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CUSTOMER BASE MANAGEMENTA High-Level Framework for Building a CBM System with Strategic ImpactImage created by the author using CanvaImagine if your customer data could tell a story — one that drove key decisions, optimized pricing, and forecast future revenue with accuracy.As a data scientist, I have spent a large part of my career designing and building customer base management (CBM) systems. These are systems that monitor and predict anything from customer churn and customer price elasticity to recommending the products customers are most likely to buy.I have witnessed how CBM systems have been successfully adopted by organizations, and enabled them to execute their strategy through effe…

Target your GPU easily with Numba’s CUDA JITContinue reading on Towards Data Science »

How to deliver outsized value on your data quality programContinue reading on Towards Data Science »

Dalle-3’s interpretation of “An AI assistant throwing documents to the wind over a clear blue ocean”. Image generated by the author.A tool that might help with your homeworkThis article is the second of a two part series where we use LangGraph and Tavily to build a simple research agent, which writes and refines short articles. To keep track of the plans, articles and comments it generates we add the ability to programmatically create and edit Google Docs. In the first article we built the agent. Now we will build the docs connection. You can find all the relevant code here.In part 1 of this series we discussed agents, and used tools from LangGraph and Tavily to build a minimal agent that c…

Dalle-3’s interpretation of “A quirky AI assistant hard at work checking documents”. Image generated by the author.A tool that might help with your homeworkThis article is the first of a two part series where we use LangGraph and Tavily to build a simple research agent, which writes and refines short articles. To keep track of the plans, articles and comments it generates we add the ability to programmatically create and edit Google Docs. In this article we focus on the agent, leaving the docs connection to the second article. You can find all the relevant code here.Large Language Models (LLMs) are quickly finding use in all sorts of applications relevant to analysts and researchers, especi…

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Real-World Healthcare Data Challenges — Part II.Continue reading on Towards Data Science »

Dance Between Dense and Sparse Embeddings: Enabling Hybrid Search in LangChain-MilvusHow to create and search multi-vector-store in langchain-milvusThis blog post was co-authored by Omri Levy and Ohad Eytan, as part of the work we have done in IBM Research Israel.IntroRecently, we — at IBM Research — needed to use hybrid search in the Milvus vector store. Since we were already using the LangChain framework, we decided to roll up our sleeves and contribute what was needed to enable it in langchain-milvus. That included support for sparse embeddings (PR) and multi-vector search (PR) through the langchain interface.In this blog post, we will briefly introduce the difference between dense and s…

Data analysis is the key to drive business decisions through answering abstract business questions but it’s hard to get rightContinue reading on Towards Data Science »

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.

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…

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

All of these terms (“AI”, “gender”, and “bias”) can be somewhat overused and ambiguous.

A Short History of Studying Gender Bias in AIHere, I cover a very small sample of papers I’ve found influential studying gender bias in AI.

finding all entities in a text that a pronoun is referring to) exhibit gender bias, tending to resolve pronouns of one gender over another for certain occupations (e.g.

This article mainly focused on gender bias — and particularly, on binary gender.

AcknowledgementsThis post was originally posted on Art Fish IntelligenceCitationFor attribution in academic contexts or books, please cite this work asYennie Jun, "Gender Bias in AI," The Gradient, 2024@article{Jun2024bia…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

CitationFor attribution in academic cont…

Created Using MidjourneyIn this issue:Understanding adversarial distillation.

Alibaba’s paper about Introspective Adversarial Distillation (IAD).

The first stop is about a method known as adversarial distillation.

As it names indicates, adversarial distillation draws inspiration from generative adversarial networks(GANs) using a generator-discriminator architecture.

In that setting, the generator creates synthetic samples close to the true data distribution while the discriminator learns to differentiate between the synthetic and original data samples.

Much of AI's impressive performance in math benchmarks relies on scenarios where the problem is perfectly articulated within a prompt.

Unlike traditional math benchmarks such as GSM-8K and MATH, where AI models now score over 90%, Frontier Math presents a significantly more challenging test.

The benchmark comprises hundreds of intricate math problems spanning diverse fields of modern mathematics, from computational number theory to abstract algebraic geometry.

Frontier Math provides a critical benchmark for measuring progress in AI reasoning as these systems continue to evolve.

Frontier MathFrontierMath is, arguably, the toughest math benchmark ever created —> Read more.

Convirza, a leader in call analytics, recently faced this exact challenge and found an answer with Predibase’s multi-LoRA serving infrastructure.

Join us on November 21st at 10:00 am PT for an exclusive look into how Convirza transitioned from Longformer models to fine-tuned SLMs, improving speed and accuracy while cutting costs.

Optimized Cost Structure : Instead of dedicated GPUs for each of the 60 indicators—each costing $500-$1,500 per month—Convirza now runs multiple indicators on a single scalable GPU deployment.

Faster Iterations with SLMs: They swapped out Longformer models for fine-tuned SLMs, reducing the training cycles from 9-24+ hours to less than 3 hours on average per adapter…

Created Using MidjourneyReasoning is one of the most interesting areas of research in the world of foundation models and one that has been accelerated since the release of GPT-o1.

The more specific trend is to develop foundation models that can “reason” before producing an output.

This concept draws inspiration from how humans tackle complex problems, taking time to ponder and strategize before arriving at an answer.

Research in the area of planning and reasoning is being tracked very closely as it can represent the next breakthrough in generative AI.

This is the area of a recent research paper from Meta AI which explores a novel technique called Thought Preference Optimization (TPO).

Created Using DALL-EMega large transformer have dominated the generative AI revolution over the last few years.

The emergent properties that surface at scale in large foundation models are nothing short of magical.

At the same time, running pretraining, fine-tuning and inference workloads in large models results cost prohibited for most organizations.

As a result, we have seen the emergence of smaller models that more specialized in a given domain.

The raise of smaller models also represents a challenge to the dependency of NVIDIA GPUs are these models are perfectly able to run in commodity hardware.

Created Using DALL-EIn this issue:Understanding the different types of model distillation.

The original model distillation paper.

💡 ML Concept of the Day: Types of Model DistillationIn the previous issue of this series, we introduced the concept of model distillation.

The core idea of this technique is to use a teacher model to train a smaller, more efficient model that maintains the accuracy of the teacher model on a specific set of domains.

Response-Based Distillation:

You can subscribe to The Sequence below:📝 Editorial: Magentic-OneMagentic-One: A Multi-Agent System for Complex TasksMulti-agent systems are one the most fascinating areas of generative AI.

We are barely getting single agents to work so thinking about systems that combine several agents is fundamentally hard.

After releasing frameworks such as AutoGen or TaskWeaver, Microsoft is dabbling into multi-agent systems.

Magentic-One is implemented using AutoGen, Microsoft's open-source framework for multi-agent applications.

🤖 AI Tech ReleasesMagentic OneMicrosoft open sourced Magentic One, a multi-agent framework for web and file tasks —> Read more.

One of the ultimate manifestations of this proposition is AI writing AI code.

But how good is AI in traditional machine learning(ML) engineering tasks such as training or validation.

This is the purpose of a new work proposed by OpenAI with MLE-Bench, a benchmark to evaluate AI agents in ML engineering tasks.

MLE-Bench is a new benchmark introduced by OpenAI to evaluate the performance of AI agents on complex machine learning engineering tasks.

The benchmark is specifically designed to assess how well AI agents can perform real-world MLE work, such as training models, preparing datasets, and running experiments.

A review of one of the first papers about knowledge distillation.

💡 ML Concept of the Day: An Intro to Knowledge DistillationMaking foundation models smaller and more cost effective is one of the key challenges of generative AI.

Distillation is one of the new emerging techniques focused on reducing the size while maintaining the accuracy of large generative AI models.

These days, we are constaintly seeing distilled versions of large models being able to run on smaller compute environments such as mobile devices.

How does distillation work exactly?

Created Using IdeogramNext Week in The Sequence:Edge 445: We start a new series about one of the most exciting topics in generative AI: model distillation.

You can subscribe to The Sequence below:📝 Editorial: Robotics is Inching Towards it ChatGPT MomentThe field of AI robotics is currently experiencing a surge in innovation, with researchers developing new techniques and technologies that are pushing the boundaries of what robots can do.

This week we saw several major research contributions in the field of robotics from NVIDIA, MIT and Meta among others.

HPT aligns data from different sources into a shared "language" that a generative AI model can process.

The advancements in AI robotics, …

Date: 13th November 2024 | Time: 4pm CETWe recommend to join this full-day virtual event featuring top speakers like Thomas Wolf – Co-founder of Hugging Face; Prashanth Chandrasekar – CEO of Stack Overflow; and Nathan Benaich from Air Street Capital; along with experts from OpenAI, Stanford, Replit, and more!

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From OpenAI’s Sora startups like Runway or Pika to the new efforts from Google, most of the large video generation models remained closed-source.

There are open-source efforts such as Stability AI’s Stable Video, but the quality is not at the same level as that of the large commercial alternatives.

The barrier of entry for pretraining video models is fundamentally larger than language or image, as datasets are more scarce and difficult to produce.

Can open-source AI compete in the video generation space?

Recently, Meta unveiled some of their recent work in video and audio generation with the publication of the Movie Gen research paper.

Created Using MidjourneyI mentioned last week that these opinion pieces are trying to offer a different perspective about various topics in AI.

Beyond completely revolutionizing the generative AI field, transformers can be considered a true marvel given its unique scaling properties and incredible adaptability.

One analogy that has worked for me recently is to think of transformers as computers.

The transformer architecture has revolutionized the field of artificial intelligence, particularly in natural language processing.

The Programmable Nature of Transformers

Today, we would like to present a summary of this series about some of the most interesting trends in foundation models.

Edge 427: Reviewed Jamba, a model that combines SSMs, transformers and MoEs including the original Jamba paper.

Edge 431: Reviewed Cobra, a multimodal SSM including its original paper.

Edge 437: Discusses the BlackMamba model that combines SSMs and MoEs in a single architecture including its original paper.

We dived into Zamba’s original paper and review the LitServe framework for high performance model serving.

In cognitive science, human thought processes are commonly divided into two systems: the fast, intuitive System 1 and the slower…Continue reading on SyncedReview »

Continue reading on SyncedReview »

Generative AI, including Language Models (LMs), holds the promise to reshape key sectors like education, healthcare, and law, which rely…Continue reading on SyncedReview »

Recent large language models (LLMs) have shown impressive performance across a diverse array of tasks.Continue reading on SyncedReview »

For artificial intelligence to thrive in a complex, constantly evolving world, it must overcome significant challenges: limited data…Continue reading on SyncedReview »

The rise of large language models (LLMs) has sparked questions about their computational abilities compared to traditional models. While…Continue reading on SyncedReview »

Multimodal Large Language Models (MLLMs) have rapidly become a focal point in AI research. Closed-source models like GPT-4o, GPT-4V…Continue reading on SyncedReview »

Large language models (LLMs) like GPTs, developed from extensive datasets, have shown remarkable abilities in understanding language…Continue reading on SyncedReview »

Cellular automata (CA) have become essential for exploring complex phenomena like emergence and self-organization across fields such as…Continue reading on SyncedReview »

Tools designed for rewriting, refactoring, and optimizing code should prioritize both speed and accuracy. Large language models (LLMs)…Continue reading on SyncedReview »

Continue reading on SyncedReview »

Sparse Mixture of Experts (MoE) models are gaining traction due to their ability to enhance accuracy without proportionally increasing…Continue reading on SyncedReview »

Continue reading on SyncedReview »

Reinforcement Learning from Human Feedback (RLHF) has become the go-to technique for refining large language models (LLMs), but it faces…Continue reading on SyncedReview »

Monocular Depth Estimation, which involves estimating depth from a single image, holds tremendous potential. It can add a third dimension…Continue reading on SyncedReview »

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).

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.

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 …

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 …

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…

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.

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?!”.

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.

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.

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 […]

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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 […]

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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 […]

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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.

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 […]

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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 […]

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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 […]

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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 […]

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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 […]

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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 […]

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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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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 […]

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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 […]

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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 .

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.

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.

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.

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

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

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

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

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

From these excerpts, sociotechnical AI seems to have something to do with being people-centred, having social-purpose, understanding and reducing risks, building trust, widening opportunities, centring rights and values, being accountable for claims, and taking a system-wide view on AI.

To do this we will need more than technical AI research; and that is the role of sociotechnical AI research.

This expresses a hope for significant public benefit, but again, the burden of proof lies with AI designers, and so requires new forms of sociotechnical AI research.

Further developing all these varied forms for social and community involvement is another focus area for sociotechnical AI.

Sociotechnic…

At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages.

We address uncertainty in estimates $\widehat \beta$ of model coefficients with subsampling, our usual strategy for variance estimation in online experiments[12].

If our experiments were smaller then we might need to model our metrics with Poisson, Binomial or other GLMs instead.

We sometimes use $L^1$ regularization when we fit models to our live experiment data to select the most important non-linear c…

Superficially this looks fine, but of course the problem is hidden in how do we arrive with these equivalences?

Let's take a closer look: Kurzweil claims we should be seeing insect brain capability in $1000 computer in 2001.

Alright what would we expect from an insect brain?

It's 2024 at the time of writing this, and the best military technology that could benefit from an insect brain like capabilities is still on remote control.

That is of course if we actually get an insect brain this year.

Both of these assumptions seem reasonable and general and we can quickly see where Gaussian distribution should start popping out.

Since Gaussian distribution is fully defined by just two numbers - mean and variance, we can, by collecting enough data rather precisely estimate these values.

Below I simulate 400x200=80000 independent pixels, each taking a random value between zero and one [code available here].

These distributions we often refer to as "fat tails" [for putting much more "weight" in the tail of the distribution, as in outside of the "central" part] are really weird and problematic.

Let's get back to why we see Gaussian distribution anywhere at all?

Skeleton-based action recognition has achieved remarkable performance with the development of graph convolutional networks (GCNs).

However, most of these methods tend to construct complex topology learning mechanisms while neglecting the inherent symmetry of the human body.

To address the issues, we (1) propose a novel Topological Symmetry Enhanced Graph Convolution (TSE-GC) to enable distinct topology learning across different channel partitions while incorporating topological symmetry awareness and (2) construct a Multi-Branch Deformable Temporal Convolution (MBDTC) for skeleton-based action recognition.

Combining TSE-GC with MBDTC, our final model, TSE-GCN, achieves competitive performan…

Recent advances in Graph Neural Networks (GNNs) and Graph Transformers (GTs) have been driven by innovations in architectures and Positional Encodings (PEs), which are critical for augmenting node features and capturing graph topology.

PEs are essential for GTs, where topological information would otherwise be lost without message-passing.

However, PEs are often tested alongside novel architectures, making it difficult to isolate their effect on established models.

To address this, we present a comprehensive benchmark of PEs in a unified framework that includes both message-passing GNNs and GTs.

Our findings demonstrate that previously untested combinations of GNN architectures and PEs can …

Seismic data is often sparse and unevenly distributed due to the high costs and logistical challenges associated with deploying physical seismometers, limiting the application of Machine Learning (ML) in earthquake analysis.

To address this gap, we introduce PyAWD, a Python library designed to generate high-resolution synthetic datasets simulating spatio-temporal acoustic wave propagation in both two-dimensional and three-dimensional heterogeneous media.

By allowing fine control over parameters such as wave speed, external forces, spatial and temporal discretization, and media composition, PyAWD enables the creation of ML-scale datasets that capture the complexity of seismic wave behavior.

…

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…

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…

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…

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…

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…

We’re changing that, and making these incredible technologies more easily accessible to millions of creators and billions of users around the world.

Over the next few months, we’re bringing our advanced generative AI models, Veo and Imagen 3 , to YouTube creators through Dream Screen .

Artificial intelligence (AI) technologies for generating creative content are improving rapidly, but seamless ways of using them still aren’t widely available.

New video generation technology in YouTube Shorts will help millions of people realize their creative visionCreators can soon generate video with Dream ScreenBy integrating Veo into Dream Screen, YouTube creators will soon be able to generate exciting …

Research Our latest advances in robot dexterity ShareCopy link ×Two new AI systems, ALOHA Unleashed and DemoStart, help robots learn to perform complex tasks that require dexterous movement People perform many tasks on a daily basis, like tying shoelaces or tightening a screw.

Today, we introduce two new papers featuring our latest artificial intelligence (AI) advances in robot dexterity research: ALOHA Unleashed which helps robots learn to perform complex and novel two-armed manipulation tasks; and DemoStart which uses simulations to improve real-world performance on a multi-fingered robotic hand.

This helps the robot learn from the data, so it can perform the same tasks on its own.

To ena…

Research AlphaProteo generates novel proteins for biology and health research ShareCopy link ×New AI system designs proteins that successfully bind to target molecules, with potential for advancing drug design, disease understanding and more.

Today, we introduce AlphaProteo, our first AI system for designing novel, high-strength protein binders to serve as building blocks for biological and health research.

AlphaProteo can generate new protein binders for diverse target proteins, including VEGF-A, which is associated with cancer and complications from diabetes.

Learning the intricate ways proteins bind to each other Protein binders that can bind tightly to a target protein are hard to desig…

Research FermiNet: Quantum physics and chemistry from first principles ShareCopy link ×Using deep learning to solve fundamental problems in computational quantum chemistry and explore how matter interacts with light Note: This blog was first published on 19 October 2020.

Our neural network architecture, FermiNet (Fermionic Neural Network), is well-suited to modeling the quantum state of large collections of electrons, the fundamental building blocks of chemical bonds.

A brief history of quantum mechanics Mention “quantum mechanics” and you’re more likely to inspire confusion than anything else.

But even the humble covalent bond — the basic building block of chemistry — is a consequence of t…

Company A new generation of African talent brings cutting-edge AI to scientific challenges ShareCopy link ×Food security, healthcare and exploring the cosmos are among the ways students of a new pan-African Master’s program aspire to apply AI At Google DeepMind, we’re committed to supporting the next generation of artificial intelligence (AI) leaders to help build a stronger, more diverse and inclusive global AI community.

Students have the opportunity to accelerate scientific discovery, with mentoring and support from Google DeepMind’s researchers and engineers.

As the next generation of AI leaders in Africa, Béria Chingnabé Kalpélbé, Olivier Mahumawon Adjagba and Diffo Mboudjiho Annette D…

Responsibility & Safety Mapping the misuse of generative AI ShareCopy link ×New research analyzes the misuse of multimodal generative AI today, in order to help build safer and more responsible technologies Generative artificial intelligence (AI) models that can produce image, text, audio, video and more are enabling a new era of creativity and commercial opportunity.

By analyzing media reports, we identified two main categories of generative AI misuse tactics: the exploitation of generative AI capabilities and the compromise of generative AI systems.

Relative frequency generative AI misuse tactics in our dataset.

By observing how bad actors combine their generative AI misuse tactics in pur…

Technologies Gemma Scope: helping the safety community shed light on the inner workings of language models ShareCopy link ×Announcing a comprehensive, open suite of sparse autoencoders for language model interpretability.

To create an artificial intelligence (AI) language model, researchers build a system that learns from vast amounts of data without human guidance.

As a result, the inner workings of language models are often a mystery, even to the researchers who train them.

Gemma Scope is a collection of hundreds of freely available, open sparse autoencoders (SAEs) for Gemma 2 9B and Gemma 2 2B.

Interpreting what happens inside a language model When you ask a language model a question, it…

Research AI achieves silver-medal standard solving International Mathematical Olympiad problems ShareCopy link ×Breakthrough models AlphaProof and AlphaGeometry 2 solve advanced reasoning problems in mathematics Artificial general intelligence (AGI) with advanced mathematical reasoning has the potential to unlock new frontiers in science and technology.

This year, we applied our combined AI system to the competition problems, provided by the IMO organizers.

Prof Sir Timothy Gowers,IMO gold medalist and Fields Medal winnerFirst, the problems were manually translated into formal mathematical language for our systems to understand.

We also tested this approach on this year’s IMO problems and t…

Research Google DeepMind at ICML 2024 ShareCopy link ×Exploring AGI, the challenges of scaling and the future of multimodal generative AI Next week the artificial intelligence (AI) community will come together for the 2024 International Conference on Machine Learning (ICML).

Running from July 21-27 in Vienna, Austria, the conference is an international platform for showcasing the latest advances, exchanging ideas and shaping the future of AI research.

Depending on their performance, generality and autonomy, our paper categorizes systems ranging from non-AI calculators to emerging AI models and other novel technologies.

New approaches in generative AI and multimodality Generative AI technolo…

Similarly, NGIS is using Google Earth Engine to power TraceMark, helping businesses deliver traceability and transparency across global supply chains.

It’s clear that AI can process large volumes of data, optimize complex systems, and drive the development of new business models.

We are seeing cloud and AI being used to de-risk investments, improve transparency, and increase profitability through the use of large-scale datasets, machine learning, and generative AI.

These technologies allow companies to analyze their ESG performance, gain insights into climate risks, and monitor supplier behaviors.

For example, Palo Alto Networks partnered with Watershed, a Google Cloud Ready - Sustainabilit…

Parallelstore: World’s first fully-managed DAOS offeringParallelstore is a fully managed, scalable, high-performance storage solution based on next-generation DAOS technology, designed for demanding HPC and AI workloads.

At the same time, we continue to invest in automating and simplifying the building of HPC and AI platforms, with:Customer success stories: Atommap and beyondAtommap, a company specializing in atomic-scale materials design, is using Google Cloud HPC to accelerate its research and development efforts.

Expect further enhancements to HPC VMs, Parallelstore, Cluster Toolkit, Slurm-gcp, and other HPC products and solutions.

Google Cloud at Supercomputing 2024The annual Supercompu…

Prompt refinement boosts the quality of the prompt, while also saving significant times during prompt design.

A powerful duo: Generate prompt meets Refine promptThese two features work in tandem to help you craft the most effective prompt for your objective – irrespective of your skill level.

Generate prompt gets you started quickly, while Refine prompt allows for iterative improvement in five steps:Define your objective: Tell Generate prompt what you want to achieve.

Generate a prompt: Generate prompt creates a ready-to-use prompt, often with helpful placeholders for context.

Refine with feedback: Use Refine prompt to provide feedback on the output and receive AI-powered suggestions for pr…

As open-source large language models (LLMs) become increasingly popular, developers are looking for better ways to access new models and deploy them on Cloud Run GPU.

That’s why Cloud Run now offers fully managed NVIDIA GPUs, which removes the complexity of driver installations and library configurations.

This means you’ll benefit from the same on-demand availability and effortless scalability that you love with Cloud Run's CPU and memory, with the added power of NVIDIA GPUs.

In this blog post, we'll guide you through deploying the Meta Llama 3.2 1B Instruction model on Cloud Run.

We'll also share best practices to streamline your development process using local model testing with Text Gene…

Many ML frameworks output their checkpoints (snapshots of model weights) to object storage such as Google Cloud Storage, a common choice for long-term storage.

Using Cloud Storage as the source of truth, there are two main products to retrieve your data at the GKE-pod level: Cloud Storage Fuse and Hyperdisk ML (HdML).

Hyperdisk ML gives you better performance and scalability than downloading files directly to the pod from Cloud Storage or other online location.

Additionally, you can attach up to 2500 nodes to a single Hyperdisk ML instance, with aggregate bandwidth up 1.2 TiB/sec.

To use Hyperdisk ML, load your data on the Hyperdisk ML disk prior to using it, and again upon each update.

Traditional performance metricsAccelerator systems can be evaluated and compared across multiple dimensions, ranging from peak throughput, to effective throughput, to throughput scaling efficiency.

Hardware specifications and peak performanceTraditionally, comparisons focused on hardware specifications like peak throughput, memory bandwidth, and network connectivity.

However, these hardware efficiency metrics don't directly translate to business-value measures like training time or model quality.

The need for convergence scaling efficiencyWhile hardware utilization and scaling metrics provide important system insights, convergence scaling efficiency focuses on the fundamental goal of traini…

Many of the successful funded gen AI startups — more than 60% of whom are building on Google Cloud — are using Vertex AI as the development host and productionalize backbone to accelerate innovation.

Abstrakt, NextNet, and Ferret are among the long list of startups using Google Cloud’s AI-optimized infrastructure and Vertex AI platform to accelerate their innovation.

Leveraging Google Cloud Vertex AI and Gemini, it identifies hidden relationships and patterns within scientific literature, allowing researchers to ask complex questions in plain language and receive accurate answers.

Specifically, NextNet uses Gemini for natural language processing and knowledge extraction, outperforming other…

The modeling and machine-learning occur within BigQuery while CRMint aids in data integration and audience creation within Google Analytics.

When Google Analytics is linked to Google Ads, audience segments are shared automatically with Google Ads where they can be activated in a number of strategic ways.

At the same time, we’ll be scaling advanced audiences to all of our 20+ international entities.

Looking toward an AI and data-driven futureThis year, we will be moving much of PUMA’s e-commerce infrastructure over to Google Cloud.

We look forward to a long-term partnership with Google Cloud and are excited to see where the future will take us.

As different gen AI models evolve, Aible can simply update the Chat Templates, allowing all Aible customers to benefit instantly.

Provide Feedback: Users are used to providing thumbs up / down feedback on ChatGPT or Gemini.

Such feedback typically has to be collected by Data Scientists from Business Users - usually manually using spreadsheets.

For business users this is too much complexity.

This is simple trial and error with immediate feedback under the control of the business users.

Multi-host deployment with vLLM and LWSvLLM is a popular open source model server and supports multi-node multi-GPU inference by employing tensor parallelism and pipeline parallelism.

vLLM supports distributed tensor parallelism with Megatron-LM’s tensor parallel algorithm.

For pipeline parallelism, vLLM manages the distributed runtime with Ray for multi-node inferencing.

On the other hand, pipeline parallelism vertically partitions the model by layer and does not demand constant communication between GPUs.

For this LWS deployment, the tensor parallel size is set to 8, while the pipeline parallel size is set to 2.

Within the decades that Deutsche Telekom has been in operation, we’ve gathered a vast amount of valuable data, as well as accruing just as much legacy infrastructure.

Modernizing infrastructure to protect — and act on — dataAs a telco organization, protecting our existing data while we scale is paramount.

These needs led us to Google Cloud.

Our team decided on a hub-and-spoke model based on BigQuery, Cloud Storage, and BigLake to build a data lakehouse architecture, using Apache Iceberg.

That pseudonymised data is then pushed to Google Cloud Storage with Apache Iceberg and then sent to BigQuery and BigLake.

Document preprocessing is a common hurdle when building retrieval-augmented generation (RAG) pipelines.

In this blog, we look at new capabilities within BigQuery and Document AI that simplify this process, and take you through a step-by-step example.

Streamline document processing in BigQueryBigQuery now offers the ability to preprocess documents for RAG pipelines and other document-centric applications through its close integration with Document AI.

The ML.PROCESS_DOCUMENT function, now generally available, can now access new processors, including Document AI's Layout Parser processor, allowing you to parse and chunk PDF documents with SQL syntax.

Layout Parser in Document AI simplifies th…

Data quality challenges in real-world applicationsBut first, let’s take a deeper look at some of the things that make NL2SQL difficult to implement.

Customer challengesIt’s not just the data that can be ambiguous or imprecisely formatted — users’ questions are often confusing or complex.

Here are three common problems with users’ questions that can make it hard to implement NL2SQL.

An ideal NL2SQL solution should proactively prompt the user to specify the appropriate column and incorporate their input during the SQL query generation process.

An ideal NL2SQL solution should recognize uncertainty in the original input and follow up with clarifying questions to get a complete query representat…

The solution: Extracting information in the blink of an AIManually verifying claims submission documents is an avoidable bottleneck in an important process.

Loadsure partnered with Google Cloud to develop an AI-powered claims verification system.

By leveraging Google Cloud's Document AI, Loadsure was able to automate the extraction of relevant data from various claim documents, such as bills of lading, invoices, and shipping documents.

Loadsure was able to seamlessly test different workflows using generative AI models and various Document AI processors.

“Using the labeling tool in Document AI significantly enhanced our collaboration with the domain specialists,” Estefany Montoya, the data t…

“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.

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…

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…

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 …

Assisted Fine-TuningAt DevDay last November, we announced a Custom Model program designed to train and optimize models for a specific domain, in partnership with a dedicated group of OpenAI researchers.

Since then, we've met with dozens of customers to assess their custom model needs and evolved our program to further maximize performance.

Today, we are formally announcing our assisted fine-tuning offering as part of the Custom Model program.

Fully custom-trained models imbue new knowledge from a specific domain by modifying key steps of the model training process using novel mid-training and post-training techniques.

Our team modified every step of the model training process, from domain-s…

We’ve also introduced additional content safeguards for this experience, such as blocking prompts and generations in a wider range of categories.

There are many benefits to creating an account including the ability to save and review your chat history, share chats, and unlock additional features like voice conversations and custom instructions.

For anyone that has been curious about AI’s potential but didn’t want to go through the steps to set-up an account, start using ChatGPT today.

We recognize that generating speech that resembles people's voices has serious risks, which are especially top of mind in an election year.

We are engaging with U.S. and international partners from across government, media, entertainment, education, civil society and beyond to ensure we are incorporating their feedback as we build.ÂThe partners testing Voice Engine today have agreed to our usage policies, which prohibit the impersonation of another individual or organization without consent or legal right.

In addition, our terms with these partners require explicit and informed consent from the original speaker and we don’t allow developers to build ways for individual users to create the…

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

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

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

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

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

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

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

They will work closely with current board members Adam D’Angelo, Larry Summers and Bret Taylor as well as Sam and OpenAI’s senior management.ÂBret Taylor, Chair of the OpenAI board, stated, “I am excited to welcome Sue, Nicole, and Fidji to the OpenAI Board of Directors.

She also served as President of Sony Entertainment, Inc., and simultaneously served as President of Sony Corporation of America.

She also serves as a member of …

The Special Committee of the OpenAI Board today announced the completion of the review by WilmerHale.

The firm conducted dozens of interviews with members of OpenAI’s prior Board, OpenAI executives, advisors to the prior Board, and other pertinent witnesses; reviewed more than 30,000 documents; and evaluated various corporate actions.

“We have unanimously concluded that Sam and Greg are the right leaders for OpenAI,” stated Bret Taylor, Chair of the OpenAI Board.

The Special Committee acknowledged the important work done by WilmerHale in conducting this extensive review and thanked OpenAI current and former Board members, advisors and employees for their cooperation.

The Special Commi…

Date: January 31, 2018 at 11:54:30 PM PSTSubject: Re: Top AI institutions todayWorking at the cutting edge of AI is unfortunately expensive.

For example,In addition to DeepMind, Google also has Google Brain, Research, and Cloud.

If historical trends are any indication, progress in AI is primarily driven by systems - compute, data, infrastructure.

Not only that, but any algorithmic advances published in a paper somewhere can be almost immediately re-implemented and incorporated.

The “second stage” would be a full self driving solution based on large-scale neural network training, which OpenAI expertise could significantly help accelerate.

This technical report focuses on (1) our method for turning visual data of all types into a unified representation that enables large-scale training of generative models, and (2) qualitative evaluation of Sora’s capabilities and limitations.

Model and implementation details are not included in this report.

Much prior work has studied generative modeling of video data using a variety of methods, including recurrent networks,[^1][^2][^3] generative adversarial networks,[^4][^5][^6][^7] autoregressive transformers,[^8][^9] and diffusion models.

[^10][^11][^12] These works often focus on a narrow category of visual data, on shorter videos, or on videos of a fixed size.

Sora is a generalist mo…

Based on collaboration and information sharing with Microsoft, we disrupted five state-affiliated malicious actors: two China-affiliated threat actors known as Charcoal Typhoon and Salmon Typhoon; the Iran-affiliated threat actor known as Crimson Sandstorm; the North Korea-affiliated actor known as Emerald Sleet; and the Russia-affiliated actor known as Forest Blizzard.

The identified OpenAI accounts associated with these actors were terminated.

Salmon Typhoon used our services to translate technical papers, retrieve publicly available information on multiple intelligence agencies and regional threat actors, assist with coding, and research common ways processes could be hidden on a system.…

We’re testing memory with ChatGPT.

Remembering things you discuss across all chats saves you from having to repeat information and makes future conversations more helpful.

You're in control of ChatGPT's memory.

You can explicitly tell it to remember something, ask it what it remembers, and tell it to forget conversationally or through settings.

We are rolling out to a small portion of ChatGPT free and Plus users this week to learn how useful it is.

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 …

He reflects on his journey, the evolution of technology, and the opportunities ahead for Microsoft Research Asia – Tokyo.

Yasuyuki Matsushita, Microsoft Research Asia – TokyoWhy return to Microsoft Research Asia?

You worked at Microsoft Research Asia in Beijing from 2003 to 2015 before transitioning to academia.

Yasuyuki Matsushita: Microsoft Research Asia has always been an exceptional place for conducting cutting-edge research, especially in the AI era.

Understanding embodied AI beyond roboticsQuestion: Your current research interests include embodied AI, which is also one of the key areas at Microsoft Research Asia – Tokyo.

In this blog, we introduce BiomedParse (opens in new tab), a new approach for holistic image analysis by treating object as the first-class citizen.

Through joint pretraining of object recognition, detection, and segmentation, BiomedParse opens new possibilities for holistic image analysis and image-based discovery in biomedicine.

Image parsing: a unifying framework for holistic image analysisBack in 2005, researchers first introduced the concept of “image parsing”—a unified approach to image analysis that jointly conducts object recognition, detection, and segmentation.

With our model, BiomedParse, we have created a foundation for biomedical image parsing that leverages interdependencies a…

Here, we introduce dynamic community selection to the global search algorithm, which leverages the knowledge graph structure of the indexed dataset.

Figure 1: Dynamic community selection workflowThe dynamic global search approach has two main benefits.

* Note that we only evaluate answers from dynamic search at community level 3, which contains more community reports than static search at level 1.

### Common Trends in Vaccination Rates for Major Diseases #### Decline in Vaccination Rates A significant trend observed across the dataset is the decline in vaccination rates for various diseases, including measles, mumps, rubella (MMR), and polio.

Generated response from static search (level 1) …

Orca-AgentInstruct is another step in this direction, where we explore using agentic flows to generate diverse and high-quality data at scale.

Synthetic Data Accelerated LLM Development: Over the past year, using synthetic data has greatly advanced the training of large language models (LLMs).

Successful use of synthetic data involves significant human effort in curating and filtering the data to ensure high quality.

AgentInstruct can create:High-quality data: AgentInstruct uses GPT-4, coupled with tools like search and code interpreters, to create high-quality data.

This has the potential to drive AI advances across multiple industries by making high-quality model training more efficient a…

I’m Bonnie Kruft, partner and deputy director of Microsoft Research AI for Science and your host for today.

KRUFT: Microsoft Research is one of the earliest institutions to apply AI in biomolecular simulation research.

Classical MD is fast but less accurate, while quantum MD is very accurate but computationally prohibitive for the protein study.

Thus, applying AI in biomolecular simulation can become the third way to achieve both ab initio—or first principles—accuracy and high efficiency.

For AI, AI2BMD proves AI can make a big difference to the dynamic protein structure study beyond AI for the protein static structure prediction.

Our team is exploring the potential of modular models by focusing on two themes: i) optimizing the training of expert models and ii) refining how expert models coordinate to form a collaborative model.

One method for coordinating expert models is to adaptively select the most relevant independently developed expert models for specific tasks or queries.

For example, in expert design, expert training can be custom or standard, and training data can be private or shared.

Nonrouter methods: Methods that do not explicitly train a router but instead initialize the router in an unsupervised manner.

Modular models allow specific expert models to be identified and, if necessary, removed or retrained.

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 paperNEW RESEARCH Building AI Agents for Autonomous Clouds: Challenges and Design Principles Using AI agents for operational resilience of cloud services, which currently require significant human effort and domain knowledge, is a high-impact application.

Using AI to generate code and proofs in proof-oriented languages helps mitigate these concerns, while also making proof-oriented programming more accessible to people.

In a recent preprint: Towards Neural Synthesis for SMT-Assisted Proof-Orien…

The ability for cloud providers to share components of the hardware stack across customers, or tenants, is essential for running efficient cloud systems.

Microsoft Azure (opens in new tab) provides strong protection via comprehensive architectural isolation through access control mechanisms implemented across the cloud platform, including the hardware and the hypervisor.

Preventing microarchitectural side-channels with resource-exclusive domainsPublication Principled Microarchitectural Isolation on Cloud CPUsIn a research paper (opens in new tab), which has received a distinguished paper award at the ACM Conference on Computer and Communications Security (ACM CCS’24 (opens in new tab)), we …

And that’s, kind of, where I ended up as a computational social scientist.

HUIZINGA: Right, right, right.

And one potential reason for this is the ways that people prompt the models, right.

HUIZINGA: Right, right, right.

I can’t get the phrase “AI whisperer” out of my head now, [LAUGHTER] and I think that’s what I want to be when I grow up.

As one approach, Molecular Dynamics (MD) simulation combines the laws of physics with numerical simulations to tackle the challenge of understanding biomolecular dynamics.

Similarly, the quantum mechanical approach—known as Density Functional Theory (DFT)—received its own Nobel Prize in 1998 (opens in new tab) (opens in new tab), marking a pivotal moment in computational chemistry.

MD simulations can be roughly divided into two classes: classical MD and quantum mechanics.

Ab initio biomolecular dynamics simulation by AIMicrosoft Research has been working on the development of efficient methods aiming for ab initio accuracy simulations of biomolecules.

This method, AI2BMD (AI-based ab initio…

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…

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…

Microsoft is proud to sponsor the 30th Symposium on Operating Systems Principles (SOSP 2024), highlighting its commitment to advancing computing systems research.

Organized annually by the Association for Computing Machinery (ACM), the symposium brings together experts to explore innovations in operating systems, distributed systems, and systems software.

This work not only contributes to theoretical knowledge but also address real-world challenges, helping ensure that as computing systems grow more complex, they remain sustainable, reliable, and secure.

T10 introduces a distributed tensor abstraction, rTensor, and maps the computation and communication of tensor operators with a generalize…

The rise of affordable small-scale renewable energy, like rooftop solar panels, is reshaping energy systems around the world.

Despite this progress, centralized energy systems still dominate, often failing to provide vulnerable communities with reliable, affordable renewable energy.

AI-powered microgrids support resilient communitiesMicrogrids, small and localized energy systems, hold promise as a solution to the challenges of centralized energy systems.

Learn more Opens in a new tabWhen powered by AI, microgrids can also contribute to energy equity.

The West Atlanta project illustrates AI’s potential to create resilient, equitable, community-driven energy systems, paving the way for a more…

A crowd gathered at the MIT Media Lab in September for a concert by musician Jordan Rudess and two collaborators.

Each time the model took its turn, a range of expressions moved across Rudess’ face: bemusement, concentration, curiosity.

The researchers set out to develop a machine learning model channeling Rudess’ distinctive musical style and technique.

Rudess contributed the data on which Blanchard trained the AI model.

Because the samples he recorded to train the model were similar to ear-training exercises he’s used with students, he thinks the model itself could someday be used for teaching.

Since the 1970s, the field has evolved from writing sophisticated programs to using deep learning, teaching robots to learn directly from human behavior.

As robots become more sophisticated, this hands-on approach hits a scaling problem: the demand for high-quality training data far outpaces humans’ ability to provide it.

Dreams In Motion does this by considering the 3D geometry of the scene and the relative changes in the robot’s perspective.

“Although collecting demonstrations is easy, scaling a real-world robot teleoperation setup to thousands of skills is challenging because a human has to physically set up each scene.

But when robots collected their own training data through LucidSim, …

In addition to MIT’s two U.S. Rhodes winners, Ouigbo and Nair, two affiliates were awarded international Rhodes Scholarships: Chen for Rhodes’ China constituency and Hector for the Global Rhodes Scholarship.

Yiming Chen ’24Yiming Chen, from Beijing, China, and the Washington area, was named one of four Rhodes China Scholars on Sept 28.

Chen graduated from MIT in 2024 with a BS in mathematics and computer science and an MEng in computer science.

Chen was a teaching assistant in the MIT math and electrical engineering and computer science departments, and received a teaching excellence award.

Oluigbo also did a summer internship with the Anyscale Learning for All Laboratory at the MIT Compute…

“We’re pursuing this by purchasing more and different types of clean energy locally, and accelerating technological innovation such as next-generation geothermal projects,” he said.

“At the start of 2024 there were about 3.5 million clean energy jobs, with 'red' states showing the fastest growth in clean energy jobs,” said David S. Miller, managing partner at Clean Energy Ventures.

“The majority (58 percent) of new jobs in energy are now in clean energy — that transition has happened.

MIT ingenuity essentialThe energy transition is placing very different demands on different regions around the world.

“We are stoked by the energy transition, because it’s not just the future, but our chance t…

“By blending generative AI with graph-based computational tools, this approach reveals entirely new ideas, concepts, and designs that were previously unimaginable.

We can accelerate scientific discovery by teaching generative AI to make novel predictions about never-before-seen ideas, concepts, and designs,” says Buehler.

By using this approach, Buehler was able to teach the AI model to systematically reason over complex scientific concepts and behaviors.

The AI model found unexpected similarities between biological materials and “Symphony No.

In another experiment, the graph-based AI model recommended creating a new biological material inspired by the abstract patterns found in Wassily Kan…

The Design Computation and Digital Engineering (DeCoDE) Lab at MIT instead explores the bounds of what is possible.

In the case of linkages, your design looks like a set of bars and how they are connected.

The combinatorial space combined with the continuous space is so big that they can basically go up to seven joints.

If you were to do this exhaustively, it would be very difficult to actually cover the entire design space.

These are all difficult problems that involve both combinatorial design variables and continuous design variables.

This means researchers don’t need to perform costly, and sometimes infeasible, interventional experiments to obtain the data needed to infer the underlying causal relationships.

These programs describe which genes function together to regulate other genes in a biological process, such as cell development or differentiation.

With only observational data, researchers can’t compare genes before and after an intervention to learn how groups of genes function together.

They can use this technique to identify causal modules and reconstruct an accurate underlying representation of the cause-and-effect mechanism.

Causal variables that don’t affect any subsequent variables should have a variance of …

Digital fabrication engineers are now working toward expanding the display capabilities of other everyday objects.

Equipped with ultraviolet (UV) and red, green, and blue (RGB) LEDs, the device can be attached to everyday objects like shirts and headphones.

Photo-Chromeleon used a projector to help activate the color-changing properties of photochromic dye, where the light on the object's surface is at a reduced intensity.

“Compared with our projector-based system from before, PortaChrome is a more portable light source that can be placed directly on top of the photochromic surface.

The photochromic dye was coated on the headphones and the team then attached the PortaChrome device to the in…

Despite the model’s uncanny ability to navigate effectively, when the researchers closed some streets and added detours, its performance plummeted.

So, the team developed two new metrics that can test a transformer’s world model.

“We needed test beds where we know what the world model is.

Now, we can rigorously think about what it means to recover that world model,” Vafa explains.

If scientists want to build LLMs that can capture accurate world models, they need to take a different approach, the researchers say.

In addition to coordinating research on systemic racism across campus, the IDSS initiative has a new project aiming to empower and support this research beyond MIT: the new ICSR Data Hub , which serves as an evolving, public web depository of datasets gathered by ICSR researchers.

“There’s extensive research showing racial discrimination and systemic inequity in essentially all sectors of American society,” explains MIT Professor Fotini Christia, who directs the MIT Institute for Data, Systems, and Society (IDSS), where she also co-leads the Initiative on Combatting Systemic Racism (ICSR).

“My motivation behind doing this project is personal,” says Lewis, who was drawn to MIT in large part …

Artist and designer Es Devlin is the recipient of the 2025 Eugene McDermott Award in the Arts at MIT .

“We look forward to presenting Es Devlin with MIT’s highest award in the arts.

The Eugene McDermott Award in the Arts at MIT was established in 1974 by Margaret McDermott (1912-2018) in honor of her husband, Eugene McDermott (1899-1973), a co-founder of Texas Instruments and longtime friend and benefactor of MIT.

The McDermott Award reflects MIT’s commitment to risk-taking, problem-solving, and connecting creative minds across disciplines.

Es Devlin, born in London in 1971, views an audience as a temporary society and often invites public participation in communal choral works.

Silicon transistors, which are used to amplify and switch signals, are a critical component in most electronic devices, from smartphones to automobiles.

Surpassing siliconIn electronic devices, silicon transistors often operate as switches.

A transistor’s switching slope reflects the sharpness of the “off” to “on” transition.

But while tunneling transistors can enable sharp switching slopes, they typically operate with low current, which hampers the performance of an electronic device.

With such small devices, even a 1-nanometer variance can change the behavior of the electrons and affect device operation.

The MIT Stephen A. Schwarzman College of Computing has announced the launch of a new program to support postdocs conducting research at the intersection of artificial intelligence and particular disciplines.

The Tayebati Postdoctoral Fellowship Program will focus on AI for addressing the most challenging problems in select scientific research areas, and on AI for music composition and performance.

“This new postdoc program is a remarkable opportunity to cultivate exceptional bilingual talent combining AI and another discipline.

The Tayebati Postdoctoral Fellowship Program is a key component of a larger focus of the MIT Schwarzman College of Computing aimed at fostering innovative research i…

Typically, engineers collect data that are specific to a certain robot and task, which they use to train the robot in a controlled environment.

To train better general-purpose robots, MIT researchers developed a versatile technique that combines a huge amount of heterogeneous data from many of sources into one system that can teach any robot a wide range of tasks.

“In robotics, people often claim that we don’t have enough training data.

These models are pretrained using an enormous amount of diverse language data and then fine-tuned by feeding them a small amount of task-specific data.

Even when the task was very different from the pretraining data, HPT still improved performance.

Due to this hallucination problem, an LLM’s responses are often verified by human fact-checkers, especially if a model is deployed in a high-stakes setting like health care or finance.

To help human validators, MIT researchers created a user-friendly system that enables people to verify an LLM’s responses much more quickly.

Users hover over highlighted portions of its text response to see data the model used to generate that specific word or phrase.

SymGen then resolves each reference using a rule-based tool that copies the corresponding text from the data table into the model’s response.

They could validate the model’s responses about 20 percent faster than if they used standard methods.

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…

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 …

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…

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?

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.

As computer vision researchers, we believe that every pixel can tell a story. However, there seems to be a writer’s block settling into the field when it comes to dealing with large images. Large images are no longer rare—the cameras we carry in our pockets and those orbiting our planet snap pictures so big and detailed that they stretch our current best models and hardware to their breaking points when handling them. Generally, we face a quadratic increase in memory usage as a function of image size.

Today, we make one of two sub-optimal choices when handling large images: down-sampling or cropping. These two methods incur significant losses in the amount of information and context present…

Modeling Extremely Large Images with xTAs computer vision researchers, we believe that every pixel can tell a story.

However, there seems to be a writer’s block settling into the field when it comes to dealing with large images.

Today, we make one of two sub-optimal choices when handling large images: down-sampling or cropping.

Why bother handling large images anyways?

That’s basically what we do with large images with $x$T.

2024 BAIR Graduate DirectoryEvery year, the Berkeley Artificial Intelligence Research (BAIR) Lab graduates some of the most talented and innovative minds in artificial intelligence and machine learning.

Our Ph.D. graduates have each expanded the frontiers of AI research and are now ready to embark on new adventures in academia, industry, and beyond.

These fantastic individuals bring with them a wealth of knowledge, fresh ideas, and a drive to continue contributing to the advancement of AI.

Join us in celebrating the achievements of BAIR’s latest PhD graduates.

Thank you to our friends at the Stanford AI Lab for this idea!

Every year, the Berkeley Artificial Intelligence Research (BAIR) Lab graduates some of the most talented and innovative minds in artificial intelligence and machine learning. Our Ph.D. graduates have each expanded the frontiers of AI research and are now ready to embark on new adventures in academia, industry, and beyond.

These fantastic individuals bring with them a wealth of knowledge, fresh ideas, and a drive to continue contributing to the advancement of AI. Their work at BAIR, ranging from deep learning, robotics, and natural language processing to computer vision, security, and much more, has contributed significantly to their fields and has had transformative impacts on society.

This…

AI caught everyone’s attention in 2023 with Large Language Models (LLMs) that can be instructed to perform general tasks, such as translation or coding, just by prompting. This naturally led to an intense focus on models as the primary ingredient in AI application development, with everyone wondering what capabilities new LLMs will bring.

As more developers begin to build using LLMs, however, we believe that this focus is rapidly changing: state-of-the-art AI results are increasingly obtained by compound systems with multiple components, not just monolithic models.

For example, Google’s AlphaCode 2 set state-of-the-art results in programming through a carefully engineered system that uses L…

In this post, we analyze the trend toward compound AI systems and what it means for AI developers.

We argue that compound AI systems will likely be the best way to maximize AI results in the future, and might be one of the most impactful trends in AI in 2024.

We define a Compound AI System as a system that tackles AI tasks using multiple interacting components, including multiple calls to models, retrievers, or external tools.

Developing Compound AI SystemsWhile compound AI systems can offer clear benefits, the art of designing, optimizing, and operating them is still emerging.

However, new compound AI systems contain non-differentiable components like search engines or code interpreters, a…

The solution combines data from an Amazon Aurora MySQL-Compatible Edition database and data stored in an Amazon Simple Storage Service (Amazon S3) bucket.

Create an Amazon Q applicationComplete the following steps to create an Amazon Q application:On the Amazon Q console, choose Applications in the navigation pane.

Configure Amazon Q to connect to Aurora MySQL-CompatibleComplete the following steps to configure Amazon Q to connect to Aurora MySQL-Compatible:On the Connect data sources page, under Data sources, choose the Aurora (MySQL) data source.

Configure Amazon Q to connect to Amazon S3Complete the following steps to configure Amazon Q to connect to Amazon S3:On the Connect data sources…

In this post, we illustrate automating the responses to email inquiries by using Amazon Bedrock Knowledge Bases and Amazon Simple Email Service (Amazon SES), both fully managed services.

Amazon Bedrock Knowledge BasesFor RAG workflows, Amazon Bedrock offers managed knowledge bases, which are vector databases that store unstructured data semantically.

For more information on RAG and Amazon Bedrock Knowledge Bases, see Connect Foundation Models to Your Company Data Sources with Agents for Amazon Bedrock.

Amazon Bedrock Knowledge Bases uses the Amazon Titan embeddings model to convert the prompt query to a vector embedding, and then finds chunks that are semantically similar.

Our description o…

For a comprehensive overview of metadata filtering and its benefits, refer to Amazon Bedrock Knowledge Bases now supports metadata filtering to improve retrieval accuracy.

Solution overviewIn this section, we illustrate the flow of the dynamic metadata filtering solution using the tool use (function calling) capability.

In the following sections, we explore how to implement dynamic metadata filtering using the tool use feature in Amazon Bedrock and Pydantic for data validation.

For detailed cleanup instructions, see Amazon Bedrock Knowledge Bases now supports metadata filtering to improve retrieval accuracy.

ConclusionBy implementing dynamic metadata filtering using Amazon Bedrock and Pydan…

Mission-critical public safety applications require the highest levels of security and reliability when implementing technology capabilities.

This solution demonstrates how generative AI can enhance public safety operations while allowing officers to focus more time on serving their communities.

Mark43’s public safety solution built on the AWS CloudMark43 offers a cloud-native Public Safety Platform with powerful computer-aided dispatch (CAD), records management system (RMS), and analytics solutions, positioning agencies at the forefront of public safety technology.

If a user doesn’t have access to the data outside of Amazon Q Business, then they cannot access the data within Amazon Q Busin…

In this post, we explore how FP8 optimization can significantly speed up large model training on Amazon SageMaker P5 instances.

LLM training using SageMaker P5In 2023, SageMaker announced P5 instances, which support up to eight of the latest NVIDIA H100 Tensor Core GPUs.

With the use of Amazon SageMaker Model Training, organizations have been able to achieve higher training speeds and efficiency by turning to P5 instances.

LLM training using FP8P5 instances, which are NVIDIA H100 GPUs underneath, also come with capabilities of training models using FP8 precision.

The impact on LLM training and beyondThe use of FP8 precision combined with SageMaker P5 instances has significant implications f…

The AWS DeepRacer League was also announced, featuring physical races at AWS Summits worldwide in 2019 and a virtual league in a simulated environment.

This event also sparked the creation of the AWS DeepRacer Community, which has since grown to over 45,000 members.

Conclusion: The lasting impact of AWS DeepRacerOver the past six years, AWS DeepRacer has profoundly impacted my professional and personal life.

For those interested in starting their own AI and ML journey, I encourage you to explore the AWS DeepRacer resources available on the AWS website.

Matt is an AWS Community Builder and continues to contribute to open source projects in the AWS DeepRacer community.

These sessions, featuring Amazon Q Business, Amazon Q Developer, Amazon Q in QuickSight, and Amazon Q Connect, span the AI/ML, DevOps and Developer Productivity, Analytics, and Business Applications topics.

Visit the Generative AI Zone (GAIZ) at AWS Village in the Venetian Expo Hall to explore hands-on experiences with our newest launches and connect with our generative AI and ML specialists.

Experience an immersive selection of innovative generative AI exhibits at the Generative AI and Innovations Pavilion through interactive displays spanning the AWS generative AI stack.

Baskar Sridharan, VP, AI/ML Services & Infrastructure | AIM276-INT | Generative AI in action: From prototype to product…

You can fine-tune and deploy models with Amazon SageMaker JumpStart or directly through Hugging Face containers.

We use Amazon SageMaker Studio, a comprehensive web-based integrated development environment (IDE) designed to facilitate all aspects of ML development.

We use Amazon SageMaker Studio, a comprehensive web-based integrated development environment (IDE) designed to facilitate all aspects of ML development.

If you deployed the model with Amazon Bedrock Custom Model Import, you can delete the imported model using the Amazon Bedrock console.

You can also improve the quality of training data using advanced data processing techniques.

In this post, we demonstrate how to create an automated email response solution using Amazon Bedrock and its features, including Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails.

The benefits of AI-powered solutionsTo address these challenges, businesses are adopting generative AI to automate and refine email response processes.

By using AI-driven solutions, organizations can overcome the limitations of manual email processing, streamlining operations and improving the overall customer experience.

The following diagram provides a detailed view of the architecture to enhance email support using generative AI.

As a part of the AI/ML community at AWS, AK gui…

In this post, we show you how agentic workflows with Amazon Bedrock Agents can help accelerate this journey for research scientists with a natural language interface.

Amazon Bedrock Agents provides default prompt templates for pre-processing users’ queries, orchestration, a knowledge base, and a post-processing template.

Amazon Bedrock Agents are designed to handle a wide range of user inputs, from simple queries to complex, multi-turn conversations.

This post has demonstrated how Amazon Bedrock Agents can offer a flexible framework and relevant tools to help accelerate this critical discovery process.

For examples to get started with Amazon Bedrock Agents, check out the Amazon Bedrock Agen…

create_response = client.create_guardrail( name='math-tutoring-guardrail', description='Prevents the model from providing non-math tutoring, in-person tutoring, or tutoring outside grades 6-12.

""", tags=[ {'key': 'purpose', 'value': 'math-tutoring-guardrail'}, {'key': 'environment', 'value': 'production'} ] )The API response will include a guardrail ID and version.

Additionally, each row of the API response is saved so the user can explore the response as needed.

Optional: Automate the workflow and iteratively improve the guardrailWe recommend reviewing your test results after each iteration.

When using TDD, it’s important to validate your test results and verify that you’re making improve…

Today, we are happy to announce the availability of Binary Embeddings for Amazon Titan Text Embeddings V2 in Amazon Bedrock Knowledge Bases and Amazon OpenSearch Serverless.

Generate Binary Embeddings with Amazon Titan Text Embeddings V2Amazon Titan Text Embeddings V2 now supports Binary Embeddings and is optimized for retrieval performance and accuracy across different dimension sizes (1024, 512, 256) with text support for more than 100 languages.

To learn more about Amazon Knowledge Bases, visit the Amazon Bedrock Knowledge Bases product page.

For more information regarding Amazon Titan Text Embeddings, visit Amazon Titan in Amazon Bedrock.

For more information on Amazon OpenSearch Server…

To address this challenge, this post demonstrates a proactive approach for security vulnerability assessment of your accounts and workloads, using Amazon GuardDuty, Amazon Bedrock, and other AWS serverless technologies.

The Step Functions state machine invokes AWS Lambda functions to get a findings summary and remediation steps through Amazon Bedrock.

The Step Functions workflow sends findings and remediation notifications to the subscribers or users using Amazon Simple Notification Service (Amazon SNS).

AWS Lambda – Lambda is a compute service that runs your code in response to events and automatically manages the compute resources, making it the fastest way to turn an idea into a modern, …

Here is the question {query} Return your answer in the following format $REASON_JUSTIFYING_CATEGORY $COMMA_SEPARETED_LABELS """Using XML tags in the output allows you to parse out the right category for the question.

H is the human and E is the expert: {history} Here is the new query {query} If you can answer the question, your answer should be formatted as follows.

A: This is a translation, I can answer. Il y a 42 puits. H: Can you summarize that in one sentence?

A: This is just rewriting, I can summarize the previous reply and answer directly. Il y a 42 puits. H: Who's the queen of England?

ConclusionIn this post, we presented an AI assistant for efficient data exploratio…

After the SQL query is generated with the placeholder, the user can swap back the actual list of codes before running it.

After the SQL query is generated with the placeholder, the user can swap back the actual list of codes before running it.

The output contains a generated SQL query, in which case we return it to the user, along with the generated explanation.

Output the generated SQL query in tag.

ConclusionIn this post, we showcased how you can use generative AI to translate natural language into SQL for complex healthcare databases like DE-SynPUF.

Of those accelerated systems, 85% use NVIDIA Hopper GPUs, driving advancements in areas like climate forecasting, drug discovery and quantum simulation.

Accelerated computing is much more than floating point operations per second (FLOPS).

A total of 249 exaflops of AI performance are now available to TOP500 systems, supercharging innovations and discoveries across industries.

Accelerated Computing Is Sustainable ComputingAs the demand for computational capacity grows, so does the need for sustainability.

In the Green500 list of the world’s most energy-efficient supercomputers, systems with NVIDIA accelerated computing rank among eight of the top 10.

Quality filtering Heuristic filtering : Applies rules-based filters to remove low-quality content.

Unicode reformatting, exact deduplication, heuristic filtering, and classifier-based filtering are used to process and refine this dataset into a high-quality final version.

Box plots of symbol, number, and whitespace percentages before and after heuristic curationThe box plots highlight a significant reduction in outliers after heuristic filtering.

Through both heuristic filtering and classifier-based filtering, the dataset maintains its broad range of domain diversity.

The pipeline uses NVIDIA NeMo Curator, a valuable tool for preparing large datasets for pretraining language models, focusin…

As COP29 attendees gather in Baku, Azerbaijan, to tackle climate change, the role AI plays in environmental sustainability is front and center.

Experts from Crusoe Energy Systems, EON, the International Energy Agency (IEA) and NVIDIA sat down for a conversation about the energy efficiency of AI.

The study looks at how organizations can achieve “Green AI” in the coming decades and addresses AI’s energy use.

Direct-to-chip liquid cooling allows data centers to cool systems more effectively than traditional air conditioning, consuming less power and water.

As AI continues to scale, the future of data centers will hinge on designing for energy efficiency from the outset.

How the Department of Energy’s AI Initiatives Are Transforming Science, Industry and GovernmentThe U.S. Department of Energy oversees national energy policy and production.

Hear more from Helena Fu by watching the on-demand session, AI for Science, Energy and Security, from AI Summit DC.

Time Stamps2:20: Four areas of focus for the CET include AI, microelectronics, quantum information science and biotechnology.

10:55: Introducing AI-related initiatives within the DOE, including FASST, or Frontiers in AI for Science, Security and Technology.

19:35: The opportunity of AI applied to materials discovery and applications across science, energy and national security.

Grace Blackwell now on Azure, new workflows for industrial AI, and tools and features for RTX AI PCs accelerate AI development.

NVIDIA and Microsoft today unveiled product integrations designed to advance full-stack NVIDIA AI development on Microsoft platforms and applications.

Plus, the NVIDIA AI platform on Azure includes new reference workflows for industrial AI and an NVIDIA Omniverse Blueprint for creating immersive, AI-powered visuals.

The Blackwell-based VM series complements previously announced Azure AI clusters with ND H200 V5 VMs, which provide increased high-bandwidth memory for improved AI inferencing.

At Ignite, NVIDIA announced its new multimodal SLM, NVIDIA Nemovision-4B Ins…

Today, over 600 Windows apps and games are already running AI locally on more than 100 million GeForce RTX AI PCs worldwide, delivering fast, reliable and low-latency performance.

At Microsoft Ignite, NVIDIA and Microsoft announced tools to help Windows developers quickly build and optimize AI-powered apps on RTX AI PCs, making local AI more accessible.

RTX AI PCs Power Digital Humans With Multimodal Small Language ModelsNVIDIA ACE is a suite of digital human technologies that brings life to agents, assistants and avatars.

Available in 8B-, 4B- and 2B-parameter versions, these models offer flexible options for balancing speed, memory usage and accuracy on RTX AI PCs.

Learn more about how de…

AI Will Drive Scientific Breakthroughs, NVIDIA CEO Says at SC24NVIDIA kicked off SC24 in Atlanta with a wave of AI and supercomputing tools set to revolutionize industries like biopharma and climate science.

“Supercomputers are among humanity’s most vital instruments, driving scientific breakthroughs and expanding the frontiers of knowledge,” Huang said.

AI Breakthroughs in Drug Discovery and ChemistryWith the open-source release of BioNeMo Framework, NVIDIA is advancing AI-driven drug discovery as researchers gain powerful tools tailored specifically for pharmaceutical applications.

And the NVIDIA ALCHEMI NIM microservice, NVIDIA introduces generative AI to chemistry, allowing researchers …

NVIDIA today at SC24 announced two new NVIDIA NIM microservices that can accelerate climate change modeling simulation results by 500x in NVIDIA Earth-2.

The new NIM microservices offer climate technology application providers advanced generative AI-driven capabilities to assist in forecasting extreme weather events.

NVIDIA is releasing the CorrDiff NIM and FourCastNet NIM microservices to help weather technology companies more quickly develop higher-resolution and more accurate predictions.

New CorrDiff NIM Microservices for Higher-Resolution ModelingNVIDIA CorrDiff is a generative AI model for kilometer-scale super resolution.

The CorrDiff NIM microservice is 500x faster and 10,000x more …

The accelerated computing library advances scientific discovery by helping researchers seamlessly scale to powerful computing clusters without modifying their Python code.

Other institutions using cuPyNumeric include:Australia National University , where researchers used cuPyNumeric to scale the Levenberg-Marquardt optimization algorithm to run on multi-GPU systems at the country’s National Computational Infrastructure.

, where researchers used cuPyNumeric to scale the Levenberg-Marquardt optimization algorithm to run on multi-GPU systems at the country’s National Computational Infrastructure.

The team used cuPyNumeric to decompose a matrix of 16 million rows and 4,000 columns.

The team use…

During the Supercomputing 2024 conference, NVIDIA announced the availability of the NVIDIA H200 NVL PCIe GPU — the latest addition to the Hopper family.

Enterprises can use H200 NVL to accelerate AI and HPC applications, while also improving energy efficiency through reduced power consumption.

The NVIDIA H200 NVL is paired with powerful software tools that enable enterprises to accelerate applications from AI to HPC.

It comes with a five-year subscription for NVIDIA AI Enterprise, a cloud-native software platform for the development and deployment of production AI.

NVIDIA AI Enterprise includes NVIDIA NIM microservices for the secure, reliable deployment of high-performance AI model inferen…

To meet demand for Blackwell, now in full production, Foxconn, the world’s largest electronics manufacturer, is using NVIDIA Omniverse.

Foxconn uses NVIDIA Omniverse to virtually integrate their facility and equipment layouts, NVIDIA Isaac Sim for autonomous robot testing and simulation, and NVIDIA Metropolis for vision AI.

World’s Largest Electronics Maker Plans With Omniverse and AITo meet demands at Foxconn, factory planners are building physical AI-powered robotic factories with Omniverse and NVIDIA AI.

Creating Efficiencies While Building Resilient Supply ChainsUsing NVIDIA Omniverse and AI, Foxconn plans to replicate its precision production lines across the world.

Foxconn’s Mexico fa…

NVIDIA ALCHEMI for Material and Chemical SimulationsExploring the universe of potential materials, using the nearly infinite combinations of chemicals — each with unique characteristics — can be extremely complex and time consuming.

All in all, evaluating 16 million structures would have taken months — with the NIM microservice, it can be done in just hours.

SES AI, a leading developer of lithium-metal batteries, is using the NVIDIA ALCHEMI NIM microservice with the AIMNet2 model to accelerate the identification of electrolyte materials used for electric vehicles.

It will also be downloadable from build.nvidia.com, and the production-grade NIM microservice will be offered through the NVIDIA…

This post introduces the NVIDIA ALCHEMI (AI Lab for Chemistry and Materials Innovation), which aims to accelerate chemical and material discovery with AI.

NVIDIA Batched Geometry Relaxation NIMWhile MLIPs accelerate energy and force calculations for geometry relaxation significantly (versus full DFT), the actual implementation can still be time-consuming.

This is why NVIDIA has developed the NVIDIA Batched Geometry Relaxation NIM to accelerate geometry relaxation calculations.

This post request transmits the data to the Batch Geometry Relaxation NIM and starts the relaxation process, asynchronous from the client code.

Sign up to receive notification when the NVIDIA Batched Geometry Relaxati…

Popularized under equivariant neural networks (ENNs), these neural network architectures are built using the mathematical concept of equivariance under symmetry-related transformations.

In this post, we describe how the new math library NVIDIA cuEquivariance tackles both challenges and accelerates AI for science models, with examples from drug discovery and material science applications.

Accelerating equivariant neural networksTo address these challenges, NVIDIA developed the new cuEquivariance math library that introduces CUDA-accelerated building blocks for equivariant neural networks.

This specialized backend is optimized for performance on NVIDIA GPUs, enabling significant speedups in m…

cuPyNumeric offers you the productivity of NumPy with the performance benefits of accelerated, distributed GPU computing.

cuPyNumeric: A drop-in replacement for NumPyYou can start using cuPyNumeric by importing cupynumeric instead of numpy in a NumPy program.

cuPyNumeric is the only existing distributed NumPy implementation that can support aliasing and mutation of distributed arrays in this manner.

Weak scaling plot for a cuPyNumeric 2D stencil computationHow cuPyNumeric parallelizes NumPy operationsBy design, NumPy APIs are built around vector operations that have ample data parallelism.

With cuPyNumeric, scaling NumPy programs across a range of systems—from laptops to supercomputers—is a…

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.

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.​

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…

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…

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 …

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…

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 …

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…

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.

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 …

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…

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.

Improving tail utilization – the utilization level of the top 5% of the servers when ranked by utilization– within our infrastructure is imperative to operate our fleet efficiently and sustainably.

Challenges of load balancingThere are two approaches to load balancing:Routing load balancing – load balancing across replicas of a single model.

Placement load balancing – balancing load on hosts by moving replicas of a model across hosts.

It also exposed a deeper problem like spiky tail utilization, which was hidden behind the high tail utilization and was fixed once identified .

Optimizing tail utilization within IPnext thereby delivering these benefits to a broader range of expanding machine …

Prediction uncertainty is inherent, which makes it difficult to define, identify, diagnose, reproduce, and debug prediction quality issues.

However, ML prediction stability implies a consistent prediction quality shift, which is harder to distinguish.

Meta’s approach and progress towards prediction robustnessMeta has developed a systematic framework to build prediction robustness.

Additionally, fundamental understanding of training data label consistency has resulted in optimizations in training data generation for better model learning.

Regarding ML development productivity, prediction robustness techniques can facilitate model development, and improve daily operations by reducing the time…

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.

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.

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…

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.

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…

In this article, I’ll explore the following questions:What are the basic and advanced strategies for prompt engineering, and when should they be used?

Basic prompt engineering strategiesWhile there is no universal way to design a prompt, there are strategies we can follow to create prompts that yield LLM outputs closer to what we expect.

The basic strategies we cover in this section are straightforward to implement in a lot of different tasks, and they involve minimal customization.

Use Cases: When to use prompt templatesFinally, I recommend trying this approach for your project if:You encounter repetitive tasks, prompt templates ensure uniformity and save a lot of time.

Lessons learnedAs w…

TL;DR Evaluating text summarization is difficult because there is no one correct solution, and summarization quality often depends on the summary’s context and purpose.

How does LLM text summarization work?

METEOR is recall-oriented and ensures that the generated text captures as much information from the reference text.

Example: Source Text: {{Document}} Summary: {{Summary}} Evaluation Form (scores ONLY): – Relevance: “””Different prompts for different evaluation criteria are available.

The extensive use of LLMs for text summarization, including the integration of summarization features in search engines, makes research in this field highly popular and relevant.

The demand for observability and the scale of the required infrastructure vary significantly along the LLMOps value chain.

The distributed structure of agentic networks adds another level of complexity, which is not yet addressed fully by LLM observability tools and practices.

The value chain of LLMOpsThe LLMOps value chain starts with training foundation models and subsequent task-specific finetuning.

Each step has different observability needs and requires different scales of observability tooling and infrastructure.

Full talk Observability in LLMOps: Different Levels of Scale Watch on Youtube

Large Language Model (LLM) observability comprises methods for monitoring, tracing, and analyzing an LLM system’s behavior and responses.

Before we discuss the different pillars of LLM observability in detail, let’s clarify how LLM observability relates to LLM monitoring and ML observability.

LLM monitoring vs. LLM observabilityThe difference between LLM monitoring and LLM observability is analogous to the one between traditional monitoring and observability.

LLM observability vs ML observabilityMachine learning (ML) observability is an established practice, so it is natural to ask why LLM applications require a new approach.

LangfuseLangfuse is an open-source LLM observability platform tha…

End-to-end (E2E) MLOps platforms promise to simplify the complicated process of building, deploying, and maintaining ML models in production.

However, while E2E MLOps platforms promise convenience and integration, they may not always align with an organization’s specific needs, existing infrastructure, or long-term goals.

She brings a wealth of experience to the discussion on end-to-end MLOps platforms.

However, she highlighted a core problem:The main challenge of using end-to-end ML platforms for your MLOps stack is that nothing works exactly as you need.

As an avid podcast fan, I’ve also learned much from listening to experienced MLOps engineers share their experiences building platforms …

Defense strategies like adversarial learning, monitoring, defensive distillation, and differential privacy improve robustness against adversarial attacks.

In this article, we’ll review common attack strategies and dive into the latest defense mechanisms for shielding machine learning systems against adversarial attacks.

Regardless of the level of access to the targeted machine learning model, adversarial attacks can be further categorized as:Evasion attacks,Data-poisoning attacks,Byzantine attacks,Model-extraction attacks.

Related Adversarial Attacks on Neural Networks: Exploring the Fast Gradient Sign Method Read moreData-poisoning attacksData-poisoning attacks are another flavor of advers…

Large Language Model (LLM): A machine-learning model that takes in a textual prompt and outputs an answer.

These documents and the user query comprise the prompt for the LLM | Source: AuthorMethods for building LLM applications with vector databasesVector databases for context retrievalThe simplest way to leverage vector databases in LLM systems is to use them to efficiently search for context that can help your LLM provide accurate answers.

Dynamic few-shot prompting: The prompt is constructed by combining the original user query and examples selected through retrieval froma vector database | Source: AuthorRelated Zero-Shot and Few-Shot Learning with LLMs Read moreHow to build LLM applicat…

Your MLflow run logs can easily be exported to the neptune.ai app using a dedicated plugin.

Seven reasons for migrating from MLflow to neptune.aiBefore diving into the migration process, let’s see how MLflow and Neptune differ and why migrating from MLflow to Neptune is worth the effort.

Export MLflow logs to neptune.ai: We will migrate your existing MLflow run logs and models to the Neptune experiment tracker.

Export MLflow logs to NeptuneDepending on whether you store your MLflow data locally or work with a remote tracking server, the export works slightly differently.

Here’s an example of how to create a Neptune tracking URI and pass it to MLflow:import os import mlflow from neptune_mlfl…

We have consolidated the separate runs table, run details, and compare runs views into one single view, freeing up a ton of space at the top of your screen.

Run groups: Level up the analysis of your experimentsTo some extent, grouping experiments has always been possible in Neptune.

But we’re now elevating run groups to be more important citizens of Neptune.

Here’s what’s new:Most importantly, you can now group experiments by string sets, e.g., tags and newly introduced group tags .

A few changes in the runs table include:We’re working on bringing the run name to the front row of the runs table.

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…

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 …

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…

#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…

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://…

#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…

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…

#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:/…

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…

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…

Paper: https://arxiv.org/abs/2404.09173 Abstract:

While Transformers have revolutionized deep learning, their quadratic attention complexity hinders their ability to process infinitely long inputs. We propose Feedback Attention Memory (FAM), a novel Transformer architecture that leverages a feedback loop to enable the network to attend to its own latent representations. This design fosters the emergence of working memory within the Transformer, allowing it to process indefinitely long sequences. TransformerFAM requires no additional weights, enabling seamless integration with pre-trained models. Our experiments show that TransformerFAM significantly improves Transformer performance on long-…

OUTLINE:

0:00 - Intro

0:21 - Debunking Devin: "First AI Software Engineer" Upwork lie exposed!

07:24 - NeurIPS 2024 will have a track for papers from high schoolers.

13:29 - Opus can operate as a Turing machine.

13:47 - An AI-Powered, Self-Running Propaganda Machine for $105

14:27 - TechScape: How cheap, outsourced labour in Africa is shaping AI English

16:25 - Is ChatGPT Transforming Academics' Writing Style? References:

https://news.ycombinator.com/item?id=40008109&s=09

https://www.youtube.com/watch?v=tNmgmwEtoWE

https://www.youtube.com/watch?v=xE2fxcETP5E

https://twitter.com/itsandrewgao/status/1779369373737668669?t=omW3DvRNmZyce8oo0Ehf1g&s=09

https://twitter.com/0interestrates/status/17…

Google researchers achieve supposedly infinite context attention via compressive memory. Paper: https://arxiv.org/abs/2404.07143 Abstract:

This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation. A key component in our proposed approach is a new attention technique dubbed Infini-attention. The Infini-attention incorporates a compressive memory into the vanilla attention mechanism and builds in both masked local attention and long-term linear attention mechanisms in a single Transformer block. We demonstrate the effectiveness of our approach on long-context language modeling benchmarks, 1M …

Meta's Llama 3 is out. New model, new license, new opportunities. References:

https://llama.meta.com/llama3/

https://ai.meta.com/blog/meta-llama-3/

https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md

https://llama.meta.com/trust-and-safety/

https://ai.meta.com/research/publications/cyberseceval-2-a-wide-ranging-cybersecurity-evaluation-suite-for-large-language-models/

https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai

https://llama.meta.com/llama3/license/

https://about.fb.com/news/2024/04/meta-ai-assistant-built-with-llama-3/?utm_source=twitter&utm_medium=organic_social&utm_content=thread&utm_campaign=imagineflash

https://twitter.com/minchoi/status/178277…

Links:

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Twitter: https://twitter.com/ykilcher

Discord: https://ykilcher.com/discord

LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):

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Litecoin (LTC): LQW2TRyKYetVC8WjFkhpP…

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…

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 …

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…

Hey everyone! Thank you so much for watching this overview of Command R+ showing you how you can use the new model in DSPy and a quick RAG demo, as well as walking through the details of the release post! Congratulations to the Cohere team! Super exciting times to be working with LLM systems! Introducing Command R+: A Scalable LLM Built for Business - https://txt.cohere.com/command-r-plus-microsoft-azure/ Link to demo notebook - https://github.com/weaviate/recipes/blob/main/integrations/dspy/llms/Command-R-Plus.ipynb Chapters

0:00 Welcome! Command R+!

1:12 Demo with Cohere, DSPy, and Weaviate

6:06 Command R+ Announcement Post

9:24 LLM Evals

Unfortunately, Large Language Models will not consistently follow the instructions that you give them. This is a massive problem when you are building AI systems that require a particular type of output from the previous step to feed into the next one! For example, imagine you are building a blog post writing system that first takes a question and retrieved context to output a list of topics. These topics have to be formatted in a particular way, such as a comma-separated list or a JSON of Topic objects, such that the system can continue writing the blog post! I am SUPER excited to share the 4th video in my DSPy series, diving into 3 solutions to structuring outputs in DSPy programs: (1) **…

Hey everyone! Thank you so much for watching the 3rd edition of the DSPy series, Adding Depth to DSPy Programs!! You can find the examples and links to community resources / news on https://github.com/weaviate/recipes! Chapters

0:00 Intro

0:50 Chapters Overview

5:06 Weaviate Recipes

5:24 DSPy News and Community Notes

13:51 Adding Depth to RAG Programs

18:40 Multi-Model DSPy Programs

20:18 DSPy Optimizers

25:30 Deep Dive Optimizers

27:55 Into the Optimizer Code!

37:48 Demo #1: Adding Depth to RAG

1:05:25 Demo #2: Questions to Blogs

1:07:48 Thank you so much for watching!

Hey everyone! Thank you so much for watching this tutorial on getting started with RAG programming in DSPy! This video will take you through 4 major aspects of building DSPy programs (1) Installation, settings, and Datasets with dspy.Example, (2) LLM Metrics, (3) The DSPy programming model, and (4) Optimization!! The notebook used in the video can be found here: https://github.com/weaviate/recipes/blob/main/integrations/dspy/1.Getting-Started-with-RAG-in-DSPy.ipynb All future videos, as well as additional utils like data import scripts, will be in this folder: https://github.com/weaviate/recipes/tree/main/integrations/dspy Please leave a star, it helps a lot! DSPy on GitHub: https://github.…

Hey everyone! Thank you so much for watching this explanation of DSPy! DSPy is a super exciting new framework for developing LLM programs! Pioneered by frameworks such as LangChain and LlamaIndex, we can build much more powerful systems by chaining together LLM calls! This means that the output of one call to an LLM is the input to the next, and so on. We can think of chains as programs, with each LLM call analogous to a function that takes text as input and produces text as output. DSPy offers a new programming model, inspired by PyTorch, that gives you a massive amount of control over these LLM programs. Further the Signature abstraction wraps prompts and structured input / outputs to cle…

From this full video: https://youtu.be/piJkuavhV50

Full video: https://youtu.be/GNcFjFmqEc8

Full video: https://youtu.be/-RdOwhmqP5s

The referenced stack exchange post by usename 2'5 9'2, http://math.stackexchange.com/questions/30046/alternative-notation-for-exponents-logs-and-roots

Full video: https://youtu.be/piJkuavhV50

I learned this from Matt Parker, who then made a full video: https://youtu.be/ga9Qk38FaHM

A series of delightful geometry puzzlesBonus video with extra puzzles: https://www.patreon.com/posts/115570453

The artwork at the end is by Kurt Bruns

Thanks to Daniel Kim for sharing the first two puzzles with me.

The idea to include the tetrahedron volume example was based on a conversation with Po Shen Lo about these puzzles, during which he mentioned the case of one dimension lower.

I received the cone correction to the proof of Monge's theorem via Akos Zahorsky. If any of you know the original source, please let me know! I referenced quaternions at the end, and if you're curious to learn more, here are a few options. This video walks through concretely what the computation is for using…

Full video: https://youtu.be/wjZofJX0v4M

A behind-the-scenes look at how I animate videos.

Code for all the videos: https://github.com/3b1b/videos

Manim: https://github.com/3b1b/manim

Community edition: https://github.com/ManimCommunity/manim/

Example scenes shown near the end: https://github.com/3b1b/manim/blob/master/example_scenes.py I added some more details about the workflow shown in this video to the readme of the videos repo: https://github.com/3b1b/videos?tab=readme-ov-file#workflow These lessons are funded directly by viewers: https://3b1b.co/support Timestamp:

0:00 - Intro

2:39 - Hello World

10:32 - Coding up a Lorenz attractor

23:46 - Add some tracking points

28:52 - The globals().update(locals()) hack

32:57 - Final st…

Full video: https://youtu.be/EmKQsSDlaa4

Full video: https://youtu.be/EmKQsSDlaa4

3d scenes on 2d film, and a diffraction lesson along the way.

Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support

An equally valuable form of support is to share the videos. Gabor's Nobel Prize lecture:

https://www.nobelprize.org/uploads/2018/06/gabor-lecture.pdf A few resources we found helpful for this video

Seeing the Light, by Falk, Brill, and Stork

https://amzn.to/3Ngdiqh Practical Holography, by Saxby and Zarcharovas

https://amzn.to/3ZR2MNN Principles of Holography by Howard Smith

https://amzn.to/3ZOihFZ Timestamps

0:00 - What is a Hologram?

3:28 - The recording process

11:45 - The simplest hologram

17:12 - Diffraction gratings

25:15 - …

Why max(rand(), rand()) is the same as sqrt(rand()) See Matt Parker's video for more:

https://youtu.be/ga9Qk38FaHM

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers Oasis: A Universe in a Transformer - try it out now:

https://oasis.decart.ai/welcome More info:

https://oasis-model.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 Albrec…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper is available here:

https://openai.com/index/introducing-simpleqa/ 📝 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 Sundv…

❤️ Try Macro for free and supercharge your learning: https://macro.com/papers 📝 The paper "3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes" is available here:

https://gaussiantracer.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 T…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers

📝 The paper is available here:

https://openai.com/index/simplifying-stabilizing-and-scaling-continuous-time-consistency-models/ 📝 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 Albre…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝Apple reasoning paper: https://arxiv.org/pdf/2410.05229 📝 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 Sundvall, Taras Bobrovytsk…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers Guide on how I use Lambda to make images: https://docs.lambdalabs.com/on-demand-cloud/how-to-serve-the-flux.1-prompt-to-image-models-using-lambda-cloud-on-demand-instances 📝 The paper "Interactive Character Control with Auto-Regressive Motion Diffusion Models" is available here:

https://xbpeng.github.io/projects/AMDM/index.html

https://yi-shi94.github.io/amdm_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 …

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papersllm 📝 The paper is available here:

https://visualcomputing.ist.ac.at/publications/2024/PDNSF/ 📝 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…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The papers are available here:

https://allenai.github.io/discoveryworld/

https://www.youtube.com/watch?v=hcBFhJKdAvk

https://cg.informatik.uni-freiburg.de/publications/2023_CGF_monolithic_contact_friction.pdf 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Gaston Ingaramo, Gordon Child, Joh…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "fVDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial Intelligence" is available here:

https://research.nvidia.com/labs/prl/publication/williams2024fvdb/

https://developer.nvidia.com/fvdb

https://blogs.nvidia.com/blog/fvdb-bigger-digital-models/ LLama 3.2: https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/

Gaussian Haircut paper: https://eth-ait.github.io/GaussianHaircut/ NVLM vision model:

https://research.nvidia.com/labs/adlr/NVLM-1/

https://huggingface.co/nvidia/NVLM-D-72B 📝 My paper on simulations that look almost like reality is a…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers Unreal Engine + 5.5 preview:

https://www.unrealengine.com/en-US

https://forums.unrealengine.com/t/unreal-engine-5-5-preview/2048423 📝 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 A…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers NotebookLM: https://notebooklm.google/

Our paper on ray tracing: https://users.cg.tuwien.ac.at/zsolnai/gfx/adaptive_metropolis/ Give it a face: https://x.com/HalimAlrasihi/status/1840052149012570283

It sings with you: https://x.com/ai_for_success/status/1840079574048133350

AlphaChip: https://deepmind.google/discover/blog/how-alphachip-transformed-computer-chip-design/

Open NotebookLM: https://huggingface.co/spaces/gabrielchua/open-notebooklm Other sousrces:

https://x.com/emollick/status/1840225474905084079?s=46

https://x.com/OpenAI/status/1838642444365369814 and more

https://x.com/ai_for_success/status/1…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The papers are available here:

https://research.nvidia.com/labs/par/maskedmimic/

https://3dtopia.github.io/3DTopia-XL/

https://kovenyu.com/wonderworld/ 📝 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 Biew…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/paper Play the Tron game: https://agpallav.com/tron.html Sources:

https://www.youtube.com/watch?v=M9YOO7N5jF8

https://x.com/ammaar/status/1834312398016074083

https://x.com/MatthewBerman/status/1834295485773054312

https://x.com/slow_developer/status/1835685158248219106

https://x.com/mehran__jalali/status/1834299971828679079

https://x.com/MattVidPro/status/1836144228084117520

https://x.com/pallavmac/status/1836197280325488792

https://agpallav.com/tron.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 clickab…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/paper 📝 The papers are available here:

https://research.nvidia.com/labs/prl/shallow-water-simulation/

https://cs.uwaterloo.ca/~c2batty/papers/Takahashi2024/Takahashi2024.pdf

https://dl.acm.org/doi/abs/10.1145/3414685.3417859

https://cgl.ethz.ch/publications/papers/paperChen20a.php 📝 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…

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…

Mikita Shchutski | Lead Machine Learning Engineer, Quantori Training large medical models using electronic health records in order to create a highly informative medical embedding space

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

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Это фрагмент выступления руководителя управления качества в Яндекс Поиске Кати Серажим и CTO Яндекс Поиска Алексея Гусакова. На Practical ML Conf 2024 Катя рассказала об архитектуре и процессе обучения LLM для больших продуктовых проектов Яндекса и вместе с Алексеем ответила на вопросы из зала. Посмотреть доклад целиком можно на нашем канале. Из него вы узнаете, что находится под капотом у Нейро и какие сложности решала команда на каждом из этапов его создания.

Это фрагмент доклада Степана Комкова, старшего разработчика службы синтеза речи в Яндекс Поиске. На Practical ML Conf 2024 Степан рассказал об опыте создания виртуального рассказчика в Яндекс Книгах. Зачем его создавали и чего хотят пользователи, как выжать максимум из технологий уходящего поколения и внедрить длинный контекст в low-resource-real-time-модель. И как GPT и диффузионные модели произвели революцию в синтезе речи. Смотрите доклад целиком на нашем канале.

Это фрагмент доклада Степана Комкова, старшего разработчика службы синтеза речи в Яндекс Поиске. На Practical ML Conf 2024 Степан рассказал об опыте создания виртуального рассказчика в Яндекс Книгах. Зачем его создавали и чего хотят пользователи, как выжать максимум из технологий уходящего поколения и внедрить длинный контекст в low-resource-real-time-модель. И как GPT и диффузионные модели произвели революцию в синтезе речи. Смотрите доклад целиком на нашем канале.

Это доклад с ML Party в Белграде. Его прочитал Антон Клочков, руководитель подгруппы распознавания текста в VLM. Антон рассказал, как в визуальных языковых моделях развивают навыки распознавания символов на картинке. И показал кейсы, где это используется (кроме расшифровки мемов). Больше интересных материалов по ML ищите тут: https://t.me/yandexforml

Это доклад с ML Party в Белграде. Его прочитал Дмитрий Солодуха, руководитель команды Алисы и умных устройств Яндекса. Дмитрий рассказал, как они с ребятами учат умные устройства активироваться по интонации и быстрым командам, чтобы пользователю нужно было меньше «алискать». Больше интересных материалов по ML ищите тут: https://t.me/yandexforml

Это доклад с ML Party в Белграде. С ним выступил Илья Ирхин, руководитель аналитики Яндекс Еды. Илья показал, как решается задача ранжирования ресторанов-партнёров при продвижении в выдаче сервиса. Больше интересных материалов по ML ищите тут: https://t.me/yandexforml

Это доклад с ML Party в Белграде. С ним выступил Пётр Вытовтов, руководитель машинного обучения рекламной платформы. Пётр объяснил, как в Яндекс Погоде прогнозируют осадки вплоть до миллиметров в любой точке земного шара. А ещё показал, как измеряют интенсивность осадков с помощью ведра и спутника. Больше интересных материалов по ML ищите тут: https://t.me/yandexforml

Это фрагмент доклада Степана Комкова, старшего разработчика службы синтеза речи в Яндекс Поиске. На Practical ML Conf 2024 Степан рассказал об опыте создания виртуального рассказчика в Яндекс Книгах. Зачем его создавали и чего хотят пользователи, как выжать максимум из технологий уходящего поколения и внедрить длинный контекст в low-resource-real-time-модель. И как GPT и диффузионные модели произвели революцию в синтезе речи. Смотрите доклад целиком на нашем канале.

Это фрагмент доклада Степана Комкова, старшего разработчика службы синтеза речи в Яндекс Поиске. На Practical ML Conf 2024 Степан рассказал об опыте создания виртуального рассказчика в Яндекс Книгах. Зачем его создавали и чего хотят пользователи, как выжать максимум из технологий уходящего поколения и внедрить длинный контекст в low-resource-real-time-модель. И как GPT и диффузионные модели произвели революцию в синтезе речи. Смотрите доклад целиком на нашем канале.

Хардовая конференция для экспертов: качественные технические доклады от ключевых инженеров, максимум пользы и знаний о практическом применении ML. Поговорим, как уже сейчас использовать ML с пользой для бизнеса.

Хардовая конференция для экспертов: качественные технические доклады от ключевых инженеров, максимум пользы и знаний о практическом применении ML. Поговорим, как уже сейчас использовать ML с пользой для бизнеса.

Спикер: Анна Ариничева

Название: Интерпретируемая модель выявления когнитивных искажений в текстах на естественном языке Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

Трек: https://ods.ai/tracks/sibfest5-student

_____

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

Спикер: Антон Колонин

Название: Интерпретируемая обработка естественного языка (фундаментальные результаты и прикладные кейсы) Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

Трек: https://ods.ai/tracks/sibfest5-ml-applied

_____

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

Спикер: Владислав Негодин 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

Спикер: Артем Вахрушев Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

Трек: https://ods.ai/tracks/sibfest5-student

_____

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

Спикер: Голощапов Влад Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

Трек Прикладной МЛ: https://ods.ai/tracks/sibfest5-ml-applied

_____

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

Спикер: Голощапов Влад

Название: Чем не является гроккинг - демонстрация опенсорсной библиотечки визуализации in sight Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

Трек Прикладной МЛ: https://ods.ai/tracks/sibfest5-ml-applied

_____

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

Спикер: Дмитрий Пилецкий 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

Спикер: Александр Мамылов

Название: Избавляемся от рутины: как AI-ассистент автоматизирует генерацию экспертного контента для SIEM 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

Спикер: Анна Латушко, Джулия Мазине

Название: Как отсмотреть всю Википедию и не сойти с ума? История создания библиотеки wiki-synonyms 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

Спикер: Павел Вешкин 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

Спикер: Виктор Паньшин Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

Трек CV: https://ods.ai/tracks/sibfest5-cv

_____

Наши соц.сети:

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Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

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Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

Спикер: Марина Руменских Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

Трек: https://ods.ai/tracks/sibfest5-ml-applied

_____

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

Спикер: Пахаруков Илья

Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

Трек ML и бизнес: https://ods.ai/tracks/sibfest5-ml-business

_____

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

Спикер: Елена Лебедева 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

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

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Captain Chinchilla

Christoph Grabo (@asaaki)

Christy Ser…

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…

Rick Spence is a historian specializing in the history of intelligence agencies, espionage, secret societies, conspiracies, the occult, and military history.

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Bernie Sanders is a US Senator from Vermont and a two-time presidential candidate.

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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…

Graham Hancock a journalist and author who for over 30 years has explored the controversial possibility that there existed a lost civilization during the last Ice Age, and that it was destroyed in a global cataclysm some 12,000 years ago.

He is the presenter of the Netflix documentary series “Ancient Apocalypse”, the 2nd season of which has just been released.

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Jordan Peterson is a psychologist, author, lecturer, and podcast host.

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Go to https://drinkLMNT.com/lexOUTLINE:(00:00) – Introduction(07:07) – Nietzsche(14:48) – Power and propaganda(19:54) – Nazism(24:54) – Religion(41:18) – Communism(47:03) – Hero myth(49:12) – Belief in God(59:24) – Advice for young people(1:12:02) – Sex(1:32:00) – Good and evil(1:44:46) – Psychopathy(1…

Aman Sanger, Arvid Lunnemark, Michael Truell, and Sualeh Asif are creators of Cursor, a popular code editor that specializes in AI-assisted programming.

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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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Vivek Ramaswamy is a conservative politician, entrepreneur, and author of many books on politics, including his latest titled Truths: The Future of America First.

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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(…

Vejas Liulevicius is a historian specializing in Germany and Eastern Europe, who has lectured extensively on Marxism and the rise, the reign, and the fall of Communism.

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Gregory Aldrete is a historian specializing in ancient Rome and military history.

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Donald Trump is the 45th President of the United States and the Republican candidate in the 2024 US Presidential Election.

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Cenk Uygur is a progressive political commentator and host of The Young Turks.

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Pieter Levels (aka levelsio on X) is a self-taught developer and entrepreneur who has designed, programmed, launched over 40 startups, many of which are highly successful.

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Craig Jones is a legendary jiu jitsu personality, competitor, co-founder of B-Team, and organizer of the CJI tournament that offers over $2 million in prize money.

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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 …

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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.

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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…

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…

Today, I’m speaking with my intern, Angela Busheska, about her work this summer and her experience as a Microsoft Research intern.

And I think I’m really grateful that we took this direction because we had a chance to understand at a granular level what is actually happening.

RANGANATHAN: And I think we realized this and you can chime in, Angela, but I feel that the traceability is a vehicle for data.

BUSHESKA: Yeah, so this will be a fall really spent with a lot of applications because of, like, graduate school.

As all the listeners can probably see, I’m a person who really speaks a lot, even more than needed sometimes.

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[MUSIC FADES]Today I’m talking to Dr. Richard Black, a senior principal research manager and the research director of Project Silica at Microsoft Research.

Richard and Dexter are involved in a unique multidisciplinary, multi-institutional, and multigenerational collaboration called Avenues Golden Record, a current effort to communicate with extraterrestrial intelligence.

So the project we’re talking about today is Avenues Golden Record, but it’s not the first Golden Record to exist.

GREENE: And I guess moving on to future attempts … so what we’re doing, my team, is we’re working on creating an updated Golden Record.

HUIZINGA: Yeah, yeah.

Model maker and fabricator Lex Story helps bring research to life through prototyping.

He discusses his take on failure; the encouragement and advice that has supported his pursuit of art and science; and the sabbatical that might inspire his next career move.

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In this episode, Microsoft Product Manager Shrey Jain and OpenAI Research Scientist Zoë Hitzig join host Amber Tingle to discuss “Personhood credentials: Artificial intelligence and the value of privacy-preserving tools to distinguish who is real online.” In their paper, Jain, Hitzig, and their coauthors describe how malicious actors can draw on increasingly advanced AI tools to carry out deception, making online deception harder to detect and more harmful.

Bringing ideas from cryptography into AI policy conversations, they identify a possible mitigation: a credential that allows its holder to prove they’re a person––not a bot––without sharing any identifying information.

This exploratory r…

Brendan and Mert are exploring the economic impact of job automation and generative AI as part of Microsoft’s AI, Cognition, and the Economy, or AICE, research initiative.

And more recently, my research focuses on AI, both, like, the adoption of AI and the productivity impact of AI.

HUIZINGA: Right, right.

Like, we need to predict what will be the effect of AI, how AI is going to affect the economy, especially in the long run.

HUIZINGA: Right, right.

Emre Kiciman shares how some keen observations and a desire to have front-end impact led him to make the jump from systems and networking to computational social science and now causal analysis and large-scale AI—and how systems thinking still impacts his work.

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ZHANG: OK, so this paper is about how to effectively extend the context window of large language models beyond 2 million tokens.

With our method, we can push LLM context window to over 2 million tokens.

For example, pretraining with an efficient model architecture, using RAG (retrieval-augmented generation), and extending the context window with RoPE positional interpolation.

RoPE positional interpolation solves this by downscaling positional embeddings to fit within the pretrained range.

Although we have managed to recover some of the short performance for long-context LLM, it has not recovered 100 percent.

Senior Researcher Arindam Mitra introduces AgentInstruct.

Using raw data sources, the automated multi-agent framework can create diverse, high-quality synthetic data at scale for the post-training of small and large language models.

Microsoft Research PodcastBy Researchers across the Microsoft research communityRedmond, WAAn ongoing series of conversations bringing you right up to the cutting edge of Microsoft Research.

Printed circuit boards are abundant—in the stuff we use and in landfills.

Researcher Jake Smith and professor Aniruddh Vashisth discuss the development of vitrimer-based PCBs that perform comparably to traditional PCBs but have less environmental impact.

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Curious about the safety of LLMs?

🤔 Join us for an insightful new episode featuring Suchin Gururangan, Young Investigator at Allen Institute for Artificial Intelligence and Data Science Engineer at Appuri.

🚀 Don't miss…

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…

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…

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.

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.

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.

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.

Join us for our capstone episode on the Animal Intelligence season. We recap what we loved, what we learned, and things we wish we had gotten to spend more time on. This is a great episode to see how the podcast is produced. Now that the season is ending, our current co-host, Becky, is moving to emeritus status. In this last installment we got to spend a little more time getting to know Becky and where her work will take her after this. Did Data Skeptic inspire her to learn more about machine learning? Tune in and find out.

David Obembe, a recent University of Tartu graduate, discussed his Masters thesis on integrating LLMs with process mining tools. He explained how process mining uses event logs to create maps that identify inefficiencies in business processes. David shared his research on LLMs' potential to enhance process mining, including experiments evaluating their performance and future improvements using Retrieval Augmented Generation (RAG).

Our guest today is Risa Shinoda, a PhD student at Kyoto University Agricultural Systems Engineering Lab, where she applies computer vision techniques. She talked about the OpenAnimalTracks dataset and what it was used for. The dataset helps researchers predict animal footprint. She also discussed how she built a model for predicting tracks of animals. She shared the algorithms used and the accuracy they achieved. She also discussed further improvement opportunities for the model.

This episode features an interview with Mélisande Teng, a PhD candidate at Université de Montréal. Her research lies in the intersection of remote sensing and computer vision for biodiversity monitoring.

In this interview with author Deborah Gordon, Kyle asks questions about the mechanisms at work in an ant colony and what ants might teach us about how to build artificial intelligence. Ants are surprisingly adaptive creatures whose behavior emerges from their complex interactions. Aspects of network theory and the statistical nature of ant behavior are just some of the interesting details you'll get in this episode.

This season it’s become clear that computing skills are vital for working in the natural sciences. In this episode, we were fortunate to speak with Madlen Wilmes, co-author of the book "Computing Skills for Biologists: A Toolbox". We discussed the book and why it’s a great resource for students and teachers. In addition to the book, Madlen shared her experience and advice on transitioning from academia to an industry career and how data analytic skills transfer to jobs that your professionals might not always consider. Join us and learn more about the book and careers using transferable skills.

In this episode, we talked shop with Hager Radi about her biodiversity monitoring work. While biodiversity modeling may sound simple, count organisms and mark their location, there is a lot more to it than that! Incomplete and biased data can make estimations hard. There are also many species with very few observations in the wild. Using machine learning and remote sensing data, scientists can build models that predict species distributions with limited data. Listen in and hear about Hager’s work tackling these challenges and the tools she has built.

Today, Ashay Aswale and Tony Lopez shared their work on swarm robotics and what they have learned from ants. Robotic swarms must solve the same problems that eusocial insects do. What if your pheromone trail goes cold? What if you’re getting bad information from a bad-actor within the swarm? Answering these questions can help tackle serious robotic challenges. For example, a swarm of robots can lose a few members to accidents and malfunctions, but a large robot cannot. Additionally, a swarm could be host to many castes like an ant colony. Specialization with redundancy built in seems like a win-win! Tune in and hear more about this fascinating topic.

Deepali is Senior Partner and Global Head of the Data, AI and Financial Technology Practice of Korn Ferry, one of the world's largest executive search firms.

00:04:31At Korn Ferry, you lead executive search and leadership consulting for strategic AI data and analytics leaders across industries.

And so there are things that you're going to have to do to show where those soft skills are more transferable.

And so they really adopted it really, really well.

I'm going to lean on my industry friends and I'm going to bring this to the younger generation."

Economist and social-impact innovator Dr. Nat Ware reveals how our expectations shape happiness and why chasing it often leaves us unfulfilled. He shares insights on the “hedonic treadmill” and the effects of constant comparison on our well-being. Find out how to build a more meaningful life by making memories, taking chances, and focusing on genuine connections. Additional materials: www.superdatascience.com/836 Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

And now we're seeing this translate into B2B use cases for us, which are where it gets really, really exciting as well.

And then for years after, it was, oh, it was obvious to me that we needed to get really, really large context windows because you need to have longer context.

But an AI system can scale way, way, way, way more than us.

Bryan McCann: 01:26:43 Kind of, general topology, and just seeing the world in terms of stuff and functions.

Bryan McCann: 01:28:29 I'm most active right now on LinkedIn, so you can follow me and connect with me there.

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Meanwhile we invite you to check out our podcasts in:Subscribe on Website, Apple Podcasts, Spotify, Stitcher Radio or TuneIn

It is really, really good at extracting characters.

That's really, really critical.

I don't think the industry has really, or the field has really, really figured this out.

It's open source model and is really, really interesting.

I've really, really enjoyed it.

(01:48): The aspect of Dario’s article that made the biggest splash was his explicit defining the term “Powerful AI”.

Instead, Powerful AI has all the interfaces available to a human that’s working virtually – text, audio, video, mouse and keyboard control, and internet access.

And while the Powerful AI doesn't have a physical body, it can control existing tools, robots, and laboratory equipment through computer interfaces.

Potentially within as few as 5-10 years after developing this Powerful AI system, we could see transformative changes across multiple domains.

(00:02): This is Episode #832 on the Anthropic CEO’s “Powerful AI” Utopia.

Jon Krohn: 00:09:57So you are the CTO of Lightning AI, the makers of AI Studio, PyTorch Lightning, and many more open source products.

So PyTorch Lightning was released, was born many years before, created by William Falcon, the CEO and founder of Lightning AI.

And incidentally, I got to know PyTorch Lightning before I got to know William because my company in Italy, Orobix landed on PyTorch Lightning to standardize their training code.

And in fact, PyTorch Lightning became one of the ways PyTorch lands in organizations because PyTorch Lightning doesn't wrap PyTorch.

To recap, in today's episode, Dr. Luca Antiga filled us in on how Lightning AI offers tools like PyTorch Lightning, Lightning…

Geoffrey Hinton and Sir Demis Hassabis: The Nobel Prize committee is an achievement of the highest order, awarding physicists, chemists, physiologists, medical practitioners, writers, pacifists and economists perhaps the greatest honor in their respective fields. In this week’s Five-Minute Friday, Jon Krohn discusses how two AI pioneers came to win prizes in chemistry and physics. Additional materials: www.superdatascience.com/830 Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Neuroscientist Bradley Voytek outlines to Jon Krohn the incredible use of data science and machine learning in his research and how recent discoveries in action potentials and neurons have completely skyrocketed the field to a new understanding of the brain and its functions. You’ll also hear what Bradley thinks is most important when hiring data scientists and his contributions to Uber’s algorithm when it was still a startup. This episode is brought to you by epic LinkedIn Learning instructor Keith McCormick, and by Gurobi, the Decision Intelligence Leader. Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this e…

The citizen data scientist: Fact or fiction? Jon Krohn holds a conversation across episodes in this Five-Minute Friday, with today’s guest Keith McCormick, in part responding to Nick Elprin’s interview in episode 811: Scaling Data Teams Effectively. Additional materials: www.superdatascience.com/828 Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Ritchie Vink, CEO and Co-Founder of Polars, Inc., speaks to Jon Krohn about the new achievements of Polars, an open-source library for data manipulation. This is the episode for any data scientist on the fence about using Polars, as it explains how Polars managed to make such improvements, the APIs and integration libraries that make it so versatile, and what’s next for this efficient library. This episode is brought to you by epic LinkedIn Learning instructor Keith McCormick, by Gurobi, the Decision Intelligence Leader, and by ODSC, the Open Data Science Conference. Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. …

Next-gen IDEs, efficiency-boosting open-source Python libraries, and changes in hiring for data scientists: This episode of In Case You Missed It gives you our best clips of September’s interviews, hosted by Jon Krohn. Additional materials: www.superdatascience.com/826 Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Data contracts are redefining data quality and governance, and Chad Sanderson, CEO of Gable.ai, joins host Jon Krohn to explain how they can transform your data strategy. He breaks down what data contracts are, how they shift data quality checks closer to production, and why they’re essential for reducing data debt. Chad also highlights how better alignment between data producers and consumers can elevate data reliability and tackle change-management challenges in modern organizations. This episode is brought to you by epic LinkedIn Learning instructor Keith McCormick, and by Gurobi, the Decision Intelligence Leader. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie…

Llama 3.2 brings a new era of AI innovation with lightweight models tailored for on-device applications and powerful vision models for handling complex image inputs. Host Jon Krohn explores how this release pushes the boundaries of open-source AI, making it more accessible and versatile for developers. He also covers the Llama Stack toolkit, designed to streamline deployment, and Llama Guard 3, Meta’s latest content moderation solution. With extensive support from major cloud and hardware partners, Llama 3.2 is set to unlock groundbreaking possibilities for AI across mobile and beyond. Tune in to hear more. Additional materials: www.superdatascience.com/824 Interested in sponsoring a SuperD…

Virtual humans are rewriting the rules of digital communication and reshaping entire industries. This week, Jon Krohn welcomes Natalie Monbiot, Head of Strategy at Hour One, to shed light on how AI avatars are revolutionizing L&D and e-commerce by turning traditional training and product listings into captivating, presenter-led content. This episode is brought to you by epic LinkedIn Learning instructor Keith McCormick, by Gurobi, the Decision Intelligence Leader, and 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:

• How do you create a virt…

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 …

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…

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

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…

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!

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?

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.

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.

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.

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?

In this insightful episode, we dive deep into the pressing issue of data privacy, where 86% of U.S. consumers express growing concerns and 40% don’t trust companies to handle their data ethically.

Join us as we chat with the Vice President of Engineering at MetaRouter, a cutting-edge platform enabling enterprises to regain control over their customer data.

We explore how MetaRouter empowers businesses to manage data in a 1st-party context, ensuring ethical, compliant handling while navigating the complexities of privacy regulations.

SponsorsIntrepid AI ( https://intrepid.ai ) is an AI assisted all-in-one platform for robotics teams.

Build robotics applications in minutes, not monthsReferenc…

Join us as David Marom, Head of Panoply Business, explores the benefits of all-in-one data platforms.

Learn how tech stack consolidation boosts efficiency, improves data accuracy, and cuts costs.

David shares insights on overcoming common challenges, enhancing data governance, and success stories from organizations thriving with Panoply.

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Join us in this exciting episode of the Data Science at Home podcast.

It’s all about GPUs.

We’ll take you on a journey through the inner workings of these powerful processors, explaining how they handle complex computations and drive everything from gaming graphics to scientific simulations.

Whether you’re a budding programmer or a tech enthusiast, understanding GPUs is key to unlocking new levels of performance and efficiency in your projects.

Tune in and get ready to turbocharge your tech knowledge!

In this episode, we sit down with Ryan Smith, Founder of QFunction LLC, to explore how AI and machine learning are revolutionizing cybersecurity.

With over 8 years of experience, including work at NASA’s Jet Propulsion Laboratory, Ryan shares insights on the future of threat detection and prevention, the challenges businesses face in maintaining effective cybersecurity, and the ethical considerations of AI implementation.

Learn about cost-effective strategies for small businesses, the importance of collaboration in combating cyber threats, and how QFunction tailors its AI solutions to meet diverse industry needs.

QFunction does cybersecurity differently.

By relying on scientific breakthroug…

In this last episode of the series “Rust in the Cosmos” we speak about what happens in space, what projects are currently active and what happened in the past that we can learn from?

What about Rust and space applications?

Build robotics applications in minutes, not months.

Amethix works to create and maximize the impact of the world’s leading corporations and startups, so they can create a better future for everyone they serve.

CommunitiesIntrepid AI, AeroRust, BytenookIntrepid AI Discord https://discord.gg/cSSzche6Cthttps://discord.gg/cSSzche6Ct Intrepid AI website: https://intrepid.aiAeroRust Discord invite: https://discord.com/invite/6jJyx5nEUqAeroRust website: AeroRust.orgReferences