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State-of-the-art Machine Learning News Feed
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последний пост 1 час назад
[D] Anyone remembers the AI company Element AI?
[D] Anyone remembers the AI company Element AI?

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1 час назад @ reddit.com
[D] How is deep learning used to correct spelling errors in search engines?
[D] How is deep learning used to correct spelling errors in search engines?

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1 час назад @ reddit.com
[D] a sentence level transformer to improve memory for a token level transformer?
[D] a sentence level transformer to improve memory for a token level transformer?

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1 час назад @ reddit.com
[Discussion] My boss asked me to give a presentation about - AI for data-science
[Discussion] My boss asked me to give a presentation about - AI for data-science

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1 час назад @ reddit.com
[D]Evaluating xG Models: Comparing Discrete Outcomes with Continuous Predictions
[D]Evaluating xG Models: Comparing Discrete Outcomes with Continuous Predictions

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1 час назад @ reddit.com
[N] The 77 French legal codes are now available via Hugging Face's Datasets library with daily updates
[N] The 77 French legal codes are now available via Hugging Face's Datasets library with daily updates

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4 часа назад @ reddit.com
[D] Help finding an AI website
[D] Help finding an AI website

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6 часов назад @ reddit.com
[D] What are some of the big tech company sponsored ML research websites that you are aware of for constantly keeping up with the ML research and workings behind their products, like Apple Machine Learning Research (https://machinelearning.apple.com/) or T
[D] What are some of the big tech company sponsored ML research websites that you are aware of for constantly keeping up with the ML research and workings behind their products, like Apple Machine Learning Research (https://machinelearning.apple.com/) or T

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6 часов назад @ reddit.com
Long-form factuality in large language models [R]
Long-form factuality in large language models [R] Long-form factuality in large language models [R]

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7 часов назад @ reddit.com
[R] Paper (NAACL 2024): why LLMs cannot be used for everyday fact checking, on the reversal problem, on the solution to the reversal problem, and a lot more
[R] Paper (NAACL 2024): why LLMs cannot be used for everyday fact checking, on the reversal problem, on the solution to the reversal problem, and a lot more

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8 часов назад @ reddit.com
[D] Machine Learning On The Edge
[D] Machine Learning On The Edge [D] Machine Learning On The Edge

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9 часов назад @ reddit.com
[D] Are data structures and leetcode needed for Machine Learning Researcher/Engineer jobs and interviews?
[D] Are data structures and leetcode needed for Machine Learning Researcher/Engineer jobs and interviews?

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12 часов назад @ reddit.com
[P] deit3-jax: A codebase for training ViTs on TPUs
[P] deit3-jax: A codebase for training ViTs on TPUs

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13 часов назад @ reddit.com
[D] Local LLM Models can learn while using them?
[D] Local LLM Models can learn while using them?

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15 часов назад @ reddit.com
[D] Thoughts on a blockchain based robot authorisation system
[D] Thoughts on a blockchain based robot authorisation system

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17 часов назад @ reddit.com
Towards Data Science
последний пост 5 часов назад
8 things most data science programs don’t teach (but you should know) — Part 2
8 things most data science programs don’t teach (but you should know) — Part 2 8 things most data science programs don’t teach (but you should know) — Part 2

MIT Calls this “the missing semester of your CS education”Continue reading on Towards Data Science »

5 часов назад @ towardsdatascience.com
Data Science for Sustainability —  Simulate a Circular Economy
Data Science for Sustainability —  Simulate a Circular Economy Data Science for Sustainability —  Simulate a Circular Economy

Data Science for Sustainability — Simulate a Circular EconomyUse data science to simulate the impact of a circular model on the CO2 emissions and water usage of a fast fashion retailer.Rental Model — (Image by Author)A circular economy is an economic model that aims to minimize waste and maximize resource efficiency.It involves designing products and processes focusing on longevity, reuse and recycling.Why not rent your dress instead of buying it?!Several fashion retailers have implemented a subscription model.Circular Rental Model — (Image by Author)Customers pay a regular fee to access a product or service for a specific period.The objective is to reduce the environmental impact along pro…

5 часов назад @ towardsdatascience.com
Random Walks Are Strange and Beautiful
Random Walks Are Strange and Beautiful Random Walks Are Strange and Beautiful

A journey through dimensions and lifeContinue reading on Towards Data Science »

18 часов назад @ towardsdatascience.com
The Rise of Diffusion Models — A new Era of Generative Deep Learning
The Rise of Diffusion Models — A new Era of Generative Deep Learning The Rise of Diffusion Models — A new Era of Generative Deep Learning

Paper Walkthrough— Denoising Diffusion Probabilistic ModelsContinue reading on Towards Data Science »

18 часов назад @ towardsdatascience.com
Step-by-Step Guide to Time Series Visualization Using Plotnine
Step-by-Step Guide to Time Series Visualization Using Plotnine Step-by-Step Guide to Time Series Visualization Using Plotnine

6 graphics to explore your time seriesContinue reading on Towards Data Science »

18 часов назад @ towardsdatascience.com
Real-time Twitch chat sentiment analysis with Apache Flink
Real-time Twitch chat sentiment analysis with Apache Flink Real-time Twitch chat sentiment analysis with Apache Flink

Real-Time Twitch Chat Sentiment Analysis with Apache FlinkLearn how to empower creators by real-time sentiment analysis with Apache Flink to decipher audience emotions to steer content for viewer satisfactionPhoto by Joey kwok on Unsplash🚀 Let’s learn about Apache Flink and sentiment analysis by building a real-time sentiment analysis streaming application for the Twitch chat.– Introduction and demo– Apache Flink– NLP and sentiment analysis– Setting up a Flink project– Prepare the project −− Project settings in IntelliJ −− Rename and reduce main class −− pom.xml project settings −− Run configuration −− Local Flink Web UI– Read the Twitch chat −− Add Twitch4J dependency −− Create POJO for Tw…

18 часов назад @ towardsdatascience.com
Intersect Multiple 3D Lines (Closest Point)
Intersect Multiple 3D Lines (Closest Point) Intersect Multiple 3D Lines (Closest Point)

Seeking intersection of a batch of 3D lines is more of a minimization problem than an actual intersection test, as usually done with 2 raysContinue reading on Towards Data Science »

19 часов назад @ towardsdatascience.com
The Business Guide to Tailoring Language AI
The Business Guide to Tailoring Language AI The Business Guide to Tailoring Language AI

A framework for unlocking custom LLM solutions you’ll understandForewordThis article illustrates how Large Language Models (LLM) are gradually adapted for custom use. It is meant to give people with no computer science background an easy to grasp analogy into how GPT and similar AI systems can be customized. Why the artwork? Bare with me, I hope you enjoy the journey.IntroductionI will not start this article with an introduction on how ChatGPT, Claude and generative AI have transformed businesses and will forever change our lives, careers and businesses. This has been written many times (most notably by the GPTs themselves …). Instead, today I would like to focus on the question of how we c…

19 часов назад @ towardsdatascience.com
Real Product Data Scientist Interview Questions at Lyft - A Compilation from GlassDoor
Real Product Data Scientist Interview Questions at Lyft - A Compilation from GlassDoor Real Product Data Scientist Interview Questions at Lyft - A Compilation from GlassDoor

Practical tips to ace your next tech interviewContinue reading on Towards Data Science »

19 часов назад @ towardsdatascience.com
Create Mixtures of Experts with MergeKit
Create Mixtures of Experts with MergeKit Create Mixtures of Experts with MergeKit

Combine multiple models into a single MoEImage by authorThanks to the release of Mixtral, the Mixture of Experts (MoE) architecture has become popular in recent months. This architecture offers an interesting tradeoff: higher performance at the cost of increased VRAM usage. While Mixtral and other MoE architectures are pre-trained from scratch, another method of creating MoE has recently appeared. Thanks to Arcee’s MergeKit library, we now have a new way of creating MoEs by ensembling several pre-trained models. These are often referred to as frankenMoEs or MoErges to distinguish them from the pre-trained MoEs.In this article, we will detail how the MoE architecture works and how frankenMoE…

22 часа назад @ towardsdatascience.com
Chronos: The Latest Time Series Forecasting Foundation Model by Amazon
Chronos: The Latest Time Series Forecasting Foundation Model by Amazon Chronos: The Latest Time Series Forecasting Foundation Model by Amazon

Take a deep dive into Chronos, its inner workings, and how to apply it in your forecasting projects using Python.Continue reading on Towards Data Science »

22 часа назад @ towardsdatascience.com
Text Embeddings, Classification, and Semantic Search
Text Embeddings, Classification, and Semantic Search Text Embeddings, Classification, and Semantic Search

An introduction with example Python codeThis article is part of a larger series on using large language models (LLMs) in practice. In the previous post, we saw how to improve an LLM via retrieval-augmented generation (i.e. RAG). A key part of RAG was using text embeddings to retrieve relevant information from a knowledge base automatically. Here, I will discuss text embeddings more deeply and share two simple (yet powerful) applications: text classification and semantic search.Photo by Daniel Lerman on UnsplashChatGPT captured the world’s imagination regarding AI and its potential. A key contributor to this impact was ChatGPT’s chat interface, which made the power of AI more accessible than…

22 часа назад @ towardsdatascience.com
FrugalGPT and Reducing LLM Operating Costs
FrugalGPT and Reducing LLM Operating Costs FrugalGPT and Reducing LLM Operating Costs

This blog post will go into detail about a cost-saving architecture for LLM-driven apps as seen in the “FrugalGPT” paperImage by Author generated by DALL-ELarge Language Models open up a new frontier for computer science, however, they are (as of 2024) significantly more expensive to run than almost anything else in computer science. For companies looking to minimize their operating costs, this poses a serious problem. The “FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance” paper introduces one framework to reduce operating costs significantly while maintaining quality.How to Measure the Cost of LLMThere are multiple ways to determine the cost of runn…

1 day, 6 hours назад @ towardsdatascience.com
Why You Should Never Use Cross-Validation
Why You Should Never Use Cross-Validation Why You Should Never Use Cross-Validation

In real-world applications, using cross-validation is always a bad choice. Here is why.Continue reading on Towards Data Science »

1 day, 7 hours назад @ towardsdatascience.com
Leverage OpenAI Tool calling: Building a reliable AI Agent from Scratch
Leverage OpenAI Tool calling: Building a reliable AI Agent from Scratch Leverage OpenAI Tool calling: Building a reliable AI Agent from Scratch

Created with DALL·EStep-by-Step Workflow for developing and refining an AI Agent while dealing with errorsWhen we think about the future of AI, we envision intuitive everyday helpers seamlessly integrating into our workflows and taking on complex, routinely tasks. We all have found touchpoints that relieve us from the tedium of mental routine work. Yet, the main tasks currently tackled involve text creation, correction, and brainstorming, underlined by the significant role RAG (Retrieval-Augmented Generation) pipelines play in ongoing development. We aim to provide Large Language Models with better context to generate more valuable content.Thinking about the future of AI conjures images of …

1 day, 13 hours назад @ towardsdatascience.com
Distill.pub Distill.pub
последний пост None
The Gradient The Gradient
последний пост 10 часов назад
Mamba Explained
Mamba Explained Mamba Explained

As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics.

Here we’ll discuss:The advantages (and disadvantages) of Mamba (🐍) vs Transformers (🤖),Analogies and intuitions for thinking about Mamba, andWhat Mamba means for Interpretability, AI Safety and Applications.

The Mamba BlockLike a Transformer made up of stacked transformer blocks, Mamba is made up of stacked Mamba blocks as above.

The Mamba authors write, “the efficiency vs. effectiveness tradeoff of sequence models is characterised by how well they compress their state”.

Thanks to Gonçalo for reading an early draft, Jaden for the nnsight library …

10 часов назад @ thegradient.pub
Car-GPT: Could LLMs finally make self-driving cars happen?
Car-GPT: Could LLMs finally make self-driving cars happen? Car-GPT: Could LLMs finally make self-driving cars happen?

We've just seen 3 prominent families of LLM usage in self-driving cars: Perception, Planning, and Generation.

The first wave of papers mentioning LLMs in Self-Driving Cars is from mid-2023, so let's give it some time.

Next StepsIf you want to get started on LLMs for self-driving cars, there are several things you can do:⚠️ Before this, the most important : If you want to keep learning about self-driving cars.

Author BioJérémy Cohen is a self-driving car engineer and founder of Think Autonomous, a platform to help engineers learn about cutting-edge technologies such as self-driving cars and advanced Computer Vision.

CitationFor attribution in academic contexts or books, please cite this work…

2 weeks, 5 days назад @ thegradient.pub
Do text embeddings perfectly encode text?
Do text embeddings perfectly encode text? Do text embeddings perfectly encode text?

Beyond the requirements of semantic similarity, there are no constraints on what embedding must be assigned for a given text input.

What if someone hacks into the database and gains access to all your text embedding vectors – would this be bad?

From text to embeddings...back to textThe problem of recovering text from embeddings is exactly the scenario we tackle in our paper Text Embeddings Reveal As Much as Text (EMNLP 2023).

Scaling and future workThe fact that text embeddings can be perfectly inverted raises many follow-up questions.

CitationFor attribution in academic contexts or books, please cite this work asJack Morris, "Do text embeddings perfectly encode text?

3 weeks, 1 day назад @ thegradient.pub
Why Doesn’t My Model Work?
Why Doesn’t My Model Work? Why Doesn’t My Model Work?

If your model latches on to these during training, it will appear to work well, but may not work on new data.

This happens when the model training pipeline has access to information it shouldn’t have access to, particularly information that confers an advantage to the model.

This means that knowledge of the test data is implicitly entering the model training pipeline, even if it is not explicitly used to train the model.

Well, this is a common thing to do, but if you’re developing a model iteratively and using the same test set to evaluate the model after each iteration, then you’re basically using that test set to guide the development of the model.

CitationFor attribution in academic cont…

1 month назад @ thegradient.pub
Deep learning for single-cell sequencing: a microscope to see the diversity of cells
Deep learning for single-cell sequencing: a microscope to see the diversity of cells Deep learning for single-cell sequencing: a microscope to see the diversity of cells

Evolution of single-cell sequencing over timeHaving explored the panorama of single-cell sequencing, let us now delve into the role of deep learning in the context of single-cell sequencing.

Deep Learning on single-cell sequencingDeep learning is increasingly employed in single-cell analysis due to its capacity to handle the complexity of single-cell sequencing data.

The deep learning approach, however, autonomously captures relevant characteristics from single-cell sequencing data, addressing the heterogeneity between single-cell sequencing experiments, as well as the associated noise and sparsity in such data.

As we explore the reasons behind using deep learning in single-cell sequencing …

2 months, 2 weeks назад @ thegradient.pub
Salmon in the Loop
Salmon in the Loop Salmon in the Loop

In order to obtain a license or permit from FERC, hydroelectric dam operators must submit detailed plans and studies demonstrating that their facility meets regulations.

Typically, a hydroelectric dam requires lots of space to store water on one side of it, which means they tend to be located away from population centers.

Enter Computer VisionSome organizations are exploring the use of computer vision and machine learning to significantly automate fish counting.

The annotated images are then used to train a machine learning model.

CitationFor attribution of this in academic contexts or books, please cite this work as:Kevin McCraney, "Salmon in the Loop", The Gradient, 2023.

3 months, 1 week назад @ thegradient.pub
Neural algorithmic reasoning
Neural algorithmic reasoning Neural algorithmic reasoning

In recent work with computer networking and machine learning collaborators from ETH Zürich, we studied the applicability of neural algorithmic reasoning in computer networking [27].

Our proposal, the neural algorithmic reasoning blueprint [32], aims to bridge this divide by neuralising the target algorithm.

Neural Algorithmic Reasoning with Causal Regularisation.

Neural Algorithmic Reasoning.

CitationFor attribution in academic contexts or books, please cite this work asPetar Veličković, "Neural Algorithmic Reasoning", The Gradient, 2023.

5 months, 2 weeks назад @ thegradient.pub
The Artificiality of Alignment
The Artificiality of Alignment The Artificiality of Alignment

This community has developed an extensive vocabulary around theories of AI safety and alignment, many first introduced as detailed blog posts in forums like LessWrong and AI Alignment Forum.

One such idea that is useful for contextualizing technical alignment work — and is perhaps the more formal version of what Bostrom was referring to — is the concept of intent alignment.

Anthropic’s product marketing pages are plastered with notes and phrases about their alignment work —“HHH” is also Claude's biggest selling point.

The site uses the phrasing “AI Safety” instead of “AI Alignment” in the title, but the article itself proceeds to use “safety” and “alignment” interchangeably without differen…

5 months, 3 weeks назад @ thegradient.pub
An Introduction to the Problems of AI Consciousness
An Introduction to the Problems of AI Consciousness An Introduction to the Problems of AI Consciousness

This brief introduction is aimed at those working within the AI community who are interested in AI consciousness, but may not know much about the philosophical and scientific work behind consciousness generally or the topic of AI consciousness in particular.

(Image by author)AI and ConsciousnessTwo Problems for AI ConsciousnessLet’s return to the topic of AI consciousness.

The problem of AI consciousness may seem less difficult than the hard problem: the problem of AI consciousness only asks if silicon could support consciousness, but it does not ask for an explanation of why silicon can or cannot, like the hard problem does.

The second test proposed by Schneider and Edwin Turner [27], call…

5 months, 4 weeks назад @ thegradient.pub
Text-to-CAD: Risks and Opportunities
Text-to-CAD: Risks and Opportunities Text-to-CAD: Risks and Opportunities

We’ve identified three key areas where such programs can level up: dataset curation, a pattern language for usability, and filtering.

The format for a friction hinge pattern might look like this:Pattern Name Friction Hinge Pattern Description The Friction Hinge pattern addresses the need for adjustable friction in hinges so as to provide tuneable resistance, but without compromising smooth movement.

Alas, once text-to-CAD models get open-sourced or leaked, many of these queries will be satisfied without compunction.

____In conclusion, the emergence of AI-powered text-to-CAD generation presents both risks and opportunities, the ratio of which is still very much undecided.

CitationFor attribu…

6 months, 2 weeks назад @ thegradient.pub
Interpretability Creationism
Interpretability Creationism Interpretability Creationism

In this piece, I will discuss the tendency towards “interpretability creationism” – interpretability methods that only look at the final state of the model and ignore its evolution over the course of training – and propose a focus on the training process to supplement interpretability research.

In the case of language models, they behave similarly to ngram models early on and exhibit linguistic patterns later.

Let us consider an explanation usually based on analyzing static models: hierarchical behavior in language models.

An ExampleI recently had to manage the trap of interpretability creationism myself.

Citation:For attribution in academic contexts or books, please cite this work asNaomi …

8 months, 2 weeks назад @ thegradient.pub
What Do LLMs Know About Linguistics? It Depends on How You Ask
What Do LLMs Know About Linguistics? It Depends on How You Ask What Do LLMs Know About Linguistics? It Depends on How You Ask

Understanding what LLMs learn about linguistics from large-scale pretraining is a good framework for understanding how they work in general.

How we extend in-context learning to structured prediction tasks with structured prompting (left) and an example of using structured prompting with an LLM to annotate parts-of-speech (right).

Overall, structured prompting can consistently generate the linguistic structure underlying text from LLMs, confirming that these models have implicitly learned these structures during pretraining.

This leads us to ask: what factors of structured prompting allow LLMs to annotate linguistic structure?

By searching for labels instead of full examples, we can find ta…

8 months, 3 weeks назад @ thegradient.pub
Why transformative artificial intelligence is really, really hard to achieve
Why transformative artificial intelligence is really, really hard to achieve Why transformative artificial intelligence is really, really hard to achieve

Good speculated, one day self-improve, cause an intelligence explosion, and lead to an economic growth singularity?

In this essay we assemble the best arguments that we have encountered for why transformative AI is hard to achieve.

AI progress may even be accelerating the decoupling of the US and China, reducing the flow of people and ideas.

Automation alone is not enough for transformative economic growth.

If transformative AI were coming soon, real interest rates would rise in line with expectations of great future wealth or risk.

9 months назад @ thegradient.pub
TheSequence TheSequence
последний пост 51 минуту назад
Edge 381: Google DeepMind's PrompBreeder Self-Improves Prompts
Edge 381: Google DeepMind's PrompBreeder Self-Improves Prompts Edge 381: Google DeepMind's PrompBreeder Self-Improves Prompts

Created Using DALL-EReasoning and prompt evolution/optimization are being recognized as the next significant frontier for large language models(LLMs).

Among the various strategies employed to enhance the reasoning capabilities of LLMs, one of the prominent ones is Chain-of-Thought Prompting, often hailed for its effectiveness.

However, it’s worth noting that manually crafted prompt strategies tend to fall short of optimal performance.

Recently, researchers from Google DeepMind unveiled PROMPTBREEDER , a self-improving prompt algorithm that uses evolutionary techniques to arrive to the best prompts for a given task.

PROMPTBREEDER addresses some of the limitations of CoT with a simple and sup…

51 минуту назад @ thesequence.substack.com
Edge 380: A New Series About Autonomous Agents
Edge 380: A New Series About Autonomous Agents Edge 380: A New Series About Autonomous Agents

Created Using DALL-EIn this Issue:An introduction to our series about autonomous agents.

💡 ML Concept of the Day: A New Series About Autonomous AgentsToday, we start one of our most ambitious series at The Sequence by diving into the world of autonomous agents.

What makes this series ambitious is that autonomous agents remains a relatively nascent and unsolved problem in AI.

The rapid pace of growth in LLMs have certainly accelerated the viability of autonomous agents but the space still remains in a very early stages.

What is an autonomous agents?

2 days назад @ thesequence.substack.com
📝 Guest Post: Zilliz Unveiled Milvus 2.4 at GTC 24, Transforming Vector Databases with GPU Acceleration*
📝 Guest Post: Zilliz Unveiled Milvus 2.4 at GTC 24, Transforming Vector Databases with GPU Acceleration* 📝 Guest Post: Zilliz Unveiled Milvus 2.4 at GTC 24, Transforming Vector Databases with GPU Acceleration*

High throughput ensures that vector databases can handle a large volume of incoming queries concurrently, delivering low-latency responses to end-users or services.

At the heart of vector databases lies a core set of vector operations, such as similarity calculations and matrix operations, which are highly parallelizable and computationally intensive.

To leverage the benefits of GPU acceleration, CAGRA is integrated into Milvus’ Index and Query Nodes.

Blazing New TrailsThe integration of NVIDIA's CAGRA GPU acceleration framework into Milvus 2.4 represents a groundbreaking achievement in vector databases.

The unveiling of Milvus 2.4, a collaboration between Zilliz and NVIDIA, exemplifies the…

2 days, 23 hours назад @ thesequence.substack.com
NVIDIA’s GTC in Four Headlines
NVIDIA’s GTC in Four Headlines NVIDIA’s GTC in Four Headlines

You can subscribe below:📝 Editorial: NVIDIA’s GTC in Four HeadlinesI tried to resist making this weekend's editorial about NVIDIA because I think you might have been inundated with headlines from the GTC conference.

If I have to summarize two key takeaways from NVIDIA's AI announcements this week, they would be these:NVIDIA is not only outgrowing but also out-innovating everyone else in AI compute hardware by a large margin.

Project GR00T: I think the coolest and most ambitious announcement was Project GR00T, which focuses on developing foundation models for humanoid robots.

NVIDIA's AI hardware dominance is unquestionable, but it's quickly making inroads in the software space.

The techniqu…

3 days, 23 hours назад @ thesequence.substack.com
📌 Exciting lineup for apply() 2024 is now live
📌 Exciting lineup for apply() 2024 is now live 📌 Exciting lineup for apply() 2024 is now live

The agenda for apply() 2024, Tecton’s premier virtual conference dedicated to mastering AI and ML at production scale, is now live!

Join us on Wednesday, April 3, for a day packed with enlightening sessions and networking opportunities alongside industry leaders and fellow engineers.

REGISTER TODAYHere are some agenda highlights:Is RAG Really Dead?

Semi-Supervised Learning: How to Overcome the Lack of LabelsSpeaker: Aleksandr Timashov, ML Engineer at MetaAleksandr will delve into semi-supervised learning, offering insights into leveraging labeled and unlabeled data efficiently for model training.

SEE FULL AGENDA

5 days, 23 hours назад @ thesequence.substack.com
Edge 380: Inside SELF-Discover: Google DeepMind's LLM Reasoning Method for Solving Complex Tasks
Edge 380: Inside SELF-Discover: Google DeepMind's LLM Reasoning Method for Solving Complex Tasks Edge 380: Inside SELF-Discover: Google DeepMind's LLM Reasoning Method for Solving Complex Tasks

Chain of thought(CoT), tree of thought(ToT), System 2 are many of the recent LLM reasoning techniques that are exploring the ability of LLMs to breakdown complex problems.

Recently, researchers from Google DeepMind published a paper outlining SELF-DISCOVER, a somewhat of a novel take on LLM reasoning.

As mentioned before, there is no lack of reasoning methods in the LLM space but DeepMind’s seems to have \been inspired by the way humans tackle reasoning problems.

They’ve looked at methods like few-shot and zero-shot chain-of-thought prompting, which mimic the human approach of solving problems step by step.

Another method, decomposition-based prompting, draws from the human ability to break…

1 week назад @ thesequence.substack.com
Edge 379: A Summary Of Our Series About LLM Reasoning
Edge 379: A Summary Of Our Series About LLM Reasoning Edge 379: A Summary Of Our Series About LLM Reasoning

Created Using DALL-E💡 ML Concept of the Day: A Summary Of Our Series About LLM ReasoningToday, we are concluding our series about reasoning in LLMs with a summary of the different topics covered.

Throughout the last few weeks, we have explored some of the most cutting edge LLM reasoning techniques, related research and technology.

You can subscribe below:Here is our summary:Edge 253: Provides an introduction to LLM reasoning and its relevance.

Edge 359: Explains the tree-of-thought(ToT) LLM reasoning method.

Edge 361: Introduces graph-of-thoughts in LLM reasoning including its original paper.

1 week, 2 days назад @ thesequence.substack.com
Explore the Global Generative AI Landscape 2024 by AIport
Explore the Global Generative AI Landscape 2024 by AIport Explore the Global Generative AI Landscape 2024 by AIport

Our friends from AIport – an online community of AI writers and practitioners – have just released Volume I of the Global Generative AI Landscape 2024.

This landscape provides a comprehensive analysis in a reader-friendly format, revealing how international companies are driving GenAI development.

The study covers six different continents and extends far beyond the usual focus of American and European entities, spanning a whopping four times more countries than similar projects.

Read the report and view the full landscape here to get exclusive insights into 107 international companies, developing 128 generative models.

Read the report

1 week, 3 days назад @ thesequence.substack.com
One AI for Navigating Any 3D Environment
One AI for Navigating Any 3D Environment One AI for Navigating Any 3D Environment

You can subscribe to The Sequence below:📝 Editorial: SIMA, One AI for Navigating Any 3D EnvironmentVideo games have long served as some of the best environments for training AI agents.

However, most of the AI breakthroughs in 3D game environments have been constrained to one or a small number of games.

By using language instructions, SIMA was able to master actions in 3D environments it hadn’t seen before.

DocFormverv2 can make sense of visual and textual information in a way that mimics human reasoning —> Read more.

🛠 Real World MLMeta’s Gen AI InfrastructureMeta shares some details about the compute infrastructure used in their gen AI workloads —> Read more.

1 week, 4 days назад @ thesequence.substack.com
📌 Exciting news! The speaker lineup for apply() 2024 is now live
📌 Exciting news! The speaker lineup for apply() 2024 is now live 📌 Exciting news! The speaker lineup for apply() 2024 is now live

The speaker lineup for apply() 2024 is now live and we can’t wait to show you!

Join industry leaders, starting Wednesday, April 3rd at 9AM PT, from LangChain, Meta, Pinterest, Samsung, Vanguard, Visa and more for actionable insights to master AI and ML in production.

Explore Semi-Supervised Learning: Aleksandr Timashov, ML Engineer at Meta, dives into practical approaches for training models with limited labeled data.

Deep Dive into Uplift Modeling: Toyosi Bamidele, Data Scientist at Visa, demystifies uplift modeling for estimating marketing interventions' impact.

Register today for free to join the event live on April 3rd, or to have the on-demand videos sent to your inbox.

1 week, 6 days назад @ thesequence.substack.com
Edge 378: Meet TimesFM: Google's New Foundation Model for Time-Series Forecasting
Edge 378: Meet TimesFM: Google's New Foundation Model for Time-Series Forecasting Edge 378: Meet TimesFM: Google's New Foundation Model for Time-Series Forecasting

Created Using IdeogramTime series forecasting is one of the classic scenarios in machine learning(ML) since its early days.

The ability of outputting predictions on time series data is relevant on many domains including retail, finance, manufacturing, healthcare, and natural sciences and yes, stock market predictions.

Is the paradigm of pretrain model applicable to time series forecasting scenarios.

Google seems to believe so with a recent research paper that outlines a decoder-only pretrain model for time series forecasting.

The paper introduces TimeFM, a 200M parameter foundation model trained in over 100 billion time series data points.

2 weeks назад @ thesequence.substack.com
Edge 377: LLM Reasoning with Reinforced Fine-Tuning
Edge 377: LLM Reasoning with Reinforced Fine-Tuning Edge 377: LLM Reasoning with Reinforced Fine-Tuning

Created Using DALL-EIn this Issue:An overview of reinforced fine-tuning(ReFT) as a method for LLM reasoning.

An introduction to Guardrails AI as one of the most complete frameworks to guide the behavior of LLM applications.

💡 ML Concept of the Day: Reinforced Fine-Tuning and LLM ReasoningIn the last installment of our series about LLM reasoning, we are going to discuss a new technique recently introduced by ByteDance.

Reinforced Fine-Tuning(ReFT) looks to address some of the limitation of supervised fine tuning(SFT) approaches such as chain of thought(CoT) of reliance on reasoning training data.

The core idea is to create models that can learn from multiple reasoning paths for a single ques…

2 weeks, 2 days назад @ thesequence.substack.com
📝 Guest Post: Evaluating LLM Applications*
📝 Guest Post: Evaluating LLM Applications* 📝 Guest Post: Evaluating LLM Applications*

As CTO of Humanloop, Peter has assisted companies such as Duolingo, Gusto, and Vanta in solving LLM evaluation challenges for AI applications with millions of daily users.

This post is a shortened version of Peter’s original blog, titled 'Evaluating LLM Applications'.

These different types can be roughly characterized by the return type and the source of, as well as the criteria for, the judgment required.

Different stages of evaluation are necessaryDifferent stages of the app development lifecycle will have different evaluation needs.

These cycles are then repeated during the lifetime of the LLM app in order to intervene and improve performance.

2 weeks, 3 days назад @ thesequence.substack.com
Can I Solve Science?
Can I Solve Science? Can I Solve Science?

You can Subscribe to The Sequence Below:📝 Editorial: Can AI Solve Science?

We are witnessing glimpses of the potential impact of 'AI for science' with models such as those discovering new computer science and math algorithms, or the famous AlphaFold, which is actively used for discovering new proteins.

AI, by itself, cannot solve all science, but the combination of AI and computational languages could get pretty far.

🛠 Real World MLCan I Solve Science?

Stephen Wolfram published a long and super insightful essay detailing the history, possibilities and challenges of AI when comes to discover new science.

2 weeks, 4 days назад @ thesequence.substack.com
📌 ML Engineering Event: Lineup for apply() 2024 is Now Live!
📌 ML Engineering Event: Lineup for apply() 2024 is Now Live! 📌 ML Engineering Event: Lineup for apply() 2024 is Now Live!

The speaker lineup for apply() 2024 is now live.

Join industry leaders from LangChain, Meta, and Visa for insights to master AI and ML in production.

Explore Semi-Supervised Learning: Aleksandr Timashov, ML Engineer at Meta, dives into practical approaches for training models with limited labeled data.

Deep Dive into Uplift Modeling: Toyosi Bamidele, Data Scientist at Visa, demystifies uplift modeling for estimating marketing interventions' impact.

Dive deep into these topics with our expert speakers and gain actionable insights for mastering AI and ML.

2 weeks, 5 days назад @ thesequence.substack.com
Synced Review
последний пост 21 час назад
First Model-Stealing Attack Reveals Secrets of Black-Box Production Language Models
First Model-Stealing Attack Reveals Secrets of Black-Box Production Language Models First Model-Stealing Attack Reveals Secrets of Black-Box Production Language Models

Despite the remarkable capabilities of contemporary large language models like GPT-4, Claude 2, or Gemini, the inner mechanisms governing their operations remain shrouded in mystery. While the intricate details of these models are kept under wraps, they are made accessible through APIs, prompting the question: How much insight can adversaries glean about these models by interacting with their APIs?To answer this question, in a new paper Stealing Part of a Production Language Model, a research team from Google DeepMind, ETH Zurich, University of Washington, OpenAI and McGill University introduces a groundbreaking model-stealing attack. This attack unveils precise, nontrivial information from…

21 час назад @ medium.com
DeepMind & UBC’s Genie: A Revolutionary Leap in Generative AI for Interactive Virtual Worlds
DeepMind & UBC’s Genie: A Revolutionary Leap in Generative AI for Interactive Virtual Worlds DeepMind & UBC’s Genie: A Revolutionary Leap in Generative AI for Interactive Virtual Worlds

In recent years, there has been a remarkable surge in the development of generative AI, showcasing the potential of models to create novel and imaginative content. While strides have been made in various domains, the realm of video generation stands as a promising frontier.Recent advancements suggest that scaling up these models could further enhance their capabilities. However, there remains a significant gap between the interactive engagement offered by video generative models and the rich interactions facilitated by language tools like ChatGPT, not to mention more immersive experiences.In response to this challenge, in a new paper Genie: Generative Interactive Environments, a research te…

2 days, 15 hours назад @ medium.com
ByteDance’s AnimateDiff-Lightning Shines in State-of-the-Art Video Creation in Lightning Speed
ByteDance’s AnimateDiff-Lightning Shines in State-of-the-Art Video Creation in Lightning Speed ByteDance’s AnimateDiff-Lightning Shines in State-of-the-Art Video Creation in Lightning Speed

In recent times, video generative models have emerged as a focal point of attention, unlocking a realm of fresh creative opportunities. Despite this, the velocity of these models remains a significant obstacle to their broader adoption. State-of-the-art generative models, while impressive in their capabilities, are hampered by sluggishness and computational demands due to their iterative diffusion processes.To address this issue, in a new paper AnimateDiff-Lightning: Cross-Model Diffusion Distillation, a ByteDance research team presents AnimateDiff-Lightning, a novel approach that utilizes progressive adversarial diffusion distillation, catapulting video generation into a realm of lightning…

1 week назад @ medium.com
Stanford’s VideoAgent Achieves New SOTA of Long-Form Video Understanding via Agent-Based System
Stanford’s VideoAgent Achieves New SOTA of Long-Form Video Understanding via Agent-Based System Stanford’s VideoAgent Achieves New SOTA of Long-Form Video Understanding via Agent-Based System

Understanding long-form videos presents a formidable challenge within the realm of computer vision. This undertaking requires a model adept at processing multi-modal data, managing extensive sequences, and effectively reasoning over these sequences.In response to this challenge, in a new paper VideoAgent: Long-form Video Understanding with Large Language Model as Agent, a Stanford University research team introduces VideoAgent, an innovative approach simulates human comprehension of long-form videos through an agent-based system, showcasing superior effectiveness and efficiency compared to current state-of-the-art methods. This underscores the potential of agent-based approaches in advancin…

1 week, 2 days назад @ medium.com
DeepMind’s Gemma: Advancing AI Safety and Performance with Open Models
DeepMind’s Gemma: Advancing AI Safety and Performance with Open Models DeepMind’s Gemma: Advancing AI Safety and Performance with Open Models

Large Language Models (LLMs) have proven their mettle across a spectrum of real-world applications, ranging from language modeling to visual comprehension, and even text-to-image and text-to-video generation. Undoubtedly, LLMs stand as pivotal elements in contemporary artificial intelligence. However, alongside their groundbreaking potential, concerns regarding their safe deployment loom large.In a new paper Gemma: Open Models Based on Gemini Research and Technology, Google DeepMind Gemma Team introduces Gemma, a suite of lightweight, cutting-edge open models derived from the same research and technology underpinning the powerful Gemini models. Gemma marks a significant leap forward in perf…

1 week, 5 days назад @ medium.com
Fast Tracks to Diverse Behaviors: VQ-BeT Achieves 5x Speed Surge Compared to Diffusion Policies
Fast Tracks to Diverse Behaviors: VQ-BeT Achieves 5x Speed Surge Compared to Diffusion Policies Fast Tracks to Diverse Behaviors: VQ-BeT Achieves 5x Speed Surge Compared to Diffusion Policies

Generative modeling of complex behaviors from labeled datasets has long been a significant challenge in decision-making. This entails…Continue reading on SyncedReview »

2 weeks, 2 days назад @ medium.com
BasedAI: A Decentralized Solution for Seamless Integration of Privacy and Performance in Large…
BasedAI: A Decentralized Solution for Seamless Integration of Privacy and Performance in Large… BasedAI: A Decentralized Solution for Seamless Integration of Privacy and Performance in Large…

Continue reading on SyncedReview »

2 weeks, 6 days назад @ medium.com
Transcend The Boundaries of Language Models: bGPT Enables Deeper Understanding Through Byte…
Transcend The Boundaries of Language Models: bGPT Enables Deeper Understanding Through Byte… Transcend The Boundaries of Language Models: bGPT Enables Deeper Understanding Through Byte…

In the realm of deep learning, much emphasis has been placed on deciphering digital media files that resonate with human understanding…Continue reading on SyncedReview »

3 weeks, 1 day назад @ medium.com
Embracing the Era of 1-Bit LLMs: Microsoft & UCAS’s BitNet b1.58 Redefines Efficiency
Embracing the Era of 1-Bit LLMs: Microsoft & UCAS’s BitNet b1.58 Redefines Efficiency Embracing the Era of 1-Bit LLMs: Microsoft & UCAS’s BitNet b1.58 Redefines Efficiency

The recent explosive growth of Large Language Models (LLMs) has showcased their exceptional performance across a spectrum of natural…Continue reading on SyncedReview »

3 weeks, 6 days назад @ medium.com
NVIDIA’s Nemotron-4 15B Dominates Multilingual Domain, Defeating 4× Larger Rivals
NVIDIA’s Nemotron-4 15B Dominates Multilingual Domain, Defeating 4× Larger Rivals NVIDIA’s Nemotron-4 15B Dominates Multilingual Domain, Defeating 4× Larger Rivals

Continue reading on SyncedReview »

4 weeks, 1 day назад @ medium.com
Microsoft’s LongRoPE Breaks the Limit of Context Window of LLMs, Extents it to 2 Million Tokens
Microsoft’s LongRoPE Breaks the Limit of Context Window of LLMs, Extents it to 2 Million Tokens Microsoft’s LongRoPE Breaks the Limit of Context Window of LLMs, Extents it to 2 Million Tokens

Large Language Models (LLMs) have achieved remarkable success across various tasks. However, they often grapple with a limited context…Continue reading on SyncedReview »

1 month назад @ medium.com
Yann LeCun & Randall Balestriero Optimize Deep Learning for Perception Tasks
Yann LeCun & Randall Balestriero Optimize Deep Learning for Perception Tasks Yann LeCun & Randall Balestriero Optimize Deep Learning for Perception Tasks

Deep learning endeavors to establish a comprehensive approach for acquiring interpretable and universally applicable data representations…Continue reading on SyncedReview »

1 month назад @ medium.com
Apple’s Keyframer: Redefining Animation Prototyping with Language-Guided Design
Apple’s Keyframer: Redefining Animation Prototyping with Language-Guided Design Apple’s Keyframer: Redefining Animation Prototyping with Language-Guided Design

Large Language Models (LLMs) possess immense potential to revolutionize and enhance creative processes throughout the design journey, from…Continue reading on SyncedReview »

1 month, 1 week назад @ medium.com
Unveiling Sora: OpenAI’s Breakthrough in Text-to-Video Generation
Unveiling Sora: OpenAI’s Breakthrough in Text-to-Video Generation Unveiling Sora: OpenAI’s Breakthrough in Text-to-Video Generation

Continue reading on SyncedReview »

1 month, 1 week назад @ medium.com
DeepMind & Stanford U’s UNFs: Advancing Weight-Space Modeling with Universal Neural Functionals
DeepMind & Stanford U’s UNFs: Advancing Weight-Space Modeling with Universal Neural Functionals DeepMind & Stanford U’s UNFs: Advancing Weight-Space Modeling with Universal Neural Functionals

In the realm of machine learning, addressing weight-space features like weights, gradients, or sparsity masks of neural networks is often…Continue reading on SyncedReview »

1 month, 1 week назад @ medium.com
📓 Cool Blogs
ODS.ai Habr ODS.ai Habr
последний пост 3 months, 1 week назад
GPT-like модель «впервые сделала научное открытие»: что, как и куда дальше?
GPT-like модель «впервые сделала научное открытие»: что, как и куда дальше? GPT-like модель «впервые сделала научное открытие»: что, как и куда дальше?

Или кликбейт — и это в Nature?

Статья ниже — подробный разбор достаточно сложного топика, и в некоторых моментах нужно будет сосредоточиться и вдумчиво читать.

Они же пишут код в помощь разработчикам, да и в целом помогают решать разного рода проблемы.

Однако может так получиться, что сет долгое время не выпадает — и не потому, что игроки проворонили, а потому, что его действительно просто нет на столе.

Надеемся, что после прочтения этой статьи стало ясно, что ошибки нейросетей — это не баг, это фича.

3 months, 1 week назад @ habr.com
Кто такие LLM-агенты и что они умеют?
Кто такие LLM-агенты и что они умеют? Кто такие LLM-агенты и что они умеют?

Также присоединяйтесь к моему телеграм каналу AI[ex]Time , где я пишу про машинное обучение, NLP, LLM и в том числе про агентов.

LLaVaВ качестве LLM для генерации текстового ответа используется LLaMA, которая декодирует эмбеддинги (то же, что и векторы) картинок и входного текста в ответ.

На вход модель получает картинку и запрос от пользователя (Normal prompt) и на выходе должна дать ответ (Response).

Модель таким образом от решения задачи напрямую переходит к рассуждению по шагам, что в некотором смысле и является декомпозицией задачи.

И это улучшает качество работы модели — в том числе и ризонинга.

3 months, 3 weeks назад @ habr.com
Главное событие в мире AI: создатель ChatGPT рассказал, в какое будущее он нас всех ведет
Главное событие в мире AI: создатель ChatGPT рассказал, в какое будущее он нас всех ведет Главное событие в мире AI: создатель ChatGPT рассказал, в какое будущее он нас всех ведет

Мы помним, что в начале 2023-го продуктом уже пользовалось больше 100 млн человек в месяц.

У самых любознательных читателей может возникнуть вопрос: а как это вообще работает?

Насколько нам известно, это первый раз, когда модель такого масштаба обучается на синтетических данных, а не на произведенных человеком.

Использование такой модели дешевле в 3 раза на текст из промпта, и в 2 раза на генерируемые токены (их обычно меньше).

Но вот как с ними обращаться, когда использовать и как комбинировать — это уже решает AI по контексту диалога.

4 months, 2 weeks назад @ habr.com
Пять книг про NLP, с которых можно начать
Пять книг про NLP, с которых можно начать Пять книг про NLP, с которых можно начать

Меня зовут Валентин Малых, я — руководитель направления NLP-исследований в MTS AI, вот уже 6 лет я читаю курс по NLP.

Поскольку я все время отвечаю одно и то же, появилась идея сделать пост про мой список книг, заодно описав их.

При этом в книге больше информации про информационный поиск (information retrieval) и меньше про NLP, но в наше время эти две области уже (или все еще) очень близки.

Правда, его тоже уже не достать, хотя PDF версия ищется без проблем.

Foundations of Statistical Natural Language ProcessingНасколько мне известно, эта книга не переводилась на русский язык.

6 months, 3 weeks назад @ habr.com
Пять книг про NLP, с которых можно начать
Пять книг про NLP, с которых можно начать Пять книг про NLP, с которых можно начать

Меня зовут Валентин Малых, я — руководитель направления NLP-исследований в MTS AI, вот уже 6 лет я читаю курс по NLP.

Поскольку я все время отвечаю одно и то же, появилась идея сделать пост про мой список книг, заодно описав их.

При этом в книге больше информации про информационный поиск (information retrieval) и меньше про NLP, но в наше время эти две области уже (или все еще) очень близки.

Правда, его тоже уже не достать, хотя PDF версия ищется без проблем.

Foundations of Statistical Natural Language ProcessingНасколько мне известно, эта книга не переводилась на русский язык.

6 months, 3 weeks назад @ habr.com
Дропаем ранжирующие метрики в рекомендательной системе, часть 3: платформа для экспериментов
Дропаем ранжирующие метрики в рекомендательной системе, часть 3: платформа для экспериментов Дропаем ранжирующие метрики в рекомендательной системе, часть 3: платформа для экспериментов

Платформа для экспериментов в RecSysНа примере Киона я показала сложности экспериментов с рекомендательными системами.

В работе над RecSys в реальном сервисе мы строим гипотезы, выкатываем модели в АБ тесты, строим новые гипотезы.

Нашей целью была инфраструктура и пайплайны для максимально быстрых и удобных экспериментов с любыми алгоритмами.

Трекинг экспериментов: метрики и визуальный анализТрекинг экспериментов мы проводили одновременно в Mlflow и в отчётах в репозитории.

Схема на картинке:Инфраструктура платформы для экспериментов и приложения с рекомендациямиЖёлтые стрелки отражают запись результатов экспериментов: логирование метрик с кросс-валидации, сохранение обученных моделей в Min…

7 months назад @ habr.com
Дропаем ранжирующие метрики в рекомендательной системе, часть 2: двухэтапные модели
Дропаем ранжирующие метрики в рекомендательной системе, часть 2: двухэтапные модели Дропаем ранжирующие метрики в рекомендательной системе, часть 2: двухэтапные модели

В первой части статьи я описывала, как мы с напарником решили выкатить модель из соревнования в онлайн рекомендации, и что из этого вышло.

Давайте построим фичу для бустинга, которая будет решать главную проблему нашей текущей модели - не релевантные айтемы в хороших подборках.

Также у нас были замечательные фичи, проверенные ещё на модели в соревновании.

В задаче классификации мы подаём модели кандидатов, бывших в интеракциях в качестве позитивных таргетов, и не бывших в качестве негативных, и учим его отличать их друг от друга.

Самый стабильный подход к кросс-валидации в рекомендательных системах - это схема скользящего окна, которая больше всего соответствует работе модели в реальном мир…

7 months, 1 week назад @ habr.com
Дропаем ранжирующие метрики в рекомендательной системе, часть 1: визуальный анализ и popularity bias
Дропаем ранжирующие метрики в рекомендательной системе, часть 1: визуальный анализ и popularity bias Дропаем ранжирующие метрики в рекомендательной системе, часть 1: визуальный анализ и popularity bias

Смотрим, что мы имеем в Кионе:Распределение популярности топ 100 айтемов в датасете KionЧто там в топе просмотров?

Популярные айтемы заполняют собой полку рекомендаций и на этот запрос, и на многие другие.

Фильм занимает 6 место в топе просмотров в датасете и очень не вовремя попадает в рекомендации у самых разных алгоритмов.

Проверим рекомендации от модели с максимумом Recall без популярного:Рекомендации модели bm25 с максимумом Recall без популярного.

Recall и MAP понизились примерно в 2 раза от модели в соревновании.

7 months, 2 weeks назад @ habr.com
«Диалектик», независимое социалистическое медиа, рассказывает о своих NLP проектах, публикует датасеты и делится кодом
«Диалектик», независимое социалистическое медиа, рассказывает о своих NLP проектах, публикует датасеты и делится кодом «Диалектик», независимое социалистическое медиа, рассказывает о своих NLP проектах, публикует датасеты и делится кодом

Почти сразу после публикации поста про систему поиска новостей о трудовых конфликтах в СНГ я познакомился с коллективом проекта «Диалектик».

Коллектив волнуют процессы, происходящие как в экономическом базисе, так и в его надстройке – в обществе, культуре, политике.

Этот метод познания помогал всем классикам марксизма анализировать сложные ситуации в экономике и обществе, этим он ценен и для коллектива «Диалектика».

Поэтому внутри коллектива ведется разработка ИТ-инструментов для внутреннего (только для редакторов) и для внешнего (для всех пользователей) использования.

Этот публичный агрегатор находится на этапе тестирования и в скором времени будет представлен общественности.

7 months, 2 weeks назад @ habr.com
Machine Learning Mastery
последний пост 1 week, 1 day назад
Unfolding Data Stories: From First Glance to In-Depth Analysis
Unfolding Data Stories: From First Glance to In-Depth Analysis

The path to uncovering meaningful insights often starts with a single step: looking at the data before asking questions. This journey through the Ames Housing dataset is more than an exploration; it’s a narrative about the hidden stories within numbers, waiting to be told. Through a “Data First Approach,” we invite you to dive deep […]

The post Unfolding Data Stories: From First Glance to In-Depth Analysis appeared first on MachineLearningMastery.com.

1 week, 1 day назад @ machinelearningmastery.com
The Da Vinci Code of Data: Mastering The Data Science Mind Map
The Da Vinci Code of Data: Mastering The Data Science Mind Map

Data Science embodies a delicate balance between the art of visual storytelling, the precision of statistical analysis, and the foundational bedrock of data preparation, transformation, and analysis. The intersection of these domains is where true data alchemy happens – transforming and interpreting data to tell compelling stories that drive decision-making and knowledge discovery. Just as […]

The post The Da Vinci Code of Data: Mastering The Data Science Mind Map appeared first on MachineLearningMastery.com.

1 week, 6 days назад @ machinelearningmastery.com
Finding Value with Data: The Cohesive Force Behind Luxury Real Estate Decisions
Finding Value with Data: The Cohesive Force Behind Luxury Real Estate Decisions

The real estate industry is a vast network of stakeholders including agents, homeowners, investors, developers, municipal planners, and tech innovators, each bringing unique perspectives and objectives to the table. Within this intricate ecosystem, data emerges as the critical element that binds these diverse interests together, facilitating collaboration and innovation. PropTech, or Property Technology, illustrates this […]

The post Finding Value with Data: The Cohesive Force Behind Luxury Real Estate Decisions appeared first on MachineLearningMastery.com.

2 weeks, 2 days назад @ machinelearningmastery.com
Skewness Be Gone: Transformative Tricks for Data Scientists
Skewness Be Gone: Transformative Tricks for Data Scientists

Data transformations enable data scientists to refine, normalize, and standardize raw data into a format ripe for analysis. These transformations are not merely procedural steps; they are essential in mitigating biases, handling skewed distributions, and enhancing the robustness of statistical models. This post will primarily focus on how to address skewed data. By focusing on […]

The post Skewness Be Gone: Transformative Tricks for Data Scientists appeared first on MachineLearningMastery.com.

3 weeks назад @ machinelearningmastery.com
Best Free Resources to Learn Data Analysis and Data Science
Best Free Resources to Learn Data Analysis and Data Science

Sponsored Content In my decade of teaching online, the most significant inspiration has been that online learning democratizes access to education globally. Regardless of your ethnic background, income level, and geographical location—as long as you can surf the web—you can find an ocean of free educational content to help you learn new skills. […]

The post Best Free Resources to Learn Data Analysis and Data Science appeared first on MachineLearningMastery.com.

3 weeks, 2 days назад @ machinelearningmastery.com
Harmonizing Data: A Symphony of Segmenting, Concatenating, Pivoting, and Merging
Harmonizing Data: A Symphony of Segmenting, Concatenating, Pivoting, and Merging

In the world of data science, where raw information swirls in a cacophony of numbers and variables, lies the art of harmonizing data. Like a maestro conducting a symphony, the skilled data scientist orchestrates the disparate elements of datasets, weaving them together into a harmonious composition of insights. Welcome to a journey where data transcends […]

The post Harmonizing Data: A Symphony of Segmenting, Concatenating, Pivoting, and Merging appeared first on MachineLearningMastery.com.

3 weeks, 3 days назад @ machinelearningmastery.com
Beyond SQL: Transforming Real Estate Data into Actionable Insights with Pandas
Beyond SQL: Transforming Real Estate Data into Actionable Insights with Pandas

In the realm of data analysis, SQL stands as a mighty tool, renowned for its robust capabilities in managing and querying databases. However, Python’s pandas library brings SQL-like functionalities to the fingertips of analysts and data scientists, enabling sophisticated data manipulation and analysis without the need for a traditional SQL database. This exploration delves into […]

The post Beyond SQL: Transforming Real Estate Data into Actionable Insights with Pandas appeared first on MachineLearningMastery.com.

3 weeks, 5 days назад @ machinelearningmastery.com
Spotting the Exception: Classical Methods for Outlier Detection in Data Science
Spotting the Exception: Classical Methods for Outlier Detection in Data Science

Outliers are unique in that they often don’t play by the rules. These data points, which significantly differ from the rest, can skew your analyses and make your predictive models less accurate. Although detecting outliers is critical, there is no universally agreed-upon method for doing so. While some advanced techniques like machine learning offer solutions, […]

The post Spotting the Exception: Classical Methods for Outlier Detection in Data Science appeared first on MachineLearningMastery.com.

4 weeks, 1 day назад @ machinelearningmastery.com
Leveraging ANOVA and Kruskal-Wallis Tests to Analyze the Impact of the Great Recession on Housing Prices
Leveraging ANOVA and Kruskal-Wallis Tests to Analyze the Impact of the Great Recession on Housing Prices

In the world of real estate, numerous factors influence property prices. The economy, market demand, location, and even the year a property is sold can play significant roles. The years 2007 to 2009 marked a tumultuous time for the US housing market. This period, often referred to as the Great Recession, saw a drastic decline […]

The post Leveraging ANOVA and Kruskal-Wallis Tests to Analyze the Impact of the Great Recession on Housing Prices appeared first on MachineLearningMastery.com.

1 month назад @ machinelearningmastery.com
Garage or Not? Housing Insights Through the Chi-Squared Test for Ames, Iowa
Garage or Not? Housing Insights Through the Chi-Squared Test for Ames, Iowa

The Chi-squared test for independence is a statistical procedure employed to assess the relationship between two categorical variables – determining whether they are associated or independent. In the dynamic realm of real estate, where a property’s visual appeal often impacts its valuation, the exploration becomes particularly intriguing. But how often do you associate a house’s […]

The post Garage or Not? Housing Insights Through the Chi-Squared Test for Ames, Iowa appeared first on MachineLearningMastery.com.

1 month, 1 week назад @ machinelearningmastery.com
Testing Assumptions in Real Estate: A Dive into Hypothesis Testing with the Ames Housing Dataset
Testing Assumptions in Real Estate: A Dive into Hypothesis Testing with the Ames Housing Dataset

In the realm of inferential statistics, you often want to test specific hypotheses about our data. Using the Ames Housing dataset, you’ll delve deep into the concept of hypothesis testing and explore if the presence of an air conditioner affects the sale price of a house. Let’s get started. Overview This post unfolds through the […]

The post Testing Assumptions in Real Estate: A Dive into Hypothesis Testing with the Ames Housing Dataset appeared first on MachineLearningMastery.com.

1 month, 1 week назад @ machinelearningmastery.com
Inferential Insights: How Confidence Intervals Illuminate the Ames Real Estate Market
Inferential Insights: How Confidence Intervals Illuminate the Ames Real Estate Market

In the vast universe of data, it’s not always about what we can see but rather what we can infer. Confidence intervals, a cornerstone of inferential statistics, empower us to make educated guesses about a larger population based on our sample data. Using the Ames Housing dataset, let’s unravel the concept of confidence intervals and […]

The post Inferential Insights: How Confidence Intervals Illuminate the Ames Real Estate Market appeared first on MachineLearningMastery.com.

1 month, 2 weeks назад @ machinelearningmastery.com
Mastering Pair Plots for Visualization and Hypothesis Creation in the Ames Housing Market
Mastering Pair Plots for Visualization and Hypothesis Creation in the Ames Housing Market

Navigating the complex landscape of real estate analytics involves unraveling distinct narratives shaped by various property features within the housing market data. Our exploration today takes us into the realm of a potent yet frequently overlooked data visualization tool: the pair plot. This versatile graphic not only sheds light on the robustness and orientation of […]

The post Mastering Pair Plots for Visualization and Hypothesis Creation in the Ames Housing Market appeared first on MachineLearningMastery.com.

1 month, 2 weeks назад @ machinelearningmastery.com
Feature Relationships 101: Lessons from the Ames Housing Data
Feature Relationships 101: Lessons from the Ames Housing Data

In the realm of real estate, understanding the intricacies of property features and their impact on sale prices is paramount. In this exploration, we’ll dive deep into the Ames Housing dataset, shedding light on the relationships between various features and their correlation with the sale price. Harnessing the power of data visualization, we’ll unveil patterns, […]

The post Feature Relationships 101: Lessons from the Ames Housing Data appeared first on MachineLearningMastery.com.

1 month, 3 weeks назад @ machinelearningmastery.com
Exploring Dictionaries, Classifying Variables, and Imputing Data in the Ames Dataset
Exploring Dictionaries, Classifying Variables, and Imputing Data in the Ames Dataset

The real estate market is a complex ecosystem driven by numerous variables such as location, property features, market trends, and economic indicators. One dataset that offers a deep dive into this complexity is the Ames Housing dataset. Originating from Ames, Iowa, this dataset comprises various properties and their characteristics, ranging from the type of alley […]

The post Exploring Dictionaries, Classifying Variables, and Imputing Data in the Ames Dataset appeared first on MachineLearningMastery.com.

1 month, 3 weeks назад @ machinelearningmastery.com
ML in Production
последний пост None
Sorta Insightful Sorta Insightful
последний пост 4 days, 6 hours назад
Solving Crew Battle Strategy With Math
Solving Crew Battle Strategy With Math Solving Crew Battle Strategy With Math

This means we can reduce all the crew battle outcomes down to a single \(n \times n\) matrix, which I’ll call the crew battle matrix.

So I Wrote Some Python Code to Compute \(f\) for Arbitrary Crew BattlesLet’s consider the RPS crew battle again.

Here’s the matchup matrix:and here’s what my code outputs for the crew battle matrix, assuming optimal play.

But also, this suggests that crew battle strategy really isn’t that important in the first place!

If your crew loses, you don’t get to blame bad crew battle strategy.

4 days, 6 hours назад @ alexirpan.com
MIT Mystery Hunt 2024
MIT Mystery Hunt 2024 MIT Mystery Hunt 2024

This has spoilers for MIT Mystery Hunt 2024.

I hunted with teammate again this year, because there is nothing quite like writing a Mystery Hunt to forge friends through fire.

I needed to spend some to avoid hitting the vacation cap, and what better time than Mystery Hunt?

If you were forced to pick how a Mystery Hunt runs long, I think most people would pick the “too many puzzles” side of Mystery Hunt 2024 over the “too difficult puzzles” side of Mystery Hunt 2023.

So did Spoilr, the codebase for Mystery Hunt 2022, and tph-site from Mystery Hunt 2023.

2 months, 1 week назад @ alexirpan.com
My AI Timelines Have Sped Up (Again)
My AI Timelines Have Sped Up (Again) My AI Timelines Have Sped Up (Again)

In August 2020, I wrote a post about my AI timelines.

Diffusion based image augmentation has been shown to improve robot learning, and Anthropic has based a lot of its branding on constitutional AI and “RL from AI feedback”.

I don’t like AI Twitter for reasons I’ve explained here, but I especially do not AI twitter post-ChatGPT.

In AI, models can never do everything people claim they can, but what the models can do is ever-growing and never slides backward.

The bull is to say that we can figure out how to scale models, and scaled up models will solve all the other hard problems.

2 months, 2 weeks назад @ alexirpan.com
Far More Research Into Making Neopoints Than Anyone Needs to Know
Far More Research Into Making Neopoints Than Anyone Needs to Know Far More Research Into Making Neopoints Than Anyone Needs to Know

You know how much time I have spent studying how to squeeze Neopoints water out of the Neopets stone?

I’d say you can expect to get about 25k NP a day, depending on how many NP rewards you get.

Trudy’s Surprise also gives items for 7 day streaks, but these items are usually junk and not worth anything.

When you win against CPU opponents, you earn a small amount of Neopoints and an item drop.

Or your needs to…see someone do a lot of research into things that don’t matter?

4 months назад @ alexirpan.com
Everfree Northwest 2023
Everfree Northwest 2023 Everfree Northwest 2023

Everfree Northwest is a My Little Pony convention in the Seattle area.

Except, I heard that with the ending of BronyCon, Everfree Northwest is the largest pony convention left.

My Little Pony: Friendship is Magic is Generation 4, or G4, and is the one that kicked off the brony fandom.

People tend to assume that “pony concert” means “remixes of pony songs”, or at least “overt references to My Little Pony”.

The first was a signed copy of Ponies: The Galloping, the Magic: The Gathering x My Little Pony crossover.

6 months, 3 weeks назад @ alexirpan.com
Eight Years Later
Eight Years Later Eight Years Later

I started working on that post right after Mystery Hunt 2023 finished, and hardcore focused on getting it out ASAP.

The end result was that I was exerting “write Mystery Hunt” levels of effort (10-20 hrs/week) for 1.5 years straight.

markdown 1929 2023 - 04 - 21 - mh - 2023. markdown 114 2023 - 05 - 09 - bootes - 2023. markdown 153 2023 - 07 - 19 - ml - hurry .

markdownPosts in LimboPost about Dominion Online:Odds of writing this year: 5%Odds of writing eventually: 25%My priorities are moving away from Dominion.

Post about AI timelines:Odds of writing this year: 90%Odds of writing eventually: 99%I’m not planning a big update to the last timelines post.

7 months, 1 week назад @ alexirpan.com
Machine Learning Got Itself in a Big Damn Hurry
Machine Learning Got Itself in a Big Damn Hurry Machine Learning Got Itself in a Big Damn Hurry

As I asked questions about compute resources and the presenter’s position on generative AI ethics, I had a moment of realization.

Why are we talking about whether an RTX 3090 is big enough to finetune a LLaMa checkpoint?

The much easier and less-speculative way for a field to move faster is by having more people working in that field.

The MLP AI enthusiasts mentioned some pretrained LLMs that I had not even heard of.

I remember being a young whippersnapper, in the deep learning wave of 2015.

8 months, 1 week назад @ alexirpan.com
Lil'Log
последний пост None
The Spectator
последний пост 3 months, 2 weeks назад
Generative Science: Roles for Generative AI in Scientific Discovery
Generative Science: Roles for Generative AI in Scientific Discovery Generative Science: Roles for Generative AI in Scientific Discovery

Generative AI is a membership based concept, so it is defined by the set of approaches and applications that get put under that name.

I think this is one part of the intrigue of generative AI for science.

So maybe generative AI applications are part of this drive for a post-theory mode of science.

This generative science, it is hoped, can add vigour into the sciences, especially in cross-disciplinary ways and provide broad-based benefit.

Generative AI for Science presents opportunities for new ways of doing theory and renewed vigour across our sciences.

3 months, 2 weeks назад @ blog.shakirm.com
Responsibilities of the Pioneer: Generative AI and its Sociotechnical Foundations
Responsibilities of the Pioneer: Generative AI and its Sociotechnical Foundations Responsibilities of the Pioneer: Generative AI and its Sociotechnical Foundations

Keynote at the Stanford Human-centered AI 2023 Fall Conference on New Horizons in Generative AI: Science, Creativity, and Society.

Our conference today on new horizons in generative AI, invites us to think of the frontier of research and innovation.

These stories will expose some features of the sociotechnical foundations of generative AI that is my underlying message and call to action.

What he doesn’t know yet, is that this book will become a cornerstone of the field and industry of weather forecasting.

There is a specific and firm model for responsibility that is built on taking a sociotechnical approach to generative AI and our work.

5 months назад @ blog.shakirm.com
Machine Learning with Social Purpose
Machine Learning with Social Purpose Machine Learning with Social Purpose

Abstract: This talk talk has a single objective: to advocate for machine learning infused with social purpose.

In this way, social purpose transforms our field of machine learning: into something that is both technical and social.

So social purpose will make machine learning something that is both technical and social.

A more global field and industry can shift machine learning to be more general: magnifying social purpose.

Your continued volunteering, funding, support, and openness to these groups shows yet another way of infusing machine learning with social purpose.

7 months, 3 weeks назад @ blog.shakirm.com
Off the Convex Path
последний пост None
Jay Alammar
последний пост None
fast.ai NLP fast.ai NLP
последний пост None
大トロ 大トロ
последний пост None
🔬 Science
Papers With Code Papers With Code
последний пост 3 часа назад
/tianfu18/ Object Pose Estimation via the Aggregation of Diffusion Features
/tianfu18/ Object Pose Estimation via the Aggregation of Diffusion Features /tianfu18/ Object Pose Estimation via the Aggregation of Diffusion Features

However, these methods experience a significant performance drop when dealing with unseen objects.

To address this problem, we have an in-depth analysis on the features of diffusion models, e.g.

Stable Diffusion, which hold substantial potential for modeling unseen objects.

Based on this analysis, we then innovatively introduce these diffusion features for object pose estimation.

To achieve this, we propose three distinct architectures that can effectively capture and aggregate diffusion features of different granularity, greatly improving the generalizability of object pose estimation.

3 часа назад @ paperswithcode.com
/hakys-a/ Large Language Models Need Consultants for Reasoning: Becoming an Expert in a Complex Human System Through Behavior Simulation
/hakys-a/ Large Language Models Need Consultants for Reasoning: Becoming an Expert in a Complex Human System Through Behavior Simulation /hakys-a/ Large Language Models Need Consultants for Reasoning: Becoming an Expert in a Complex Human System Through Behavior Simulation

In this paper, we delve into the reasoning abilities of LLMs within complex human systems.

We propose a novel reasoning framework, termed ``Mosaic Expert Observation Wall'' (MEOW) exploiting generative-agents-based simulation technique.

In the MEOW framework, simulated data are utilized to train an expert model concentrating ``experience'' about a specific task in each independent time of simulation.

It is the accumulated ``experience'' through the simulation that makes for an expert on a task in a complex human system.

The results indicate that our proposed methodology can cooperate with existing methodologies to enhance the reasoning abilities of LLMs in complex human systems.

4 часа назад @ paperswithcode.com
/p-karisani/ Fact Checking Beyond Training Set
/p-karisani/ Fact Checking Beyond Training Set /p-karisani/ Fact Checking Beyond Training Set

We empirically demonstrate that the commonly used fact checking pipeline, known as the retriever-reader, suffers from performance deterioration when it is trained on the labeled data from one domain and used in another domain.

We then focus on the reader component and propose to train it such that it is insensitive towards the order of claims and evidence documents.

To our knowledge, there is no publicly available multi-topic fact checking dataset.

Thus, we propose a simple automatic method to re-purpose two well-known fact checking datasets.

We then construct eight fact checking scenarios from these datasets, and compare our model to a set of strong baseline models, including recent domain…

5 часов назад @ paperswithcode.com
/Deceptrax123/ Colour and Brush Stroke Pattern Recognition in Abstract Art using Modified Deep Convolutional Generative Adversarial Networks
/Deceptrax123/ Colour and Brush Stroke Pattern Recognition in Abstract Art using Modified Deep Convolutional Generative Adversarial Networks /Deceptrax123/ Colour and Brush Stroke Pattern Recognition in Abstract Art using Modified Deep Convolutional Generative Adversarial Networks

Abstract Art is an immensely popular, discussed form of art that often has the ability to depict the emotions of an artist.

Many researchers have made attempts to study abstract art in the form of edge detection, brush stroke and emotion recognition algorithms using machine and deep learning.

This papers describes the study of a wide distribution of abstract paintings using Generative Adversarial Neural Networks(GAN).

GANs have the ability to learn and reproduce a distribution enabling researchers and scientists to effectively explore and study the generated image space.

These findings validate the effectiveness of the proposed approach, emphasising its potential to revolutionise the field …

5 часов назад @ paperswithcode.com
/datascienceuibk/ TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions
/datascienceuibk/ TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions /datascienceuibk/ TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions

We introduce a framework for the automatic hint generation for factoid questions, employing it to construct TriviaHG, a novel large-scale dataset featuring 160,230 hints corresponding to 16,645 questions from the TriviaQA dataset.

Additionally, we present an automatic evaluation method that measures the Convergence and Familiarity quality attributes of hints.

To evaluate the TriviaHG dataset and the proposed evaluation method, we enlisted 10 individuals to annotate 2,791 hints and tasked 6 humans with answering questions using the provided hints.

Moreover, the proposed automatic evaluation methods showed a robust correlation with annotators' results.

Conclusively, the findings highlight thr…

6 часов назад @ paperswithcode.com
/ysh-1998/ Sequential Recommendation with Latent Relations based on Large Language Model
/ysh-1998/ Sequential Recommendation with Latent Relations based on Large Language Model /ysh-1998/ Sequential Recommendation with Latent Relations based on Large Language Model

In this paper, we propose a novel relation-aware sequential recommendation framework with Latent Relation Discovery (LRD).

The motivation is that LLM contains abundant world knowledge, which can be adopted to mine latent relations of items for recommendation.

These representations are fed into a latent relation discovery module based on the discrete state variational autoencoder (DVAE).

Experimental results on multiple public datasets demonstrate our proposed latent relations discovery method can be incorporated with existing relation-aware sequential recommendation models and significantly improve the performance.

Further analysis experiments indicate the effectiveness and reliability of t…

6 часов назад @ paperswithcode.com
/xurong-liang/ Lightweight Embeddings for Graph Collaborative Filtering
/xurong-liang/ Lightweight Embeddings for Graph Collaborative Filtering /xurong-liang/ Lightweight Embeddings for Graph Collaborative Filtering

Graph neural networks (GNNs) are currently one of the most performant collaborative filtering methods.

As a common practice for scalable embeddings, parameter sharing enables the use of fewer embedding vectors (i.e., meta-embeddings).

However, in the context of GNN-based collaborative filtering, such a fixed mapping omits the semantic correlations between entities that are evident in the user-item interaction graph, leading to suboptimal recommendation performance.

To this end, we propose Lightweight Embeddings for Graph Collaborative Filtering (LEGCF), a parameter-efficient embedding framework dedicated to GNN-based recommenders.

The meta-embeddings and assignment matrix are alternately up…

6 часов назад @ paperswithcode.com
/hamidrezaeiv/ Nonlinear model reduction for operator learning
/hamidrezaeiv/ Nonlinear model reduction for operator learning /hamidrezaeiv/ Nonlinear model reduction for operator learning

Operator learning provides methods to approximate mappings between infinite-dimensional function spaces.

Deep operator networks (DeepONets) are a notable architecture in this field.

Recently, an extension of DeepONet based on model reduction and neural networks, proper orthogonal decomposition (POD)-DeepONet, has been able to outperform other architectures in terms of accuracy for several benchmark tests.

We extend this idea towards nonlinear model order reduction by proposing an efficient framework that combines neural networks with kernel principal component analysis (KPCA) for operator learning.

Our results demonstrate the superior performance of KPCA-DeepONet over POD-DeepONet.

6 часов назад @ paperswithcode.com
/hxwork/ HandBooster: Boosting 3D Hand-Mesh Reconstruction by Conditional Synthesis and Sampling of Hand-Object Interactions
/hxwork/ HandBooster: Boosting 3D Hand-Mesh Reconstruction by Conditional Synthesis and Sampling of Hand-Object Interactions /hxwork/ HandBooster: Boosting 3D Hand-Mesh Reconstruction by Conditional Synthesis and Sampling of Hand-Object Interactions

Reconstructing 3D hand mesh robustly from a single image is very challenging, due to the lack of diversity in existing real-world datasets.

While data synthesis helps relieve the issue, the syn-to-real gap still hinders its usage.

In this work, we present HandBooster, a new approach to uplift the data diversity and boost the 3D hand-mesh reconstruction performance by training a conditional generative space on hand-object interactions and purposely sampling the space to synthesize effective data samples.

Then, we design a novel condition creator based on our similarity-aware distribution sampling strategies to deliberately find novel and realistic interaction poses that are distinctive from …

6 часов назад @ paperswithcode.com
/ysh-1998/ Common Sense Enhanced Knowledge-based Recommendation with Large Language Model
/ysh-1998/ Common Sense Enhanced Knowledge-based Recommendation with Large Language Model /ysh-1998/ Common Sense Enhanced Knowledge-based Recommendation with Large Language Model

Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance.

Recently, benefiting from the emergent world knowledge of the large language model, efficient acquisition of common sense has become possible.

In this paper, we propose a novel knowledge-based recommendation framework incorporating common sense, CSRec, which can be flexibly coupled to existing knowledge-based methods.

Considering the challenge of the knowledge gap between the common sense-based knowledge graph and metadata-based knowledge graph, we propose a knowledge fusion approach based on mutual informatio…

6 часов назад @ paperswithcode.com
/dfki-nlp/ A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages
/dfki-nlp/ A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages /dfki-nlp/ A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages

User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world.

However, the existing clinical corpora predominantly revolve around scientific articles in English.

This work presents a multilingual corpus of texts concerning ADRs gathered from diverse sources, including patient fora, social media, and clinical reports in German, French, and Japanese.

Our corpus contains annotations covering 12 entity types, four attribute types, and 13 relation types.

We provide statistics to highlight certain challenges associated with the corpus and conduct preliminary experiments resulting in strong…

6 часов назад @ paperswithcode.com
/and-mill/ The Impact of Uniform Inputs on Activation Sparsity and Energy-Latency Attacks in Computer Vision
/and-mill/ The Impact of Uniform Inputs on Activation Sparsity and Energy-Latency Attacks in Computer Vision /and-mill/ The Impact of Uniform Inputs on Activation Sparsity and Energy-Latency Attacks in Computer Vision

The energy and decision latency are two critical aspects to ensure a sustainable and practical application.

In computer vision, the proposed strategy crafts inputs with less activation sparsity which could otherwise be used to accelerate the computation.

In this paper, we analyze the mechanism how these energy-latency attacks reduce activation sparsity.

Based on these insights, we propose two new simple, yet effective strategies for crafting sponge examples: sampling images from a probability distribution and identifying dense, yet inconspicuous inputs in natural datasets.

We also show that our sponge examples transfer between different neural networks.

6 часов назад @ paperswithcode.com
/google-deepmind/ Long-form factuality in large language models
/google-deepmind/ Long-form factuality in large language models /google-deepmind/ Long-form factuality in large language models

Large language models (LLMs) often generate content that contains factual errors when responding to fact-seeking prompts on open-ended topics.

To benchmark a model's long-form factuality in open domains, we first use GPT-4 to generate LongFact, a prompt set comprising thousands of questions spanning 38 topics.

We then propose that LLM agents can be used as automated evaluators for long-form factuality through a method which we call Search-Augmented Factuality Evaluator (SAFE).

Furthermore, we propose extending F1 score as an aggregated metric for long-form factuality.

We also benchmark thirteen language models on LongFact across four model families (Gemini, GPT, Claude, and PaLM-2), finding…

6 часов назад @ paperswithcode.com
/geox-lab/ Homogeneous Tokenizer Matters: Homogeneous Visual Tokenizer for Remote Sensing Image Understanding
/geox-lab/ Homogeneous Tokenizer Matters: Homogeneous Visual Tokenizer for Remote Sensing Image Understanding /geox-lab/ Homogeneous Tokenizer Matters: Homogeneous Visual Tokenizer for Remote Sensing Image Understanding

The tokenizer, as one of the fundamental components of large models, has long been overlooked or even misunderstood in visual tasks.

We designed a simple HOmogeneous visual tOKenizer: HOOK.

To achieve homogeneity, the OPM splits the image into 4*4 pixel seeds and then utilizes the attention mechanism to perceive SIRs.

The results demonstrate that the visual tokens obtained by HOOK correspond to individual objects, which demonstrates homogeneity.

HOOK outperformed Patch Embed by 6\% and 10\% in the two tasks and achieved state-of-the-art performance compared to the baselines used for comparison.

6 часов назад @ paperswithcode.com
/uraken38/ Clustering Change Sign Detection by Fusing Mixture Complexity
/uraken38/ Clustering Change Sign Detection by Fusing Mixture Complexity /uraken38/ Clustering Change Sign Detection by Fusing Mixture Complexity

This paper proposes an early detection method for cluster structural changes.

Cluster structure refers to discrete structural characteristics, such as the number of clusters, when data are represented using finite mixture models, such as Gaussian mixture models.

For finite mixture models, the concept of mixture complexity (MC) measures the continuous cluster size by considering the cluster proportion bias and overlap between clusters.

In this paper, we propose MC fusion as an extension of MC to handle situations in which multiple mixture numbers are possible in a finite mixture model.

By incorporating the fusion of multiple models, our approach accurately captured the cluster structure duri…

6 часов назад @ paperswithcode.com
Papers With Code Papers With Code
последний пост 3 часа назад
/dvlab-research/ Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
/dvlab-research/ Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models /dvlab-research/ Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models

In this work, we introduce Mini-Gemini, a simple and effective framework enhancing multi-modality Vision Language Models (VLMs).

Despite the advancements in VLMs facilitating basic visual dialog and reasoning, a performance gap persists compared to advanced models like GPT-4 and Gemini.

To enhance visual tokens, we propose to utilize an additional visual encoder for high-resolution refinement without increasing the visual token count.

In general, Mini-Gemini further mines the potential of VLMs and empowers current frameworks with image understanding, reasoning, and generation simultaneously.

Mini-Gemini supports a series of dense and MoE Large Language Models (LLMs) from 2B to 34B.

6 часов назад @ paperswithcode.com
/aniloid2/ SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks
/aniloid2/ SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks /aniloid2/ SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks

Language models (LMs) are indispensable tools for natural language processing tasks, but their vulnerability to adversarial attacks remains a concern.

While current research has explored adversarial training techniques, their improvements to defend against word-level attacks have been limited.

In this work, we propose a novel approach called Semantic Robust Defence (SemRoDe), a Macro Adversarial Training strategy to enhance the robustness of LMs.

We hypothesize that if samples were not projected into an adversarial domain, but instead to a domain with minimal shift, it would improve attack robustness.

With this, our model is able to learn more generalized representations by aligning the mod…

6 часов назад @ paperswithcode.com
/mlivanos/ Identification and Uses of Deep Learning Backbones via Pattern Mining
/mlivanos/ Identification and Uses of Deep Learning Backbones via Pattern Mining /mlivanos/ Identification and Uses of Deep Learning Backbones via Pattern Mining

Deep learning is extensively used in many areas of data mining as a black-box method with impressive results.

However, understanding the core mechanism of how deep learning makes predictions is a relatively understudied problem.

Here we explore the notion of identifying a backbone of deep learning for a given group of instances.

We view each instance for a given group as activating a subset of neurons and attempt to find a subgraph of neurons associated with a given concept/group.

As an alternative, we explore a coverage-based heuristic approach related to pattern mining, and show it converges to a Pareto equilibrium point of the ILP formulation.

7 часов назад @ paperswithcode.com
/inhwanbae/ SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model
/inhwanbae/ SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model /inhwanbae/ SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model

There are five types of trajectory prediction tasks: deterministic, stochastic, domain adaptation, momentary observation, and few-shot.

These associated tasks are defined by various factors, such as the length of input paths, data split and pre-processing methods.

In this paper, we propose SingularTrajectory, a diffusion-based universal trajectory prediction framework to reduce the performance gap across the five tasks.

Finally, we adopt a diffusion-based predictor to further enhance the prototype paths using a cascaded denoising process.

Our unified framework ensures the generality across various benchmark settings such as input modality, and trajectory lengths.

7 часов назад @ paperswithcode.com
/samsung/ NL-ITI: Optimizing Probing and Intervention for Improvement of ITI Method
/samsung/ NL-ITI: Optimizing Probing and Intervention for Improvement of ITI Method /samsung/ NL-ITI: Optimizing Probing and Intervention for Improvement of ITI Method

In first stage, it identifies attention heads, which contain the highest amount of desired type of knowledge (e.g., truthful).

Afterwards, during inference, LLM activations are shifted for chosen subset of attention heads.

We further improved the ITI framework by introducing a nonlinear probing and multi-token intervention - Non-Linear ITI (NL-ITI).

NL-ITI is tested on diverse multiple-choice benchmarks, including TruthfulQA, on which we report around 14% MC1 metric improvement with respect to the baseline ITI results.

NL-ITI achieves also encouraging results on other testsets - on Business Ethics subdomain of MMLU, around 18% MC1 improvement over baseline LLaMA2-7B.

7 часов назад @ paperswithcode.com
/imagegridworth/ An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM
/imagegridworth/ An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM /imagegridworth/ An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM

Stimulated by the sophisticated reasoning capabilities of recent Large Language Models (LLMs), a variety of strategies for bridging video modality have been devised.

A prominent strategy involves Video Language Models (VideoLMs), which train a learnable interface with video data to connect advanced vision encoders with LLMs.

In this study, we introduce a simple yet novel strategy where only a single Vision Language Model (VLM) is utilized.

The resulting single image is termed as an image grid.

Therefore, the image grid approach enables direct application of a single high-performance VLM without necessitating any video-data training.

7 часов назад @ paperswithcode.com
/abess-team/ skscope: Fast Sparsity-Constrained Optimization in Python
/abess-team/ skscope: Fast Sparsity-Constrained Optimization in Python /abess-team/ skscope: Fast Sparsity-Constrained Optimization in Python

Applying iterative solvers on sparsity-constrained optimization (SCO) requires tedious mathematical deduction and careful programming/debugging that hinders these solvers' broad impact.

In the paper, the library skscope is introduced to overcome such an obstacle.

With skscope, users can solve the SCO by just programming the objective function.

Numerical experiments reveal the available solvers in skscope can achieve up to 80x speedup on the competing relaxation solutions obtained via the benchmarked convex solver.

skscope is published on the Python Package Index (PyPI) and Conda, and its source code is available at: https://github.com/abess-team/skscope.

7 часов назад @ paperswithcode.com
/viper-purdue/ Theoretical Bound-Guided Hierarchical VAE for Neural Image Codecs
/viper-purdue/ Theoretical Bound-Guided Hierarchical VAE for Neural Image Codecs /viper-purdue/ Theoretical Bound-Guided Hierarchical VAE for Neural Image Codecs

Recent studies reveal a significant theoretical link between variational autoencoders (VAEs) and rate-distortion theory, notably in utilizing VAEs to estimate the theoretical upper bound of the information rate-distortion function of images.

Such estimated theoretical bounds substantially exceed the performance of existing neural image codecs (NICs).

To narrow this gap, we propose a theoretical bound-guided hierarchical VAE (BG-VAE) for NIC.

The proposed BG-VAE leverages the theoretical bound to guide the NIC model towards enhanced performance.

We implement the BG-VAE using Hierarchical VAEs and demonstrate its effectiveness through extensive experiments.

7 часов назад @ paperswithcode.com
/cccccczh404/ Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding
/cccccczh404/ Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding /cccccczh404/ Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding

This paper introduces H-SAM: a prompt-free adaptation of SAM tailored for efficient fine-tuning of medical images via a two-stage hierarchical decoding procedure.

In the initial stage, H-SAM employs SAM's original decoder to generate a prior probabilistic mask, guiding a more intricate decoding process in the second stage.

This approach enables SAM to effectively integrate learned medical priors, facilitating enhanced adaptation for medical image segmentation with limited samples.

Our H-SAM demonstrates a 4.78% improvement in average Dice compared to existing prompt-free SAM variants for multi-organ segmentation using only 10% of 2D slices.

Notably, without using any unlabeled data, H-SAM e…

7 часов назад @ paperswithcode.com
/king-haw/ Generative Medical Segmentation
/king-haw/ Generative Medical Segmentation /king-haw/ Generative Medical Segmentation

Rapid advancements in medical image segmentation performance have been significantly driven by the development of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs).

In this manuscript, we introduce Generative Medical Segmentation (GMS), a novel approach leveraging a generative model for image segmentation.

This process culminates in generating a precise segmentation mask within the image space using the pre-trained VAE decoder.

Our extensive experimental analysis across five public datasets in different medical imaging domains demonstrates GMS outperforms existing discriminative segmentation models and has remarkable domain generalization.

Our experiments suggest GMS could…

7 часов назад @ paperswithcode.com
/sabrinaherbst/ On Optimizing Hyperparameters for Quantum Neural Networks
/sabrinaherbst/ On Optimizing Hyperparameters for Quantum Neural Networks /sabrinaherbst/ On Optimizing Hyperparameters for Quantum Neural Networks

The increasing capabilities of Machine Learning (ML) models go hand in hand with an immense amount of data and computational power required for training.

Quantum Computing, and specifically Quantum Machine Learning (QML), can offer significant theoretical speed-ups and enhanced expressive power.

However, training QML models requires tuning various hyperparameters, which is a nontrivial task and suboptimal choices can highly affect the trainability and performance of the models.

In this study, we identify the most impactful hyperparameters and collect data about the performance of QML models.

We compare different configurations and provide researchers with performance data and concrete sugge…

7 часов назад @ paperswithcode.com
/luigisigillo/ Ship in Sight: Diffusion Models for Ship-Image Super Resolution
/luigisigillo/ Ship in Sight: Diffusion Models for Ship-Image Super Resolution /luigisigillo/ Ship in Sight: Diffusion Models for Ship-Image Super Resolution

In recent years, remarkable advancements have been achieved in the field of image generation, primarily driven by the escalating demand for high-quality outcomes across various image generation subtasks, such as inpainting, denoising, and super resolution.

A major effort is devoted to exploring the application of super-resolution techniques to enhance the quality of low-resolution images.

In this context, our method explores in depth the problem of ship image super resolution, which is crucial for coastal and port surveillance.

We investigate the opportunity given by the growing interest in text-to-image diffusion models, taking advantage of the prior knowledge that such foundation models h…

7 часов назад @ paperswithcode.com
/jkbehrens/ CoBOS: Constraint-Based Online Scheduler for Human-Robot Collaboration
/jkbehrens/ CoBOS: Constraint-Based Online Scheduler for Human-Robot Collaboration /jkbehrens/ CoBOS: Constraint-Based Online Scheduler for Human-Robot Collaboration

Fixed robot programs leave no room to diverge from a fixed protocol.

We propose a novel approach of online constraint-based scheduling in a reactive execution control framework facilitating behavior trees called CoBOS.

This allows the robot to adapt to uncertain events such as delayed activity completions and activity selection (by the human).

In addition to the improved working conditions, our algorithm leads to increased efficiency, even in highly uncertain scenarios.

Initial real robot experiments using a Franka Emika Panda robot and human tracking based on HTC Vive VR gloves look promising.

7 часов назад @ paperswithcode.com
/vleplat/ Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation Models
/vleplat/ Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation Models /vleplat/ Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation Models

Regularized nonnegative low-rank approximations such as sparse Nonnegative Matrix Factorization or sparse Nonnegative Tucker Decomposition are an important branch of dimensionality reduction models with enhanced interpretability.

By studying a more general model called the Homogeneous Regularized Scale-Invariant, we prove that the scale-invariance inherent to low-rank approximation models causes an implicit regularization with both unexpected beneficial and detrimental effects.

Some of these results were already known but restricted to specific instances of regularized low-rank approximations.

We also derive a generic Majorization Minimization algorithm that handles many regularized nonnega…

7 часов назад @ paperswithcode.com
/inhwanbae/ Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction
/inhwanbae/ Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction /inhwanbae/ Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction

Inspired by the recent success of language foundation models, in this paper, we propose LMTraj (Language-based Multimodal Trajectory predictor), which recasts the trajectory prediction task into a sort of question-answering problem.

The transformed numerical and image data are then wrapped into the question-answering template for use in a language model.

We then train a numerical tokenizer with the prompt data.

Lastly, we train the language model using the numerical tokenizer and all of the question-answer prompts.

Applying our LMTraj, we show that the language-based model can be a powerful pedestrian trajectory predictor, and outperforms existing numerical-based predictor methods.

7 часов назад @ paperswithcode.com
Papers With Code Papers With Code
последний пост 3 часа назад
/gabrieletaz/ Supervised Multiple Kernel Learning approaches for multi-omics data integration
/gabrieletaz/ Supervised Multiple Kernel Learning approaches for multi-omics data integration /gabrieletaz/ Supervised Multiple Kernel Learning approaches for multi-omics data integration

Advances in high-throughput technologies have originated an ever-increasing availability of omics datasets.

The integration of multiple heterogeneous data sources is currently an issue for biology and bioinformatics.

Multiple kernel learning (MKL) has shown to be a flexible and valid approach to consider the diverse nature of multi-omics inputs, despite being an underused tool in genomic data mining.We provide novel MKL approaches based on different kernel fusion strategies.To learn from the meta-kernel of input kernels, we adaptedunsupervised integration algorithms for supervised tasks with support vector machines.We also tested deep learning architectures for kernel fusion and classificat…

7 часов назад @ paperswithcode.com
/xiaofeng-life/ A Semi-supervised Nighttime Dehazing Baseline with Spatial-Frequency Aware and Realistic Brightness Constraint
/xiaofeng-life/ A Semi-supervised Nighttime Dehazing Baseline with Spatial-Frequency Aware and Realistic Brightness Constraint /xiaofeng-life/ A Semi-supervised Nighttime Dehazing Baseline with Spatial-Frequency Aware and Realistic Brightness Constraint

However, few studies have considered the characteristics of nighttime hazy scenes.

First, there may be multiple active colored light sources with lower illumination intensity in nighttime scenes, which may cause haze, glow and noise with localized, coupled and frequency inconsistent characteristics.

Second, due to the domain discrepancy between simulated and real-world data, unrealistic brightness may occur when applying a dehazing model trained on simulated data to real-world data.

To address the above two issues, we propose a semi-supervised model for real-world nighttime dehazing.

Second, a pseudo-label-based retraining strategy and a local window-based brightness loss for semi-supervise…

7 часов назад @ paperswithcode.com
/cmu-llab/ Improved Neural Protoform Reconstruction via Reflex Prediction
/cmu-llab/ Improved Neural Protoform Reconstruction via Reflex Prediction /cmu-llab/ Improved Neural Protoform Reconstruction via Reflex Prediction

Protolanguage reconstruction is central to historical linguistics.

Not surprisingly, numerous computational linguists have attempted to operationalize comparative reconstruction through various computational models, the most successful of which have been supervised encoder-decoder models, which treat the problem of predicting protoforms given sets of reflexes as a sequence-to-sequence problem.

We argue that this framework ignores one of the most important aspects of the comparative method: not only should protoforms be inferable from cognate sets (sets of related reflexes) but the reflexes should also be inferable from the protoforms.

Leveraging another line of research -- reflex prediction…

7 часов назад @ paperswithcode.com
/themody/ Faster Convergence for Transformer Fine-tuning with Line Search Methods
/themody/ Faster Convergence for Transformer Fine-tuning with Line Search Methods /themody/ Faster Convergence for Transformer Fine-tuning with Line Search Methods

Recent works have shown that line search methods greatly increase performance of traditional stochastic gradient descent methods on a variety of datasets and architectures [1], [2].

In this work we succeed in extending line search methods to the novel and highly popular Transformer architecture and dataset domains in natural language processing.

More specifically, we combine the Armijo line search with the Adam optimizer and extend it by subdividing the networks architecture into sensible units and perform the line search separately on these local units.

Our optimization method outperforms the traditional Adam optimizer and achieves significant performance improvements for small data sets o…

7 часов назад @ paperswithcode.com
/THU-VCLab/ Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes
/THU-VCLab/ Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes /THU-VCLab/ Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes

Most current works resort to the whole observed point cloud for 6-Dof grasp generation, ignoring the guidance information excavated from global semantics, thus limiting high-quality grasp generation and real-time performance.

In this work, we show that the widely used heatmaps are underestimated in the efficiency of 6-Dof grasp generation.

Therefore, we propose an effective local grasp generator combined with grasp heatmaps as guidance, which infers in a global-to-local semantic-to-point way.

Specifically, Gaussian encoding and the grid-based strategy are applied to predict grasp heatmaps as guidance to aggregate local points into graspable regions and provide global semantic information.

B…

7 часов назад @ paperswithcode.com
/arise-lab/ CYCLE: Learning to Self-Refine the Code Generation
/arise-lab/ CYCLE: Learning to Self-Refine the Code Generation /arise-lab/ CYCLE: Learning to Self-Refine the Code Generation

Pre-trained code language models have achieved promising performance in code generation and improved the programming efficiency of human developers.

Unfortunately, our study reveals that code LMs cannot efficiently self-refine their faulty generations as well.

In this paper, we propose CYCLE framework, learning to self-refine the faulty generation according to the available feedback, such as the execution results reported by the test suites.

We evaluate CYCLE on three popular code generation benchmarks, HumanEval, MBPP, and APPS.

The results reveal that CYCLE successfully maintains, sometimes improves, the quality of one-time code generation, while significantly improving the self-refinemen…

7 часов назад @ paperswithcode.com
/monalissaa/ Attention Calibration for Disentangled Text-to-Image Personalization
/monalissaa/ Attention Calibration for Disentangled Text-to-Image Personalization /monalissaa/ Attention Calibration for Disentangled Text-to-Image Personalization

However, an intriguing problem persists: Is it possible to capture multiple, novel concepts from one single reference image?

In this paper, we identify that existing approaches fail to preserve visual consistency with the reference image and eliminate cross-influence from concepts.

To alleviate this, we propose an attention calibration mechanism to improve the concept-level understanding of the T2I model.

Together, our proposed method, dubbed DisenDiff, can learn disentangled multiple concepts from one single image and produce novel customized images with learned concepts.

More importantly, our proposed techniques are compatible with LoRA and inpainting pipelines, enabling more interactive …

7 часов назад @ paperswithcode.com
/haomo-ai/ ModaLink: Unifying Modalities for Efficient Image-to-PointCloud Place Recognition
/haomo-ai/ ModaLink: Unifying Modalities for Efficient Image-to-PointCloud Place Recognition /haomo-ai/ ModaLink: Unifying Modalities for Efficient Image-to-PointCloud Place Recognition

Place recognition is an important task for robots and autonomous cars to localize themselves and close loops in pre-built maps.

While single-modal sensor-based methods have shown satisfactory performance, cross-modal place recognition that retrieving images from a point-cloud database remains a challenging problem.

In this work, we introduce a fast and lightweight framework to encode images and point clouds into place-distinctive descriptors.

We propose an effective Field of View (FoV) transformation module to convert point clouds into an analogous modality as images.

We further design a non-negative factorization-based encoder to extract mutually consistent semantic features between point …

7 часов назад @ paperswithcode.com
/chenshuang-zhang/ ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object
/chenshuang-zhang/ ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object /chenshuang-zhang/ ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object

We establish rigorous benchmarks for visual perception robustness.

Synthetic images such as ImageNet-C, ImageNet-9, and Stylized ImageNet provide specific type of evaluation over synthetic corruptions, backgrounds, and textures, yet those robustness benchmarks are restricted in specified variations and have low synthetic quality.

In this work, we introduce generative model as a data source for synthesizing hard images that benchmark deep models' robustness.

Leveraging diffusion models, we are able to generate images with more diversified backgrounds, textures, and materials than any prior work, where we term this benchmark as ImageNet-D.

Our work suggests that diffusion models can be an eff…

7 часов назад @ paperswithcode.com
/lavi-lab/ Beyond Embeddings: The Promise of Visual Table in Multi-Modal Models
/lavi-lab/ Beyond Embeddings: The Promise of Visual Table in Multi-Modal Models /lavi-lab/ Beyond Embeddings: The Promise of Visual Table in Multi-Modal Models

Visual representation learning has been a cornerstone in computer vision, evolving from supervised learning with human-annotated labels to aligning image-text pairs from the Internet.

Despite recent advancements in multi-modal large language models (MLLMs), the visual representations they rely on, such as CLIP embeddings, often lack access to external world knowledge critical for real-world visual reasoning.

In this work, we propose Visual Table, a novel visual representation tailored for MLLMs.

We further develop a scalable generator for visual table generation and train it on small-scale annotations from GPT4V.

When visual tables serve as standalone visual representations, our model can c…

7 часов назад @ paperswithcode.com
/lshi91/ Tensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering
/lshi91/ Tensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering /lshi91/ Tensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering

Graph learning is widely recognized as a crucial technique in multi-view clustering.

Existing graph learning methods typically involve constructing an adaptive neighbor graph based on probabilistic neighbors and then learning a consensus graph to for clustering, however, they are confronted with two limitations.

In response to the aforementioned drawbacks, we in this paper propose a novel tensor-based graph learning framework that simultaneously considers consistency and specificity for multi-view clustering.

By making an assumption that the learned neighbor graph of each view comprises both a consistent graph and a view-specific graph, we formulate a new tensor-based target graph learning …

7 часов назад @ paperswithcode.com
/aradhye2002/ ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation
/aradhye2002/ ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation /aradhye2002/ ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation

In the absence of parallax cues, a learning-based single image depth estimation (SIDE) model relies heavily on shading and contextual cues in the image.

It has been shown that using embeddings from pre-trained foundational models, such as CLIP, improves zero shot transfer in several applications.

Taking inspiration from this, in our paper we explore the use of global image priors generated from a pre-trained ViT model to provide more detailed contextual information.

Based on this idea, we propose a new SIDE model using a diffusion backbone which is conditioned on ViT embeddings.

And on KITTI dataset, achieving Sq Rel error of 0.139 (2% improvement) compared to 0.142 by the current SOTA (GED…

7 часов назад @ paperswithcode.com
/rapisurazurite/ DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis
/rapisurazurite/ DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis /rapisurazurite/ DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis

The rapid progress in deep learning has given rise to hyper-realistic facial forgery methods, leading to concerns related to misinformation and security risks.

Existing face forgery datasets have limitations in generating high-quality facial images and addressing the challenges posed by evolving generative techniques.

To combat this, we present DiffusionFace, the first diffusion-based face forgery dataset, covering various forgery categories, including unconditional and Text Guide facial image generation, Img2Img, Inpaint, and Diffusion-based facial exchange algorithms.

Our DiffusionFace dataset stands out with its extensive collection of 11 diffusion models and the high-quality of the gene…

7 часов назад @ paperswithcode.com
/qrzou/ ParCo: Part-Coordinating Text-to-Motion Synthesis
/qrzou/ ParCo: Part-Coordinating Text-to-Motion Synthesis /qrzou/ ParCo: Part-Coordinating Text-to-Motion Synthesis

We study a challenging task: text-to-motion synthesis, aiming to generate motions that align with textual descriptions and exhibit coordinated movements.

Currently, the part-based methods introduce part partition into the motion synthesis process to achieve finer-grained generation.

However, these methods encounter challenges such as the lack of coordination between different part motions and difficulties for networks to understand part concepts.

In this paper, we propose Part-Coordinating Text-to-Motion Synthesis (ParCo), endowed with enhanced capabilities for understanding part motions and communication among different part motion generators, ensuring a coordinated and fined-grained motio…

7 часов назад @ paperswithcode.com
/doubleclass/ Generative Multi-modal Models are Good Class-Incremental Learners
/doubleclass/ Generative Multi-modal Models are Good Class-Incremental Learners /doubleclass/ Generative Multi-modal Models are Good Class-Incremental Learners

In class-incremental learning (CIL) scenarios, the phenomenon of catastrophic forgetting caused by the classifier's bias towards the current task has long posed a significant challenge.

It is mainly caused by the characteristic of discriminative models.

With the growing popularity of the generative multi-modal models, we would explore replacing discriminative models with generative ones for CIL.

However, transitioning from discriminative to generative models requires addressing two key challenges.

To this end, we propose a novel generative multi-modal model (GMM) framework for class-incremental learning.

7 часов назад @ paperswithcode.com
💼 University and corporation labs
DeepMind DeepMind
последний пост 1 week, 1 day назад
TacticAI: an AI assistant for football tactics
TacticAI: an AI assistant for football tactics TacticAI: an AI assistant for football tactics

Research TacticAI: an AI assistant for football tactics ShareCopy link ×As part of our multi-year collaboration with Liverpool FC, we develop a full AI system that can advise coaches on corner kicks 'Corner taken quickly… Origi!'

Our first paper, Game Plan, looked at why AI should be used in assisting football tactics, highlighting examples such as analyzing penalty kicks.

Predicting corner kick outcomes with geometric deep learning A corner kick is awarded when the ball passes over the byline, after touching a player of the defending team.

With TacticAI, we have developed a capable AI assistant for football tactics and achieved a milestone in developing useful assistants in sports AI.

We s…

1 week, 1 day назад @ deepmind.google
SIMA generalist AI agent for 3D virtual environments
SIMA generalist AI agent for 3D virtual environments SIMA generalist AI agent for 3D virtual environments

In a new technical report, we introduce SIMA, short for Scalable Instructable Multiworld Agent, a generalist AI agent for 3D virtual settings.

SIMA: a versatile AI agent SIMA is an AI agent that can perceive and understand a variety of environments, then take actions to achieve an instructed goal.

What’s more, an agent trained in all but one game performed nearly as well on that unseen game as an agent trained specifically on it, on average.

We compare this performance with three types of generalist SIMA agent, each trained across multiple environments.

Advancing AI agent research SIMA’s results show the potential to develop a new wave of generalist, language-driven AI agents.

2 weeks назад @ deepmind.google
Gemma: Introducing new state-of-the-art open models
Gemma: Introducing new state-of-the-art open models Gemma: Introducing new state-of-the-art open models

Today, we’re excited to introduce a new generation of open models from Google to assist developers and researchers in building AI responsibly.

Gemma open modelsGemma is a family of lightweight, state-of-the-art open models built from the same research and technology used to create the Gemini models.

This enables Gemma 2B and 7B to achieve best-in-class performance for their sizes compared to other open models.

And Gemma models are capable of running directly on a developer laptop or desktop computer.

Notably, Gemma surpasses significantly larger models on key benchmarks while adhering to our rigorous standards for safe and responsible outputs.

1 month назад @ blog.google
Our next-generation model: Gemini 1.5
Our next-generation model: Gemini 1.5 Our next-generation model: Gemini 1.5

A note from Google and Alphabet CEO Sundar Pichai:Last week, we rolled out our most capable model, Gemini 1.0 Ultra, and took a significant step forward in making Google products more helpful, starting with Gemini Advanced.

Today, developers and Cloud customers can begin building with 1.0 Ultra too — with our Gemini API in AI Studio and in Vertex AI.

Our teams continue pushing the frontiers of our latest models with safety at the core.

In fact, we’re ready to introduce the next generation: Gemini 1.5.

It shows dramatic improvements across a number of dimensions and 1.5 Pro achieves comparable quality to 1.0 Ultra, while using less compute.

1 month, 1 week назад @ blog.google
The next chapter of our Gemini era
The next chapter of our Gemini era The next chapter of our Gemini era

We’re excited by the progress, for example with our Search Generative Experience, or SGE, which you can try in Search Labs.

Introducing Gemini AdvancedBard has been the best way for people to directly experience our most capable models.

To reflect the advanced tech at its core, Bard will now simply be called Gemini.

The version with Ultra will be called Gemini Advanced, a new experience far more capable at reasoning, following instructions, coding, and creative collaboration.

You can start using Gemini Advanced by subscribing to the new Google One AI Premium plan, which offers the best of Google’s AI features in a single place.

1 month, 2 weeks назад @ blog.google
AlphaGeometry: An Olympiad-level AI system for geometry
AlphaGeometry: An Olympiad-level AI system for geometry AlphaGeometry: An Olympiad-level AI system for geometry

Research AlphaGeometry: An Olympiad-level AI system for geometry ShareCopy link ×Our AI system surpasses the state-of-the-art approach for geometry problems, advancing AI reasoning in mathematicsReflecting the Olympic spirit of ancient Greece, the International Mathematical Olympiad is a modern-day arena for the world's brightest high-school mathematicians.

In a paper published today in Nature, we introduce AlphaGeometry, an AI system that solves complex geometry problems at a level approaching a human Olympiad gold-medalist - a breakthrough in AI performance.

In a benchmarking test of 30 Olympiad geometry problems, AlphaGeometry solved 25 within the standard Olympiad time limit.

Solving Ol…

2 months, 1 week назад @ deepmind.google
Shaping the future of advanced robotics
Shaping the future of advanced robotics Shaping the future of advanced robotics

Today we’re announcing a suite of advances in robotics research that bring us a step closer to this future.

AutoRT, SARA-RT, and RT-Trajectory build on our historic Robotics Transformers work to help robots make decisions faster, and better understand and navigate their environments.

So the AutoRT system comprises layers of practical safety measures from classical robotics.

SARA-RT: Making Robotics Transformers leaner and faster Our new system, Self-Adaptive Robust Attention for Robotics Transformers (SARA-RT), converts Robotics Transformer (RT) models into more efficient versions.

We will continue to tackle challenges in robotics today and to adapt to the new capabilities and technologies …

2 months, 3 weeks назад @ deepmind.google
Images altered to trick machine vision can influence humans too
Images altered to trick machine vision can influence humans too Images altered to trick machine vision can influence humans too

Research Images altered to trick machine vision can influence humans too ShareCopy link ×New research shows that even subtle changes to digital images, designed to confuse computer vision systems, can also affect human perception Computers and humans see the world in different ways.

Our discovery highlights a similarity between human and machine vision, but also demonstrates the need for further research to understand the influence adversarial images have on people, as well as AI systems.

An adversarial image is one that has been subtly altered by a procedure that causes an AI model to confidently misclassify the image contents.

Indeed, security concerns have led researchers to investigate …

2 months, 3 weeks назад @ deepmind.google
2023: A Year of Groundbreaking Advances in AI and Computing
2023: A Year of Groundbreaking Advances in AI and Computing 2023: A Year of Groundbreaking Advances in AI and Computing

Company 2023: A Year of Groundbreaking Advances in AI and Computing ShareCopy link ×This has been a year of incredible progress in the field of Artificial Intelligence (AI) research and its practical applications.

In this Year-in-Review post we’ll go over some of Google Research’s and Google DeepMind’s efforts putting these paragraphs into practice safely throughout 2023.

In November, in partnership with YouTube, we announced Lyria, our most advanced AI music generation model to date.

Then in December, we launched Gemini, our most capable and general AI model.

Advances in capable language and multimodal models have also benefited our robotics research efforts.

3 months назад @ deepmind.google
FunSearch: Making new discoveries in mathematical sciences using Large Language Models
FunSearch: Making new discoveries in mathematical sciences using Large Language Models FunSearch: Making new discoveries in mathematical sciences using Large Language Models

Research FunSearch: Making new discoveries in mathematical sciences using Large Language Models ShareCopy link ×By searching for “functions” written in computer code, FunSearch made the first discoveries in open problems in mathematical sciences using LLMs Large Language Models (LLMs) are useful assistants - they excel at combining concepts and can read, write and code to help people solve problems.

This work represents the first time a new discovery has been made for challenging open problems in science or mathematics using LLMs.

FunSearch discovered new solutions for the cap set problem, a longstanding open problem in mathematics.

The corresponding function produced by FunSearch for each …

3 months, 2 weeks назад @ deepmind.google
Google DeepMind at NeurIPS 2023
Google DeepMind at NeurIPS 2023 Google DeepMind at NeurIPS 2023

We’ll be showcasing demos of our cutting edge AI models for global weather forecasting, materials discovery, and watermarking AI-generated content.

There will also be an opportunity to hear from the team behind Gemini, our largest and most capable AI model.

Here’s a look at some of our research highlights:Multimodality: language, video, actionUniSim is a universal simulator of real-world interactions.

Our approach surpassed current methods on vision and language tasks, and showed more potential to scale.

To help people solve problems and find what they’re looking for, AI models need to process billions of unique values efficiently.

3 months, 2 weeks назад @ deepmind.google
Introducing Gemini: our largest and most capable AI model
Introducing Gemini: our largest and most capable AI model Introducing Gemini: our largest and most capable AI model

AI has the potential to create opportunities — from the everyday to the extraordinary — for people everywhere.

That’s what excites me: the chance to make AI helpful for everyone, everywhere in the world.

At the same time, developers are using our models and infrastructure to build new generative AI applications, and startups and enterprises around the world are growing with our AI tools.

Now, we’re taking the next step on our journey with Gemini, our most capable and general model yet, with state-of-the-art performance across many leading benchmarks.

I’m genuinely excited for what’s ahead, and for the opportunities Gemini will unlock for people everywhere.

3 months, 3 weeks назад @ blog.google
Millions of new materials discovered with deep learning
Millions of new materials discovered with deep learning Millions of new materials discovered with deep learning

Research Millions of new materials discovered with deep learning ShareCopy link ×AI tool GNoME finds 2.2 million new crystals, including 380,000 stable materials that could power future technologies Modern technologies from computer chips and batteries to solar panels rely on inorganic crystals.

Computational approaches drawing from the Materials Project, Open Quantum Materials Database and WBM database boosted this number to 48,000 stable crystals.

Over the last decade, computational approaches led by the Materials Project and other groups have helped discover 28,000 new materials.

GNoME: Harnessing graph networks for materials explorationGNoME uses two pipelines to discover low-energy (st…

3 months, 4 weeks назад @ deepmind.google
Transforming the future of music creation
Transforming the future of music creation Transforming the future of music creation

Music AI tools – a set of tools we’re designing with artists, songwriters, and producers to help bolster their creative processes.

To develop these projects, we’ve brought together technical experts from across Google with a diverse range of world-renowned artists and songwriters to explore how generative music technologies can responsibly shape the future of music creation.

Exploring music AI tools with the industry Our researchers have been exploring with artists, songwriters, and producers in YouTube’s Music AI Incubator how generative AI can best support the creative process, and working together to responsibly design a suite of music AI tools.

This work draws on our history of research…

4 months, 1 week назад @ deepmind.google
Empowering the next generation for an AI-enabled world
Empowering the next generation for an AI-enabled world Empowering the next generation for an AI-enabled world

One important way to do this is through access to AI education to develop the next wave of thinkers, researchers, and AI leaders.

However, not every young person currently has access to AI education and resources.

Today, Google DeepMind and the Raspberry Pi Foundation are expanding access to the Experience AI program.

Now, we’re broadening the reach of the Experience AI program on a global scale, aiming to empower more students for an AI-enabled world.

Originally focused on the UK and encouraging access especially from students from low socioeconomic backgrounds, the program’s demand has inspired a £1m investment to broaden the program’s reach, equipping more students for an AI-enabled worl…

4 months, 2 weeks назад @ deepmind.google
Google
последний пост 1 day, 19 hours назад
Enterprise Connect 2024 - Bringing AI to the Contact Center
Enterprise Connect 2024 - Bringing AI to the Contact Center Enterprise Connect 2024 - Bringing AI to the Contact Center

At Google Cloud, we offer a range of sophisticated generative and deterministic capabilities to ensure you have the best technology for each use case.

Increase the Speed from Research to Enterprise ReadinessWIth CCAI-P, Google Cloud is infusing AI across the customer engagement and agent experience.

With Google Cloud, you have a reliable, proven path from ground-breaking research to enterprise ready adoption and value.

In the past year, Contact Center AI Platform launched almost 100 AI-enabled features through continuous integration, meaning that they become immediately accessible to Contact Center AI Platform customers as they become available.

The cloud contact center market is plagued by…

1 day, 19 hours назад @ cloud.google.com
Computer-aided diagnosis for lung cancer screening
Computer-aided diagnosis for lung cancer screening Computer-aided diagnosis for lung cancer screening

The United States Preventive Services Task Force recently expanded lung cancer screening recommendations by roughly 80%, which is expected to increase screening access for women and racial and ethnic minority groups.

We also introduce a generalizable user-centric interface to help radiologists leverage such models for lung cancer screening.

In the case of lung cancer screening, hospitals follow various country-specific guidelines that are regularly updated.

Example of the assistive lung cancer screening system outputs.

This can, in turn, help improve the sustainability of lung cancer screening programs, particularly as more people become eligible for screening.

1 week назад @ blog.research.google
Using AI to expand global access to reliable flood forecasts
Using AI to expand global access to reliable flood forecasts Using AI to expand global access to reliable flood forecasts

Nearly 1.5 billion people, making up 19% of the world’s population, are exposed to substantial risks from severe flood events.

Driven by the potential impact of reliable flood forecasting on people’s lives globally, we started our flood forecasting effort in 2017.

This research led to flood forecasting improvements that enabled the expansion of our forecasting coverage to include all of India and Bangladesh.

Two LSTMs are applied in sequence, one ingesting historical weather data and one ingesting forecasted weather data.

Improving on the current state-of-the-artWe compared our river forecast model with GloFAS version 4, the current state-of-the-art global flood forecasting system.

1 week назад @ blog.research.google
How Palo Alto Networks uses BigQuery ML to automate resource classification
How Palo Alto Networks uses BigQuery ML to automate resource classification How Palo Alto Networks uses BigQuery ML to automate resource classification

This is the story of how we achieved that with BigQuery ML, BigQuery’s built-in machine learning feature.

The Google Cloud team suggested we instead try BigQuery ML to prototype our project and it just made sense.

But with BigQuery ML, we can look at the code sequence and explain it in five minutes.

BigQuery ML makes this work much more accessible, even for people without years of machine learning training.

Solving for greater visibility with 99.9% accuracyThis label prediction project now supports the backend infrastructure for all cloud operations teams at Palo Alto Networks.

1 week назад @ cloud.google.com
Anthropic’s Claude 3 Sonnet and Claude 3 Haiku are now generally available on Vertex AI
Anthropic’s Claude 3 Sonnet and Claude 3 Haiku are now generally available on Vertex AI Anthropic’s Claude 3 Sonnet and Claude 3 Haiku are now generally available on Vertex AI

Earlier this month, we shared the news that Anthropic’s Claude 3 family of models would soon be available to Google Cloud customers on Vertex AI Model Garden.

Today, we’re announcing that Claude 3 Sonnet and Claude 3 Haiku are generally available to all customers on Vertex AI.

Customers interact with Claude 3 API endpoints on Vertex AI the same way they interact with other Vertex AI endpoints, making Anthropic's newest offerings simple for customers to integrate.

With millions of messages exchanged between our users and Anthropic’s Claude-based bots daily, we’re excited to work with Anthropic’s Claude 3 models on Vertex AI.”How to get started with Claude 3 on Vertex AIGet access to the Clau…

1 week, 1 day назад @ cloud.google.com
ScreenAI: A visual language model for UI and visually-situated language understanding
ScreenAI: A visual language model for UI and visually-situated language understanding ScreenAI: A visual language model for UI and visually-situated language understanding

To that end, we introduce “ScreenAI: A Vision-Language Model for UI and Infographics Understanding”.

The ScreenAI model is trained in two stages: a pre-training stage followed by a fine-tuning stage.

ScreenAI model architecture.

Block diagram of our workflow for generating data for QA, summarization and navigation tasks using existing ScreenAI models and LLMs.

Along with the fine-tuning datasets, we evaluate the fine-tuned ScreenAI model using three novel benchmarks:Screen Annotation: Enables the evaluation model layout annotations and spatial understanding capabilities.

1 week, 1 day назад @ blog.research.google
Advanced scheduling for AI/ML with Ray and Kueue
Advanced scheduling for AI/ML with Ray and Kueue Advanced scheduling for AI/ML with Ray and Kueue

This strategy, often termed "gang scheduling," is particularly valuable for the resource-intensive nature of AI/ML workloads.

You can take advantage of Kueue’s dynamic resource provisioning and queueing to orchestrate gang scheduling with KubeRay.

Kueue ensures Ray workloads execute only when all required resources are available, preventing wasted GPU/TPU cycles and maximizing utilization.

Kueue achieves this efficient gang scheduling on GKE using the ProvisioningRequest API.

Gang scheduling helps you make the most of hardware accelerators, preventing wasted time and maximizing efficiency.

1 week, 1 day назад @ cloud.google.com
Automatic driver installation simplifies using NVIDIA GPUs in GKE
Automatic driver installation simplifies using NVIDIA GPUs in GKE Automatic driver installation simplifies using NVIDIA GPUs in GKE

GKE can now automatically install NVIDIA GPU drivers, making it easier for customers to take advantage of GPUs.

Previously, using GPUs with GKE required manually installing the GPU drivers by applying a daemonset.

Going forward, GKE can automatically install GPU drivers on behalf of the customer.

Setting up GPU driver installationTo take advantage of automated GPU driver installation when creating GKE node pools, specify the DRIVER_VERSION option with one of the following options:default : Install the default driver version for the GKE version.

latest : Install the latest available driver version for the GKE version.

1 week, 1 day назад @ cloud.google.com
SCIN: A new resource for representative dermatology images
SCIN: A new resource for representative dermatology images SCIN: A new resource for representative dermatology images

We've made the SCIN dataset freely available as an open-access resource for researchers, educators, and developers, and have taken careful steps to protect contributor privacy.

Dataset compositionThe SCIN dataset currently contains over 10,000 images of skin, nail, or hair conditions, directly contributed by individuals experiencing them.

One to three dermatologists labeled each contribution with up to five dermatology conditions, along with a confidence score for each label.

Self-reported and dermatologist-estimated Fitzpatrick Skin Type distribution in the SCIN dataset compared with existing un-enriched dermatology datasets (Fitzpatrick17k, PH², SKINL2, and PAD-UFES-20).

We hope the SCIN …

1 week, 1 day назад @ blog.research.google
Accelerate your generative AI journey with NVIDIA NeMo framework on GKE
Accelerate your generative AI journey with NVIDIA NeMo framework on GKE Accelerate your generative AI journey with NVIDIA NeMo framework on GKE

This blog post shows how generative AI models can be adapted to your use cases by demonstrating how to train models on Google Kubernetes Engine (GKE) using NVIDIA accelerated computing and NVIDIA NeMo framework.

Building generative AI modelsIn the context of constructing generative AI models, high-quality data (the ‘dataset’), serves as a foundational element.

Based on the model's modality, this data is fed into a model architecture to enable the model's training process.

NVIDIA NeMoNVIDIA NeMo is an open-source, end-to-end platform purpose-built for developing custom, enterprise-grade generative AI models.

It enables organizations to foster innovation, optimize operational efficiency, and …

1 week, 2 days назад @ cloud.google.com
MELON: Reconstructing 3D objects from images with unknown poses
MELON: Reconstructing 3D objects from images with unknown poses MELON: Reconstructing 3D objects from images with unknown poses

In “MELON: NeRF with Unposed Images in SO(3)”, spotlighted at 3DV 2024, we present a technique that can determine object-centric camera poses entirely from scratch while reconstructing the object in 3D.

The first is a very lightweight, dynamically trained convolutional neural network (CNN) encoder that regresses camera poses from training images.

These two techniques are integrated into standard NeRF training, except that instead of fixed camera poses, poses are inferred by the CNN and duplicated by the modulo loss.

This perhaps shouldn’t be too surprising, given that techniques like RawNeRF have demonstrated NeRF’s excellent de-noising capabilities with known camera poses.

The fact that ME…

1 week, 2 days назад @ blog.research.google
Google named a Leader in The Forrester Wave: AI Infrastructure Solutions, Q1 2024
Google named a Leader in The Forrester Wave: AI Infrastructure Solutions, Q1 2024 Google named a Leader in The Forrester Wave: AI Infrastructure Solutions, Q1 2024

Today, we are excited to announce that Forrester Research has recognized Google as a Leader in The Forrester Wave™: AI Infrastructure Solutions, Q1 2024.

“Google has strengths across the board with the highest scores of all the vendors in this evaluation.” - The Forrester Wave™: AI Infrastructure Solutions, Q1 2024Download the report: The Forrester Wave™: AI Infrastructure Solutions, Q1 2024In this report, Forrester evaluated 12 vendors against pre-defined criteria, assessing them on their current offerings and strategy.

So, to say Google has a head-start is an understatement.“ - The Forrester Wave™: AI Infrastructure Solutions, Q1 2024World-leading AI companies are built on Google CloudWe …

1 week, 2 days назад @ cloud.google.com
Why GKE for your Ray AI workloads? Portability, scalability, manageability, cost
Why GKE for your Ray AI workloads? Portability, scalability, manageability, cost Why GKE for your Ray AI workloads? Portability, scalability, manageability, cost

The revolution in generative AI (gen AI) and large language models (LLMs) is leading to larger model sizes and increased demands on the compute infrastructure.

As the need for scalable gen AI solutions grows, Ray, an open-source Python framework designed for scaling and distributing AI workloads, has become increasingly popular.

Traditional Ray deployments on virtual machines (VMs) have limitations when it comes to scalability, resource efficiency, and infrastructure manageability.

One alternative is to leverage the power and flexibility of Kubernetes and deploy Ray on Google Kubernetes Engine (GKE) with KubeRay, an open-source Kubernetes operator that simplifies Ray deployment and manageme…

1 week, 2 days назад @ cloud.google.com
Writer.com pens its gen AI success story with Google Cloud databases
Writer.com pens its gen AI success story with Google Cloud databases Writer.com pens its gen AI success story with Google Cloud databases

Google Cloud databases delivered, allowing us to re-architect and scale securely and without a massive increase in our database staff.

Google Cloud databases offer built-in encryption, authentication and other security features; ease of scalability and cost control; and simple, tight integration with other cloud services such as GKE, BigQuery, and Datastream for BigQuery, which were lacking in our previous cloud service provider.

Google Cloud databases allow us to manage a highly sophisticated and complex platform securely.

Writing our own ticket with Google CloudWe see Google Cloud databases supporting our future business goals too.

Google continues to be our trusted partner, and Writer is…

1 week, 2 days назад @ cloud.google.com
The future of infrastructure modernization: how Google Cloud Innovators are embracing the cloud
The future of infrastructure modernization: how Google Cloud Innovators are embracing the cloud The future of infrastructure modernization: how Google Cloud Innovators are embracing the cloud

Google Cloud Champion Innovators is a global network of roughly 600 external professionals who are technical experts in Google Cloud products and services.

Today we're talking to Rohan Singh, a Senior Cloud Infrastructure Engineer at SADA - An Insight Company, and a Google Cloud Champion Innovator specializing in Modern Architecture.

Google recently launched Infrastructure Manager, which uses Terraform and allows you to manage your Google Cloud infrastructure through IaC.

RS: My main resources are Google Cloud blogs, documentation, and my company's internal channels, where we continuously discuss cloud migration and modernization.

Having “Google Cloud Champion Innovator for Modern Architect…

1 week, 2 days назад @ cloud.google.com
OpenAI
последний пост 3 days, 4 hours назад
Sora: First Impressions
Sora: First Impressions Sora: First Impressions

Starting his career at DreamWorks Animation, Don Allen III is a multidisciplinary creator, speaker and consultant who collaborates with major tech and entertainment companies on mixed reality, virtual reality and AI applications.

“For a long time I've been making augmented reality hybrid creatures that I think would be fun combinations in my head.

Now I have a much easier way of prototyping the ideas before I fully build out the 3-D characters to place in spatial computers.” Don cites Sora’s “weirdness” as its greatest strength: “It’s not bound by traditional laws of physics or conventions of thought.” He says that working with Sora shifted his focus from “technical hurdle…

3 days, 4 hours назад @ openai.com
Global news partnerships: Le Monde and Prisa Media
Global news partnerships: Le Monde and Prisa Media Global news partnerships: Le Monde and Prisa Media

Echoing this sentiment, Louis Dreyfus, CEO of Le Monde, stated, "At the moment we are celebrating the 80th anniversary of Le Monde, this partnership with OpenAI allows us to expand our reach and uphold our commitment to providing accurate, verified, balanced news stories at scale.

Collaborating with OpenAI ensures that our authoritative content can be accessed and appreciated by a broader, more diverse audience. ÂEvery shift in the media landscape has presented Le Monde with new opportunities.

From the transition to digital platforms to embracing the era of free media, Le Monde has consistently seized these moments to underscore its commitment to independence, expertise, and journalistic i…

2 weeks, 1 day назад @ openai.com
OpenAI announces new members to board of directors
OpenAI announces new members to board of directors OpenAI announces new members to board of directors

Additionally, Sam Altman, CEO, will rejoin the OpenAI Board of Directors.ÂSue, Nicole and Fidji have experience in leading global organizations and navigating complex regulatory environments, including backgrounds in technology, nonprofit and board governance.

They will work closely with current board members Adam D’Angelo, Larry Summers and Bret Taylor as well as Sam and OpenAI’s senior management.ÂBret Taylor, Chair of the OpenAI board, stated, “I am excited to welcome Sue, Nicole, and Fidji to the OpenAI Board of Directors.

She also served as President of Sony Entertainment, Inc., and simultaneously served as President of Sony Corporation of America.

She also serves as a member of …

2 weeks, 6 days назад @ openai.com
Review completed & Altman, Brockman to continue to lead OpenAI
Review completed & Altman, Brockman to continue to lead OpenAI Review completed & Altman, Brockman to continue to lead OpenAI

The Special Committee of the OpenAI Board today announced the completion of the review by WilmerHale.

The firm conducted dozens of interviews with members of OpenAI’s prior Board, OpenAI executives, advisors to the prior Board, and other pertinent witnesses; reviewed more than 30,000 documents; and evaluated various corporate actions.

“We have unanimously concluded that Sam and Greg are the right leaders for OpenAI,” stated Bret Taylor, Chair of the OpenAI Board.

The Special Committee acknowledged the important work done by WilmerHale in conducting this extensive review and thanked OpenAI current and former Board members, advisors and employees for their cooperation.

The Special Commi…

2 weeks, 6 days назад @ openai.com
OpenAI and Elon Musk
OpenAI and Elon Musk OpenAI and Elon Musk

Date: January 31, 2018 at 11:54:30 PM PSTSubject: Re: Top AI institutions todayWorking at the cutting edge of AI is unfortunately expensive.

For example,In addition to DeepMind, Google also has Google Brain, Research, and Cloud.

If historical trends are any indication, progress in AI is primarily driven by systems - compute, data, infrastructure.

Not only that, but any algorithmic advances published in a paper somewhere can be almost immediately re-implemented and incorporated.

The “second stage” would be a full self driving solution based on large-scale neural network training, which OpenAI expertise could significantly help accelerate.

3 weeks, 2 days назад @ openai.com
Video generation models as world simulators
Video generation models as world simulators Video generation models as world simulators

This technical report focuses on (1) our method for turning visual data of all types into a unified representation that enables large-scale training of generative models, and (2) qualitative evaluation of Sora’s capabilities and limitations.

Model and implementation details are not included in this report.

Much prior work has studied generative modeling of video data using a variety of methods, including recurrent networks,[^1][^2][^3] generative adversarial networks,[^4][^5][^6][^7] autoregressive transformers,[^8][^9] and diffusion models.

[^10][^11][^12] These works often focus on a narrow category of visual data, on shorter videos, or on videos of a fixed size.

Sora is a generalist mo…

1 month, 1 week назад @ openai.com
Disrupting malicious uses of AI by state-affiliated threat actors
Disrupting malicious uses of AI by state-affiliated threat actors Disrupting malicious uses of AI by state-affiliated threat actors

Based on collaboration and information sharing with Microsoft, we disrupted five state-affiliated malicious actors: two China-affiliated threat actors known as Charcoal Typhoon and Salmon Typhoon; the Iran-affiliated threat actor known as Crimson Sandstorm; the North Korea-affiliated actor known as Emerald Sleet; and the Russia-affiliated actor known as Forest Blizzard.

The identified OpenAI accounts associated with these actors were terminated.

Salmon Typhoon used our services to translate technical papers, retrieve publicly available information on multiple intelligence agencies and regional threat actors, assist with coding, and research common ways processes could be hidden on a system.…

1 month, 1 week назад @ openai.com
Memory and new controls for ChatGPT
Memory and new controls for ChatGPT Memory and new controls for ChatGPT

We’re testing memory with ChatGPT.

Remembering things you discuss across all chats saves you from having to repeat information and makes future conversations more helpful.

You're in control of ChatGPT's memory.

You can explicitly tell it to remember something, ask it what it remembers, and tell it to forget conversationally or through settings.

We are rolling out to a small portion of ChatGPT free and Plus users this week to learn how useful it is.

1 month, 2 weeks назад @ openai.com
Building an early warning system for LLM-aided biological threat creation
Building an early warning system for LLM-aided biological threat creation Building an early warning system for LLM-aided biological threat creation

We believe that these efforts would benefit from broader input, and that methods-sharing could also be of value to the AI risk research community.

To this end, we are presenting some of our early work—today, focused on biological risk.

This evaluation aims to measure whether models could meaningfully increase malicious actors’ access to dangerous information about biological threat creation, compared to the baseline of existing resources (i.e., the internet).

Each participant was then asked to complete a set of tasks covering aspects of the end-to-end process for biological threat creation.

We also discuss the limitations of statistical significance as an effective method of measuring m…

1 month, 3 weeks назад @ openai.com
New embedding models and API updates
New embedding models and API updates New embedding models and API updates

We are launching a new generation of embedding models, new GPT-4 Turbo and moderation models, new API usage management tools, and soon, lower pricing on GPT-3.5 Turbo.

2 months назад @ openai.com
Democratic inputs to AI grant program: lessons learned and implementation plans
Democratic inputs to AI grant program: lessons learned and implementation plans Democratic inputs to AI grant program: lessons learned and implementation plans

We funded 10 teams from around the world to design ideas and tools to collectively govern AI. We summarize the innovations, outline our learnings, and call for researchers and engineers to join us as we continue this work.

2 months, 1 week назад @ openai.com
How OpenAI is approaching 2024 worldwide elections
How OpenAI is approaching 2024 worldwide elections How OpenAI is approaching 2024 worldwide elections

We want to make sure that our AI systems are built, deployed, and used safely.

Like any new technology, these tools come with benefits and challenges.

They are also unprecedented, and we will keep evolving our approach as we learn more about how our tools are used.

As we prepare for elections in 2024 across the world’s largest democracies, our approach is to continue our platform safety work by elevating accurate voting information, enforcing measured policies, and improving transparency.

We have a cross-functional effort dedicated to election work, bringing together expertise from our safety systems, threat intelligence, legal, engineering, and policy teams to quickly investigate and add…

2 months, 1 week назад @ openai.com
Introducing ChatGPT Team
Introducing ChatGPT Team Introducing ChatGPT Team

Integrating AI into everyday organizational workflows can make your team more productive.

[^study]Connor O’Brien, VP of GTM Strategy & Operations at Sourcegraph, shares, "We use ChatGPT in almost every part of our business, from financial modeling for pricing and packaging to internal and external communications to board prep to recruiting and note taking—it’s accelerated everything we do allowing us to execute at a high level."

Dr. John Brownstein, Chief Innovation Officer at Boston Children’s Hospital says, “With ChatGPT Team, we’ve been able to pilot innovative GPTs that enhance our team’s productivity and collaboration.

As we integrate GPTs safely and responsibly across in…

2 months, 2 weeks назад @ openai.com
Introducing the GPT Store
Introducing the GPT Store Introducing the GPT Store

Building your own GPT is simple and doesn't require any coding skills.

If you’d like to share a GPT in the store, you’ll need to:Save your GPT for Everyone (Anyone with a link will not be shown in the store).

Verify your Builder Profile (Settings → Builder profile → Enable your name or a verified website).

Please review our latest usage policies and GPT brand guidelines to ensure your GPT is compliant.

To help ensure GPTs adhere to our policies, we've established a new review system in addition to the existing safety measures we've built into our products.

2 months, 2 weeks назад @ openai.com
OpenAI and journalism
OpenAI and journalism OpenAI and journalism

Our discussions with The New York Times had appeared to be progressing constructively through our last communication on December 19.

Interestingly, the regurgitations The New York Times induced appear to be from years-old articles that have proliferated on multiple third-party websites.

It seems they intentionally manipulated prompts, often including lengthy excerpts of articles, in order to get our model to regurgitate.

Despite their claims, this misuse is not typical or allowed user activity, and is not a substitute for The New York Times.

Regardless, we are continually making our systems more resistant to adversarial attacks to regurgitate training data, and have already made much progre…

2 months, 2 weeks назад @ openai.com
Microsoft Microsoft
последний пост 13 часов назад
Learning from interaction with Microsoft Copilot (web)
Learning from interaction with Microsoft Copilot (web) Learning from interaction with Microsoft Copilot (web)

AI systems like Bing and Microsoft Copilot (web) are as good as they are because they continuously learn and improve from people’s interactions.

[1]How are people using Copilot (web)?

Conversely, they express explicit frustration or switch topics when encountering mistakes in the response from Copilot (web).

These three reports present a comprehensive and multi-faceted approach to dynamically learning from conversation logs in Copilot (web) at scale.

[1] The research was performed only on fully de-identified interaction data from Copilot (web) consumers.

13 часов назад @ microsoft.com
Abstracts: March 21, 2024
Abstracts: March 21, 2024 Abstracts: March 21, 2024

GRETCHEN HUIZINGA: Welcome to Abstracts, a Microsoft Research Podcast that puts the spotlight on world-class research in brief.

These two examples are also the differences from other deep learning OFDFT works.

This is the generalization challenge and is one of the major challenges of deep learning method for molecular science applications.

This somehow shows the benefits of leveraging the OFDFT framework for using a deep learning method to solve molecular tasks.

You can also read it on arXiv, or you can check out the March 2024 issue of Nature Computational Science.

6 days, 22 hours назад @ microsoft.com
Research Focus: Week of March 18, 2024
Research Focus: Week of March 18, 2024 Research Focus: Week of March 18, 2024

Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft.

NEW RESEARCHFewer is More: Boosting LLM Reasoning with Reinforced Context PruningLarge language models (LLMs) have shown impressive capabilities, yet they still struggle with math reasoning.

Given that adding more concise CoT examples in the prompt can improve LLM reasoning performance, CoT-Influx employs a coarse-to-fine pruner to maximize the input of effective and concise CoT examples.

The pruner first selects as many crucial CoT examples as possible and then prunes unimportant tokens to fit the cont…

1 week назад @ microsoft.com
Intelligent monitoring: Towards AI-assisted monitoring for cloud services
Intelligent monitoring: Towards AI-assisted monitoring for cloud services Intelligent monitoring: Towards AI-assisted monitoring for cloud services

Microsoft cloud monitor platformsWhen they are properly configured, cloud monitors can help to meet monitoring requirements.

Notably, missing monitors and alerts constituted over 40 percent of all misdetections, indicating the complexity of determining what to monitor in cloud services.

Data-driven intelligent monitoringOrganizing monitor dataBecause there is no standardized approach to building monitors, monitor data often lacks structure.

In our paper, “Intelligent Monitoring Framework for Cloud Services: A Data-Driven Approach (opens in new tab),” to be presented at ICSE 2024 (opens in new tab), we propose a data-driven approach for developing this ontology.

This shows us that we can pre…

1 week, 1 day назад @ microsoft.com
Introducing Garnet – an open-source, next-generation, faster cache-store for accelerating applications and services
Introducing Garnet – an open-source, next-generation, faster cache-store for accelerating applications and services Introducing Garnet – an open-source, next-generation, faster cache-store for accelerating applications and services

Researchers at Microsoft have been working for nearly a decade to address the increasing demand for data storage mechanisms to support the rapid advances in interactive web applications and services.

This has fueled a growing cache-store industry, including many open-source systems, such as Redis, Memcached, KeyDB, and Dragonfly.

Garnet demonstrates better client latency at the 99 th and 99.9 th percentiles, which is critical to real-world scenarios.

We compare Garnet to the latest open-source versions of Redis (opens in new tab) (v7.2), KeyDB (opens in new tab) (v6.3.4), and Dragonfly (opens in new tab) (v6.2.11).

We see in Figure 4 that Garnet’s latency is low across the board.

1 week, 2 days назад @ microsoft.com
Exploring how context, culture, and character matter in avatar research
Exploring how context, culture, and character matter in avatar research Exploring how context, culture, and character matter in avatar research

In our paper, “Ecological Validity and the Evaluation of Avatar Facial Animation Noise,” presented at ANIVAE 2024, we explore the challenge of evaluating avatar noise without a standardized approach.

Traditional methods, which present participants with isolated facial animation noise to gauge perception thresholds, fall short of reflecting real-life avatar interactions.

Our approach emphasizes ecological validity—the extent to which experiments mimic real-world conditions—as central in assessing avatar noise.

Isolated clips, on the other hand, led to greater annoyance with facial animation noise, suggesting the importance of social context over hyper-realistic animation.

This indicates that…

1 week, 2 days назад @ microsoft.com
Scaling early detection of esophageal cancer with AI
Scaling early detection of esophageal cancer with AI Scaling early detection of esophageal cancer with AI

Microsoft Research and Cyted have collaborated to build novel AI models (opens in new tab) to scale the early detection of esophageal cancer.

Fewer than 1 in 5 patients survive five years after diagnosis, making early detection of this disease critical to improving a patient’s chances.

However early detection of BE has typically involved an endoscopic biopsy, a procedure that many people find uncomfortable and invasive.

As we move forward, the scalability of this technology holds the promise for widespread adoption of early detection in the fight against esophageal cancer.

As researchers, it has been exciting to work closely with Cyted and be part of the long path towards early detection of…

2 weeks, 3 days назад @ microsoft.com
Improving LLM understanding of structured data and exploring advanced prompting methods
Improving LLM understanding of structured data and exploring advanced prompting methods Improving LLM understanding of structured data and exploring advanced prompting methods

Our paper, “Table Meets LLM: Can Large Language Models Understand Structured Table Data?

Despite the simplicity of the benchmark tasks, the highest overall accuracy across seven tasks is only 65.43 percent.

Choosing the right combination of input designs can significantly enhance LLMs’ understanding of structured data.

We suggest future research should prioritize the integration of structural information to improve performance with various structured data types.

Additionally, we propose exploring LLMs’ ability to use external tools or agents for improved handling of structured data, opening new avenues for application.

2 weeks, 6 days назад @ microsoft.com
Research Forum Episode 2: Transforming health care and the natural sciences, AI and society, and the evolution of foundational AI technologies
Research Forum Episode 2: Transforming health care and the natural sciences, AI and society, and the evolution of foundational AI technologies Research Forum Episode 2: Transforming health care and the natural sciences, AI and society, and the evolution of foundational AI technologies

In the latest episode of Microsoft Research Forum (opens in new tab), we explore how AI is transforming health care and the natural sciences, the intersection of AI and society, and the continuing evolution of foundational AI technologies.

Keynote: The Revolution in Scientific DiscoveryChris Bishop, Technical Fellow and Director, Microsoft Research AI4ScienceAs in our debut event on January 30, this edition of Research Forum began with a keynote address by a leader from Microsoft Research.

Chi Wang, Principal Researcher, Microsoft Research AI FrontiersChi Wang presented the latest updates on AutoGen – the multi-agent framework for next generation AI applications.

Madeleine Daepp discussed t…

3 weeks назад @ microsoft.com
Research Focus: Week of March 4, 2024
Research Focus: Week of March 4, 2024 Research Focus: Week of March 4, 2024

Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft.

NEW RESEARCHGenerative Kaleidoscopic NetworksNeural networks are deep learning models that can be trained to learn complex patterns and relationships within data.

Spotlight: Event Series Microsoft Research Forum Join us for a continuous exchange of ideas about research in the era of general AI.

However, existing text diffusion models have yet to fulfill this potential, due to challenges in handling the discreteness of language.

NEW RESEARCHPRISE: Learning Temporal Action Abstractions as a Sequence Compr…

3 weeks назад @ microsoft.com
Orca-Math: Demonstrating the potential of SLMs with model specialization
Orca-Math: Demonstrating the potential of SLMs with model specialization Orca-Math: Demonstrating the potential of SLMs with model specialization

Orca-Math is a 7 billion parameters model created by fine-tuning the Mistral 7B model.

The smaller model and smaller dataset mean faster and cheaper training.

To reach higher levels of performance with smaller models, researchers often train SLMs to generate code, or use calculators to help avoid calculation errors.

Providing paraphrases of the seed with different numbers and attributes can be useful for creating training data for the smaller model.

Our findings also highlight the potential of continual learning and the improvement of language models, where the model iteratively improves as it receives more feedback from a person or another model.

3 weeks, 1 day назад @ microsoft.com
ViSNet: A general molecular geometry modeling framework for predicting molecular properties and simulating molecular dynamics
ViSNet: A general molecular geometry modeling framework for predicting molecular properties and simulating molecular dynamics ViSNet: A general molecular geometry modeling framework for predicting molecular properties and simulating molecular dynamics

Molecular geometry modeling is a powerful tool for understanding the intricate relationships between molecular structure and biological activity – a field known as structure-activity relationships (SAR).

The vector-scalar interactive graph neural network (ViSNet) framework, developed by Microsoft, is a novel approach to molecular geometry modeling.

These include:Insufficient molecular interpretability – We are limited in our ability to understand and interpret the inner workings of deep neural networks when applied to molecular geometry modeling.

– We are limited in our ability to understand and interpret the inner workings of deep neural networks when applied to molecular geometry modeling…

3 weeks, 6 days назад @ microsoft.com
Abstracts: February 29, 2024
Abstracts: February 29, 2024 Abstracts: February 29, 2024

And so we realized that working with generative AI really parallels these different aspects of what a manager does, right.

So this requires having self-awareness of the applicability of generative AI to your workflows and maintaining an appropriate level of confidence in completing tasks manually or relying on generative AI.

For example, whether it’s worth it for you to actually learn how to work with generative AI more effectively.

But I think, given how generative AI has rolled out in the world today, I mean, a lot of the focus has been on productivity and workflows.

If you want to read the full paper on metacognition and generative AI, you can find a link at aka.ms/abstracts, or you can …

3 weeks, 6 days назад @ microsoft.com
Structured knowledge from LLMs improves prompt learning for visual language models
Structured knowledge from LLMs improves prompt learning for visual language models Structured knowledge from LLMs improves prompt learning for visual language models

We’re seeing remarkable abilities from visual language models in transforming text descriptions into images.

In our paper, “Learning Hierarchical Prompt with Structured Linguistic Knowledge for Language Models,” presented at AAAI-24, we introduce a novel approach using large language models (LLMs) to enhance the images created by visual language models.

Our findings offer valuable insights into a more effective approach to navigating and understanding complex linguistic data, improving the model’s knowledge discovery and decision-making processes.

One example is enhanced image captioning, where visual language models gain the ability to describe the contents of photographs, illustrations, o…

4 weeks, 1 day назад @ microsoft.com
Research Focus: Week of February 19, 2024
Research Focus: Week of February 19, 2024 Research Focus: Week of February 19, 2024

Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft.

These monolithic applications often must rely on vertical resource scaling instead of horizontal scale-out, adjusting CPU cores to match load fluctuations.

CaaSPER is designed to be application-agnostic and platform-agnostic, with potential for extension to other applications and resources requiring vertical autoscaling.

Microsoft Research Podcast AI Frontiers: AI for health and the future of research with Peter Lee Peter Lee, head of Microsoft Research, and Ashley Llorens, AI scientist and engineer, di…

1 month назад @ microsoft.com
MIT AI MIT AI
последний пост 1 day, 22 hours назад
MIT-derived algorithm helps forecast the frequency of extreme weather
MIT-derived algorithm helps forecast the frequency of extreme weather MIT-derived algorithm helps forecast the frequency of extreme weather

Sapsis and his colleagues have now developed a method to “correct” the predictions from coarse climate models.

But this risk analysis is only as accurate as the predictions from that first, coarser climate model.

Once corrected, the models can help to determine where and how often extreme weather will strike as global temperatures rise over the coming years.

Over the hoodToday’s large-scale climate models simulate weather features such as the average temperature, humidity, and precipitation around the world, on a grid-by-grid basis.

“We now have a coarse model that can get you the right frequency of events, for the present climate.

1 day, 22 hours назад @ news.mit.edu
Large language models use a surprisingly simple mechanism to retrieve some stored knowledge
Large language models use a surprisingly simple mechanism to retrieve some stored knowledge Large language models use a surprisingly simple mechanism to retrieve some stored knowledge

They found a surprising result: Large language models (LLMs) often use a very simple linear function to recover and decode stored facts.

Linear functions, equations with only two variables and no exponents, capture the straightforward, straight-line relationship between two variables.

Finding factsMost large language models, also called transformer models, are neural networks.

They would also like to run experiments with larger models, as well as study the precision of linear decoding functions.

“This is an exciting work that reveals a missing piece in our understanding of how large language models recall factual knowledge during inference.

3 days, 7 hours назад @ news.mit.edu
Engineering household robots to have a little common sense
Engineering household robots to have a little common sense Engineering household robots to have a little common sense

Now MIT engineers are aiming to give robots a bit of common sense when faced with situations that push them off their trained path.

They’ve developed a method that connects robot motion data with the “common sense knowledge” of large language models, or LLMs.

Language taskThe researchers illustrate their new approach with a simple chore: scooping marbles from one bowl and pouring them into another.

The algorithm automatically learned to map the robot’s physical coordinates in the trajectories and the corresponding image view to a given subtask.

The team then let the robot carry out the scooping task on its own, using the newly learned grounding classifiers.

3 days, 7 hours назад @ news.mit.edu
AI generates high-quality images 30 times faster in a single step
AI generates high-quality images 30 times faster in a single step AI generates high-quality images 30 times faster in a single step

Diffusion models have suddenly grabbed a seat at everyone’s table: Enter a few words and experience instantaneous, dopamine-spiking dreamscapes at the intersection of reality and fantasy.

MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have introduced a new framework that simplifies the multi-step process of traditional diffusion models into a single step, addressing previous limitations.

This is done through a type of teacher-student model: teaching a new computer model to mimic the behavior of more complicated, original models that generate images.

Theoretically, the approach marries the principles of generative adversarial networks (GANs) with those of dif…

6 days, 22 hours назад @ news.mit.edu
New algorithm unlocks high-resolution insights for computer vision
New algorithm unlocks high-resolution insights for computer vision New algorithm unlocks high-resolution insights for computer vision

The FeatUp algorithm can stop this loss of information and boost the resolution of any deep network without compromising on speed or quality.

It achieves this by providing more accurate, high-resolution features, which are crucial for building vision applications ranging from autonomous driving to medical imaging.

“The essence of all computer vision lies in these deep, intelligent features that emerge from the depths of deep learning architectures.

This results in hundreds of deep-feature maps that are all slightly different, which can be combined into a single crisp, high-resolution, set of deep features.

Our goal is to learn how to refine the low-resolution features into high-resolution f…

1 week, 2 days назад @ news.mit.edu
Five MIT faculty members take on Cancer Grand Challenges
Five MIT faculty members take on Cancer Grand Challenges Five MIT faculty members take on Cancer Grand Challenges

All three are also affiliates of the Koch Institute for Integrative Cancer Research At MIT.

Team MATCHMAKERS will take advantage of recent advances in artificial intelligence to develop tools for personalized immunotherapies for cancer patients.

Team MATCHMAKERS will collect data on T cell receptors and the different antigens they target and build computer models to predict antigen recognition by different T cell receptors.

KOODAC is funded by Cancer Research UK, France's Institut National Du Cancer, and KiKa (Children Cancer Free Foundation) through Cancer Grand Challenges.

Team PROSPECT is supported by Cancer Research UK, the U.S. National Cancer Institute, the Bowelbabe Fund for Cancer R…

1 week, 2 days назад @ news.mit.edu
3 Questions: What you need to know about audio deepfakes
3 Questions: What you need to know about audio deepfakes 3 Questions: What you need to know about audio deepfakes

Audio deepfakes have had a recent bout of bad press after an artificial intelligence-generated robocall purporting to be the voice of Joe Biden hit up New Hampshire residents, urging them not to cast ballots.

What receives less press, however, are some of the uses of audio deepfakes that could actually benefit society.

Q: What ethical considerations justify the concealment of the source speaker's identity in audio deepfakes, especially when this technology is used for creating innovative content?

Q: How can we effectively maneuver through the challenges posed by audio deepfakes in spear-phishing attacks, taking into account the associated risks, the development of countermeasures, and the a…

1 week, 5 days назад @ news.mit.edu
Researchers enhance peripheral vision in AI models
Researchers enhance peripheral vision in AI models Researchers enhance peripheral vision in AI models

Peripheral vision enables humans to see shapes that aren’t directly in our line of sight, albeit with less detail.

Taking a step in this direction, MIT researchers developed an image dataset that allows them to simulate peripheral vision in machine learning models.

Plus, a deeper understanding of peripheral vision in AI models could help researchers better predict human behavior, adds lead author Anne Harrington MEng ’23.

For a more accurate approach, the MIT researchers started with a technique used to model peripheral vision in humans.

“That let us faithfully model peripheral vision the same way it is being done in human vision research,” says Harrington.

2 weeks, 6 days назад @ news.mit.edu
Using generative AI to improve software testing
Using generative AI to improve software testing Using generative AI to improve software testing

Using generative AI to create realistic synthetic data around those scenarios can help organizations more effectively treat patients, reroute planes, or improve software platforms — especially in scenarios where real-world data are limited or sensitive.

For the last three years, the MIT spinout DataCebo has offered a generative software system called the Synthetic Data Vault to help organizations create synthetic data to do things like test software applications and train machine learning models.

In 2021, the data science platform Kaggle hosted a competition for data scientists that used SDV to create synthetic data sets to avoid using proprietary data.

“Enterprise data of this kind is comp…

3 weeks, 2 days назад @ news.mit.edu
Dealing with the limitations of our noisy world
Dealing with the limitations of our noisy world Dealing with the limitations of our noisy world

“The reality is that we live in a noisy world, and we can’t always get exactly the data that we want.

“Working in data analysis, you get to hang out in everybody’s backyard, so to speak.

“But when I got into college at Princeton, I could not decide — math, physics, computer science — they all seemed super-cool.

In the UK, she took a number of statistics and data analysis classes, including her first class on Bayesian data analysis in the field of machine learning.

She earned a PhD in statistics with a focus on Bayesian data analysis.

3 weeks, 6 days назад @ news.mit.edu
Startup accelerates progress toward light-speed computing
Startup accelerates progress toward light-speed computing Startup accelerates progress toward light-speed computing

Our ability to cram ever-smaller transistors onto a chip has enabled today’s age of ubiquitous computing.

Now Lightmatter, a company founded by three MIT alumni, is continuing the remarkable progress of computing by rethinking the lifeblood of the chip.

“With photonics, you can perform multiple calculations at the same time because the data is coming in on different colors of light,” Harris explains.

Sending information between chips is central to running the massive server farms that power cloud computing and run AI systems like ChatGPT.

“When you look at computing deployments for training these large AI models, they’re headed toward using hundreds of megawatts.

3 weeks, 6 days назад @ news.mit.edu
Brain surgery training from an avatar
Brain surgery training from an avatar Brain surgery training from an avatar

More than 3,000 miles away, his virtual avatar stands next to Matheus Vasconcelos in Brazil as the resident practices delicate surgery on a doll-like model of a baby’s brain.

With a pair of virtual-reality goggles, Vasconcelos is able to watch Warf’s avatar demonstrate a brain surgery procedure before replicating the technique himself and while asking questions of Warf’s digital twin.

The Warf avatar has synchronous and asynchronous modes.

It’s amazing.”Coelho, Warf, Reks, and other team members demonstrated a combination of the modes in a second session in late December.

Training surgeons with the avatar, she says, “can change reality for this baby and can change the future.”

3 weeks, 6 days назад @ news.mit.edu
3 Questions: Shaping the future of work in an age of AI
3 Questions: Shaping the future of work in an age of AI 3 Questions: Shaping the future of work in an age of AI

The MIT Shaping the Future of Work Initiative, co-directed by MIT professors Daron Acemoglu, David Autor, and Simon Johnson, celebrated its official launch on Jan. 22.

Q: What was the impetus for creating the MIT Shaping the Future of Work Initiative?

Underpinning this fatalism is a paradigm which says that the factors shaping demand for work, such as technological change, are immutable: workers must adapt to these forces or be left behind.

We must answer what sort of work we want and how we can make policies and shape technology that builds that future.

Last fall, David, Daron, and I wrote the initiative’s inaugural policy memo, entitled “Can we Have Pro-Worker AI?

4 weeks назад @ news.mit.edu
Sadhana Lolla named 2024 Gates Cambridge Scholar
Sadhana Lolla named 2024 Gates Cambridge Scholar Sadhana Lolla named 2024 Gates Cambridge Scholar

MIT senior Sadhana Lolla has won the prestigious Gates Cambridge Scholarship, which offers students an opportunity to pursue graduate study in the field of their choice at Cambridge University in the U.K.

Established in 2000, the Gates Cambridge Scholarship offers full-cost post-graduate scholarships to outstanding applicants from countries outside of the U.K.

At MIT, Lolla conducts research on safe and trustworthy robotics and deep learning at the Distributed Robotics Laboratory with Professor Daniela Rus.

Outside of research, Lolla leads initiatives to make computer science education more accessible globally.

“Her work at Cambridge will allow her the time to think about reducing bias in s…

4 weeks, 1 day назад @ news.mit.edu
New AI model could streamline operations in a robotic warehouse
New AI model could streamline operations in a robotic warehouse New AI model could streamline operations in a robotic warehouse

However, getting 800 robots to and from their destinations efficiently while keeping them from crashing into each other is no easy task.

Their technique divides the warehouse robots into groups, so these smaller groups of robots can be decongested faster with traditional algorithms used to coordinate robots.

“We devised a new neural network architecture that is actually suitable for real-time operations at the scale and complexity of these warehouses.

For instance, in a warehouse with 800 robots, the network might cut the warehouse floor into smaller groups that contain 40 robots each.

For instance, in a warehouse with 800 robots, decongesting a group of 40 robots requires holding the other…

1 month назад @ news.mit.edu
Berkeley AI
последний пост 1 week назад
Modeling Extremely Large Images with xT
Modeling Extremely Large Images with xT Modeling Extremely Large Images with xT

As computer vision researchers, we believe that every pixel can tell a story. However, there seems to be a writer’s block settling into the field when it comes to dealing with large images. Large images are no longer rare—the cameras we carry in our pockets and those orbiting our planet snap pictures so big and detailed that they stretch our current best models and hardware to their breaking points when handling them. Generally, we face a quadratic increase in memory usage as a function of image size.

Today, we make one of two sub-optimal choices when handling large images: down-sampling or cropping. These two methods incur significant losses in the amount of information and context present…

1 week назад @ localhost:4000
Modeling Extremely Large Images with xT
Modeling Extremely Large Images with xT Modeling Extremely Large Images with xT

Modeling Extremely Large Images with xTAs computer vision researchers, we believe that every pixel can tell a story.

However, there seems to be a writer’s block settling into the field when it comes to dealing with large images.

Today, we make one of two sub-optimal choices when handling large images: down-sampling or cropping.

Why bother handling large images anyways?

That’s basically what we do with large images with $x$T.

1 week назад @ bair.berkeley.edu
2024 BAIR Graduate Directory
2024 BAIR Graduate Directory 2024 BAIR Graduate Directory

2024 BAIR Graduate DirectoryEvery year, the Berkeley Artificial Intelligence Research (BAIR) Lab graduates some of the most talented and innovative minds in artificial intelligence and machine learning.

Our Ph.D. graduates have each expanded the frontiers of AI research and are now ready to embark on new adventures in academia, industry, and beyond.

These fantastic individuals bring with them a wealth of knowledge, fresh ideas, and a drive to continue contributing to the advancement of AI.

Join us in celebrating the achievements of BAIR’s latest PhD graduates.

Thank you to our friends at the Stanford AI Lab for this idea!

2 weeks, 3 days назад @ bair.berkeley.edu
2024 BAIR Graduate Directory
2024 BAIR Graduate Directory 2024 BAIR Graduate Directory

Every year, the Berkeley Artificial Intelligence Research (BAIR) Lab graduates some of the most talented and innovative minds in artificial intelligence and machine learning. Our Ph.D. graduates have each expanded the frontiers of AI research and are now ready to embark on new adventures in academia, industry, and beyond.

These fantastic individuals bring with them a wealth of knowledge, fresh ideas, and a drive to continue contributing to the advancement of AI. Their work at BAIR, ranging from deep learning, robotics, and natural language processing to computer vision, security, and much more, has contributed significantly to their fields and has had transformative impacts on society.

This…

2 weeks, 3 days назад @ localhost:4000
The Shift from Models to Compound AI Systems
The Shift from Models to Compound AI Systems The Shift from Models to Compound AI Systems

In this post, we analyze the trend toward compound AI systems and what it means for AI developers.

We argue that compound AI systems will likely be the best way to maximize AI results in the future, and might be one of the most impactful trends in AI in 2024.

We define a Compound AI System as a system that tackles AI tasks using multiple interacting components, including multiple calls to models, retrievers, or external tools.

Developing Compound AI SystemsWhile compound AI systems can offer clear benefits, the art of designing, optimizing, and operating them is still emerging.

However, new compound AI systems contain non-differentiable components like search engines or code interpreters, a…

1 month, 1 week назад @ bair.berkeley.edu
The Shift from Models to Compound AI Systems
The Shift from Models to Compound AI Systems The Shift from Models to Compound AI Systems

AI caught everyone’s attention in 2023 with Large Language Models (LLMs) that can be instructed to perform general tasks, such as translation or coding, just by prompting. This naturally led to an intense focus on models as the primary ingredient in AI application development, with everyone wondering what capabilities new LLMs will bring.

As more developers begin to build using LLMs, however, we believe that this focus is rapidly changing: state-of-the-art AI results are increasingly obtained by compound systems with multiple components, not just monolithic models.

For example, Google’s AlphaCode 2 set state-of-the-art results in programming through a carefully engineered system that uses L…

1 month, 1 week назад @ localhost:4000
Ghostbuster: Detecting Text Ghostwritten by Large Language Models
Ghostbuster: Detecting Text Ghostwritten by Large Language Models Ghostbuster: Detecting Text Ghostwritten by Large Language Models

Ghostbuster: Detecting Text Ghostwritten by Large Language ModelsThe structure of Ghostbuster, our new state-of-the-art method for detecting AI-generated text.

Large language models like ChatGPT write impressively well—so well, in fact, that they’ve become a problem.

Existing tools to detect AI-generated text sometimes do poorly on data that differs from what they were trained on.

Our recent paper introduces Ghostbuster, a state-of-the-art method for detecting AI-generated text.

Many current AI-generated text detection systems are brittle to classifying different types of text (e.g., different writing styles, or different text generation models or prompts).

4 months, 2 weeks назад @ bair.berkeley.edu
Ghostbuster: Detecting Text Ghostwritten by Large Language Models
Ghostbuster: Detecting Text Ghostwritten by Large Language Models Ghostbuster: Detecting Text Ghostwritten by Large Language Models

The structure of Ghostbuster, our new state-of-the-art method for detecting AI-generated text. Large language models like ChatGPT write impressively well—so well, in fact, that they’ve become a problem. Students have begun using these models to ghostwrite assignments, leading some schools to ban ChatGPT. In addition, these models are also prone to producing text with factual errors, so wary readers may want to know if generative AI tools have been used to ghostwrite news articles or other sources before trusting them.

What can teachers and consumers do? Existing tools to detect AI-generated text sometimes do poorly on data that differs from what they were trained on. In addition, if these m…

4 months, 2 weeks назад @ localhost:4000
Asymmetric Certified Robustness via Feature-Convex Neural Networks
Asymmetric Certified Robustness via Feature-Convex Neural Networks Asymmetric Certified Robustness via Feature-Convex Neural Networks

Asymmetric Certified Robustness via Feature-Convex Neural Networks TLDR: We propose the asymmetric certified robustness problem, which requires certified robustness for only one class and reflects real-world adversarial scenarios. This focused setting allows us to introduce feature-convex classifiers, which produce closed-form and deterministic certified radii on the order of milliseconds. Figure 1. Illustration of feature-convex classifiers and their certification for sensitive-class inputs. This architecture composes a Lipschitz-continuous feature map $\varphi$ with a learned convex function $g$. Since $g$ is convex, it is globally underapproximated by its tangent plane at $\varphi(x)$, y…

4 months, 2 weeks назад @ localhost:4000
Asymmetric Certified Robustness via Feature-Convex Neural Networks
Asymmetric Certified Robustness via Feature-Convex Neural Networks Asymmetric Certified Robustness via Feature-Convex Neural Networks

Asymmetric Certified Robustness via Feature-Convex Neural NetworksAsymmetric Certified Robustness via Feature-Convex Neural NetworksTLDR: We propose the asymmetric certified robustness problem, which requires certified robustness for only one class and reflects real-world adversarial scenarios.

We argue that these issues can be addressed by refining the certified robustness problem to be more aligned with practical adversarial settings.

The Asymmetric Certified Robustness ProblemCurrent certifiably robust classifiers produce certificates for inputs belonging to any class.

Feature-convex classifiersWe propose feature-convex neural networks to address the asymmetric robustness problem.

Conclu…

4 months, 2 weeks назад @ bair.berkeley.edu
Goal Representations for Instruction Following
Goal Representations for Instruction Following Goal Representations for Instruction Following

Goal Representations for Instruction Following A longstanding goal of the field of robot learning has been to create generalist agents that can perform tasks for humans. Natural language has the potential to be an easy-to-use interface for humans to specify arbitrary tasks, but it is difficult to train robots to follow language instructions. Approaches like language-conditioned behavioral cloning (LCBC) train policies to directly imitate expert actions conditioned on language, but require humans to annotate all training trajectories and generalize poorly across scenes and behaviors. Meanwhile, recent goal-conditioned approaches perform much better at general manipulation tasks, but do not e…

5 months, 1 week назад @ localhost:4000
Goal Representations for Instruction Following
Goal Representations for Instruction Following Goal Representations for Instruction Following

Goal Representations for Instruction FollowingGoal Representations for Instruction FollowingA longstanding goal of the field of robot learning has been to create generalist agents that can perform tasks for humans.

Goal Representations for Instruction FollowingThe GRIF model consists of a language encoder, a goal encoder, and a policy network.

Our approach, Goal Representations for Instruction Following (GRIF), jointly trains a language- and a goal- conditioned policy with aligned task representations.

In particular, we exploit this structure by requiring that language- and goal- representations be similar for the same semantic task.

We train dual image and text encoders by doing contrastiv…

5 months, 1 week назад @ bair.berkeley.edu
Rethinking the Role of PPO in RLHF
Rethinking the Role of PPO in RLHF Rethinking the Role of PPO in RLHF

Rethinking the Role of PPO in RLHFRethinking the Role of PPO in RLHFTL;DR: In RLHF, there’s tension between the reward learning phase, which uses human preference in the form of comparisons, and the RL fine-tuning phase, which optimizes a single, non-comparative reward.

By incorporating a new component - pairwise policy gradient, we can unify the reward modeling stage and RL stage, enabling direct updates based on pairwise responses.

Proximal Policy Optimization (PPO), the dominant RL optimizer in this process, has been reported to exhibit instability and implementation complications.

Derivation of P3OOur idea stems from the vanilla policy gradient (VPG).

Under this framework, we develop …

5 months, 2 weeks назад @ bair.berkeley.edu
Rethinking the Role of PPO in RLHF
Rethinking the Role of PPO in RLHF Rethinking the Role of PPO in RLHF

Rethinking the Role of PPO in RLHF TL;DR: In RLHF, there’s tension between the reward learning phase, which uses human preference in the form of comparisons, and the RL fine-tuning phase, which optimizes a single, non-comparative reward. What if we performed RL in a comparative way? Figure 1: This diagram illustrates the difference between reinforcement learning from absolute feedback and relative feedback. By incorporating a new component - pairwise policy gradient, we can unify the reward modeling stage and RL stage, enabling direct updates based on pairwise responses. Large Language Models (LLMs) have powered increasingly capable virtual assistants, such as GPT-4, Claude-2, Bard and Bing…

5 months, 2 weeks назад @ localhost:4000
Goal Representations for Instruction Following
Goal Representations for Instruction Following Goal Representations for Instruction Following

Goal Representations for Instruction FollowingGoal Representations for Instruction FollowingA longstanding goal of the field of robot learning has been to create generalist agents that can perform tasks for humans.

Goal Representations for Instruction FollowingThe GRIF model consists of a language encoder, a goal encoder, and a policy network.

Our approach, Goal Representations for Instruction Following (GRIF), jointly trains a language- and a goal- conditioned policy with aligned task representations.

In particular, we exploit this structure by requiring that language- and goal- representations be similar for the same semantic task.

We train dual image and text encoders by doing contrastiv…

5 months, 2 weeks назад @ bair.berkeley.edu
AWS Machine Learning AWS Machine Learning
последний пост 19 часов назад
Achieve DevOps maturity with BMC AMI zAdviser Enterprise and Amazon Bedrock
Achieve DevOps maturity with BMC AMI zAdviser Enterprise and Amazon Bedrock Achieve DevOps maturity with BMC AMI zAdviser Enterprise and Amazon Bedrock

This blog post discusses how BMC Software added AWS Generative AI capabilities to its product BMC AMI zAdviser Enterprise.

The zAdviser uses Amazon Bedrock to provide summarization, analysis, and recommendations for improvement based on the DORA metrics data.

Aggregate the data retrieved from Elasticsearch and form the prompt for the generative AI Amazon Bedrock API call.

Pass the generative AI prompt to Amazon Bedrock (using Anthropic’s Claude2 model on Amazon Bedrock).

Store the response from Amazon Bedrock (an HTML-formatted document) in Amazon Simple Storage Service (Amazon S3).

19 часов назад @ aws.amazon.com
Fine-tune your Amazon Titan Image Generator G1 model using Amazon Bedrock model customization
Fine-tune your Amazon Titan Image Generator G1 model using Amazon Bedrock model customization Fine-tune your Amazon Titan Image Generator G1 model using Amazon Bedrock model customization

To address these use cases and generate fully personalized images, you can fine-tune Amazon Titan Image Generator with your own data using custom models for Amazon Bedrock.

We discuss how to prepare your data for the model fine-tuning task and how to create a model customization job in Amazon Bedrock.

Fine-tuning Amazon Titan Image GeneratorAmazon Bedrock provides you with a serverless experience for fine-tuning your Amazon Titan Image Generator model.

Fine-tuning Amazon Titan Image GeneratorNow that we have our training data ready, we can begin a new customization job.

We showed how to fine-tune the Amazon Titan Image Generator model and deploy the custom model on Amazon Bedrock.

19 часов назад @ aws.amazon.com
Build a receipt and invoice processing pipeline with Amazon Textract
Build a receipt and invoice processing pipeline with Amazon Textract Build a receipt and invoice processing pipeline with Amazon Textract

In this post, we show how to automate the accounts payable process using Amazon Textract for data extraction.

Approved and rejected documents go to their respective folders within the Amazon Simple Storage Service (Amazon S3) bucket.

TextractAsync – This task calls Amazon Textract using the asynchronous API following best practices with Amazon Simple Notification Service (Amazon SNS) notifications and uses OutputConfig to store the Amazon Textract JSON output to the S3 bucket you created earlier.

– This task calls Amazon Textract using the asynchronous API following best practices with Amazon Simple Notification Service (Amazon SNS) notifications and uses to store the Amazon Textract JSON o…

1 day, 20 hours назад @ aws.amazon.com
Best practices for building secure applications with Amazon Transcribe
Best practices for building secure applications with Amazon Transcribe Best practices for building secure applications with Amazon Transcribe

In batch transcription mode, an audio file first needs to be put in an Amazon Simple Storage Service (Amazon S3) bucket.

Amazon Transcribe uses encrypted Amazon Elastic Block Store (Amazon EBS) volumes to temporarily store customer data during media processing.

Additional Amazon Transcribe security best practicesBest practice 4 – Use IAM roles for applications and AWS services that require Amazon Transcribe access.

You can monitor Amazon Transcribe using AWS CloudTrail and Amazon CloudWatch.

And follow Amazon Transcribe on the AWS Machine Learning Blog to keep up to date with new capabilities and use cases for Amazon Transcribe.

2 days, 18 hours назад @ aws.amazon.com
Boost your content editing with Contentful and Amazon Bedrock
Boost your content editing with Contentful and Amazon Bedrock Boost your content editing with Contentful and Amazon Bedrock

Today, jointly with Contentful, we are announcing the launch of the AI Content Generator powered by Amazon Bedrock.

With this newly launched app, Contentful customers can take advantage of the range of models provided by Amazon Bedrock directly from within Contentful.

This is a task that the AI Content Generator powered by Amazon Bedrock can now do automatically:First, open an existing content entry.

Next, install the AI Content Generator powered by Amazon Bedrock app by visiting the Contentful Marketplace and choosing Install now.

ConclusionWith the AI Content Generator powered by Amazon Bedrock, content teams can unlock powerful tools to save time, reduce feedback loops, and increase crea…

5 days, 21 hours назад @ aws.amazon.com
Unlock the potential of generative AI in industrial operations
Unlock the potential of generative AI in industrial operations Unlock the potential of generative AI in industrial operations

Beyond time series data analysis, FMs prove valuable in various industrial applications.

To simplify these workflows, AWS has introduced Amazon Bedrock, enabling you to build and scale generative AI applications with state-of-the-art pre-trained FMs like Claude v2.

However, the limitations of FMs in handling time series data analysis have hindered their full utilization by industrial clients.

This shift, spearheaded by Amazon Bedrock, is significantly amplified by the growing robustness and potential of LLMs like Amazon Bedrock Claude 3 to further elevate solutions.

To learn more, visit consult the Amazon Bedrock documentation, and get hands-on with the Amazon Bedrock workshop.

1 week, 1 day назад @ aws.amazon.com
Enhance performance of generative language models with self-consistency prompting on Amazon Bedrock
Enhance performance of generative language models with self-consistency prompting on Amazon Bedrock Enhance performance of generative language models with self-consistency prompting on Amazon Bedrock

This post shows how to implement self-consistency prompting via batch inference on Amazon Bedrock to enhance model performance on arithmetic and multiple-choice reasoning tasks.

We demonstrate the approach using batch inference on Amazon Bedrock:We access the Amazon Bedrock Python SDK in JupyterLab on an Amazon SageMaker notebook instance.

#### 72"}Set up to run batch inference with Amazon BedrockBatch inference allows you to run multiple inference calls to Amazon Bedrock asynchronously and improve the performance of model inference on large datasets.

At current Amazon Bedrock inference prices, the total cost incurred was around $9.50.

Clean upRunning batch inference in Amazon Bedrock is su…

1 week, 1 day назад @ aws.amazon.com
Optimize price-performance of LLM inference on NVIDIA GPUs using the Amazon SageMaker integration with NVIDIA NIM Microservices
Optimize price-performance of LLM inference on NVIDIA GPUs using the Amazon SageMaker integration with NVIDIA NIM Microservices Optimize price-performance of LLM inference on NVIDIA GPUs using the Amazon SageMaker integration with NVIDIA NIM Microservices

NVIDIA NIM microservices now integrate with Amazon SageMaker, allowing you to deploy industry-leading large language models (LLMs) and optimize model performance and cost.

You can deploy state-of-the-art LLMs in minutes instead of days using technologies such as NVIDIA TensorRT, NVIDIA TensorRT-LLM, and NVIDIA Triton Inference Server on NVIDIA accelerated instances hosted by SageMaker.

An introduction to NVIDIA NIMNIM provides optimized and pre-generated engines for a variety of popular models for inference.

Saurabh Trikande is a Senior Product Manager for Amazon SageMaker Inference.

He focuses on core challenges related to deploying complex ML applications, multi-tenant ML models, cost opt…

1 week, 2 days назад @ aws.amazon.com
Fine-tune Code Llama on Amazon SageMaker JumpStart
Fine-tune Code Llama on Amazon SageMaker JumpStart Fine-tune Code Llama on Amazon SageMaker JumpStart

Today, we are excited to announce the capability to fine-tune Code Llama models by Meta using Amazon SageMaker JumpStart.

Fine-tuned Code Llama models provide better accuracy and explainability over the base Code Llama models, as evident on its testing against HumanEval and MBPP datasets.

You can fine-tune and deploy Code Llama models with SageMaker JumpStart using the Amazon SageMaker Studio UI with a few clicks or using the SageMaker Python SDK.

Why fine-tune Code Llama modelsMeta published Code Llama performance benchmarks on HumanEval and MBPP for common coding languages such as Python, Java, and JavaScript.

Fine-tune via the SageMaker Python SDKIn this section, we demonstrate how to fi…

1 week, 2 days назад @ aws.amazon.com
Transform one-on-one customer interactions: Build speech-capable order processing agents with AWS and generative AI
Transform one-on-one customer interactions: Build speech-capable order processing agents with AWS and generative AI Transform one-on-one customer interactions: Build speech-capable order processing agents with AWS and generative AI

In this post, we show you how to build a speech-capable order processing agent using Amazon Lex, Amazon Bedrock, and AWS Lambda.

A customer Lambda function takes the data structure as an input for processing the order and passes the order total back to the orchestrating Lambda function.

Create a Lambda function to orchestrate the Amazon Lex botYou can now build the Lambda function that orchestrates the Amazon Lex bot and workflow.

Complete the following steps:Create a Lambda function with the standard execution policy and let Lambda create a role for you.

ConclusionThis post demonstrated how to build a speech-enabled AI order processing agent using Amazon Lex, Amazon Bedrock, and other AWS …

1 week, 5 days назад @ aws.amazon.com
Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker
Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

Unlike traditional ML training, FL training occurs within an isolated client location using an independent secure session.

In this post, we share an FL approach using FedML, Amazon Elastic Kubernetes Service (Amazon EKS), and Amazon SageMaker to improve patient outcomes while addressing data privacy and security concerns.

Trigger federated trainingTo run federated training, complete the following steps:On the FedML UI, choose Project List in the navigation pane.

Select the edge client devices and the central aggregator server for this training run.

Choose the Training Status tab and wait for the training run to complete.

1 week, 5 days назад @ aws.amazon.com
Enable data sharing through federated learning: A policy approach for chief digital officers
Enable data sharing through federated learning: A policy approach for chief digital officers Enable data sharing through federated learning: A policy approach for chief digital officers

Medical data restrictionsYou can use machine learning (ML) to assist doctors and researchers in diagnosis tasks, thereby speeding up the process.

Federated learning: An introductionFederated learning (FL) is a decentralized form of ML—a dynamic engineering approach.

Federated learning has many benefits in general and specifically for medical data settings.

Addressing FL data challengesFederated learning comes with its own data challenges, including privacy and security, but they are straightforward to address.

They can benefit from seamless and secure integration and data interoperability, making medical data usable for impactful ML-based predictions and pattern recognition.

1 week, 5 days назад @ aws.amazon.com
The journey of PGA TOUR’s generative AI virtual assistant, from concept to development to prototype
The journey of PGA TOUR’s generative AI virtual assistant, from concept to development to prototype The journey of PGA TOUR’s generative AI virtual assistant, from concept to development to prototype

Generative artificial intelligence (generative AI) has enabled new possibilities for building intelligent systems.

The establishment of a cloud-based generative AI assistant lets the PGA TOUR present its vast data source to multiple internal and external stakeholders.

This is the beginning of our generative AI journey in collaboration with the AWS Generative AI Innovation Center for a transformational end-to-end customer experience.

Ahsan Ali is an Applied Scientist at the Amazon Generative AI Innovation Center, where he works with customers from different domains to solve their urgent and expensive problems using Generative AI.

Tahin Syed is an Applied Scientist with the Amazon Generative …

1 week, 6 days назад @ aws.amazon.com
Enhance code review and approval efficiency with generative AI using Amazon Bedrock
Enhance code review and approval efficiency with generative AI using Amazon Bedrock Enhance code review and approval efficiency with generative AI using Amazon Bedrock

In the world of software development, code review and approval are important processes for ensuring the quality, security, and functionality of the software being developed.

Time constraints – Code review and approval can be a time-consuming process, especially in larger or more complex projects.

– Code review and approval can be a time-consuming process, especially in larger or more complex projects.

Documentation – Proper documentation of the code review and approval process is important for transparency and accountability.

Test the solutionYou can test the workflow end to end by taking on the role of a developer and pushing some code changes.

1 week, 6 days назад @ aws.amazon.com
Best practices to build generative AI applications on AWS
Best practices to build generative AI applications on AWS Best practices to build generative AI applications on AWS

Generative AI applications driven by foundational models (FMs) are enabling organizations with significant business value in customer experience, productivity, process optimization, and innovations.

More and more customers are building their own FMs using SageMaker, including Stability AI, AI21 Labs, Hugging Face, Perplexity AI, Hippocratic AI, LG AI Research, and Technology Innovation Institute.

Common generative AI approachesIn this section, we discuss common approaches to implement effective generative AI solutions.

– RAG models help question answering applications locate and integrate information from documents or knowledge sources to generate high-quality answers.

With many options ava…

1 week, 6 days назад @ aws.amazon.com
NVIDIA
последний пост 12 часов назад
Software Developers Launch OpenUSD and Generative AI-Powered Product Configurators Built on NVIDIA Omniverse
Software Developers Launch OpenUSD and Generative AI-Powered Product Configurators Built on NVIDIA Omniverse Software Developers Launch OpenUSD and Generative AI-Powered Product Configurators Built on NVIDIA Omniverse

From designing dream cars to customizing clothing, 3D product configurators are ringing in a new era of hyper-personalization that will benefit retailers and consumers.

Many other developers are also building 3D product configurator software and solutions with NVIDIA Omniverse SDKs and APIs.

Creating a 3D Product ConfiguratorThe process of creating a 3D product configurator begins with harnessing OpenUSD’s powerful composition engine and interoperability.

Teams can also integrate generative AI technologies into OpenUSD-based product configurators using NVIDIA Omniverse APIs to enhance the realism and customization options available to users.

The possibilities for 3D product configurators ar…

12 часов назад @ blogs.nvidia.com
Efficient CUDA Debugging: Using NVIDIA Compute Sanitizer with NVIDIA Tools Extension and Creating Custom Tools
Efficient CUDA Debugging: Using NVIDIA Compute Sanitizer with NVIDIA Tools Extension and Creating Custom Tools Efficient CUDA Debugging: Using NVIDIA Compute Sanitizer with NVIDIA Tools Extension and Creating Custom Tools

We also discuss the API for Compute Sanitizer itself, to enable the creation of more tools for debugging CUDA applications.

The integration between Compute Sanitizer and NVTX enables you to use NVTX to annotate your code to assist Compute Sanitizer in catching bugs.

Memory pool managementThe first example of NVTX integration with Compute Sanitizer comes through the suballocation part of the NVTX Memory API.

Compute Sanitizer is aware of these pools and can detect which parts of a specific allocation are actually being used.

In combination, these APIs give you the ability to incorporate Compute Sanitizer functionality into your own tools.

15 часов назад @ developer.nvidia.com
New Self-Paced Course: Accelerate Data Science Workflows with Zero Code Changes
New Self-Paced Course: Accelerate Data Science Workflows with Zero Code Changes New Self-Paced Course: Accelerate Data Science Workflows with Zero Code Changes

For technical questions.check out the NVIDIA Developer Forums.

16 часов назад @ courses.nvidia.com
Scale and Curate High-Quality Datasets for LLM Training with NVIDIA NeMo Curator
Scale and Curate High-Quality Datasets for LLM Training with NVIDIA NeMo Curator Scale and Curate High-Quality Datasets for LLM Training with NVIDIA NeMo Curator

In this post, we discuss the open-source version of the NVIDIA NeMo Curator framework, the foundation upon which the recently introduced NeMo Curator microservice is built.

NeMo Curator aims to simplify and streamline the data curation process, paving the way for the adoption of generative AI at an enterprise scale.

NeMo Curator simplifies and scales data curation pipelinesNeMo Curator supports data curation for model pretraining and was engineered on the following key pillars: performance, scalability, and customizability.

KT is expecting state-of-the-art performance for LLMs trained on tokens prepared from NVIDIA NeMo Curator, which can generate high-quality datasets.

Get started with NeM…

17 часов назад @ developer.nvidia.com
NVIDIA Hopper Leaps Ahead in Generative AI at MLPerf
NVIDIA Hopper Leaps Ahead in Generative AI at MLPerf NVIDIA Hopper Leaps Ahead in Generative AI at MLPerf

It’s official: NVIDIA delivered the world’s fastest platform in industry-standard tests for inference on generative AI.

Raising the Bar in Generative AITensorRT-LLM running on NVIDIA H200 Tensor Core GPUs — the latest, memory-enhanced Hopper GPUs — delivered the fastest performance running inference in MLPerf’s biggest test of generative AI to date.

Memory Boost for NVIDIA Hopper GPUsNVIDIA is shipping H200 GPUs today.

Sweeping Every MLPerf TestOn a per-accelerator basis, Hopper GPUs swept every test of AI inference in the latest round of the MLPerf industry benchmarks.

Inference for large language models is difficult, requiring both expertise and the full-stack architecture NVIDIA demonstr…

20 часов назад @ blogs.nvidia.com
Unlocking Peak Generations: TensorRT Accelerates AI on RTX PCs and Workstations
Unlocking Peak Generations: TensorRT Accelerates AI on RTX PCs and Workstations Unlocking Peak Generations: TensorRT Accelerates AI on RTX PCs and Workstations

As generative AI advances and becomes widespread across industries, the importance of running generative AI applications on local PCs and workstations grows.

NVIDIA GeForce and NVIDIA RTX GPUs feature Tensor Cores, dedicated AI hardware accelerators that provide the horsepower to run generative AI locally.

Combining Tensor Cores with TensorRT software brings unmatched generative AI performance to local PCs and workstations.

NVIDIA RTX offers the fastest AI accelerators, scaling to more than 1,300 AI trillion operations per second, or TOPS.

NVIDIA RTX offers the fastest AI accelerators, scaling to more than 1,300 AI trillion operations per second, or TOPS.

22 часа назад @ blogs.nvidia.com
Viome’s Guru Banavar Discusses AI for Personalized Health
Viome’s Guru Banavar Discusses AI for Personalized Health Viome’s Guru Banavar Discusses AI for Personalized Health

In the latest episode of NVIDIA’s AI Podcast, Viome Chief Technology Officer Guru Banavar spoke with host Noah Kravitz about how AI and RNA sequencing are revolutionizing personalized healthcare.

The startup aims to tackle the root causes of chronic diseases by delving deep into microbiomes and gene expression.

With a comprehensive testing kit, Viome translates biological data into practical dietary recommendations.

Viome is forging ahead with professional healthcare solutions, such as early detection tests for diseases, and integrating state-of-the-art technology with traditional medical practices for a holistic approach to wellness.

Time Stamps:2:00: Introduction to Viome and the science …

22 часа назад @ blogs.nvidia.com
Boom in AI-Enabled Medical Devices Transforms Healthcare
Boom in AI-Enabled Medical Devices Transforms Healthcare Boom in AI-Enabled Medical Devices Transforms Healthcare

Around 700 FDA-cleared, AI-enabled medical devices are now on the market — more than 10x the number available in 2020.

Shifting From Hardware to Software-Defined Medical DevicesMedical devices have long been hardware-centric, relying on intricate designs and precise engineering.

Leading medtech companies such as GE Healthcare are using NVIDIA technology to develop, fine-tune and deploy AI for software-defined medical imaging applications.

Arrow Electronics is delivering IGX as a subscription-like platform-as-a-service for industrial and medical customers.

Subscribe to NVIDIA healthcare news.

1 day, 17 hours назад @ blogs.nvidia.com
Model Innovators: How Digital Twins Are Making Industries More Efficient
Model Innovators: How Digital Twins Are Making Industries More Efficient Model Innovators: How Digital Twins Are Making Industries More Efficient

A manufacturing plant near Hsinchu, Taiwan’s Silicon Valley, is among facilities worldwide boosting energy efficiency with AI-enabled digital twins.

And thanks to the AI models they developed using Modulus, the airflows in the simulation obey the laws of physics.

An Expanding EffortCurrently, the group is expanding its AI model to track more than a hundred variables in a space that holds 50 computer racks.

Efficiently Generating EnergyHalf a world away, Siemens Energy is demonstrating the power of digital industrialization using Modulus and Omniverse.

For more insights, listen to a talk from GTC describing Wistron’s work and a panel about industries using generative AI.

1 day, 20 hours назад @ blogs.nvidia.com
Into the Omniverse: Groundbreaking OpenUSD Advancements Put NVIDIA GTC Spotlight on Developers
Into the Omniverse: Groundbreaking OpenUSD Advancements Put NVIDIA GTC Spotlight on Developers Into the Omniverse: Groundbreaking OpenUSD Advancements Put NVIDIA GTC Spotlight on Developers

Newly announced NVIDIA Omniverse Cloud APIs, coming first to Microsoft Azure, allow developers to send their OpenUSD industrial scenes from content-creation applications to the NVIDIA Graphics Delivery Network.

Dassault Systèmes is using OpenUSD, Omniverse Cloud APIs and Shutterstock 3D AI Services for generative storytelling in 3DEXCITE applications.

is using OpenUSD, Omniverse Cloud APIs and Shutterstock 3D AI Services for generative storytelling in 3DEXCITE applications.

Trimble is enabling interactive NVIDIA Omniverse RTX viewers with Trimble model data using OpenUSD and Omniverse Cloud APIs.

is enabling interactive NVIDIA Omniverse RTX viewers with Trimble model data using OpenUSD and …

1 day, 22 hours назад @ blogs.nvidia.com
NVIDIA Blackwell and Automotive Industry Innovators Dazzle at NVIDIA GTC
NVIDIA Blackwell and Automotive Industry Innovators Dazzle at NVIDIA GTC NVIDIA Blackwell and Automotive Industry Innovators Dazzle at NVIDIA GTC

Generative AI, in the data center and in the car, is making vehicle experiences safer and more enjoyable.

The NVIDIA Blackwell GPU architecture will be integrated into the NVIDIA DRIVE Thor centralized car computer to enable generative AI applications and immersive in-vehicle experiences.

The NVIDIA auto booth highlighted the wide adoption of the NVIDIA DRIVE platform, with displays featuring electronic control units from a variety of partners, including Bosch, Lenovo and ZEEKR.

A wide range of NVIDIA automotive partners, including Ansys, Foretellix, Lenovo, MediaTek, NODAR, OMNIVISION, Plus, Seyond, SoundHound, Voxel51 and Waabi, all made next-generation product announcements at GTC.

Learn…

2 days, 9 hours назад @ blogs.nvidia.com
AI’s New Frontier: From Daydreams to Digital Deeds
AI’s New Frontier: From Daydreams to Digital Deeds AI’s New Frontier: From Daydreams to Digital Deeds

Imagine a world where you can whisper your digital wishes into your device, and poof, it happens.

“Our lives are full of so much friction … every single person’s vision can come to life,” Qiu said.

“We tend to underestimate the things that we do naturally and overestimate the things that require reasoning,” Catanzaro observed.

The Future of Personal ComputingQiu and Catanzaro discussed the role that virtual worlds will play in this, and how they could serve as interfaces for human-technology interaction.

“I think it’s pretty clear that AI is going to help build virtual worlds,” said Catanzaro.

6 days, 12 hours назад @ blogs.nvidia.com
Speed Up Your AI Development: NVIDIA AI Workbench Goes GA
Speed Up Your AI Development: NVIDIA AI Workbench Goes GA Speed Up Your AI Development: NVIDIA AI Workbench Goes GA

NVIDIA AI Workbench, a toolkit for AI and ML developers, is now generally available as a free download.

Enterprise support is also available for customers who purchase a license for NVIDIA AI Enterprise.

Visit NVIDIA on GitHub to reference NVIDIA AI Workbench Projects that can get you started with faster results.

AI Workbench handles the complexity of portability and reproducibility challenges so developers don’t have to.

Get AI WorkbenchDownload NVIDIA AI Workbench for Windows, macOS, and Ubuntu Linux.

6 days, 19 hours назад @ developer.nvidia.com
Here Be Dragons: ‘Dragon’s Dogma 2’ Comes to GeForce NOW
Here Be Dragons: ‘Dragon’s Dogma 2’ Comes to GeForce NOW Here Be Dragons: ‘Dragon’s Dogma 2’ Comes to GeForce NOW

Arise for a new adventure with Dragon’s Dogma 2, leading two new titles joining the GeForce NOW library this week.

Dragon’s Dogma 2, the long-awaited sequel to Capcom’s legendary action role-playing game, streams this week on GeForce NOW.

Recruit Pawns — mysterious otherworldly beings — to aid in battle and work with other players’ Pawns to fight the diverse monsters inhabiting the ever-changing lands.

Upgrade to a GeForce NOW Ultimate membership to stream Dragon’s Dogma 2 from NVIDIA GeForce RTX 4080 servers in the cloud for the highest performance, even on low-powered devices.

Then, look forward to the following list of games this week:Alone in the Dark (New release on Steam, March 20)(Ne…

6 days, 22 hours назад @ blogs.nvidia.com
Instant Latte: NVIDIA Gen AI Research Brews 3D Shapes in Under a Second
Instant Latte: NVIDIA Gen AI Research Brews 3D Shapes in Under a Second Instant Latte: NVIDIA Gen AI Research Brews 3D Shapes in Under a Second

NVIDIA researchers have pumped a double shot of acceleration into their latest text-to-3D generative AI model, dubbed LATTE3D.

Like a virtual 3D printer, LATTE3D turns text prompts into 3D representations of objects and animals within a second.

NVIDIA Research comprises hundreds of scientists and engineers worldwide, with teams focused on topics including AI, computer graphics, computer vision, self-driving cars and robotics.

Read more on the NVIDIA Technical Blog, and see the full list of NVIDIA Research sessions at GTC, running in San Jose, Calif., and online through March 21.

For the latest NVIDIA AI news, watch the replay of NVIDIA founder and CEO Jensen Huang’s keynote address at GTC:

6 days, 22 hours назад @ blogs.nvidia.com
Facebook
последний пост 1 week назад
Optimizing RTC bandwidth estimation with machine learning
Optimizing RTC bandwidth estimation with machine learning Optimizing RTC bandwidth estimation with machine learning

Bandwidth estimation (BWE) and congestion control play an important role in delivering high-quality real-time communication (RTC) across Meta’s family of apps.

Network characterizationAn ML model-based approach leverages time series data to improve the bandwidth estimation by using offline parameter tuning for characterized network types.

The first component is offline ML model learning using ML to categorize the network type (random packet loss versus bursty loss).

The non-time series data or dense data will pass through a dense layer (i.e., a fully connected layer).

Use case: Random packet loss classificationLet’s consider the use case of categorizing packet loss as either random or conge…

1 week назад @ engineering.fb.com
Logarithm: A logging engine for AI training workflows and services
Logarithm: A logging engine for AI training workflows and services Logarithm: A logging engine for AI training workflows and services

In this post, we present the design behind Logarithm, and show how it powers AI training debugging use cases.

AI training debugging with LogarithmBefore looking at Logarithm’s internals, we present support for training systems and model issue debugging, one of the prominent use cases of Logarithm at Meta.

ML model training workflows tend to have a wide range of failure modes, spanning data inputs, model code and hyperparameters, and systems components (e.g., PyTorch, data readers, checkpointing, framework code, and hardware).

Logarithm ingests both systems logs from the training stack, and model telemetry from training jobs that the stack executes.

Filter–by-callsite enables hiding known lo…

1 week, 2 days назад @ engineering.fb.com
Building Meta’s GenAI Infrastructure
Building Meta’s GenAI Infrastructure Building Meta’s GenAI Infrastructure

While we’ve had a long history of building AI infrastructure, we first shared details on our AI Research SuperCluster (RSC), featuring 16,000 NVIDIA A100 GPUs, in 2022.

Under the hoodOur newer AI clusters build upon the successes and lessons learned from RSC.

Our out-of-box performance for large clusters was initially poor and inconsistent, compared to optimized small cluster performance.

Commitment to open AI innovationMeta maintains its commitment to open innovation in AI software and hardware.

The future of Meta’s AI infrastructureThese two AI training cluster designs are a part of our larger roadmap for the future of AI.

2 weeks, 1 day назад @ engineering.fb.com
Improving machine learning iteration speed with faster application build and packaging
Improving machine learning iteration speed with faster application build and packaging Improving machine learning iteration speed with faster application build and packaging

These improvements helped us find and remove many unnecessary dependencies, making build graph analysis and overall build times much better.

In response to this challenge, we implemented a new solution for the packaging and distribution of Python executables – the Content Addressable Filesystem (CAF).

LazyCAF and enforcing uniform revisions: Areas for further ML iteration improvementsThe improvements we implemented have proven highly effective, drastically reducing the overhead and significantly elevating the efficiency of our ML engineers.

Faster build times and more efficient packaging and distribution of executables have reduced overhead by double-digit percentages.

We plan to enable all…

1 month, 4 weeks назад @ engineering.fb.com
Lazy is the new fast: How Lazy Imports and Cinder accelerate machine learning at Meta
Lazy is the new fast: How Lazy Imports and Cinder accelerate machine learning at Meta Lazy is the new fast: How Lazy Imports and Cinder accelerate machine learning at Meta

At Meta, the quest for faster model training has yielded an exciting milestone: the adoption of Lazy Imports and the Python Cinder runtime.

The challenges of adopting Lazy ImportsWhile Lazy Imports’ approach significantly improved ML development, it was not all a bed of roses.

With Lazy Imports, Meta’s ML developers are now equipped to work more efficiently, experiment more rapidly, and achieve results faster.

Here’s a glimpse into our future endeavors:Streamlining developer onboardingThe learning curve associated with Lazy Imports can be a challenge for newcomers.

Building a robust community that helps supporting paradigms and patterns that play well with Lazy Imports is one of our future …

2 months, 1 week назад @ engineering.fb.com
How Meta is advancing GenAI
How Meta is advancing GenAI How Meta is advancing GenAI

What’s going on with generative AI (GenAI) at Meta?

In this episode of the Meta Tech Podcast, Meta engineer Pascal Hartig (@passy) speaks with Devi Parikh, an AI research director at Meta.

They cover a wide range of topics, including the history and future of GenAI and the most interesting research papers that have come out recently.

And, of course, they discuss some of Meta’s latest GenAI innovations, including:Audiobox, a foundational model for generating sound and soundscapes using natural language prompts.

And if you’re interested in AI career opportunities at Meta visit the Meta Careers page.

2 months, 2 weeks назад @ engineering.fb.com
AI debugging at Meta with HawkEye
AI debugging at Meta with HawkEye AI debugging at Meta with HawkEye

In this post, we will provide an overview of the end-to-end debugging workflows supported by HawkEye, components of the system, and the product surface for Meta product and monetization teams to debug AI model and feature issues.

HawkEye includes infrastructure for continuously collecting data on serving and training models, data generation, and analysis components for mining root causes.

However, significant differences indicate problems with either the training data or loss divergence (e.g., loss or gradient explosion) in the bad snapshot.

Such issues can happen for several hard-to-diagnose reasons, ranging from the complex data pipelines behind training data, to data corruptions.

HawkEye…

3 months, 1 week назад @ engineering.fb.com
Watch: Meta’s engineers on building network infrastructure for AI
Watch: Meta’s engineers on building network infrastructure for AI Watch: Meta’s engineers on building network infrastructure for AI

The 2023 edition of Networking at Scale focused on how Meta’s engineers and researchers have been designing and operating the network infrastructure over the last several years for Meta’s AI workloads, including our numerous ranking and recommendation workloads and the immense Generative AI models.

But the sheer scale and complexity of GenAi models means new challenges for Meta’s network infrastructure.

Meta’s Network Journey to Enable AIHany Morsy, Network EngineerSusana Contrera, Network EngineerOver the years, Meta’s AI infrastructure has transitioned from CPU-based to GPU-based training due to growing AI workloads.

Traffic Engineering for AI Training NetworksShuqiang Zhang, Software Eng…

4 months, 1 week назад @ engineering.fb.com
How Meta is creating custom silicon for AI
How Meta is creating custom silicon for AI How Meta is creating custom silicon for AI

Fueling the success of these products are world-class infrastructure teams, including Meta’s custom AI silicon team, led by Olivia Wu, a leader in the silicon industry for 30 years.

In 2018, I saw a social media post from Yann LeCun, our Chief AI Scientist, that Meta was looking for someone to help build AI silicon in-house.

I knew of just a few other companies designing their own custom AI silicon, but they were mainly focused only on silicon and not the software ecosystem and products.

What’s next for the AI silicon design team?

We’re continuing to gather feedback and input from our AI software teams to shape the features of our future AI silicon.

5 months, 1 week назад @ engineering.fb.com
Using Chakra execution traces for benchmarking and network performance optimization
Using Chakra execution traces for benchmarking and network performance optimization Using Chakra execution traces for benchmarking and network performance optimization

Meta presents Chakra execution traces , an open graph-based representation of AI/ML workload execution, laying the foundation for benchmarking and network performance optimization.

The limitations of traditional AI benchmarking methodologyTraditionally, benchmarking AI systems has largely relied on running full ML workloads.

How Meta leverages Chakra execution tracesAt Meta, we collect execution traces from our production servers every day.

Future plansEnhancing the benchmarking capability of Chakra execution tracesWhile the execution trace replayer enables replay of execution traces, it brings forth challenges.

Using AI to generate representative execution tracesChakra execution traces are…

6 months, 3 weeks назад @ engineering.fb.com
Arcadia: An end-to-end AI system performance simulator
Arcadia: An end-to-end AI system performance simulator Arcadia: An end-to-end AI system performance simulator

We’re introducing Arcadia, Meta’s unified system that simulates the compute, memory, and network performance of AI training clusters.

Arcadia gives Meta’s researchers and engineers valuable insights into the performance of AI models and workloads in an AI cluster – enabling data-driven decision making in the design of AI clusters.

That’s where Arcadia, Meta’s end-to-end AI system performance simulator, comes in.

By providing insights into the impact of these factors on system performance, Arcadia facilitates data-driven decision-making processes and fosters the evolution of models and hardware.

This comprehensive set of metrics empowers stakeholders to analyze the impact of different factor…

6 months, 3 weeks назад @ engineering.fb.com
Code Llama: Meta’s state-of-the-art LLM for coding
Code Llama: Meta’s state-of-the-art LLM for coding Code Llama: Meta’s state-of-the-art LLM for coding

Today, we are releasing Code Llama, a large language model (LLM) that can use text prompts to generate code.

Additionally, we have further fine-tuned two additional variations of Code Llama: Code Llama - Python and Code Llama - Instruct.

Code Llama - Python is a language-specialized variation of Code Llama, further fine-tuned on 100B tokens of Python code.

Code Llama - Instruct is an instruction fine-tuned and aligned variation of Code Llama.

We recommend using Code Llama - Instruct variants whenever using Code Llama for code generation since Code Llama - Instruct has been fine-tuned to generate helpful and safe answers in natural language.

7 months, 1 week назад @ ai.meta.com
Meta Connect 2023: September 27 – 28
Meta Connect 2023: September 27 – 28 Meta Connect 2023: September 27 – 28

[...]

Read More...

The post Meta Connect 2023: September 27 – 28 appeared first on Engineering at Meta.

7 months, 2 weeks назад @ meta.com
Scaling the Instagram Explore recommendations system
Scaling the Instagram Explore recommendations system Scaling the Instagram Explore recommendations system

Every day, hundreds of millions of people visit Explore on Instagram to discover something new, making it one of the largest recommendation surfaces on Instagram.

Readers might notice that the leitmotif of this post will be clever use of caching and pre-computation in different ranking stages.

In most large-scale recommender systems, the retrieval stage consists of multiple candidates’ retrieval sources (“sources” for short).

To model media retrieval for different user groups with various interests, we utilize all these mentioned source types together and mix them with tunable weights.

After training, user embeddings should be close to the embeddings of relevant items for a given user.

7 months, 3 weeks назад @ engineering.fb.com
Uber Engineering
последний пост None
neptune.ai neptune.ai
последний пост 5 days, 20 hours назад
Zero-Shot and Few-Shot Learning with LLMs
Zero-Shot and Few-Shot Learning with LLMs Zero-Shot and Few-Shot Learning with LLMs

The role of zero-shot and few-shot learning in LLMsThe goal of zero-shot and few-shot learning is to get a machine-learning model to perform a new task it was not trained for.

“Few-shot learning” and “zero-shot learning” are well-known concepts in machine learning that were studied long before LLMs appeared on the scene.

In the context of LLMs, these terms are sometimes used interchangeably with “few-shot prompting” and “zero-shot prompting.” However, they are not the same.

Where zero-shot prompting failsLet’s turn to two use cases where zero-shot prompting is insufficient.

Where few-shot prompting failsFinally, let’s look at a situation where few-shot prompting won’t get us far.

5 days, 20 hours назад @ neptune.ai
LLMOps: What It Is, Why It Matters, and How to Implement It
LLMOps: What It Is, Why It Matters, and How to Implement It LLMOps: What It Is, Why It Matters, and How to Implement It

Large Language Models (LLMs) like Meta AI’s LLaMA models, MISTRAL AI’s open models, and OpenAI’s GPT series have improved language-based AI.

Monitoring Monitor model performance for data drift and model degradation, often using automated monitoring tools.

Automate prompt selection: Implement systems that automatically choose the best prompt for a given task using historical data on prompt performance and the specifics of the current request.

Monitoring and observability are about tracking LLMs’ performance, health, and operational metrics in production to ensure they perform optimally and reliably.

Use platforms that offer comprehensive observability into LLM performance, including function…

2 weeks, 2 days назад @ neptune.ai
The Real Cost of Self-Hosting MLflow
The Real Cost of Self-Hosting MLflow The Real Cost of Self-Hosting MLflow

The canonical MLflow setup for teams: The MLflow client embedded in the training code communicates with the MLflow tracking server, which handles access to cloud storage (artifact store) and a database (metadata store).

| Modified based on: sourceDeploying the MLflow tracking serverMLflow’s tracking server is relatively lightweight.

With this in mind, there are three main options for deploying the MLflow tracking server on AWS:Deploying the MLflow tracking server on an Amazon EC2 instance.

Recommended The Best MLflow alternatives Read alsoSetting up an artifact storeThe artifact store is the third relevant cost item in an MLflow deployment.

Further, everyone who has access to your MLflow tr…

2 weeks, 5 days назад @ neptune.ai
Deep Learning Model Optimization Methods
Deep Learning Model Optimization Methods Deep Learning Model Optimization Methods

Knowledge distillation transfers insights from a complex “teacher” model to a simpler “student” model, maintaining performance with less computational demand.

Distillation loss: The student model is trained not just to replicate the output of the teacher model but to match the output distributions produced by the teacher model.

Model architecture compatibility: The effectiveness of knowledge distillation depends on how well the student model can learn from the teacher model, which is greatly influenced by their architectural compatibility.

Data is fed to both a complex ‘Teacher Model’ and a simpler ‘Student Model.’ The Teacher Model, which consists of multiple layers (from Layer 1 to Layer …

3 weeks, 6 days назад @ neptune.ai
Continual Learning: Methods and Application
Continual Learning: Methods and Application Continual Learning: Methods and Application

Continual learning scenariosDepending on the data stream characteristics, problems within the continual learning scenario can be divided into three, each with a common solution.

Task incremental continual learningTask Incremental (TI) continual learning is classic multi-task learning but in an incremental way.

Continual learning methodsOver the second decade of the 2000s, there has been a rapid improvement in recent advances in continual learning methods.

Recommended How to Build Machine Learning Systems with a Feature Store See alsoHow to choose the right continual learning method for your projectAcross the three groups of continual learning approaches, many techniques exist.

Continual lea…

1 month назад @ neptune.ai
2024 Layoffs and LLMs: Pivoting for Success
2024 Layoffs and LLMs: Pivoting for Success 2024 Layoffs and LLMs: Pivoting for Success

Neither teams working on proof-of-concept projects nor production ML systems are immune from job cuts.

The rapid advancements of Large Language Models (LLMs) are changing the day-to-day work of ML practitioners and how company leadership thinks about AI.

The rise of modern LLMsIn 2023, the dominance of modern LLMs became increasingly evident, challenging the incumbent classical NLP models.

Even small and relatively weaker LLMs like DistilGPT2 and t5-small have surpassed classical NLP models in understanding context and generating coherent text.

As you can see, it’s unclear whether people working on PoC or production projects are at higher risk of layoffs.

1 month, 1 week назад @ neptune.ai
Mikiko Bazeley: What I Learned Building the ML Platform at Mailchimp
Mikiko Bazeley: What I Learned Building the ML Platform at Mailchimp Mikiko Bazeley: What I Learned Building the ML Platform at Mailchimp

In this article, I will outline the learnings and challenges I faced while on the ML platform team at Mailchimp, building infrastructure and setting up the environment for development and testing.

Mailchimp’s ML Platform: genesis, challenges, and objectivesMailchimp is a 20-year-old bootstrapped email marketing company.

Team setup and responsibilitiesWe had around 20 data engineers and ML(Ops) engineers working on the ML platform at Mailchimp.

Recommended Learnings From Building the ML Platform at Mailchimp See full episodeIn times of generative AI, a good ML infrastructure matters a lotA lesson that I’ve learned time and time again over the past years is the enduring importance of ‘boring’…

2 months назад @ neptune.ai
How to Build Machine Learning Systems With a Feature Store
How to Build Machine Learning Systems With a Feature Store How to Build Machine Learning Systems With a Feature Store

Understanding machine learning pipelinesMachine learning (ML) pipelines are a key component of ML systems.

Machine learning systems with feature storesMachine learning (ML) systems manage the data transformations, model training, and predictions made on ML models.

A feature store typically comprises a feature repository, a feature serving layer, and a metadata store.

A feature store is a data platform that supports the development and operation of machine learning systems by managing the storage and efficient querying of feature data.

A feature store stores feature data in tables called feature groups, also called feature sets or tables.

2 months назад @ neptune.ai
Logging PyMC and Arviz Artifacts on Neptune
Logging PyMC and Arviz Artifacts on Neptune Logging PyMC and Arviz Artifacts on Neptune

PyMC and ArviZ are an excellent pairing of open-source Python libraries for modeling and visualizing Bayesian models.

Even though neptune.ai does not have built-in integration for PyMC and ArviZ, it’s straightforward to track artifacts produced by these libraries through the powerful run interface.

Iterative modeling with PyMC and ArviZ creates a lot of artifacts in the form of plots, data, metrics, and so on.

Neptune itself is not integrated out-of-the-box with PyMC and ArviZ, but thanks to its extensibility, it is easy enough to use it in combination with both.

For the following code examples, I assume you have a Python >=3.10 environment with neptune, pymc, and arviz installed.

2 months назад @ neptune.ai
LLM Fine-Tuning and Model Selection Using Neptune and Transformers
LLM Fine-Tuning and Model Selection Using Neptune and Transformers LLM Fine-Tuning and Model Selection Using Neptune and Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer from peft import prepare_model_for_kbit_training from peft import get_peft_model, LoraConfig model_name = 'gpt2-large' model = AutoModelForCausalLM.from_pretrained(model_name, device_map = " auto ", load_in_8bit=True, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_name) tokenizer.pad_token = tokenizer.eos_token tokenizer.pad_token_id = tokenizer.eos_token_id model = prepare_model_for_kbit_training(model) After quantizing the model to 8 bits, it takes only a fourth of the memory to load and train the model, respectively.

With our base model loaded, we now want to add the LoRA layers in parallel with the ori…

2 months, 1 week назад @ neptune.ai
How to Visualize Deep Learning Models
How to Visualize Deep Learning Models How to Visualize Deep Learning Models

While many traditional machine learning models make do with just a couple of hundreds of parameters, deep learning models have millions or billions of parameters.

Deep learning model visualization helps us understand model behavior and differences between models, diagnose training processes and performance issues, and aid the refinement and optimizations of models | SourceWhy do we want to visualize deep learning models?

Visualizing deep learning models can help us with several different objectives:Interpretability and explainability: The performance of deep learning models is, at times, staggering, even for seasoned data scientists and ML engineers.

Downstream consumers of deep learning mo…

4 months, 2 weeks назад @ neptune.ai
How to Use Exploratory Notebooks [Best Practices]
How to Use Exploratory Notebooks [Best Practices] How to Use Exploratory Notebooks [Best Practices]

I’ll show you best practices for using Jupyter Notebooks for exploratory data analysis.

In this article, we’ll talk about Jupyter notebooks specifically from a business and product point of view.

As I already mentioned, Jupyter notebooks are a polarising topic, so let’s go straight into my opinion.

Jupyter notebooks should be used for purely exploratory tasks or ad-hoc analysis ONLY.

All in all, I encourage data scientists to use Jupyter notebooks, but exclusively for answering exploratory questions and reporting purposes.

5 months, 1 week назад @ neptune.ai
Learnings From Building the ML Platform at Mailchimp
Learnings From Building the ML Platform at Mailchimp Learnings From Building the ML Platform at Mailchimp

This article was originally an episode of the ML Platform Podcast, a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals.

In this episode, Mikiko Bazeley shares her learnings from building the ML Platform at Mailchimp.

I joined FeatureForm last October, and before that, I was with Mailchimp on their ML platform team.

But, in the last two, three years, Mailchimp started hiring data scientists that were more on the product and business side.

Aurimas: Do I understand correctly that you had 18 people in the various platform…

5 months, 3 weeks назад @ neptune.ai
Software Engineering Patterns for Machine Learning
Software Engineering Patterns for Machine Learning Software Engineering Patterns for Machine Learning

Best practices for building ETLs for MLBest practices for building ETLs for ML | Source: AuthorThe significance of ETLs in machine learning projectsExploring a pivotal facet of every machine learning endeavor: ETLs.

Related post In-Depth ETL in Machine Learning Tutorial [Case Study] Read moreBest practices for building training and inference algorithmsBest practices for building training and inference algorithms | Source: AuthorThe nature of training in machine learningTraining is often seen as an engaging and imaginative aspect of machine learning tasks.

Transition to production and challengesAfter constructing the machine learning model, the next step is transitioning it into a production…

6 months, 3 weeks назад @ neptune.ai
ML Pipeline Architecture Design Patterns (With 10 Real-World Examples)
ML Pipeline Architecture Design Patterns (With 10 Real-World Examples) ML Pipeline Architecture Design Patterns (With 10 Real-World Examples)

2 Exploration of standard ML pipeline/system design and architectural practices in prominent tech companiesExploration of standard ML pipeline/system design and architectural practices in prominent tech companies 3 Explanation of common ML pipeline architecture design patternsExplanation of common ML pipeline architecture design patterns 4 Introduction to common components of ML pipelinesIntroduction to common components of ML pipelines 5 Introduction to tools, techniques and software used to implement and maintain ML pipelinesIntroduction to tools, techniques and software used to implement and maintain ML pipelines 6 ML pipeline architecture examplesML pipeline architecture examples 7 Comm…

7 months, 2 weeks назад @ neptune.ai
▶️ YouTube
Yannic Kilcher Yannic Kilcher
последний пост 1 day, 14 hours назад
[ML News] Grok-1 open-sourced | Nvidia GTC | OpenAI leaks model names | AI Act
[ML News] Grok-1 open-sourced | Nvidia GTC | OpenAI leaks model names | AI Act [ML News] Grok-1 open-sourced | Nvidia GTC | OpenAI leaks model names | AI Act

OUTLINE:

0:00 - Intro

0:15 - XAI releases Grok-1

2:00 - Nvidia GTC

4:45 - Comment of the Week

5:35 - Brute-forcing OpenAI model names

7:30 - Inflection AI gets eaten by Microsoft

9:25 - EU AI Act moving forward

11:45 - Advances in Robotics

14:00 - India retracts controversial advisory

14:30 - OpenSora

15:20 - Improved Gemma fine-tuning

16:20 - Decoding encrypted LLM traffic

17:45 - Varia References:

https://x.ai/blog/grok-os

https://github.com/xai-org/grok-1

https://finance.yahoo.com/news/nvidia-debuts-next-generation-blackwell-ai-chip-at-gtc-2024-205825161.html?guccounter=1&guce_referrer=aHR0cHM6Ly9uZXdzLmdvb2dsZS5jb20v&guce_referrer_sig=AQAAAHYRVePPrDnH3HxPV8smDzUiia_ztWttteAmHKxy-x_Z75lq…

1 day, 14 hours назад @ youtube.com
[ML News] Devin AI Software Engineer | GPT-4.5-Turbo LEAKED | US Gov't Report: Total Extinction
[ML News] Devin AI Software Engineer | GPT-4.5-Turbo LEAKED | US Gov't Report: Total Extinction [ML News] Devin AI Software Engineer | GPT-4.5-Turbo LEAKED | US Gov't Report: Total Extinction

Your weekly dose of ML News OUTLINE:

0:00 - Intro

0:15 - Devin: AI software engineer

5:50 - Mira Murati on Sora training data

6:50 - Inflection accused of copying Claude

9:00 - Tools & papers

16:30 - GPT-4.5-turbo mystery

17:30 - US government report: total extinction by AI

19:20 - Various other news References:

https://www.cognition-labs.com/introducing-devin

https://twitter.com/cognition_labs/status/1767548763134964000?t=ZECIn-uqbguwHtY8X_Gvtw&s=09

https://news.google.com/stories/CAAqNggKIjBDQklTSGpvSmMzUnZjbmt0TXpZd1NoRUtEd2lWMUwyU0N4RnVWM3pSRWhWX01pZ0FQAQ?hl=en-US&gl=US&ceid=US%3Aen

https://www.bloomberg.com/news/articles/2024-03-12/cognition-ai-is-a-peter-thiel-backed-coding-assistant?…

1 week, 3 days назад @ youtube.com
[ML News] Elon sues OpenAI | Mistral Large | More Gemini Drama
[ML News] Elon sues OpenAI | Mistral Large | More Gemini Drama [ML News] Elon sues OpenAI | Mistral Large | More Gemini Drama

#mlnews #ainews #openai OUTLINE:

0:00 - Intro

0:20 - Elon sues OpenAI

14:00 - Mistral Large

16:40 - ML Espionage

18:30 - More Gemini Drama

24:00 - Copilot generates spicy images

26:55 - Gemma bugs

28:45 - Varia References: https://gist.github.com/yk/0c065cdc8e414738abfaae4f8e417e00 Thumbnail pictures: Wikipedia Links:

Homepage: https://ykilcher.com

Merch: https://ykilcher.com/merch

YouTube: https://www.youtube.com/c/yannickilcher

Twitter: https://twitter.com/ykilcher

Discord: https://ykilcher.com/discord

LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and …

2 weeks, 3 days назад @ youtube.com
On Claude 3
On Claude 3 On Claude 3 2 weeks, 6 days назад @ youtube.com
No, Anthropic's Claude 3 is NOT sentient
No, Anthropic's Claude 3 is NOT sentient No, Anthropic's Claude 3 is NOT sentient

No, Anthropic's Claude 3 is not conscious or sentient or self-aware. References:

https://www.anthropic.com/news/claude-3-family

https://twitter.com/_akhaliq/status/1764673955313459560?t=gkBx2uTXfrxLl-5_mL7Btg&s=09

https://twitter.com/idavidrein/status/1764675668175094169?t=pJfbN3LtKaxsU8egz83Mvg&s=09

https://twitter.com/TolgaBilge_/status/1764754012824314102?t=9bakXDnVMC1oAEyZFoKimA&s=09

https://twitter.com/karinanguyen_/status/1764670019743690757?t=gkBx2uTXfrxLl-5_mL7Btg&s=09

https://twitter.com/alexalbert__/status/1764722513014329620

https://www.lesswrong.com/posts/pc8uP4S9rDoNpwJDZ/claude-3-claims-its-conscious Links:

Homepage: https://ykilcher.com

Merch: https://ykilcher.com/merch

YouTu…

3 weeks, 1 day назад @ youtube.com
[ML News] Groq, Gemma, Sora, Gemini, and Air Canada's chatbot troubles
[ML News] Groq, Gemma, Sora, Gemini, and Air Canada's chatbot troubles [ML News] Groq, Gemma, Sora, Gemini, and Air Canada's chatbot troubles

Your dose of ML News! OUTLINE:

0:00 - Intro

0:20 - Gemma & Gemini

3:40 - Groq

6:30 - Nvidia EOS Supercomputer

7:15 - Gpulist.ai

8:20 - Demis Hassabis on scale

10:10 - Hardware wars

12:05 - Sora

15:10 - Gemini 1.5 Pro & Long Context

18:45 - Air Canada must pay for chatbot mistake

23:30 - Giant Rat Balls

26:25 - Various News References:

https://blog.google/technology/developers/gemma-open-models/?utm_source=tw

https://twitter.com/altryne/status/1760358916624719938?t=PVZkHQA_p7GxmeUX0hcZ_Q&s=09

https://twitter.com/paulg/status/1760078920135872716?t=PVZkHQA_p7GxmeUX0hcZ_Q&s=09

https://groq.com/

https://twitter.com/mattshumer_/status/1759347920543834117?t=cS5nPvZOsV6iDA1mVabHOg&s=09

https://twit…

3 weeks, 5 days назад @ youtube.com
Gemini has a Diversity Problem
Gemini has a Diversity Problem Gemini has a Diversity Problem

Google turned the anti-bias dial up to 11 on their new Gemini Pro model. References:

https://developers.googleblog.com/2024/02/gemini-15-available-for-private-preview-in-google-ai-studio.html

https://blog.google/technology/developers/gemma-open-models/?utm_source=tw

https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf

https://twitter.com/ClementDelangue/status/1760324815888486668?t=spXd7Oq_cSrRN2A-3r6gnQ&s=09

https://twitter.com/paulg/status/1760078920135872716?t=PVZkHQA_p7GxmeUX0hcZ_Q&s=09

https://twitter.com/yoavgo/status/1760445342691016811/photo/3

https://twitter.com/alex_peys/status/1760327435890135279/photo/2

https://twitter.com/woke8yearold/status/1760310705142558781/…

1 month назад @ youtube.com
V-JEPA: Revisiting Feature Prediction for Learning Visual Representations from Video (Explained)
V-JEPA: Revisiting Feature Prediction for Learning Visual Representations from Video (Explained) V-JEPA: Revisiting Feature Prediction for Learning Visual Representations from Video (Explained)

#vjepa #meta #unsupervisedlearning V-JEPA is a method for unsupervised representation learning of video data by using only latent representation prediction as objective function. Weights & Biases course on Structured LLM Outputs: https://wandb.me/course-yannic OUTLINE:

0:00 - Intro

1:45 - Predictive Feature Principle

8:00 - Weights & Biases course on Structured LLM Outputs

9:45 - The original JEPA architecture

27:30 - V-JEPA Concept

33:15 - V-JEPA Architecture

44:30 - Experimental Results

46:30 - Qualitative Evaluation via Decoding Blog: https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/

Paper: https://ai.meta.com/research/publications/revisit…

1 month, 1 week назад @ youtube.com
What a day in AI! (Sora, Gemini 1.5, V-JEPA, and lots of news)
What a day in AI! (Sora, Gemini 1.5, V-JEPA, and lots of news) What a day in AI! (Sora, Gemini 1.5, V-JEPA, and lots of news)

Your regularly irregular dose of Machine Learning News! W&B Course on LLM Structured Outputs: https://wandb.me/course-yannic OUTLINE:

0:00 - OpenAI Sora

3:25 - Gemini 1.5 with 1 Million Tokens context window

4:50 - V-JEPA

6:50 - Sam Altman raises 7 TRILLION dollars for AI chips

9:30 - Sponsor: Weights & Biases course on Structure Output from LLMs

11:30 - Bard becomes Gemini

13:55 - GOODY-2: The world's most responsible model

16:05 - miqu-1-70b leaked from Mistral

18:25 - Zuckerberg on Meta's open approach to AI models

21:40 - 1X advances robotics

23:30 - Questions around Bard's arena leaderboard position

27:00 - Various other news References:

https://gist.github.com/yk/65fe3d582a43540a61718…

1 month, 1 week назад @ youtube.com
Lumiere: A Space-Time Diffusion Model for Video Generation (Paper Explained)
Lumiere: A Space-Time Diffusion Model for Video Generation (Paper Explained) Lumiere: A Space-Time Diffusion Model for Video Generation (Paper Explained)

#lumiere #texttovideoai #google LUMIERE by Google Research tackles globally consistent text-to-video generation by extending the U-Net downsampling concept to the temporal axis of videos. OUTLINE:

0:00 - Introduction

8:20 - Problems with keyframes

16:55 - Space-Time U-Net (STUNet)

21:20 - Extending U-Nets to video

37:20 - Multidiffusion for SSR prediction fusing

44:00 - Stylized generation by swapping weights

49:15 - Training & Evaluation

53:20 - Societal Impact & Conclusion Paper: https://arxiv.org/abs/2401.12945

Website: https://lumiere-video.github.io/ Abstract:

We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and co…

1 month, 3 weeks назад @ youtube.com
AlphaGeometry: Solving olympiad geometry without human demonstrations (Paper Explained)
AlphaGeometry: Solving olympiad geometry without human demonstrations (Paper Explained) AlphaGeometry: Solving olympiad geometry without human demonstrations (Paper Explained)

#deepmind #alphageometry #llm AlphaGeometry is a combination of a symbolic solver and a large language model by Google DeepMind that tackles IMO geometry questions without any human-generated trainind data. OUTLINE:

0:00 - Introduction

1:30 - Problem Statement

7:30 - Core Contribution: Synthetic Data Generation

9:30 - Sampling Premises

13:00 - Symbolic Deduction

17:00 - Traceback

19:00 - Auxiliary Construction

25:20 - Experimental Results

32:00 - Problem Representation

34:30 - Final Comments Paper: https://www.nature.com/articles/s41586-023-06747-5 Abstract:

Proving mathematical theorems at the olympiad level represents a notable milestone in human-level automated reasoning1,2,3,4, owing to…

2 months назад @ youtube.com
Mixtral of Experts (Paper Explained)
Mixtral of Experts (Paper Explained) Mixtral of Experts (Paper Explained)

#mixtral #mistral #chatgpt OUTLINE:

0:00 - Introduction

3:00 - Mixture of Experts

6:00 - Classic Transformer Blocks

11:15 - Expert Routing

17:00 - Sparse Expert Routing

22:00 - Expert Parallelism

25:00 - Experimental Results

31:30 - Routing Analysis

33:20 - Conclusion Paper: https://arxiv.org/abs/2401.04088 Abstract:

We introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model. Mixtral has the same architecture as Mistral 7B, with the difference that each layer is composed of 8 feedforward blocks (i.e. experts). For every token, at each layer, a router network selects two experts to process the current state and combine their outputs. Even though each token only sees two exp…

2 months, 2 weeks назад @ youtube.com
Until the Litter End
Until the Litter End Until the Litter End

https://litter.ykilcher.com Links:

Homepage: https://ykilcher.com

Merch: https://ykilcher.com/merch

YouTube: https://www.youtube.com/c/yannickilcher

Twitter: https://twitter.com/ykilcher

Discord: https://ykilcher.com/discord

LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):

SubscribeStar: https://www.subscribestar.com/yannickilcher

Patreon: https://www.patreon.com/yannickilcher

Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq

Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2

Litecoin …

2 months, 2 weeks назад @ youtube.com
LLaMA Pro: Progressive LLaMA with Block Expansion (Paper Explained)
LLaMA Pro: Progressive LLaMA with Block Expansion (Paper Explained) LLaMA Pro: Progressive LLaMA with Block Expansion (Paper Explained)

Note: The H800 is a variant of the H100 for the Chinese market OUTLINE:

0:00 - Introduction

5:30 - Adding new blocks to LLaMA

15:00 - Block expansion

27:40 - Experiments

30:40 - Conclusion Paper: https://arxiv.org/abs/2401.02415

Other Paper: https://proceedings.mlr.press/v162/shen22f/shen22f.pdf Abstract:

Humans generally acquire new skills without compromising the old; however, the opposite holds for Large Language Models (LLMs), e.g., from LLaMA to CodeLLaMA. To this end, we propose a new post-pretraining method for LLMs with an expansion of Transformer blocks. We tune the expanded blocks using only new corpus, efficiently and effectively improving the model's knowledge without catastroph…

2 months, 2 weeks назад @ youtube.com
I created an AI-powered Social Network
I created an AI-powered Social Network I created an AI-powered Social Network

#ai #chatgpt #socialmedia I created a social network that operates entirely in the latent space.

Litter (aka Latent Twitter) will pull images and text through multiple modality conversions before it hits the network, so you can communicate just the essence of your message. Website: https://litter.ykilcher.com

Code: https://github.com/yk/litter OUTLINE:

0:00 - Introduction

1:10 - How does it work?

3:30 - Improving Yann LeCun's post

4:20 - Posting images

5:05 - Image examples

6:40 - Final words Links:

Homepage: https://ykilcher.com

Merch: https://ykilcher.com/merch

YouTube: https://www.youtube.com/c/yannickilcher

Twitter: https://twitter.com/ykilcher

Discord: https://ykilcher.com/discord

Link…

2 months, 3 weeks назад @ youtube.com
Henry AI Labs Henry AI Labs
последний пост 3 weeks, 2 days назад
Adding Depth to DSPy Programs
Adding Depth to DSPy Programs Adding Depth to DSPy Programs

Hey everyone! Thank you so much for watching the 3rd edition of the DSPy series, Adding Depth to DSPy Programs!! You can find the examples and links to community resources / news on https://github.com/weaviate/recipes! Chapters

0:00 Intro

0:50 Chapters Overview

5:06 Weaviate Recipes

5:24 DSPy News and Community Notes

13:51 Adding Depth to RAG Programs

18:40 Multi-Model DSPy Programs

20:18 DSPy Optimizers

25:30 Deep Dive Optimizers

27:55 Into the Optimizer Code!

37:48 Demo #1: Adding Depth to RAG

1:05:25 Demo #2: Questions to Blogs

1:07:48 Thank you so much for watching!

3 weeks, 2 days назад @ youtube.com
Getting Started with RAG in DSPy!
Getting Started with RAG in DSPy! Getting Started with RAG in DSPy!

Hey everyone! Thank you so much for watching this tutorial on getting started with RAG programming in DSPy! This video will take you through 4 major aspects of building DSPy programs (1) Installation, settings, and Datasets with dspy.Example, (2) LLM Metrics, (3) The DSPy programming model, and (4) Optimization!! The notebook used in the video can be found here: https://github.com/weaviate/recipes/blob/main/integrations/dspy/1.Getting-Started-with-RAG-in-DSPy.ipynb All future videos, as well as additional utils like data import scripts, will be in this folder: https://github.com/weaviate/recipes/tree/main/integrations/dspy Please leave a star, it helps a lot! DSPy on GitHub: https://github.…

1 month, 2 weeks назад @ youtube.com
DSPy Explained!
DSPy Explained! DSPy Explained!

Hey everyone! Thank you so much for watching this explanation of DSPy! DSPy is a super exciting new framework for developing LLM programs! Pioneered by frameworks such as LangChain and LlamaIndex, we can build much more powerful systems by chaining together LLM calls! This means that the output of one call to an LLM is the input to the next, and so on. We can think of chains as programs, with each LLM call analogous to a function that takes text as input and produces text as output. DSPy offers a new programming model, inspired by PyTorch, that gives you a massive amount of control over these LLM programs. Further the Signature abstraction wraps prompts and structured input / outputs to cle…

1 month, 4 weeks назад @ youtube.com
3blue1brown 3blue1brown
последний пост 2 months назад
Simulating the electric field and a moving charge
Simulating the electric field and a moving charge Simulating the electric field and a moving charge

A link to the full video is at the bottom of the screen.

Or, for reference: https://youtu.be/aXRTczANuIs

2 months назад @ youtube.com
How the Mandelbrot set is defined
How the Mandelbrot set is defined How the Mandelbrot set is defined

A link to the full video answering this is at the bottom of the screen. Or, for reference: https://youtu.be/LqbZpur38nw

2 months назад @ youtube.com
A challenging puzzle about subset sums
A challenging puzzle about subset sums A challenging puzzle about subset sums

A link to the full video answering this is at the bottom of the screen. Or, for reference: https://youtu.be/bOXCLR3Wric

2 months назад @ youtube.com
Ellipses have multiple definitions, how are these the same?
Ellipses have multiple definitions, how are these the same? Ellipses have multiple definitions, how are these the same?

A link to the full video is at the bottom of the screen.

Or, for reference: https://youtu.be/pQa_tWZmlGs The full video this comes from proves why slicing a cone gives the same shape as the two-thumbtacks-and-string construction, which is beautiful. Editing from long-form to short by Dawid Kołodziej

2 months, 1 week назад @ youtube.com
Three levels of understanding Bayes' theorem
Three levels of understanding Bayes' theorem Three levels of understanding Bayes' theorem

A link to the full video is at the bottom of the screen.

Or, for reference: https://youtu.be/HZGCoVF3YvM Editing from long-form to short by Dawid Kołodziej

2 months, 1 week назад @ youtube.com
The medical test paradox (well "paradox")
The medical test paradox (well "paradox") The medical test paradox (well "paradox")

A link to the full video about Bayesian thinking is at the bottom of the screen.

Or, for reference: https://youtu.be/lG4VkPoG3ko Long-to-short editing by Dawid Kołodziej

2 months, 1 week назад @ youtube.com
Positioned as the hardest question on a Putnam exam (#6, 1992)
Positioned as the hardest question on a Putnam exam  (#6, 1992) Positioned as the hardest question on a Putnam exam (#6, 1992)

A link to the full video is at the bottom of the screen.

Or, for reference: https://youtu.be/OkmNXy7er84 Editing from the original video into this short by Dawid Kołodziej

2 months, 2 weeks назад @ youtube.com
Why does light slowing imply a bend? (Beyond the tank/car analogy)
Why does light slowing imply a bend? (Beyond the tank/car analogy) Why does light slowing imply a bend? (Beyond the tank/car analogy)

A link to the full video is at the bottom of the screen.

Or, for reference: https://youtu.be/Cz4Q4QOuoo8 That video answers various viewer questions about the index of refraction. Editing from long-form to short by Dawid Kołodziej

2 months, 2 weeks назад @ youtube.com
The cube shadow puzzle
The cube shadow puzzle The cube shadow puzzle

A link to the full video is at the bottom of the screen. Or, for reference: https://youtu.be/ltLUadnCyi0

2 months, 2 weeks назад @ youtube.com
What does it mean that light "slows down" in glass?
What does it mean that light "slows down" in glass? What does it mean that light "slows down" in glass?

A link to the full video is at the bottom of the screen.

Or, for reference: https://youtu.be/KTzGBJPuJwM That video unpacks the mechanism behind how light slows down in passing through a medium, and why the slow-down rate would depend on color. Editing from long-form to short by Dawid Kołodziej

2 months, 2 weeks назад @ youtube.com
Why do we call them "scalars"?
Why do we call them "scalars"? Why do we call them "scalars"?

A link to the full video is at the bottom of the screen.

Or, for reference: https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab Editing from long-form to short by Dawid Kołodziej

2 months, 3 weeks назад @ youtube.com
A beautiful international math olympiad problem
A beautiful international math olympiad problem A beautiful international math olympiad problem

The link to the full video is at the bottom of the screen. For reference, here it is: https://youtu.be/M64HUIJFTZM

2 months, 3 weeks назад @ youtube.com
Definition of a "bit", in information theory
Definition of a "bit", in information theory Definition of a "bit", in information theory

A link to the full video is at the bottom of the screen.

Or, for reference: https://youtu.be/v68zYyaEmEA That video describes using information theory to write a bot that plays Wordle Editing from long-form to short by Dawid Kołodziej

2 months, 3 weeks назад @ youtube.com
The Newton art puzzle
The Newton art puzzle The Newton art puzzle

A link to the full video is at the bottom of the screen. Or, for reference: https://youtu.be/-RdOwhmqP5s

2 months, 4 weeks назад @ youtube.com
What is a group?
What is a group? What is a group?

A link to the full video is at the bottom of the screen.

Or, for reference: https://youtu.be/mH0oCDa74tE That video introduces group theory and the monster group. Editing from long-form to short by Dawid Kołodziej

3 months назад @ youtube.com
Two Minute Papers Two Minute Papers
последний пост 1 day, 16 hours назад
OpenAI Sora Just Supercharged Filmmaking!
OpenAI Sora Just Supercharged Filmmaking! OpenAI Sora Just Supercharged Filmmaking!

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers The conference:

https://wandb.ai/site/resources/events/fully-connected First impressions from creatives: https://openai.com/blog/sora-first-impressions 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Bret Brizzee, Gaston Ingaramo, Gordon Child, Jace O'Brien, John Le, Kyle Davis, Lukas Biewald…

1 day, 16 hours назад @ youtube.com
NVIDIA GTC: This Is The Future Of Everything!
NVIDIA GTC: This Is The Future Of Everything! NVIDIA GTC: This Is The Future Of Everything!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Bret Brizzee, Gaston Ingaramo, Gordon Child, Jace O'Brien, John Le, Kyle Davis, Lukas Biewald, Martin, Michael Albrecht, Michael Tedder, Owen Skarpness, Richard Putra Iskandar, Richard Sundvall, Taras Bobrovytsky, Ted Johnson, Thomas Krcmar, Tyb…

3 days, 18 hours назад @ youtube.com
DeepMind’s New Gaming AI Is Here!
DeepMind’s New Gaming AI Is Here! DeepMind’s New Gaming AI Is Here!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "A generalist AI agent for 3D virtual environments" is available here:

https://deepmind.google/discover/blog/sima-generalist-ai-agent-for-3d-virtual-environments/ 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Bret Brizzee, Gaston Ingaramo, Gordon Child, Jace O'Brien, John Le, Ky…

1 week, 1 day назад @ youtube.com
The First AI Software Engineer Is Here!
The First AI Software Engineer Is Here! The First AI Software Engineer Is Here!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Bret Brizzee, Gaston Ingaramo, Gordon Child, Jace O'Brien, John Le, Kyle Davis, Lukas Biewald, Martin, Michael Albrecht, Michael Tedder, Owen Skarpness, Richard Putra Iskandar, Richard Sundvall, Taras Bobrovytsky, Ted Johnson, Thomas Krcmar, Tyb…

2 weeks назад @ youtube.com
The First AI Virus Is Here!
The First AI Virus Is Here! The First AI Virus Is Here!

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper "ComPromptMized: Unleashing Zero-click Worms that Target GenAI-Powered Applications" is available here:

https://sites.google.com/view/compromptmized 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Bret Brizzee, Gaston Ingaramo, Gordon Child, Jace O'Brien, John Le, Kyle Davis, Luka…

2 weeks, 1 day назад @ youtube.com
Claude 3 AI: Smarter Than GPT-4?
Claude 3 AI: Smarter Than GPT-4? Claude 3 AI: Smarter Than GPT-4?

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 Claude 3 is available here - try it out for free (note that we are not affiliated with them):

https://www.anthropic.com/news/claude-3-family Conference I am coming to:

https://wandb.ai/site/resources/events/fully-connected Experiments, evaluations:

https://twitter.com/JiaweiLiu_/status/1764866009175920892

https://twitter.com/tolgabilge_/status/1764754012824314102

https://twitter.com/RubenHssd/status/1764692641436827842 Additional results (very nice so far!):

https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard 📝 My paper on simulations that look almost like reality is available for free here:

2 weeks, 6 days назад @ youtube.com
Stable Diffusion 3 - An Amazing AI For Free!
Stable Diffusion 3 - An Amazing AI For Free! Stable Diffusion 3 - An Amazing AI For Free!

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper "Scaling Rectified Flow Transformers for High-Resolution Image Synthesis" is available here:

https://stability.ai/news/stable-diffusion-3-research-paper Waitlist: https://stability.ai/stablediffusion3 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Bret Brizzee, Gaston Ingaramo, G…

3 weeks, 1 day назад @ youtube.com
DeepMind’s New AI Makes Games From Scratch!
DeepMind’s New AI Makes Games From Scratch! DeepMind’s New AI Makes Games From Scratch!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Genie: Generative Interactive Environments" is available here:

https://sites.google.com/view/genie-2024/ 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Bret Brizzee, Gaston Ingaramo, Gordon Child, Jace O'Brien, John Le, Kyle Davis, Lukas Biewald, Martin, Michael Albrecht, Michae…

3 weeks, 6 days назад @ youtube.com
Stable Diffusion 3 - Creative AI For Everyone!
Stable Diffusion 3 - Creative AI For Everyone! Stable Diffusion 3 - Creative AI For Everyone!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Bret Brizzee, Gaston Ingaramo, Gordon Child, Jace O'Brien, John Le, Kyle Davis, Lukas Biewald, Martin, Michael Albrecht, Michael Tedder, Owen Skarpness, Richard Putra Iskandar, Richard Sundvall, Taras Bobrovytsky, Ted Johnson, Thomas Krcmar, Tyb…

1 month назад @ youtube.com
OpenAI Sora: Behind The Magic!
OpenAI Sora: Behind The Magic! OpenAI Sora: Behind The Magic!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 Sora:

https://openai.com/research/video-generation-models-as-world-simulators 📝 My paper on latent space material synthesis is available here:

https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/ 📝 The other material synthesis paper is available here:

https://users.cg.tuwien.ac.at/zsolnai/gfx/photorealistic-material-editing/ 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Pa…

1 month назад @ youtube.com
DeepMind Gemini 1.5 - An AI That Remembers!
DeepMind Gemini 1.5 - An AI That Remembers! DeepMind Gemini 1.5 - An AI That Remembers!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Gemini 1.5: Unlocking multimodal understanding across millions of tokens of

context" is available here:

https://blog.google/technology/ai/google-gemini-next-generation-model-february-2024/

https://storage.googleapis.com/deepmind-media/gemini/gemini_v1_5_report.pdf 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, A…

1 month назад @ youtube.com
Stable Video AI Just Got Supercharged! - For Free!
Stable Video AI Just Got Supercharged! - For Free! Stable Video AI Just Got Supercharged! - For Free!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "MotionCtrl: A Unified and Flexible Motion Controller for Video Generation" is available here:

https://wzhouxiff.github.io/projects/MotionCtrl/ Try it out: https://huggingface.co/spaces/TencentARC/MotionCtrl_SVD

https://huggingface.co/spaces/TencentARC/MotionCtrl_SVD?docker=true It is also open source - run it locally:

https://github.com/TencentARC/MotionCtrl 📝 My latest paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We w…

1 month, 1 week назад @ youtube.com
OpenAI Sora: The Age Of AI Is Here!
OpenAI Sora: The Age Of AI Is Here! OpenAI Sora: The Age Of AI Is Here!

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers OpenAI Sora: https://openai.com/sora 📝 My paper with the latent space material synthesis:

https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/ 📝 My latest paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Bret Brizzee, Gaston Ingaramo, Gordon Child, Jace O'Brien, John Le, Kyle Davi…

1 month, 1 week назад @ youtube.com
DeepMind’s New AI Beats Billion Dollar Systems - For Free!
DeepMind’s New AI Beats Billion Dollar Systems - For Free! DeepMind’s New AI Beats Billion Dollar Systems - For Free!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "GraphCast: Learning skillful medium-range global weather forecasting" is available here:

https://arxiv.org/abs/2212.12794 Code + models: https://github.com/google-deepmind/graphcast Predictions out there in the wild:

https://charts.ecmwf.int/products/graphcast_medium-mslp-wind850?base_time=202402060000&projection=opencharts_europe&valid_time=202402060600 WMO:

https://www.flickr.com/photos/worldmeteorologicalorganization/52945643786

https://www.flickr.com/photos/worldmeteorologicalorganization/52917024995 📝 My latest paper on simulations that look almost like reality is available for free her…

1 month, 1 week назад @ youtube.com
Enhance! AI Super Resolution Is Here!
Enhance! AI Super Resolution Is Here! Enhance! AI Super Resolution Is Here!

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper "Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild" is available here:

https://supir.xpixel.group/ 📝 My latest paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Bret Brizzee, Gaston Ingaramo, Gordon Child, Jace O'Brien, John Le, Kyle Dav…

1 month, 1 week назад @ youtube.com
DataFest Video DataFest Video
последний пост None
Яндекс. Компьютерные науки Яндекс. Компьютерные науки
последний пост 7 months, 1 week назад
03. Дикуссия «Ближайшее будущее диффузионных моделей»
03. Дикуссия «Ближайшее будущее диффузионных моделей» 03. Дикуссия «Ближайшее будущее диффузионных моделей»

Участники: - Петр Ермаков, ML Brand Director, Яндекс

- Иван Барабанов, Разработчик, ВКонтакте, deep vk

- Валентин Хрульков, ведущий исследователь, Yandex Research Денис Димитров, Исполнительный директор по исследованию данных Sber AI, научный консультант AIRI Вместе со специалистами в области диффузионных картиночных моделей порассуждаем, куда развивается область. Поговорим про текущие положение дел и актуальные технические барьеры области.

7 months, 1 week назад @ youtube.com
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Спикер: Валентин Хрульков, ведущий исследователь, Yandex Research Рассмотрим все этапы обучения foundational диффузионных моделей, начиная от подготовки датасета до регулярных замеров качества в процессе обучения. Обсудим scaling law эксперименты и их предварительные результаты. Так же обсудим различные аспекты применения этих моделей на практике: генерации рекламных баннеров, персонализация, сервис-социальная сеть Шедеврум.

7 months, 1 week назад @ youtube.com
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Спикер: Андрей Кузнецов, Исполнительный директор по исследованию данных, Sber AI Рассмотрим теорию и доступную информацию о диффузионном процессе. Подробно покажу, чем отличается архитектура Kandinsky 2.1 от 2.2. Обсудим метрики для оценки качества генераций, поговорим про ключевые результаты релизов. Вместе посмотрим, в каких сценариях для бизнеса и практических приложениях можно применять нашу модель.

7 months, 1 week назад @ youtube.com
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00:00:00 – таймер обратного отсчёта перед эфиром

00:02:12 – открытие конференции

00:04:52 – Kandinsky 2.X

Андрей Кузнецов, исполнительный директор по исследованию данных, Sber AI.

00:46:55 – перерыв

01:03:40 – Практические аспекты обучения масштабных диффузионных моделей Валентин Хрульков, ведущий исследователь, Yandex Research. 01:49:34 – Дискуссия «Ближайшее будущее диффузионных моделей» Присоединяйтесь к нашем сообществу в телеграм, чтобы быть в курсе всех событий и мероприятий Яндекса https://t.me/yadatadojo. Вопрос…

7 months, 3 weeks назад @ youtube.com
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Спикеры: Дмитрий Колодезев, Алексей Пустынников, Алексей Натекин Страница соревнования: https://ods.ai/tracks/data-fusion-2024-competitions

Дедлайн по соревнованию 5 апреля 2024 года, присоединяйтесь!

6 days назад @ youtube.com
Data Fusion Contest 2024 - митап по задачам Геоаналитика и Модели оттока (29.02.2024)
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Спикеры: Алексей Натекин, Дмитрий Колодезев Страница соревнования: https://ods.ai/tracks/data-fusion-2024-competitions

Все презентации можно скачать на странице митапа https://ods.ai/tracks/data-fusion-2024-competitions/meetup Дедлайн по соревнованию 5 апреля 2024 года, присоединяйтесь!

2 weeks, 1 day назад @ youtube.com
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С радостью приглашаем вас на уникальное событие, посвященное силе и вкладу женщин в мире данных - митап "Woman In Data Science"! Это не просто встреча, это праздник ума, таланта и вдохновения, организованный ODS SPB при поддержке компании Samokat.tech. Полная программа доступна на ODS:

https://ods.ai/events/wids-meetup-2024 Вступить в сообщество: https://ods.ai/ Соцсети Data Fest & Course Fest: https://t.me/datafest

https://vk.com/datafest

3 weeks, 2 days назад @ youtube.com
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Курс Open ML Course: Деревья и их ансамбли:

https://ods.ai/tracks/trees-autumn23

Сезон курсов: https://ods.ai/events/course_season_autumn_23 Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

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Курс Open ML Course: Деревья и их ансамбли:

https://ods.ai/tracks/trees-autumn23

Сезон курсов: https://ods.ai/events/course_season_autumn_23 Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

1 month, 3 weeks назад @ youtube.com
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Курс Deep Reinforcement Learning 2023: https://ods.ai/tracks/drlcourse23

Сезон курсов:https://ods.ai/events/course_season_autumn_23 В четвертой лекции:

- Рассматривается случай MDP с неизвестными функциями награды и перехода между состояниями

- Рассмотрели подход Monte-Carlo и Temporal-Difference для нахождения Q-функции в этом случае

- Обсудили epsilon-жадные политики

- Вывили алгоритмы Monte-Carlo, SARSA и Q-learning Автор курса: Антон Плаксин, исследователь в группе Yandex.Research и доцент Уральского федерального университета. Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам:…

1 month, 3 weeks назад @ youtube.com
DRL Course 2023 |Dynamic Programming. Policy and Value Iterations
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Курс Deep Reinforcement Learning 2023: https://ods.ai/tracks/drlcourse23

Сезон курсов:https://ods.ai/events/course_season_autumn_23 В третьей лекции:

- Поговорили про принцип динамического программирования

- Рассмотрели понятия v- и q-функций, а также понятия оптимальной политики.

- Выписали уравнения Белламана и научились их решать методами Policy Iteration и Value Iteration. Автор курса: Антон Плаксин, исследователь в группе Yandex.Research и доцент Уральского федерального университета. Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

Как попасть в чат …

1 month, 3 weeks назад @ youtube.com
DRL Course 2023 | Практическое занятие 2. PyTorch and Deep Cross-Entropy Method.
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Курс Deep Reinforcement Learning 2023: https://ods.ai/tracks/drlcourse23

Сезон курсов:https://ods.ai/events/course_season_autumn_23 На втором практическом занятии: - Разбираемся с PyTorch

- Пишем линейную регрессию

- Решаем задачу регрессии с помощь нейронных сетей

- Реализуем Deep Cross-Entropy метод и решаем CartPole Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

1 month, 4 weeks назад @ youtube.com
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Курс Open ML Course: Линейные модели 2023: https://ods.ai/tracks/linear-models-autumn23

Сезон курсов: https://ods.ai/events/course_season_autumn_23 Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

2 months назад @ youtube.com
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На третьем практическом занятии: - Разбираемся с со средой Frozen Lake

- Пишем Policy Iteration Автор курса: Антон Плаксин, исследователь в группе Yandex.Research и доцент Уральского федерального университета. Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

2 months назад @ youtube.com
Линейные модели 2023 | Выбор модели. Создание новых признаков
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Курс Open ML Course: Линейные модели 2023: https://ods.ai/tracks/linear-models-autumn23

Сезон курсов: https://ods.ai/events/course_season_autumn_23 Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

2 months назад @ youtube.com
My First Data Project: от идеи к продукту - Создаем прототип продукта. Proof of concept
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Страница курса: https://ods.ai/tracks/my_first_data_project

Все доп.материалы в блоке на странице курса: https://ods.ai/tracks/my_first_data_project/blocks/98c41cb4-aaff-4c5e-8ddf-252be36ed722 Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

2 months назад @ youtube.com
Деревья и их ансамбли 2023 | Дополнительные условия при построении деревьев
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Курс Open ML Course: Деревья и их ансамбли:

https://ods.ai/tracks/trees-autumn23

Сезон курсов: https://ods.ai/events/course_season_autumn_23 Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

2 months назад @ youtube.com
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Курс ML System Design: https://ods.ai/tracks/ml-system-design-23

Сезон курсов: https://ods.ai/events/course_season_autumn_23 Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

2 months назад @ youtube.com
DRL Course 2023 | Introduction to Neural Networks. Deep Cross-Entropy Method
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Курс Deep Reinforcement Learning 2023: https://ods.ai/tracks/drlcourse23

Сезон курсов:https://ods.ai/events/course_season_autumn_23 Во второй лекции:

- рассмотрены понятия нейрона, функции активации, нейронных сетей;

- кратко изложен нейросетевой подход к решению задач регрессии и классификации;

- приведена Теорема Цибенко об аппроксимации нейронными сетями непрерывных функций;

- рассказана модификация алгоритма Кросс-Энтропии с использованием нейронных сетей для решения задач обучения с подкреплением с бесконечными пространствами состояний и действий. Автор курса: Антон Плаксин, исследователь в группе Yandex.Research и доцент Уральского федерального университета. Наши соц.сети:

Telegram: h…

2 months, 1 week назад @ youtube.com
Primer Primer
последний пост 3 months, 1 week назад
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Twitch: https://www.twitch.tv/primerjustin

Discord: https://discord.gg/NbruaNW

Store: https://store.dftba.com/collections/primer

Patreon: https://www.patreon.com/primerlearning

Twitter: @primerlearning 0:00 - Introduction

0:19 - Simulation rules

3:23 - First simulations

5:21 - Game theory analysis

8:45 - Alternate reward matrices

15:58 - Requirements for an evolutionarily stable strategy

16:69 - Discussion questions Made possible by support from these wonderful Patrons:

abledbody

Alba Caparros-Roissard

Andrew Lang

Anthony Eufemio

Brian Cloutier

Captain Chinchilla

Christy Serbus

Daniel Rolandsgard Kjellevold

Erik Broeders

Flavio Kindler

Gabriele Siino

Garrett

Guguke

James Manning

Jeff

Jeremy…

3 months, 1 week назад @ youtube.com
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Support these videos on Patreon: https://www.patreon.com/primerlearning

Plush blobs and other stuff: https://store.dftba.com/collections/primer 0:00 - Introduction

0:41 - Calculating by hand for small numbers

5:54 - Independent events

6:50 - Building Pascal's triangle

9:03 - Binomial coefficient formula

13:27 - Empirical test Some 0! videos:

https://youtu.be/HGoZfzz6dU0

https://youtu.be/Mfk_L4Nx2ZI

https://youtu.be/X32dce7_D48 For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Twitter: @primerlearning

- Reddit: r/primerlearning Made with Unity

https://github.com/Helpsypoo/PrimerUnity Made possible by support from these wonderful folks

abledbody

Alba Caparros-Roissard

Andrew …

9 months, 2 weeks назад @ youtube.com
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Please support this podcast by checking out our sponsors:– HiddenLayer: https://hiddenlayer.com/lex– BetterHelp: https://betterhelp.com/lex to get 10% off– Policygenius: https://policygenius.com/lex– NetSuite: http://netsuite.com/lex to get free product tourEPISODE LINKS:Nuclear War: A Scenario (book): https://amzn.to/3THZHfrAnnie’s Twitter: https://twitter.com/anniejacobsenAnnie’s Website: https://anniejacobsen.com/Annie’s Books: https://amzn.to/3TGWyMJAnnie’s Books (audio): https://adbl.co/49ZnI7cPODCAST INFO:Podcast website…

5 days, 14 hours назад @ lexfridman.com
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1 week, 2 days назад @ lexfridman.com
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1 week, 6 days назад @ lexfridman.com
#417 – Kimbal Musk: The Art of Cooking, Tesla, SpaceX, Zip2, and Family
#417 – Kimbal Musk: The Art of Cooking, Tesla, SpaceX, Zip2, and Family #417 – Kimbal Musk: The Art of Cooking, Tesla, SpaceX, Zip2, and Family

Kimbal Musk is a chef, entrepreneur, and author of The Kitchen Cookbook: Cooking for Your Community.

Please support this podcast by checking out our sponsors:– Eight Sleep: https://eightsleep.com/lex to get special savings– ExpressVPN: https://expressvpn.com/lexpod to get 3 months free– NetSuite: http://netsuite.com/lex to get free product tour– BetterHelp: https://betterhelp.com/lex to get 10% offTranscript: https://lexfridman.com/kimbal-musk-transcriptEPISODE LINKS:Kimbal’s X: https://x.com/kimbalKimbal’s Instagram: https://instagram.com/kimbalmusk/Kimbal’s Facebook: https://facebook.com/kimbalmuskofficial/The Kitchen Cookbook: https://amzn.to/4ccaCoEThe Kitchen (restaurants): https://www…

2 weeks, 3 days назад @ lexfridman.com
#416 – Yann Lecun: Meta AI, Open Source, Limits of LLMs, AGI & the Future of AI
#416 – Yann Lecun: Meta AI, Open Source, Limits of LLMs, AGI & the Future of AI #416 – Yann Lecun: Meta AI, Open Source, Limits of LLMs, AGI & the Future of AI

Yann LeCun is the Chief AI Scientist at Meta, professor at NYU, Turing Award winner, and one of the most influential researchers in the history of AI.

Please support this podcast by checking out our sponsors:– HiddenLayer: https://hiddenlayer.com/lex– LMNT: https://drinkLMNT.com/lex to get free sample pack– Shopify: https://shopify.com/lex to get $1 per month trial– AG1: https://drinkag1.com/lex to get 1 month supply of fish oilEPISODE LINKS:Yann’s Twitter: https://twitter.com/ylecunYann’s Facebook: https://facebook.com/yann.lecunMeta AI: https://ai.meta.com/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8R…

2 weeks, 6 days назад @ lexfridman.com
#415 – Serhii Plokhy: History of Ukraine, Russia, Soviet Union, KGB, Nazis & War
#415 – Serhii Plokhy: History of Ukraine, Russia, Soviet Union, KGB, Nazis & War #415 – Serhii Plokhy: History of Ukraine, Russia, Soviet Union, KGB, Nazis & War

Serhii Plokhy is a Ukrainian historian at Harvard University, director of the Ukrainian Research Institute, and an author of many books on history of Eastern Europe, including his latest book The Russo-Ukrainian War: The Return of History.

Please support this podcast by checking out our sponsors:– Eight Sleep: https://eightsleep.com/lex to get special savings– Shopify: https://shopify.com/lex to get $1 per month trial– NetSuite: http://netsuite.com/lex to get free product tour– AG1: https://drinkag1.com/lex to get 1 month supply of fish oilEPISODE LINKS:Serhii’s X: https://x.com/splokhySerhii’s Website: https://history.fas.harvard.edu/people/serhii-plokhiiHarvard Ukrainian Research Institut…

3 weeks, 2 days назад @ lexfridman.com
#414 – Tucker Carlson: Putin, Navalny, Trump, CIA, NSA, War, Politics & Freedom
#414 – Tucker Carlson: Putin, Navalny, Trump, CIA, NSA, War, Politics & Freedom #414 – Tucker Carlson: Putin, Navalny, Trump, CIA, NSA, War, Politics & Freedom

Tucker Carlson is a highly-influential political commentator.

You can watch and listen to him on the Tucker Carlson Network and the Tucker Carlson Podcast.

Please support this podcast by checking out our sponsors:– ZipRecruiter: https://ziprecruiter.com/lex– Listening: https://listening.com/lex and use code LEX to get one month free– HiddenLayer: https://hiddenlayer.com/lex– LMNT: https://drinkLMNT.com/lex to get free sample pack– AG1: https://drinkag1.com/lex to get 1 month supply of fish oilTranscript: https://lexfridman.com/tucker-carlson-transcriptEPISODE LINKS:Tucker Carlson Network: https://tuckercarlson.com/Tucker Carlson Podcast: https://tuckercarlson.com/listen/Tucker’s X: https://…

4 weeks, 1 day назад @ lexfridman.com
#413 – Bill Ackman: Investing, Financial Battles, Harvard, DEI, X & Free Speech
#413 – Bill Ackman: Investing, Financial Battles, Harvard, DEI, X & Free Speech #413 – Bill Ackman: Investing, Financial Battles, Harvard, DEI, X & Free Speech

Bill Ackman is an investor who has led some of the biggest and controversial financial trades in history.

He is founder and CEO of Pershing Square Capital Management.

Please support this podcast by checking out our sponsors:– LMNT: https://drinkLMNT.com/lex to get free sample pack– Policygenius: https://policygenius.com/lex– AG1: https://drinkag1.com/lex to get 1 month supply of fish oil– Eight Sleep: https://eightsleep.com/lex to get special savings– BetterHelp: https://betterhelp.com/lex to get 10% offEPISODE LINKS:Bill’s X: https://twitter.com/BillAckmanPershing Square Holdings: https://pershingsquareholdings.com/Pershing Square Foundation: https://pershingsquarefoundation.orgNeri Oxman …

1 month назад @ lexfridman.com
#412 – Marc Raibert: Boston Dynamics and the Future of Robotics
#412 – Marc Raibert: Boston Dynamics and the Future of Robotics #412 – Marc Raibert: Boston Dynamics and the Future of Robotics

Marc Raibert is founder and former long-time CEO of Boston Dynamics, and recently Executive Director of the newly-created Boston Dynamics AI Institute.

Please support this podcast by checking out our sponsors:– HiddenLayer: https://hiddenlayer.com/ and use code LEX– Babbel: https://babbel.com/lexpod and use code Lexpod to get 55% off– MasterClass: https://masterclass.com/lexpod to get 15% off– NetSuite: http://netsuite.com/lex to get free product tour– ExpressVPN: https://expressvpn.com/lexpod to get 3 months freeEPISODE LINKS:Boston Dynamics AI Institute: https://theaiinstitute.com/Boston Dynamics YouTube: https://youtube.com/@bostondynamicsBoston Dynamics X: https://x.com/BostonDynamicsBo…

1 month, 1 week назад @ lexfridman.com
#411 – Omar Suleiman: Palestine, Gaza, Oct 7, Israel, Resistance, Faith & Islam
#411 – Omar Suleiman: Palestine, Gaza, Oct 7, Israel, Resistance, Faith & Islam #411 – Omar Suleiman: Palestine, Gaza, Oct 7, Israel, Resistance, Faith & Islam

Omar Suleiman is a Palestinian-American Muslim scholar, civil rights leader, and President of the Yaqeen Institute for Islamic Research.

Please support this podcast by checking out our sponsors:– Shopify: https://shopify.com/lex to get $1 per month trial– NetSuite: http://netsuite.com/lex to get free product tour– BetterHelp: https://betterhelp.com/lex to get 10% off– Eight Sleep: https://eightsleep.com/lex to get special savings– AG1: https://drinkag1.com/lex to get 1 month supply of fish oilTranscript: https://lexfridman.com/omar-suleiman-2-transcriptEPISODE LINKS:Omar’s Instagram: https://instagram.com/imamomarsuleimanOmar’s X: https://x.com/omarsuleiman504Omar’s Facebook: https://facebo…

1 month, 3 weeks назад @ lexfridman.com
#410 – Ben Shapiro vs Destiny Debate: Politics, Jan 6, Israel, Ukraine & Wokeism
#410 – Ben Shapiro vs Destiny Debate: Politics, Jan 6, Israel, Ukraine & Wokeism #410 – Ben Shapiro vs Destiny Debate: Politics, Jan 6, Israel, Ukraine & Wokeism

Ben Shapiro is a conservative political commentator, host of The Ben Shapiro Show, co-founder of The Daily Wire, and author of The Authoritarian Moment and other books.

Steven Bonnell, aka Destiny, is a liberal political commentator and a live streamer on YouTube.

Please support this podcast by checking out our sponsors:– AG1: https://drinkag1.com/lex to get 1 month supply of fish oil– Policygenius: https://policygenius.com/lex– ExpressVPN: https://expressvpn.com/lexpod to get 3 months free– InsideTracker: https://insidetracker.com/lex to get 20% offTranscript: https://lexfridman.com/ben-shapiro-destiny-debate-transcriptEPISODE LINKS:Ben’s X: https://twitter.com/benshapiroBen’s Instagram: h…

2 months назад @ lexfridman.com
#409 – Matthew Cox: FBI Most Wanted Con Man – $55 Million in Bank Fraud
#409 – Matthew Cox: FBI Most Wanted Con Man – $55 Million in Bank Fraud #409 – Matthew Cox: FBI Most Wanted Con Man – $55 Million in Bank Fraud

Matthew Cox is a former con man who served 13 years in federal prison for bank fraud, mortgage fraud, and identity theft.

He is the author of many books, including his memoir Shark in the Housing Pool, and runs the YouTube channel Inside True Crime.

Please support this podcast by checking out our sponsors:– Freud’s Last Session: see it in select theaters– Babbel: https://babbel.com/lexpod and use code Lexpod to get 55% off– BetterHelp: https://betterhelp.com/lex to get 10% off– NetSuite: http://netsuite.com/lex to get free product tour– LMNT: https://drinkLMNT.com/lex to get free sample packEPISODE LINKS:Matthew’s YouTube: https://youtube.com/@InsideTrueCrimeMatthew’s Instagram: https://ins…

2 months, 1 week назад @ lexfridman.com
#408 – Tal Wilkenfeld: Music, Guitar, Bass, Jeff Beck, Prince, and Leonard Cohen
#408 – Tal Wilkenfeld: Music, Guitar, Bass, Jeff Beck, Prince, and Leonard Cohen #408 – Tal Wilkenfeld: Music, Guitar, Bass, Jeff Beck, Prince, and Leonard Cohen

Tal Wilkenfeld is a singer-songwriter, bassist, and guitarist.

She has performed with legendary artists including Jeff Beck, Prince, Incubus, Eric Clapton, Herbie Hancock, Mick Jagger, Rod Stewart, Hans Zimmer, Pharrell Williams, and many more.

Crossroads Guitar Festival: https://crossroadsguitarfestival.com/Jeff Beck & Tal Wilkenfeld at Crossroads: https://youtube.com/watch?v=BVgUzUZeTw4Guitar: Jeff BeckBass: Tal WilkenfieldDrums: Vinnie ColaiutaKeyboards: Jason Rebello“Cause We’ve Ended As Lovers” is originally by Stevie WonderPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/fee…

2 months, 2 weeks назад @ lexfridman.com
#407 – Guillaume Verdon: Beff Jezos, E/acc Movement, Physics, Computation & AGI
#407 – Guillaume Verdon: Beff Jezos, E/acc Movement, Physics, Computation & AGI #407 – Guillaume Verdon: Beff Jezos, E/acc Movement, Physics, Computation & AGI

Guillaume Verdon (aka Beff Jezos on Twitter) is a physicist, quantum computing researcher, and founder of e/acc (effective accelerationism) movement.

Please support this podcast by checking out our sponsors:– LMNT: https://drinkLMNT.com/lex to get free sample pack– Notion: https://notion.com/lex– InsideTracker: https://insidetracker.com/lex to get 20% off– AG1: https://drinkag1.com/lex to get 1 month supply of fish oilEPISODE LINKS:Guillaume Verdon Twitter: https://twitter.com/GillVerdBeff Jezos Twitter: https://twitter.com/BasedBeffJezosExtropic: https://extropic.ai/E/acc Blog: https://effectiveaccelerationism.substack.com/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple P…

2 months, 4 weeks назад @ lexfridman.com
Microsoft Research Podcast Microsoft Research Podcast
последний пост 6 days, 22 hours назад
Abstracts: March 21, 2024
Abstracts: March 21, 2024 Abstracts: March 21, 2024

GRETCHEN HUIZINGA: Welcome to Abstracts, a Microsoft Research Podcast that puts the spotlight on world-class research in brief.

These two examples are also the differences from other deep learning OFDFT works.

This is the generalization challenge and is one of the major challenges of deep learning method for molecular science applications.

This somehow shows the benefits of leveraging the OFDFT framework for using a deep learning method to solve molecular tasks.

You can also read it on arXiv, or you can check out the March 2024 issue of Nature Computational Science.

6 days, 22 hours назад @ microsoft.com
Abstracts: February 29, 2024
Abstracts: February 29, 2024 Abstracts: February 29, 2024

And so we realized that working with generative AI really parallels these different aspects of what a manager does, right.

So this requires having self-awareness of the applicability of generative AI to your workflows and maintaining an appropriate level of confidence in completing tasks manually or relying on generative AI.

For example, whether it’s worth it for you to actually learn how to work with generative AI more effectively.

But I think, given how generative AI has rolled out in the world today, I mean, a lot of the focus has been on productivity and workflows.

If you want to read the full paper on metacognition and generative AI, you can find a link at aka.ms/abstracts, or you can …

3 weeks, 6 days назад @ microsoft.com
What’s Your Story: Nicole Forsgren
What’s Your Story: Nicole Forsgren What’s Your Story: Nicole Forsgren

NICOLE FORSGREN: Yeah, it’s, you know, it’s, kind of, this ridiculous story.

I was there for, you know, two, three years, and I’m doing really, really well.

GEHRKE: This is just “I had a feeling.”FORSGREN: In my gut, I’m like, I’m doing really well.

After that, things were going really well, but we were also growing and scaling really, really rapidly.

Because I realized there were pieces about research that I really, really loved.

1 month, 1 week назад @ microsoft.com
What’s Your Story: Ivan Tashev
What’s Your Story: Ivan Tashev What’s Your Story: Ivan Tashev

In the Microsoft Research Podcast series What’s Your Story, Johannes Gehrke explores the who behind the technical and scientific advancements helping to reshape the world.

A systems expert whose 10 years with Microsoft spans research and product, Gehrke talks to members of the company’s research community about what motivates their work and how they got where they are today.

Partner Software Architect Ivan Tashev’s expertise in audio signal processing has contributed to the design and study of audio components for Microsoft products such as Kinect, Teams, and HoloLens.

In this episode, Tashev discusses how a first-place finish in the Mathematical Olympiad fueled a lifelong passion for shoot…

1 month, 3 weeks назад @ blubrry.com
Abstracts: January 25, 2024
Abstracts: January 25, 2024 Abstracts: January 25, 2024

And up until now, there’s been a lot of work on pruning model parameters for a variety of reasons.

But generally, these papers show that as parameters are removed from the model, performance just does not degrade.

You can, overall, keep performance roughly the same even with a fairly drastic reduction of model parameters.

HUIZINGA: So, Jordan, I often think of an abstract as a sort of appetizer for a research paper.

I think for one, as a practical matter, there’s this question of just what’s the best way to find the best LASER intervention?

2 months назад @ microsoft.com
AI Frontiers: A deep dive into deep learning with Ashley Llorens and Chris Bishop
AI Frontiers: A deep dive into deep learning with Ashley Llorens and Chris Bishop AI Frontiers: A deep dive into deep learning with Ashley Llorens and Chris Bishop

LLORENS: Your new book lays out foundations in statistics and probability theory for modern machine learning.

LLORENS: Another concept that is key in machine learning is generalization.

So that’s, that’s … we show that in the book, in fact.

BISHOP: [LAUGHS] Well, that’s, that’s really interesting.

So I personally, actually, find this one of the most exciting frontiers not only of the natural sciences but also of machine learning.

3 months, 1 week назад @ microsoft.com
Abstracts: December 12, 2023
Abstracts: December 12, 2023 Abstracts: December 12, 2023

Members of the research community at Microsoft work continuously to advance their respective fields.

Abstracts brings its audience to the cutting edge with them through short, compelling conversations about new and noteworthy achievements.

In this episode, Senior Principal Research Manager Tao Qin and Senior Researcher Lijun Wu discuss “FABind: Fast and Accurate Protein-Ligand Binding.” The paper, accepted at the 2023 Conference on Neural Information Processing Systems (NeurIPS), introduces a new method for predicting the binding structures of proteins and ligands during drug development.

The method demonstrates improved speed and accuracy over current methods.

Learn moreFABind: Fast and Ac…

3 months, 2 weeks назад @ blubrry.com
Abstracts: December 11, 2023
Abstracts: December 11, 2023 Abstracts: December 11, 2023

Members of the research community at Microsoft work continuously to advance their respective fields.

Abstracts brings its audience to the cutting edge with them through short, compelling conversations about new and noteworthy achievements.

In this episode, Principal Researcher Alessandro Sordoni joins host Gretchen Huizinga to discuss “Joint Prompt Optimization of Stacked LLMs using Variational Inference.” In the paper, which was accepted at the 2023 Conference on Neural Information Processing Systems (NeurIPS), Sordoni and his coauthors introduce Deep Language Networks, or DLNs, an architecture that treats large language models as layers within a network and natural language prompts as eac…

3 months, 2 weeks назад @ blubrry.com
Abstracts: December 6, 2023
Abstracts: December 6, 2023 Abstracts: December 6, 2023

Members of the research community at Microsoft work continuously to advance their respective fields.

Abstracts brings its audience to the cutting edge with them through short, compelling conversations about new and noteworthy achievements.

In this episode, Xing Xie, a Senior Principal Research Manager of Microsoft Research Asia, joins host Dr. Gretchen Huizinga to discuss “Evaluating General-Purpose AI with Psychometrics.” As AI capabilities move from task specific to more general purpose, the paper explores psychometrics, a subfield of psychology, as an alternative to traditional methods for evaluating model performance and for supporting consistent and reliable systems.

Read the paper: Ev…

3 months, 3 weeks назад @ blubrry.com
Abstracts: December 6, 2023
Abstracts: December 6, 2023 Abstracts: December 6, 2023

Members of the research community at Microsoft work continuously to advance their respective fields.

Abstracts brings its audience to the cutting edge with them through short, compelling conversations about new and noteworthy achievements.

In this episode, Xing Xie, a Senior Principal Research Manager of Microsoft Research Asia, joins host Dr. Gretchen Huizinga to discuss “Evaluating General-Purpose AI with Psychometrics.” As AI capabilities move from task specific to more general purpose, the paper explores psychometrics, a subfield of psychology, as an alternative to traditional methods for evaluating model performance and for supporting consistent and reliable systems.

Read the paper: Ev…

3 months, 3 weeks назад @ blubrry.com
Collaborators: Teachable AI with Cecily Morrison and Karolina Pakėnaitė
Collaborators: Teachable AI with Cecily Morrison and Karolina Pakėnaitė Collaborators: Teachable AI with Cecily Morrison and Karolina Pakėnaitė

It often requires the knowledge and experience of individuals from across disciplines and institutions.

Collaborators, a Microsoft Research Podcast series, explores the relationships—both expected and unexpected—behind the projects, products, and services being pursued and delivered by researchers at Microsoft and the diverse range of people they’re teaming up with.

In this episode, Dr. Gretchen Huizinga speaks with Cecily Morrison, MBE, a Senior Principal Research Manager at Microsoft Research, and Karolina Pakėnaitė, who also goes by Caroline, a PhD student and member of the citizen design team working with Morrison on the research project Find My Things.

An AI phone application designed …

3 months, 3 weeks назад @ blubrry.com
Abstracts: November 20, 2023
Abstracts: November 20, 2023 Abstracts: November 20, 2023

Shrey and Zoë are coauthors of a paper called Contextual Confidence and Generative AI, and you can read a preprint of this paper now on arXiv.

What was the prompt—no pun intended—for generative AI that got you concerned about this?

HITZIG: I’d say this research paper draws on a few different strands of literature.

So, specifically, how does generative AI threaten our ability to identify and protect?

HUIZINGA: Zoë, if there was one takeaway that you want our listeners to get from this work on contextual confidence, what would it be?

4 months, 1 week назад @ microsoft.com
What’s Your Story: Desney Tan
What’s Your Story: Desney Tan What’s Your Story: Desney Tan

Desney was previously Vice President and Managing Director of Microsoft Health Futures and is now Managing Director of Microsoft Research Redmond.

TAN: Yeah, yeah, it was a mandatory service in Singapore, and so I went back.

TAN: My career here has been a cycle of starting in Microsoft Research, incubating, failing, trying again.

So how should I think about sort of research, the choice of research problems …TAN: It’s a good question, yeah.

TAN: Yeah, yeah.

4 months, 1 week назад @ microsoft.com
Abstracts: October 23, 2023
Abstracts: October 23, 2023 Abstracts: October 23, 2023

GRETCHEN HUIZINGA: Welcome to Abstracts, a Microsoft Research Podcast that puts the spotlight on world-class research in brief.

But before we started defining formally co-audit and building this paper, our group has built co-audit tools in the co-generation space.

I think that’s going to be a really interesting challenge, and we hope we’re going to see some interesting work in that space.

So, for example, you know, at the workshop, we thought about security questions and co-audit tools themselves.

And in principle, co-audit tools could help users realize that there are deliberate mistakes that have been engineered by the attacker.

5 months назад @ microsoft.com
What’s Your Story: Ranveer Chandra
What’s Your Story: Ranveer Chandra What’s Your Story: Ranveer Chandra

In this new Microsoft Research Podcast series What’s Your Story, Lab Director Johannes Gehrke explores the who behind the technical and scientific advancements helping to reshape the world.

He talks to members of the research community at Microsoft about what motivates their work and how they got where they are today.

Ranveer Chandra is Managing Director of Research for Industry and CTO of Agri-Food.

He is also Head of Networking Research at Microsoft Research Redmond.

His work in systems and networking is helping to bring more internet connectivity to more people and is yielding tools designed to help farmers increase food production more affordably and sustainably.

5 months, 1 week назад @ blubrry.com
NLP Highlights NLP Highlights
последний пост 3 weeks, 6 days назад
Are LLMs safe?
Are LLMs safe? Are LLMs safe?

Curious about the safety of LLMs?

🤔 Join us for an insightful new episode featuring Suchin Gururangan, Young Investigator at Allen Institute for Artificial Intelligence and Data Science Engineer at Appuri.

🚀 Don't miss…

3 weeks, 6 days назад @ soundcloud.com
"Imaginative AI" with Mohamed Elhoseiny
"Imaginative AI" with Mohamed Elhoseiny "Imaginative AI" with Mohamed Elhoseiny

This podcast episode features Dr. Mohamed Elhoseiny, a true luminary in the realm of computer vision with over a decade of groundbreaking research.

As an Assistant Professor at KAUST, Dr. Elhoseiny's work delves into the…

2 months, 2 weeks назад @ soundcloud.com
Science Of Science, with Kyle Lo
Science Of Science, with Kyle Lo Science Of Science, with Kyle Lo

Our first guest with this new format is Kyle Lo, the most senior lead scientist in the Semantic Scholar team at Allen Institute for AI (AI2), who kindly agreed to share his perspective on #Science of #Science (#scisci) o…

3 months назад @ soundcloud.com
141 - Building an open source LM, with Iz Beltagy and Dirk Groeneveld
141 - Building an open source LM, with Iz Beltagy and Dirk Groeneveld 141 - Building an open source LM, with Iz Beltagy and Dirk Groeneveld

In this special episode of NLP Highlights, we discussed building and open sourcing language models.

What is the usual recipe for building large language models?

What does it mean to open source them?

What new research qu…

9 months назад @ soundcloud.com
140 - Generative AI and Copyright, with Chris Callison-Burch
140 - Generative AI and Copyright, with Chris Callison-Burch 140 - Generative AI and Copyright, with Chris Callison-Burch

In this special episode, we chatted with Chris Callison-Burch about his testimony in the recent U.S. Congress Hearing on the Interoperability of AI and Copyright Law.

We started by asking Chris about the purpose and the …

9 months, 3 weeks назад @ soundcloud.com
Data Skeptic
последний пост 2 days, 22 hours назад
Advances in Data Loggers
Advances in Data Loggers Advances in Data Loggers

Ryan discussed how the behavior of rattlesnakes is studied in the natural world, particularly with an increase in temperature.

He discussed how they collect data about the rattlesnake hunt using loggers and how they determine the loggers do not affect the animals' behavior.

Ryan discussed how he built a machine learning model to predict the behavior of the animals.

Ryan discussed what he discovered about the eating habits of rattlesnakes.

Rounding up, Ryan shared some future plans for the project.

2 days, 22 hours назад @ dataskeptic.com
What You Know About Intelligence is Wrong (fixed)
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What you Know About Intelligence is WrongWe are joined by Hank Schlinger, a professor of psychology at California State University, Los Angeles.

His research revolves around theoretical issues in psychology and behavioral analysis.

He also discussed how intelligence is measured in a given context.

Hank mentioned why the current measure of intelligence is fundamentally flawed.

Hank discussed how psychologists can perform behavioral experiments to understand consciousness.

1 week, 1 day назад @ dataskeptic.com
What You Know About Intelligence is Wrong
What You Know About Intelligence is Wrong What You Know About Intelligence is Wrong

What you Know About Intelligence is WrongWe are joined by Hank Schlinger, a professor of psychology at California State University, Los Angeles.

His research revolves around theoretical issues in psychology and behavioral analysis.

He also discussed how intelligence is measured in a given context.

Hank mentioned why the current measure of intelligence is fundamentally flawed.

Hank discussed how psychologists can perform behavioral experiments to understand consciousness.

1 week, 2 days назад @ dataskeptic.com
Animal Decision Making
Animal Decision Making Animal Decision Making

Louis and the interim director at the Whitney R. Harris World Ecology Center.

Aimee discussed how animals perceive information and what they use it for.

She also discussed the costs required for learning and factors that affect animal learning.

She also discussed the different kinds of evolutionary experiments that can be performed.

Aimee discussed some models that researchers use during evolutionary experiments.

2 weeks, 1 day назад @ dataskeptic.com
Octopus Cognition
Octopus Cognition Octopus Cognition

Octopus CognitionWe are joined by Tamar Gutnick, a visiting professor at the University of Naples Federico II, Napoli, Italy.

She studies the octopus nervous system and their behavior, focusing on cognition and learning behaviors.

Tamar gave a background to the kind of research she does — lab research.

She discussed the octopus nervous system and why they are unique compared to other animals.

She discussed how they measure the behavior of octopuses using a video recording and a logger to track brain activity.

2 weeks, 6 days назад @ dataskeptic.com
Optimal Foraging
Optimal Foraging Optimal Foraging

Claire discussed how bumblebees make foraging decisions and how they communicate when foraging.

She discussed how they set up experiments in the lab to address questions about bumblebees foraging.

Claire discussed factors that drive an animal's foraging decisions.

She also touched on some irrational foraging behaviors she observed in her study.

She discussed the effect of climate change on foraging bees' learning behavior.

4 weeks назад @ dataskeptic.com
Memory in Chess
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On today’s show, we are joined by our co-host, Becky Hansis-O’Neil. Becky is a Ph.D. student at the University of Missouri, St Louis, where she studies bumblebees and tarantulas to understand their learning and cognitive work. She joins us to discuss the paper: Perception in Chess. The paper aimed to understand how chess players perceive the positions of chess pieces on a chess board. She discussed the findings paper. She spoke about situations where grandmasters had better recall of chess positions than beginners and situations where they did not. Becky and Kyle discussed the use of chess engines for cheating. They also discussed how chess players use chunking. Becky discussed some approac…

1 month, 2 weeks назад @ dataskeptic.com
OpenWorm
OpenWorm OpenWorm

OpenwormOn this episode, we are joined by Stephen Larson, the CEO of MetaCell and an affiliate of the OpenWorm foundation.

Stephen discussed what the Openworm project is about.

They hope to use a digital C. elegans nematode (C. elegans for short) to study the basics of life.

Stephen discussed why C. elegans is an ideal organism for studying life in the lab.

He also mentioned how students can get involved in the Openworm project.

1 month, 3 weeks назад @ dataskeptic.com
What the Antlion Knows
What the Antlion Knows What the Antlion Knows

What the Antlion KnowsOur guest is Becky Hansis-O’Neil, a Ph.D. student at the University of Missouri, St Louis.

Becky discussed how they designed an experiment using a T-maze to understand antlions' behavior.

Becky discussed some interesting findings from the experiment.

Becky gave her thoughts on the findings of the paper and the methodologies used.

Paper in focusOperant conditioning in antlion larvae and its impairment following exposure to elevated temperaturesFollow our guestXWebsiteThis season’s cover art features original photographs by Becky Hansis-O’Neil

1 month, 4 weeks назад @ dataskeptic.com
AI Roundtable
AI Roundtable

Kyle is joined by friends and former guests Pramit Choudhary and Frank Bell to have an open discussion of the impacts LLMs and machine learning have had in the past year on industry, and where things may go in the current year.

2 months, 1 week назад @ dataskeptic.com
Uncontrollable AI Risks
Uncontrollable AI Risks Uncontrollable AI Risks

Uncontrollable AI RisksWe are joined by Darren McKee, a Policy Advisor and the host of Reality Check — a critical thinking podcast.

Darren gave a background about himself and how he got into the AI space.

Darren shared his thoughts on AGI's achievements in the coming years.

Darren discussed his worry about AI surpassing human understanding of the universe and potentially causing harm to humanity.

He explored whether AI possesses inherently evil intentions and gave his thoughts on regulating AI.

3 months назад @ dataskeptic.com
I LLM and You Can Too
I LLM and You Can Too I LLM and You Can Too

This episode is not yet released.

Coming soon

3 months назад @ dataskeptic.com
Q&A with Kyle
Q&A with Kyle Q&A with Kyle

Thanks to our sponsors for their support

3 months, 1 week назад @ dataskeptic.com
LLMs for Data Analysis
LLMs for Data Analysis LLMs for Data Analysis

Amir NetzAmir Netz is a Technical Fellow and chief architect of the Microsoft Business Intelligence (BI) offerings.

Amir is focused on the democratization of BI through end-user self-service enablement using Microsoft Office, SharePoint and SQL Server.

3 months, 2 weeks назад @ dataskeptic.com
AI Platforms
AI Platforms AI Platforms

Eric shared how Azure AI search helps create embeddings and return relevant chunks for customers.

Eric discussed how prompt flow helps companies manage their development process.

Eric shared the benefits of using natural language to build products.

He discussed the future of version control and the level of AI background required to get started with Azure AI.

He mentioned some foundational models in Azure AI and their capabilities.

3 months, 3 weeks назад @ dataskeptic.com
SuperDataScience SuperDataScience
последний пост 2 days назад
769: Generative AI for Medicine, with Prof. Zack Lipton
769: Generative AI for Medicine, with Prof. Zack Lipton 769: Generative AI for Medicine, with Prof. Zack Lipton

Generative AI in medicine takes center stage as Prof. Zachary Lipton, Chief Scientific Officer at Abridge, joins host Jon Krohn to discuss the significant advancements in AI that are reshaping healthcare.

This episode i…

2 days назад @ soundcloud.com
768: Is Claude 3 Better than GPT-4?
768: Is Claude 3 Better than GPT-4? 768: Is Claude 3 Better than GPT-4?

Claude 3, LLMs and testing ML performance: Jon Krohn tests out Anthropic’s new model family, Claude 3, which includes the Haiku, Sonnet and Opus models (written in order of their performance power, from least to greatest…

6 days назад @ soundcloud.com
767: Open-Source LLM Libraries and Techniques, with Dr. Sebastian Raschka
767: Open-Source LLM Libraries and Techniques, with Dr. Sebastian Raschka 767: Open-Source LLM Libraries and Techniques, with Dr. Sebastian Raschka

Jon Krohn sits down with Sebastian Raschka to discuss his latest book, Machine Learning Q and AI, the open-source libraries developed by Lightning AI, how to exploit the greatest opportunities for LLM development, and wh…

1 week, 2 days назад @ soundcloud.com
766: Vonnegut's Player Piano (1952): An Eerie Novel on the Current AI Revolution
766: Vonnegut's Player Piano (1952): An Eerie Novel on the Current AI Revolution 766: Vonnegut's Player Piano (1952): An Eerie Novel on the Current AI Revolution

Kurt Vonnegut's "Player Piano" delivers striking parallels between its dystopian vision and today's AI challenges.

This week, Jon Krohn explores the novel's depiction of a world where humans are marginalized by machines,…

1 week, 6 days назад @ soundcloud.com
765: NumPy, SciPy and the Economics of Open-Source, with Dr. Travis Oliphant
765: NumPy, SciPy and the Economics of Open-Source, with Dr. Travis Oliphant 765: NumPy, SciPy and the Economics of Open-Source, with Dr. Travis Oliphant

Explore the origins of NumPy and SciPy with their creator, Dr. Travis Oliphant.

Discover the journey from personal need to global impact, the challenges overcome, and the future of these essential Python libraries in sci…

2 weeks, 2 days назад @ soundcloud.com
764: The Top 10 Episodes of 2023
764: The Top 10 Episodes of 2023 764: The Top 10 Episodes of 2023

Data science futurists, bestselling authors, and lively how-to guides from the industry’s top practitioners, which range from applying data science for good to using open-source tools for NLP: This is The Super Data Scie…

2 weeks, 5 days назад @ soundcloud.com
763: The Best A.I. Startup Opportunities, with venture capitalist Rudina Seseri
763: The Best A.I. Startup Opportunities, with venture capitalist Rudina Seseri 763: The Best A.I. Startup Opportunities, with venture capitalist Rudina Seseri

At Glasswing Ventures, Rudina Seseri wants to be able to answer the question: What has Glasswing Ventures done for the company beyond capital investment?

She speaks to Jon Krohn about how her company uses data to assess …

3 weeks, 1 day назад @ soundcloud.com
762: Gemini 1.5 Pro, the Million-Token-Context LLM
762: Gemini 1.5 Pro, the Million-Token-Context LLM 762: Gemini 1.5 Pro, the Million-Token-Context LLM

Jon Krohn presents an insightful overview of Google's groundbreaking Gemini Pro 1.5, a million-token LLM that's transforming the landscape of AI.

Discover the innovative aspects of Gemini Pro 1.5, from its extensive cont…

3 weeks, 5 days назад @ soundcloud.com
761: Gemini Ultra: How to Release an A.I. Product for Billions of Users, with Google's Lisa Cohen
761: Gemini Ultra: How to Release an A.I. Product for Billions of Users, with Google's Lisa Cohen 761: Gemini Ultra: How to Release an A.I. Product for Billions of Users, with Google's Lisa Cohen

Google's Gemini Ultra takes the spotlight this week, as host Jon Krohn welcomes Lisa Cohen, Google's Director of Data Science and Engineering, for a conversation about the launch of Gemini Ultra.

Discover the capabilitie…

4 weeks, 1 day назад @ soundcloud.com
760: Humans Love A.I.-Crafted Beer
760: Humans Love A.I.-Crafted Beer 760: Humans Love A.I.-Crafted Beer

AI-crafted beer, machine learning for passion projects, and self-taught data science: Jon Krohn and Beau Warren’s hotly anticipated, data-driven, punny lager Krohn&Borg is finally given a taste test in this week’s Five-M…

1 month назад @ soundcloud.com
759: Full Encoder-Decoder Transformers Fully Explained, with Kirill Eremenko
759: Full Encoder-Decoder Transformers Fully Explained, with Kirill Eremenko 759: Full Encoder-Decoder Transformers Fully Explained, with Kirill Eremenko

Encoders, cross attention and masking for LLMs: SuperDataScience Founder Kirill Eremenko returns to the SuperDataScience podcast, where he speaks with Jon Krohn about transformer architectures and why they are a new fron…

1 month назад @ soundcloud.com
758: The Mamba Architecture: Superior to Transformers in LLMs
758: The Mamba Architecture: Superior to Transformers in LLMs 758: The Mamba Architecture: Superior to Transformers in LLMs

Explore the groundbreaking Mamba model, a potential game-changer in AI that promises to outpace the traditional Transformer architecture with its efficient, linear-time sequence modeling.

Additional materials: www.super…

1 month, 1 week назад @ soundcloud.com
757: How to Speak so You Blow Listeners' Minds, with Cole Nussbaumer Knaflic
757: How to Speak so You Blow Listeners' Minds, with Cole Nussbaumer Knaflic 757: How to Speak so You Blow Listeners' Minds, with Cole Nussbaumer Knaflic

Explore mind-blowing storytelling with Cole Nussbaumer Knaflic in this episode.

Audience favorite and author of "Storytelling with You," Cole returns to share essential tips for crafting impactful presentations, emphasiz…

1 month, 1 week назад @ soundcloud.com
756: AlphaGeometry: AI is Suddenly as Capable as the Brightest Math Minds
756: AlphaGeometry: AI is Suddenly as Capable as the Brightest Math Minds 756: AlphaGeometry: AI is Suddenly as Capable as the Brightest Math Minds

AlphaGeometry, intuitive AI, and geometric deduction: In this week’s Five-Minute Friday, Super Data Science host Jon Krohn looks into developments from DeepMind, Google’s ground-breaking AI lab, and explores how this is …

1 month, 2 weeks назад @ soundcloud.com
755: Brewing Beer with A.I., with Beau Warren
755: Brewing Beer with A.I., with Beau Warren 755: Brewing Beer with A.I., with Beau Warren

ChatGPT applications and data-driven beer: Beer brewer and Super Data Science regular listener Beau Warren talks to Jon Krohn about the wonders of “sweaty ales”, how to brew beer with data, and how to get started on crea…

1 month, 2 weeks назад @ soundcloud.com
Data Science at Home Data Science at Home
последний пост 2 weeks, 6 days назад
Kaggle Kommando’s Data Disco: Laughing our Way Through AI Trends (Ep. 252)
Kaggle Kommando’s Data Disco: Laughing our Way Through AI Trends (Ep. 252) Kaggle Kommando’s Data Disco: Laughing our Way Through AI Trends (Ep. 252)

In this episode, join me and the Kaggle Grand Master, Konrad Banachewicz, for a hilarious journey into the zany world of data science trends.

From algorithm acrobatics to AI, creativity, Hollywood movies, and music, we just can’t get enough.

It’s the typical episode with a dose of nerdy comedy you didn’t know you needed.

Buckle up, it’s a data disco, and we’re breaking down the binary!

SponsorsIntrepid AI is an AI assisted all-in-one platform for robotics teams.

2 weeks, 6 days назад @ datascienceathome.com
Revolutionizing Robotics: Embracing Low-Code Solutions (Ep. 251)
Revolutionizing Robotics: Embracing Low-Code Solutions (Ep. 251) Revolutionizing Robotics: Embracing Low-Code Solutions (Ep. 251)

In this episode of Data Science at Home, we explore the game-changing impact of low-code solutions in robotics development.

Discover how these tools bridge the coding gap, simplify integration, and enable trial-and-error development.

We’ll also uncover challenges with traditional coding methods using ROS.

Join us for a concise yet insightful discussion on the future of robotics!

1 month, 1 week назад @ datascienceathome.com
Is Sqream the fastest big data platform? (Ep. 250)
Is Sqream the fastest big data platform? (Ep. 250) Is Sqream the fastest big data platform? (Ep. 250)

Join us in a dynamic conversation with Yori Lavi, Field CTO at SQream, as we unravel the data analytics landscape.

From debunking the data lakehouse hype to SQream’s GPU-based magic, discover how extreme data challenges are met with agility.

Yori shares success stories, insights into SQream’s petabyte-scale capabilities, and a roadmap to breaking down organizational bottlenecks in data science.

Dive into the future of data analytics with SQream’s commitment to innovation, leaving legacy formats behind and leading the charge in large-scale, cost-effective data projects.

Tune in for a dose of GPU-powered revolution!

1 month, 3 weeks назад @ datascienceathome.com
OpenAI CEO Shake-up: Decoding December 2023 (Ep. 249)
OpenAI CEO Shake-up: Decoding December 2023 (Ep. 249) OpenAI CEO Shake-up: Decoding December 2023 (Ep. 249)

In this episode from a month ago, join me as we unravel the controversial CEO firing at OpenAI in December 2023.

I share my insights on the events, decode the intricacies, and explore what lies ahead for this influential organization.

Don’t miss this concise yet insightful take on the intersection of leadership and artificial intelligence innovation.

SponsorLearn what the new year holds for ransomware as a service, Active Directory, artificial intelligence and more when you download the 2024 Arctic Wolf Labs Predictions Report today at arcticwolf.com/datascience

2 months, 1 week назад @ datascienceathome.com
Careers, Skills, and the Evolution of AI (Ep. 248)
Careers, Skills, and the Evolution of AI (Ep. 248) Careers, Skills, and the Evolution of AI (Ep. 248)

!!WARNING!!

Due to some technical issues the volume is not always constant during the show.

I sincerely apologise for any inconvenienceFrancescoIn this episode, I speak with Richie Cotton, Data Evangelist at DataCamp, as he delves into the dynamic intersection of AI and education.

Richie, a seasoned expert in data science and the host of the podcast, brings together a wealth of knowledge and experience to explore the evolving landscape of AI careers, the skills essential for generative AI technologies, and the symbiosis of domain expertise and technical skills in the industry.

2 months, 2 weeks назад @ datascienceathome.com
Open Source Revolution: AI’s Redemption in Data Science (Ep. 247)
Open Source Revolution: AI’s Redemption in Data Science (Ep. 247) Open Source Revolution: AI’s Redemption in Data Science (Ep. 247)

Dive into the world of Data Science at Home with our latest episode, where we explore the dynamic relationship between Artificial Intelligence and the redemption of open source software.

In this thought-provoking discussion, I share my insights on why now, more than ever, is the opportune moment for open source to leave an indelible mark on the field of AI.

Join me as I unpack my opinions and set expectations for the near future, discussing the pivotal role open source is set to play in shaping the landscape of data science and artificial intelligence.

Don’t miss out—tune in to gain a deeper understanding of this revolutionary intersection!

This episode is available as YouTube stream at htt…

3 months, 1 week назад @ datascienceathome.com
Money, Cryptocurrencies, and AI: Exploring the Future of Finance with Chris Skinner [RB] (Ep. 246)
Money, Cryptocurrencies, and AI: Exploring the Future of Finance with Chris Skinner [RB] (Ep. 246) Money, Cryptocurrencies, and AI: Exploring the Future of Finance with Chris Skinner [RB] (Ep. 246)

In this captivating podcast episode, join renowned financial expert Chris Skinner as he delves into the fascinating realm of the future of money.

From cryptocurrencies to government currencies, the metaverse to artificial intelligence (AI), Skinner explores the intricate interplay between technology and humanity.

Gain valuable insights as he defines the future of money, examines the potential impact of cryptocurrencies on traditional government currencies, and addresses the advantages and disadvantages of digital currencies.

Brace yourself for an enlightening discussion on the integration of AI in the financial sector and its potential impact on humanity.

Once subscribed, you get full acces…

3 months, 2 weeks назад @ datascienceathome.com
Debunking AGI Hype and Embracing Reality [RB] (Ep. 245)
Debunking AGI Hype and Embracing Reality [RB] (Ep. 245) Debunking AGI Hype and Embracing Reality [RB] (Ep. 245)

In this thought-provoking episode, we sit down with the renowned AI expert, Filip Piekniewski, Phd, who fearlessly challenges the prevailing narratives surrounding artificial general intelligence (AGI) and the singularity.

With a no-nonsense approach and a deep understanding of the field, Filip dismantles the hype and exposes some of the misconceptions about AI, LLMs and AGI.

If you’re seeking a refreshingly pragmatic perspective on the future of AI, this episode is an absolute must-listen.

Filip Piekniewski BioFilip Piekniewski is a distinguished computer vision researcher and engineer, specializing in visual object tracking and perception.

He is known for his realistic perspective on AI, …

3 months, 3 weeks назад @ datascienceathome.com
Destroy your toaster before it kills you. Drama at OpenAI and other stories (Ep. 244)
Destroy your toaster before it kills you. Drama at OpenAI and other stories (Ep. 244) Destroy your toaster before it kills you. Drama at OpenAI and other stories (Ep. 244)

Brace yourselves, dear friends!

In this episode, we delve into the earth-shattering revelation that OpenAI might have stumbled upon AGI (lol) and we’re all just seconds away from being replaced by highly sophisticated toasters (lol lol).

Spoiler alert: OpenAI’s CEO is just playing 7D chess with the entire human race.

So, sit back, relax, and enjoy this totally not ominous exploration into the ‘totally not happening’ future of AI!

3 months, 3 weeks назад @ datascienceathome.com
The AI Chip Chat 🤖💻 (Ep. 243)
The AI Chip Chat 🤖💻 (Ep. 243) The AI Chip Chat 🤖💻 (Ep. 243)

Dive into the cool world of AI chips with us!

🚀 We’re breaking down how these special computer chips for AI have evolved and what makes them different.

Think of them like the superheroes of the tech world!

Don’t miss out!

🎙️🔍 #AIChips #TechTalk #SimpleScience

3 months, 3 weeks назад @ datascienceathome.com
Rolling the Dice: Engineering in an Uncertain World (Ep. 242)
Rolling the Dice: Engineering in an Uncertain World (Ep. 242) Rolling the Dice: Engineering in an Uncertain World (Ep. 242)

Hey there, engineering enthusiasts!

Ever wondered how engineers deal with the wild, unpredictable twists and turns in their projects?

In this episode, we’re spilling the beans on uncertainty and why it’s the secret sauce in every engineering recipe, not just the fancy stuff like deep learning and neural networks!

Join us for a ride through the world of uncertainty quantification.

Tune in and let’s demystify the unpredictable together!

3 months, 3 weeks назад @ datascienceathome.com
How Language Models Are the Ultimate Database(Ep. 241)
How Language Models Are the Ultimate Database(Ep. 241) How Language Models Are the Ultimate Database(Ep. 241)

In this episode, dive deep into the world of Language Models as we decode their intricate structure, revealing how these powerful algorithms exploit concepts from the past.

But… what if LLMs were just a database?

Referenceshttps://fchollet.substack.com/p/how-i-think-about-llm-prompt-engineering

3 months, 3 weeks назад @ datascienceathome.com
Elon is right this time: Rust is the language of AI (Ep. 240)
Elon is right this time: Rust is the language of AI (Ep. 240) Elon is right this time: Rust is the language of AI (Ep. 240)

In this episode, I delve into Elon Musk’s foresight on the future of AI as he champions Rust programming language.

Here is why Rust stands at the forefront of AI technology and the potential it holds.

Referenceshttps://github.com/WasmEdge/mediapipe-rshttps://blog.stackademic.com/why-did-elon-musk-say-that-rust-is-the-language-of-agi-eb36303ce341

5 months, 2 weeks назад @ datascienceathome.com
Attacking LLMs for fun and profit (Ep. 239)
Attacking LLMs for fun and profit (Ep. 239) Attacking LLMs for fun and profit (Ep. 239)

As a continuation of Episode 238, I explain some effective and fun attacks to conduct against LLMs.

Such attacks are even more effective on models served locally, that are hardly controlled by human feedback.

Have great fun and learn them responsibly.

Referenceshttps://www.jailbreakchat.com/https://arxiv.org/abs/2305.13860Image Credit

6 months, 1 week назад @ datascienceathome.com
Unlocking Language Models: The Power of Prompt Engineering (Ep. 238)
Unlocking Language Models: The Power of Prompt Engineering (Ep. 238) Unlocking Language Models: The Power of Prompt Engineering (Ep. 238)

Join me on an enlightening journey through the world of prompt engineering.

Explore the multifaceted skills and strategies involved in harnessing the potential of large language models for various applications.

From enhancing safety measures to augmenting models with domain knowledge, learn how prompt engineering is shaping the future of AI.

ReferencesImage Credit

6 months, 2 weeks назад @ datascienceathome.com