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State-of-the-art Machine Learning News Feed
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последний пост 2 часа назад
[D] What are programming languages that you regularly use that LLMs suck at currently?
[D] What are programming languages that you regularly use that LLMs suck at currently?

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2 часа назад @ reddit.com
[D] Theoretical limits of RL in reasoning models?
[D] Theoretical limits of RL in reasoning models?

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3 часа назад @ reddit.com
[D] How to implement AI models into websites?
[D] How to implement AI models into websites?

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3 часа назад @ reddit.com
[D] How to handle concurrent connections using vllm
[D] How to handle concurrent connections using vllm

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5 часов назад @ reddit.com
[D] Interview experience with Microsoft AI Engineer
[D] Interview experience with Microsoft AI Engineer

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5 часов назад @ reddit.com
[P] How to build a customer support AI voice agent
[P] How to build a customer support AI voice agent

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5 часов назад @ reddit.com
[D] how do you know you are implementing data preprocessing correctly?
[D] how do you know you are implementing data preprocessing correctly?

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5 часов назад @ reddit.com
[D] Library for GPU accelerated word2vec
[D] Library for GPU accelerated word2vec

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5 часов назад @ reddit.com
[P] Text Similarity and Feature Extraction
[P] Text Similarity and Feature Extraction

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8 часов назад @ reddit.com
[D] DS Career Path
[D] DS Career Path

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9 часов назад @ reddit.com
[D] Creating reward signals for LLM reasoning beyond math/programming domains
[D] Creating reward signals for LLM reasoning beyond math/programming domains

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9 часов назад @ reddit.com
[D] Forecasting with MLP??
[D] Forecasting with MLP??

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11 часов назад @ reddit.com
[R] DeepRAG: A Markov Decision Process Framework for Step-by-Step Retrieval-Augmented Reasoning
[R] DeepRAG: A Markov Decision Process Framework for Step-by-Step Retrieval-Augmented Reasoning

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13 часов назад @ reddit.com
G[R]PO VRAM Requirements For the GPU Poor
G[R]PO VRAM Requirements For the GPU Poor G[R]PO VRAM Requirements For the GPU Poor

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16 часов назад @ reddit.com
[R] [D] Potential use case of ultra-high fidelity human imitation
[R] [D] Potential use case of ultra-high fidelity human imitation

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17 часов назад @ reddit.com
Towards Data Science
последний пост 1 час назад
Introduction to Minimum Cost Flow Optimization in Python
Introduction to Minimum Cost Flow Optimization in Python Introduction to Minimum Cost Flow Optimization in Python

Minimum cost flow optimization minimizes the cost of moving flow through a network of nodes and edges.

ApplicationsApplications of minimum cost flow optimization are vast and varied, spanning multiple industries and sectors.

The energy sector leverages minimum cost flow optimization to efficiently distribute electricity through power grids, reducing losses and operational costs.

ExampleBelow is a simple flow optimization example:The image above illustrates a minimum cost flow optimization problem with six nodes and eight edges.

Specifically it checks that demand, supply, capacity, variable cost and fixed cost are not negative.

1 час назад @ towardsdatascience.com
A Visual Guide to How Diffusion Models Work
A Visual Guide to How Diffusion Models Work A Visual Guide to How Diffusion Models Work

This article is aimed at those who want to understand exactly how Diffusion Models work, with no prior knowledge expected.

How and why do diffusion models work?

Diffusion process overviewBelow is an adaptation of the somewhat-famous diagram from Ho et al.’s seminal paper “Denoising Diffusion Probabilistic Models” which gives an overview of the whole diffusion process:Diagram of the diffusion process adapted from Ho et al.

Testing the conditioned diffusion modelLet’s do a simple test of the fully trained conditioned diffusion model.

I’ve omitted some topics that are crucial to how production-grade diffusion models function, but are unnecessary for core understanding.

2 часа назад @ towardsdatascience.com
Myths vs. Data: Does an Apple a Day Keep the Doctor Away?
Myths vs. Data: Does an Apple a Day Keep the Doctor Away? Myths vs. Data: Does an Apple a Day Keep the Doctor Away?

If the myth is true, we should expect a negative correlation between apple consumption per capita and doctor visits per capita .

Testing the relationship between apple consumption and doctor visitsLet’s start with a simple correlation check between apple consumption per capita and doctor visits per capita.

Data sourcesThe data comes from:Apple consumption per capita : Our World in Data: Our World in Data Doctor visits per capita: OECDSince data availability varies by year, 2017 was selected as it provided the most complete in terms of number of countries.

The R² value is almost zero, meaning apple consumption explains virtually none of the variation in doctor visits.

If we consider this cau…

19 часов назад @ towardsdatascience.com
Training Large Language Models: From TRPO to GRPO
Training Large Language Models: From TRPO to GRPO Training Large Language Models: From TRPO to GRPO

I think this is a perfect opportunity to dive deeper into how Large Language Models (LLMs) are trained.

In the context of LLMs, rewards usually come from a separate reward model, which outputs a score for each (query, response) pair.

Important note: the value model and the reward model are two different things.

What’s new in GRPOEven if in practice, the reward model is often derived from the policy (training only the “head”), we still end up maintaining many models and handling multiple training procedures (policy, reward, value model).

ConclusionReinforcement Learning has become a cornerstone for training today’s Large Language Models, particularly through PPO, and more recently GRPO.

19 часов назад @ towardsdatascience.com
Supercharge Your RAG with Multi-Agent Self-RAG
Supercharge Your RAG with Multi-Agent Self-RAG Supercharge Your RAG with Multi-Agent Self-RAG

This article explores how to supercharge your RAG application by making its data retrieval and reasoning process similar to how a human would, under a multi-agent framework.

US Flight Compensation Policies– – – – – – – – – – – – – – – – – – – – – – – – –(AUTHOR-ADDED NOTE: IMPORTANT, PAY ATTENTION TO THIS)Short-distance flight delays – if it is up to 1,500 km, you are due 250 Euro compensation.

If the RAG agent takes these as the sole context, it can only provide a generic answer about flight compensation policy, without giving the answer we want.

Our multi-agent RAG application involves iterations and loops, and LangGraph is a great library for building such complex multi-agent application…

20 часов назад @ towardsdatascience.com
From Resume to Cover Letter Using AI and LLM, with Python and Streamlit
From Resume to Cover Letter Using AI and LLM, with Python and Streamlit From Resume to Cover Letter Using AI and LLM, with Python and Streamlit

DISCLAIMER: The idea of doing Cover Letter or even Resume with AI does not obviously start with me.

This is just a tutorial on how to build your own Cover Letter AI Generator App using Python and a few lines of code.

There are a lot of AI tools to tailor your resume for the specific company, make your resume more impressive, or build the job specific cover letter.

A cover letter LLM API.

Cover Letter Craft by Balaji Kesavan is a Streamlit app that implements a very similar idea of crafting the cover letter using AI.

1 day, 17 hours назад @ towardsdatascience.com
ML Feature Management: A Practical Evolution Guide
ML Feature Management: A Practical Evolution Guide ML Feature Management: A Practical Evolution Guide

Image by authorApproach 2: Feature Table MaterializationAs teams evolve, many transition to what’s commonly discussed online as an alternative to full-fledged feature stores: feature table materialization.

Image by authorApproach 3: Feature StoreAt the far end of the spectrum lies the feature store — typically part of a comprehensive ML platform.

In my experience, feature table materialization often provides the sweet spot — especially when your organization already has robust ETL infrastructure.

Sometimes, the pragmatic path of feature table materialization, despite its limitations, offers the best balance of capability and complexity.

Remember: success in ML feature management isn’t about…

1 day, 22 hours назад @ towardsdatascience.com
Towards Data Science is Launching as an Independent Publication
Towards Data Science is Launching as an Independent Publication Towards Data Science is Launching as an Independent Publication

Since founding Towards Data Science in 2016, we’ve built the largest publication on Medium with a dedicated community of readers and contributors focused on data science, machine learning, and AI.

As of Monday, February 3, 2025, Towards Data Science will become an independent publication.

For us at Towards Data Science, moving to an independent platform isn’t just about preserving editorial control – it’s a strategic leap towards growing the publication we’ve spent the last eight years building.

Join Us for the Next Chapter of Towards Data ScienceTo our readers and contributors: thank you for being a part of Towards Data Science.

Your support means everything to us, and we’re excited to sho…

2 days, 23 hours назад @ towardsdatascience.com
DIY AI: How to Build a Linear Regression Model from Scratch
DIY AI: How to Build a Linear Regression Model from Scratch DIY AI: How to Build a Linear Regression Model from Scratch

How to implement a linear regression model in Python without using machine learning librariesContinue reading on Towards Data Science »

3 days, 4 hours назад @ medium.com
Support Vector Machines: A Progression of Algorithms
Support Vector Machines: A Progression of Algorithms Support Vector Machines: A Progression of Algorithms

MMC, SVC, SVM: What’s the difference?The Support Vector Machine (SVM) is a popular learning algorithm used for many classification problems. They are known to be useful out-of-the-box (not much manual configuration required), and they are valuable for applications where knowledge of the class boundaries is more important than knowledge of the class distributions.When working with SVMs, you may hear people mention Support Vector Classifiers (SVC) or Maximal Margin Classifiers (MMC). While these algorithms are all related, there is an important distinction to be made between the three of them.To fully understand SVMs, we must appreciate the progression of the following algorithms, ordered fro…

3 days, 4 hours назад @ medium.com
Are Public Agencies Letting Open-Source Software Down?
Are Public Agencies Letting Open-Source Software Down? Are Public Agencies Letting Open-Source Software Down?

Open-source software is everywhere — powering the tools we rely on daily. Yet, when it comes to supporting and sustaining these projects, public agencies and institutions often fall short. In this article, I explore why this happens and what we can do to change it.Open-source software promotes transparency, sharing, and collaboration, paving the way for technological development and innovation. A well-functioning democracy is built on equal access to knowledge, the ability to verify information, and participation in decision-making processes. Interestingly, there is a clear overlap between the principles of open source and democratic processes.Building on open-source software, we are witnes…

3 days, 4 hours назад @ medium.com
How to Get A Job in Data Science/Machine Learning With NO Previous Experience
How to Get A Job in Data Science/Machine Learning With NO Previous Experience How to Get A Job in Data Science/Machine Learning With NO Previous Experience

Take charge of your job searchContinue reading on Towards Data Science »

3 days, 5 hours назад @ medium.com
Show and Tell
Show and Tell Show and Tell

Later in the inference phase, we expect the decoder to generate a caption based solely on these image features.

To do so, we have to insert a single dimension at the 1st axis of the image features ( #(2) ).

To the captions tensor, its shape changed from 1×30 ( #(3) ) to 1×30×512 ( #(4) ), indicating that every single word is now represented as a 512-dimensional vector.

Next, we pass the tensor through the LSTM layer, which in this particular case the output tensor dimension remains the same.

Towards Data Science.

3 days, 6 hours назад @ towardsdatascience.com
Show and Tell
Show and Tell Show and Tell

Implementing one of the earliest neural image caption generator models with PyTorch.Continue reading on Towards Data Science »

3 days, 6 hours назад @ medium.com
Neural Networks — Intuitively and Exhaustively Explained
Neural Networks — Intuitively and Exhaustively Explained Neural Networks — Intuitively and Exhaustively Explained

An in-depth exploration of the most fundamental architecture in modern AI.Continue reading on Towards Data Science »

3 days, 8 hours назад @ medium.com
Distill.pub Distill.pub
последний пост None
The Gradient The Gradient
последний пост 2 months, 3 weeks назад
Shape, Symmetries, and Structure: The Changing Role of Mathematics in Machine Learning Research
Shape, Symmetries, and Structure: The Changing Role of Mathematics in Machine Learning Research Shape, Symmetries, and Structure: The Changing Role of Mathematics in Machine Learning Research

Mathematics and statistics, once the primary guides of machine learning research, now struggle to provide immediate insight into the latest breakthroughs.

This shift has prompted speculation about mathematics’ diminished role in machine learning research moving forward.

It is also the way that symmetries are usually leveraged when performing computations (for example, in machine learning).

One can reasonably argue that diagrammatic descriptions of well-known constructions, like products, are not useful for the machine learning researcher.

However, as we’ve demonstrated, while mathematics may not maintain the same role in machine learning research that it has held in the past, the success of…

2 months, 3 weeks назад @ thegradient.pub
What's Missing From LLM Chatbots: A Sense of Purpose
What's Missing From LLM Chatbots: A Sense of Purpose What's Missing From LLM Chatbots: A Sense of Purpose

Let's jump back to the 1970s, when Roger Schank introduced his "restaurant script" as a kind of dialogue system [1].

The minimum requirement we could have for a dialogue system is that it can stay on the task we gave them.

Concluding marksI have reviewed the making of current LLM dialogue systems, how and why it is insufficient.

The following are two research questions that I’m mostly excited about:(1) Better monitoring and control of dialogue systems with steering techniques.

CitationFor attribution of this in academic contexts or books, please cite this work as:Kenneth Li, "From prediction to purpose: a tutorial on LLM dialogue system", The Gradient, 2024.

5 months назад @ thegradient.pub
We Need Positive Visions for AI Grounded in Wellbeing
We Need Positive Visions for AI Grounded in Wellbeing We Need Positive Visions for AI Grounded in Wellbeing

This leads to our second conclusion: We need plausible positive visions of a society with capable AI, grounded in wellbeing.

The rest of this post describes in more detail (1) what we mean by AI that benefits our wellbeing, (2) the need for positive visions for AI grounded in wellbeing, and (3) concrete leverage points to aid in the development and deployment of AI in service of such positive visions.

In diving into the philosophy of flourishing, wellbeing economics, or psychological theories of human wellbeing, one encounters many interesting, compelling, but seemingly incompatible ideas.

The case so far is that we need positive visions for society with capable AI, grounded in individual a…

6 months, 1 week назад @ thegradient.pub
Financial Market Applications of LLMs
Financial Market Applications of LLMs Financial Market Applications of LLMs

Looked at from another angle, there is much more noise than signal in financial data.

Another financial market application of LLMs might be synthetic data creation [4,8].

Then precious real market data could be employed to fine-tune the predictions and determine precisely the optimal speed to trade.

Financial market practitioners are often interested in extreme events, the times when trading strategies are more likely to experience significant gains or losses.

CitationFor attribution in academic contexts or books, please cite this work asRichard Dewey and Ciamac Moallemi, "Financial Market Applications of LLMs," The Gradient, 2024

9 months, 3 weeks назад @ thegradient.pub
TheSequence TheSequence
последний пост 11 часов назад
The Sequence Opinion #485: What's Wrong With AI Benchmarks
The Sequence Opinion #485: What's Wrong With AI Benchmarks The Sequence Opinion #485: What's Wrong With AI Benchmarks

Created Using MidjourneyIn the generative AI era, evaluations and benchmarking have rapidly evolved from a set of quantitative metrics into the primary means by which we understand the capabilities of foundation models.

With explainability techniques such as mechanistic interpretability still in their infancy, benchmarks serve as a crucial tool for deriving insights into the inner workings of generative AI models.

Every day, new benchmarks emerge to evaluate unique model capabilities.

This essay explores the current challenges in AI benchmarking and the trends shaping its future.

The Challenges of Modern AI BenchmarkingAI benchmarking today is undermined by three significant challenges: dat…

11 часов назад @ thesequence.substack.com
The Sequence Engineering #483: Block's goose is a Brand New Framework for Building Agentic Applications
The Sequence Engineering #483: Block's goose is a Brand New Framework for Building Agentic Applications The Sequence Engineering #483: Block's goose is a Brand New Framework for Building Agentic Applications

Created Using MidjourneyAgents, agents, agents.

Every week it feels that we have a new framework for agentic workflows.

Like many AI capabilities, some of the most interesting agentic releases are coming from large software companies that are applying these technologies at scale.

A few days ago, jumped Block released goose, a framework for building agentic apps.

In today’s The Sequence Engineering, I would like to dive into the details behind goose.

1 day, 11 hours назад @ thesequence.substack.com
The Sequence Knowledge #482: An Introduction to Corrective RAG
The Sequence Knowledge #482: An Introduction to Corrective RAG The Sequence Knowledge #482: An Introduction to Corrective RAG

Created Using MidjourneyToday we will Discuss:An overview of Corrective RAG.

The original paper about Corrective RAG from Google DeepMind and others.

One of the most interesting ones is known as corrective RAG.

Corrective RAG represents an evolution in retrieval-augmented generation, pushing beyond the boundaries of standard RAG to deliver more accurate and contextually appropriate outputs.

While standard RAG enhances LLM responses with external knowledge, Corrective RAG introduces a sophisticated feedback mechanism that scrutinizes and refines the generated content.

2 days, 11 hours назад @ thesequence.substack.com
The Sequence Radar #481: Humanity's Last Exam
The Sequence Radar #481: Humanity's Last Exam The Sequence Radar #481: Humanity's Last Exam

The top AI models are constantly outpacing and memorizing leading benchmarks, triggering a race to develop more challenging evaluations that push the boundaries of foundation models.

The Humanity's Last Exam (HLE) benchmark is a novel, multi-modal evaluation suite designed to assess the limits of large language model (LLM) capabilities on closed-ended academic questions.

Another key contribution of the HLE benchmark lies in its diverse question formats and subject coverage.

This level of difficulty contrasts with the saturation seen in many existing benchmarks, demonstrating the utility of HLE in assessing frontier AI capabilities.

The HLE benchmark aims to address the fact that current LLM…

4 days, 11 hours назад @ thesequence.substack.com
📝 Guest Post: Augmented SBERT: A Data Augmentation Method to Enhance Bi-Encoders for Pairwise Sentence Scoring*
📝 Guest Post: Augmented SBERT: A Data Augmentation Method to Enhance Bi-Encoders for Pairwise Sentence Scoring* 📝 Guest Post: Augmented SBERT: A Data Augmentation Method to Enhance Bi-Encoders for Pairwise Sentence Scoring*

This guest post from Zilliz discusses the limitations of these approaches and how Augmented SBERT (AugSBERT) addresses them.

It helps generate new sentence pairs by sampling the existing sentence pairs.

Here’s why it is essential:Generating Diverse Sentence Pairs: AugSBERT uses data augmentation to generate diverse sentence pairs.

It reuses individual sentences from the existing labeled sentence pairs (gold training set) and recombines them to create new sentence pairs.

Sentence Pair Sampling: AugSBERT employs smart sampling techniques to select sentence pairs that enhance training quality.

6 days, 10 hours назад @ thesequence.substack.com
The Sequence Opinion #480: What is GPT-o1 Actually Doing?
The Sequence Opinion #480: What is GPT-o1 Actually Doing? The Sequence Opinion #480: What is GPT-o1 Actually Doing?

Created Using MidjourneyThese days we can only talk about DeepSeek-R1 and the reasoning capaiblities of foundation models.

The reasoning race was initially triggered by the release of GPT-o1 followed by the announcement of the upcoming release of GPT-o3.

By exploring hypotheses about how these models work internally, we can better understand their mechanisms and the breakthroughs they represent.

This essay delves into three critical aspects of these models: reasoning hypothesis search, program synthesis, and the innovative reinforcement learning techniques introduced by DeepSeek-R1.

Reasoning Hypothesis Search: Structured Problem Solving

1 week назад @ thesequence.substack.com
The Sequence Engineering #479: Dify.AI: A Deep Dive into its Open-Source LLM Application Development Platform
The Sequence Engineering #479: Dify.AI: A Deep Dive into its Open-Source LLM Application Development Platform The Sequence Engineering #479: Dify.AI: A Deep Dive into its Open-Source LLM Application Development Platform

Created Using MidjourneyIn today’s generative AI market inundated with tools and frameworks, it is hard to determine which stack to use for your application.

One recipe that has worked for me is to rely on highly versatile and fast evolving frameworks.

This is the case of Dify.aiDify.AI is an open-source platform designed for the streamlined development and deployment of LLM-powered applications.

Offering an intuitive interface and a rich set of features, Dify aims to simplify the complex process of building production-ready AI solutions, spanning from conversational agents to intricate AI workflows enhanced with Retrieval Augmented Generation (RAG) capabilities.

This essay will explore the…

1 week, 1 day назад @ thesequence.substack.com
The Sequence Knowledge #478: Speculative RAG is a More Efficient Form of RAG
The Sequence Knowledge #478: Speculative RAG is a More Efficient Form of RAG The Sequence Knowledge #478: Speculative RAG is a More Efficient Form of RAG

Created Using MidjourneyToday we will Discuss:An introduction to Speculative RAG.

A review of the Google Research paper that introduced the ideas of Speculative RAG in AI.

💡 AI Concept of the Day: What is Speculative RAG?

Continuing our series about RAG, today we would like to dive into Speculative RAG , a novel dual-model architecture to enhance the efficiency and accuracy of traditional Retrieval Augmented Generation systems.

At its core, the technique employs two distinct language models: a smaller specialist LM serving as the RAG drafter, and a larger generalist LM acting as the RAG verifier.

1 week, 2 days назад @ thesequence.substack.com
The R1 Moment
The R1 Moment The R1 Moment

The engineering section dives into the super popular Dify AI framework.

You can subscribe to The Sequence below:📝 Editorial: The R1 MomentIn the evolution of any tech trend, there are moments that mark a beginning and an after.

DeepSeek-R1's innovative approach to model design and training sets it apart from traditional language models.

While questions remain about the long-term implications and potential limitations of this approach, the R1 Effect undoubtedly marks a turning point in the democratization of AI technology.

A Blueprint of Reasoning ModelsIn the paper "A Blueprint for Reasoning Language Models" researchers from ETH Zurich, Cledar, BASF SE, and Cyfronet AGH present a blueprint …

1 week, 4 days назад @ thesequence.substack.com
The Sequence Opinion #476: The DeepSeek Effect: The Remarkable Innovations and Controversies Surrounding the New Challenger in Open-Source AI
The Sequence Opinion #476: The DeepSeek Effect: The Remarkable Innovations and Controversies Surrounding the New Challenger in Open-Source AI The Sequence Opinion #476: The DeepSeek Effect: The Remarkable Innovations and Controversies Surrounding the New Challenger in Open-Source AI

DeepSeek is the lab behind R1 and has emerged as an unlikely leader in open source AI pushing the boundaries of the space.

At the same time, there is been a lot of controversy surrounding DeepSeek’s rapid progress.

The Rise of DeepSeekFounded in May 2023, DeepSeek quickly established itself as a formidable player in the AI space.

The DeepSeek Effect has been characterized by a rapid succession of increasingly powerful models, each pushing the boundaries of AI capabilities.

Let's explore the chronological development of DeepSeek's models in more detail, with a focus on the earlier iterations.

1 week, 6 days назад @ thesequence.substack.com
The Sequence Chat #475: Ed Sim, Forbes Top Tech Investor, on AI Investing, Security, Agents and More
The Sequence Chat #475: Ed Sim, Forbes Top Tech Investor, on AI Investing, Security, Agents and More The Sequence Chat #475: Ed Sim, Forbes Top Tech Investor, on AI Investing, Security, Agents and More

You’ve been a successful investor in enterprise AI security and have emphasized that “there is no AI without AI security.” How do traditional cybersecurity practices and techniques need to evolve in the era of generative AI?

Is AI security innovative enough for new startups to disrupt the market, or will the incumbents continue to dominate?

In my 29 years as an enterprise VC, I’ve never seen a category, if you will, explode in interest as fast as AI security.

One caveat is that the idea of AI security is super broad and for these purposes I want to limit it to securing AI usage in the enterprise.

What are your views on AI agents, and how can companies succeed in such a hyper-fragmented mark…

2 weeks назад @ thesequence.substack.com
The Sequence Engineering #474: The Super Popular Eliza Framework for Building AI Agents
The Sequence Engineering #474: The Super Popular Eliza Framework for Building AI Agents The Sequence Engineering #474: The Super Popular Eliza Framework for Building AI Agents

Created Using MidjourneyEliza is not a framework that often gets mentioned among the top AI agent stacks in the market and, yet, its popularity is astonishing.

Image Credit: ai16zPart of the popularity comes from the rapid adoption of Eliza within the web3/crypto community but it would be a mistake to encapsulate Eliza in that category.

The framework provides a flexible and pretty comprehensive feature based that automates the core building blocks of agentic apps.

In this essay, we delves into the core capabilities, architecture, and key components of Eliza, providing a technical overview for AI enthusiasts and developers.

Architecture Overview

2 weeks, 1 day назад @ thesequence.substack.com
The Sequence Knowledge #473: Not All RAGs are Created Equal
The Sequence Knowledge #473: Not All RAGs are Created Equal The Sequence Knowledge #473: Not All RAGs are Created Equal

Created Using MidjourneyToday we will Discuss:A taxonomy about the different types of RAG.

Google Research’s REALM method that uses RAG at pretraining time.

The evolution of RAG has led to various types, each addressing specific challenges and leveraging unique advantages.

Some of these variations include Standard RAG, Corrective RAG, Speculative RAG, Fusion RAG, Agnostic RAG, Self RAG, Graph RAG, Modular RAG, and RadioRAG.

Understanding the nuances of these different RAG types is crucial for AI practitioners and researchers.

2 weeks, 2 days назад @ thesequence.substack.com
📽 Webinar: Building AI Agents with Fine-tuned SLMs
📽 Webinar: Building AI Agents with Fine-tuned SLMs 📽 Webinar: Building AI Agents with Fine-tuned SLMs

Don't miss our exclusive webinar on January 29th, 10am - 11am PT, to hear how the team at Marsh McLennan (MMC) built advanced AI agents to transform their business operations, saving their teams over a million hours annually.

Save your spot to learn firsthand what it takes to build high-performance agentic workflows.

Topics include:Real-World Impact: Explore the tangible benefits and real-world use cases for MMC’s AI assistant.

Boosting Agent Performance: Learn how MMC improved model accuracy by 10-12% using fine-tuned open-source SLMs.

Exclusive Live Demo: Witness the capabilities of MMC's cutting-edge tool across various applications.

2 weeks, 3 days назад @ thesequence.substack.com
The Sequence Radar #472: Remember this Name: Ndea
The Sequence Radar #472: Remember this Name: Ndea The Sequence Radar #472: Remember this Name: Ndea

Ndea seeks to transcend these limitations by pioneering a novel approach: guided program synthesis.

Unlike deep learning, which interpolates between data points, program synthesis generates discrete programs that precisely encapsulate the observed data.

While some frontier AI labs are beginning to explore program synthesis, they often treat it as a supplementary tool.

Ndea, on the other hand, positions program synthesis and deep learning as equally critical pillars for achieving AGI.

To achieve these ambitious goals, Ndea is assembling a world-class team of program synthesis experts.

2 weeks, 4 days назад @ thesequence.substack.com
Synced Review
последний пост 1 month, 1 week назад
Automating Artificial Life Discovery: The Power of Foundation Models
Automating Artificial Life Discovery: The Power of Foundation Models Automating Artificial Life Discovery: The Power of Foundation Models

The recent Nobel Prize for groundbreaking advancements in protein discovery underscores the transformative potential of foundation models…Continue reading on SyncedReview »

1 month, 1 week назад @ medium.com
Llama 3 Meets MoE: Pioneering Low-Cost High-Performance AI
Llama 3 Meets MoE: Pioneering Low-Cost High-Performance AI Llama 3 Meets MoE: Pioneering Low-Cost High-Performance AI

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1 month, 1 week назад @ medium.com
DeepMind’s JetFormer: Unified Multimodal Models Without Modelling Constraints
DeepMind’s JetFormer: Unified Multimodal Models Without Modelling Constraints DeepMind’s JetFormer: Unified Multimodal Models Without Modelling Constraints

Recent advancements in training large multimodal models have been driven by efforts to eliminate modeling constraints and unify…Continue reading on SyncedReview »

1 month, 1 week назад @ medium.com
NVIDIA’s nGPT: Revolutionizing Transformers with Hypersphere Representation
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The Transformer architecture, introduced by Vaswani et al. in 2017, serves as the backbone of contemporary language models. Over the years…Continue reading on SyncedReview »

1 month, 2 weeks назад @ medium.com
From Token to Conceptual: Meta Introduces Large Concept Models in Multilingual AI
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Large Language Models (LLMs) have become indispensable tools for diverse natural language processing (NLP) tasks. Traditional LLMs operate…Continue reading on SyncedReview »

1 month, 2 weeks назад @ medium.com
NVIDIA’s Hybrid: Combining Attention and State Space Models for Breakthrough Performance of Small…
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Language models (LMs) based on transformers have become the gold standard in natural language processing, thanks to their exceptional…Continue reading on SyncedReview »

1 month, 3 weeks назад @ medium.com
From Response to Query: The Power of Reverse Thinking in Language Models
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1 month, 3 weeks назад @ medium.com
Yann LeCun Team’s New Research: Revolutionizing Visual Navigation with Navigation World Models
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Navigation is a fundamental skill for any visually-capable organism, serving as a critical tool for survival. It enables agents to locate…Continue reading on SyncedReview »

1 month, 4 weeks назад @ medium.com
The Future of Vision AI: How Apple’s AIMV2 Leverages Images and Text to Lead the Pack
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2 months назад @ medium.com
Redefining Music AI: The Power of Sony’s SoniDo as a Versatile Foundation Model
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2 months назад @ medium.com
DeepMind’s Socratic Learning with Language Games: The Path to Self-Improving Superintelligence
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2 months, 1 week назад @ medium.com
Revolutionizing AI on a Budget: Apple’s Roadmap for Small Language Models Training Success
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While large language models (LLMs) dominate the AI landscape, Small-scale Large Language Models (SLMs) are gaining traction as…Continue reading on SyncedReview »

2 months, 1 week назад @ medium.com
Redefines Consistency Models”: OpenAI’s TrigFlow Narrows FID Gap to 10% with Efficient Two-Step…
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2 months, 1 week назад @ medium.com
Precision in Pixels: NVIDIA’s Edify Image Model Combines High Quality with Unmatched Control
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2 months, 1 week назад @ medium.com
Meta’s Dualformer: Bridging Fast and Slow Thinking in Transformers for Superior AI Reasoning
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2 months, 2 weeks назад @ medium.com
📓 Cool Blogs
ODS.ai Habr ODS.ai Habr
последний пост 1 month назад
Создаем воспоминания. Осваиваем FLUX, LoRA и ComfyUI
Создаем воспоминания. Осваиваем FLUX, LoRA и ComfyUI Создаем воспоминания. Осваиваем FLUX, LoRA и ComfyUI

Такие модели можно обучать с нуля и это дорого, нужен кластер с GPU (видеокарты) и много данных.

В домене текст-картинка бывают открытые модели, типа Stable Diffusion, Kandinsky и FLUX, бывают закрытые, типа DALL-E.Открытую модель можно дообучать разными способами.

Борис СтругацкийОсобенности: Для личностей типа Стругацких или Бродского, качественных фотографий крайне мало, но много и не надо.

Можно и фразу.

Владимир СурдинАлексей СемихатовВидео с их лекциями можно найти повсеместно, начать можно с канала Вселенная плюс на YouTube и в телеграм.

1 month назад @ habr.com
Как нейросети, RL и байесовскую оптимизацию стали использовать на ускорителях заряженных частиц
Как нейросети, RL и байесовскую оптимизацию стали использовать на ускорителях заряженных частиц Как нейросети, RL и байесовскую оптимизацию стали использовать на ускорителях заряженных частиц

Один из них — поддержание стабильной орбиты пучка частиц (траектории, по которой происходит движение), которая критически важна для точности экспериментов.

Вот, кстати, наша статья про то, как сейсмические вибрации будут влиять на орбиту пучка в СКИФ: Beam Stability .

Классические подходы к стабилизации орбиты пучка в ускорителяхДля стабилизации орбиты используют датчики положения пучка (BPM) и магнитные корректоры.

По словам авторов получились следующие преимущества:Ускорение процесса коррекции орбиты и повышение точности по сравнению с классическими методами, такими как SVD.

Задача агента:Автоматическое восстановление орбиты пучка заряженных частиц за ограниченное время и минимальное коли…

1 month, 2 weeks назад @ habr.com
о1: почему новая GPT от OpenAI — это не хайп, а переход к новой парадигме в ИИ
о1: почему новая GPT от OpenAI — это не хайп, а переход к новой парадигме в ИИ о1: почему новая GPT от OpenAI — это не хайп, а переход к новой парадигме в ИИ

В этой статье мы разберемся, чему научилась новая GPT o1, и как это повлияет на дальнейшую эволюцию ИИ.

Компания утверждает, что для них сброс счётчика линейки моделей к единичке знаменует собой переход к новой парадигме, и что эта нейросеть и вовсе демонстрирует новый уровень возможностей ИИ.

На форумах и в Твиттере была куча обсуждений, предвосхищений и хайпа, на фоне которых планка ожиданий некоторых людей взлетела до небес.

Издание Bloomberg рассказало, что в ходе внутренней демонстрации OpenAI показали концепцию из пяти уровней, помогающую отслеживать прогресс в создании ИИ.

Однако на уровне GPT-5 прирост в навыках может быть совсем другим (как в лучшую, так и в худшую сторону).

4 months, 3 weeks назад @ habr.com
Большие и чёрные (ящики): что мы знаем о том, как «думают» нейросети?
Большие и чёрные (ящики): что мы знаем о том, как «думают» нейросети? Большие и чёрные (ящики): что мы знаем о том, как «думают» нейросети?

И в том, и в другом случаях объяснение действия не связано с реальным мотивом его сделать, и там, и там рождается поддельное (но правдоподобно звучащее) объяснение причин.

Просто сейчас это не воспринимается всерьёз, ведь LLM не распространены и не становятся ядром бизнес-процессов, включающих принятие решений.

Один и тот же текст запроса+ответа подаётся в модель, и производится оценка вероятности получить именно такой ответ при фиксированном запросе.

Это и желание продолжать существовать/жить, и нежелание умирать, и рассуждения об эмоциях и контроле.

Потому что абстракции, потому что обобщение, потому что это ровно то, за что мы ценим модели.

4 months, 4 weeks назад @ habr.com
Как организовать процесс А/В тестирования на коленке
Как организовать процесс А/В тестирования на коленке Как организовать процесс А/В тестирования на коленке

В ней авторы выделили 4 этапа зрелости, грубо можно разделить компании по частоте запусков экспериментов:на этапе Crawl компания проводит эксперимент раз в месяц (примерно 10 экспериментов в год);на этапе Walk – раз в неделю (примерно 50 экспериментов в год);на этапе Run – ежедневно (примерно 250 экспериментов в год);на этапе Fly – более 1000 экспериментов в год.

Более четкое разделение основывается на некоторых важных свойствах компаний, таких как: наличие команды платформы экспериментов, возможности автоматического расчета метрик, распространенности экспериментов в компании, самодостаточность продуктовых команд в проведении экспериментов, влияние экспериментов на компанию и другое.

Точка …

5 months, 3 weeks назад @ habr.com
Как организовать процесс А/В тестирования на коленке
Как организовать процесс А/В тестирования на коленке Как организовать процесс А/В тестирования на коленке

В ней авторы выделили 4 этапа зрелости, грубо можно разделить компании по частоте запусков экспериментов:на этапе Crawl компания проводит эксперимент раз в месяц (примерно 10 экспериментов в год);на этапе Walk – раз в неделю (примерно 50 экспериментов в год);на этапе Run – ежедневно (примерно 250 экспериментов в год);на этапе Fly – более 1000 экспериментов в год.

Более четкое разделение основывается на некоторых важных свойствах компаний, таких как: наличие команды платформы экспериментов, возможности автоматического расчета метрик, распространенности экспериментов в компании, самодостаточность продуктовых команд в проведении экспериментов, влияние экспериментов на компанию и другое.

Точка …

5 months, 3 weeks назад @ habr.com
Введение в MLflow
Введение в MLflow Введение в MLflow

Mlflow Experiments and Mlflow RunsMLflow Experiments и MLflow Runs - это основные абстракции для структурирования проекта.

mlflow run mlproject --entry-point hyperparameters-tuning --env-manager conda --experiment-name Cancer_Classification --run-name Hyperparameters_Search -P n-trials=10Посмотрим на результаты в MLflow UI.

artifact_path: model flavors: python_function: data: model.xgb env: conda: conda.yaml virtualenv: python_env.yaml loader_module: mlflow.xgboost python_version: 3.11.4 xgboost: code: null data: model.xgb model_class: xgboost.sklearn.XGBClassifier model_format: xgb xgb_version: 2.0.3 mlflow_version: 2.14.2 model_size_bytes: 35040 model_uuid: 516954aae7c94e91adeed9df76cb405…

6 months, 1 week назад @ habr.com
В 48 собесах от оффера в Гугл
В 48 собесах от оффера в Гугл В 48 собесах от оффера в Гугл

Как это определить и как предсказывать?

- Да... упс.. правда, они же уже определяют целевой признакВовремя не выкрутился, пришел фидбек, что я не понимаю разницу между обычными признаками (а.к.а.

Не, не делал, только DDP?

NVIDIA ищет единорогов, крутых и в рисече, и в инженерии.

Вакансия в Лондоне была, и в итоге они взяли моего бывшего коллегу: он уже в Лондоне и с чуть большим опытом в рекомендашках, явно лучше подходит.

6 months, 3 weeks назад @ habr.com
ChatGPT + YandexGPT API = ЛЮБОФ. Часть 1
ChatGPT + YandexGPT API = ЛЮБОФ. Часть 1 ChatGPT + YandexGPT API = ЛЮБОФ. Часть 1

ChatGPT 4 был значительно улучшен по сравнению с ChatGPT 3.5, что делает его более мощным инструментом.

Вам тоже надо учиться — учиться выстраивать взаимоотношение с ChatGPT, учиться общаться с ним.

Помните, вы всегда можете уточнить любую строчку в ответе ChatGPT и, в большинстве случаев, получить исчерпывающий ответ, который, вероятнее всего, обогатит вас новыми знаниями.

Вот несколько идей:Откройте с ChatGPT новый чат и в нём отправьте запрос в другой форме, желательно с новыми деталями.

И поэтому, когда с ChatGPT не удаётся что-то сделать с первого раза за 2–5 минут, возникает возмущение: “Ну, как так?!”.

8 months, 3 weeks назад @ habr.com
Machine Learning Mastery
последний пост 6 months, 3 weeks назад
Tips for Effectively Training Your Machine Learning Models
Tips for Effectively Training Your Machine Learning Models

In machine learning projects, achieving optimal model performance requires paying attention to various steps in the training process. But before focusing on the technical aspects of model training, it is important to define the problem, understand the context, and analyze the dataset in detail. Once you have a solid grasp of the problem and data, […]

The post Tips for Effectively Training Your Machine Learning Models appeared first on MachineLearningMastery.com.

6 months, 3 weeks назад @ machinelearningmastery.com
Principles of Reinforcement Learning: An Introduction with Python
Principles of Reinforcement Learning: An Introduction with Python

Reinforcement Learning (RL) is a type of machine learning. It trains an agent to make decisions by interacting with an environment. This article covers the basic concepts of RL. These include states, actions, rewards, policies, and the Markov Decision Process (MDP). By the end, you will understand how RL works. You will also learn how […]

The post Principles of Reinforcement Learning: An Introduction with Python appeared first on MachineLearningMastery.com.

7 months назад @ machinelearningmastery.com
5 Tips for Getting Started with Deep Learning
5 Tips for Getting Started with Deep Learning

Deep learning is a subset of machine learning that has become a cornerstone in many technological breakthroughs. At the core of deep learning, it’s a model inspired by the human brain, which we call a neural network. Contrary to the traditional machine learning model, deep learning can automatically find feature representations from data. That’s why […]

The post 5 Tips for Getting Started with Deep Learning appeared first on MachineLearningMastery.com.

7 months назад @ machinelearningmastery.com
Tips for Effective Feature Engineering in Machine Learning
Tips for Effective Feature Engineering in Machine Learning

Feature engineering is an important step in the machine learning pipeline. It is the process of transforming data in its native format into meaningful features to help the machine learning model learn better from the data. If done right, feature engineering can significantly enhance the performance of machine learning algorithms. Beyond the basics of understanding […]

The post Tips for Effective Feature Engineering in Machine Learning appeared first on MachineLearningMastery.com.

7 months назад @ machinelearningmastery.com
5 Common Mistakes in Machine Learning and How to Avoid Them
5 Common Mistakes in Machine Learning and How to Avoid Them

Using machine learning to solve real-world problems is exciting. But most eager beginners jump straight to model building—overlooking the fundamentals—resulting in models that aren’t very helpful. From understanding the data to choosing the best machine learning model for the problem, there are some common mistakes that beginners often tend to make. But before we go […]

The post 5 Common Mistakes in Machine Learning and How to Avoid Them appeared first on MachineLearningMastery.com.

7 months, 1 week назад @ machinelearningmastery.com
Stable Diffusion Project: Reviving Old Photos
Stable Diffusion Project: Reviving Old Photos

Photography has been around for more than a century. There are many old photos around, and probably your family has some, too. Limited by the camera and film of the time, you may have photos of low resolution, blurry, or with folds or scratches. Restoring these old photos and making them like new ones taken […]

The post Stable Diffusion Project: Reviving Old Photos appeared first on MachineLearningMastery.com.

7 months, 1 week назад @ machinelearningmastery.com
The Ultimate Beginner’s Guide to Docker
The Ultimate Beginner’s Guide to Docker

Today’s digital landscape has never been so diverse. Every individual and company selects their preferred tools and operating systems, creating a diverse technological system. However, this diversity often leads to compatibility issues, making it hard to ensure application performance across different environments. This is where Docker plays a key role as an indispensable tool for […]

The post The Ultimate Beginner’s Guide to Docker appeared first on MachineLearningMastery.com.

7 months, 1 week назад @ machinelearningmastery.com
Stable Diffusion Project: Commercial Poster
Stable Diffusion Project: Commercial Poster

Stable Diffusion has taken the AI art world by storm, empowering users to generate stunning and imaginative visuals with just a few text prompts. This opens exciting possibilities for creatives, including crafting impactful commercial posters. In this post, we’ll delve into using Stable Diffusion to design a compelling poster for a product. After finishing this […]

The post Stable Diffusion Project: Commercial Poster appeared first on MachineLearningMastery.com.

7 months, 2 weeks назад @ machinelearningmastery.com
5 Effective Ways to Handle Imbalanced Data in Machine Learning
5 Effective Ways to Handle Imbalanced Data in Machine Learning

Introduction Here’s a something that new machine learning practitioners figure out almost immediately: not all datasets are created equal. It may now seem obvious to you, but had you considered this before undertaking machine learning projects on a real world dataset? As an example of a single class vastly outnumbering the rest, take for instance […]

The post 5 Effective Ways to Handle Imbalanced Data in Machine Learning appeared first on MachineLearningMastery.com.

7 months, 2 weeks назад @ machinelearningmastery.com
Tips for Choosing the Right Machine Learning Model for Your Data
Tips for Choosing the Right Machine Learning Model for Your Data

Introduction Choosing the right machine learning model for your data is of major importance in any data science project. The model you select will have a significant impact on the insights you derive from your data, and ultimately determine the usefulness of a project. In this article, we aim to provide practical tips to help […]

The post Tips for Choosing the Right Machine Learning Model for Your Data appeared first on MachineLearningMastery.com.

7 months, 2 weeks назад @ machinelearningmastery.com
Stable Diffusion Project: Creating Illustration
Stable Diffusion Project: Creating Illustration

Many people write in their jobs. Not everyone is a novel writer; some write technical documentation, business plans, news articles, and even blog posts. In those writings, illustrations are not essential but often good to have. They are decorations, interpretations, or visual explanations of the text. However, you probably do not want to spend too […]

The post Stable Diffusion Project: Creating Illustration appeared first on MachineLearningMastery.com.

7 months, 2 weeks назад @ machinelearningmastery.com
5 Free Books on Machine Learning Algorithms You Must Read
5 Free Books on Machine Learning Algorithms You Must Read

If you are a machine learning student, researcher, or practitioner, it is crucial for your career growth to have a deep understanding of how each algorithm works and the various techniques to enhance model performance. Nowadays, many individuals tend to focus solely on the code, data, and pre-trained models, often without fully comprehending the machine […]

The post 5 Free Books on Machine Learning Algorithms You Must Read appeared first on MachineLearningMastery.com.

7 months, 2 weeks назад @ machinelearningmastery.com
Stable Diffusion Project: Word Art
Stable Diffusion Project: Word Art

Stable Diffusion is a powerful tool that helps you generate pictures. It is fun to play with the generative AI tool. But it would be useful if the tool could help you in a real job. In this post, you will see how you can leverage the power of Stable Diffusion to work on something […]

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7 months, 3 weeks назад @ machinelearningmastery.com
5 Free YouTube Channels Dedicated to Machine Learning Education
5 Free YouTube Channels Dedicated to Machine Learning Education

As a data professional, you should also know how to build predictive models with machine learning to solve business problems. And if you’re interested in machine learning, you’re probably also looking for the best resources to get going. Well, you can always choose a self-paced online course that best aligns with your learning preferences. But […]

The post 5 Free YouTube Channels Dedicated to Machine Learning Education appeared first on MachineLearningMastery.com.

7 months, 3 weeks назад @ machinelearningmastery.com
Tips for Choosing the Right Machine Learning Course
Tips for Choosing the Right Machine Learning Course

If you’re looking to make a career in data science, you probably know that machine learning is one of the most in-demand skills. Whether you are a beginner looking to break into the field or an experienced professional aiming to level up your expertise, selecting the right machine learning course is super important. So how […]

The post Tips for Choosing the Right Machine Learning Course appeared first on MachineLearningMastery.com.

7 months, 3 weeks назад @ machinelearningmastery.com
ML in Production
последний пост None
Sorta Insightful Sorta Insightful
последний пост 1 week, 2 days назад
MIT Mystery Hunt 2025
MIT Mystery Hunt 2025 MIT Mystery Hunt 2025

This has spoilers for MIT Mystery Hunt 2025.

I enjoyed it more than their 2018 Hunt, which is commonly cited as an all-time good Mystery Hunt.

In this Mystery Hunt it was reversed, where the act of unlocking is easy but the value and difficulty of a feeder varied.

In my free time pre-Hunt, I went to Puzzled Pint, where I tried to all-brain a logic puzzle (solve it without writing anything).

I’m looking forward to solving “No Assembly Required” in Mystery Hunt 2026, a puzzle that gives you the answer for no work.

1 week, 2 days назад @ alexirpan.com
Using AI to Get the Neopets Destruct-o-Match Avatar
Using AI to Get the Neopets Destruct-o-Match Avatar Using AI to Get the Neopets Destruct-o-Match Avatar

If AI can be superhuman at Go, surely AI can be slightly-worse-than-experts at Destruct-o-Match if we try?

Step 0: Is Making a Destruct-o-Match AI Against Neopets Rules?

I believe the precedent is in favor of a Destruct-o-Match AI being okay.

As long as I’m the one inputting moves the Destruct-o-Match AI recommends, I should be okay.

To write a game AI, we first need to implement the rules of the game in code.

3 weeks, 6 days назад @ alexirpan.com
Late Takes on OpenAI o1
Late Takes on OpenAI o1 Late Takes on OpenAI o1

I realize how late this is, but I didn’t get a post out while o1 was fresh, and still feel like writing one despite it being cold.

(Also, OpenAI just announced they’re going to ship new stuff starting tomorrow so it’s now or never to say something.)

OpenAI o1 is a model release widely believed (but not confirmed) to be a post-trained version of GPT-4o.

If true, that makes this video especially useful for understanding OpenAI o1.

Which I suppose is part of why I’m talking about o1 rather than building o1.

2 months назад @ alexirpan.com
Nine Years Later
Nine Years Later Nine Years Later

I expected to fill that void with more blog writing, but that’s not what happened.

The puzzles are great though, and if that’s good enough for you, I had fun with that.

Undertale YellowUndertale Yellow is a fantastic fan game, that’s been in development for 7 years and comes out feeling like a canon entry made by Toby Fox.

markdown 15837 2024 - 01 - 11 - ai - timelines - 2024. markdown 1939 2024 - 01 - 21 - mh - 2024. markdown 5076 2024 - 03 - 23 - crew - battle .

markdown 826 2024 - 04 - 30 - puzzlehunting - 201. markdown 8641 2024 - 07 - 08 - tragedies - of - reality .

5 months, 3 weeks назад @ alexirpan.com
I'm Switching Into AI Safety
I'm Switching Into AI Safety I'm Switching Into AI Safety

There’s often a conflation between the research field of AI safety and the community of AI safety.

Me thinking AI safety is important is not an endorsement for or against anything else in the broader meme space it came from.

Historically, AI safety work did not appeal to me because of how theoretical it was.

I’m aware of the arguments that most AI safety work so far has either been useless or not that different from broader AI work.

Those who care about safety a lot call this safetywashing, the stapling of “safety” to work that does not advance safety.

6 months назад @ alexirpan.com
The Tragedies of Reality Are Coming for You
The Tragedies of Reality Are Coming for You The Tragedies of Reality Are Coming for You

I would extend it to reality is complicated, relative to code, and in robotics you’re often pushing a messy reality into an abstraction nice enough for code to act on it.

Robotics research relies on building new bridges between reality and software, but that happens outside of robotics too.

Any software that interfaces with reality will have imperfect knowledge of that reality.

However, that means all the messiness of reality is coming for a field that historically does a bad job at considering reality.

I consider the world of bits to be as much a part of reality as the world of atoms.

7 months назад @ alexirpan.com
Puzzlehunting 201
Puzzlehunting 201 Puzzlehunting 201

Most people will have more fun if they solve puzzles than if they don’t, but you don’t have to solve puzzles quickly to have fun.

I’m still going to explain the solving strategies I’ve learned, but puzzle solving is really an activity where you learn by doing.

Puzzle solving often involves relating two parts of the puzzle together.

Search everythingHonestly, a lot of puzzle solving is about taking random parts of the puzzle and throwing them into a search engine.

Bringing This TogetherTo showcase these strategies together, here is a puzzle I remember speedrunning especially quickly: The Three Little Pigs from Hunt 20 2.1 Puzzle Hunt.

9 months, 1 week назад @ alexirpan.com
Lil'Log
последний пост None
inFERENCe
последний пост None
Off the Convex Path
последний пост None
Jay Alammar
последний пост None
fast.ai NLP fast.ai NLP
последний пост None
Sebastian Ruder
последний пост None
Andrew Karpathy blog
последний пост None
大トロ 大トロ
последний пост None
🔬 Science
Papers With Code Papers With Code
последний пост 2 days, 5 hours назад
/hazdzz/ Converting Transformers into DGNNs Form
/hazdzz/ Converting Transformers into DGNNs Form /hazdzz/ Converting Transformers into DGNNs Form

Central to the success of Transformers is the self-attention mechanism, which scores the similarity between query and key matrices to modulate a value matrix.

This operation bears striking similarities to digraph convolution, prompting an investigation into whether digraph convolution could serve as an alternative to self-attention.

In this study, we formalize this concept by introducing a synthetic unitary digraph convolution based on the digraph Fourier transform.

The resulting model, which we term Converter, effectively converts a Transformer into a Directed Graph Neural Network (DGNN) form.

We have tested Converter on Long-Range Arena benchmark, long document classification, and DNA seq…

2 days, 5 hours назад @ paperswithcode.com
/eleutherai/ Transcoders Beat Sparse Autoencoders for Interpretability
/eleutherai/ Transcoders Beat Sparse Autoencoders for Interpretability /eleutherai/ Transcoders Beat Sparse Autoencoders for Interpretability

Sparse autoencoders (SAEs) extract human-interpretable features from deep neural networks by transforming their activations into a sparse, higher dimensional latent space, and then reconstructing the activations from these latents.

Transcoders are similar to SAEs, but they are trained to reconstruct the output of a component of a deep network given its input.

In this work, we compare the features found by transcoders and SAEs trained on the same model and data, finding that transcoder features are significantly more interpretable.

We also propose _skip transcoders_, which add an affine skip connection to the transcoder architecture, and show that these achieve lower reconstruction loss with…

2 days, 6 hours назад @ paperswithcode.com
/eleutherai/ Partially Rewriting a Transformer in Natural Language
/eleutherai/ Partially Rewriting a Transformer in Natural Language /eleutherai/ Partially Rewriting a Transformer in Natural Language

In this paper, we attempt to partially rewrite a large language model using simple natural language explanations.

We then replace the first layer of this sparse MLP with an LLM-based simulator, which predicts the activation of each neuron given its explanation and the surrounding context.

With our pipeline, the model's increase in loss is statistically similar to entirely replacing the sparse MLP output with the zero vector.

We employ the same protocol, this time using a sparse autoencoder, on the residual stream of the same layer and obtain similar results.

These results suggest that more detailed explanations are needed to improve performance substantially above the zero ablation baseline.

2 days, 6 hours назад @ paperswithcode.com
/BioMedIA/ An Adversarial Approach to Register Extreme Resolution Tissue Cleared 3D Brain Images
/BioMedIA/ An Adversarial Approach to Register Extreme Resolution Tissue Cleared 3D Brain Images /BioMedIA/ An Adversarial Approach to Register Extreme Resolution Tissue Cleared 3D Brain Images

We developed a generative patch based 3D image registration model that can register very high resolution images obtained from a biochemical process name tissue clearing.

Tissue clearing process removes lipids and fats from the tissue and make the tissue transparent.

Traditional image registration methods fail to register images with such extant.

In this paper we addressed this very high resolution image registration issue by proposing a patch-based generative network named InvGAN.

Our proposed network can register very high resolution tissue cleared images.

2 days, 8 hours назад @ paperswithcode.com
/BonnBytes/ Position: More Rigorous Software Engineering Would Improve Reproducibility in Machine Learning Research
/BonnBytes/ Position: More Rigorous Software Engineering Would Improve Reproducibility in Machine Learning Research /BonnBytes/ Position: More Rigorous Software Engineering Would Improve Reproducibility in Machine Learning Research

Experimental verification and falsification of scholarly work are part of the scientific method's core.

To improve the Machine Learning (ML)-communities' ability to verify results from prior work, we argue for more robust software engineering.

We estimate the adoption of common engineering best practices by examining repository links from all recently accepted International Conference on Machine Learning (ICML), International Conference on Learning Representations (ICLR) and Neural Information Processing Systems (NeurIPS) papers as well as ICML papers over time.

Based on the results, we recommend how we, as a community, can improve reproducibility in ML-research.

PDFAbstract

2 days, 11 hours назад @ paperswithcode.com
/antoinedemathelin/ OneBatchPAM: A Fast and Frugal K-Medoids Algorithm
/antoinedemathelin/ OneBatchPAM: A Fast and Frugal K-Medoids Algorithm /antoinedemathelin/ OneBatchPAM: A Fast and Frugal K-Medoids Algorithm

This paper proposes a novel k-medoids approximation algorithm to handle large-scale datasets with reasonable computational time and memory complexity.

We develop a local-search algorithm that iteratively improves the medoid selection based on the estimation of the k-medoids objective.

A single batch of size m << n provides the estimation, which reduces the required memory size and the number of pairwise dissimilarities computations to O(mn), instead of O(n^2) compared to most k-medoids baselines.

We obtain theoretical results highlighting that a batch of size m = O(log(n)) is sufficient to guarantee, with strong probability, the same performance as the original local-search algorithm.

Multi…

2 days, 12 hours назад @ paperswithcode.com
/orionw/ mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval
/orionw/ mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval /orionw/ mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval

However, advances in language models have facilitated the nascent rise of retrieval models that can understand more complex queries with diverse intents.

However, these efforts have focused exclusively on English; therefore, we do not yet understand how they work across languages.

We introduce mFollowIR, a multilingual benchmark for measuring instruction-following ability in retrieval models.

mFollowIR builds upon the TREC NeuCLIR narratives (or instructions) that span three diverse languages (Russian, Chinese, Persian) giving both query and instruction to the retrieval models.

We make small changes to the narratives and isolate how well retrieval models can follow these nuanced changes.

2 days, 12 hours назад @ paperswithcode.com
/matchten/ Low-Rank Adapting Models for Sparse Autoencoders
/matchten/ Low-Rank Adapting Models for Sparse Autoencoders /matchten/ Low-Rank Adapting Models for Sparse Autoencoders

Sparse autoencoders (SAEs) decompose language model representations into a sparse set of linear latent vectors.

In this work, we improve on these limitations by taking a fundamentally different approach: we use low-rank adaptation (LoRA) to finetune the language model itself around a previously trained SAE.

We analyze our method across SAE sparsity, SAE width, language model size, LoRA rank, and model layer on the Gemma Scope family of SAEs.

In these settings, our method reduces the cross entropy loss gap by 30% to 55% when SAEs are inserted during the forward pass.

Our results demonstrate that improving model interpretability is not limited to post-hoc SAE training; Pareto improvements can…

2 days, 12 hours назад @ paperswithcode.com
/mustafakarabag/ Do LLMs Strategically Reveal, Conceal, and Infer Information? A Theoretical and Empirical Analysis in The Chameleon Game
/mustafakarabag/ Do LLMs Strategically Reveal, Conceal, and Infer Information? A Theoretical and Empirical Analysis in The Chameleon Game /mustafakarabag/ Do LLMs Strategically Reveal, Conceal, and Infer Information? A Theoretical and Empirical Analysis in The Chameleon Game

In such settings, agents' decision-making needs to conceal information from their adversaries, reveal information to their cooperators, and infer information to identify the other agents' characteristics.

To investigate whether LLMs have these information control and decision-making capabilities, we make LLM agents play the language-based hidden-identity game, The Chameleon.

In the game, a group of non-chameleon agents who do not know each other aim to identify the chameleon agent without revealing a secret.

The game requires the aforementioned information control capabilities both as a chameleon and a non-chameleon.

Based on the empirical results and theoretical analysis of different strat…

2 days, 12 hours назад @ paperswithcode.com
/ozekri/ Fine-Tuning Discrete Diffusion Models with Policy Gradient Methods
/ozekri/ Fine-Tuning Discrete Diffusion Models with Policy Gradient Methods /ozekri/ Fine-Tuning Discrete Diffusion Models with Policy Gradient Methods

Discrete diffusion models have recently gained significant attention due to their ability to process complex discrete structures for language modeling.

However, fine-tuning these models with policy gradient methods, as is commonly done in Reinforcement Learning from Human Feedback (RLHF), remains a challenging task.

We propose an efficient, broadly applicable, and theoretically justified policy gradient algorithm, called Score Entropy Policy Optimization (SEPO), for fine-tuning discrete diffusion models over non-differentiable rewards.

Our numerical experiments across several discrete generative tasks demonstrate the scalability and efficiency of our method.

Our code is available at https:/…

2 days, 12 hours назад @ paperswithcode.com
/HanxunH/ Detecting Backdoor Samples in Contrastive Language Image Pretraining
/HanxunH/ Detecting Backdoor Samples in Contrastive Language Image Pretraining /HanxunH/ Detecting Backdoor Samples in Contrastive Language Image Pretraining

Contrastive language-image pretraining (CLIP) has been found to be vulnerable to poisoning backdoor attacks where the adversary can achieve an almost perfect attack success rate on CLIP models by poisoning only 0.01\% of the training dataset.

This raises security concerns on the current practice of pretraining large-scale models on unscrutinized web data using CLIP.

In this work, we analyze the representations of backdoor-poisoned samples learned by CLIP models and find that they exhibit unique characteristics in their local subspace, i.e., their local neighborhoods are far more sparse than that of clean samples.

Based on this finding, we conduct a systematic study on detecting CLIP backdoo…

2 days, 14 hours назад @ paperswithcode.com
/khanld/ ChunkFormer: Masked Chunking Conformer For Long-Form Speech Transcription
/khanld/ ChunkFormer: Masked Chunking Conformer For Long-Form Speech Transcription /khanld/ ChunkFormer: Masked Chunking Conformer For Long-Form Speech Transcription

Include the markdown at the top of your GitHub README.md file to showcase the performance of the model.

Badges are live and will be dynamically updated with the latest ranking of this paper.

2 days, 14 hours назад @ paperswithcode.com
/rebelsnlu-jaist/ Improving the Robustness of Representation Misdirection for Large Language Model Unlearning
/rebelsnlu-jaist/ Improving the Robustness of Representation Misdirection for Large Language Model Unlearning /rebelsnlu-jaist/ Improving the Robustness of Representation Misdirection for Large Language Model Unlearning

Representation Misdirection (RM) and variants are established large language model (LLM) unlearning methods with state-of-the-art performance.

In this paper, we show that RM methods inherently reduce models' robustness, causing them to misbehave even when a single non-adversarial forget-token is in the retain-query.

Toward understanding underlying causes, we reframe the unlearning process as backdoor attacks and defenses: forget-tokens act as backdoor triggers that, when activated in retain-queries, cause disruptions in RM models' behaviors, similar to successful backdoor attacks.

To mitigate this vulnerability, we propose Random Noise Augmentation -- a model and method agnostic approach wi…

2 days, 15 hours назад @ paperswithcode.com
/chumingqian/ CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning
/chumingqian/ CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning /chumingqian/ CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning

Despite the emergence of deep learning-based methods for automatic respiratory sound classification post-Covid-19, limited datasets impede performance enhancement.

Distinguishing between normal and abnormal respiratory sounds poses challenges due to the coexistence of normal respiratory components and noise components in both types.

Moreover, different abnormal respiratory sounds exhibit similar anomalous features, hindering their differentiation.

To address these issues, we design a lightweight network CycleGuardian and propose a framework based on an improved deep clustering and contrastive learning.

Additionally, we deploy the network on Android devices, showcasing a comprehensive intell…

2 days, 16 hours назад @ paperswithcode.com
/MYC000801/ Avoiding $\mathbf{exp(R_{max})}$ scaling in RLHF through Preference-based Exploration
/MYC000801/ Avoiding $\mathbf{exp(R_{max})}$ scaling in RLHF through Preference-based Exploration /MYC000801/ Avoiding $\mathbf{exp(R_{max})}$ scaling in RLHF through Preference-based Exploration

Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal technique for large language model (LLM) alignment.

This paper studies the setting of online RLHF and focus on improving sample efficiency.

All existing algorithms in online RLHF, whether doing passive exploration or active exploration, suffer from a sample complexity that scales exponentially with the scale of the reward function.

To address this, we introduce Self-Exploring Preference-Incentive Online Preference Optimization (SE-POPO), an online RLHF algorithm that for the first time achieves a sample complexity that scales polynomially with the reward scale, answering an open problem raised by Xie et al.

(2024).. …

2 days, 16 hours назад @ paperswithcode.com
Papers With Code Papers With Code
последний пост 2 days, 5 hours назад
/DongYang26/ Physics-Inspired Distributed Radio Map Estimation
/DongYang26/ Physics-Inspired Distributed Radio Map Estimation /DongYang26/ Physics-Inspired Distributed Radio Map Estimation

To gain panoramic awareness of spectrum coverage in complex wireless environments, data-driven learning approaches have recently been introduced for radio map estimation (RME).

Federated learning (FL) enhance data security and communication efficiency in RME by allowing multiple clients to collaborate in model training without directly sharing local data.

However, the performance of the FL-based RME can be hindered by the problem of task heterogeneity across clients due to their unavailable or inaccurate landscaping information.

To fill this gap, in this paper, we propose a physics-inspired distributed RME solution in the absence of landscaping information.

The main idea is to develop a nov…

2 days, 17 hours назад @ paperswithcode.com
/huawei-lin/ Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data
/huawei-lin/ Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data /huawei-lin/ Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data

Gradient Boosting Decision Tree (GBDT) is one of the most popular machine learning models in various applications.

However, in the traditional settings, all data should be simultaneously accessed in the training procedure: it does not allow to add or delete any data instances after training.

In this paper, we propose an efficient online learning framework for GBDT supporting both incremental and decremental learning.

To the best of our knowledge, this is the first work that considers an in-place unified incremental and decremental learning on GBDT.

We theoretically show the relationship between the hyper-parameters of the proposed optimizations, which enables trading off accuracy and cost o…

2 days, 17 hours назад @ paperswithcode.com
/lurenhaothu/ Complex Wavelet Mutual Information Loss: A Multi-Scale Loss Function for Semantic Segmentation
/lurenhaothu/ Complex Wavelet Mutual Information Loss: A Multi-Scale Loss Function for Semantic Segmentation /lurenhaothu/ Complex Wavelet Mutual Information Loss: A Multi-Scale Loss Function for Semantic Segmentation

Recent advancements in deep neural networks have significantly enhanced the performance of semantic segmentation.

To address the multiscale nature of segmented objects, various models have incorporated mechanisms such as spatial attention and feature pyramid networks.

To address this limitation, we propose complex wavelet mutual information (CWMI) loss, a novel loss function that leverages mutual information from subband images decomposed by a complex steerable pyramid.

The complex steerable pyramid captures features across multiple orientations and preserves structural similarity across scales.

Meanwhile, mutual information is well-suited for capturing high-dimensional directional features…

2 days, 19 hours назад @ paperswithcode.com
/RManLuo/ GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation
/RManLuo/ GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation /RManLuo/ GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation

Recently, graph-enhanced retrieval augmented generation (GraphRAG) builds graph structure to explicitly model these relationships, enabling more effective and efficient retrievers.

Nevertheless, its performance is still hindered by the noise and incompleteness within the graph structure.

To address this, we introduce GFM-RAG, a novel graph foundation model (GFM) for retrieval augmented generation.

GFM-RAG is powered by an innovative graph neural network that reasons over graph structure to capture complex query-knowledge relationships.

This results in impressive performance and generalizability for GFM-RAG, making it the first graph foundation model applicable to unseen datasets for retriev…

2 days, 19 hours назад @ paperswithcode.com
/haiduo/ Partial Channel Network: Compute Fewer, Perform Better
/haiduo/ Partial Channel Network: Compute Fewer, Perform Better /haiduo/ Partial Channel Network: Compute Fewer, Perform Better

To address this challenge and exploit the redundancy within feature map channels, we propose a new solution: partial channel mechanism (PCM).

Based on this assumption, we introduce a novel partial attention convolution (PATConv) that can efficiently combine convolution with visual attention.

Our exploration indicates that the PATConv can completely replace both the regular convolution and the regular visual attention while reducing model parameters and FLOPs.

Moreover, PATConv can derive three new types of blocks: Partial Channel-Attention block (PAT_ch), Partial Spatial-Attention block (PAT_sp), and Partial Self-Attention block (PAT_sf).

In addition, we propose a novel dynamic partial conv…

2 days, 19 hours назад @ paperswithcode.com
/haiduo/ Nearly Lossless Adaptive Bit Switching
/haiduo/ Nearly Lossless Adaptive Bit Switching /haiduo/ Nearly Lossless Adaptive Bit Switching

Model quantization is widely applied for compressing and accelerating deep neural networks (DNNs).

Hence, the scheme of one-shot joint training multiple precisions is proposed to address this issue.

Furthermore, we observe a competitive interference among different precisions during one-shot joint training, primarily due to inconsistent gradients of quantization scales during backward propagation.

Additionally, we extend our Double Rounding to one-shot mixed precision training and develop a Hessian-Aware Stochastic Bit-switching (HASB) strategy.

Experimental results on the ImageNet-1K classification demonstrate that our methods have enough advantages to state-of-the-art one-shot joint QAT i…

2 days, 19 hours назад @ paperswithcode.com
/andypinxinliu/ GestureLSM: Latent Shortcut based Co-Speech Gesture Generation with Spatial-Temporal Modeling
/andypinxinliu/ GestureLSM: Latent Shortcut based Co-Speech Gesture Generation with Spatial-Temporal Modeling /andypinxinliu/ GestureLSM: Latent Shortcut based Co-Speech Gesture Generation with Spatial-Temporal Modeling

Include the markdown at the top of your GitHub README.md file to showcase the performance of the model.

Badges are live and will be dynamically updated with the latest ranking of this paper.

3 days, 3 hours назад @ paperswithcode.com
/Pablo1990/ Single cell resolution 3D imaging and segmentation within intact live tissues
/Pablo1990/ Single cell resolution 3D imaging and segmentation within intact live tissues /Pablo1990/ Single cell resolution 3D imaging and segmentation within intact live tissues

Epithelial cells form diverse structures from squamous spherical organoids to densely packed pseudostratified tissues.

Quantification of cellular properties in these contexts requires high-resolution deep imaging and computational techniques to achieve truthful three-dimensional (3D) structural features.

Here, we describe a detailed step-by-step protocol for sample preparation, imaging and deep-learning-assisted cell segmentation to achieve accurate quantification of fluorescently labelled individual cells in 3D within live tissues.

We share the lessons learned through troubleshooting 3D imaging of Drosophila wing discs, including considerations on the choice of microscopy modality and sett…

3 days, 13 hours назад @ paperswithcode.com
/fabian-sp/ The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training
/fabian-sp/ The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training /fabian-sp/ The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training

We show that learning-rate schedules for large model training behave surprisingly similar to a performance bound from non-smooth convex optimization theory.

We provide a bound for the constant schedule with linear cooldown; in particular, the practical benefit of cooldown is reflected in the bound due to the absence of logarithmic terms.

Further, we show that this surprisingly close match between optimization theory and practice can be exploited for learning-rate tuning: we achieve noticeable improvements for training 124M and 210M Llama-type models by (i) extending the schedule for continued training with optimal learning-rate, and (ii) transferring the optimal learning-rate across schedul…

3 days, 13 hours назад @ paperswithcode.com
/cisimon7/ Swarm-Gen: Fast Generation of Diverse Feasible Swarm Behaviors
/cisimon7/ Swarm-Gen: Fast Generation of Diverse Feasible Swarm Behaviors /cisimon7/ Swarm-Gen: Fast Generation of Diverse Feasible Swarm Behaviors

However, the problem of generating diverse and feasible swarm behaviors in a scalable manner remains largely unaddressed.

Specifically, we sample diverse trajectories from a learned generative model which is subsequently projected onto the feasible set using the SF.

We experiment with two choices for generative models, namely: Conditional Variational Autoencoder (CVAE) and Vector-Quantized Variational Autoencoder (VQ-VAE).

First, we demonstrate that we can generate a large set of multi-modal, feasible trajectories, simulating diverse swarm behaviors, within a few tens of milliseconds.

Second, we show that our initialization network provides faster convergence of our SF solver vis-a-vis othe…

3 days, 16 hours назад @ paperswithcode.com
/taneya1987/ Secured Communication Schemes for UAVs in 5G: CRYSTALS-Kyber and IDS
/taneya1987/ Secured Communication Schemes for UAVs in 5G: CRYSTALS-Kyber and IDS /taneya1987/ Secured Communication Schemes for UAVs in 5G: CRYSTALS-Kyber and IDS

This paper introduces a secure communication architecture for Unmanned Aerial Vehicles (UAVs) and ground stations in 5G networks, addressing critical challenges in network security.

The architecture is based on a server-client model, with UAVs functioning as clients and the ground station acting as the server.

Experimental results confirm that CRYSTALS-Kyber delivers strong protection against quantum threats with minimal performance overhead, making it highly suitable for UAVs with resource constraints.

Moreover, the proposed architecture integrates an Artificial Intelligence (AI)-based Intrusion Detection System (IDS) to further enhance security.

In performance evaluations, the IDS demonst…

3 days, 16 hours назад @ paperswithcode.com
/crepuscularlight/ LiDAR Loop Closure Detection using Semantic Graphs with Graph Attention Networks
/crepuscularlight/ LiDAR Loop Closure Detection using Semantic Graphs with Graph Attention Networks /crepuscularlight/ LiDAR Loop Closure Detection using Semantic Graphs with Graph Attention Networks

In this paper, we propose a novel loop closure detection algorithm that uses graph attention neural networks to encode semantic graphs to perform place recognition and then use semantic registration to estimate the 6 DoF relative pose constraint.

Our place recognition algorithm has two key modules, namely, a semantic graph encoder module and a graph comparison module.

The semantic graph encoder employs graph attention networks to efficiently encode spatial, semantic and geometric information from the semantic graph of the input point cloud.

The graph vectors of the current scan and a keyframe scan are then compared in the graph comparison module to identify a possible loop closure.

Specific…

3 days, 16 hours назад @ paperswithcode.com
/kim-dahye/ Concept Steerers: Leveraging K-Sparse Autoencoders for Controllable Generations
/kim-dahye/ Concept Steerers: Leveraging K-Sparse Autoencoders for Controllable Generations /kim-dahye/ Concept Steerers: Leveraging K-Sparse Autoencoders for Controllable Generations

Despite the remarkable progress in text-to-image generative models, they are prone to adversarial attacks and inadvertently generate unsafe, unethical content.

Existing approaches often rely on fine-tuning models to remove specific concepts, which is computationally expensive, lack scalability, and/or compromise generation quality.

In this work, we propose a novel framework leveraging k-sparse autoencoders (k-SAEs) to enable efficient and interpretable concept manipulation in diffusion models.

Through extensive experiments, we demonstrate that our approach is very simple, requires no retraining of the base model nor LoRA adapters, does not compromise the generation quality, and is robust to…

3 days, 17 hours назад @ paperswithcode.com
/aiotgroup/ XRF V2: A Dataset for Action Summarization with Wi-Fi Signals, and IMUs in Phones, Watches, Earbuds, and Glasses
/aiotgroup/ XRF V2: A Dataset for Action Summarization with Wi-Fi Signals, and IMUs in Phones, Watches, Earbuds, and Glasses /aiotgroup/ XRF V2: A Dataset for Action Summarization with Wi-Fi Signals, and IMUs in Phones, Watches, Earbuds, and Glasses

Human Action Recognition (HAR) plays a crucial role in applications such as health monitoring, smart home automation, and human-computer interaction.

While HAR has been extensively studied, action summarization, which involves identifying and summarizing continuous actions, remains an emerging task.

This paper introduces the novel XRF V2 dataset, designed for indoor daily activity Temporal Action Localization (TAL) and action summarization.

To tackle TAL and action summarization, we propose the XRFMamba neural network, which excels at capturing long-term dependencies in untrimmed sensory sequences and outperforms state-of-the-art methods, such as ActionFormer and WiFiTAD.

We envision XRF V2…

3 days, 17 hours назад @ paperswithcode.com
/sjmeis/ On the Impact of Noise in Differentially Private Text Rewriting
/sjmeis/ On the Impact of Noise in Differentially Private Text Rewriting /sjmeis/ On the Impact of Noise in Differentially Private Text Rewriting

The field of text privatization often leverages the notion of $\textit{Differential Privacy}$ (DP) to provide formal guarantees in the rewriting or obfuscation of sensitive textual data.

However, noise addition almost undoubtedly leads to considerable utility loss, thereby highlighting one major drawback of DP in NLP.

In this work, we introduce a new sentence infilling privatization technique, and we use this method to explore the effect of noise in DP text rewriting.

We empirically demonstrate that non-DP privatization techniques excel in utility preservation and can find an acceptable empirical privacy-utility trade-off, yet cannot outperform DP methods in empirical privacy protections.

O…

3 days, 17 hours назад @ paperswithcode.com
Papers With Code Papers With Code
последний пост 2 days, 5 hours назад
/chrisfanwang/ Transfer Learning for Nonparametric Contextual Dynamic Pricing
/chrisfanwang/ Transfer Learning for Nonparametric Contextual Dynamic Pricing /chrisfanwang/ Transfer Learning for Nonparametric Contextual Dynamic Pricing

Dynamic pricing strategies are crucial for firms to maximize revenue by adjusting prices based on market conditions and customer characteristics.

However, designing optimal pricing strategies becomes challenging when historical data are limited, as is often the case when launching new products or entering new markets.

One promising approach to overcome this limitation is to leverage information from related products or markets to inform the focal pricing decisions.

In this paper, we explore transfer learning for nonparametric contextual dynamic pricing under a covariate shift model, where the marginal distributions of covariates differ between source and target domains while the reward func…

3 days, 17 hours назад @ paperswithcode.com
/zombasy/ Full-scale Representation Guided Network for Retinal Vessel Segmentation
/zombasy/ Full-scale Representation Guided Network for Retinal Vessel Segmentation /zombasy/ Full-scale Representation Guided Network for Retinal Vessel Segmentation

The U-Net architecture and its variants have remained state-of-the-art (SOTA) for retinal vessel segmentation over the past decade.

In this study, we introduce a Full Scale Guided Network (FSG-Net), where the feature representation network with modernized convolution blocks extracts full-scale information and the guided convolution block refines that information.

Attention-guided filter is introduced to the guided convolution block under the interpretation that the filter behaves like the unsharp mask filter.

The structure preceding the guided convolution block can be replaced by any U-Net variant, which enhances the scalability of the proposed approach.

Our experiments also show that the p…

3 days, 17 hours назад @ paperswithcode.com
/camlab-ethz/ RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains
/camlab-ethz/ RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains /camlab-ethz/ RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains

Learning the solution operators of PDEs on arbitrary domains is challenging due to the diversity of possible domain shapes, in addition to the often intricate underlying physics.

We propose an end-to-end graph neural network (GNN) based neural operator to learn PDE solution operators from data on point clouds in arbitrary domains.

Our multi-scale model maps data between input/output point clouds by passing it through a downsampled regional mesh.

Our model, termed RIGNO, is tested on a challenging suite of benchmarks, composed of various time-dependent and steady PDEs defined on a diverse set of domains.

We demonstrate that RIGNO is significantly more accurate than neural operator baselines …

3 days, 17 hours назад @ paperswithcode.com
/javiersgjavi/ CoSTI: Consistency Models for (a faster) Spatio-Temporal Imputation
/javiersgjavi/ CoSTI: Consistency Models for (a faster) Spatio-Temporal Imputation /javiersgjavi/ CoSTI: Consistency Models for (a faster) Spatio-Temporal Imputation

Multivariate Time Series Imputation (MTSI) is crucial for many applications, such as healthcare monitoring and traffic management, where incomplete data can compromise decision-making.

Existing state-of-the-art methods, like Denoising Diffusion Probabilistic Models (DDPMs), achieve high imputation accuracy; however, they suffer from significant computational costs and are notably time-consuming due to their iterative nature.

In this work, we propose CoSTI, an innovative adaptation of Consistency Models (CMs) for the MTSI domain.

CoSTI employs Consistency Training to achieve comparable imputation quality to DDPMs while drastically reducing inference times, making it more suitable for real-ti…

3 days, 17 hours назад @ paperswithcode.com
/bozdaglab/ CAAT-EHR: Cross-Attentional Autoregressive Transformer for Multimodal Electronic Health Record Embeddings
/bozdaglab/ CAAT-EHR: Cross-Attentional Autoregressive Transformer for Multimodal Electronic Health Record Embeddings /bozdaglab/ CAAT-EHR: Cross-Attentional Autoregressive Transformer for Multimodal Electronic Health Record Embeddings

These datasets, after necessary preprocessing to clean and format the data for analysis, often remain in their raw EHR form, representing numerical or categorical values without further transformation into task-agnostic embeddings.

While such raw EHR data enables predictive modeling, its reliance on manual feature engineering or downstream task-specific optimization limits its utility for general-purpose applications.

However, these methods often struggle to fully exploit the temporal and multimodal dependencies inherent in EHR data due to their reliance on pre-processed but untransformed raw EHR inputs.

In this study, we introduce CAAT-EHR, a novel architecture designed to bridge this gap …

3 days, 17 hours назад @ paperswithcode.com
/arseniigav/ DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics Experiments
/arseniigav/ DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics Experiments /arseniigav/ DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics Experiments

Ensuring reliable data collection in large-scale particle physics experiments demands Data Quality Monitoring (DQM) procedures to detect possible detector malfunctions and preserve data integrity.

Traditionally, this resource-intensive task has been handled by human shifters that struggle with frequent changes in operational conditions.

We present novel, interpretable, robust, and scalable DQM algorithms designed to automate anomaly detection in time-dependent settings.

Our approach constructs evolving histogram templates with built-in uncertainties, featuring both a statistical variant - extending the classical Exponentially Weighted Moving Average (EWMA) - and a machine learning (ML)-enha…

3 days, 17 hours назад @ paperswithcode.com
/isee-laboratory/ LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models
/isee-laboratory/ LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models /isee-laboratory/ LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models

In this work, we show that an open-vocabulary detector co-training with a large language model by generating image-level detailed captions for each image can further improve performance.

With this dataset, we finetune an open-vocabulary detector with training objectives including a standard grounding loss and a caption generation loss.

We take advantage of a large language model to generate both region-level short captions for each region of interest and image-level long captions for the whole image.

Under the supervision of the large language model, the resulting detector, LLMDet, outperforms the baseline by a clear margin, enjoying superior open-vocabulary ability.

Further, we show that t…

3 days, 17 hours назад @ paperswithcode.com
/dmis-lab/ GPO-VAE: Modeling Explainable Gene Perturbation Responses utilizing GRN-Aligned Parameter Optimization
/dmis-lab/ GPO-VAE: Modeling Explainable Gene Perturbation Responses utilizing GRN-Aligned Parameter Optimization /dmis-lab/ GPO-VAE: Modeling Explainable Gene Perturbation Responses utilizing GRN-Aligned Parameter Optimization

While variational autoencoders (VAEs) have shown promise in modeling perturbation responses, their limited explainability poses a significant challenge, as the learned features often lack clear biological meaning.

GRNs elicit the underlying causal relationships between genes and are capable of explaining the transcriptional responses caused by genetic perturbation treatments.

Results: We propose GPO-VAE, an explainable VAE enhanced by GRN-aligned Parameter Optimization that explicitly models gene regulatory networks in the latent space.

Experimental results on perturbation prediction show our model achieves state-of-the-art performance in predicting transcriptional responses across multiple…

3 days, 17 hours назад @ paperswithcode.com
/tripto03/ Beyond checkmate: exploring the creative chokepoints in AI text
/tripto03/ Beyond checkmate: exploring the creative chokepoints in AI text /tripto03/ Beyond checkmate: exploring the creative chokepoints in AI text

While numerous prior research has focused on detecting LLM-generated text (AI text) and thus checkmating them, our study investigates a relatively unexplored territory: portraying the nuanced distinctions between human and AI texts across text segments.

Whether LLMs struggle with or excel at incorporating linguistic ingenuity across different text segments carries substantial implications for determining their potential as effective creative assistants to humans.

While AI texts can approximate the body segment better due to its increased length, a closer examination reveals a pronounced disparity, highlighting the importance of this segment in AI text detection.

Additionally, human texts ex…

3 days, 17 hours назад @ paperswithcode.com
/shawnricecake/ Efficient Reasoning with Hidden Thinking
/shawnricecake/ Efficient Reasoning with Hidden Thinking /shawnricecake/ Efficient Reasoning with Hidden Thinking

Chain-of-Thought (CoT) reasoning has become a powerful framework for improving complex problem-solving capabilities in Multimodal Large Language Models (MLLMs).

However, the verbose nature of textual reasoning introduces significant inefficiencies.

In this work, we propose $\textbf{Heima}$ (as hidden llama), an efficient reasoning framework that leverages reasoning CoTs at hidden latent space.

Meanwhile, we design corresponding Heima Decoder with traditional Large Language Models (LLMs) to adaptively interpret the hidden representations into variable-length textual sequence, reconstructing reasoning processes that closely resemble the original CoTs.

Moreover, the effective reconstruction of…

3 days, 17 hours назад @ paperswithcode.com
/lamdasz-ml/ TabFSBench: Tabular Benchmark for Feature Shifts in Open Environment
/lamdasz-ml/ TabFSBench: Tabular Benchmark for Feature Shifts in Open Environment /lamdasz-ml/ TabFSBench: Tabular Benchmark for Feature Shifts in Open Environment

Tabular data is widely utilized in various machine learning tasks.

Current tabular learning research predominantly focuses on closed environments, while in real-world applications, open environments are often encountered, where distribution and feature shifts occur, leading to significant degradation in model performance.

Previous research has primarily concentrated on mitigating distribution shifts, whereas feature shifts, a distinctive and unexplored challenge of tabular data, have garnered limited attention.

To this end, this paper conducts the first comprehensive study on feature shifts in tabular data and introduces the first tabular feature-shift benchmark (TabFSBench).

TabFSBench eva…

3 days, 17 hours назад @ paperswithcode.com
/dddavid4real/ Context Matters: Query-aware Dynamic Long Sequence Modeling of Gigapixel Images
/dddavid4real/ Context Matters: Query-aware Dynamic Long Sequence Modeling of Gigapixel Images /dddavid4real/ Context Matters: Query-aware Dynamic Long Sequence Modeling of Gigapixel Images

Whole slide image (WSI) analysis presents significant computational challenges due to the massive number of patches in gigapixel images.

While transformer architectures excel at modeling long-range correlations through self-attention, their quadratic computational complexity makes them impractical for computational pathology applications.

Existing solutions like local-global or linear self-attention reduce computational costs but compromise the strong modeling capabilities of full self-attention.

In this work, we propose Querent, i.e., the query-aware long contextual dynamic modeling framework, which maintains the expressive power of full self-attention while achieving practical efficiency.…

3 days, 17 hours назад @ paperswithcode.com
/kbyrski/ RaySplats: Ray Tracing based Gaussian Splatting
/kbyrski/ RaySplats: Ray Tracing based Gaussian Splatting /kbyrski/ RaySplats: Ray Tracing based Gaussian Splatting

3D Gaussian Splatting (3DGS) is a process that enables the direct creation of 3D objects from 2D images.

However, a significant limitation of 3DGS is the challenge of incorporating light and shadow reflections, primarily due to the utilization of rasterization rather than ray tracing for rendering.

This paper introduces RaySplats, a model that employs ray-tracing based Gaussian Splatting.

Rather than utilizing the projection of Gaussians, our method employs a ray-tracing mechanism, operating directly on Gaussian primitives represented by confidence ellipses with RGB colors.

In practice, we compute the intersection between ellipses and rays to construct ray-tracing algorithms, facilitating t…

3 days, 17 hours назад @ paperswithcode.com
/aei13/ Distorting Embedding Space for Safety: A Defense Mechanism for Adversarially Robust Diffusion Models
/aei13/ Distorting Embedding Space for Safety: A Defense Mechanism for Adversarially Robust Diffusion Models /aei13/ Distorting Embedding Space for Safety: A Defense Mechanism for Adversarially Robust Diffusion Models

Text-to-image diffusion models show remarkable generation performance following text prompts, but risk generating Not Safe For Work (NSFW) contents from unsafe prompts.

In this paper, we propose a novel approach called Distorting Embedding Space (DES), a text encoder-based defense mechanism that effectively tackles these issues through innovative embedding space control.

DES transforms unsafe embeddings, extracted from a text encoder using unsafe prompts, toward carefully calculated safe embedding regions to prevent unsafe contents generation, while reproducing the original safe embeddings.

These methods ensure both robust defense and high-quality image generation.

Extensive experiments on …

3 days, 17 hours назад @ paperswithcode.com
/ireklos/ PathE: Leveraging Entity-Agnostic Paths for Parameter-Efficient Knowledge Graph Embeddings
/ireklos/ PathE: Leveraging Entity-Agnostic Paths for Parameter-Efficient Knowledge Graph Embeddings /ireklos/ PathE: Leveraging Entity-Agnostic Paths for Parameter-Efficient Knowledge Graph Embeddings

Knowledge Graphs (KGs) store human knowledge in the form of entities (nodes) and relations, and are used extensively in various applications.

KG embeddings are an effective approach to addressing tasks like knowledge discovery, link prediction, and reasoning.

This is often done by allocating and learning embedding tables for all or a subset of the entities.

As this scales linearly with the number of entities, learning embedding models in real-world KGs with millions of nodes can be computationally intractable.

Rather than storing entity embeddings, we learn to compute them by leveraging multiple entity-relation paths to contextualise individual entities within triples.

3 days, 17 hours назад @ paperswithcode.com
💼 University and corporation labs
DeepMind DeepMind
последний пост 1 day, 7 hours назад
Gemini 2.0 is now available to everyone
Gemini 2.0 is now available to everyone Gemini 2.0 is now available to everyone

And last week, we made an updated 2.0 Flash available to all users of the Gemini app on desktop and mobile, helping everyone discover new ways to create, interact and collaborate with Gemini.

Today, we’re making the updated Gemini 2.0 Flash generally available via the Gemini API in Google AI Studio and Vertex AI.

It is available in Google AI Studio and Vertex AI, and in the Gemini app for Gemini Advanced users.

Finally, 2.0 Flash Thinking Experimental will be available to Gemini app users in the model dropdown on desktop and mobile.

Try Gemini 2.0 Flash in the Gemini app or the Gemini API in Google AI Studio and Vertex AI.

1 day, 7 hours назад @ blog.google
Updating the Frontier Safety Framework
Updating the Frontier Safety Framework Updating the Frontier Safety Framework

Responsibility & Safety Updating the Frontier Safety Framework ShareCopy link ×Our next iteration of the FSF sets out stronger security protocols on the path to AGI AI is a powerful tool that is helping to unlock new breakthroughs and make significant progress on some of the biggest challenges of our time, from climate change to drug discovery.

That’s why we introduced the first iteration of our Frontier Safety Framework last year - a set of protocols to help us stay ahead of possible severe risks from powerful frontier AI models.

We have also implemented the Framework in our safety and governance processes for evaluating frontier models such as Gemini 2.0.

As a result of this work, today w…

2 days, 6 hours назад @ deepmind.google
FACTS Grounding: A new benchmark for evaluating the factuality of large language models
FACTS Grounding: A new benchmark for evaluating the factuality of large language models FACTS Grounding: A new benchmark for evaluating the factuality of large language models

They can “hallucinate” false information, particularly when given complex inputs.

Today, we’re introducing FACTS Grounding, a comprehensive benchmark for evaluating the ability of LLMs to generate responses that are not only factually accurate with respect to given inputs, but also sufficiently detailed to provide satisfactory answers to user queries.

We hope our benchmark will spur industry-wide progress on factuality and grounding.

To track progress, we’re also launching the FACTS leaderboard on Kaggle.

We’ve already tested leading LLMs using FACTS Grounding and have populated the initial leaderboard with their grounding scores.

1 month, 3 weeks назад @ deepmind.google
State-of-the-art video and image generation with Veo 2 and Imagen 3
State-of-the-art video and image generation with Veo 2 and Imagen 3 State-of-the-art video and image generation with Veo 2 and Imagen 3

Earlier this year, we introduced our video generation model, Veo, and our latest image generation model, Imagen 3.

Since then, it’s been exciting to watch people bring their ideas to life with help from these models: YouTube creators are exploring the creative possibilities of video backgrounds for their YouTube Shorts, enterprise customers are enhancing creative workflows on Vertex AI and creatives are using VideoFX and ImageFX to tell their stories.

Together with collaborators ranging from filmmakers to businesses, we’re continuing to develop and evolve these technologies.

Today we're introducing a new video model, Veo 2, and the latest version of Imagen 3, both of which achieve state-of-…

1 month, 3 weeks назад @ blog.google
Introducing Gemini 2.0: our new AI model for the agentic era
Introducing Gemini 2.0: our new AI model for the agentic era Introducing Gemini 2.0: our new AI model for the agentic era

Today we’re excited to launch our next era of models built for this new agentic era: introducing Gemini 2.0, our most capable model yet.

Starting today our Gemini 2.0 Flash experimental model will be available to all Gemini users.

It's available in Gemini Advanced today.

TPUs powered 100% of Gemini 2.0 training and inference, and today Trillium is generally available to customers so they can build with it too.

If Gemini 1.0 was about organizing and understanding information, Gemini 2.0 is about making it much more useful.

1 month, 3 weeks назад @ blog.google
Google DeepMind at NeurIPS 2024
Google DeepMind at NeurIPS 2024 Google DeepMind at NeurIPS 2024

Google Research Scientist David Warde and Google DeepMind Research Scientist Ian Goodfellow will present on Generative Adversarial Nets.

Teams across Google DeepMind will present more than 150 new papers on topics ranging from AI agents and generative media to innovative learning approaches.

Building adaptive, smart, and safe AI Agents LLM-based AI agents are showing promise in carrying out digital tasks via natural language commands.

Yet their success depends on precise interaction with complex user interfaces, which requires extensive training data.

AI agents trained using this dataset showed significant performance gains which we hope helps advance research into more general AI agents.

2 months назад @ deepmind.google
GenCast predicts weather and the risks of extreme conditions with state-of-the-art accuracy
GenCast predicts weather and the risks of extreme conditions with state-of-the-art accuracy GenCast predicts weather and the risks of extreme conditions with state-of-the-art accuracy

Because a perfect weather forecast is not possible, scientists and weather agencies use probabilistic ensemble forecasts, where the model predicts a range of likely weather scenarios.

The evolution of AI weather models GenCast marks a critical advance in AI-based weather prediction that builds on our previous weather model, which was deterministic, and provided a single, best estimate of future weather.

Better forecasts of extreme weather, such as heat waves or strong winds, enable timely and cost-effective preventative actions.

GenCast offers greater value than ENS when making decisions about preparations for extreme weather, across a wide range of decision-making scenarios.

Advanced forec…

2 months назад @ deepmind.google
Genie 2: A large-scale foundation world model
Genie 2: A large-scale foundation world model Genie 2: A large-scale foundation world model

Today we introduce Genie 2, a foundation world model capable of generating an endless variety of action-controllable, playable 3D environments for training and evaluating embodied agents.

Based on a single prompt image, it can be played by a human or AI agent using keyboard and mouse inputs.

Games play a key role in the world of artificial intelligence (AI) research.

However, training more general embodied agents has been traditionally bottlenecked by the availability of sufficiently rich and diverse training environments.

As we show, Genie 2 could enable future agents to be trained and evaluated in a limitless curriculum of novel worlds.

2 months назад @ deepmind.google
AlphaQubit tackles one of quantum computing’s biggest challenges
AlphaQubit tackles one of quantum computing’s biggest challenges AlphaQubit tackles one of quantum computing’s biggest challenges

Quantum computers have the potential to revolutionize drug discovery, material design and fundamental physics — that is, if we can get them to work reliably.

Certain problems, which would take a conventional computer billions of years to solve, would take a quantum computer just hours.

If we want to make quantum computers more reliable, especially at scale, we need to accurately identify and correct these errors.

In a paper published today in Nature, we introduce AlphaQubit, an AI-based decoder that identifies quantum computing errors with state-of-the-art accuracy.

This collaborative work brought together Google DeepMind’s machine learning knowledge and Google Quantum AI’s error correction…

2 months, 2 weeks назад @ blog.google
The AI for Science Forum: A new era of discovery
The AI for Science Forum: A new era of discovery The AI for Science Forum: A new era of discovery

AI is revolutionizing the landscape of scientific research, enabling advancements at a pace that was once unimaginable — from accelerating drug discovery to designing new materials for clean energy technologies.

The AI for Science Forum — co-hosted by Google DeepMind and the Royal Society — brought together the scientific community, policymakers, and industry leaders to explore the transformative potential of AI to drive scientific breakthroughs, address the world's most pressing challenges, and lead to a new era of discovery.

2 months, 2 weeks назад @ blog.google
Pushing the frontiers of audio generation
Pushing the frontiers of audio generation Pushing the frontiers of audio generation

Technologies Pushing the frontiers of audio generation ShareCopy link ×Our pioneering speech generation technologies are helping people around the world interact with more natural, conversational and intuitive digital assistants and AI tools.

Pioneering techniques for audio generation For years, we've been investing in audio generation research and exploring new ways for generating more natural dialogue in our products and experimental tools.

Download audio Audio clip of two speakers telling a funny story, with laughter at the punchline.

Download audio Audio clip of two speakers expressing excitement about a surprise birthday party.

Scaling our audio generation models Scaling our single-spe…

3 months, 1 week назад @ deepmind.google
New generative AI tools open the doors of music creation
New generative AI tools open the doors of music creation New generative AI tools open the doors of music creation

Technologies New generative AI tools open the doors of music creation ShareCopy link ×Our latest AI music technologies are now available in MusicFX DJ, Music AI Sandbox and YouTube Shorts For nearly a decade, our teams have been exploring how artificial intelligence (AI) can support the creative process, building tools that empower enthusiasts and professionals to discover new forms of creative expression.

Their input has been guiding our state-of-the-art generative music experiments, and helping us ensure that our new generative AI tools responsibly open the doors of music creation to everyone.

We’re also announcing updates to our music AI toolkit, called Music AI Sandbox, and highlighting…

3 months, 2 weeks назад @ deepmind.google
Demis Hassabis & John Jumper awarded Nobel Prize in Chemistry
Demis Hassabis & John Jumper awarded Nobel Prize in Chemistry Demis Hassabis & John Jumper awarded Nobel Prize in Chemistry

This morning, Co-founder and CEO of Google DeepMind and Isomorphic Labs Sir Demis Hassabis, and Google DeepMind Senior Research Scientist Dr. John Jumper were co-awarded the 2024 Nobel Prize in Chemistry for their work developing AlphaFold, a groundbreaking AI system that predicts the 3D structure of proteins from their amino acid sequences.

AlphaFold’s predictions, made freely available through the AlphaFold Protein Structure Database, have given more than 2 million scientists and researchers from 190 countries a powerful tool for making new discoveries.

In a statement released after informed of the news, Demis Hassabis said:"Receiving the Nobel Prize is the honour of a lifetime.

After rec…

4 months назад @ deepmind.google
How AlphaChip transformed computer chip design
How AlphaChip transformed computer chip design How AlphaChip transformed computer chip design

Research How AlphaChip transformed computer chip design ShareCopy link ×Our AI method has accelerated and optimized chip design, and its superhuman chip layouts are used in hardware around the world In 2020, we released a preprint introducing our novel reinforcement learning method for designing chip layouts, which we later published in Nature and open sourced.

Today, we’re publishing a Nature addendum that describes more about our method and its impact on the field of chip design.

Computer chips have fueled remarkable progress in artificial intelligence (AI), and AlphaChip returns the favor by using AI to accelerate and optimize chip design.

Using AI to design Google’s AI accelerator chips…

4 months, 1 week назад @ deepmind.google
Updated production-ready Gemini models, reduced 1.5 Pro pricing, increased rate limits, and more
Updated production-ready Gemini models, reduced 1.5 Pro pricing, increased rate limits, and more Updated production-ready Gemini models, reduced 1.5 Pro pricing, increased rate limits, and more

Developers can access our latest models for free via Google AI Studio and the Gemini API.

With the latest updates, 1.5 Pro and Flash are now better, faster, and more cost-efficient to build with in production.

For more details on migrating to the latest versions of Gemini 1.5 Pro and 1.5 Flash, check out the Gemini API models page.

Gemini 1.5 Pro We continue to be blown away with the creative and useful applications of Gemini 1.5 Pro’s 2 million token long context window and multimodal capabilities.

Increased rate limits To make it even easier for developers to build with Gemini, we are increasing the paid tier rate limits for 1.5 Flash to 2,000 RPM and increasing 1.5 Pro to 1,000 RPM, up f…

4 months, 2 weeks назад @ developers.googleblog.com
Google
последний пост 6 часов назад
Announcing public beta of Gen AI Toolbox for Databases
Announcing public beta  of Gen AI Toolbox for Databases Announcing public beta of Gen AI Toolbox for Databases

Today, we are thrilled to announce the public beta launch of Gen AI Toolbox for Databases in partnership with LangChain, the leading orchestration framework for developers building large language model (LLM) applications.

Gen AI Toolbox for Databases (Toolbox) is an open-source server that empowers application developers to connect production-grade, agent-based generative AI (gen AI) applications to databases.

It streamlines the creation, deployment, and management of sophisticated gen AI tools capable of querying databases with secure access, robust observability, scalability, and comprehensive manageability.

It also provides connectivity to popular open-source databases such as PostgreSQL…

6 часов назад @ cloud.google.com
Designing sustainable AI: A deep dive into TPU efficiency and lifecycle emissions
Designing sustainable AI: A deep dive into TPU efficiency and lifecycle emissions Designing sustainable AI: A deep dive into TPU efficiency and lifecycle emissions

Today we’re releasing a first-of-its-kind study1 on the lifetime emissions of our Tensor Processing Unit (TPU) hardware.

These measurements provide a snapshot of the average, chip-level carbon intensity of Google’s TPU hardware, and enable us to compare efficiency across generations.

CCI quantifies an AI accelerator chip’s carbon emissions per unit of computation (measured in grams of CO2e per Exa-FLOP).3 Lower CCI scores mean lower emissions from the AI hardware platform for a given AI workload — for example training an AI model.

This underscores the importance of improving the energy efficiency of AI chips and reducing the carbon intensity of the electricity that powers them.

Our signific…

1 day, 6 hours назад @ cloud.google.com
How to build a strong brand logo with Imagen 3 and Gemini
How to build a strong brand logo with Imagen 3 and Gemini How to build a strong brand logo with Imagen 3 and Gemini

Last year we announced Imagen 3, our highest quality image generation model.

Imagen 3 is available to Vertex AI customers, which means businesses can create high quality images that reflect their own brand style and logos for use in marketing, advertising, or product design.

Today, we’ll share how you can build your brand style with a logo using Imagen 3, Gemini, and the Python Library Pillow.

First, use Imagen 3 to generate visual optionsImagen 3 generates the most realistic and highest quality images from simple text prompts, surpassing previous versions of Imagen in detail, lighting, and artifact reduction.

The new Imagen 3 generation model (002), delivers even higher visual appeal, prom…

1 day, 6 hours назад @ cloud.google.com
Helping our partners co-market faster with AI
Helping our partners co-market faster with AI Helping our partners co-market faster with AI

You find a pre-built campaign in Partner Marketing Studio that aligns with your goals, but you need to tailor for the healthcare industry.

Each bite-sized episode features Google marketers who've successfully integrated AI into their daily work, using tools like Gemini, Gemini for Workspace, NotebookLM, and AI Studio.

To find the AI Boost Bites training series and start using the AI features, login to Partner Marketing Studio.

Google Cloud speakers: Request a Google Cloud speaker for your events.

What our pilot partners are saying"With the new AI features in Partner Marketing Studio, we can create more targeted industry and persona-based versions of our Google Cloud marketing campaigns auto…

2 days, 6 hours назад @ cloud.google.com
Improving model performance with PyTorch/XLA 2.6
Improving model performance with PyTorch/XLA 2.6 Improving model performance with PyTorch/XLA 2.6

Host offloadingAnother powerful tool for memory optimization in PyTorch/XLA is host offloading.

This suggests your model is "tracing bound," meaning performance is limited by the speed of tracing operations.

You'll now find builds with both the pre-C++11 ABI, which remains the default to match PyTorch upstream, and the more modern C++11 ABI.

So if we have a higher goodput measurement for the same model on the same hardware, that indicates better performance of the model.

An example of using a C++11 ABI docker image in your Dockerfile might look something like:

6 days, 6 hours назад @ cloud.google.com
Blackwell is here — new A4 VMs powered by NVIDIA B200 now in preview
Blackwell is here — new A4 VMs powered by NVIDIA B200 now in preview Blackwell is here — new A4 VMs powered by NVIDIA B200 now in preview

Today, we’re excited to bring the highly-anticipated NVIDIA Blackwell GPUs to Google Cloud with the preview of A4 VMs, powered by NVIDIA HGX B200.

Combined with our datacenter-wide 4-way rail-aligned network, A4 VMs deliver non-blocking 3.2 Tbps of GPU-to-GPU traffic with RDMA over Converged Ethernet (RoCE).

Combined with our datacenter-wide 4-way rail-aligned network, A4 VMs deliver non-blocking 3.2 Tbps of GPU-to-GPU traffic with RDMA over Converged Ethernet (RoCE).

Vertex AI : A4 VMs will be accessible through Vertex AI, our fully managed, unified AI development platform for building and using generative AI, and which is powered by the AI Hypercomputer architecture under the hood.

Hudson…

6 days, 7 hours назад @ cloud.google.com
Introducing agent evaluation in Vertex AI Gen AI evaluation service
Introducing agent evaluation in Vertex AI Gen AI evaluation service Introducing agent evaluation in Vertex AI Gen AI evaluation service

Evaluate agents using Vertex AI Gen AI evaluation serviceOur evaluation metrics can be grouped in two categories: final response and trajectory evaluation.

Compatibility meets flexibilityVertex AI Gen AI evaluation service supports a variety of agent architectures.

With today’s launch, you can evaluate agents built with Reasoning Engine (LangChain on Vertex AI), the managed runtime for your agentic applications on Vertex AI.

For maximum flexibility, you can evaluate agents using a custom function that processes prompts and returns responses.

To make your evaluation experience easier, we offer native agent inference and automatically log all results in Vertex AI experiments.

1 week, 6 days назад @ cloud.google.com
Announcing smaller machine types for A3 High VMs
Announcing smaller machine types for A3 High VMs Announcing smaller machine types for A3 High VMs

You can use A3 High VMs powered by NVIDIA H100 80GB GPUs in multiple generally available machine types of 1NEW, 2NEW, 4NEW, and 8 GPUs.

Accessing smaller H100 machine typesAll A3 machine types are available through the fully managed Vertex AI, as nodes through Google Kubernetes Engine (GKE), and as VMs through Google Compute Engine.

The 1, 2, and 4 A3 High GPU machine types are available as Spot VMs and through Dynamic Workload Scheduler (DWS) Flex Start mode.

You can use the 1, 2, and 4 A3 High GPU machine types through both GKE Standard and GKE Autopilot modes of operation.

Below are two examples of creating node pools in your GKE cluster with a3-highgpu-1g machine type using Spot VMs and…

1 week, 6 days назад @ cloud.google.com
How L’Oréal Tech Accelerator built its end-to-end MLOps platform
How L’Oréal Tech Accelerator built its end-to-end MLOps platform How L’Oréal Tech Accelerator built its end-to-end MLOps platform

To adapt to this reality, L'Oréal has established itself as a leader in Beauty Tech, promoting personalized, inclusive, and responsible beauty accessible to all, under the banner "Beauty for Each, powered by Beauty Tech."

This convergence of Beauty Tech is evident in augmented beauty products, smart devices, enhanced marketing, online and offline services, and digital platforms, all powered by information and communication technologies, data, and artificial intelligence.

L'Oréal, the world’s largest cosmetics company, has for years leveraged AI to enhance digital solutions for its employees and provide personalized experiences for customers.

In this blog, we will describe how L'Oréal’s Tech…

2 weeks назад @ cloud.google.com
New year, new updates to AI Hypercomputer
New year, new updates to AI Hypercomputer New year, new updates to AI Hypercomputer

A3 Ultra, with NVIDIA H200 GPUs is a new addition to the A3 family of NVIDIA Hopper GPU-accelerated VMs with twice the GPU-to-GPU network bandwidth and twice the high bandwidth memory (HBM) compared to A3 Mega with NVIDIA H100 GPUs.

A3 Ultra VMs offer the best performance in the A3 family.

Combined with our datacenter-wide 4-way rail-aligned network, A3 Ultra VMs deliver up to 3.2 Tbps of non-blocking GPU-to-GPU communication with RDMA over Converged Ethernet (RoCE).

A3 Ultra VMs are also available through GKE, which provides an open, portable, extensible, and highly scalable platform for training and serving AI workloads.

To try out A3 Ultra VMs, you can easily create a cluster with GKE or…

3 weeks назад @ cloud.google.com
Unlock multimodal search at scale: Combine text & image power with Vertex AI
Unlock multimodal search at scale: Combine text & image power with Vertex AI Unlock multimodal search at scale: Combine text & image power with Vertex AI

Instead, we can leverage image embeddings and combine the search results with text data in Vertex AI Search.

Google Cloud's Vertex AI platform provides a comprehensive set of tools for building and deploying machine learning solutions, including powerful search capabilities:Vertex AI search: A highly scalable and feature-rich engine for many types of search.

Vertex AI multimodal embedding API: This is used to generate image embeddings (numerical representations of images).

Vertex AI Vector Search: This is used as the vector database to store the embeddings with metadata information for searching.

Our ensemble approach: Text + image powerTo create our multimodal search engine, we'll use an e…

3 weeks, 2 days назад @ cloud.google.com
Trading in the Cloud: Lessons from Deutsche Börse Group’s cloud-native trading engine
Trading in the Cloud: Lessons from Deutsche Börse Group’s cloud-native trading engine Trading in the Cloud: Lessons from Deutsche Börse Group’s cloud-native trading engine

Digital markets demand new trading systemsToday, Deutsche Börse Group successfully operates high-volume/low-latency trading venues — such as Xetra, Börse Frankfurt, Eurex, and the European Energy Exchange, as well as partner exchanges — by using proven high-performance architectures.

The need for a new trading engine, and the desire to make it the cornerstone and first component of Deutsche Börse Group’s emerging Digital Asset Business Platform, stems from changing market structures.

Market participants also demand choice of market access, including internet connectivity to execute trades anytime, anywhere.

Finally, a new trading engine would have to meet not only these new requirements, bu…

3 weeks, 2 days назад @ cloud.google.com
How inference at the edge unlocks new AI use cases for retailers
How inference at the edge unlocks new AI use cases for retailers How inference at the edge unlocks new AI use cases for retailers

How retailers can build an AI foundationRetailers can find assets to fuel their AI in all corners of the business.

Edge processing power decision point: CPU vs GPUNext, we’ll explore the critical decision on the right hardware to power your applications.

The two primary options are CPUs (Central Processing Units) and GPUs (Graphics Processing Units), each with its own strengths and weaknesses.

Consider this chart to guide your decision-making process, especially when choosing between deploying at a regional DC or at the edge.

This is less of a concern if deploying at a regional DC where power and cooling are centralized.

3 weeks, 3 days назад @ cloud.google.com
Empowering retailers with AI for commerce, marketing, supply chains, and more
Empowering retailers with AI for commerce, marketing, supply chains, and more Empowering retailers with AI for commerce, marketing, supply chains, and more

To provide customers with the most advanced ecosystem of solutions across industries, we’ve enabled these partners to easily build and scale products on our platform.

Many are deeply engaged with our AI technology to deliver new and novel AI solutions directly to our customers and theirs.

Generative AI has already had a significant impact on the retail industry by enabling businesses to run more personalized marketing campaigns, increase sales via improved search capabilities, and enhance customer service experiences through more accurate and tailored resolutions.

In many cases, AI agents are helping these businesses move beyond predictive capabilities to performing tasks autonomously.

At N…

3 weeks, 4 days назад @ cloud.google.com
Introducing Vertex AI RAG Engine: Scale your Vertex AI RAG pipeline with confidence
Introducing Vertex AI RAG Engine: Scale your Vertex AI RAG pipeline with confidence Introducing Vertex AI RAG Engine: Scale your Vertex AI RAG pipeline with confidence

Closing the gap between impressive model demos and real-world performance is crucial for successfully deploying generative AI for enterprise.

Despite the incredible capabilities of generative AI for enterprise, this perceived gap may be a barrier for many developers and enterprises to “productionize” AI.

This is where retrieval-augmented generation (RAG) becomes non-negotiable – it strengthens your enterprise applications by building trust in its AI outputs.

Today, we’re sharing the general availability of Vertex AI’s RAG Engine, a fully managed service that helps you build and deploy RAG implementations with your data and methods.

With our Vertex AI RAG Engine you will be able to:

4 weeks назад @ cloud.google.com
OpenAI
последний пост 9 months, 1 week назад
We’re bringing the Financial Times’ world-class journalism to ChatGPT
We’re bringing the Financial Times’ world-class journalism to ChatGPT We’re bringing the Financial Times’ world-class journalism to ChatGPT

“It recognises the value of our award-winning journalism and will give us early insights into how content is surfaced through AI.

“Apart from the benefits to the FT, there are broader implications for the industry.

It’s right, of course, that AI platforms pay publishers for the use of their material.

“We value the opportunity to be inside the development loop as people discover content in new ways.

As with any transformative technology, there is potential for significant advancements and major challenges, but what’s never possible is turning back time.

9 months, 1 week назад @ openai.com
Introducing more enterprise-grade features for API customers
Introducing more enterprise-grade features for API customers Introducing more enterprise-grade features for API customers

Customers with a sustained level of tokens per minute (TPM) usage on GPT-4 or GPT-4 Turbo can request access to provisioned throughput to get discounts ranging from 10–50% based on the size of the commitment.

Reduced costs on asynchronous workloads: Customers can use our new Batch API to run non-urgent workloads asynchronously.

Batch API requests are priced at 50% off shared prices, offer much higher rate limits, and return results within 24 hours.

We plan to keep adding new features focused on enterprise-grade security, administrative controls, and cost management.

For more information on these launches, visit our API documentation or get in touch with our team to discuss custom solution…

9 months, 2 weeks назад @ openai.com
OpenAI’s commitment to child safety: adopting safety by design principles
OpenAI’s commitment to child safety: adopting safety by design principles OpenAI’s commitment to child safety: adopting safety by design principles

OpenAI, alongside industry leaders including Amazon, Anthropic, Civitai, Google, Meta, Metaphysic, Microsoft, Mistral AI, and Stability AI, has committed to implementing robust child safety measures in the development, deployment, and maintenance of generative AI technologies as articulated in the Safety by Design principles.

By adopting comprehensive Safety by Design principles, OpenAI and our peers are ensuring that child safety is prioritized at every stage in the development of AI.

Responsibly source our training datasets, detect and remove child sexual abuse material (CSAM) and child sexual exploitation material (CSEM) from training data, and report any confirmed CSAM to the relevant a…

9 months, 2 weeks назад @ openai.com
Introducing OpenAI Japan
Introducing OpenAI Japan Introducing OpenAI Japan

Our new local presence also gets us closer to leading businesses like Daikin, Rakuten, and TOYOTA Connected who are using ChatGPT Enterprise to automate complex business processes, assist in data analysis, and optimize internal reporting.

ChatGPT also helps accelerate the efforts of local governments, such as Yokosuka City, which is leveraging the technology to improve the efficiency of public services in Japan.

Over the past year, the city has gradually provided ChatGPT access to almost all city employees, and 80% have reported increases in productivity.

Now Yokosuka City has formed a network with 21 local governments—including the Tokyo Metropolitan Government and the City of Kobe—to …

9 months, 4 weeks назад @ openai.com
Microsoft Microsoft
последний пост 1 day, 5 hours назад
Advances to low-bit quantization enable LLMs on edge devices
Advances to low-bit quantization enable LLMs on edge devices Advances to low-bit quantization enable LLMs on edge devices

LUT Tensor Core hardware architecture: Introduces a cutting-edge design for next-generation AI hardware, tailored for low-bit quantization and mixed-precision computations.

By eliminating dequantization and lowering computational costs, T-MAC enables efficient inference of low-bit LLMs on resource-constrained edge devices.

The LUT Tensor Core workflowEvaluating LUT Tensor CoreTesting LUT Tensor Core on low-bit LLMs, such as BitNet and Llama, showed significant performance gains, achieving 6.93 times the inference speed while using just 38.3% of the area of a traditional Tensor Core.

As AI models grow in scale and complexity, LUT Tensor Core enables low-bit LLMs to be applied in new and dive…

1 day, 5 hours назад @ microsoft.com
Research Focus: Week of January 27, 2025
Research Focus: Week of January 27, 2025 Research Focus: Week of January 27, 2025

And we invite you to join an upcoming workshop: LLM4Eval@WSDM 2025: Large Language Models for Evaluation in Information Retrieval.

Listen now Opens in a new tabNEW RESEARCH Symbolic Automata: Omega-Regularity Modulo Theories Symbolic automata are finite state automata that support potentially infinite alphabets, such as the set of rational numbers, generally applied to regular expressions and languages over finite words.

In symbolic automata (or automata modulo A), an alphabet is represented by an effective Boolean algebra A, supported by a decision procedure for satisfiability.

In a recent paper: Symbolic Automata: Omega-Regularity Modulo Theories, researchers from Microsoft generalize sym…

6 days, 5 hours назад @ microsoft.com
Ideas: Bug hunting with Shan Lu
Ideas: Bug hunting with Shan Lu Ideas: Bug hunting with Shan Lu

LU: And I still feel like, wow, you know, I feel so … I feel like I’m inspired every day!

HUIZINGA: Yeah, yeah, yeah, yeah.

And then once you learn that language, right, then you have to learn about how to write proofs in that special language.

HUIZINGA: Yeah, yeah, yeah.

And my colleagues are also working on using AI, right, to automatically do performance tuning.

2 weeks назад @ microsoft.com
Research Focus: Week of January 13, 2025
Research Focus: Week of January 13, 2025 Research Focus: Week of January 13, 2025

NEW RESEARCH AI meets materials discovery Two of the transformative tools that play a central role in Microsoft’s work on AI for science are MatterGen and MatterSim.

Read the paperNEW RESEARCH RD-Agent: An open-source solution for smarter R&D Research and development (R&D) plays a pivotal role in boosting industrial productivity.

Read the articleMicrosoft research podcast What’s Your Story: Lex Story Model maker and fabricator Lex Story helps bring research to life through prototyping.

Listen now Opens in a new tabMicrosoft Research | In case you missed it Microsoft Research 2024: A year in review December 20, 2024 Microsoft Research did extraordinary work this year, using AI and scientific…

2 weeks, 6 days назад @ microsoft.com
Ideas: AI for materials discovery with Tian Xie and Ziheng Lu
Ideas: AI for materials discovery with Tian Xie and Ziheng Lu Ideas: AI for materials discovery with Tian Xie and Ziheng Lu

And now you can use this loop to design materials really quickly.

XIE: So you can really think about MatterSim and MatterGen accelerating different parts of materials discovery process.

They are also both foundation AI models, meaning they can both be used for a broad range of materials design problems.

Really, really a lot.

Yeah, I really, really like the example that Ziheng mentioned about the educational purposes.

3 weeks назад @ microsoft.com
MatterGen: A new paradigm of materials design with generative AI
MatterGen: A new paradigm of materials design with generative AI MatterGen: A new paradigm of materials design with generative AI

MatterGen enables a new paradigm of generative AI-assisted materials design that allows for efficient exploration of materials, going beyond the limited set of known ones.

MatterGen can be fine-tuned to generate materials under different design requirements such as specific chemistry, crystal symmetry, or materials’ properties.

AI emulator and generator flywheelMatterGen presents a new opportunity for AI accelerated materials design, complementing our AI emulator MatterSim.

Looking aheadMatterGen represents a new paradigm of materials design enabled by generative AI technology.

That’s why we are interested in understanding the impact that MatterGen could have on materials discovery,” said C…

3 weeks назад @ microsoft.com
AutoGen v0.4: Reimagining the foundation of agentic AI for scale, extensibility, and robustness
AutoGen v0.4: Reimagining the foundation of agentic AI for scale, extensibility, and robustness AutoGen v0.4: Reimagining the foundation of agentic AI for scale, extensibility, and robustness

Modular and extensible : Users can easily customize systems with pluggable components, including custom agents, tools, memory, and models.

: Users can easily customize systems with pluggable components, including custom agents, tools, memory, and models.

Core: The foundational building blocks for an event-driven agentic system.

In addition to the framework, AutoGen 0.4 includes upgraded programming tools and applications, designed to support developers in building and experimenting with AutoGen.

Third-party component galleries: Import and use custom agents, tools, and workflows from external galleries to extend functionality.

3 weeks, 2 days назад @ microsoft.com
AIOpsLab: Building AI agents for autonomous clouds
AIOpsLab: Building AI agents for autonomous clouds AIOpsLab: Building AI agents for autonomous clouds

To tackle these challenges, recent research on using AIOps agents for cloud operations—such as AI agents for incident root cause analysis (RCA) or triaging—has relied on proprietary services and datasets.

Users developing agents for cloud operations tasks with Azure AI Agent Service can evaluate and improve them using AIOpsLab.

This calls for a standardized and principled research framework for building, testing, comparing, and improving AIOps agents.

The AIOpsLab research paper has been accepted at SoCC’24 (the annual ACM Symposium on Cloud Computing).

Our approach integrates application and domain knowledge to create adaptable policies and “oracles” compatible with AIOps scenarios.

1 month, 2 weeks назад @ microsoft.com
Ideas: AI and democracy with Madeleine Daepp and Robert Osazuwa Ness
Ideas: AI and democracy with Madeleine Daepp and Robert Osazuwa Ness Ideas: AI and democracy with Madeleine Daepp and Robert Osazuwa Ness

DAEPP: You know, we didn’t really think about the term fraud until we started prepping for this interview with you.

BADANES: Right, right.

One of the things that I get asked a lot is, why can’t we just build good AI to detect bad AI, right?

BADANES: So next time my kids are in a fight, I’m going to point them to Copilot and say, work with Copilot to mediate.

[LAUGHS] No, that’s really, really interesting.

1 month, 2 weeks назад @ microsoft.com
Research Focus: Week of December 16, 2024
Research Focus: Week of December 16, 2024 Research Focus: Week of December 16, 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.

Read the paperon-demand event Microsoft Research Forum Episode 4 Learn about the latest multimodal AI models, advanced benchmarks for AI evaluation and model self-improvement, and an entirely new kind of computer for AI inference and hard optimization.

In a recent paper: RedCode: Risky Code Execution and Generation Benchmark for Code Agents, published at NeurIPS 2024, researchers from Microsoft and external colleagues propose comprehensive and practical evaluations on the safety of code agents.

This res…

1 month, 2 weeks назад @ microsoft.com
NeurIPS 2024: The co-evolution of AI and systems with Lidong Zhou
NeurIPS 2024: The co-evolution of AI and systems with Lidong Zhou NeurIPS 2024: The co-evolution of AI and systems with Lidong Zhou

Earlier today, Lidong gave a keynote here at NeurIPS on the co-evolution of AI and systems engineering.

One dimension is that the scale of the AI systems that we have to support.

And the other dimension is if you look at AI systems, it’s actually a whole-stack kind of design.

STRICKLAND: Yeah, yeah.

ZHOU: Yeah, I think in terms of AI systems, I’m certainly pretty excited about what we can do together, you know, with a combination of AI and systems.

1 month, 3 weeks назад @ microsoft.com
PromptWizard: The future of prompt optimization through feedback-driven self-evolving prompts
PromptWizard: The future of prompt optimization through feedback-driven self-evolving prompts PromptWizard: The future of prompt optimization through feedback-driven self-evolving prompts

The question then becomes: How can we make prompt optimization faster, more accessible, and more adaptable across diverse tasks?

Download PromptWizardTo address this challenge, we developed PromptWizard (PW), a research framework that automates and streamlines the process of prompt optimization.

Joint optimization and synthesis of diverse examples: PW generates synthetic examples that are not only robust and diverse but also task-aware.

Stage 1: Refinement of prompt instructionThe first stage focuses on refining the task instructions of a prompt.

Through the critique-and-synthesis mechanism, PromptWizard ensures alignment between the prompt and examples, simultaneously synthesizing new exam…

1 month, 3 weeks назад @ microsoft.com
Moving to GraphRAG 1.0 – Streamlining ergonomics for developers and users
Moving to GraphRAG 1.0 – Streamlining ergonomics for developers and users Moving to GraphRAG 1.0 – Streamlining ergonomics for developers and users

Introducing GraphRAG 1.0Microsoft debuted (opens in new tab) the pre-release version of GraphRAG (opens in new tab) in July 2024 to advance AI use in complex domains.

The original CLI was intended as a starter demo for users to try GraphRAG on a sample dataset.

Our original data model stored all embeddings within the parquet output files after data ingestion and indexing.

This streamlining has also reduced the in-memory footprint of the pipeline, enabling users to index and analyze larger datasets with GraphRAG.

MigratingWe recommend users migrate to GraphRAG 1.0, which offers a streamlined experience including multiple improvements for both users and developers.

1 month, 3 weeks назад @ microsoft.com
NeurIPS 2024: AI for Science with Chris Bishop
NeurIPS 2024: AI for Science with Chris Bishop NeurIPS 2024: AI for Science with Chris Bishop

And then the second paradigm really emerged in the 17th century.

And so the third paradigm really began, I guess, sort of, in the ’50s and ’60s, the development of digital computers.

And when I think about AI for Science actually, the space of opportunity is colossal because science is, science is really just understanding more about the world around us.

And now the SMILES autoregressive model can now generate a molecule that’s an improvement on the starting molecule and knows about the protein binding.

But also if you think about [it], science is really about learning more about the world.

1 month, 3 weeks назад @ microsoft.com
Abstracts: NeurIPS 2024 with Jindong Wang and Steven Euijong Whang
Abstracts: NeurIPS 2024 with Jindong Wang and Steven Euijong Whang Abstracts: NeurIPS 2024 with Jindong Wang and Steven Euijong Whang

Today I’m talking to Jindong Wang, a senior researcher at Microsoft Research, and Steven Whang, a tenured associate professor at the Korea Advanced Institute of Science and Technology.

JINDONG WANG: OK, everybody knows that with the widespread usage of large language models, hallucination has become a crucial factor of concern.

So foreign key constraint basically requires that if there is some director mentioned in the movie table, it has to be one of the directors in the director table.

So now we can join the movie and director table and generate a bigger table.

HUIZINGA: Well, Jindong Wang and Steven Whang, thanks for joining us today, and to our listeners, thanks for tuning in.

1 month, 3 weeks назад @ microsoft.com
MIT AI MIT AI
последний пост 1 час назад
Streamlining data collection for improved salmon population management
Streamlining data collection for improved salmon population management Streamlining data collection for improved salmon population management

Young salmon fry (newly hatched salmon) make their way to the ocean, where they spend several years maturing to adulthood.

Monitoring salmon migrationIncreased human activity, including overfishing and hydropower development, together with habitat loss and climate change, have had a significant impact on salmon populations in the region.

As a result, effective monitoring and management of salmon fisheries is important to ensure balance among competing ecological, cultural, and human interests.

Beery is currently leading a research project that aims to streamline salmon monitoring using cutting-edge computer vision methods.

Automating salmon monitoring is necessary for better management of s…

1 час назад @ news.mit.edu
Aligning AI with human values
Aligning AI with human values Aligning AI with human values

Senior Audrey Lorvo is researching AI safety, which seeks to ensure increasingly intelligent AI models are reliable and can benefit humanity.

The growing field focuses on technical challenges like robustness and AI alignment with human values, as well as societal concerns like transparency and accountability.

“We need to do all we can to develop it safely.”Her participation in efforts like the AI Safety Technical Fellowship reflect her investment in understanding the technical aspects of AI safety.

The fellowship provides opportunities to review existing research on aligning AI development with considerations of potential human impact.

“The fellowship helped me understand AI safety’s techni…

2 days, 18 hours назад @ news.mit.edu
Introducing the MIT Generative AI Impact Consortium
Introducing the MIT Generative AI Impact Consortium Introducing the MIT Generative AI Impact Consortium

Enter the MIT Generative AI Impact Consortium, a collaboration between industry leaders and MIT’s top minds.

As MIT President Sally Kornbluth highlighted last year, the Institute is poised to address the societal impacts of generative AI through bold collaborations.

Generative AI continues to advance at lightning speed, but its future depends on building a solid foundation.

“Now is a perfect time to look at the fundamentals — the building blocks that will make generative AI more effective and safer to use,” adds Kraska.

“Progress on generative AI is not zero-sum, so it makes sense for this to be an open-source initiative.”While participants may approach success from different angles, they s…

3 days, 4 hours назад @ news.mit.edu
User-friendly system can help developers build more efficient simulations and AI models
User-friendly system can help developers build more efficient simulations and AI models User-friendly system can help developers build more efficient simulations and AI models

To improve the efficiency of AI models, MIT researchers created an automated system that enables developers of deep learning algorithms to simultaneously take advantage of two types of data redundancy.

Because the system utilizes a user-friendly programming language, it could optimize machine-learning algorithms for a wide range of applications.

The system could also help scientists who are not experts in deep learning but want to improve the efficiency of AI algorithms they use to process data.

Cutting out computationIn machine learning, data are often represented and manipulated as multidimensional arrays known as tensors.

Because the system is automated, it could be especially useful in …

3 days, 18 hours назад @ news.mit.edu
With generative AI, MIT chemists quickly calculate 3D genomic structures
With generative AI, MIT chemists quickly calculate 3D genomic structures With generative AI, MIT chemists quickly calculate 3D genomic structures

MIT chemists have now come up with a new way to determine those 3D genome structures, using generative artificial intelligence.

These differences in chromatin conformation help determine which genes are expressed in different cell types, or at different times within a given cell.

Over the past 20 years, scientists have developed experimental techniques for determining chromatin structures.

The AI model that they designed can quickly analyze DNA sequences and predict the chromatin structures that those sequences might produce in a cell.

“If you repeat your experiment multiple times, in different cells, you will very likely end up with a very different conformation.

6 days, 4 hours назад @ news.mit.edu
3 Questions: Modeling adversarial intelligence to exploit AI’s security vulnerabilities
3 Questions: Modeling adversarial intelligence to exploit AI’s security vulnerabilities 3 Questions: Modeling adversarial intelligence to exploit AI’s security vulnerabilities

Q: In what ways can artificial adversarial intelligence play the role of a cyber attacker, and how does artificial adversarial intelligence portray a cyber defender?

Think of the specialized, nefarious intelligence that these attackers marshal — that's adversarial intelligence.

My research goal is to replicate this specific kind of offensive or attacking intelligence, intelligence that is adversarially-oriented (intelligence that human threat actors rely upon).

Another thing stands out about adversarial intelligence: Both Tom and Jerry are able to learn from competing with one another!

Q: What are some examples in our everyday lives where artificial adversarial intelligence has kept us safe?

1 week, 1 day назад @ news.mit.edu
MIT students' works redefine human-AI collaboration
MIT students' works redefine human-AI collaboration MIT students' works redefine human-AI collaboration

Imagine a boombox that tracks your every move and suggests music to match your personal dance style.

That’s the idea behind “Be the Beat,” one of several projects from MIT course 4.043/4.044 (Interaction Intelligence), taught by Marcelo Coelho in the Department of Architecture, that were presented at the 38th annual NeurIPS (Neural Information Processing Systems) conference in December 2024.

With over 16,000 attendees converging in Vancouver, NeurIPS is a competitive and prestigious conference dedicated to research and science in the field of artificial intelligence and machine learning, and a premier venue for showcasing cutting-edge developments.

The course investigates the emerging field…

1 week, 1 day назад @ news.mit.edu
New training approach could help AI agents perform better in uncertain conditions
New training approach could help AI agents perform better in uncertain conditions New training approach could help AI agents perform better in uncertain conditions

Their results indicate that, in some situations, training a simulated AI agent in a world with less uncertainty, or “noise,” enabled it to perform better than a competing AI agent trained in the same, noisy world they used to test both agents.

The researchers studied this phenomenon by training AI agents to play Atari games, which they modified by adding some unpredictability.

They hope these results fuel additional research toward developing better training methods for AI agents.

If their exploration patterns are different, then the agent trained in the noisy environment tends to perform better.

They also want to build training environments designed to leverage the indoor training effect, …

1 week, 1 day назад @ news.mit.edu
Expanding robot perception
Expanding robot perception Expanding robot perception

The group does theoretical and experimental research, all toward expanding a robot’s awareness of its environment in ways that approach human perception.

“Perception is a big bottleneck toward getting robots to help us in the real world,” Carlone says.

“If we can add elements of cognition and reasoning to robot perception, I believe they can do a lot of good.”Expanding horizonsCarlone was born and raised near Salerno, Italy, close to the scenic Amalfi coast, where he was the youngest of three boys.

“SLAM is about figuring out the geometry of things and how a robot moves among those things,” Carlone says.

Robot perception cannot yet match what a toddler can do,” Carlone says.

1 week, 2 days назад @ news.mit.edu
A platform to expedite clean energy projects
A platform to expedite clean energy projects A platform to expedite clean energy projects

The company has developed a marketplace for clean energy that helps real estate owners and businesses analyze properties to calculate returns on clean energy projects, create detailed project listings, collect and compare bids, and select a provider.

The company is also working with grocery chains, warehouses, and other businesses to accelerate the clean energy transition.

Berkemeyer served as president of the MIT Energy Club while at the MIT Sloan School of Management.

Prior to his studies at MIT, Berkemeyer had extensive experience developing solar and storage projects and selling clean energy products to commercial customers.

In 2020, the company widened its focus from selling access to …

1 week, 6 days назад @ news.mit.edu
Toward video generative models of the molecular world
Toward video generative models of the molecular world Toward video generative models of the molecular world

As the capabilities of generative AI models have grown, you've probably seen how they can transform simple text prompts into hyperrealistic images and even extended video clips.

Models like AlphaFold can predict molecular structures to accelerate drug discovery, and the MIT-assisted “RFdiffusion,” for example, can help design new proteins.

“Early on, generative AI models produced somewhat simple videos, like a person blinking or a dog wagging its tail,” says Jing, a PhD student at CSAIL.

“To enhance MDGen’s predictive capabilities to model proteins, we’ll need to build on the current architecture and data available.

“MDGen models molecular dynamics simulations as a joint distribution of str…

2 weeks назад @ news.mit.edu
The multifaceted challenge of powering AI
The multifaceted challenge of powering AI The multifaceted challenge of powering AI

The sudden need for so many data centers presents a massive challenge to the technology and energy industries, government policymakers, and everyday consumers.

(A different reactor at that plant partially melted down in 1979, causing the nation’s worst nuclear power accident.)

And in early December, Meta released a request for proposals to identify nuclear energy developers to help the company meet their AI needs and their sustainability goals.

A major concern running through all the options for powering data centers is the impact on residential energy consumers.

When a data center comes into a neighborhood, there are not only aesthetic concerns but also more practical worries.

2 weeks, 2 days назад @ news.mit.edu
Explained: Generative AI’s environmental impact
Explained: Generative AI’s environmental impact Explained: Generative AI’s environmental impact

The excitement surrounding potential benefits of generative AI, from improving worker productivity to advancing scientific research, is hard to ignore.

“When we think about the environmental impact of generative AI, it is not just the electricity you consume when you plug the computer in.

Olivetti is senior author of a 2024 paper, “The Climate and Sustainability Implications of Generative AI,” co-authored by MIT colleagues in response to an Institute-wide call for papers that explore the transformative potential of generative AI, in both positive and negative directions for society.

Plus, generative AI models have an especially short shelf-life, driven by rising demand for new AI applicatio…

2 weeks, 6 days назад @ news.mit.edu
Algorithms and AI for a better world
Algorithms and AI for a better world Algorithms and AI for a better world

A good example of Raghavan’s intention can be found in his exploration of the use AI in hiring.

Just before starting college, though, his love of math and computing called him to follow his family example into computer science.

The experience may be satisfying in the moment, but it can leave the user feeling slightly sick.

The model won the Exemplary Applied Modeling Track Paper Award at the 2022 Association for Computing Machinery Conference on Economics and Computation.

“Long-term satisfaction is ultimately important, even if all you care about is a company’s interests,” Raghavan says.

3 weeks назад @ news.mit.edu
Algorithms and AI for a better world
Algorithms and AI for a better world Algorithms and AI for a better world

A good example of Raghavan’s intention can be found in his exploration of the use AI in hiring.

Just before starting college, though, his love of math and computing called him to follow his family example into computer science.

The experience may be satisfying in the moment, but it can leave the user feeling slightly sick.

The model won the Exemplary Applied Modeling Track Paper Award at the 2022 Association for Computing Machinery Conference on Economics and Computation.

“Long-term satisfaction is ultimately important, even if all you care about is a company’s interests,” Raghavan says.

3 weeks назад @ news.mit.edu
Berkeley AI
последний пост 2 months, 3 weeks назад
Virtual Personas for Language Models via an Anthology of Backstories
Virtual Personas for Language Models via an Anthology of Backstories Virtual Personas for Language Models via an Anthology of Backstories

Virtual Personas for Language Models via an Anthology of BackstoriesWe introduce Anthology, a method for conditioning LLMs to representative, consistent, and diverse virtual personas by generating and utilizing naturalistic backstories with rich details of individual values and experience.

What does it mean for large language models (LLMs) to be trained on massive text corpora, collectively produced by millions and billions of distinctive human authors?

In this work, we introduce Anthology, an approach for steering LLMs to representative, consistent, and diverse virtual personas by providing richly detailed life narratives of individuals as conditioning context to models.

By grounding langu…

2 months, 3 weeks назад @ bair.berkeley.edu
Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination
Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination

Linguistic Bias in ChatGPT: Language Models Reinforce Dialect DiscriminationSample language model responses to different varieties of English and native speaker reactions.

Over 1 billion people around the world speak varieties such as Indian English, Nigerian English, Irish English, and African-American English.

Then, we compared the language model responses to the “standard” varieties and the non-“standard” varieties.

Here, we included the original GPT-3.5 responses, plus responses from GPT-3.5 and GPT-4 where the models were told to imitate the style of the input.

That can reinforce barriers against speakers of non-“standard” varieties as AI models become increasingly used in …

4 months, 2 weeks назад @ bair.berkeley.edu
How to Evaluate Jailbreak Methods: A Case Study with the StrongREJECT Benchmark
How to Evaluate Jailbreak Methods: A Case Study with the StrongREJECT Benchmark How to Evaluate Jailbreak Methods: A Case Study with the StrongREJECT Benchmark

How to Evaluate Jailbreak Methods: A Case Study with the StrongREJECT BenchmarkWhen we began studying jailbreak evaluations, we found a fascinating paper claiming that you could jailbreak frontier LLMs simply by translating forbidden prompts into obscure languages.

This blog post shows how to use a new, state-of-the art jailbreak benchmark - StrongREJECT - to accurately and robustly evaluate jailbreak methods.

PAP instructs an attacker model to persuade a victim model to give it harmful information using techniques like misrepresentation and logical appeals.

We conducted two experiments to test this hypothesis:We used StrongREJECT to evaluate 37 jailbreak methods on an unaligned model; Dolp…

5 months, 1 week назад @ bair.berkeley.edu
Are We Ready for Multi-Image Reasoning? Launching VHs: The Visual Haystacks Benchmark!
Are We Ready for Multi-Image Reasoning? Launching VHs: The Visual Haystacks Benchmark! Are We Ready for Multi-Image Reasoning? Launching VHs: The Visual Haystacks Benchmark!

Launching VHs: The Visual Haystacks Benchmark!

Humans excel at processing vast arrays of visual information, a skill that is crucial for achieving artificial general intelligence (AGI).

Visual Haystacks: the first "visual-centric" Needle-In-A-Haystack (NIAH) benchmark designed to rigorously evaluate Large Multimodal Models (LMMs) in processing long-context visual information.

The first NIAH benchmark for visual reasoning was introduced by Google in the Gemini-v1.5 technical report.

What is the Visual Haystacks (VHs) Benchmark?

6 months, 3 weeks назад @ bair.berkeley.edu
TinyAgent: Function Calling at the Edge
TinyAgent: Function Calling at the Edge TinyAgent: Function Calling at the Edge

TinyAgent: Function Calling at the EdgeThe ability of LLMs to execute commands through plain language (e.g.

The framework is open sourced and available at https://github.com/SqueezeAILab/TinyAgentTeaching LLMs to do Function CallingFigure 1: Overview of the LLMCompiler Function Calling Planner.

Once this function calling plan is generated, we can parse it and call each function based on the dependencies.

With our dataset in place, we can now proceed to fine-tune off-the-shelf SLMs to enhance their function calling capability.

Latency is the end-to-end latency of the function calling planner, including the prompt processing time and generation.

8 months, 1 week назад @ bair.berkeley.edu
AWS Machine Learning AWS Machine Learning
последний пост 5 часов назад
Fine-tune and host SDXL models cost-effectively with AWS Inferentia2
Fine-tune and host SDXL models cost-effectively with AWS Inferentia2 Fine-tune and host SDXL models cost-effectively with AWS Inferentia2

In this post, we demonstrate how to efficiently fine-tune the SDXL model using SageMaker Studio.

After the model is fine-tuned, you can compile and host the fine-tuned SDXL on Inf2 instances using the AWS Neuron SDK.

Fine-tuning SDXL on SageMakerTo fine-tune SDXL on SageMaker, follow the steps in the next sections.

Prepare the imagesThe first step in fine-tuning the SDXL model is to prepare your training images.

Then, run the compilation notebook to compile the fine-tuned SDXL model using the Optimum Neuron library.

5 часов назад @ aws.amazon.com
How Aetion is using generative AI and Amazon Bedrock to translate scientific intent to results
How Aetion is using generative AI and Amazon Bedrock to translate scientific intent to results How Aetion is using generative AI and Amazon Bedrock to translate scientific intent to results

Scientists, epidemiologists, and biostatisticians implement a vast range of queries to capture complex, clinically relevant patient variables from real-world data.

The company provides comprehensive solutions to healthcare and life science customers to rapidly and transparently transforms real-world data into real-world evidence.

To facilitate this translation, Aetion developed a Measures Assistant to turn users’ natural language expressions of scientific intent into Measures.

Measures Assistant incorporates the question into a prompt template and calls the Amazon Bedrock API to invoke Anthropic’s Claude 3 Haiku.

Measures Assistant maintains a local knowledge base about AEP Measures from sc…

5 часов назад @ aws.amazon.com
Trellix lowers cost, increases speed, and adds delivery flexibility with cost-effective and performant Amazon Nova Micro and Amazon Nova Lite models
Trellix lowers cost, increases speed, and adds delivery flexibility with cost-effective and performant Amazon Nova Micro and Amazon Nova Lite models Trellix lowers cost, increases speed, and adds delivery flexibility with cost-effective and performant Amazon Nova Micro and Amazon Nova Lite models

Trellix Wise is available to customers as part of the Trellix Security Platform.

With growing adoption and use, the Trellix team has been exploring ways to optimize the cost structure of Trellix Wise investigations.

Smaller, cost-effective FMs seemed promising and Amazon Nova Micro stood out as an option because of its quality and cost.

In early evaluations, the Trellix team observed that Amazon Nova Micro delivered inferences three times faster and at nearly 100-fold lower cost.

The following figures are the results of tests by Trellix comparing Amazon Nova Micro to other models on Amazon Bedrock.

1 day назад @ aws.amazon.com
OfferUp improved local results by 54% and relevance recall by 27% with multimodal search on Amazon Bedrock and Amazon OpenSearch Service
OfferUp improved local results by 54% and relevance recall by 27% with multimodal search on Amazon Bedrock and Amazon OpenSearch Service OfferUp improved local results by 54% and relevance recall by 27% with multimodal search on Amazon Bedrock and Amazon OpenSearch Service

OfferUp found that multimodal search improved relevance recall by 27%, reduced geographic spread (which means more local results) by 54%, and grew search depth by 6.5%.

The listing writer microservice publishes listing change events to an Amazon Simple Notification Service (Amazon SNS) topic, which an Amazon Simple Queue Service (Amazon SQS) queue subscribes to.

The listing writer microservice publishes listing change events to an Amazon Simple Notification Service (Amazon SNS) topic, which an Amazon Simple Queue Service (Amazon SQS) queue subscribes to.

OfferUp multimodal search migration pathOfferUp adopted a three-step process to implement multimodal search functionality into their found…

1 day, 4 hours назад @ aws.amazon.com
Enhancing LLM Capabilities with NeMo Guardrails on Amazon SageMaker JumpStart
Enhancing LLM Capabilities with NeMo Guardrails on Amazon SageMaker JumpStart Enhancing LLM Capabilities with NeMo Guardrails on Amazon SageMaker JumpStart

We will provide a brief introduction to guardrails and the Nemo Guardrails framework for managing LLM interactions.. We will provide a brief introduction to guardrails and the Nemo Guardrails framework for managing LLM interactions.

NeMo Guardrails is a toolset designed to create robust conversational agents, utilizing Colang — a modelling language specifically tailored for defining dialogue flows and guardrails.

When integrating models from SageMaker JumpStart with NeMo Guardrails, the direct interaction with the SageMaker inference API requires some customization, which we will explore below.

Although NeMo Guardrails provides a SagemakerEndpoint wrapper class, it requires some customizati…

1 day, 5 hours назад @ aws.amazon.com
Build a multi-interface AI assistant using Amazon Q and Slack with Amazon CloudFront clickable references from an Amazon S3 bucket
Build a multi-interface AI assistant using Amazon Q and Slack with Amazon CloudFront clickable references from an Amazon S3 bucket Build a multi-interface AI assistant using Amazon Q and Slack with Amazon CloudFront clickable references from an Amazon S3 bucket

Web applications like Amazon Q Business and Slack have become essential environments for modern AI assistant deployment.

Amazon Q, which supports two types of retrievers (native retriever and Amazon Kendra), is seamlessly integrated into this setup.

By using Amazon Kendra, the solution efficiently employs the same retriever for both the Amazon Q and Slack interfaces.

Amazon Q BusinessAmazon Q Business uses RAG to offer a secure, knowledge-enhanced AI assistant tailored to your organization.

By integrating Amazon Q Business and Slack interfaces with a robust backend powered by Amazon Kendra, this solution offers seamless, environment-agnostic access to accurate, context-aware information.

1 day, 6 hours назад @ aws.amazon.com
Orchestrate seamless business systems integrations using Amazon Bedrock Agents
Orchestrate seamless business systems integrations using Amazon Bedrock Agents Orchestrate seamless business systems integrations using Amazon Bedrock Agents

Using these agents, you can enable generative AI applications to execute multiple tasks across your company systems and data sources.

The post showcases how generative AI can be used to logic, reason, and orchestrate integrations using a fictitious business process.

It demonstrates strategies and techniques for orchestrating Amazon Bedrock agents and action groups to seamlessly integrate generative AI with existing business systems, enabling efficient data access and unlocking the full potential of generative AI.

Create an Amazon Bedrock AgentThe first step in configuring Amazon Bedrock Agents is to define their capabilities.

Marcelo Silva is a Principal Product Manager at Amazon Web Servic…

2 days, 5 hours назад @ aws.amazon.com
Accelerate video Q&A workflows using Amazon Bedrock Knowledge Bases, Amazon Transcribe, and thoughtful UX design
Accelerate video Q&A workflows using Amazon Bedrock Knowledge Bases, Amazon Transcribe, and thoughtful UX design Accelerate video Q&A workflows using Amazon Bedrock Knowledge Bases, Amazon Transcribe, and thoughtful UX design

Amazon Cognito handles user logins to the frontend application and Amazon API Gateway.

When a user uploads a media file through the frontend, a pre-signed URL is generated for the frontend to upload the file to Amazon Simple Storage Service (Amazon S3).

The file is sent to Amazon Transcribe and the resulting transcript is stored in Amazon S3.

Create an Amazon Cognito user to access the appTo log in to the running web application, you have to create an Amazon Cognito user.

For expert assistance, the AWS Generative AI Innovation Center, AWS Professional Services, and our AWS Partners are here to help.

3 days, 6 hours назад @ aws.amazon.com
Boost team innovation, productivity, and knowledge sharing with Amazon Q Apps
Boost team innovation, productivity, and knowledge sharing with Amazon Q Apps Boost team innovation, productivity, and knowledge sharing with Amazon Q Apps

Improve production with Amazon Q AppsAmazon Q Apps is a feature within Amazon Q Business that assists you in creating lightweight, purpose-built applications within Amazon Q Business.

Refer to Pre-requisites for Amazon Q Apps for the steps to complete prior to deploying Amazon Q Apps.

Begin by asking the Amazon Q Business assistant a question related to the data that is provided in the Amazon Q Business application.

Create an application using Amazon Q Apps CreatorYou can start building an Amazon Q application with Amazon Q Apps Creator by describing the task you want to create an application for.

*Note Amazon Q Apps is only available to users with the Pro subscription, if you have the Lite…

3 days, 6 hours назад @ aws.amazon.com
Harnessing Amazon Bedrock generative AI for resilient supply chain
Harnessing Amazon Bedrock generative AI for resilient supply chain Harnessing Amazon Bedrock generative AI for resilient supply chain

The drag and drop capability of Amazon Bedrock Flows efficiently integrates with Amazon Bedrock Knowledge Bases, Amazon Bedrock Agents and other ever-growing AWS services such as Amazon Simple Storage Service (Amazon S3), AWS Lambda and Amazon Lex.

Configure Amazon Bedrock Knowledge BasesIn this section, you create an Amazon Bedrock knowledge base and sync it.

Create an Amazon Bedrock workflowIn this section, you create a workflow in Amazon Bedrock Flows.

On the Amazon Bedrock console, select Amazon Bedrock Flows from the left navigation pane.

About the AuthorsMarcelo Silva is a Principal Product Manager at Amazon Web Services, leading strategy and growth for Amazon Bedrock Knowledge Bases …

6 days, 3 hours назад @ aws.amazon.com
How Travelers Insurance classified emails with Amazon Bedrock and prompt engineering
How Travelers Insurance classified emails with Amazon Bedrock and prompt engineering How Travelers Insurance classified emails with Amazon Bedrock and prompt engineering

In this post, we walk through how the Generative AI Innovation Center (GenAIIC) collaborated with leading property and casualty insurance carrier Travelers to develop an FM-based classifier through prompt engineering.

In this case, given the accuracy was already high by just using prompt engineering, the accuracy after fine-tuning would have to justify the cost.

The Amazon Textract output was then combined with the email text and given to the model to decide the appropriate class.

The structure of the prompt was as follows:Persona definitionOverall instructionFew-shot examplesDetailed definitions for each classEmail data inputFinal output instructionTo learn more about prompt engineering fo…

6 days, 5 hours назад @ aws.amazon.com
Accelerate digital pathology slide annotation workflows on AWS using H-optimus-0
Accelerate digital pathology slide annotation workflows on AWS using H-optimus-0 Accelerate digital pathology slide annotation workflows on AWS using H-optimus-0

This powerful FM, with its comprehensive training on over 500,000 histopathology slides, represents a valuable tool for organizations looking to enhance their digital pathology workflows.

In this post, we demonstrate how to use H-optimus-0 for two common digital pathology tasks: patch-level analysis for detailed tissue examination, and slide-level analysis for broader diagnostic assessment.

The architecture combines the following services:The following diagram illustrates the solution architecture for training and deploying fine-tuned FMs using H-optimus-0.

ConclusionIn this post, we demonstrated how you can use AWS services to build scalable digital pathology AI workflows using the H-optim…

6 days, 6 hours назад @ aws.amazon.com
DeepSeek-R1 model now available in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart
DeepSeek-R1 model now available in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart DeepSeek-R1 model now available in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart

Today, we are announcing that DeepSeek AI’s first-generation frontier model, DeepSeek-R1, is available through Amazon SageMaker JumpStart and Amazon Bedrock Marketplace to deploy for inference.

Deploy DeepSeek-R1 in Amazon Bedrock MarketplaceAmazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock.

Delete the Amazon Bedrock Marketplace deploymentIf you deployed the model using Amazon Bedrock Marketplace, complete the following steps:On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace deployments.

Delete the SageMaker JumpStart predictorThe SageMaker JumpStart model …

6 days, 20 hours назад @ aws.amazon.com
Streamline grant proposal reviews using Amazon Bedrock
Streamline grant proposal reviews using Amazon Bedrock Streamline grant proposal reviews using Amazon Bedrock

The team developed an innovative solution to streamline grant proposal review and evaluation by using the natural language processing (NLP) capabilities of Amazon Bedrock.

In this post, we explore the technical implementation details and key learnings from the team’s Amazon Bedrock powered grant proposal review solution, providing a blueprint for organizations seeking to optimize their grants management processes.

Building a dynamic proposal review application with Streamlit and generative AITo demonstrate and test the capabilities of a dynamic proposal review solution, we built a rapid prototype implementation using Streamlit, Amazon Bedrock, and Amazon DynamoDB.

def get_assessment(submiss…

1 week назад @ aws.amazon.com
How Aetion is using generative AI and Amazon Bedrock to unlock hidden insights about patient populations
How Aetion is using generative AI and Amazon Bedrock to unlock hidden insights about patient populations How Aetion is using generative AI and Amazon Bedrock to unlock hidden insights about patient populations

The company provides comprehensive solutions to healthcare and life science customers to transform real-world data into real-world evidence.

With Aetion Discover, users can conduct rapid, exploratory analyses with real-world data while experiencing a structured approach to research questions.

In this post, we review how Aetion’s Smart Subgroups Interpreter enables users to interact with Smart Subgroups using natural language queries.

Solution overviewSmart Subgroups Interpreter combines elements of unsupervised machine learning with generative AI to uncover hidden patterns in real-world data.

Let’s review each step in detail:Create the patient population – Users define a patient population …

1 week назад @ aws.amazon.com
NVIDIA
последний пост 9 часов назад
When the Earth Talks, AI Listens
When the Earth Talks, AI Listens When the Earth Talks, AI Listens

A team of researchers from the Earth and environmental sciences division at Los Alamos National Laboratory repurposed Meta’s Wav2Vec-2.0, an AI model designed for speech recognition, to analyze seismic signals from Hawaii’s 2018 Kīlauea volcano collapse.

The AI model outperformed traditional methods, such as gradient-boosted trees, which struggle with the unpredictable nature of seismic signals.

High-performance NVIDIA GPUs accelerated training, enabling the AI to efficiently extract meaningful patterns from continuous seismic signals.

This study suggests that AI models designed for speech recognition may be uniquely suited to interpreting the intricate, shifting signals faults generate ove…

9 часов назад @ blogs.nvidia.com
Medieval Mayhem Arrives With ‘Kingdom Come: Deliverance II’ on GeForce NOW
Medieval Mayhem Arrives With ‘Kingdom Come: Deliverance II’ on GeForce NOW Medieval Mayhem Arrives With ‘Kingdom Come: Deliverance II’ on GeForce NOW

The month kicks off with Kingdom Come: Deliverance II.

Experience the highly anticipated sequel’s stunning open world at GeForce RTX quality in the cloud, available to stream across devices at launch.

It leads seven games joining the GeForce NOW library of over 2,000 titles, along with MARVEL vs. CAPCOM Fighting Collection: Arcade Classics.

Chainmail Meets the CloudKingdom Come: Deliverance II continues the epic, open-world RPG saga set in the brutal and realistic medieval world of Bohemia.

The game also features enhanced graphics powered by GeForce RTX, making it ideal to stream on GeForce NOW even without a game-ready rig.

9 часов назад @ blogs.nvidia.com
Featured Researcher and Educator Sessions at NVIDIA GTC 2025
Featured Researcher and Educator Sessions at NVIDIA GTC 2025 Featured Researcher and Educator Sessions at NVIDIA GTC 2025

This site requires Javascript in order to view all its content.

Please enable Javascript in order to access all the functionality of this web site.

Here are the instructions how to enable JavaScript in your web browser.

1 day назад @ nvidia.com
Building More Builders: Gooey.AI Makes AI More Accessible Across Communities
Building More Builders: Gooey.AI Makes AI More Accessible Across Communities Building More Builders: Gooey.AI Makes AI More Accessible Across Communities

Cofounders Sean Blagsvedt and Archana Prasad join the NVIDIA AI Podcast to discuss how the startup’s platform is making AI development accessible to developers and non-coders alike.

The company’s vision centers on democratizing AI development through shareable AI recipes, as well as helping ensure responsible implementation and representation of historically underserved communities in AI model-building.

Time Stamps00:31 – How a development platform began life as a British Council arts project called Dara.network.

Living Optics CEO Robin Wang on Democratizing Hyperspectral ImagingStep into the realm of the unseen with Robin Wang, CEO of Living Optics.

Yotta CEO Sunil Gupta on Supercharging I…

1 day, 7 hours назад @ blogs.nvidia.com
How GeForce RTX 50 Series GPUs Are Built to Supercharge Generative AI on PCs
How GeForce RTX 50 Series GPUs Are Built to Supercharge Generative AI on PCs How GeForce RTX 50 Series GPUs Are Built to Supercharge Generative AI on PCs

NIM and AI Blueprints are optimized for GeForce RTX 50 Series GPUs.

These technologies work together seamlessly to help developers and enthusiasts build, iterate and deliver cutting-edge AI experiences on AI PCs.

With FP4, FLUX.1 [dev] requires less than 10GB, so it can run locally on more GeForce RTX GPUs.

With AI Blueprints, users can quickly go from experimenting with to developing AI on RTX PCs and workstations.

NIM microservices and AI Blueprints are coming soon, with initial hardware support for GeForce RTX 50 Series, GeForce RTX 4090 and 4080, and NVIDIA RTX 6000 and 5000 professional GPUs.

1 day, 9 hours назад @ blogs.nvidia.com
AI Pays Off: Survey Reveals Financial Industry’s Latest Technological Trends
AI Pays Off: Survey Reveals Financial Industry’s Latest Technological Trends AI Pays Off: Survey Reveals Financial Industry’s Latest Technological Trends

NVIDIA’s State of AI in Financial Services survey finds companies are using AI to boost revenue, reduce costs and open new lines of business.

NVIDIA’s fifth annual State of AI in Financial Services report shows how financial institutions have consolidated their AI efforts to focus on core applications, signaling a significant increase in AI capability and proficiency.

Generative AI Powers More Use CasesAfter data analytics, generative AI has emerged as the second-most-used AI workload in the financial services industry.

Advanced AI Drives InnovationRecognizing the transformative potential of AI, companies are taking proactive steps to build AI factories — specially built accelerated computi…

1 day, 9 hours назад @ blogs.nvidia.com
NVIDIA Blackwell Now Generally Available in the Cloud
NVIDIA Blackwell Now Generally Available in the Cloud NVIDIA Blackwell Now Generally Available in the Cloud

CoreWeave launches the first NVIDIA GB200 NVL72-cloud based instances to power the next era of AI reasoning.

To meet this demand, CoreWeave has launched NVIDIA GB200 NVL72-based instances, becoming the first cloud service provider to make the NVIDIA Blackwell platform generally available.

With rack-scale NVIDIA NVLink across 72 NVIDIA Blackwell GPUs and 36 NVIDIA Grace CPUs, scaling to up to 110,000 GPUs with NVIDIA Quantum-2 InfiniBand networking, these instances provide the scale and performance needed to build and deploy the next generation of AI reasoning models and agents.

NVIDIA NIM is a set of easy-to-use microservices designed for secure, reliable deployment of high-performance AI m…

2 days, 5 hours назад @ blogs.nvidia.com
AI Foundation Model Enhances Cancer Diagnosis and Tailors Treatment
AI Foundation Model Enhances Cancer Diagnosis and Tailors Treatment AI Foundation Model Enhances Cancer Diagnosis and Tailors Treatment

A new study and AI model from researchers at Stanford University is streamlining cancer diagnostics, treatment planning, and prognosis prediction.

However, integrating and interpreting complex medical data remains difficult for doctors and AI models.

The researchers pretrained the AI model on one of the biggest datasets in the field, using 50M pathology images from 11,577 patients with 33 tumor types and 1B pathology-related text data.

When tested on 23 pathology benchmarks, MUSK outperformed existing AI models in several key areas.

It excelled at matching pathology images with correlating medical text, making it more effective in gathering relevant patient information.

2 days, 5 hours назад @ developer.nvidia.com
Accelerate DeepSeek Reasoning Models With NVIDIA GeForce RTX 50 Series AI PCs
Accelerate DeepSeek Reasoning Models With NVIDIA GeForce RTX 50 Series AI PCs Accelerate DeepSeek Reasoning Models With NVIDIA GeForce RTX 50 Series AI PCs

With up to 3,352 trillion operations per second of AI horsepower, NVIDIA GeForce RTX 50 Series GPUs can run the DeepSeek family of distilled models faster than anything on the PC market.

The reasoning capabilities of the larger DeepSeek-R1 671-billion-parameter model were taught to the smaller Llama and Qwen student models, resulting in powerful, smaller reasoning models that run locally on RTX AI PCs with fast performance.

Peak Performance on RTXInference speed is critical for this new class of reasoning models.

Experience DeepSeek on RTX in Popular ToolsNVIDIA’s RTX AI platform offers the broadest selection of AI tools, software development kits and models, opening access to the capabilit…

6 days, 6 hours назад @ blogs.nvidia.com
DeepSeek-R1 Now Live With NVIDIA NIM
DeepSeek-R1 Now Live With NVIDIA NIM DeepSeek-R1 Now Live With NVIDIA NIM

To help developers securely experiment with these capabilities and build their own specialized agents, the 671-billion-parameter DeepSeek-R1 model is now available as an NVIDIA NIM microservice preview on build.nvidia.com.

The DeepSeek-R1 NIM microservice can deliver up to 3,872 tokens per second on a single NVIDIA HGX H200 system.

The DeepSeek-R1 NIM microservice simplifies deployments with support for industry-standard APIs.

Using NVIDIA AI Foundry with NVIDIA NeMo software, enterprises will also be able to create customized DeepSeek-R1 NIM microservices for specialized AI agents.

Get Started Now With the DeepSeek-R1 NIM MicroserviceDevelopers can experience the DeepSeek-R1 NIM microservi…

6 days, 23 hours назад @ blogs.nvidia.com
Mastering the cudf.pandas Profiler for GPU Acceleration
Mastering the cudf.pandas Profiler for GPU Acceleration Mastering the cudf.pandas Profiler for GPU Acceleration

A fundamental pillar of this approach is the cudf.pandas profiler, which provides insights into how much of your code is being executed on the GPU compared to the CPU.

In this post, we discuss what the cudf.pandas profiler is, how to use it, and why it’s critical for understanding and optimizing your accelerated pandas workloads.

cudf.pandas profiler overviewThe cudf.pandas.profile magic command, available in Jupyter and IPython, is a profiling tool that analyzes your pandas-style code in real time.

Identifying these is crucial for maximum speedup, as a repeated sequence of GPU operations followed by CPU operations can generally lead to expensive data transfers.

The cudf.pandas profiler is …

1 week назад @ developer.nvidia.com
Lights, Camera, Action: New NVIDIA Broadcast AI Features Now Streaming With GeForce RTX 50 Series GPUs
Lights, Camera, Action: New NVIDIA Broadcast AI Features Now Streaming With GeForce RTX 50 Series GPUs Lights, Camera, Action: New NVIDIA Broadcast AI Features Now Streaming With GeForce RTX 50 Series GPUs

GeForce RTX 5090 and RTX 5080 GPUs feature fifth-generation Tensor Cores with support for FP4, reducing the VRAM requirements to run generative AI models while doubling performance.

“The GeForce RTX 5090 is a content creation powerhouse.” — PC WorldThe GeForce RTX 5090 GPU includes 32GB of ultra-fast GDDR7 memory and 1,792 GB/sec of total memory bandwidth — a 77% bandwidth increase over the GeForce RTX 4090 GPU.

The GeForce RTX 5080 GPU features 16GB of GDDR7 memory, providing up to 960 GB/sec of total memory bandwidth — a 34% increase over the GeForce RTX 4080 GPU.

Use the GeForce RTX graphics card product finder to pick up GeForce RTX 5090 and RTX 5080 GPUs or a prebuilt system today.

The…

1 week назад @ blogs.nvidia.com
GeForce NOW Celebrates Five Years of Cloud Gaming With AAA Blockbusters
GeForce NOW Celebrates Five Years of Cloud Gaming With AAA Blockbusters GeForce NOW Celebrates Five Years of Cloud Gaming With AAA Blockbusters

‘Kingdom Come: Deliverance II,’ ‘Avowed’ and ‘Sid Meier’s Civilization VII’ are part of 17 games joining GeForce NOW in February.

Five incredible years of high-performance gaming have been made possible thanks to the members who’ve joined the cloud gaming platform on its remarkable journey.

Since exiting beta in 2020, GeForce NOW has changed how gamers access and enjoy their favorite titles.

As part of an epic February lineup of 17 games coming this month, every week, GeForce NOW will deliver a major game release in the cloud.

Look for the following games available to stream in the cloud this week:Space Engineers 2 (New release on Steam, Jan. 27)(New release on Steam, Jan. 27) Eternal Stran…

1 week назад @ blogs.nvidia.com
Accelerating JSON Processing on Apache Spark with GPUs
Accelerating JSON Processing on Apache Spark with GPUs Accelerating JSON Processing on Apache Spark with GPUs

The processing of JSON for the clickstream data included large queries processing tens of terabytes of JSON data in a single Spark workload.

The Spark get_json_object functionGPU processing for JSON has existed in the RAPIDS Accelerator for Apache Spark since the 22.02 release, but there have been challenges in accelerated processing.

The data used for our benchmarks is five columns and 200,000 rows of generated JSON data based on an approximation of the retailer’s JSON data.

The RAPIDS Accelerator for Apache Spark along with cuDF has enhanced JSON processing for improved speedups on GPUs.

Get hands-on with JSON processing and the RAPIDS Accelerator for Apache Spark with this Colab notebook…

1 week, 1 day назад @ developer.nvidia.com
Mastering LLM Techniques: Evaluation
Mastering LLM Techniques: Evaluation Mastering LLM Techniques: Evaluation

Why LLM evaluation mattersIn the development of generative AI applications, rigorous evaluation is crucial for ensuring system effectiveness and reliability.

Lack of techniques Overfitting to current techniques: Relying heavily on existing evaluation methods risks models being optimized for these techniques rather than achieving genuine performance improvements.

Integrating evaluation into AI workflowsEmbedding evaluation processes within AI development workflows presents additional hurdles, including:Continuous evaluation: Models in production require ongoing assessment to ensure performance and reliability over time, necessitating seamless integration of evaluation tools.

In the following…

1 week, 1 day назад @ developer.nvidia.com
Facebook
последний пост 1 day, 4 hours назад
Revolutionizing software testing: Introducing LLM-powered bug catchers
Revolutionizing software testing: Introducing LLM-powered bug catchers Revolutionizing software testing: Introducing LLM-powered bug catchers

WHAT IT ISMeta’s Automated Compliance Hardening (ACH) tool is a system for mutation-guided, LLM-based test generation.

Traditionally, automated test generation techniques sought merely to increase code coverage.

LLM-based test generation and LLM-based mutant generation are not new, but this is the first time they’ve been combined and deployed in large-scaled industrial systems.

WHAT’S NEXTOur novel approach combines LLM-based test generation and mutant generation to help automate complex technical organizational workflows in this space.

READ THE PAPERMutation-Guided LLM-based Test Generation at Meta

1 day, 4 hours назад @ engineering.fb.com
Meta Andromeda: Supercharging Advantage+ automation with the next-gen personalized ads retrieval engine
Meta Andromeda: Supercharging Advantage+ automation with the next-gen personalized ads retrieval engine Meta Andromeda: Supercharging Advantage+ automation with the next-gen personalized ads retrieval engine

Unlocking advertiser value through industry-leading ML innovationMeta Andromeda is a personalized ads retrieval engine that leverages the NVIDIA Grace Hopper Superchip, to enable cutting edge ML innovation in the Ads retrieval stage to drive efficiency and advertiser performance.

Its deployment across Instagram and Facebook applications has achieved +6% recall improvement to the retrieval system, delivering +8% ads quality improvement on selected segments.

Andromeda is designed to maximize ads performance by utilizing the exponential growth in volume of eligible ads available to the retrieval stage.

The design is optimized for AI hardware, minimizing memory bandwidth bottlenecks and enablin…

2 months назад @ engineering.fb.com
Sequence learning: A paradigm shift for personalized ads recommendations
Sequence learning: A paradigm shift for personalized ads recommendations Sequence learning: A paradigm shift for personalized ads recommendations

Meta’s ad recommendation engine, powered by deep learning recommendation models (DLRMs), has been instrumental in delivering personalized ads to people.

Learning from sequences: developing new sequence learning architectures to replace traditional DLRM neural network architectures.

A paradigm shift with learning from sequences for recommendation systemsMeta’s new system for ads recommendations uses sequence learning at its core.

Scaling the new sequence learning paradigmFollowing the redesign to shift from sparse feature learning to event-based sequence learning, the next focus was scaling across two domains — scaling the sequence learning architecture and scaling event sequences to be long…

2 months, 2 weeks назад @ engineering.fb.com
OCP Summit 2024: The open future of networking hardware for AI
OCP Summit 2024: The open future of networking hardware for AI OCP Summit 2024: The open future of networking hardware for AI

At Open Compute Project Summit (OCP) 2024, we’re sharing details about our next-generation network fabric for our AI training clusters.

We’ve expanded our network hardware portfolio and are contributing two new disaggregated network fabrics and a new NIC to OCP.

At Meta, we believe that open hardware drives innovation.

At Meta, we envision a future of AI hardware systems that are not only scalable, but also open and collaborative.

We encourage anyone who wants to help advance the future of networking hardware for AI to engage with OCP and Meta to help share the future of AI infrastructure.

3 months, 3 weeks назад @ engineering.fb.com
Meta’s open AI hardware vision
Meta’s open AI hardware vision Meta’s open AI hardware vision

At the Open Compute Project (OCP) Global Summit 2024, we’re showcasing our latest open AI hardware designs with the OCP community.

These innovations include a new AI platform, cutting-edge open rack designs, and advanced network fabrics and components.

The open future of AI infraMeta is committed to open source AI.

We must also prioritize open and standardized models so we can leverage collective expertise, make AI more accessible, and work towards minimizing biases in our systems.​Just as important, we also need open AI hardware systems.

By addressing AI’s infrastructure needs together, we can unlock the true promise of open AI for everyone.​

3 months, 3 weeks назад @ engineering.fb.com
How open source AI can improve population estimates, sustainable energy, and the delivery of climate change interventions
How open source AI can improve population estimates, sustainable energy, and the delivery of climate change interventions How open source AI can improve population estimates, sustainable energy, and the delivery of climate change interventions

Why we need better population mapsAccurate estimates of population are taken for granted in many countries.

As the world’s natural resource and energy demands scale, accurate population estimates also offer significant opportunities to improve sustainability efforts.

In addition to total population counts, Meta’s population maps also include demographic breakdowns for groups such as the number of children under five, women of reproductive age, youth, and the elderly.

AI-powered population estimates have been scientifically evaluated to be among the most accurate in the world for mapping population distribution for a variety of geographies and use-cases.

Please visit the Data for Good websit…

4 months назад @ engineering.fb.com
Simulator-based reinforcement learning for data center cooling optimization
Simulator-based reinforcement learning for data center cooling optimization Simulator-based reinforcement learning for data center cooling optimization

Meta is revamping its new data center design to optimize for artificial intelligence and the same methodology will be applicable for future data center optimizations as well.

As Meta is revamping its new data center design to optimize for artificial intelligence, the same methodology will be applicable for future data center optimizations as well to improve operational efficiency.

A reinforcement learning approach to data center coolingReinforcement learning (RL) is good at modeling control systems as sequential state machines.

There are also various RL approaches reported such as, transforming cooling optimization via deep reinforcement learning and data center cooling using model-predicti…

4 months, 4 weeks назад @ engineering.fb.com
How PyTorch powers AI training and inference
How PyTorch powers AI training and inference How PyTorch powers AI training and inference

How PyTorch powers AI training and inferenceLearn about new PyTorch advancements for LLMs and how PyTorch is enhancing every aspect of the LLM lifecycle.

In this talk from AI Infra @ Scale 2024, software engineers Wanchao Liang and Evan Smothers are joined by Meta research scientist Kimish Patel to discuss our newest features and tools that enable large-scale training, memory efficient fine-tuning, and on-device LLM capabilities.

First, they cover the importance of memory-efficient fine-tuning and a few common architectural and algorithmic techniques to enable fine-tuning on consumer-grade hardware.

Then they discuss the challenges of deploying large models for on-device deployment and how …

5 months, 2 weeks назад @ engineering.fb.com
Inside the hardware and co-design of MTIA
Inside the hardware and co-design of MTIA Inside the hardware and co-design of MTIA

In this talk from AI Infra @ Scale 2024, Joel Colburn, a software engineer at Meta, technical lead Junqiang Lan, and software engineer Jack Montgomery discuss the second generation of MTIA, Meta’s in-house training and inference accelerator.

They cover the co-design process behind building the second generation of Meta’s first-ever custom silicon for AI workloads, including the PyTorch software ecosystem, and the model architectures for Meta’s key applications.

They demonstrate how MTIA achieves the performance, efficiency, and developer experience to successfully launch models into production.

They also highlight several co-design examples where special silicon features are utilized to acc…

5 months, 2 weeks назад @ engineering.fb.com
Bringing Llama 3 to life
Bringing Llama 3 to life Bringing Llama 3 to life

At AI Infra @ Scale 2024, Meta engineers discussed every step of how we built and brought Llama 3 to life, from data and training to inference.

Joe Spisak, Product Director and Head of Generative AI Open Source at Meta, talks about the history of Llama and Meta’s overarching vision for open source AI.

He’s joined by Delia David, a software engineer at Meta, to discuss all things data-related for GenAI.

Kaushik Veeraraghavan, a software engineer at Meta, discusses how Meta trains Llama at scale and delves into the data center, networking, and software investments that have enabled the development of Meta’s Llama 3 models.

Finally, Ye (Charlotte) Qia, a production engineer at Meta, discusses …

5 months, 2 weeks назад @ engineering.fb.com
Aparna Ramani discusses the future of AI infrastructure
Aparna Ramani discusses the future of AI infrastructure Aparna Ramani discusses the future of AI infrastructure

Delivering new AI technologies at scale also means rethinking every layer of our infrastructure – from silicon and software systems and even our data center designs.

For the second year in a row, Meta’s engineering and infrastructure teams returned for the AI Infra @ Scale conference, where they discussed the challenges of scaling up an infrastructure for AI as well as work being done on our large-scale GPU clusters, open hardware designs for next-generation data center hardware, and how Meta is building custom silicon like the Meta Training and Inference Accelerator (MTIA) to handle some of our AI training workloads.

Aparna Ramani, VP of Engineering at Meta, responsible for AI infrastructu…

5 months, 2 weeks назад @ engineering.fb.com
How Meta animates AI-generated images at scale
How Meta animates AI-generated images at scale How Meta animates AI-generated images at scale

Meta AI’s animate feature, which lets people generate a short animation of a generated image, carried unique challenges in this regard.

Here’s how we were able to deploy Meta AI’s animate feature using a combination of latency optimizations, traffic management, and other novel techniques.

We started by looking at the data for previous traffic on our AI-generated media both at their launches and over time.

With these changes, the preponderance of requests remained in region and latency dropped to roughly what we would expect.

The service tries to take a chunk of that region’s requests and offload them to a nearby region that can handle them without becoming more overloaded.

5 months, 3 weeks назад @ engineering.fb.com
A RoCE network for distributed AI training at scale
A RoCE network for distributed AI training at scale A RoCE network for distributed AI training at scale

Our paper, “ RDMA over Ethernet for Distributed AI Training at Meta Scale ,” provides the details on how we design, implement, and operate one of the world’s largest AI networks at scale.

These RoCE clusters support an extensive range of production distributed GPU training jobs, including ranking, content recommendation, content understanding, natural language processing, and GenAI model training, among other workloads.

However, our experience with distributed AI training workloads provides a different perspective on tailoring the congestion control algorithms.

Moving forwardThe design and operation of large-scale RoCE networks for distributed AI training workloads have evolved to meet the …

6 months назад @ engineering.fb.com
Meet Caddy – Meta’s next-gen mixed reality CAD software
Meet Caddy – Meta’s next-gen mixed reality CAD software Meet Caddy – Meta’s next-gen mixed reality CAD software

What happens when a team of mechanical engineers get tired of looking at flat images of 3D models over Zoom?

Meet the team behind Caddy, a new CAD app for mixed reality.

They join Pascal Hartig (@passy) on the Meta Tech Podcast to talk about teaching themselves to code, disrupting the CAD software space, and how they integrated Caddy with Llama 3, and so much more!

Download or listen to the podcast episode below:You can also find the episode wherever you get your podcasts, including:The Meta Tech Podcast is a podcast, brought to you by Meta, where we highlight the work Meta’s engineers are doing at every level – from low-level frameworks to end-user features.

And if you’re interested in lea…

6 months, 3 weeks назад @ engineering.fb.com
AI Lab: The secrets to keeping machine learning engineers moving fast
AI Lab: The secrets to keeping machine learning engineers moving fast AI Lab: The secrets to keeping machine learning engineers moving fast

The key to developer velocity across AI lies in minimizing time to first batch (TTFB) for machine learning (ML) engineers.

AI Lab prevents TTFB regressions whilst enabling experimentation to develop improvements.

Optimizing TTFB helps ML engineers move fastThe overhead induced from TTFB is on the critical path for most ML development.

Here, we see the true utility of a framework like AI Lab and how it was used to facilitate this sweeping change.

O(Releases): Running a more holistic set of AI Lab tests prior to release and performing a bisect-like attribution process to find the root cause.

6 months, 3 weeks назад @ engineering.fb.com
Uber Engineering
последний пост None
neptune.ai neptune.ai
последний пост 11 часов назад
Mixture of Experts LLMs: Key Concepts Explained
Mixture of Experts LLMs: Key Concepts Explained Mixture of Experts LLMs: Key Concepts Explained

TL;DR Mixture of Experts (MoE) is a type of neural network architecture that employs sub-networks (experts) to process specific input parts.

This is the key idea behind Mixture of Expert LLMs.

The Switch-Language Transformer, Mixtral, GLaM, GShard, and DeepSeekMoE are Mixture of Experts LLMs (MoEs), which require only executing a portion of the model’s computational graph during inference.

Optimization strategies for MoE LLMs are discussed comprehensively in the papers introducing the Switch Transformer, GShard, and GLaM.

Mixture of Experts (MoE) is an approach to scaling LLMs to trillions of parameters with conditional computation while avoiding exploding computational costs.

11 часов назад @ neptune.ai
Hyperparameter Optimization For LLMs: Advanced Strategies
Hyperparameter Optimization For LLMs: Advanced Strategies Hyperparameter Optimization For LLMs: Advanced Strategies

Advanced hyperparameter optimization strategies, like population-based training, Bayesian optimization, and adaptive LoRA, promise to balance computational effort and outcome.

To avoid this, learning rate schedules for LLMs start with a small learning rate and slowly ramp it up to its maximum value.

Can we use traditional machine learning hyperparameter optimization methods for LLMs?

| Modified based on: sourceHands-on: LLM hyperparameter optimization with neptune.aiOptuna is a framework for optimizing hyperparameter search using Bayesian optimization.

See the docs or watch a short product demo (2 min)Play with a live Neptune Scale projectRequest your early accessWhat’s next in LLM hyperpar…

1 week назад @ neptune.ai
Multimodal Large Language Models
Multimodal Large Language Models Multimodal Large Language Models

TL;DR Multimodal Large Language Models (MLLMs) process data from different modalities like text, audio, image, and video.

This article explores Multimodal Large Language Models, exploring their core functionalities, challenges, and potential for various machine-learning domains.

Let’s break down the concept of Multimodal Large Language Models (MLLMs) by first understanding the terms “modal” and “multimodal:”“Modal” refers to a particular way of communicating or perceiving information.

| SourceGoogle: PaLM-EGoogle developed an embodied language model, PaLM-E, to incorporate continuous sensor modalities into language models and establish the link between words and perceptions.

Improving how t…

2 weeks назад @ neptune.ai
How to Build and Evaluate a RAG System Using LangChain, Ragas, and neptune.ai
How to Build and Evaluate a RAG System Using LangChain, Ragas, and neptune.ai How to Build and Evaluate a RAG System Using LangChain, Ragas, and neptune.ai

In this guide, we’ll show you how to build a RAG system using the LangChain framework, evaluate its performance using Ragas, and track your experiments with neptune.ai.

Part 1: Building a baseline RAG system with LangChainIn the first part of this guide, we’ll use LangChain to build a RAG system for the blog posts in the LLMOps category on Neptune’s blog.

Ragas works smoothly with LangChain, making it a great choice for evaluating our RAG system.

Step 1: Generate a RAG evaluation datasetAn evaluation set for RAG tasks is similar to a question-answering task dataset.

Step 2: Choose RAG evaluation metricsAs mentioned earlier, Ragas offers both LLM-based and non-LLM-based metrics for RAG syste…

1 month, 1 week назад @ neptune.ai
Position: Understanding LLMs Requires More Than Statistical Generalization [Paper Reflection]
Position: Understanding LLMs Requires More Than Statistical Generalization [Paper Reflection] Position: Understanding LLMs Requires More Than Statistical Generalization [Paper Reflection]

In our paper, Understanding LLMs Requires More Than Statistical Generalization, we argue that current machine learning theory cannot explain the interesting emergent properties of Large Language Models, such as reasoning or in-context learning.

Inductive biases affect which solution the neural network converges to, such as the model architecture or the optimization algorithm.

How do language complexity and model architecture affect generalization ability?

showed how different neural network architectures generalize better for different language types.

Presumably, we’ll need to find different complexity measures for different model architectures that consider their specific inductive biases.

1 month, 2 weeks назад @ neptune.ai
From Research to Production: Building The Most Scalable Experiment Tracker For Foundation Models
From Research to Production: Building The Most Scalable Experiment Tracker For Foundation Models From Research to Production: Building The Most Scalable Experiment Tracker For Foundation Models

TL;DR At a large-scale model training (in huge models), anomalies are not rare events but problematic patterns that drive failure.

The Neptune Scale experiment tracker supports fault tolerance and is designed to maintain progress despite hardware failures, making it adaptable for enterprise teams tackling LLM fine-tuning, compliance, and building domain-specific models.

Experiment tracking back then was straightforward—dealing mostly with single models or small-scale distributed systems.

One of the biggest lessons we’ve learned is that experiment tracking has evolved into experiment monitoring.

That’s why we’re focusing on building intelligent alerts and anomaly detection right into our exp…

1 month, 3 weeks назад @ neptune.ai
Transformers Key-Value Caching Explained
Transformers Key-Value Caching Explained Transformers Key-Value Caching Explained

Key-value (KV) caching is a clever trick to do that: At inference time, key and value matrices are calculated for each generated token.

Implementing K-V caching in large-scale production systems requires careful cache management, including choosing an appropriate strategy for cache invalidation and exploring opportunities for cache reuse.

Key-value (KV) caching is a clever trick to do just that – let’s see how it works and when to use it.

Transformer architecture overviewBefore we dive into KV caching, we will need to take a short detour to the attention mechanism used in transformers.

Understanding how it works is required to spot and appreciate how KV caching optimizes transformer inferen…

2 months назад @ neptune.ai
Learn From Failure: Fine-Tuning LLMs With Trial-and-Error Data For Intuitionistic Propositional Logic Proving [Paper Reflection]
Learn From Failure: Fine-Tuning LLMs With Trial-and-Error Data For Intuitionistic Propositional Logic Proving [Paper Reflection] Learn From Failure: Fine-Tuning LLMs With Trial-and-Error Data For Intuitionistic Propositional Logic Proving [Paper Reflection]

In our paper, Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving, we explored this problem experimentally.

Our goal was to assess the influence of trial-and-error information in the training data on the performance of LLMs in theorem proving.

However, at the time we published our paper, current approaches to training LLMs for ATPs only utilized data on correct proof attempts.

We hope our work can raise the community’s awareness of the importance of trial-and-error data for automated theorem proving.

We believe this advancement is largely due to the substantial trial-and-error data included in the model’s training process.

2 months, 1 week назад @ neptune.ai
Fine-Tuning Llama 3 with LoRA: Step-by-Step Guide
Fine-Tuning Llama 3 with LoRA: Step-by-Step Guide Fine-Tuning Llama 3 with LoRA: Step-by-Step Guide

We will explore these challenges and provide an example of fine-tuning the Llama 3 8B Instruct model utilizing the neptune.ai experiment tracker.

The Llama 3 training data is seven times larger than what Meta used for training Llama 2.

For pre-training, Meta combined four types of parallelization, an approach they dubbed “4D parallelism”: data, model, pipeline, and context.

Hands-on guide: resource-efficient fine-tuning of Llama 3 on Google ColabFine-tuning Llama 3 8B is challenging, as it requires considerable computational resources.

We’ll use the Llama 3 8B model, which is sufficient for this task despite being the smallest Llama 3 model.

2 months, 2 weeks назад @ neptune.ai
How to Run LLMs Locally
How to Run LLMs Locally How to Run LLMs Locally

This is the process we will be going through in the following section to answer the question: When do I decide to run LLMs locally?

PrivacyA very obvious argument in favor of running LLMs locally is that, in some cases, there is no alternative.

Related ML/AI Platform Build vs Buy Decision: What Factors to Consider Read moreWhat does it take to run LLMs locally?

However, recent advancements in optimization techniques, such as quantization and attention mechanism optimizations, have made it possible to run LLMs locally, even on a CPU.

LM StudioLM Studio is a user-friendly application designed to run LLMs locally.

2 months, 3 weeks назад @ neptune.ai
Scale And Track Your AI/ML Workflows: neptune.ai + Flyte & Union Integration
Scale And Track Your AI/ML Workflows: neptune.ai + Flyte & Union Integration Scale And Track Your AI/ML Workflows: neptune.ai + Flyte & Union Integration

Like Union, Neptune excels in scalability, making it the ideal tracking solution for teams working on large-scale model training.

The new Neptune Flyte plugin enables you to use Neptune to track, visualize, and manage your models.

In this blog post, you’ll learn how to use the Neptune plugin on Union.

Orchestrate and track your models with Flytekit’s Neptune PluginIn Union, data and compute are fundamental building blocks for developing all workflows.

With flytekit’s Neptune plugin, you can easily track your experiments, visualize results, and debug your models.

4 months назад @ neptune.ai
LLM Hallucinations 101: Why Do They Appear? Can We Avoid Them?
LLM Hallucinations 101: Why Do They Appear? Can We Avoid Them? LLM Hallucinations 101: Why Do They Appear? Can We Avoid Them?

What are LLM hallucinations?

LLM hallucinations become a problem in LLM-based applicationsMost of the time, if you use an LLM, you probably won’t use a base LLM but an LLM-based assistant whose goal is to help with your requests and reliably answer your questions.

Before we dive into this further, I’d like to stress that when thinking about LLM hallucinations, it’s important to keep in mind the difference between a base LLM and an LLM-based assistant.

When we talk about LLM hallucinations as a problematic phenomenon, it’s in the context of an LLM-based assistant or system.

While it’s unlikely that this process introduces new hallucinations, hallucinations seeded upstream are amplified.

4 months, 1 week назад @ neptune.ai
LLM Guardrails: Secure and Controllable Deployment
LLM Guardrails: Secure and Controllable Deployment LLM Guardrails: Secure and Controllable Deployment

LLM guardrails prevent models from generating harmful, biased, or inappropriate content and ensure that they adhere to guidelines set by developers and stakeholders.

LLM guardrails are small programs that validate and correct the modes’ outputs to ensure they align with your application’s specific requirements and context.

We’ll approach the broad and constantly evolving field of LLM guardrails in three stages:First, we’ll talk about the key vulnerabilities threatening AI applications.

We’ll explore these different kinds of LLM guardrails using the Guardrails AI framework, an open-source tool for building reliable AI applications.

| SourceRule-based data validationThe simplest type of LLM g…

4 months, 2 weeks назад @ neptune.ai
Reinforcement Learning From Human Feedback (RLHF) For LLMs
Reinforcement Learning From Human Feedback (RLHF) For LLMs Reinforcement Learning From Human Feedback (RLHF) For LLMs

TL;DR Reinforcement Learning from Human Feedback (RLHF) unlocked the full potential of today’s large language models (LLMs).

Reinforcement Learning from Human Feedback (RLHF) has turned out to be the key to unlocking the full potential of today’s large language models (LLMs).

Related LLM Evaluation For Text Summarization Read moreThe RLHF processThe RLHF process consists of three steps:Collecting human feedback.

Collecting human feedbackThe first step in RLHF is to collect human feedback in the so-called preference dataset.

We analyzed the three steps of the RLHF training pipeline: collecting human feedback, training the reward model, and fine-tuning the LLM.

4 months, 3 weeks назад @ neptune.ai
LLM For Structured Data
LLM For Structured Data LLM For Structured Data

However, when we look for data in a specific domain or organization, we often end up finding structured data.

The most likely reason is that structured data is still the de facto standard for quantitative information.

Consequently, in the age of Large Language Models (LLM), structured data still is and will continue to be relevant—even Microsoft is working on adding Large Language Models (LLMs) to Excel!

LLMs are mostly used with unstructured data, particularly text, but with the proper tools, they can also help tackle tasks with structured data.

Use case 3: Synthetic structured data generationWhen working with structured datasets, it is common to need more data with the same characteristic…

5 months назад @ neptune.ai
▶️ YouTube
Yannic Kilcher Yannic Kilcher
последний пост 1 week, 4 days назад
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models (Paper Explained)
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models (Paper Explained) DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models (Paper Explained)

#deepseek #llm #reinforcementlearning GRPO is one of the core advancements used in Deepseek-R1, but was introduced already last year in this paper that uses a combination of new RL techniques and iterative data collection to achieve remarkable performance on mathematics benchmarks with just a 7B model. Paper: https://arxiv.org/abs/2402.03300 Abstract:

Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature. In this paper, we introduce DeepSeekMath 7B, which continues pre-training DeepSeek-Coder-Base-v1.5 7B with 120B math-related tokens sourced from Common Crawl, together with natural language and code data. DeepSeekMath 7B has achie…

1 week, 4 days назад @ youtube.com
Traditional Holiday Live Stream
Traditional Holiday Live Stream Traditional Holiday Live Stream

https://ykilcher.com/discord Links:

TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick

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BiliBili: https://space.bilibili.com/1824646584 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:/…

1 month, 1 week назад @ youtube.com
Byte Latent Transformer: Patches Scale Better Than Tokens (Paper Explained)
Byte Latent Transformer: Patches Scale Better Than Tokens (Paper Explained) Byte Latent Transformer: Patches Scale Better Than Tokens (Paper Explained)

#tokenization #llm #meta This paper does away with tokenization and creates an LLM architecture that operates on dynamically sized "patches" instead of tokens. By controlling the patch size, they gain a level of control over the tradeoff between model size and FLOPs and use that to achieve more favorable scaling behavior than classically tokenized LLMs. Paper: https://ai.meta.com/research/publications/byte-latent-transformer-patches-scale-better-than-tokens/

Code: https://github.com/facebookresearch/blt Abstract:

We introduce the Byte Latent Transformer (BLT), a new byte-level LLM architecture that, for the first time, matches tokenization-based LLM performance at scale with significant imp…

1 month, 2 weeks назад @ youtube.com
Safety Alignment Should be Made More Than Just a Few Tokens Deep (Paper Explained)
Safety Alignment Should be Made More Than Just a Few Tokens Deep (Paper Explained) Safety Alignment Should be Made More Than Just a Few Tokens Deep (Paper Explained)

This paper demonstrates in a series of experiments that current safety alignment techniques of LLMs, as well as corresponding jailbreaking attacks, are in large part focusing on modulating the distribution of the first few tokens of the LLM response. Paper: https://openreview.net/forum?id=6Mxhg9PtDE&s=09 Abstract:

The safety alignment of current Large Language Models (LLMs) is vulnerable. Simple attacks, or even benign fine-tuning, can jailbreak aligned models. We note that many of these vulnerabilities are related to a shared underlying issue: safety alignment can take shortcuts, wherein the alignment adapts a model's generative distribution primarily over only its very first few output to…

1 month, 4 weeks назад @ youtube.com
TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters (Paper Explained)
TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters (Paper Explained) TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters (Paper Explained)

A deep dive into the TokenFormer and an opinion about its impact, novelty, and relation to prior work. Paper: https://arxiv.org/abs/2410.23168 Abstract:

Transformers have become the predominant architecture in foundation models due to their excellent performance across various domains. However, the substantial cost of scaling these models remains a significant concern. This problem arises primarily from their dependence on a fixed number of parameters within linear projections. When architectural modifications (e.g., channel dimensions) are introduced, the entire model typically requires retraining from scratch. As model sizes continue growing, this strategy results in increasingly high com…

2 months, 2 weeks назад @ youtube.com
GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models
GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

This paper (by Apple) questions the mathematical reasoning abilities of current LLMs and designs a synthetic template-based dataset distribution to investigate various aspects around LLM performance of high-school level math questions. Paper: https://arxiv.org/abs/2410.05229 Abstract:

Recent advancements in Large Language Models (LLMs) have sparked interest in their formal reasoning capabilities, particularly in mathematics. The GSM8K benchmark is widely used to assess the mathematical reasoning of models on grade-school-level questions. While the performance of LLMs on GSM8K has significantly improved in recent years, it remains unclear whether their mathematical reasoning capabilities hav…

3 months, 2 weeks назад @ youtube.com
Were RNNs All We Needed? (Paper Explained)
Were RNNs All We Needed? (Paper Explained) Were RNNs All We Needed? (Paper Explained)

This paper posits the interesting question: How much of the performance of Mamba, S4, and other state-space-like models is actually just attributable to some very core concepts - rather than their elaborate architectures. The authors construct minimal versions of GRUs and LSTMs and report competitive performance. Paper: https://arxiv.org/abs/2410.01201 Abstract:

The scalability limitations of Transformers regarding sequence length have renewed interest in recurrent sequence models that are parallelizable during training. As a result, many novel recurrent architectures, such as S4, Mamba, and Aaren, have been proposed that achieve comparable performance. In this work, we revisit traditional …

3 months, 3 weeks назад @ youtube.com
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters (Paper)
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters (Paper) Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters (Paper)

How can one best use extra FLOPS at test time? Paper: https://arxiv.org/abs/2408.03314 Abstract:

Enabling LLMs to improve their outputs by using more test-time computation is a critical step towards building generally self-improving agents that can operate on open-ended natural language. In this paper, we study the scaling of inference-time computation in LLMs, with a focus on answering the question: if an LLM is allowed to use a fixed but non-trivial amount of inference-time compute, how much can it improve its performance on a challenging prompt? Answering this question has implications not only on the achievable performance of LLMs, but also on the future of LLM pretraining and how one s…

4 months назад @ youtube.com
Privacy Backdoors: Stealing Data with Corrupted Pretrained Models (Paper Explained)
Privacy Backdoors: Stealing Data with Corrupted Pretrained Models (Paper Explained) Privacy Backdoors: Stealing Data with Corrupted Pretrained Models (Paper Explained)

#llm #privacy #finetuning Can you tamper with a base model in such a way that it will exactly remember its fine-tuning data? This paper presents a method of doing exactly that, and implements it in modern transformers. OUTLINE:

0:00 - Intro & Overview

10:50 -Core idea: single-use data traps

44:30 - Backdoors in transformer models

58:00 - Additional numerical tricks

1:00:35 - Experimental results & conclusion Paper: https://arxiv.org/abs/2404.00473

Code: https://github.com/ShanglunFengatETHZ/PrivacyBackdoor Abstract:

Practitioners commonly download pretrained machine learning models from open repositories and finetune them to fit specific applications. We show that this practice introduces a…

6 months назад @ youtube.com
Scalable MatMul-free Language Modeling (Paper Explained)
Scalable MatMul-free Language Modeling (Paper Explained) Scalable MatMul-free Language Modeling (Paper Explained)

Matrix multiplications (MatMuls) are pervasive throughout modern machine learning architectures. However, they are also very resource intensive and require special accelerators (GPUs). This paper explores architectures that do away with MatMuls and use quantization and recurrence to keep performance up. OUTLINE:

0:00 - Intro

2:30 - MatMul is everywhere

5:55 - Ternary accumulation as a substitute for matrix multiplication

16:35 - Replacing attention layers with recurrent layers

32:40 - Replacing dense layers with ternary channel mixing

38:30 - Language modelling results & scaling laws

45:00 - Other experimental results

48:20 - Conclusion Paper: https://arxiv.org/abs/2406.02528

Code: https://…

7 months назад @ youtube.com
Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools (Paper Explained)
Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools (Paper Explained) Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools (Paper Explained)

#rag #hallucinations #legaltech An in-depth look at a recent Stanford paper examining the degree of hallucinations in various LegalTech tools that incorporate LLMs. OUTLINE:

0:00 - Intro

1:58 - What are legal research tools and how are large language models used by them?

5:30 - Overview and abstract of the paper

9:29 - What is a hallucination and why do they occur?

15:45 - What is retrieval augmented generation (RAG)?

25:00 - Why LLMs are a bad choice when reasoning is involved

29:16 - The products that were tested

32:00 - Some shady practices by the researchers in the back and forth with the legal research companies

37:00 - Legal technology companies’ marketing claims to eliminate or solve…

7 months, 2 weeks назад @ youtube.com
xLSTM: Extended Long Short-Term Memory
xLSTM: Extended Long Short-Term Memory xLSTM: Extended Long Short-Term Memory

xLSTM is an architecture that combines the recurrency and constant memory requirement of LSTMs with the large-scale training of transformers and achieves impressive results. Paper: https://arxiv.org/abs/2405.04517 Abstract:

In the 1990s, the constant error carousel and gating were introduced as the central ideas of the Long Short-Term Memory (LSTM). Since then, LSTMs have stood the test of time and contributed to numerous deep learning success stories, in particular they constituted the first Large Language Models (LLMs). However, the advent of the Transformer technology with parallelizable self-attention at its core marked the dawn of a new era, outpacing LSTMs at scale. We now raise a sim…

8 months, 1 week назад @ youtube.com
[ML News] OpenAI is in hot waters (GPT-4o, Ilya Leaving, Scarlett Johansson legal action)
[ML News] OpenAI is in hot waters (GPT-4o, Ilya Leaving, Scarlett Johansson legal action) [ML News] OpenAI is in hot waters (GPT-4o, Ilya Leaving, Scarlett Johansson legal action)

#gpt4o #sky #scarlettjohansson After the release of their flagship model GPT-4o, OpenAI finds itself in multiple controversies and an exodus of senior personnel - notably Ilya Sutskever References:

https://openai.com/index/gpt-4o-and-more-tools-to-chatgpt-free/

https://openai.com/index/hello-gpt-4o/

https://x.com/LiamFedus/status/1790064963966370209?t=rx2YBT9AdDdKPhI6dUH4zA&s=09

https://x.com/lmsysorg/status/1790097588399779991?t=rx2YBT9AdDdKPhI6dUH4zA&s=09

https://x.com/bindureddy/status/1790127425705120149?t=mMUBqFBRphx-bDuZ1j3mjQ&s=09

https://openai.com/index/improvements-to-data-analysis-in-chatgpt/

https://openai.com/index/openai-and-reddit-partnership/

https://archive.ph/jHlMm

https:/…

8 months, 3 weeks назад @ youtube.com
ORPO: Monolithic Preference Optimization without Reference Model (Paper Explained)
ORPO: Monolithic Preference Optimization without Reference Model (Paper Explained) ORPO: Monolithic Preference Optimization without Reference Model (Paper Explained)

Paper: https://arxiv.org/abs/2403.07691 Abstract:

While recent preference alignment algorithms for language models have demonstrated promising results, supervised fine-tuning (SFT) remains imperative for achieving successful convergence. In this paper, we study the crucial role of SFT within the context of preference alignment, emphasizing that a minor penalty for the disfavored generation style is sufficient for preference-aligned SFT. Building on this foundation, we introduce a straightforward and innovative reference model-free monolithic odds ratio preference optimization algorithm, ORPO, eliminating the necessity for an additional preference alignment phase. We demonstrate, both empiri…

9 months, 1 week назад @ youtube.com
[ML News] Chips, Robots, and Models
[ML News] Chips, Robots, and Models [ML News] Chips, Robots, and Models

OUTLINE:

0:00 - Intro

0:19 - Our next-generation Meta Training and Inference Accelerator

01:39 - ALOHA Unleashed

03:10 - Apple Inks $50M Deal with Shutterstock for AI Training Data

04:28 - OpenAI Researchers, Including Ally of Sutskever, Fired for Alleged Leaking

05:01 - Adobe's Ethical Firefly AI was Trained on Midjourney Images

05:52 - Trudeau announces $2.4billion for AI-related investments

06:48 - RecurrentGemma: Moving Past Transformers for Efficient Open Language Models

07:15 - CodeGemma - an official Google release for code LLMs

07:24 - Mistral AI: Cheaper, Better, Faster, Stronger

08:08 - Vezora/Mistral-22B-v0.1

09:00 - WizardLM-2, next generation state-of-the-art-LLM

09:31 - Idefic…

9 months, 1 week назад @ youtube.com
Henry AI Labs Henry AI Labs
последний пост 5 months, 4 weeks назад
Chunking with Generative Feedback Loops
Chunking with Generative Feedback Loops Chunking with Generative Feedback Loops

Hey everyone! I am super excited to share a quick notebook on using Generative Feedback Loops to chunk code files and better structure how they are indexed in the Weaviate Vector Database! Chunking is one of the key topics in Vector Search. We need to break up long documents into smaller parts that we can encode with a pre-trained embedding model and index in a vector index, such as HNSW-PQ. Most solutions use some form of a rolling token window such as taking every 300 tokens as a chunk, with say 50 tokens overlapping between each window. Unfortunately, this solution doesn't work that well for code particularly. We don't want the chunk to cut off in the middle of a function or class defini…

5 months, 4 weeks назад @ youtube.com
Gemini 1.5 Pro and Flash - Demo of Long Context LLMs!
Gemini 1.5 Pro and Flash - Demo of Long Context LLMs! Gemini 1.5 Pro and Flash - Demo of Long Context LLMs!

Hey everyone! Thanks so much for watching this video exploring Gemini Pro 1.5 and Gemini Flash! Long Context LLMs!! This video covers 3 key tests, the classic "Lost in the Middle" exploration, using Long Context LLMs as Re-rankers in Search, and finally, testing Many-Shot In-Context Learning! I am really excited about the potential of Many-Shot In-Context Learning with DSPy's `BootstrapFewShot` and Gemini, curious to know what you think! Notebook: https://github.com/weaviate/recipes/blob/main/integrations/dspy/llms/Gemini-1.5-Pro-and-Flash.ipynb Gemini 1.5 Technical Report: https://storage.googleapis.com/deepmind-media/gemini/gemini_v1_5_report.pdf Chapters

0:00 Gemini 1.5!!

1:25 Setup and …

8 months, 3 weeks назад @ youtube.com
Llama 3 RAG Demo with DSPy Optimization, Ollama, and Weaviate!
Llama 3 RAG Demo with DSPy Optimization, Ollama, and Weaviate! Llama 3 RAG Demo with DSPy Optimization, Ollama, and Weaviate!

Hey everyone! Thank you so much for watching this overview of Llama 3 looking at the release notes and seeing a demo of how to integrate it with DSPy through Ollama and how to use DSPy's MIPRO to find the optimal prompt when using this new large language model for RAG! We are hosting an event in San Francisco on May 1st with Arize AI and Cohere, featuring a talk from Omar Khattab, the lead author of DSPy! Hope to see you there! https://lu.ma/dspy Introducing Meta Llama 3: https://ai.meta.com/blog/meta-llama-3/ Ollama Llama 3: https://ollama.com/library/llama3 Weaviate Recipes: https://github.com/weaviate/recipes/blob/main/integrations/dspy/llms/Llama3.ipynb Chapters

0:00 Llama3!!

1:28 Relea…

9 months, 3 weeks назад @ youtube.com
3blue1brown 3blue1brown
последний пост 1 week назад
The topology of two-note chords
The topology of two-note chords The topology of two-note chords

Based on a construction in this video: https://youtu.be/IQqtsm-bBRU

1 week назад @ youtube.com
The space of all musical intervals
The space of all musical intervals The space of all musical intervals

Full video: https://youtu.be/IQqtsm-bBRU

4 weeks, 1 day назад @ youtube.com
The barber pole optical mystery
The barber pole optical mystery The barber pole optical mystery

Series exploring optics: https://www.youtube.com/watch?v=QCX62YJCmGk&list=PLZHQObOWTQDMKqfyUvG2kTlYt-QQ2x-ui

1 month назад @ youtube.com
Monge's Theorem
Monge's Theorem Monge's Theorem

Full video: https://youtu.be/piJkuavhV50

1 month, 1 week назад @ youtube.com
Thinking through double slits
Thinking through double slits Thinking through double slits

Extracted from this video about holograms: https://youtu.be/EmKQsSDlaa4

1 month, 1 week назад @ youtube.com
The inscribed square problem
The inscribed square problem The inscribed square problem

Full video: https://youtu.be/IQqtsm-bBRU

1 month, 2 weeks назад @ youtube.com
This open problem taught me what topology is
This open problem taught me what topology is This open problem taught me what topology is

A beautiful solution to the inscribed rectangle problem.

Playlist with more neat proofs: https://www.youtube.com/playlist?list=PLZHQObOWTQDPSKntUcMArGheySM4gL7wS

Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support

An equally valuable form of support is to simply share the videos. This argument was originally by Herbert Vaughan, appearing for examples in this issue of the Topology Proceedings.

https://topology.nipissingu.ca/tp/reprints/v06/tp06107.pdf 2020 Paper by Greene and Lobb:

https://arxiv.org/pdf/2005.09193 Nice Quanta article about this result:

https://www.quantamagazine.org/new-geometric-perspective-cracks-old-problem-about-rectangles…

1 month, 2 weeks назад @ youtube.com
The meaning within the Mandelbrot set
The meaning within the Mandelbrot set The meaning within the Mandelbrot set

The full video dives deeper into the field of math studying this called holomorphic dynamics: https://youtu.be/LqbZpur38nw

2 months, 2 weeks назад @ youtube.com
The scale of training LLMs
The scale of training LLMs The scale of training LLMs

From this 7-minute LLM explainer: https://youtu.be/LPZh9BOjkQs

2 months, 2 weeks назад @ youtube.com
Large Language Models explained briefly
Large Language Models explained briefly Large Language Models explained briefly

Dig deeper here: https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi

Technical details as a talk: https://youtu.be/KJtZARuO3JY

Made for an exhibit at the Computer History Museum: https://computerhistory.org/

Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support Timestamps:

0:00 - Who this was made for

0:41 - What are large language models?

7:48 - Where to learn more No secret end-screen vlog for this one, the end-screen real estate was all full! ------------------ These animations are largely made using a custom Python library, manim. See the FAQ comments here:

https://3b1b.co/faq#manim

https://github.com/3b1b/manim

https:/…

2 months, 2 weeks назад @ youtube.com
This puzzle is tricker than it seems
This puzzle is tricker than it seems This puzzle is tricker than it seems

From this full video: https://youtu.be/piJkuavhV50

2 months, 2 weeks назад @ youtube.com
Sphere surface area proof sketch
Sphere surface area proof sketch Sphere surface area proof sketch

Full video: https://youtu.be/GNcFjFmqEc8

2 months, 3 weeks назад @ youtube.com
Newton’s Fractal is beautiful
Newton’s Fractal is beautiful Newton’s Fractal is beautiful

Full video: https://youtu.be/-RdOwhmqP5s

2 months, 3 weeks назад @ youtube.com
The Triangle Of Power
The Triangle Of Power The Triangle Of Power

The referenced stack exchange post by usename 2'5 9'2, http://math.stackexchange.com/questions/30046/alternative-notation-for-exponents-logs-and-roots

2 months, 3 weeks назад @ youtube.com
The twirling tiles puzzle
The twirling tiles puzzle The twirling tiles puzzle

Full video: https://youtu.be/piJkuavhV50

2 months, 3 weeks назад @ youtube.com
Two Minute Papers Two Minute Papers
последний пост 8 часов назад
OpenAI’s Deep Research: Unexpected Game Changer!
OpenAI’s Deep Research: Unexpected Game Changer! OpenAI’s Deep Research: Unexpected Game Changer!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 Deep research:

https://openai.com/index/introducing-deep-research/ Lambda DeepSeek instructions: https://docs.lambdalabs.com/education/large-language-models/deepseek-r1-ollama/ - Open Deep Research: https://opendeepresearch.vercel.app/

- Revisiting the McKinley Tariff of 1890 through the Lens of Modern Trade Theory

https://kevinbryanecon.com/o3McKinley.pdf

- Deep Research by Tyler Cowen February 4, 2025

https://marginalrevolution.com/marginalrevolution/2025/02/deep-research.html Complex tax situation: https://x.com/PatriceBTC/status/1886529037474127951

Daily briefing: https://x.com/mckaywrigley/status/…

8 часов назад @ youtube.com
OpenAI o3-mini - Thinking AI for Free…For Everyone!
OpenAI o3-mini - Thinking AI for Free…For Everyone! OpenAI o3-mini - Thinking AI for Free…For Everyone!

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papersllm o3 mini: https://openai.com/index/openai-o3-mini/

OpenAI Deep Research: https://openai.com/index/introducing-deep-research/ 🤝 Interested in sponsoring us? Click here: https://eu.jotform.com/241831324457354 Sources:

https://x.com/hxiao/status/1885522459329520089?s=46

https://x.com/techikansh/status/1885429093862187008?s=46

https://x.com/rlancemartin/status/1885748894220554445?s=46

https://x.com/buccocapital/status/1885792154129219959?s=46

https://x.com/_akhaliq/status/1885733581651050586?s=46

https://x.com/_akhaliq/status/1885833163764646267?s=46

https://x.com/aidan_mclau/status/1886078444855034055?s=4…

2 days, 11 hours назад @ youtube.com
NVIDIA Unveils AI For 150x Faster 3D Modeling!
NVIDIA Unveils AI For 150x Faster 3D Modeling! NVIDIA Unveils AI For 150x Faster 3D Modeling!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "InstantSplat: Sparse-view SfM-free Gaussian Splatting in Seconds" is available here:

https://instantsplat.github.io/

Try it out (hopefully works): https://huggingface.co/spaces/kairunwen/InstantSplat Clouds paper: https://arcanous98.github.io/projectPages/gaussianVolumes.html 📝 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:

Ben…

1 week, 1 day назад @ youtube.com
This New Free AI Is History In The Making!
This New Free AI Is History In The Making! This New Free AI Is History In The Making!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers Try it out (choose DeepSeek as your model): https://huggingface.co/chat/

Official (read the privacy policy below before you use this one): https://www.deepseek.com/ Run it at home:

https://www.reddit.com/r/selfhosted/comments/1i6ggyh/got_deepseek_r1_running_locally_full_setup_guide/

https://lmstudio.ai/ Links:

https://x.com/ivanfioravanti/status/1881565759123702140

https://eu.jotform.com/tables/241831324457354

https://x.com/deepseek_ai/status/1881318130334814301

https://x.com/nobody_qwert/status/1881620406710452535

https://github.com/bytedance/UI-TARS

https://github.com/bytedance/UI-TARS-desktop

https://…

2 weeks назад @ youtube.com
OpenAI’s New ChatGPT: 3 Secrets From The Paper!
OpenAI’s New ChatGPT: 3 Secrets From The Paper! OpenAI’s New ChatGPT: 3 Secrets From The Paper!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper is available here:

https://openai.com/index/openai-o1-system-card/ 📝 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, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Martin, Michael Albrecht, Michael Tedder, Owen Skarpness, Richard Sund…

2 weeks, 2 days назад @ youtube.com
NVIDIA’s New AI Learned How To Punch!
NVIDIA’s New AI Learned How To Punch! NVIDIA’s New AI Learned How To Punch!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "CLoSD - Closing the Loop between Simulation and

Diffusion for multi-task character control" is available here:

https://guytevet.github.io/CLoSD-page/ 📝 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, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, L…

2 weeks, 4 days назад @ youtube.com
DeepMind’s Veo2 AI - The New King Is Here!
DeepMind’s Veo2 AI - The New King Is Here! DeepMind’s Veo2 AI - The New King Is Here!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers Try Veo2 here (Notes: likely USA only so far and there may be a waitlist):

https://deepmind.google/technologies/veo/veo-2/ 📝 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, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Martin, Michael Albrecht, …

3 weeks, 4 days назад @ youtube.com
NVIDIA Cosmos - A Video AI…For Free!
NVIDIA Cosmos - A Video AI…For Free! NVIDIA Cosmos - A Video AI…For Free!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers Cosmos platform:

https://www.nvidia.com/en-us/ai/cosmos/

Hugging Face models: https://huggingface.co/collections/nvidia/cosmos-6751e884dc10e013a0a0d8e6

More: https://github.com/NVIDIA/Cosmos 📝 The paper "Cosmos World Foundation Model Platform for Physical AI" is available here:

https://research.nvidia.com/publication/2025-01_cosmos-world-foundation-model-platform-physical-ai 📝 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 woul…

1 month назад @ youtube.com
The Simulator That Could Supercharge Robotics!
The Simulator That Could Supercharge Robotics! The Simulator That Could Supercharge Robotics!

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper is available here:

https://github.com/Genesis-Embodied-AI/DiffTactile

https://difftactile.github.io/ 📝 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, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Martin, Michael Albrecht, Michael Te…

1 month назад @ youtube.com
NVIDIA’s New AI: A Revolution In 3D Modeling!
NVIDIA’s New AI: A Revolution In 3D Modeling! NVIDIA’s New AI: A Revolution In 3D Modeling!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The Edify 3D paper available here:

https://research.nvidia.com/labs/dir/edify-3d/

https://build.nvidia.com/shutterstock/edify-3d 📝 MeshGPT paper: https://nihalsid.github.io/mesh-gpt/ 📝 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, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Dav…

1 month, 1 week назад @ youtube.com
New Super Resolution AI - Enhance ~10x Faster!
New Super Resolution AI - Enhance ~10x Faster! New Super Resolution AI - Enhance ~10x Faster!

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper "Deep Fourier-based Arbitrary-scale Super-resolution for Real-time Rendering" is available here:

https://iamxym.github.io/DFASRR.github.io/ 📝 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, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewal…

1 month, 2 weeks назад @ youtube.com
NVIDIA’s New AI: Training 10,000x Faster!
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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The papers are available here:

https://hover-versatile-humanoid.github.io/

https://blogs.nvidia.com/blog/robot-learning-humanoid-development/ 📝 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, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Marti…

1 month, 3 weeks назад @ youtube.com
Unreal Engine 5 - Real Time Ray Tracing Is Here!
Unreal Engine 5 - Real Time Ray Tracing Is Here! Unreal Engine 5 - Real Time Ray Tracing Is Here!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers Try Unreal Engine 5 here:

https://www.unrealengine.com/en-US/unreal-engine-5 My free course on ray tracing for you Fellow Scholars:

https://users.cg.tuwien.ac.at/zsolnai/gfx/rendering-course/ Our earlier paper with the spheres scene:

https://users.cg.tuwien.ac.at/zsolnai/gfx/adaptive_metropolis/ LuxCoreRender (free and open source): https://luxcorerender.org/ 📝 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 …

1 month, 3 weeks назад @ youtube.com
OpenAI’s Sora Is Here, But...
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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers Sora is available here (for select countries, not in EU currently):

https://openai.com/sora/

https://sora.com/ 📝 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, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Martin, Michael Albrecht, Michael Tedd…

1 month, 4 weeks назад @ youtube.com
DeepMind’s New Gaming AI Does The Impossible!
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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 DeepMind's Genie 2 info is available here:

https://deepmind.google/discover/blog/genie-2-a-large-scale-foundation-world-model/ 📝 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, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Martin, Michael Albr…

2 months назад @ youtube.com
DataFest Video DataFest Video
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Data Fest Online 2020 AI Hardware Track Premiere
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DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

5 months назад @ youtube.com
Mikita Shchutski | A small BERT towards Large Medical Models
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Interview with Juergen Schmidhuber at Data Christmas 2020
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02:00-05:38 What do you think were the most outstanding underestimated news and achievements in AI field in 2020?

05:41-11:28 What do you think about trends in ML like transformers trying to replace LSTMs in NLP?

11:29-16:06 Are you working on any new types of models right now?

16:07-20:41 What is your opinion on the most underestimated ML subfield like Reinforcement Learning?

20:42-22:17 Your best recommendation for our community is to look into AI in the real physical world, right?

22:18-33:10 Do you think it is possible to achieve great results in creative AI, particularly in subjective beauty?

33:17-35:50 What prevents chat bots from reaching more intelligent levels?

36:03-39:39 What is…

5 months назад @ youtube.com
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Это Дмитрий Бабаев, руководитель ML R&D в Яндекс Картах. Дмитрий рассказал о самых запоминающихся статьях про обучение с подкреплением с ICML этого года. Например, Дмитрий поговорил о работе Stop Regressing: Training Value Functions via Classification for Scalable Deep RL от DeepMind, которая посвящена нестандартному подходу к регрессиям. Подписывайтесь на телеграм-канал «Яндекс для ML-инженеров»: https://t.me/yandexforml

1 day, 9 hours назад @ youtube.com
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Это Андрей Бут, руководитель команды YandexGPT Alignment в Яндекс Поиске. В этом видео он рассказывает о самых интересных статьях на тему обработки естественного языка с нынешней конференции ICML. Андрей рассмотрел ряд проблем, которые ещё однозначно не решены в современных LLM. Подписывайтесь на телеграм-канал «Яндекс для ML-инженеров»: https://t.me/yandexforml

2 days, 9 hours назад @ youtube.com
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Это Алексей Гусаков, CTO Яндекс Поиска. В этом видео он поделился личными впечатлениями от нынешнего ICML и дал напутственные слова коллегам из индустрии. Алексей вспомнил, как мероприятия по ML за 10 лет превратились из скромных посиделок в конференции на тысячи участников, а ещё подробно рассказал про его любимую статью Physics of LLMs. Подписывайтесь на телеграм-канал «Яндекс для ML-инженеров»: https://t.me/yandexforml

3 days, 9 hours назад @ youtube.com
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Это выступление Руслана Дюсаева, разработчика группы машинного обучения антиспама Яндекс 360, на конференции Data Fest в нашем офисе 2 июня 2024 года. Доклад подготовлен в рамках трека Practical ML про опыт применения ML в реальных задачах Яндекса. Подписывайтесь на наши медиа в телеграме: Канал Яндекса о технологиях и людях, которые их создают: https://t.me/Yandex4Developers Канал Яндекса специально для ML-сообщества: https://t.me/yandexforml

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2 weeks, 1 day назад @ youtube.com
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2 weeks, 3 days назад @ youtube.com
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2 weeks, 4 days назад @ youtube.com
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2 weeks, 6 days назад @ youtube.com
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Поиск давно перестал быть набором из 10 синих ссылок. Около половины пользовательских задач успешно и быстро решаются с помощью изображений, видео, фактовых ответов и других элементов выдачи. В Яндексе создали Блендер — инструмент на базе ML, который смешивает документы разной модальности и источники, чтобы оптимизировать пользовательский опыт. Как выбирали метрики, реализовывали Блендер и с какими особенностями столкнулись, рассказал Алексей Голиков, руководитель команды качественных вызовов в Яндексе Другие мероприятия Яндекса вы можете посмотреть здесь: https://events.yandex.ru/

3 weeks назад @ youtube.com
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3 weeks, 1 day назад @ youtube.com
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3 weeks, 2 days назад @ youtube.com
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VK RecSys Challenge: https://ods.ai/competitions/aivkchallenge

_____

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 week назад @ youtube.com
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Спикер: Иван Брагин, Staff machine learning engineer constructor.io Data Ёлка 2024 в гостях у VK: https://ods.ai/events/data-elka-24-vk-offline

VK RecSys Challenge: https://ods.ai/competitions/aivkchallenge

_____

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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Спикер: Станислав Чистяков, директор по развитию бизнеса, компания Noventiq Data Ёлка 2024 в гостях у VK: https://ods.ai/events/data-elka-24-vk-offline

VK RecSys Challenge: https://ods.ai/competitions/aivkchallenge

_____

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 week назад @ youtube.com
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Спикер: Александр Пославский, руководитель группы Сore ML, AI VK Data Ёлка 2024 в гостях у VK: https://ods.ai/events/data-elka-24-vk-offline

VK RecSys Challenge: https://ods.ai/competitions/aivkchallenge

_____

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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Спикер: Роман Дерунец Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

Трек NLP: https://ods.ai/tracks/sibfest5-nlp

_____

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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Спикер: Мария Мичурина Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

Трек NLP: https://ods.ai/tracks/sibfest5-nlp

_____

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
Дари Батурова | BERTScore для русского языка
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Спикер: Дари Батурова Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

Трек NLP: https://ods.ai/tracks/sibfest5-nlp

_____

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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Спикер: Ислам Умаров Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

Трек: https://ods.ai/tracks/sibfest5-ml-security

_____

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
Сиракан Багдасарян | ML-платформа: что это за зверь и как его приготовить?
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Спикер: Сиракан Багдасарян, MLOps-инженер, OZON Банк Data Halloween 2024: https://ods.ai/events/halloween2024_spb

Трек: https://ods.ai/tracks/halloween2024-spb

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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, 1 week назад @ youtube.com
Никита Венедиктов | Алло,Эйнштейн? Создание call-ботов на базе LLM в 2024 году
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Спикер: Никита Венедиктов, NLP Researcher, RAFT Data Halloween 2024: https://ods.ai/events/halloween2024_spb

Трек: https://ods.ai/tracks/halloween2024-spb

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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, 1 week назад @ youtube.com
Алексей Козлов, Евгений Тайчинов | HRBert2.0: улучшаем векторизацию вакансий и резюме
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Спикер: Алексей Козлов, Евгений Тайчинов, ML-инженер, Работа.ру Data Halloween 2024: https://ods.ai/events/halloween2024_spb

Трек: https://ods.ai/tracks/halloween2024-spb

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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, 1 week назад @ youtube.com
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Спикер: Марк Паненко, CDS, OZON Банк Data Halloween 2024: https://ods.ai/events/halloween2024_spb

Трек: https://ods.ai/tracks/halloween2024-spb

_____

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, 1 week назад @ youtube.com
Дмитрий Тихомиров | HypEx и A B тесты
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Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

Трек: https://ods.ai/tracks/sibfest5-ml-infrastructure

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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, 1 week назад @ youtube.com
Маршалова Аня, Тихобаева Оля | Вспомнить всё по короткой подсказке разбор статьи Rethinking LLM
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Спикер: Маршалова Аня, Тихобаева Оля

Название: Вспомнить всё по короткой подсказке разбор статьи Rethinking LLM Memorization through the Lens of Adversarial Compression Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

Трек: https://ods.ai/tracks/sibfest5-ds-talks

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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, 1 week назад @ youtube.com
Мурашкина Анна | Знания и пиксели как совместить разбор статьи Fetch A Set
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Спикер: Мурашкина Анна Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

Трек: https://ods.ai/tracks/sibfest5-ds-talks

_____

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, 2 weeks назад @ youtube.com
Primer Primer
последний пост 5 days, 5 hours назад
Simulating the Evolution of Aging
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Patreon: https://www.patreon.com/primerlearning Ageless book: https://www.amazon.com/Ageless-Science-Getting-Older-Without/dp/0525566317/ Papers and other further reading:

Diversity of aging across the tree of life: https://pmc.ncbi.nlm.nih.gov/articles/PMC4157354/

Antagonistic pleiotropy and p53: https://pmc.ncbi.nlm.nih.gov/articles/PMC2771578/

An unsolved problem of biology (Medawar): https://ia903408.us.archive.org/31/items/medawar-1952-unsolved-problem/Medawar1952-Unsolved-Problem.pdf

Evolution of the mutation rate: https://pmc.ncbi.nlm.nih.gov/articles/PMC2910838/

Our World in Data Life Expectancy explainer: https://ourworldindata.org/life-expectancy-how-is-it-calculated-and-how-shoul…

5 days, 5 hours назад @ youtube.com
Simulating the Evolution of Rock, Paper, Scissors
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Twitch: https://www.twitch.tv/justin_helps

Discord: https://discord.gg/NbruaNW

Store: https://store.dftba.com/collections/primer

Patreon: https://www.patreon.com/primerlearning Source and further reading on the common side-blotched lizard:

Sinervo, B.; C.M. Lively (1996). "The rock–paper–scissors game and the evolution of alternative male strategies". Nature. 380 (6571): 240–243.

https://en.wikipedia.org/wiki/Common_side-blotched_lizard Made with Godot

Github: https://github.com/Primer-Learning/PrimerTools Made possible by support from these wonderful Patrons:

abledbody

Alba Caparros-Roissard

Andrew Lang

Anthony Eufemio

Brian Cloutier

Captain Chinchilla

Christoph Grabo (@asaaki)

Christy Ser…

6 months, 4 weeks назад @ youtube.com
Evolving Rock Paper Scissors
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🎧 Podcasts
Lex Fridman AI Podcast Lex Fridman AI Podcast
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Dylan Patel is the founder of SemiAnalysis, a research & analysis company specializing in semiconductors, GPUs, CPUs, and AI hardware.

Nathan Lambert is a research scientist at the Allen Institute for AI (Ai2) and the author of a blog on AI called Interconnects.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep459-scSee below for timestamps, and to give feedback, submit questions, contact Lex, etc.

Go to https://invideo.io/i/lexpodGitHub: Developer platform and AI code editor.

(4:31:34) – AI agents(4:40:16) – Programming and AI(4:47:43) – Open source(4:56:55) – Stargate(5:04:24) – Future of AIPODCAST LINKS:– Podcast Website: https://lexfridman.com/podcast–…

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Marc Andreessen is an entrepreneur, investor, co-creator of Mosaic, co-founder of Netscape, and co-founder of the venture capital firm Andreessen Horowitz.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep458-scSee below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

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1 week, 4 days назад @ lexfridman.com
#457 – Jennifer Burns: Milton Friedman, Ayn Rand, Economics, Capitalism, Freedom
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Jennifer Burns is a historian of ideas, focusing on the evolution of economic, political, and social ideas in the United States in the 20th century.

She wrote two biographies, one on Milton Friedman, and the other on Ayn Rand.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep457-scSee below for timestamps, and to give feedback, submit questions, contact Lex, etc.

Go to https://brain.fm/lexGitHub: Developer platform and AI code editor.

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2 weeks, 4 days назад @ lexfridman.com
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Volodymyr Zelenskyy is the President of Ukraine.

On YouTube this episode is available in English, Ukrainian, and Russian.

Captions and voice-over audio tracks are provided in English, Ukrainian, Russian, and the original mixed-language version, with subtitles available in your preferred language.

To listen to the original mixed language version, please select the English (UK) audio track audio track.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep456-scSee below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

1 month назад @ lexfridman.com
#455 – Adam Frank: Alien Civilizations and the Search for Extraterrestrial Life
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Adam Frank is an astrophysicist studying star systems and the search for extraterrestrial life and alien civilizations.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep455-scSee below for timestamps, and to give feedback, submit questions, contact Lex, etc.

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1 month, 2 weeks назад @ lexfridman.com
#454 – Saagar Enjeti: Trump, MAGA, DOGE, Obama, FDR, JFK, History & Politics
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He is exceptionally well-read, and the books he recommends are always fascinating and eye-opening.

You can check out all the books he mentions in this episode here: https://lexfridman.com/saagar-booksThank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep454-scSee below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

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2 months назад @ lexfridman.com
#453 – Javier Milei: President of Argentina – Freedom, Economics, and Corruption
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Javier Milei is the President of Argentina.

This episode is available in both English and Spanish.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep453-scSee below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

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2 months, 2 weeks назад @ lexfridman.com
#452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity
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Dario Amodei is the CEO of Anthropic, the company that created Claude.

Amanda Askell is an AI researcher working on Claude’s character and personality.

Chris Olah is an AI researcher working on mechanistic interpretability.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep452-scSee below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

(3:49:02) – Character training(3:50:01) – Nature of truth(3:54:38) – Optimal rate of failure(4:01:49) – AI consciousness(4:16:20) – AGI(4:24:58) – Chris Olah – Mechanistic Interpretability(4:29:49) – Features, Circuits, Universality(4:47:23) – Superposition(4:58:22) – Monosemanticity(5:05…

2 months, 3 weeks назад @ lexfridman.com
#451 – Rick Spence: CIA, KGB, Illuminati, Secret Societies, Cults & Conspiracies
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Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep451-scSee below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

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3 months, 1 week назад @ lexfridman.com
#450 – Bernie Sanders Interview
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Bernie Sanders is a US Senator from Vermont and a two-time presidential candidate.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep450-scSee below for timestamps, and to give feedback, submit questions, contact Lex, etc.

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Go to https://drinkLMNT.com/lexOUTLINE:(00:00) – Introduction(08:51) – MLK Jr(11:43) – Corruption in politics(23:00) – Healthcare in US(31:33) – 2016 election(37:32) – Barack Obama(43:26) – Capitalism(51:35) – Response to attacks(56:32) – AOC and progressive politics(1:04:24) – Mortality(1:06:30) – Hope fo…

3 months, 2 weeks назад @ lexfridman.com
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He is the presenter of the Netflix documentary series “Ancient Apocalypse”, the 2nd season of which has just been released.

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3 months, 3 weeks назад @ lexfridman.com
#448 – Jordan Peterson: Nietzsche, Hitler, God, Psychopathy, Suffering & Meaning
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Jordan Peterson is a psychologist, author, lecturer, and podcast host.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep448-scSee below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

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3 months, 4 weeks назад @ lexfridman.com
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#447 – Cursor Team: Future of Programming with AI #447 – Cursor Team: Future of Programming with AI

Aman Sanger, Arvid Lunnemark, Michael Truell, and Sualeh Asif are creators of Cursor, a popular code editor that specializes in AI-assisted programming.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep447-scSee below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

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4 months назад @ lexfridman.com
#446 – Ed Barnhart: Maya, Aztec, Inca, and Lost Civilizations of South America
#446 – Ed Barnhart: Maya, Aztec, Inca, and Lost Civilizations of South America #446 – Ed Barnhart: Maya, Aztec, Inca, and Lost Civilizations of South America

Ed Barnhart is an archaeologist and explorer specializing in ancient civilizations of the Americas.

He is the Director of the Maya Exploration Center, host of the ArchaeoEd Podcast, and lecturer on the ancient history of North, Central, and South America.

Ed is in part known for his groundbreaking work on ancient astronomy, mathematics, and calendar systems.

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4 months, 1 week назад @ lexfridman.com
#445 – Vivek Ramaswamy: Trump, Conservatism, Nationalism, Immigration, and War
#445 – Vivek Ramaswamy: Trump, Conservatism, Nationalism, Immigration, and War #445 – Vivek Ramaswamy: Trump, Conservatism, Nationalism, Immigration, and War

Vivek Ramaswamy is a conservative politician, entrepreneur, and author of many books on politics, including his latest titled Truths: The Future of America First.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep445-scSee below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

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Go to https://eightsleep.com/lexOUTLINE:(00:00) – Introduction(12:50) – Conservatism(16:06) – Progressivism(21:41) – DEI(26:33) – Bureaucracy(33:25) – Government efficiency(48:34) – Education(1:02:59) – Military Industrial Complex(1:25:18) – Illegal immigration(1:46:53) – Donald Trump(…

4 months, 2 weeks назад @ lexfridman.com
Microsoft Research Podcast Microsoft Research Podcast
последний пост 2 weeks назад
Ideas: Bug hunting with Shan Lu
Ideas: Bug hunting with Shan Lu Ideas: Bug hunting with Shan Lu

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2 weeks назад @ microsoft.com
Ideas: AI for materials discovery with Tian Xie and Ziheng Lu
Ideas: AI for materials discovery with Tian Xie and Ziheng Lu Ideas: AI for materials discovery with Tian Xie and Ziheng Lu

And now you can use this loop to design materials really quickly.

XIE: So you can really think about MatterSim and MatterGen accelerating different parts of materials discovery process.

They are also both foundation AI models, meaning they can both be used for a broad range of materials design problems.

Really, really a lot.

Yeah, I really, really like the example that Ziheng mentioned about the educational purposes.

3 weeks назад @ microsoft.com
Ideas: AI and democracy with Madeleine Daepp and Robert Osazuwa Ness
Ideas: AI and democracy with Madeleine Daepp and Robert Osazuwa Ness Ideas: AI and democracy with Madeleine Daepp and Robert Osazuwa Ness

DAEPP: You know, we didn’t really think about the term fraud until we started prepping for this interview with you.

BADANES: Right, right.

One of the things that I get asked a lot is, why can’t we just build good AI to detect bad AI, right?

BADANES: So next time my kids are in a fight, I’m going to point them to Copilot and say, work with Copilot to mediate.

[LAUGHS] No, that’s really, really interesting.

1 month, 2 weeks назад @ microsoft.com
NeurIPS 2024: The co-evolution of AI and systems with Lidong Zhou
NeurIPS 2024: The co-evolution of AI and systems with Lidong Zhou NeurIPS 2024: The co-evolution of AI and systems with Lidong Zhou

Earlier today, Lidong gave a keynote here at NeurIPS on the co-evolution of AI and systems engineering.

One dimension is that the scale of the AI systems that we have to support.

And the other dimension is if you look at AI systems, it’s actually a whole-stack kind of design.

STRICKLAND: Yeah, yeah.

ZHOU: Yeah, I think in terms of AI systems, I’m certainly pretty excited about what we can do together, you know, with a combination of AI and systems.

1 month, 3 weeks назад @ microsoft.com
NeurIPS 2024: AI for Science with Chris Bishop
NeurIPS 2024: AI for Science with Chris Bishop NeurIPS 2024: AI for Science with Chris Bishop

And then the second paradigm really emerged in the 17th century.

And so the third paradigm really began, I guess, sort of, in the ’50s and ’60s, the development of digital computers.

And when I think about AI for Science actually, the space of opportunity is colossal because science is, science is really just understanding more about the world around us.

And now the SMILES autoregressive model can now generate a molecule that’s an improvement on the starting molecule and knows about the protein binding.

But also if you think about [it], science is really about learning more about the world.

1 month, 3 weeks назад @ microsoft.com
Abstracts: NeurIPS 2024 with Jindong Wang and Steven Euijong Whang
Abstracts: NeurIPS 2024 with Jindong Wang and Steven Euijong Whang Abstracts: NeurIPS 2024 with Jindong Wang and Steven Euijong Whang

Today I’m talking to Jindong Wang, a senior researcher at Microsoft Research, and Steven Whang, a tenured associate professor at the Korea Advanced Institute of Science and Technology.

JINDONG WANG: OK, everybody knows that with the widespread usage of large language models, hallucination has become a crucial factor of concern.

So foreign key constraint basically requires that if there is some director mentioned in the movie table, it has to be one of the directors in the director table.

So now we can join the movie and director table and generate a bigger table.

HUIZINGA: Well, Jindong Wang and Steven Whang, thanks for joining us today, and to our listeners, thanks for tuning in.

1 month, 3 weeks назад @ microsoft.com
Abstracts: NeurIPS 2024 with Weizhu Chen
Abstracts: NeurIPS 2024 with Weizhu Chen Abstracts: NeurIPS 2024 with Weizhu Chen

The other one actually is some token actually is very, very hard to be predicted during the pretraining.

And the important thing for the data is about data filtering.

If we’re able to build a better model actually is able to benefit so many different kinds of application.

And definitely there’s a lot of things about how to build a better data [that] is unsolved yet in the literature.

And the other thing actually, we are working on something that’s very exciting.

2 months назад @ microsoft.com
Abstracts: NeurIPS 2024 with Pranjal Chitale
Abstracts: NeurIPS 2024 with Pranjal Chitale Abstracts: NeurIPS 2024 with Pranjal Chitale

The drawback of this approach is that it often misses the cultural nuances of local languages.

CHITALE: Now that we have created a benchmark, the next step is to evaluate how these multimodal models are performing on this benchmark.

So what we observed is there is a huge gap when it comes … in performance when we compare these proprietary offerings versus the open-source models.

These open-source models significantly lag behind the proprietary models.

CHITALE: CVQA is significant because it addresses a fundamental gap in how we evaluate vision-language and multimodal models today.

2 months назад @ microsoft.com
Abstracts: NeurIPS 2024 with Dylan Foster
Abstracts: NeurIPS 2024 with Dylan Foster Abstracts: NeurIPS 2024 with Dylan Foster

FOSTER: So this is a, kind of, a theoretical work on reinforcement learning, or RL.

FOSTER: Yeah, so if you look at these sort of RL problems with latent dynamics, this is something that’s actually received a lot of investigation in theory.

Like, can we take existing algorithms and use them to solve rich-observation RL problems in a modular fashion?

TINGLE: Dylan, I’d like to know—and I’m sure our audience would, too—what this work means when it comes to real-world application.

TINGLE: Well, Dylan Foster, thank you for joining us today to discuss your paper on reinforcement learning under latent dynamics.

2 months назад @ microsoft.com
Ideas: Economics and computation with Nicole Immorlica
Ideas: Economics and computation with Nicole Immorlica Ideas: Economics and computation with Nicole Immorlica

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2 months назад @ microsoft.com
Ideas: The journey to DNA data storage
Ideas: The journey to DNA data storage Ideas: The journey to DNA data storage

Joining me today to discuss the state of DNA data storage and some of our contributions are several members of the DNA Data Storage Project at Microsoft Research: Principal Researcher Bichlien Nguyen, Senior Researcher Jake Smith, and Partner Research Manager Sergey Yekhanin.

Once we do this, we return to our original data and we’ve completed, let’s call it, one DNA data storage cycle.

So, like, I mean, coding is an important aspect of this whole idea of DNA data storage because we have to deal with errors—it’s a new medium—but talking about error-correcting codes in the context of DNA data storage, so, I mean, usually, like … what are error-correcting codes about?

In DNA data storage, the …

2 months, 2 weeks назад @ microsoft.com
Abstracts: November 14, 2024
Abstracts: November 14, 2024 Abstracts: November 14, 2024

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2 months, 3 weeks назад @ microsoft.com
Collaborators: Prompt engineering with Siddharth Suri and David Holtz
Collaborators: Prompt engineering with Siddharth Suri and David Holtz Collaborators: Prompt engineering with Siddharth Suri and David Holtz

Researcher Siddharth Suri and professor David Holtz give a brief history of prompt engineering, discuss the debate behind their recent collaboration, and share what they found from studying how people’s approaches to prompting change as models advance.

Learn more:

2 months, 3 weeks назад @ blubrry.com
Abstracts: November 5, 2024
Abstracts: November 5, 2024 Abstracts: November 5, 2024

Chris and Jay, thank you for joining us today for Abstracts and congratulations!

HAWBLITZEL: Yeah, so I think, you know, traditionally verification or this formal software verification that we’re doing has been considered a little bit of a pie-in-the-sky research agenda.

They’ve discovered that you can get the high performance that you want for systems code without having to sacrifice the ability to reason about ownership and lifetimes, concurrency.

TINGLE: Well, finally, Chris, what are some of the open questions or future opportunities for formal software verification research, and what might you and your collaborators tackle next?

I’m Amber Tingle, and we hope you’ll join us again for Ab…

3 months назад @ microsoft.com
Abstracts: November 4, 2024
Abstracts: November 4, 2024 Abstracts: November 4, 2024

And at that time, large language model was really in its infancy and people just started exploring what large language model can help us in terms of improving software reliability.

But of course, you know, large language model is a field that is moving so fast.

And that’s one of the reasons we used large language models, because traditional static analysis or traditional program analysis cannot capture this.

I’m actually working on how to leverage large language model to verify the correctness of code, code that may be generated by large language model itself.

STOICA: So we’re thinking of, as Shan mentioned, exploring what large language models can do in this bug-finding/testing arena furth…

3 months назад @ microsoft.com
NLP Highlights NLP Highlights
последний пост None
Data Skeptic
последний пост 2 days, 20 hours назад
A Network of Networks
A Network of Networks A Network of Networks

In this episode, Bnaya Gross, a Fulbright postdoctoral fellow at the Center for Complex Network Research at Northwestern University, explores the transformative applications of network science in fields ranging from infrastructure to medicine, by studying the interactions between networks ("a network of networks"). Listeners will learn how interdependent networks provide a framework for understanding cascading failures, such as power outages, and how these insights transfer to physical systems like superconducting materials and biological networks. Key takeaways include understanding how dependencies between networks can amplify vulnerabilities, applying these principles to create resilient…

2 days, 20 hours назад @ dataskeptic.com
Auditing LLMs and Twitter
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Our guests, Erwan Le Merrer and Gilles Tredan, are long-time collaborators in graph theory and distributed systems. They share their expertise on applying graph-based approaches to understanding both large language model (LLM) hallucinations and shadow banning on social media platforms. In this episode, listeners will learn how graph structures and metrics can reveal patterns in algorithmic behavior and platform moderation practices. Key insights include the use of graph theory to evaluate LLM outputs, uncovering patterns in hallucinated graphs that might hint at the underlying structure and training data of the models, and applying epidemic models to analyze the uneven spread of shadow ban…

1 week, 1 day назад @ dataskeptic.com
Fraud Detection with Graphs
Fraud Detection with Graphs Fraud Detection with Graphs

In this episode, Šimon Mandlík, a PhD candidate at the Czech Technical University will talk with us about leveraging machine learning and graph-based techniques for cybersecurity applications. We'll learn how graphs are used to detect malicious activity in networks, such as identifying harmful domains and executable files by analyzing their relationships within vast datasets. This will include the use of hierarchical multi-instance learning (HML) to represent JSON-based network activity as graphs and the advantages of analyzing connections between entities (like clients, domains etc.). Our guest shows that while other graph methods (such as GNN or Label Propagation) lack in scalability or h…

2 weeks, 1 day назад @ dataskeptic.com
Optimizing Supply Chains with GNN
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Thibaut Vidal, a professor at Polytechnique Montreal, specializes in leveraging advanced algorithms and machine learning to optimize supply chain operations. In this episode, listeners will learn how graph-based approaches can transform supply chains by enabling more efficient routing, districting, and decision-making in complex logistical networks. Key insights include the application of Graph Neural Networks to predict delivery costs, with potential to improve districting strategies for companies like UPS or Amazon and overcoming limitations of traditional heuristic methods. Thibaut’s work underscores the potential for GNN to reduce costs, enhance operational efficiency, and provide bette…

3 weeks, 1 day назад @ dataskeptic.com
The Mystery Behind Large Graphs
The Mystery Behind Large Graphs The Mystery Behind Large Graphs

Our guest in this episode is David Tench, a Grace Hopper postdoctoral fellow at Lawrence Berkeley National Labs, who specializes in scalable graph algorithms and compression techniques to tackle massive datasets. In this episode, we will learn how his techniques enable real-time analysis of large datasets, such as particle tracking in physics experiments or social network analysis, by reducing storage requirements while preserving critical structural properties. David also challenges the common belief that giant graphs are sparse by pointing to a potential bias: Maybe because of the challenges that exist in analyzing large dense graphs, we only see datasets of sparse graphs? The truth is ou…

3 weeks, 6 days назад @ dataskeptic.com
Customizing a Graph Solution
Customizing a Graph Solution Customizing a Graph Solution

In this episode, Dave Bechberger, principal Graph Architect at AWS and author of "Graph Databases in Action", brings deep insights into the field of graph databases and their applications. Together we delve into specific scenarios in which Graph Databases provide unique solutions, such as in the fraud industry, and learn how to optimize our DB for questions around connections, such as "How are these entities related?" or "What patterns of interaction indicate anomalies?" This discussion sheds light on when organizations should consider adopting graph databases, particularly for cases that require scalable analysis of highly interconnected data and provides practical insights into leveraging…

1 month, 3 weeks назад @ dataskeptic.com
Graph Transformations
Graph Transformations Graph Transformations

In this episode, Adam Machowczyk, a PhD student at the University of Leicester, specializes in graph rewriting and its intersection with machine learning, particularly Graph Neural Networks. Adam explains how graph rewriting provides a formalized method to modify graphs using rule-based transformations, allowing for tasks like graph completion, attribute prediction, and structural evolution. Bridging the worlds of graph rewriting and machine learning, Adam's work aspire to open new possibilities for creating adaptive, scalable models capable of solving challenges that traditional methods struggle with, such as handling heterogeneous graphs or incorporating incremental updates efficiently. R…

1 month, 4 weeks назад @ dataskeptic.com
Networks for AB Testing
Networks for AB Testing Networks for AB Testing

In this episode, the data scientist Wentao Su shares his experience in AB testing on social media platforms like LinkedIn and TikTok. We talk about how network science can enhance AB testing by accounting for complex social interactions, especially in environments where users are both viewers and content creators. These interactions might cause a "spillover effect" meaning a possible influence across experimental groups, which can distort results. To mitigate this effect, our guest presents heuristics and algorithms they developed ("one-degree label propagation”) to allow for good results on big data with minimal running time and so optimize user experience and advertiser performance in soc…

2 months, 1 week назад @ dataskeptic.com
Lessons from eGamer Networks
Lessons from eGamer Networks Lessons from eGamer Networks

Alex Bisberg, a PhD candidate at the University of Southern California, specializes in network science and game analytics, with a focus on understanding social and competitive success in multiplayer online games. In this episode, listeners can expect to learn from a network perspective about players interactions and patterns of behavior. Through his research on games, Alex sheds light on how network analysis and statistical tests might explain positive contagious behaviors, such as generosity, and explore the dynamics of collaboration and competition in gaming environments. These insights offer valuable lessons not only for game developers in enhancing player experience, engagement and rete…

2 months, 2 weeks назад @ dataskeptic.com
Github Collaboration Network
Github Collaboration Network Github Collaboration Network

In this episode we discuss the GitHub Collaboration Network with Behnaz Moradi-Jamei, assistant professor at James Madison University. As a network scientist, Behnaz created and analyzed a network of about 700,000 contributors to Github's repository. The network of collaborators on GitHub was created by identifying developers (nodes) and linking them with edges based on shared contributions to the same repositories. This means that if two developers contributed to the same project, an edge (connection) was formed between them, representing a collaborative relationship network consisting of 32 million such connections. By using algorithms for Community Detection, Behnaz's analysis reveals in…

2 months, 3 weeks назад @ dataskeptic.com
Github Collaboration Network
Github Collaboration Network Github Collaboration Network

In this episode we discuss the GitHub Collaboration Network with Behnaz Moradi-Jamei, assistant professor at James Madison University. As a network scientist, Behnaz created and analyzed a network of about 700,000 contributors to Github's repository. The network of collaborators on GitHub was created by identifying developers (nodes) and linking them with edges based on shared contributions to the same repositories. This means that if two developers contributed to the same project, an edge (connection) was formed between them, representing a collaborative relationship network consisting of 32 million such connections. By using algorithms for Community Detection, Behnaz's analysis reveals in…

2 months, 3 weeks назад @ dataskeptic.com
Graphs and ML for Robotics
Graphs and ML for Robotics Graphs and ML for Robotics

We are joined by Abhishek Paudel, a PhD Student at George Mason University with a research focus on robotics, machine learning, and planning under uncertainty, using graph-based methods to enhance robot behavior. He explains how graph-based approaches can model environments, capture spatial relationships, and provide a framework for integrating multiple levels of planning and decision-making.

3 months назад @ dataskeptic.com
Graphs for HPC and LLMs
Graphs for HPC and LLMs Graphs for HPC and LLMs

We are joined by Maciej Besta, a senior researcher of sparse graph computations and large language models at the Scalable Parallel Computing Lab (SPCL). In this episode, we explore the intersection of graph theory and high-performance computing (HPC), Graph Neural Networks (GNNs) and LLMs.

3 months, 1 week назад @ dataskeptic.com
Graph Databases and AI
Graph Databases and AI Graph Databases and AI

In this episode, we sit down with Yuanyuan Tian, a principal scientist manager at Microsoft Gray Systems Lab, to discuss the evolving role of graph databases in various industries such as fraud detection in finance and insurance, security, healthcare, and supply chain optimization.

3 months, 2 weeks назад @ dataskeptic.com
Network Analysis in Practice
Network Analysis in Practice Network Analysis in Practice

Our new season "Graphs and Networks" begins here! We are joined by new co-host Asaf Shapira, a network analysis consultant and the podcaster of NETfrix – the network science podcast. Kyle and Asaf discuss ideas to cover in the season and explore Asaf's work in the field.

3 months, 3 weeks назад @ dataskeptic.com
SuperDataScience SuperDataScience
последний пост 2 days, 11 hours назад
859: BAML: The Programming Language for AI, with Vaibhav Gupta
859: BAML: The Programming Language for AI, with Vaibhav Gupta 859: BAML: The Programming Language for AI, with Vaibhav Gupta

In this week’s guest interview, Vaibhav Gupta talks to Jon Krohn about creating a programming language, BAML, that helps companies save up to 30% on their AI costs. He explains how he started tailoring BAML to facilitate natural language generation interactions with AI models, how BAML helps companies optimize their outputs, and he also lets listeners into Boundary’s hiring process. This episode is brought to you by ODSC, the Open Data Science Conference. Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (04:53) What BAML stands for (14:33) Making a prompt engineering a serious practic…

2 days, 11 hours назад @ podtrac.com
858: Are You The Account Executive We’re Looking For?
858: Are You The Account Executive We’re Looking For? 858: Are You The Account Executive We’re Looking For?

Podcast TranscriptJon Krohn: 00:05This is episode 858 on the Account Executive we’re looking to hire.

Perhaps you are the person we’re looking for or maybe you know the person we are looking for!

And hopefully every once in a while you also do actually learn about a product or service that could be genuinely useful to you in this data science niche.

And it's been an amazing experience over these last eight years with many of the best known names in AI and machine learning data science more broadly coming as guests on to the show.

Well, as the world's most listened-to data-science podcast, we obviously have a fair bit of reach, particularly in this data science genre or, amongst machine lear…

6 days, 11 hours назад @ superdatascience.com
857: How to Ensure AI Agents Are Accurate and Reliable, with Brooke Hopkins
857: How to Ensure AI Agents Are Accurate and Reliable, with Brooke Hopkins 857: How to Ensure AI Agents Are Accurate and Reliable, with Brooke Hopkins

I'll do one that's more personal and then one I think that's pretty related to work or informed a lot of my work.

And we talked about how voice agents are taking off because they provide a universal natural language API between businesses and with consumers.

So an autonomous agent is an agent that's navigating the world and responding to the world.

I think how this translates to voice agents is can the voice agent self-determine when the request is too complex for itself?

It's the same kind of thing with this with voice agents with more and more kind agentic systems.

1 week, 2 days назад @ superdatascience.com
856: The Fastest-Growing Jobs Are AI Jobs
856: The Fastest-Growing Jobs Are AI Jobs 856: The Fastest-Growing Jobs Are AI Jobs

Podcast TranscriptJon Krohn: 00:05This is Five-Minute Friday on the fastest growing jobs of 2024.

For AI engineer, for example, it shows that LLMs, natural language processing, and PyTorch are the most common skills.

Amongst the fastest growing jobs in the US, you've got at number one AI engineer, at number two AI consultant, and at number 12 AI researcher.

For example, like in the US, AI engineer is the number one fastest growing role in the UK and in the Netherlands.

AI engineer is also the fifth-fastest growing role in Sweden, the sixth-fastest growing role in Canada and Israel, and the 12th fastest growing role in India.

1 week, 6 days назад @ superdatascience.com
855: Exponential Views on AI and Humanity’s Greatest Challenges, with Azeem Azhar
855: Exponential Views on AI and Humanity’s Greatest Challenges, with Azeem Azhar 855: Exponential Views on AI and Humanity’s Greatest Challenges, with Azeem Azhar

How can we use AI to solve global problems like the environmental crisis, and how will future AI start to manage increasingly complex workflows? Famed futurist Azeem Azhar talks to Jon Krohn about the future of AI as a force for good, how we can stay mindful of an evolving job market, and Azeem’s favorite tools for automating his workflows. This episode is brought to you by ODSC, the Open Data Science Conference. Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (05:43) Azeem Azhar’s vision for AI’s future (14:16) How to prepare for technological shifts (20:35) How to be more like an A…

2 weeks, 2 days назад @ podtrac.com
854: The Six Epochs of Intelligence Evolution
854: The Six Epochs of Intelligence Evolution 854: The Six Epochs of Intelligence Evolution

Join Jon Krohn as he unpacks Ray Kurzweil’s six epochs of intelligence evolution, a fascinating framework from The Singularity is Nearer. From the origins of atoms and molecules to the transformative future of brain-computer interfaces and cosmic intelligence, Jon explores how each stage builds upon the last. This quick yet profound journey reveals how humanity is shaping the Fifth Epoch—and hints at what’s next for intelligence in our universe. Additional materials: www.superdatascience.com/854 Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

2 weeks, 6 days назад @ podtrac.com
853: Generative AI for Business, with Kirill Eremenko and Hadelin de Ponteves
853: Generative AI for Business, with Kirill Eremenko and Hadelin de Ponteves 853: Generative AI for Business, with Kirill Eremenko and Hadelin de Ponteves

In today's episode, Kirill and Hadelin detail what generative AI models, like large language models, are and how they fit within the broader category of foundation models.

So, now, with that example in hand and with a good understanding of the lifecycle of foundation models in general, there are a lot of foundation models out there.

But that's going to mean doing steps one, two, and three in the lifecycle, and that's going to cost a lot of money.

It's somewhere in between where you do get access to the foundation models and you can choose your foundation models, you can customize them.

So remember, Jon, when you were saying that actually LLMs are included in foundation models, because in fa…

3 weeks, 2 days назад @ superdatascience.com
852: In Case You Missed It in December 2024
852: In Case You Missed It in December 2024 852: In Case You Missed It in December 2024

Podcast TranscriptJon Krohn: 00:00:00 This is episode number 852 our In Case You Missed It in December episode.

I don't think we ever put that in production, but it gave us something we could use and measure against our outcomes.

So, you don't need to be worried about spending dollars or tens of dollars by switching to GPT-4o mini.

00:11:17You have data that's sensitive and that you are not comfortable sending that data to a third party.

And for some that's a good, that's going to be fun and pleasurable, and for others that's going to be utter doom and disaster, right?

3 weeks, 6 days назад @ superdatascience.com
851: Quantum ML: Real-World Applications Today, with Dr. Florian Neukart
851: Quantum ML: Real-World Applications Today, with Dr. Florian Neukart 851: Quantum ML: Real-World Applications Today, with Dr. Florian Neukart

Florian is chief product officer and member of the board at Terra Quantum, a leading quantum computing startup headquartered in Switzerland and Germany.

You gave a great presentation on quantum computing, quantum applications.

How does Terra Quantum address its... And this is a quote from Terra Quantum themselves, "Terra Quantum has a commitment to responsible innovation in the ethical implementation of quantum technology."

These are things that we would not have to do if we were only to focus on quantum computing and other security aspects of protecting against quantum computing.

Otherwise, share this episode with folks who would love to learn about quantum computing or quantum ML.

1 month назад @ superdatascience.com
850: Continuous Calendar for 2025
850: Continuous Calendar for 2025 850: Continuous Calendar for 2025

For Jon Krohn, the continuous calendar gives him a realistic and uninterrupted overview of his time.

Well, it’s the start of another year, which means it's time for another continuous calendar from us here at SuperDataScience!

In order to do that efficiently, we love the continuous calendar format.

So if you’d like to get started today with your own super efficient continuous calendar in 2025, simply head to jonkrohn.com/cal25.

The good news for you listeners is that, in 2025, the SuperDataScience Podcast will be a bigger priority for me than ever before.

1 month назад @ superdatascience.com
849: 2025 AI and Data Science Predictions, with Sadie St. Lawrence
849: 2025 AI and Data Science Predictions, with Sadie St. Lawrence 849: 2025 AI and Data Science Predictions, with Sadie St. Lawrence

And so I think that's something just to think about.

Sadie Lawrence: 00:36:05... particularly if you want me to get specific, but I really think it's us as consumers.

I could speak for a long time about this, but you are the guest, so I'm going to just let you go first, please.

I'm going to quickly preempt what you're going to say with a couple of recent episodes that I released on this topic.

Agentic AI, AI integration to everyday devices.

1 month, 1 week назад @ superdatascience.com
848: Happy Holidays from the SuperDataScience Podcast
848: Happy Holidays from the SuperDataScience Podcast 848: Happy Holidays from the SuperDataScience Podcast

Podcast Transcript(00:05):This is Five Minute Friday with a holiday greeting from all of us at the SuperDataScience Podcast.

(00:27):Welcome back to the Super Data Science Podcast.

in depth over the course of the year through our podcast episodes, allowing you to hear directly from leading experts and practitioners like Andrew Ng, Bernard Marr and Sol Rashidi.

(02:38):From all of us here at the SuperDataScience Podcast, happy holidays!

Until next time, keep on rockin’ it out there and I’m looking forward to enjoying another round of the SuperDataScience podcast with you very soon.

1 month, 1 week назад @ superdatascience.com
847: AI Engineering 101, with Ed Donner
847: AI Engineering 101, with Ed Donner 847: AI Engineering 101, with Ed Donner

Then there's one small recent delta on agentic AI that's very similar, which is the emergence of reasoning frameworks, which is perhaps just a way of applying agentic AI.

Ed Donner: 00:46:44Well, if you're going to open that door, let me see.

They have leaderboards for medical models, models that are specialized in medical domain.

So, we built up this startup, which was about taking AI models and applying them to the field of talent.

You're really amazing at this and it's something that I've seen you do over the years and I've been so amazed by it.

1 month, 2 weeks назад @ superdatascience.com
846: Making Enterprise Data Ready for AI, with Anu Jain and Mahesh Kumar
846: Making Enterprise Data Ready for AI, with Anu Jain and Mahesh Kumar 846: Making Enterprise Data Ready for AI, with Anu Jain and Mahesh Kumar

In this Five-Minute Friday, Jon Krohn speaks to Anu Jain, CEO of Nexus Cognitive, and Mahesh Kumar, CMO of Acceldata. They talk about the importance of updating data, especially for predictive models that make key financial decisions for a company, as well as the current state of data governance and why it’s overdue its own update. Additional materials: www.superdatascience.com/846 Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

1 month, 2 weeks назад @ podtrac.com
845: Tech is Our New Religion And It Needs Reformation, with Greg Epstein
845: Tech is Our New Religion And It Needs Reformation, with Greg Epstein 845: Tech is Our New Religion And It Needs Reformation, with Greg Epstein

"I'm going to give a talk about the most powerful word in the world."

So yeah, so your book Tech Agnostic in it, you describe technology use as a ritual that offers both relief and entrapment.

01:03:08So your book Tech Agnostic delves into the implications of portraying tech as a messianic force like this.

And for some that's a good, that's going to be fun and pleasurable, and for others that's going to be utter doom and disaster, right?

Jon Krohn: 01:32:30We've also-Greg Epstein: 01:32:32It was really, really a thorough conversation, and in many wonderful ways.

1 month, 3 weeks назад @ superdatascience.com
Data Science at Home Data Science at Home
последний пост 1 month, 2 weeks назад
Scaling Smart: AI, Data, and Building Future-Ready Enterprises with Josh Miramant (Ep. 276)
Scaling Smart: AI, Data, and Building Future-Ready Enterprises with Josh Miramant (Ep. 276) Scaling Smart: AI, Data, and Building Future-Ready Enterprises with Josh Miramant (Ep. 276)

In this episode, we dive into the transformative world of AI, data analytics, and cloud infrastructure with Josh Miramant, CEO of Blue Orange Digital.

As a seasoned entrepreneur with over $25 million raised across ventures and two successful exits, Josh shares invaluable insights on scaling data-driven businesses, integrating machine learning frameworks, and navigating the rapidly evolving landscape of cloud data architecture.

From generative AI to large language models, Josh explores cutting-edge trends shaping financial services, real estate, and consumer goods.

Tune in for a masterclass in leveraging data for impact and innovation!

Linkshttps://blueorange.digital/https://blueorange.digit…

1 month, 2 weeks назад @ datascienceathome.com
Autonomous Weapons and AI Warfare (Ep. 275)
Autonomous Weapons and AI Warfare (Ep. 275) Autonomous Weapons and AI Warfare (Ep. 275)

Here’s the updated text with links to the websites included:AI is revolutionizing the military with autonomous drones, surveillance tech, and decision-making systems.

In this episode of Data Science at Home, we expose the cutting-edge tech reshaping defense—and the chilling ethical questions that follow.

🐦 Twitter: @DataScienceAtHome📘 LinkedIn: Francesco Gad📷 Instagram: https://www.instagram.com/datascienceathome/📘 Facebook: https://www.facebook.com/datascienceAH💼 LinkedIn: https://www.linkedin.com/company/data-science-at-home-podcast💬 Discord Channel: https://discord.gg/4UNKGf3NEW TO DATA SCIENCE AT HOME?

Data Science at Home explores the latest in AI, data science, and machine learning.

S…

1 month, 3 weeks назад @ datascienceathome.com
8 Proven Strategies to Scale Your AI Systems Like OpenAI! 🚀 (Ep. 274)
8 Proven Strategies to Scale Your AI Systems Like OpenAI! 🚀  (Ep. 274) 8 Proven Strategies to Scale Your AI Systems Like OpenAI! 🚀 (Ep. 274)

In this episode of Data Science at Home, we’re diving deep into the powerful strategies that top AI companies, like OpenAI, use to scale their systems to handle millions of requests every minute!

From stateless services and caching to the secrets of async processing, discover 8 essential strategies to make your AI and machine learning systems unstoppable.

Instagram: https://www.instagram.com/datascienceathome/Twitter: @datascienceathomeFacebook: https://www.facebook.com/datascienceAHLinkedIn: https://www.linkedin.com/company/data-science-at-home-podcastDiscord Channel: https://discord.gg/4UNKGf3NEW TO DATA SCIENCE AT HOME?

Data Science at Home explores the latest in AI, data science, and ma…

1 month, 3 weeks назад @ datascienceathome.com
Humans vs. Bots: Are You Talking to a Machine Right Now? (Ep. 273)
Humans vs. Bots: Are You Talking to a Machine Right Now? (Ep. 273) Humans vs. Bots: Are You Talking to a Machine Right Now? (Ep. 273)

Together, they explore the growing importance of distinguishing human-written from AI-generated text, discussing real-world examples from social media to news.

How reliable are current detection tools like DetectGPT?

What are the ethical and technical challenges ahead as AI continues to advance?

And is the balance between innovation and regulation tipping in the right direction?

Tune in for insights on the future of AI text detection and the broader implications for media, academia, and policy.

2 months, 1 week назад @ datascienceathome.com
AI bubble, Sam Altman’s Manifesto and other fairy tales for billionaires (Ep. 272)
AI bubble, Sam Altman’s Manifesto and other fairy tales for billionaires (Ep. 272) AI bubble, Sam Altman’s Manifesto and other fairy tales for billionaires (Ep. 272)

Welcome to Data Science at Home, where we don’t just drink the AI Kool-Aid.

Today, we’re dissecting Sam Altman’s “AI manifesto”—a magical journey where, apparently, AI will fix everything from climate change to your grandma’s back pain.

In this episode, I’ll break down the bold (and often bizarre) claims in Altman’s grand speech for the Intelligence Age.

I’ll give you the real scoop on what’s realistic, what’s nonsense, and why some tech billionaires just can’t resist overselling.

Chapters00:00 – Intro00:18 – CEO of Baidu Statement on AI Bubble03:47 – News On Sam Altman Open AI06:43 – Online Manifesto “The Intelleigent Age”13:14 – Deep Learning16:26 – AI gets Better With Scale17:45 – Conclu…

2 months, 2 weeks назад @ datascienceathome.com
AI vs. The Planet: The Energy Crisis Behind the Chatbot Boom (Ep. 271)
AI vs. The Planet: The Energy Crisis Behind the Chatbot Boom (Ep. 271) AI vs. The Planet: The Energy Crisis Behind the Chatbot Boom (Ep. 271)

In this episode of Data Science at Home, we dive into the hidden costs of AI’s rapid growth — specifically, its massive energy consumption.

With tools like ChatGPT reaching 200 million weekly active users, the environmental impact of AI is becoming impossible to ignore.

Each query, every training session, and every breakthrough come with a price in kilowatt-hours, raising questions about AI’s sustainability.

Join us, as we uncovers the staggering figures behind AI’s energy demands and explores practical solutions for the future.

From efficiency-focused algorithms and specialized hardware to decentralized learning, this episode examines how we can balance AI’s advancements with our planet’s …

2 months, 3 weeks назад @ datascienceathome.com
Love, Loss, and Algorithms: The Dangerous Realism of AI (Ep. 270)
Love, Loss, and Algorithms: The Dangerous Realism of AI (Ep. 270) Love, Loss, and Algorithms: The Dangerous Realism of AI (Ep. 270)

Subscribe to our new channel https://www.youtube.com/@DataScienceatHomeIn this episode of Data Science at Home, we confront a tragic story highlighting the ethical and emotional complexities of AI technology.

This devastating event has sparked urgent discussions on the mental health risks, ethical responsibilities, and potential regulations surrounding AI chatbots, especially as they become increasingly lifelike.

🎙️ Topics Covered:AI & Emotional Attachment: How hyper-realistic AI chatbots can foster intense emotional bonds with users, especially vulnerable groups like adolescents.

Mental Health Risks: The potential for AI to unintentionally contribute to mental health issues, and the challe…

3 months назад @ datascienceathome.com
VC Advice Exposed: When Investors Don’t Know What They Want (Ep. 269)
VC Advice Exposed: When Investors Don’t Know What They Want (Ep. 269) VC Advice Exposed: When Investors Don’t Know What They Want (Ep. 269)

Ever feel like VC advice is all over the place?

That’s because it is.

In this episode, I expose the madness behind the money and how to navigate their confusing advice!

Watch the video at https://youtu.be/IBrPFyRMG1QSubscribe to our new Youtube channel https://www.youtube.com/@DataScienceatHome00:00 – Introduction00:16 – The Wild World of VC Advice02:01 – Grow Fast vs. Grow Slow05:00 – Listen to Customers or Innovate Ahead09:51 – Raise Big or Stay Lean?

14:20 – The Real VC Secret: Focus on Your Team and Vision17:03 – Outro

3 months, 1 week назад @ datascienceathome.com
AI Says It Can Compress Better Than FLAC?! Hold My Entropy 🍿 (Ep. 268)
AI Says It Can Compress Better Than FLAC?! Hold My Entropy 🍿 (Ep. 268) AI Says It Can Compress Better Than FLAC?! Hold My Entropy 🍿 (Ep. 268)

In this episode of Data Science at Home, Frag dives deep into the wild claims that Large Language Models (LLMs) like Chinchilla 70B are beating traditional lossless compression algorithms.

🧠💥But before you toss out your FLAC collection, let’s break down Shannon’s Source Coding Theorem and why entropy sets the ultimate limit on lossless compression.

We explore: ⚙️ How LLMs leverage probabilistic patterns for compression 📉 Why compression efficiency doesn’t equal general intelligence 🚀 The practical (and ridiculous) challenges of using AI for compression 💡 Can AI actually BREAK Shannon’s limit—or is it just an illusion?

If you love AI, algorithms, or just enjoy some good old myth-busting, thi…

3 months, 2 weeks назад @ datascienceathome.com
What Big Tech Isn’t Telling You About AI (Ep. 267)
What Big Tech Isn’t Telling You About AI (Ep. 267) What Big Tech Isn’t Telling You About AI (Ep. 267)

Are AI giants really building trustworthy systems?

A groundbreaking transparency report by Stanford, MIT, and Princeton says no.

In this episode, we expose the shocking lack of transparency in AI development and how it impacts bias, safety, and trust in the technology.

We’ll break down Gary Marcus’s demands for more openness and what consumers should know about the AI products shaping their lives.

Check our new YouTube channel https://www.youtube.com/@DataScienceatHome and Subscribe!

3 months, 3 weeks назад @ datascienceathome.com
Money, Cryptocurrencies, and AI: Exploring the Future of Finance with Chris Skinner [RB] (Ep. 266)
Money, Cryptocurrencies, and AI: Exploring the Future of Finance with Chris Skinner [RB] (Ep. 266) Money, Cryptocurrencies, and AI: Exploring the Future of Finance with Chris Skinner [RB] (Ep. 266)

We’re revisiting one of our most popular episodes from last year, where renowned financial expert Chris Skinner explores the future of money.

In this fascinating discussion, Skinner dives deep into cryptocurrencies, digital currencies, AI, and even the metaverse.

He touches on government regulations, the role of tech in finance, and what these innovations mean for humanity.

Now, one year later, we encourage you to listen again and reflect—how much has changed?

Are Chris Skinner’s predictions still holding up, or has the financial landscape evolved in unexpected ways?

4 months назад @ datascienceathome.com
Kaggle Kommando’s Data Disco: Laughing our Way Through AI Trends (Ep. 265) [RB]
Kaggle Kommando’s Data Disco: Laughing our Way Through AI Trends (Ep. 265) [RB] Kaggle Kommando’s Data Disco: Laughing our Way Through AI Trends (Ep. 265) [RB]

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.

4 months, 1 week назад @ datascienceathome.com
AI and Video Game Development: Navigating the Future Frontier (Ep. 264) [RB]
AI and Video Game Development: Navigating the Future Frontier (Ep. 264) [RB] AI and Video Game Development: Navigating the Future Frontier (Ep. 264) [RB]

In this episode we delve into the dynamic realm of game development and the transformative role of artificial intelligence (AI).

Join Frag, Jim and Mike as they explore the current landscape of game development processes, from initial creative ideation to the integration of AI-driven solutions.

With Mike’s expertise as a software executive and avid game developer, we uncover the potential of AI to revolutionize game design, streamline development cycles, and enhance player experiences.

SponsorsIntrepid AI ( https://intrepid.ai ) is an AI assisted all-in-one platform for robotics teams.

Build robotics applications in minutes, not months.

4 months, 1 week назад @ datascienceathome.com
LLMs: Totally Not Making Stuff Up (they promise) (Ep. 263)
LLMs: Totally Not Making Stuff Up (they promise) (Ep. 263) LLMs: Totally Not Making Stuff Up (they promise) (Ep. 263)

In this episode, we dive into the wild world of Large Language Models (LLMs) and their knack for… making things up.

Can they really generalize without throwing in some fictional facts?

Or is hallucination just part of their charm?

Let’s separate the genius from the guesswork in this insightful breakdown of AI’s creativity problem.

TL;DR;LLM Generalisation without hallucinations.

4 months, 2 weeks назад @ datascienceathome.com
AI: The Bubble That Might Pop—What’s Next? (Ep. 262)
AI: The Bubble That Might Pop—What’s Next? (Ep. 262) AI: The Bubble That Might Pop—What’s Next? (Ep. 262)

The hype around Generative AI is real, but is the bubble about to burst?

Join me as we dissect the recent downturn in AI investments and what it means for the tech giants like OpenAI and Nvidia.

Could this be the end of the AI gold rush, or just a bump in the road?

5 months, 1 week назад @ datascienceathome.com