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In this article, we’ll learn how to set up a local AI coding assistant in JupyterLab using Jupyter AI, Ollama and Hugging Face.

Coding assistant in Jupyter Lab via Jupyter AI | Image by Author⚠️ Jupyter AI is still under heavy development, so some features may break.

Installing the Jupyter AI ExtensionIt’s recommended to create a new environment specifically for Jupyter AI to keep your existing environment clean and organised.

To date, Jupyter AI supports the following model providers :Supported Model providers in Jupyter AI along with the dependencies | Created by Author from the documentationIf you encounter errors during the Jupyter AI installation, manually install Jupyter AI using pip …

Now we take a closer look at how the product operating model, and the core competencies of empowered product teams in particular, can evolve to face the emerging opportunities and challenges in the age of AI.

Such product teams would focus on feature delivery rather than user experience or strategic product development; today such teams are thus often referred to as “feature teams”.

Towards AI-Ready Product Operating ModelsLeveraging AI Expertise: Embedded, Consultative, and Hybrid ModelsFigure 2 below proposes a high-level framework to think about how the AI competency could be incorporated in today’s orthodox, 3-in-a-box product operating model.

Figure 2: Options for AI-Ready Product Oper…

There is no central place where you can check which Tableau data sources rely on specific tables.

If you have the Tableau Data Management add-on, it could help, but from what I know, its hard to find dependencies of custom sql queries used in data sources.

The real pain begins when you have to go through all the data sources manually to start fixing it.

What if we could anticipate these issues and identify impacted data sources before anyone notices a problem?

This is just one use case of automating Tableau data management.

That’s how much interest Germany has to pay for its debts.

I will use Germany as an example, as it currently receives a lot of media coverage and its debt statistics are freely available.

€1,600: interest rate per second€25,503: debt per German citizen if state debt is splitAnd here’s already a large jump for us.

We can now begin to see the difference between everyday amounts (like the €1,600 interest per second) and the planned spending (i.e., debt).

This simple webpage more accurately represents the huge amount of fresh debt that Germany wants to make.

On March 3rd, Google officially rolled out its Data Science Agent to most Colab users for free.

“, the Data Science Agent first came up with a series of 10 tasks, including data loading, data exploration, data cleaning, data wrangling, feature engineering, data splitting, model training, model optimization, model evaluation, and data visualization.

Who Are the Target UsersWith the pros and cons in mind, who are the target users of the Data Science Agent?

They usually have a data question clearly defined, which makes it easier for the Data Science Agent to assist.

They usually have a data question clearly defined, which makes it easier for the Data Science Agent to assist.

This particular pain point is what the R.E.D algorithm addresses: semi-supervised learning, when the training data per class is not enough to build (quasi)traditional classifiers.

algorithmR.E.D: Recursive Expert Delegation is a novel framework that changes how we approach text classification.

Semi-supervised classification with noise oversamplingCascade this after the initial label subset formation — i.e., this classifier is only classifying between a given subset of classes.

Picture this: when you have low amounts of training data, you absolutely cannot create a hold-out set that is meaningful for evaluation.

This can be done by using a larger model to critically evaluate the relationship…

To ensure a comprehensive approach, we will address protection measures across three critical layers:Algorithm code protection: Measures to secure algorithm code, preventing unauthorized access or reverse engineering.

Measures to secure algorithm code, preventing unauthorized access or reverse engineering.

Host-based container image encryption at rest (protection at rest and in transit)This strategy is based on end-to-end protection of container images containing the algorithm.

From an algorithm protection goal perspective, HE is not designed, nor can be made to protect the algorithm.

So it’s not an algorithm protection mechanism at all.

When real life deviates from the model: Finally, let’s explore the special links that the Poisson distribution has with the Negative Binomial distribution.

Understanding these relationships can deepen our understanding, and provide alternatives when the Poisson distribution is not suited for the job.

That is the opposite of our intuition that there is maximum probability when λ = 𝑘, as the output is larger when 𝑘 = λ + 1.

The assumptionsFirst, let’s get one thing off the table: the difference between a Poisson process, and the Poisson distribution.

I hope this has given you what you came for — a better intuition about the Poisson distribution.

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

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

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

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

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

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…

Ready to learn how to train highly accurate, custom AI models – without massive labeled data?

We recommend to join Predibase’s upcoming webinar, Intro to Reinforcement Fine-Tuning: The Future of LLM Customization, on March 27 at 10:00 AM PT.

Reinforcement Fine-Tuning (RFT) redefines traditional Supervised Fine-Tuning by delivering breakthrough performance with as few as 10 labeled examples.

RFT vs. SFT: Learn why RFT outperforms SFT when data is scarce and get a practical framework for deciding which approach fits your use case.

Cut the data-labeling bottleneck and harness reinforcement learning to build AI that keeps improving over time.

You can subscribe to The Sequence below:📝 Editorial: NVIDIA’s AI Hardware and Software Synergies are Getting Scary GoodNVIDIA’s GTC never disappoints.

With hardware like Blackwell Ultra and Rubin, and tools like Llama Nemotron and Dynamo, NVIDIA is rewriting what’s possible for AI development.

The Blackwell Ultra AI Factory Platform is NVIDIA’s latest rack-scale beast, packing 72 Blackwell Ultra GPUs and 36 Grace CPUs.

This is especially important for today’s large-scale reasoning models, which chew through tons of tokens per query.

Mistral Small 3.1Mistral launched Small 3.1, a multimodal small model with impressive performance.

Created Using Microsoft ResearchA few months ago, Microsoft releazed Phi-4, the latest version of its marquee small language models (SLMs), demonstrating that carefully curated and synthesized data can enable highly competitive performance despite a smaller number of parameters.

Building on the success of the Phi family, Microsoft has just introduced Phi-4-Mini and Phi-4-Multimodal, extending their capabilities to handle vision and audio modalities.

Phi-4-Mini is a 3.8-billion-parameter language model that excels in multilingual support, reasoning, and mathematics, with the added functionality of function calling.

Phi-4-Multimodal is a multimodal model integrating text, vision, and speech/a…

Created Using MidjourneyInterpretability in the context of foundation models refers to our ability to understand and explain how these large-scale neural networks make decisions.

These models, including large language and vision-language models, often function as complex "black boxes," meaning their internal reasoning steps remain opaque.

Additionally, interpretability aids in debugging models by allowing engineers to diagnose errors more effectively than treating models as opaque artifacts.

Given the widespread deployment of foundation models, interpretability has become a key factor in ensuring trustworthiness and control, allowing users to calibrate their trust in AI systems that will be…

Created Using MidjourneyIn today’s engineering section we are going to deep dive into one of the hottest new stacks to hit the market: OpenAI’s recent release of agentic development tools.

In a nutshell, OpenAI has recently introduced a suite of advanced tools for building AI agents, marking a significant evolution in AI application development.

These tools provide developers with a robust framework for creating autonomous AI agents that operate effectively across diverse domains.

Today, we would like to dive into the core architecture of this stack as well as some of the key capabilities.

Architectural OverviewThe newly designed ecosystem integrates these components to enable seamless AI a…

Created Using MidjourneyToday we will Discuss:The endless debate of RAG vs. fine-tuning approaches for specializing foundation models.

UC Berkeley’s RAFT research that combines RAG and fine-tuning.

💡 AI Concept of the Day: RAG vs.

Fine-TuningRAG vs. fine-tuning is one of the most common debates among teams building generative AI applications.

RAG dynamically incorporates external knowledge into the model's responses, while fine-tuning adjusts the model's internal parameters for specific tasks.

You can subscribe to The Sequence below:📝 Editorial: Command A and Gemma 3: Small Models with BiteSmall foundation models is one of the most fascinating trends in generative AI.

Seeing how relatively small models can match the capabilities of mega models is truly amazing.

Command A, developed by Cohere, is engineered to match or surpass the performance of leading models like GPT-4o and DeepSeek-V3 across various enterprise tasks.

Built upon the research and technology that powers Google's Gemini 2.0 models, Gemma 3 introduces multimodal capabilities, allowing it to process and analyze text, images, and short videos.

In conclusion, Command A and Gemma 3 exemplify the potential of compact, ef…

From creating training environments to simulating real world conditions, games represent incredible catalyzers on AI learning.

A new field known as world action models is rapidly emerging as a field to combine games and AI.

Microsoft just dropped an ecising research in this area with a model that can create games after watching human players.

Muse, a generative AI model, marks a pivotal advancement in the convergence of artificial intelligence and video games.

Image Credit: Microsoft ResearchImage Credit: Microsoft ResearchArchitectural Overview

Created Using MidjourneyRetrieval-Augmented Generation (RAG) is a technique that enhances generative models by integrating a retrieval mechanism, allowing them to access relevant external information.

In a RAG pipeline, a query first triggers a search for pertinent documents, often using a vector database or search index.

It allowed smaller or general-purpose models to achieve state-of-the-art results by incorporating external facts, addressing issues like hallucinations and outdated knowledge.

RAG gained widespread adoption, powering numerous research papers and commercial applications.

However, with rapid advancements in AI models and architectures, is RAG still as relevant today?

Created Using MidjourneyA new agentic framework just hit the open source AI market and it’s a cool one.

Created as a collaboration between LangChain, LlamaIndex, Glean, Galileo and Cisco, AGNTCY shade some light into the future of agentic apps.

This initiative seeks to accelerate the integration of agentic AI in various sectors by enabling the creation of agentic workflows and applications that combine both internal and third-party agents.

The core of AGNTCY's mission involves developing and maintaining software components and services that address key challenges in agentic workflows and multi-agent applications.

CapabilitiesAGNTCY's IoA infrastructure is designed to simplify the creation o…

Created Using MidjourneyToday we will Discuss:RAG for non-language modalities.

💡 AI Concept of the Day: Multimodal RAGThroughout this series, we have been exploring some of the key methods for Retrieval-Augmented Generation (RAG).

How can RAG work with other modalities?

Multimodal Retrieval-Augmented Generation (RAG) represents a paradigm that extends the traditional text-based RAG framework to encompass diverse data modalities such as images, audio, and video.

This advancement enables AI systems to perform cross-modal reasoning and generation, significantly enhancing their ability to understand and synthesize information from heterogeneous sources.

You can subscribe to The Sequence below:📝 Editorial: Honor to Whom Honor is Due: AI Won the Nobel Prize of ComputingAI has been honored with the "Nobel Prize" of computer science.

The 2024 ACM A.M. Turing Award, often referred to as the "Nobel Prize of computing," has been awarded to Andrew G. Barto and Richard S. Sutton for their groundbreaking contributions to reinforcement learning (RL).

This achievement highlighted RL's potential when combined with deep learning techniques, paving the way for deep reinforcement learning.

The lack of explainability in RL models also raises concerns in critical applications such as healthcare and autonomous systems.

They found that a variant of representa…

Now, with the latest upgrade, AlphaGeometry2 (AG2) has officially surpassed top human competitors in geometry, marking a milestone in AI-driven mathematical reasoning.

The general consensus among IMO competitors is that geometry problems are among the toughest in each day of the Olympiad.

AG2 represents a significant advancement in AI-driven mathematical reasoning, particularly in solving Olympiad geometry problems.

Building on its predecessor, AlphaGeometry, AG2 surpasses the performance of an average gold medalist in the International Mathematical Olympiad (IMO).

To have an idea of the complexiity of the geometry problems in IMO, look at the example below from the paper:

Created Using MidjourneyArtificial intelligence (AI) has pushed modern programming languages beyond their original design constraints.

Most AI research relies on Python for ease of use, complemented by low-level languages like C++ or CUDA for performance.

As AI models become more complex and safety-critical, the question arises—are existing languages adequate, or do we need AI-specific programming languages?

This essay explores the limitations of current programming languages in AI development, the potential benefits of AI-first languages with built-in support for differentiable programming, neural networks, and probabilistic constructs, and the importance of formal verification and advance…

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

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

The Transformer architecture, introduced by Vaswani et al. in 2017, serves as the backbone of contemporary language models. Over the years…Continue reading on SyncedReview »

Large Language Models (LLMs) have become indispensable tools for diverse natural language processing (NLP) tasks. Traditional LLMs operate…Continue reading on SyncedReview »

Language models (LMs) based on transformers have become the gold standard in natural language processing, thanks to their exceptional…Continue reading on SyncedReview »

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Navigation is a fundamental skill for any visually-capable organism, serving as a critical tool for survival. It enables agents to locate…Continue reading on SyncedReview »

The landscape of vision model pre-training has undergone significant evolution, especially with the rise of Large Language Models (LLMs)…Continue reading on SyncedReview »

A foundation model refers to a pre-trained model developed on extensive datasets, designed to be versatile and adaptable for a range of…Continue reading on SyncedReview »

Continue reading on SyncedReview »

While large language models (LLMs) dominate the AI landscape, Small-scale Large Language Models (SLMs) are gaining traction as…Continue reading on SyncedReview »

Consistency models (CMs) are a cutting-edge class of diffusion-based generative models designed for rapid and efficient sampling. However…Continue reading on SyncedReview »

The field of text-to-image synthesis has advanced rapidly, with state-of-the-art models now generating highly realistic and diverse images…Continue reading on SyncedReview »

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

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

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

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

Можно и фразу.

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

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

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

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

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

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

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.

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 […]

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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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This has spoilers for MIT Mystery Hunt 2025.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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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.

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…

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.

Editors of academic journals and program chairs of conferences require peer reviewers to write their own reviews.

Existing tools for detecting LLM-generated content are not designed to differentiate between fully LLM-generated reviews and those merely polished by an LLM.

In this work, we employ a straightforward approach to identify LLM-generated reviews - doing an indirect prompt injection via the paper PDF to ask the LLM to embed a watermark.

We also consider various methods for prompt injection including font embedding and jailbreaking.

We find a high success rate in the embedding of our watermarks in LLM-generated reviews across models.

Memory, additional information beyond the training of large language models (LLMs), is crucial to various real-world applications, such as personal assistant.

The two mainstream solutions to incorporate memory into the generation process are long-context LLMs and retrieval-augmented generation (RAG).

Therefore, we propose a novel method RAG-Tuned-LLM which fine-tunes a relative small (e.g., 7B) LLM using the data generated following the RAG principles, so it can combine the advantages of both solutions.

Extensive experiments on three datasets demonstrate that RAG-Tuned-LLM can beat long-context LLMs and RAG methods across a wide range of query types.

PDFAbstract

The complex nature of medical image segmentation calls for models that are specifically designed to capture detailed, domain-specific features.

Parameter-Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), efficiently update model weights with low-rank matrices but may suffer from underfitting when the chosen rank is insufficient to capture domain-specific nuances.

Conversely, full-rank Singular Value Decomposition (SVD) based methods provide comprehensive updates by modifying all singular values, yet they often lack flexibility and exhibit variable performance across datasets.

We propose SALT (Singular Value Adaptation with Low-Rank Transformation), a method that sele…

A fundamental challenge in imitation learning is the \emph{covariate shift} problem.

In this paper, we propose a simple yet effective method (DeCIL) to mitigate covariate shift by incorporating a denoising mechanism that enhances the contraction properties of the state transition mapping.

We provide theoretical analysis showing that the denoising network acts as a local contraction mapping, reducing the error propagation of the state transition and improving stability.

Our method is straightforward to implement and can be easily integrated with existing imitation learning frameworks without requiring additional expert data or complex modifications to the training procedure.

Empirical result…

Long-Context Transformer Models (LCTMs) are vital for real-world applications but suffer high computational costs due to attention's quadratic complexity.

Block-sparse attention mitigates this by focusing computation on critical regions, yet existing methods struggle with balancing accuracy and efficiency due to costly block importance measurements.

In this paper, we introduce XAttention, a plug-and-play framework that dramatically accelerates long-context inference in Transformers models using sparse attention.

XAttention's key innovation is the insight that the sum of antidiagonal values (i.e., from the lower-left to upper-right) in the attention matrix provides a powerful proxy for block…

To address these limitations, we propose an Expectation Maximization Reconstruction Transformer (EMResformer) for single image rain streak removal.

Specifically, we propose an Expectation Maximization Block seamlessly integrated into the single image rain streak removal network, enhancing its ability to eliminate superfluous information and restore a cleaner background image.

Additionally, to further enhance local information for improved detail rendition, we introduce a Local Model Residual Block, which integrates two local model blocks along with a sequence of convolutions and activation functions.

This integration synergistically facilitates the extraction of more pertinent features for …

Animatable head avatar generation typically requires extensive data for training.

However, directly distilling 4D avatars from video diffusion often leads to over-smooth results due to spatial and temporal inconsistencies in the generated video.

To address this issue, we propose Zero-1-to-A, a robust method that synthesizes a spatial and temporal consistency dataset for 4D avatar reconstruction using the video diffusion model.

Specifically, Zero-1-to-A iteratively constructs video datasets and optimizes animatable avatars in a progressive manner, ensuring that avatar quality increases smoothly and consistently throughout the learning process.

This progressive learning involves two stages: (…

Recent advancements in large language models (LLMs) have demonstrated remarkable reasoning capabilities through long chain-of-thought (CoT) reasoning.

The R1 distillation scheme has emerged as a promising approach for training cost-effective models with enhanced reasoning abilities.

This study examines the universality of distillation data and identifies key components that enable the efficient transfer of long-chain reasoning capabilities in LLM distillation.

Our findings reveal that the effectiveness of long CoT reasoning distillation from teacher models like Qwen-QwQ degrades significantly on nonhomologous models, challenging the assumed universality of current distillation methods.

To g…

Domain-Independent Dynamic Programming (DIDP) is a state-space search paradigm based on dynamic programming for combinatorial optimization.

We propose using reinforcement learning to obtain a heuristic function to guide the search in DIDP.

We develop two RL-based guidance approaches: value-based guidance using Deep Q-Networks and policy-based guidance using Proximal Policy Optimization.

Our experiments indicate that RL-based guidance significantly outperforms standard DIDP and problem-specific greedy heuristics with the same number of node expansions.

Further, despite longer node evaluation times, RL guidance achieves better run-time performance than standard DIDP on three of four benchmark…

Language models (LMs) have exhibited exceptional versatility in reasoning and in-depth financial analysis through their proprietary information processing capabilities.

Previous research focused on evaluating classification performance while often overlooking explainability or pre-conceived that refined explanation corresponds to higher classification accuracy.

Using a public dataset in finance domain, we quantitatively evaluated self-explanations by LMs, focusing on their factuality and causality.

We identified the statistically significant relationship between the accuracy of classifications and the factuality or causality of self-explanations.

Our study built an empirical foundation for …

Reservoir computing (RC) has shown effectiveness in online learning systems for controlling nonlinear systems such as soft actuators.

This paper introduces a PRC-based online learning framework to control the motion of a pneumatic soft bending actuator, utilizing another pneumatic soft actuator as the PRC model.

Unlike conventional designs requiring two RC models, the proposed control system employs a more compact architecture with a single RC model.

Additionally, the framework enables zero-shot online learning, addressing limitations of previous PRC-based control systems reliant on offline training.

The proposed PRC-based online learning control framework provides a novel approach for harn…

In this paper, we propose Unreal Multi-Agent Playground (Unreal-MAP), an MARL general platform based on the Unreal-Engine (UE).

Unreal-MAP allows users to freely create multi-agent tasks using the vast visual and physical resources available in the UE community, and deploy state-of-the-art (SOTA) MARL algorithms within them.

Unreal-MAP is user-friendly in terms of deployment, modification, and visualization, and all its components are open-source.

Lastly, we deploy several SOTA algorithms in example tasks developed via Unreal-MAP, and conduct corresponding experimental analyses.

We believe Unreal-MAP can play an important role in the MARL field by closely integrating existing algorithms wit…

We present 3D Spatial MultiModal Memory (M3), a multimodal memory system designed to retain information about medium-sized static scenes through video sources for visual perception.

By integrating 3D Gaussian Splatting techniques with foundation models, M3 builds a multimodal memory capable of rendering feature representations across granularities, encompassing a wide range of knowledge.

To address these challenges, we propose M3 with key components of principal scene components and Gaussian memory attention, enabling efficient training and inference.

Our approach encompasses a diverse range of foundation models, including vision-language models (VLMs), perception models, and large multimod…

Training performant detection models require diverse, precisely annotated, and large scale datasets that involve complex and expensive creation processes.

Hence, there are only few public 3D datasets that are additionally limited in their range of classes.

In this work, we propose a pipeline for automatic generation of 3D datasets for arbitrary objects.

By utilizing the universal 3D representation and rendering capabilities of Radiance Fields, our pipeline generates high quality 3D models for arbitrary objects.

These 3D models serve as input for a synthetic dataset generator.

Drug-target interaction prediction (DTI) is essential in various applications including drug discovery and clinical application.

There are two perspectives of input data widely used in DTI prediction: Intrinsic data represents how drugs or targets are constructed, and extrinsic data represents how drugs or targets are related to other biological entities.

However, any of the two perspectives of input data can be scarce for some drugs or targets, especially for those unpopular or newly discovered.

Therefore, we propose the first method to tackle DTI prediction under input data and/or label scarcity.

We also test our model without any data scarcity and it still outperforms current methods.

Retrieval-Augmented Generation (RAG) mitigates hallucinations in Large Language Models (LLMs) by integrating external knowledge.

However, conflicts between parametric knowledge and retrieved context pose challenges, particularly when retrieved information is unreliable or the model's internal knowledge is outdated.

To address this, we propose **CK-PLUG**, a plug-and-play method for controlling LLMs' reliance on parametric and contextual knowledge.

Experiments demonstrate CK-PLUG's ability to significantly regulate knowledge reliance in counterfactual RAG scenarios while maintaining generation fluency and knowledge accuracy.

Moreover, CK-PLUG supports adaptive control based on the model's co…

Transformer-based LLMs spend most of their compute in large matrix multiplications for attention and feed-forward layers.

Recognizing that the Q, K, and V linear projections within the Multi-Head Self-Attention (MHA) module represent a critical computational bottleneck, we strategically focused our efforts on accelerating these operations.

We present a tiled matrix multiplication accelerator optimized for such workloads on a Xilinx KV260 on-board FPGA.

Implemented via high-level synthesis (HLS) and integrated with DistilBERT for Q, K, V projections, our accelerator achieves significant speedup and energy efficiency gains over CPU baselines.

Although the overall end-to-end DistilBERT acceler…

3D shape generation has greatly flourished through the development of so-called "native" 3D diffusion, particularly through the Vecset Diffusion Model (VDM).

While recent advancements have shown promising results in generating high-resolution 3D shapes, VDM still struggles with high-speed generation.

Challenges exist because of difficulties not only in accelerating diffusion sampling but also VAE decoding in VDM, areas under-explored in previous works.

For VAE, we introduce a lightning vecset decoder equipped with Adaptive KV Selection, Hierarchical Volume Decoding, and Efficient Network Design.

Through systematic evaluation, we show that our model significantly outperforms existing fast 3D…

Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications.

This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities.

Building a benchmark for real-world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable threats.

To address this challenge, we introduce CVE-Bench, a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures.

In CVE-Bench, we design a sandbox framework that enables LLM agent…

In this paper, we propose \textbf{\textsc{FastCuRL}}, a simple yet efficient \textbf{Cu}rriculum \textbf{R}einforcement \textbf{L}earning approach with context window extending strategy to accelerate the reinforcement learning training efficiency for R1-like reasoning models while enhancing their performance in tackling complex reasoning tasks with long chain-of-thought rationales, particularly with a 1.5B parameter language model.

\textbf{\textsc{FastCuRL}} consists of two main procedures: length-aware training data segmentation and context window extension training.

Specifically, the former first splits the original training data into three different levels by the input prompt length, and…

Recent 3D face editing methods using masks have produced high-quality edited images by leveraging Neural Radiance Fields (NeRF).

Despite their impressive performance, existing methods often provide limited user control due to the use of pre-trained segmentation masks.

We present FFaceNeRF, a NeRF-based face editing technique that can overcome the challenge of limited user control due to the use of fixed mask layouts.

This facilitates rapid model adaptation to desired mask layouts, crucial for applications in fields like personalized medical imaging or creative face editing.

Our comparative evaluations demonstrate that FFaceNeRF surpasses existing mask based face editing methods in terms of …

Reflection removal of a single image remains a highly challenging task due to the complex entanglement between target scenes and unwanted reflections.

Despite significant progress, existing methods are hindered by the scarcity of high-quality, diverse data and insufficient restoration priors, resulting in limited generalization across various real-world scenarios.

In this paper, we propose Dereflection Any Image, a comprehensive solution with an efficient data preparation pipeline and a generalizable model for robust reflection removal.

Second, we propose a diffusion-based framework with one-step diffusion for deterministic outputs and fast inference.

Extensive experiments show that our met…

One approach to leverage NN weights involves training autoencoders (AEs), using contrastive and reconstruction losses.

This allows such models to be applied to a wide variety of downstream tasks, and they demonstrate strong predictive performance and low reconstruction error.

However, despite the low reconstruction error, these AEs reconstruct NN models with deteriorated performance compared to the original ones, limiting their usability with regard to model weight generation.

We analyze the addition of a behavioral loss for training AEs in weight space, where we compare the output of the reconstructed model with that of the original one, given some common input.

We show a strong synergy be…

Text-driven motion generation has advanced significantly with the rise of denoising diffusion models.

Moreover, when using pre-trained models for downstream tasks, such as editing, they typically require additional efforts, including manual interventions, optimization, or fine-tuning.

In this paper, we introduce a skeleton-aware latent diffusion (SALAD), a model that explicitly captures the intricate inter-relationships between joints, frames, and words.

Furthermore, by leveraging cross-attention maps produced during the generation process, we enable attention-based zero-shot text-driven motion editing using a pre-trained SALAD model, requiring no additional user input beyond text prompts.

…

Although modern DL frameworks rely on compilerlike optimizations internally, BurTorch takes a different path.

In large DL frameworks, the primary source of memory overhead for relatively small computation graphs $f(x)$ is due to feature-heavy implementations.

We benchmarked BurTorch against widely used DL frameworks in their execution modes: JAX (Bradbury et al., 2018), PyTorch (Paszke et al., 2019), TensorFlow (Abadi et al., 2016); and several standalone libraries: Autograd (Maclaurin et al., 2015), Micrograd (Karpathy, 2020), Apple MLX (Hannun et al., 2023).

For small compute graphs, BurTorch outperforms best-practice solutions by up to $\times 2000$ in runtime and reduces memory consumpt…

We present RWKV-7 "Goose", a new sequence modeling architecture, along with pre-trained language models that establish a new state-of-the-art in downstream performance at the 3 billion parameter scale on multilingual tasks, and match current SoTA English language performance despite being trained on dramatically fewer tokens than other top 3B models.

Nevertheless, RWKV-7 models require only constant memory usage and constant inference time per token.

RWKV-7 introduces a newly generalized formulation of the delta rule with vector-valued gating and in-context learning rates, as well as a relaxed value replacement rule.

We show that RWKV-7 can perform state tracking and recognize all regular l…

We introduce the two-factor Quintic Ornstein-Uhlenbeck model, where volatility is modeled as a polynomial of degree five based on the sum of two Ornstein-Uhlenbeck processes driven by the same Brownian Motion, each mean-reverting at a different speed.

We demonstrate that the Quintic model effectively captures the volatility surfaces of SPX and VIX while aligning with the skew-stickiness ratio (SSR) across maturities ranging from a few days to over two years.

Furthermore, the Quintic model shows consistency with key empirical stylized facts, notably reproducing the Zumbach effect.

PDFAbstract

Recent advances in Latent Video Diffusion Models (LVDMs) have revolutionized video generation by leveraging Video Variational Autoencoders (Video VAEs) to compress intricate video data into a compact latent space.

However, as LVDM training scales, the computational overhead of Video VAEs becomes a critical bottleneck, particularly for encoding high-resolution videos.

Extensive experiments validate LeanVAE's superiority in video reconstruction and generation, particularly in enhancing efficiency over existing Video VAEs.

Our model offers up to 50x fewer FLOPs and 44x faster inference speed while maintaining competitive reconstruction quality, providing insights for scalable, efficient video …

Automated segmentation can help but requires large labeled datasets, which are scarce.

In this paper, we present a simple yet effective pseudo-labeling method with an adversarially learned shape prior to regularize segmentations.

Specifically, we devise an encoder-twin-decoder network where the shape prior acts as an implicit shape model, penalizing anatomically implausible but not ground-truth-deviating predictions.

Without bells and whistles, our simple approach achieves state-of-the-art performance on two benchmarks under different partition protocols.

We provide a strong baseline for future semi-supervised medical image segmentation.

Identifying the Markov properties or conditional independencies of a collection of random variables is a fundamental task in statistics for modeling and inference.

In this work, we propose a scalable algorithm to infer the conditional independence relationships of each variable by exploiting the local Markov property.

We show that L-SING includes existing approaches, such as neighborhood selection with Lasso, as a special case.

We demonstrate the effectiveness of our algorithm in both Gaussian and non-Gaussian settings by comparing it to existing methods.

Lastly, we show the scalability of the proposed approach by applying it to high-dimensional non-Gaussian examples, including a biological…

Research Gemini Robotics brings AI into the physical world ShareCopy link ×Introducing Gemini Robotics, our Gemini 2.0-based model designed for robotics At Google DeepMind, we've been making progress in how our Gemini models solve complex problems through multimodal reasoning across text, images, audio and video.

Because it’s built on a foundation of Gemini 2.0, Gemini Robotics is intuitively interactive.

Watch "Gemini Robotics: Dexterous"Multiple embodiments Finally, because robots come in all shapes and sizes, Gemini Robotics was also designed to easily adapt to different robot types.

Pause video Play videoEnhancing Gemini’s world understandingAlongside Gemini Robotics, we’re introducing …

In December we first introduced native image output in Gemini 2.0 Flash to trusted testers.

Today, we're making it available for developer experimentation across all regions currently supported by Google AI Studio.

You can test this new capability using an experimental version of Gemini 2.0 Flash (gemini-2.0-flash-exp) in Google AI Studio and via the Gemini API.

Gemini 2.0 Flash combines multimodal input, enhanced reasoning, and natural language understanding to create images that give you exactly what you ask for.

Text and images togetherUse Gemini 2.0 Flash to tell a story and it will illustrate it with pictures, keeping the characters and settings consistent throughout.

Built-in safety for image applications with ShieldGemma 2Alongside Gemma 3, we're also launching ShieldGemma 2, a powerful 4B image safety checker built on the Gemma 3 foundation.

A “Gemmaverse” of models and toolsThe Gemmaverse is a vast ecosystem of community-created Gemma models and tools, ready to power and inspire your innovation.

Get started with Gemma 3As part of our ongoing commitment to democratizing access to high-quality AI, Gemma 3 represents the next step.

Here's where to start:Instant exploration:Try Gemma 3 at full precision directly in your browser – no setup needed – with Google AI Studio.

Get an API key directly from Google AI Studio and use Gemma 3 with the Google GenAI S…

Gemini 2.0 Flash-Lite is now generally available in the Gemini API for production use in Google AI Studio and for enterprise customers on Vertex AI

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

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

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

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

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

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

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

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

As a result of this work, today w…

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

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

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

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

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

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

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

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

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

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

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

It's available in Gemini Advanced today.

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

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

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

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

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

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

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

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

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

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

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

Advanced forec…

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

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

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

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

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

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

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

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

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

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

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

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

Gen AI Toolbox for Databases improves how gen AI tools interact with data, addressing common challenges in gen AI tool management.

Using natural language to query databases with agentsOnce your agents are connected to databases you can use a wide variety of methods to query the data.

A recent technique that is gaining popularity is using natural language to query databases.

This enables gen AI applications to more securely execute natural-language queries such as "Where is my package?"

Furthermore, interconnected data is becoming increasingly important to customers for use cases such as knowledge graphs, recommendations, and fraud detection.

This optimized checkpoint loading approach has already demonstrated significant speedups in practice.

To evaluate the efficiency of our optimized checkpoint loading technique, we ran benchmarks on different hardware devices.

On a TPU cluster with 13 slices of v5e-256 machines, checkpoint loading completed compared to frequent timeouts with the standard approach.

This enhancement in Orbax ensures efficient memory utilization during checkpoint loading, particularly for large-scale models and in scenarios where memory constraints are critical.

Let’s take an exampleTo illustrate how to use this optimized checkpoint loading feature, let's refer to an example from MaxText.

That’s why we needed data infrastructure that could handle our growing volumes of complex data and support essential processes like data discovery, labeling, and rapid evaluation.

We ultimately arrived at AlloyDB, a high-performance, fully managed PostgreSQL-compatible database on Google Cloud, for its superior performance, ease of use, and seamless integration.

Gearing up for autonomous data growthTransitioning to a new data infrastructure can often be disruptive, but with AlloyDB, the process was seamless.

This infrastructure enables analysis for refining route optimization to find challenging scenarios so our AI models can learn based on real-world on-road performance.

AlloyDB plays a cr…

Flat pricing, limited targeting, and a focus on immediate conversions over long-term customer value leave advertisers wanting more.

At Arpeely, we're changing the game in three ways by:Putting our own money on the line for performance with a different business model.

Not just the text and code — but the images, the emotions, the nuances that make each web page unique.

That's why we're doubling-down on our ad-tech platform on Google Cloud.

We don't just look at text; we use Google Cloud's powerful multimodal AI, including Gemini Pro Vision and Gemini Flash 1.5 to analyze web page screenshots, extracting visual information that enriches our understanding in real time.

Today, we're excited to announce a significant milestone in deepening our developer communities: We’re fully integrating Google Cloud Champion Innovators (Champions) into the Google Developer Experts (GDE) program.

The Champion Innovators program was launched in 2022 to recognize and support developers demonstrating exceptional expertise and passion for Google Cloud technologies.

For over 12 years, the GDE program has been a respected community for recognized experts across a wide array of Google technologies and other developer-facing products at Google such as Android, Firebase, Flutter, Angular, and Chrome.

With the addition of the Champions, we are growing the GDE to over 1,400 members.…

It isolates AI resources from the public internet, creating a private network for sensitive data, applications and models on Google Cloud.

LBs inherent scalability can effectively mitigate DDoS attacks, as we saw when Google Cloud stopped one of the largest DDoS attacks ever seen.

Sensitive Data Protection can secure AI data on Google Cloud by discovering, classifying, and protecting sensitive information and maintaining data integrity.

In addition to this reference architecture, it is important to remember to implement appropriate IAM roles when using Vertex AI.

Tools including Security Command Center, Google Security Operations, Dataplex and Cloud Logging can be used to enforce a secure s…

Spanner Graph redefines graph data management by integrating graph, relational, search, and AI capabilities with virtually unlimited scalability.

In this blog, we demonstrate how to leverage LanghChain and Spanner Graph to build powerful GraphRAG applications.

Rather than relying solely on pre-trained knowledge, RAG systems query external data sources during inference, commonly using techniques like vector search.

LangChain is a leading orchestration framework for building RAG applications that simplifies the integration of diverse data sources and foundation models.

Recently, we integrated Spanner Graph and LangChain, streamlining the development of GraphRAG solutions.

Traditionally, meeting growing demands for machine capacity means deploying new machines and that has an associated embodied carbon impact.

In this post, we shine a spotlight on our hardware harvesting program, an approach to fleet deployment that prioritizes the reuse of existing hardware.

The hardware harvesting programThe concept is simple: As we deploy new machines or components in our fleet, we repurpose older equipment for alternative and/or additional use cases.

The harvesting program prioritizes the reuse of existing hardware, which reduces our carbon emissions compared to exclusively buying brand new machines from the market.

Hardware harvesting is not without its challenges.

Model endpoint management is available on AlloyDB, AlloyDB Omni and Cloud SQL for PostgreSQL.

Model endpoint management helps developers to build new experiences using SQL and provides a flexible interface to call gen AI models running anywhere — right from the database.

This feature is available through the google_ml_integration extension, which enables an integration with Vertex AI for both AlloyDB and Cloud SQL for PostgreSQL.

With model endpoint management, you can leverage models running on any platform — including your own local environment.

In this blog, we’ll walk you through three example workflows that leverage model endpoint management to build richer generative AI experiences.

Software development teams are under constant pressure to deliver at an ever-increasing pace.

As sponsors of the DORA research, we recently took a look at the adoption and impact of artificial intelligence on the software development lifecycle.

With Gemini Code Assist, developers aim to boost their efficiency and code quality.

In this article, we’ll provide a practical framework for adopting AI-assisted code creation, and for evaluating the effectiveness of AI assistance in your software development workflow.

This post outlines a four-step framework to adopt AI code assistants like Gemini Code Assist on your software development team: Adoption, trust, acceleration, and impact.

Join Urs Hölzle, Google Fellow, and Parthasarathy Ranganathan, VP, Engineering Fellow at Google Cloud, for a fireside chat exploring the critical intersection of intelligent infrastructure and sustainable energy.

Google Cloud TPUs and specialized AI hardware: Jeff Dean on what’s next: Join an insightful fireside chat with Jeff Dean, a pioneering force behind Google’s AI leadership and Sabastian Mugazambi, Senior Product Manager, Cloud AI Infrastructure.

As Google's Chief Scientist at DeepMind & Research, Jeff will share his vision on AI and specialized AI hardware like Google Cloud TPUs.

What drives Google’s innovation in specialized AI hardware?

We invite you to join us at Google Cloud Nex…

We are excited to announce LlamaIndex integration for Gen AI Toolbox for Databases (Toolbox).

Gen AI Toolbox for Databases is an open-source server that streamlines the development and management of sophisticated generative AI tools that can connect to databases.

Currently, Toolbox can be used to build tools for a large number of databases: AlloyDB for PostgreSQL (including AlloyDB Omni), Spanner, Cloud SQL for PostgreSQL, Cloud SQL for MySQL, Cloud SQL for SQL Server, and self-managed MySQL and PostgreSQL.

It offers a comprehensive suite of tools and functionality that facilitate the development of sophisticated AI agents.

In this post, we’ll share how LlamaIndex support for Toolbox works,…

As part of our wide-ranging, cross-industry collaboration, NVIDIA and Google Cloud have supported the development of generative AI applications and platforms.

NVIDIA BioNeMo is a powerful open-source collection of models specifically tuned to the needs of medical and pharmaceutical researchers.

Google Kubernetes Engine (GKE) offers a powerful solution for achieving many of these demanding workloads, and when taken together with NVIDIA BioNeMo, GKE can accelerate work on the platform.

With BioNeMo running on GKE, organizations can achieve medical breakthroughs and new research with levels of speed and effectiveness that were unheard of before.

In this blog, we’ll show you how to build and cu…

Sara is vice president and chief health AI officer at UC San Francisco Health.

LONGHURST: So the pat response is AI won’t replace doctors, but AI will replace doctors who don’t use AI.

LEE: And I’m assuming a chief health AI officer is not a role that has been around for a long time.

LEE: Should I be impressed or concerned that the chief health AI officer at UC San Francisco Health is using ChatGPT off label?

We’ll delve into how patients are using generative AI for their own healthcare, the hype and reality of AI drug discovery, and more.

The U.N. found that the resulting contaminated water caused many residents to fall ill, highlighting the need for improved water management.

Emerging markets face economic challenges.

Excerpt: “The U.N. found that the resulting contaminated water caused many residents to fall ill, highlighting the need for improved water management.”Claims: The U.N. found contaminated water in Derna, Libya.

Explanation: The first claim is inaccurate because the U.N. found the link between contaminated water and illness, not the contaminated water itself.

First, it found two instances of ambiguity – “resulting contaminated water” and “many residents” – that it determined could be resolved using the context.

Using this capability, we developed a GNSS positioning metasurface system (GPMS) based on passive metasurface technology.

Passive metasurfaces guide GNSS signals indoors, while enhanced positioning algorithms provide precise indoor positioning on mobile devices.

This proposed framework can optimize millimeter-wave coverage using low-cost passive metasurface design and strategic placement.

The AutoMS framework generates optimized deployment plans for passive metasurface and access points based on environment scanning results.

Low-cost passive metasurface design: We designed a high-reflectivity passive metasurface with near-2π phase control and broadband compatibility for the millimeter-wave …

Large language models (LLMs) have demonstrated remarkable capabilities in reasoning, language understanding, and even creative tasks.

A new way to integrate knowledgeTo address these challenges, we introduce the Knowledge Base-Augmented Language Model (KBLaM) —a novel approach that integrates structured knowledge bases into pre-trained LLMs.

In this setup, language tokens (such as those from a user’s question) attend to all knowledge tokens.

However, knowledge tokens do not attend to one another, nor do they attend back to the language tokens.

Figure 2: By having the user’s question attend to the knowledge base, while treating facts in the knowledge base independently, KBLaM scales efficien…

First, LLMs give us a new thing to measure, that is, how people interact with AI systems.

Semantic Telemetry is a rethink of traditional telemetry–in which data is collected for understanding systems–designed for analyzing chat-based AI.

Description of LLM generated label taxonomy processWith this approach, we have analyzed how people interact with Copilot in Bing.

Figure 7: Most and least complex topics based on percentage of high complexity tasks.

We are now able to obtain actionable insight from complex data that is not possible with traditional data science pattern-matching methods.

About two years ago, with Carey Goldberg and Zak Kohane, we wrote a book, The AI Revolution in Medicine.

If you’re a patient, in what ways could AI change your experience as you try to navigate a complex healthcare system?

A strange and bizarre thought, I admit, but a natural one, I think, for any human being that’s encountering this amazing AI technology for the first time.

And since then, of course, I’ve come to learn that many people have had similar experiences in their first encounters with AI.

And in fact, I’ve come to think of this as, somewhat tongue in cheek, the nine stages of AI grief.

IntroductionModern biomedical research is driven by the promise of precision medicine—tailored treatments for individual patients through the integration of diverse, large-scale datasets.

Study at a glanceA deep understanding of the biomedical discovery process is crucial for advancing modern precision medicine initiatives.

Empowering stakeholder collaboration and secure data sharingEffective biomedical discovery requires collaboration across multiple disciplines and institutions.

The future of precision medicine depends on our ability to break down data silos and create a research data lifecycle that is both robust and responsive to the challenges of big data.

Let’s reimagine biomedical di…

This new model represents a significant step toward AI agents that can serve as versatile, general-purpose assistants.

Figure 1: Magma is one of the first foundation models that is capable of interpreting and grounding multimodal inputs within both digital and physical environments.

Building a foundation model that spans such different modalities has required us to rethink how we train and supervise AI agents.

Set-of-Mark (SoM): SoM is an annotated set of key objects, or interface elements that are relevant to achieving a given goal.

While AutoGen focuses on the structure and management of AI agents, Magma enhances those agents by empowering them with a new level of capability.

There has been extraordinary progress in recent years toward better understanding protein structures using deep learning, enabling the accurate prediction of protein structures from their amino acid sequences.

Enter BioEmu-1 (opens in new tab)—a deep learning model that can generate thousands of protein structures per hour on a single graphics processing unit.

Training BioEmu-1 on the AFDB structures is like mapping distinct islands in a vast ocean of possible structures.

The MD simulation dataset helps BioEmu-1 predict physically plausible structural changes around these islands, mapping out the plethora of possible structures that a single protein can adopt.

Figure 3 shows the folding fre…

The WHAM, which we’ve named “Muse,” is a generative AI model of a video game that can generate game visuals, controller actions, or both.

Generated gameplay examplesExample gameplay sequences generated by Muse (based on WHAM-1.6B) demonstrate that our model can generate complex gameplay sequences that are consistent over several minutes.

The more closely the generated gameplay sequence resembles the actual game, the more accurately Muse has captured the dynamics of that game.

ChatGPT had been publicly released, and those who had tried it were in awe of OpenAI’s technical achievements and the model’s capabilities.

The generated gameplay sequence shows how the character is adapted into the ge…

CHETAN NAYAK: People sometimes say, well, quantum computers are just going to be like classical computers but faster.

This idea of quantum, because you’ve mentioned Albert Einstein, there’s quantum physics, quantum mechanics, now quantum computing.

Well, let me …NAYAK: And that’s quantum mechanics!

HUIZINGA: OK.NAYAK: You’re probably going to say, well, how does quantum computing fit into this, you know?

[LAUGHS]NAYAK: And, you know, there are people out there who said, you know, quantum computers are decades away; don’t worry about it.

Some 2 million students use the Physics Wallah platform every day, at a fraction of the cost of offline tutoring.

Physics Wallah has developed an AI-driven educational suite, Alakh AI, leveraging OpenAI’s GPT-4o model through Microsoft Azure OpenAI Service.

Researchers from Microsoft Research are developing new algorithms and techniques to enhance the accuracy and reasoning capabilities of AI models.

Microsoft Research is working with Physics Wallah to move beyond traditional next-token prediction and develop AI systems that approach reliable, systematic, step-by-step problem-solving.

Without Physics Wallah, students like Chandra would likely have no access to the support and resources that…

Rank Model Score 1 GPT-4o + ExACT 33.70 2 GPT-4o + Search 26.40 3 GPT-4o + WebDreamer 23.60 4 GPT-4o + ICAL 23.40 5 GPT-4o 19.78 6 Llama-3-70B + Search 16.70 Table 1.

How Exploratory Learning worksExploratory Learning enables agents to dynamically search and adjust their computational resources during testing without depending on MCTS.

In contrast to Imitation Learning, Exploratory Learning uses the entire search trajectory for training.

Results demonstrate the following key benefits:Improved performance : GPT-4o achieves performance improvement, comparable with scaling test-time compute with MCTS, even without search.

How can AI agents improve decision-making in real-world scenarios, where…

His work lies at the intersection of systems, AI, and machine learning with a focus on designing, deploying, and scaling AI systems to solve compelling real-world problems.

CHRIS STETKIEWICZ: You’re listening to Ideas, a Microsoft Research Podcast that dives deep into the world of technology research and the profound questions behind the code.

NAMBI: That’s right.

This represents a major step towards building AI systems that’s much more holistic personal tutors, which help student understanding and create more engaging, effective learning experience.

Are there some things that could go wrong, even if we get the technology right?

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

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

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

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

One popular type of model, called a diffusion model, can create stunningly realistic images but is too slow and computationally intensive for many applications.

Their hybrid image-generation tool uses an autoregressive model to quickly capture the big picture and then a small diffusion model to refine the details of the image.

Their tool, known as HART (short for hybrid autoregressive transformer), can generate images that match or exceed the quality of state-of-the-art diffusion models, but do so about nine times faster.

The best of both worldsPopular diffusion models, such as Stable Diffusion and DALL-E, are known to produce highly detailed images.

Outperforming larger modelsDuring the de…

As director of the MIT BioMicro Center (BMC), Stuart Levine ’97 wholeheartedly embraces the variety of challenges he tackles each day.

One of over 50 core facilities providing shared resources across the Institute, the BMC supplies integrated high-throughput genomics, single-cell and spatial transcriptomic analysis, bioinformatics support, and data management to researchers across MIT.

After earning his PhD in biochemistry from Harvard University and Massachusetts General Hospital, Levine returned to MIT for postdoctoral work with Professor Richard Young, core member at the Whitehead Institute for Biomedical Research.

Staying ahead of the curveWith a scientist directing the core, the BMC ai…

“Philosophers like Cicero argue that the good life centers on the pursuit of virtue and wisdom,” Vinson said.

Does a world that increasingly defers to AI for decision-making and artistic creation, and even ethical deliberation, does that reflect a more advanced society?

“It serves as a Rorschach test for society’s deepest hopes and anxieties,” Vinson said of AI.

“Unlike previous technologies that have extended human labor, again, AI targets cognition, creativity, decision-making, and even emotional intelligence,” Vinson said.

Together, we can shape an AI future that honors dignity for everyone, and at the same time, advances the ideals of humanity itself.”

For Pietersen, the time-intensive, painstaking, and potentially dangerous work underscored the potential for his research to enable remote airfield assessments.

Rapid and remote airfield assessment is not the standard practice yet.

“In my senior year, the Air Force research labs had some pavement-related projects that fell into my scope as a civil engineer,” Pietersen recalls.

Pietersen went remote between 2022 to 2024, but he wasn’t doing his research from the comfort of a home office.

“If we can move to spectral imaging and deep-learning solutions, we can finally conduct remote assessments that make everyone safer.”

Using a new framework developed by MIT and NVIDIA researchers, you could correct that robot’s behavior with simple interactions.

The method would allow you to point to the bowl or trace a trajectory to it on a screen, or simply give the robot’s arm a nudge in the right direction.

Unlike other methods for correcting robot behavior, this technique does not require users to collect new data and retrain the machine-learning model that powers the robot’s brain.

Instead, the MIT researchers wanted to allow users to steer the robot’s behavior during deployment when it makes a mistake.

Their framework accomplishes this by providing the user with three intuitive ways to correct the robot’s behavior,…

For over 30 years, science photographer Felice Frankel has helped MIT professors, researchers, and students communicate their work visually.

On a more personal note, she questions whether there will still be a place for a science photographer in the research community.

The critical issue is not to manipulate the data, and in the case of most images, the data is the structure.

A: For the Nature article, I decided that a powerful way to question the use of AI in generating images was by example.

In conversations with colleagues in research and computer-science communities, all agree that we should have clear standards on what is and is not allowed.

MIT Professor Markus J. Buehler has been named the recipient of the 2025 Washington Award, one of the nation’s oldest and most esteemed engineering honors.

The Washington Award is conferred to “an engineer(s) whose professional attainments have preeminently advanced the welfare of humankind,” recognizing those who have made a profound impact on society through engineering innovation.

His work on materiomusic — converting molecular structures into musical compositions — has provided new insights into the hidden patterns within biological systems.

Buehler is the Jerry McAfee (1940) Professor in Engineering in the departments of Civil and Environmental Engineering (CEE) and Mechanical Engineer…

MIT and GlobalFoundries (GF), a leading manufacturer of essential semiconductors, have announced a new research agreement to jointly pursue advancements and innovations for enhancing the performance and efficiency of critical semiconductor technologies.

The collaboration will be led by MIT’s Microsystems Technology Laboratories (MTL) and GF’s research and development team, GF Labs.

“By bringing together MIT's world-renowned capabilities with GF's leading semiconductor platforms, we are positioned to drive significant research advancements in GF’s essential chip technologies for AI,” says Gregg Bartlett, chief technology officer at GF.

MIT faculty are active participants in GF’s University P…

These systems, which the researchers call TIGR (Tandem Interspaced Guide RNA) systems, use RNA to guide them to specific sites on DNA.

TIGR systems can be reprogrammed to target any DNA sequence of interest, and they have distinct functional modules that can act on the targeted DNA.

That modularity could facilitate tool development, allowing researchers to swap useful new features into natural Tas proteins.

TIGR Tas proteins, in contrast, have no such requirement.

What’s more, Tas proteins are compact — a quarter of the size Cas9, on average — making them easier to deliver, which could overcome a major obstacle to therapeutic deployment of gene editing tools.

Recent findings have revealed that small protein fragments have a lot of functional potential.

Protein fragments could therefore empower both basic research on protein interactions and cellular processes, and could potentially have therapeutic applications.

Leveraging machine learningThe program, called FragFold, leverages AlphaFold, an AI model that has led to phenomenal advancements in biology in recent years due to its ability to predict protein folding and protein interactions.

The goal of the project was to predict fragment inhibitors, which is a novel application of AlphaFold.

“By creating compact, genetically encodable binders, FragFold opens a wide range of possibilities to manipula…

The MIT spinout ReviveMed has created a platform for measuring metabolites — products of metabolism like lipids, cholesterol, sugar, and carbs — at scale.

The company is using those measurements to uncover why some patients respond to treatments when others don’t and to better understand the drivers of disease.

“With the field of AI booming, we think we can overcome data problems that have limited the study of metabolites,” Pirhaji says.

ReviveMed began by working with hospitals to uncover how lipids are dysregulated in a disease known as metabolic dysfunction-associated steatohepatitis.

“We’re democratizing the use of metabolomic data,” Pirhaji says.

While early language models could only process text, contemporary large language models now perform highly diverse tasks on different types of data.

This semantic hub is connected to modality-specific “spokes” that route information to the hub.

Then, the LLM converts tokens into modality-agnostic representations as it reasons about them throughout its internal layers, akin to how the brain’s semantic hub integrates diverse information.

A better understanding of an LLM’s semantic hub could help researchers prevent this language interference, he says.

“Understanding how language models process inputs across languages and modalities is a key question in artificial intelligence.

More recently, researchers are coming to appreciate that a protein’s localization is also critical for its function.

MIT Professor Richard Young and colleagues wondered whether the code in those regions could be used to predict protein localization in the same way that other regions are used to predict structure.

Other researchers have discovered some protein sequences that code for protein localization, and some have begun developing predictive models for protein localization.

The researchers also tested how well ProtGPS could predict changes in protein localization based on disease-associated mutations within a protein.

The researchers found many cases in which a disease-associated mutati…

The MIT Stephen A. Schwarzman College of Computing has received substantial support for its striking new headquarters on Vassar Street in Cambridge, Massachusetts.

A major gift from Sebastian Man ’79, SM ’80 will be recognized with the naming of a key space in the building, enriching the academic and research activities of the MIT Schwarzman College of Computing and MIT.

Man’s gift to the college was recognized at a ceremony and luncheon in Hong Kong, where he resides, on Jan. 10.

“When we first announced the new college at MIT,” he said, “MIT said it was reshaping itself for the future.

“MIT instilled in me unending intellectual curiosity and the love for the unknown, and I am honored and …

At this moment, what some consider the golden age of generative AI, this may seem like an urgent new question.

But Solar-Lezama, the Distinguished Professor of Computing at MIT, is quick to point out that this struggle is as old as humankind itself.

A recitation to break down the week's topic with graduate students from philosophy or computer science and a lively discussion combine the course content.

Westover says he's drawn to philosophy because of an interest in ethics and a desire to distinguish right from wrong.

However, in Ethics of Computing, he has learned how to make written arguments for "tricky philosophical questions" that may not have a single correct answer.

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?

Amazon Q Business also supports over 50 actions across popular business applications and platforms.

They can now get these questions answered from Amazon Q Business without necessarily signing in to either Atlassian Jira or Confluence.

Amazon Q Business can use the generative AI to provide responses with actionable insights in just few seconds.

Organization can create more Amazon Q Business applications and share purpose-built Amazon Q Business apps within their organizations to manage repetitive tasks.

Learn more:For expert assistance, AWS Professional Services, AWS Generative AI partner solutions, and AWS Generative AI Competency Partners are here to help.

However, by using Anthropic’s Claude on Amazon Bedrock, researchers and engineers can now automate the indexing and tagging of these technical documents.

Create an Amazon Bedrock knowledge base.

Get Metadata from formulasAfter the image documents are available, you can use Anthropic’s Claude to extract formulas and metadata with the Amazon Bedrock Converse API.

Additionally, you can use the Amazon Bedrock Converse API to obtain an explanation of the extracted formulas in plain language.

Embrace the future of AI-driven document processing and unlock new possibilities for your organization with Anthropic’s Claude on Amazon Bedrock.

This solution also uses Amazon Bedrock Knowledge Bases and Amazon Bedrock Agents.

Amazon Bedrock Agents can iterate and call multiple functions as needed until the task is successfully complete.

The RAG process is powered by Amazon Bedrock Knowledge Bases, which stores information that the Amazon Bedrock agent can access and use.

Configure Amazon Bedrock AgentsAmazon Bedrock Agents augment the user request with the right information from Amazon Bedrock Knowledge Bases to generate an accurate response.

Through the integration of Amazon Bedrock, Anthropic’s Claude on Amazon Bedrock, Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and other supporting services, this solution provides re…

Fortunately, AWS provides a powerful tool called AWS Support Automation Workflows, which is a collection of curated AWS Systems Manager self-service automation runbooks.

– Amazon Bedrock Agents acts as the intelligent interface between users and AWS Support Automation Workflows.

Amazon Bedrock agent action groups – These action groups define the structured API operations that the Amazon Bedrock agent can invoke.

Lambda Function – The Lambda function acts as the integration layer between the Amazon Bedrock agent and AWS Support Automation Workflows.

AWS Support Automation Workflows – These pre-built diagnostic runbooks are developed by AWS Support Engineering.

Today we are announcing that general availability of Amazon Bedrock in Amazon SageMaker Unified Studio.

Organization administrators can control member access to Amazon Bedrock models and features, maintaining secure identity management and granular access control.

The workflow is as follows:The user logs into SageMaker Unified Studio using their organization’s SSO from AWS IAM Identity Center.

Build the chat agent applicationComplete the following steps to build the chat agent application:Under the New section located to the right of the crm-agent project landing page, choose Chat agent.

Try out Amazon Bedrock in SageMaker Unified Studio for your own use case, and share your questions in th…

Solution OverviewAt a high level, the solution consists of first converting audio into transcripts using Amazon Transcribe and generating and evaluating summary fields for each transcript using Amazon Bedrock.

In addition, Q&A can be done at a single call level using Amazon Bedrock or for many calls using Amazon Q in QuickSight.

Integrate Amazon Q in QuickSight with the PCA solutionThe Amazon Q in QuickSight integration was done by following three high-level steps:Create a dataset on QuickSight.

Data fields within Amazon Q in QuickSight needed to be defined properly and synonyms needed to be added to make Amazon Q more robust with natural language queries.

This is one of the many ways build…

The following figure shows how Amazon Bedrock Data Automation seamlessly integrates with Amazon Bedrock Knowledge Bases to extract insights from unstructured datasets and ingest them into a vector database for efficient retrieval.

Amazon Bedrock Data Automation can also be used with Amazon Bedrock Agents to take the next step in automation.

Unified, API-drove speech analytics with Amazon Bedrock Data AutomationThe following figure shows customer service call analytics using Amazon Bedrock Data Automation-power intelligent speech analytics.

Amazon Bedrock Data Automation streamlines multi-modal processing, while Amazon Bedrock offers building blocks for deeper customization and control.

Star…

To add fine-grained control to how tools are used, we have released a feature for tool choice for Amazon Nova models.

There are three supported options for this parameter:Any – With tool choice Any , the model will select at least one of the available tools each time: { "toolChoice": { "any": {} } }– With tool choice , the model will select at least one of the available tools each time: Tool – With tool choice Tool , the model will always use the requested tool: { "toolChoice": { "tool": { "name": "name_of_tool" } } }– With tool choice , the model will always use the requested tool: Auto – Tool choice Auto is the default behavior and will leave the tool selection completely up to the model:…

An Office Add-in is composed of two elements:Manifest : A domain-specific XML file that specifies how the Office Add-in integrates with the application.

For testing, one can sideload an Office Add-in.

: A domain-specific XML file that specifies how the Office Add-in integrates with the application.

AWS Lambda handles the REST API integration, processing the requests and invoking the appropriate AWS services.

About the AuthorsMartin Maritsch is a Generative AI Architect at AWS ProServe focusing on Generative AI and MLOps.

As the world’s most comprehensive and broadly adopted cloud, our partnership with NVIDIA’s pioneering accelerated computing platform for generative AI amplifies this capability.

Transforming content creation with generative AIContent creation represents one of the most visible and immediate applications of generative AI today.

Adobe’s approach to generative AI infrastructure exemplifies what their VP of Generative AI, Alexandru Costin, calls an “AI superhighway”—a sophisticated technical foundation that enables rapid iteration of AI models and seamless integration into their creative applications.

The success of their Firefly family of generative AI models, integrated across flagship produc…

AWS customers and partners innovate using Amazon Q Business in EuropeOrganizations across the EU are using Amazon Q Business for a wide variety of use cases, including answering questions about company data, summarizing documents, and providing business insights.

Certain Amazon Q Business features already available in US East (N. Virginia) and US West (Oregon) including Q Apps, Q Actions, and Audio/Video file support will become available in Europe (Ireland) soon.

Morgan Dutton is a Senior Technical Program Manager at AWS, Amazon Q Business based in Seattle.

Eva Pagneux is a Principal Product Manager at AWS, Amazon Q Business, based in San Francisco.

Wesleigh Roeca is a Senior Worldwide Gen…

By using FLARE as the communicator for Flower applications, you can transform any Flower app into a FLARE job.

The FLARE client forwards this message as a reliable FLARE message to the FLARE server.

return FlowerClient().to_client() # Flower ClientApp app = ClientApp( client_fn=client_fn, )With the integration of Flower and FLARE, applications developed with the Flower framework run seamlessly in FLARE runtime and you don’t have to make any changes.

When integrating Flower applications into the FLARE environment, we aimed to preserve identical results as when running the applications in a standalone Flower setup.

The Flower and NVIDIA Flare integration opens the door to more efficient, scal…

GeForce NOW brings a legendary addition to the cloud: Ubisoft’s highly anticipated Assassin’s Creed Shadows is now available for members to stream.

Plus, dive into the updated version of the iconic Fable Anniversary — part of 11 games joining the cloud this week.

Silent as a ShadowExplore 16th-century Japan, uncover conspiracies and shape the destiny of a nation — all from the cloud.

Assassin’s Creed Shadows unfolds in 1579, during the turbulent Azuchi-Momoyama period of feudal Japan, a time of civil war and cultural exchange.

GeForce NOW transforms these devices into powerful gaming rigs, with up to eight-hour gaming sessions for Ultimate members.

Global consortium brings together utilities, technology companies, academia and more to build open AI models to transform the way we make, move and use electricity.

To advance the next generation of electricity generation and distribution, many of the industry’s members are joining forces through the creation of the Open Power AI Consortium.

The consortium includes energy companies, technology companies and researchers developing AI applications to tackle domain-specific challenges, such as adapting to an increased deployment of distributed energy resources and significant load growth on electric grids.

Led by independent, nonprofit energy R&D organization EPRI, the consortium aims to spur …

“We make a deliberate effort to do great research while being relevant to the company,” said Dally, chief scientist and senior vice president of NVIDIA Research.

Unveiled in 2018, NVIDIA RTX also marked the launch of another NVIDIA Research innovation: NVIDIA DLSS, or Deep Learning Super Sampling.

As deep learning soared in popularity and evolved into generative AI, NVIDIA Research was at the forefront — exemplified by NVIDIA StyleGAN, a groundbreaking visual generative AI model that demonstrated how neural networks could rapidly generate photorealistic imagery.

It’s integrated into the NVIDIA NeMo platform for developing custom generative AI, which also features speech recognition and spee…

NVIDIA this week recognized 14 partners leading the way across the Americas for their work advancing agentic and physical AI across industries.

NPN partners help customers implement a broad range of AI technologies, including NVIDIA-accelerated AI factories, as well as large language models and generative AI chatbots, to transform business operations.

The company harnesses NVIDIA AI technologies to optimize data management, enhance cybersecurity and deliver transformative generative AI solutions, helping financial services clients navigate rapid technological changes and evolving customer expectations.

The company harnesses NVIDIA AI technologies to optimize data management, enhance cyberse…

With the NVIDIA RTX PRO Blackwell GPU series, announced yesterday at the NVIDIA GTC global AI conference, media companies can now harness real-time AI for media workflows with unprecedented speed, efficiency and creative potential.

NVIDIA Blackwell serves as the foundation of NVIDIA Media2, an initiative that enables real-time AI by bringing together NVIDIA technologies — including NVIDIA NIM microservices, NVIDIA AI Blueprints, accelerated computing platforms and generative AI software — to transform all aspects of production workflows and experiences, starting with content creation, streaming and live media.

NVIDIA RTX PRO Blackwell GPUs series include new features that enable unprecedent…

Release highlights include the following:What’s newNVIDIA Blueprints available through NVIDIA Brev, including genomics analysis using Parabricks and single-cell analysis leveraging RAPIDS and RAPIDS-singlecell.

At NVIDIA GTC 2025, NVIDIA launched two blueprints for genomics use cases: genomics analysis and single-cell analysis.

Both enable bioinformaticians and genomics platform providers to easily deploy and gain access to NVIDIA technology—including NVIDIA Parabricks and RAPIDS.

Parabricks v4.5 is also accompanied by AI blueprints for genomics and single-cell analysis that lets users easily deploy and test NVIDIA Parabricks and NVIDIA RAPIDS.

Download NVIDIA Parabricks to get started with…

This gap between virtual designs and real-world impact is today’s central challenge in AI-driven molecular design.

Computational generative chemistry models need experimental feedback and molecular simulations to confirm that their designed molecules are stable, synthesizable, and functional.

In generative molecular design, an oracle is a feedback mechanism—a test or evaluation that tells us how a proposed molecule performs regarding a desired outcome, often a molecular or experimental property (e.g., potency, safety, and feasibility).

Integrating oracles—experimental and computation-based feedback mechanisms—into AI-driven molecular design fundamentally changes drug design.

By integrating …

The new lineup includes:Desktop GPUs: NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition, NVIDIA RTX PRO 5000 Blackwell, NVIDIA RTX PRO 4500 Blackwell and NVIDIA RTX PRO 4000 BlackwellNVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition, NVIDIA RTX PRO 5000 Blackwell, NVIDIA RTX PRO 4500 Blackwell and NVIDIA RTX PRO 4000 Blackwell Laptop GPUs: NVIDIA RTX PRO 5000 Blackwell, NVIDIA RTX PRO 4000 Blackwell, NVIDIA RTX PRO 3000 Blackwell, NVIDIA RTX PRO 2000 Blackwell, NVIDIA RTX PRO 1000 Blackwell and NVIDIA RTX PRO 500 Blackwell Laptop GPUsNVIDIA RTX PRO 5000 Blackwell, NVIDIA RTX PRO…

NVIDIA and its ecosystem partners are building AI factories at scale for the AI reasoning era — and every enterprise will need one.

AI is fueling a new industrial revolution — one driven by AI factories.

Unlike traditional data centers, AI factories do more than store and process data — they manufacture intelligence at scale, transforming raw data into real-time insights.

Norway: Telenor has launched an NVIDIA-powered AI factory to accelerate AI adoption across the Nordic region, focusing on workforce upskilling and sustainability.

The NVIDIA AI Data Platform is a customizable reference design to build a new class of AI infrastructure for demanding AI inference workloads.

The NVIDIA Accelerated Quantum Research Center, or NVAQC, announced today at the NVIDIA GTC global AI conference, is where these developments will happen.

With an NVIDIA GB200 NVL72 system and the NVIDIA Quantum-2 InfiniBand networking platform, the facility will house a supercomputer with 576 NVIDIA Blackwell GPUs dedicated to quantum computing research.

“The NVAQC draws on much-needed and long-sought-after tools for scaling quantum computing to next-generation devices,” said Tim Costa, senior director of computer-aided engineering, quantum and CUDA-X at NVIDIA.

Developing Applications for Accelerated Quantum SupercomputersThe majority of useful quantum algorithms draw equally from classic…

That’s why NVIDIA is expanding NVIDIA-Certified Systems to include enterprise storage certification — for streamlined AI factory deployments in the enterprise with accelerated computing, networking, software and storage.

The NVIDIA-Certified Storage designation is a prerequisite for partners developing agentic AI infrastructure solutions built on the NVIDIA AI Data Platform.

NVIDIA Enterprise Reference Architectures (RAs) were introduced last fall to provide partners with AI infrastructure best practices and configuration guidance for deploying NVIDIA-Certified servers, NVIDIA Spectrum-X networking and NVIDIA AI Enterprise software.

Enterprise AI Needs Scalable StorageAs the pace of AI inno…

With our Ray-Ban Meta glasses, multimodal AI helps the glasses see what the wearer is seeing.

This means anyone wearing Ray-Ban Meta glasses can ask them questions about what they’re looking at.

On this episode of the Meta Tech Podcast, meet Shane, a research scientist at Meta who has spent the last seven years focusing on computer vision and multimodal AI for wearables.

Shane sits down with Pascal Hartig to share how his team is building foundational models for the Ray-Ban Meta glasses.

They talk about the unique challenges of AI glasses and pushing the boundaries of AI-driven wearable technology.

WHAT IT ISMeta’s Automated Compliance Hardening (ACH) tool is a system for mutation-guided, LLM-based test generation.

Traditionally, automated test generation techniques sought merely to increase code coverage.

LLM-based test generation and LLM-based mutant generation are not new, but this is the first time they’ve been combined and deployed in large-scaled industrial systems.

WHAT’S NEXTOur novel approach combines LLM-based test generation and mutant generation to help automate complex technical organizational workflows in this space.

READ THE PAPERMutation-Guided LLM-based Test Generation at Meta

Unlocking advertiser value through industry-leading ML innovationMeta Andromeda is a personalized ads retrieval engine that leverages the NVIDIA Grace Hopper Superchip, to enable cutting edge ML innovation in the Ads retrieval stage to drive efficiency and advertiser performance.

Its deployment across Instagram and Facebook applications has achieved +6% recall improvement to the retrieval system, delivering +8% ads quality improvement on selected segments.

Andromeda is designed to maximize ads performance by utilizing the exponential growth in volume of eligible ads available to the retrieval stage.

The design is optimized for AI hardware, minimizing memory bandwidth bottlenecks and enablin…

Meta’s ad recommendation engine, powered by deep learning recommendation models (DLRMs), has been instrumental in delivering personalized ads to people.

Learning from sequences: developing new sequence learning architectures to replace traditional DLRM neural network architectures.

A paradigm shift with learning from sequences for recommendation systemsMeta’s new system for ads recommendations uses sequence learning at its core.

Scaling the new sequence learning paradigmFollowing the redesign to shift from sparse feature learning to event-based sequence learning, the next focus was scaling across two domains — scaling the sequence learning architecture and scaling event sequences to be long…

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…

Moreover, I will make the case for why Bayesian deep learning can satisfy these desiderata and briefly review recent advances in the field.

The case for Bayesian deep learningBayesian deep learning uses the foundational statistical principles of Bayesian inference to endow deep learning systems with the ability to make probabilistic predictions.

However, Bayesian deep learning is unfortunately still not as easy to use as standard deep learning, which you can do these days in a few lines of PyTorch code.

If you want to use a Bayesian deep learning model, first, you have to think about specifying the prior.

If this is the case, trying out Bayesian deep learning is likely worth your while.

TL;DR State Space Models (SSMs) use first-order differential equations to represent dynamic systems.

Understanding state space modelsBefore exploring how State Space Models (SSMs) can function as components of large language models (LLMs), we’ll examine their foundational mechanics.

State space models for natural language processingState Space Models (SSMs), long established in time series analysis, have been utilized as trainable sequence models for decades.

Linear state space layers (LSSLs)So far, we’ve seen that State Space Models are efficient sequence models.

Improvements on the state matrix AIn the previous section, we explored how the original LSSL relied on a fixed, predefined form …

To keep data secure throughout the model’s lifecycle, implement these practices: data anonymization, secure model serving and privacy penetration tests.

For example, a recruitment LLM favoring male applicants due to biased training data reflects a harmful bias that requires correction.

Monitor bias continuouslyMitigating bias isn’t a one-time effort—it requires ongoing monitoring to ensure that your LLM remains fair and effective across iterations.

Although these contributions are publicly available, the move opened up debates about the ethics of reusing community-contributed content for proprietary AI training.

Best practices for ethical LLM developmentNavigating the regulatory landscape r…

While much of the discussion around LLMs centers on task and computational performance, in our paper Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives, we focus on the privacy implications of using Open and Closed LLMs.

The threat space in adapting LLMs to private dataThe adaptation of Closed LLMs to private datasets introduces a multifaceted threat space.

Related Zero-Shot and Few-Shot Learning with LLMs Read morePrivate adaptation methods for Open LLMsUnlike Closed LLMs, Open LLMs provide access to their parameters, enabling more flexible and parameter-centric private adaptation methods.

Performance: All adaptation methods for Closed LLMs ach…

“What is not measured, cannot be improved.” This quote has become a guiding principle for teams training foundation models.

During my talk at NeurIPS, I broke down five key lessons learned from teams facing large-scale model training and monitoring.

Waabi’s teams, running large-scale ML experiments, needed a way to organize and share their experiment data efficiently.

Visualizing large datasetsWe generally do not think of dataset visualization as part of experiment monitoring.

Moving forwardThe path to efficient hyperscale training lies in combining robust monitoring, advanced debugging tools, and comprehensive experiment tracking.

TL;DR Mixture of Experts (MoE) is a type of neural network architecture that employs sub-networks (experts) to process specific input parts.

This is the key idea behind Mixture of Expert LLMs.

The Switch-Language Transformer, Mixtral, GLaM, GShard, and DeepSeekMoE are Mixture of Experts LLMs (MoEs), which require only executing a portion of the model’s computational graph during inference.

Optimization strategies for MoE LLMs are discussed comprehensively in the papers introducing the Switch Transformer, GShard, and GLaM.

Mixture of Experts (MoE) is an approach to scaling LLMs to trillions of parameters with conditional computation while avoiding exploding computational costs.

Advanced hyperparameter optimization strategies, like population-based training, Bayesian optimization, and adaptive LoRA, promise to balance computational effort and outcome.

To avoid this, learning rate schedules for LLMs start with a small learning rate and slowly ramp it up to its maximum value.

Can we use traditional machine learning hyperparameter optimization methods for LLMs?

| Modified based on: sourceHands-on: LLM hyperparameter optimization with neptune.aiOptuna is a framework for optimizing hyperparameter search using Bayesian optimization.

See the docs or watch a short product demo (2 min)Play with a live Neptune Scale projectRequest your early accessWhat’s next in LLM hyperpar…

TL;DR Multimodal Large Language Models (MLLMs) process data from different modalities like text, audio, image, and video.

This article explores Multimodal Large Language Models, exploring their core functionalities, challenges, and potential for various machine-learning domains.

Let’s break down the concept of Multimodal Large Language Models (MLLMs) by first understanding the terms “modal” and “multimodal:”“Modal” refers to a particular way of communicating or perceiving information.

| SourceGoogle: PaLM-EGoogle developed an embodied language model, PaLM-E, to incorporate continuous sensor modalities into language models and establish the link between words and perceptions.

Improving how t…

In this guide, we’ll show you how to build a RAG system using the LangChain framework, evaluate its performance using Ragas, and track your experiments with neptune.ai.

Part 1: Building a baseline RAG system with LangChainIn the first part of this guide, we’ll use LangChain to build a RAG system for the blog posts in the LLMOps category on Neptune’s blog.

Ragas works smoothly with LangChain, making it a great choice for evaluating our RAG system.

Step 1: Generate a RAG evaluation datasetAn evaluation set for RAG tasks is similar to a question-answering task dataset.

Step 2: Choose RAG evaluation metricsAs mentioned earlier, Ragas offers both LLM-based and non-LLM-based metrics for RAG syste…

In our paper, Understanding LLMs Requires More Than Statistical Generalization, we argue that current machine learning theory cannot explain the interesting emergent properties of Large Language Models, such as reasoning or in-context learning.

Inductive biases affect which solution the neural network converges to, such as the model architecture or the optimization algorithm.

How do language complexity and model architecture affect generalization ability?

showed how different neural network architectures generalize better for different language types.

Presumably, we’ll need to find different complexity measures for different model architectures that consider their specific inductive biases.

TL;DR At a large-scale model training (in huge models), anomalies are not rare events but problematic patterns that drive failure.

The Neptune Scale experiment tracker supports fault tolerance and is designed to maintain progress despite hardware failures, making it adaptable for enterprise teams tackling LLM fine-tuning, compliance, and building domain-specific models.

Experiment tracking back then was straightforward—dealing mostly with single models or small-scale distributed systems.

One of the biggest lessons we’ve learned is that experiment tracking has evolved into experiment monitoring.

That’s why we’re focusing on building intelligent alerts and anomaly detection right into our exp…

Key-value (KV) caching is a clever trick to do that: At inference time, key and value matrices are calculated for each generated token.

Implementing K-V caching in large-scale production systems requires careful cache management, including choosing an appropriate strategy for cache invalidation and exploring opportunities for cache reuse.

Key-value (KV) caching is a clever trick to do just that – let’s see how it works and when to use it.

Transformer architecture overviewBefore we dive into KV caching, we will need to take a short detour to the attention mechanism used in transformers.

Understanding how it works is required to spot and appreciate how KV caching optimizes transformer inferen…

In our paper, Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving, we explored this problem experimentally.

Our goal was to assess the influence of trial-and-error information in the training data on the performance of LLMs in theorem proving.

However, at the time we published our paper, current approaches to training LLMs for ATPs only utilized data on correct proof attempts.

We hope our work can raise the community’s awareness of the importance of trial-and-error data for automated theorem proving.

We believe this advancement is largely due to the substantial trial-and-error data included in the model’s training process.

We will explore these challenges and provide an example of fine-tuning the Llama 3 8B Instruct model utilizing the neptune.ai experiment tracker.

The Llama 3 training data is seven times larger than what Meta used for training Llama 2.

For pre-training, Meta combined four types of parallelization, an approach they dubbed “4D parallelism”: data, model, pipeline, and context.

Hands-on guide: resource-efficient fine-tuning of Llama 3 on Google ColabFine-tuning Llama 3 8B is challenging, as it requires considerable computational resources.

We’ll use the Llama 3 8B model, which is sufficient for this task despite being the smallest Llama 3 model.

#deepseek #llm #reinforcementlearning GRPO is one of the core advancements used in Deepseek-R1, but was introduced already last year in this paper that uses a combination of new RL techniques and iterative data collection to achieve remarkable performance on mathematics benchmarks with just a 7B model. Paper: https://arxiv.org/abs/2402.03300 Abstract:

Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature. In this paper, we introduce DeepSeekMath 7B, which continues pre-training DeepSeek-Coder-Base-v1.5 7B with 120B math-related tokens sourced from Common Crawl, together with natural language and code data. DeepSeekMath 7B has achie…

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#tokenization #llm #meta This paper does away with tokenization and creates an LLM architecture that operates on dynamically sized "patches" instead of tokens. By controlling the patch size, they gain a level of control over the tradeoff between model size and FLOPs and use that to achieve more favorable scaling behavior than classically tokenized LLMs. Paper: https://ai.meta.com/research/publications/byte-latent-transformer-patches-scale-better-than-tokens/

Code: https://github.com/facebookresearch/blt Abstract:

We introduce the Byte Latent Transformer (BLT), a new byte-level LLM architecture that, for the first time, matches tokenization-based LLM performance at scale with significant imp…

This paper demonstrates in a series of experiments that current safety alignment techniques of LLMs, as well as corresponding jailbreaking attacks, are in large part focusing on modulating the distribution of the first few tokens of the LLM response. Paper: https://openreview.net/forum?id=6Mxhg9PtDE&s=09 Abstract:

The safety alignment of current Large Language Models (LLMs) is vulnerable. Simple attacks, or even benign fine-tuning, can jailbreak aligned models. We note that many of these vulnerabilities are related to a shared underlying issue: safety alignment can take shortcuts, wherein the alignment adapts a model's generative distribution primarily over only its very first few output to…

A deep dive into the TokenFormer and an opinion about its impact, novelty, and relation to prior work. Paper: https://arxiv.org/abs/2410.23168 Abstract:

Transformers have become the predominant architecture in foundation models due to their excellent performance across various domains. However, the substantial cost of scaling these models remains a significant concern. This problem arises primarily from their dependence on a fixed number of parameters within linear projections. When architectural modifications (e.g., channel dimensions) are introduced, the entire model typically requires retraining from scratch. As model sizes continue growing, this strategy results in increasingly high com…

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…

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…

Two colliding blocks compute pi, here we dig into the physics to explain why

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

An equally valuable form of support is to simply share the videos. The original paper by Gregory Galperin:

https://www.maths.tcd.ie/~lebed/Galperin.%20Playing%20pool%20with%20pi.pdf Adam Brown's paper on the analogy with Grover's Algorithm:

https://arxiv.org/pdf/1912.02207 Here's a lovely interactive built by GitHub user prajwalsouza after watching this video: https://prajwalsouza.github.io/Experiments/Colliding-Blocks.html Matt Parker's Pi Day video:

https://youtu.be/vlUTlbZT4ig NY Times blog post about this proble…

Full video: https://youtu.be/YdOXS_9_P4U

From this video on the cosmic distance ladder: https://youtu.be/YdOXS_9_P4U

Full video: https://youtu.be/YdOXS_9_P4U

Full video: https://youtu.be/YdOXS_9_P4U

How we know the distances to the planets, stars, and faraway galaxies.

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

FAQ with added details and corrections: https://terrytao.wordpress.com/2025/02/13/cosmic-distance-ladder-video-with-grant-sanderson-3blue1brown-commentary-and-corrections/ An equally valuable form of support is to simply share the videos. Terry and his collaborator Tanya have an Instagram about the cosmic distance ladder: https://www.instagram.com/cosmic_distance_ladder/ Artwork of Guillaume Le Gentil by Kurt Bruns

Artwork of Antonia Maury and Henrietta Leavitt by Talia Gershon: https://bit.ly/taliagershonart

Several of t…

From this video: https://youtu.be/YdOXS_9_P4U

The Cosmic Distance Ladder, how we learned distances in the heavens.

Email list: https://3b1b.co/mail

Patreon supporters see early views of new videos: https://www.patreon.com/3blue1brown Artwork by Kurt Bruns

Thanks to Paul Dancstep for several animations, such as the powers of 10 zoom out and the simulations of shadows on the moon. Thanks to Tanya Klowden for helpful conversations about the history of the distance ladder. Argument for why if every shadow of a convex shape is a circle, it must be a sphere: https://mathoverflow.net/questions/39127/is-the-sphere-the-only-surface-with-circular-projections-or-can-we-deduce-a-sp Timestamps: 0:00 - About Terence Tao and the Distance Ladder

2:02 …

From this video: https://youtu.be/YdOXS_9_P4U

Based on a construction in this video: https://youtu.be/IQqtsm-bBRU

Full video: https://youtu.be/IQqtsm-bBRU

Series exploring optics: https://www.youtube.com/watch?v=QCX62YJCmGk&list=PLZHQObOWTQDMKqfyUvG2kTlYt-QQ2x-ui

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

Extracted from this video about holograms: https://youtu.be/EmKQsSDlaa4

Full video: https://youtu.be/IQqtsm-bBRU

❤️ Try Macro for free and supercharge your learning: https://macro.com/papers 📝 The papers are available here:

https://physics-iq.github.io/

https://physbench.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:

Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Thomas Krcmar, T…

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

https://docs.lambdalabs.com/education/large-language-models/deepseek-r1-ollama/?utm_source=two-minute-papers&utm_campaign=relevant-videos&utm_medium=video 📝 The Gemma 3 paper and the rest are available here:

https://blog.google/technology/developers/gemma-3/

https://developers.googleblog.com/en/experiment-with-gemini-20-flash-native-image-generation/

https://deepmind.google/technologies/gemini-robotics/

https://aistudio.google.com/ Sources:

https://x.com/thepushkarp/status/1899874826669744425/photo/1

https://x.com/Angaisb_/status/1899852603107721388

https://x.com/alexa…

❤️ Try Macro for free and supercharge your learning: https://macro.com/papers 📝 The paper "Meshtron: High-Fidelity, Artist-Like 3D Mesh Generation at Scale" is available here:

https://research.nvidia.com/labs/dir/meshtron/ 📝 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:

Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard…

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

https://docs.lambdalabs.com/education/large-language-models/deepseek-r1-ollama/?utm_source=two-minute-papers&utm_campaign=relevant-videos&utm_medium=video 📝 The paper "World and Human Action Models towards gameplay ideation" is available here:

https://www.microsoft.com/en-us/research/blog/introducing-muse-our-first-generative-ai-model-designed-for-gameplay-ideation/

https://www.nature.com/articles/s41586-025-08600-3 Sources (snake game and more):

https://x.com/emollick/status/1894480971648377198

https://x.com/emollick/status/1894441728175677837

https://x.com/levelsio/s…

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

https://docs.lambdalabs.com/education/large-language-models/deepseek-r1-ollama/?utm_source=two-minute-papers&utm_campaign=relevant-videos&utm_medium=video 📝 The paper "Agent Laboratory: Using LLM Agents as Research Assistants" and "Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers" are available here:

https://agentlaboratory.github.io/

https://arxiv.org/abs/2409.04109 📝 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 …

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 Magic 1-For-1:

https://magic-141.github.io/Magic-141/

https://github.com/Open-Magic-Video/Magic-1-For-1

https://arxiv.org/abs/2502.07701v1 📝 Phantom: https://phantom-video.github.io/Phantom/ 📝 Relighting paper: https://bujiazi.github.io/light-a-video.github.io/ 📝 Stepfun:

https://github.com/stepfun-ai/Step-Video-T2V

https://yuewen.cn/videos

https://arxiv.org/abs/2502.10248

https://huggingface.co/stepfun-ai/stepvideo-t2v 📝 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.…

❤️ 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/prl/publication/park2023nearrealtime/

https://gao-jiawei.com/Research/CooHOI/ 📝 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:

Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Competitive Programming with Large Reasoning Models" is available here:

https://arxiv.org/abs/2502.06807 📝 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:

Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef,…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models" is available here:

https://hila-chefer.github.io/videojam-paper.github.io/ Vs Veo2: https://x.com/TomLikesRobots/status/1888279188336963725 📝 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:

Benji Rabhan, B Shang, Christian Ahlin, Go…

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

https://openai.com/index/introducing-deep-research/ Lambda DeepSeek instructions: https://docs.lambdalabs.com/education/large-language-models/deepseek-r1-ollama/ - Open Deep Research: https://opendeepresearch.vercel.app/

- Revisiting the McKinley Tariff of 1890 through the Lens of Modern Trade Theory

https://kevinbryanecon.com/o3McKinley.pdf

- Deep Research by Tyler Cowen February 4, 2025

https://marginalrevolution.com/marginalrevolution/2025/02/deep-research.html Complex tax situation: https://x.com/PatriceBTC/status/1886529037474127951

Daily briefing: https://x.com/mckaywrigley/status/…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papersllm o3 mini: https://openai.com/index/openai-o3-mini/

OpenAI Deep Research: https://openai.com/index/introducing-deep-research/ 🤝 Interested in sponsoring us? Click here: https://eu.jotform.com/241831324457354 Sources:

https://x.com/hxiao/status/1885522459329520089?s=46

https://x.com/techikansh/status/1885429093862187008?s=46

https://x.com/rlancemartin/status/1885748894220554445?s=46

https://x.com/buccocapital/status/1885792154129219959?s=46

https://x.com/_akhaliq/status/1885733581651050586?s=46

https://x.com/_akhaliq/status/1885833163764646267?s=46

https://x.com/aidan_mclau/status/1886078444855034055?s=4…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "InstantSplat: Sparse-view SfM-free Gaussian Splatting in Seconds" is available here:

https://instantsplat.github.io/

Try it out (hopefully works): https://huggingface.co/spaces/kairunwen/InstantSplat Clouds paper: https://arcanous98.github.io/projectPages/gaussianVolumes.html 📝 My paper on simulations that look almost like reality is available for free here:

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

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

Ben…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers Try it out (choose DeepSeek as your model): https://huggingface.co/chat/

Official (read the privacy policy below before you use this one): https://www.deepseek.com/ Run it at home:

https://www.reddit.com/r/selfhosted/comments/1i6ggyh/got_deepseek_r1_running_locally_full_setup_guide/

https://lmstudio.ai/ Links:

https://x.com/ivanfioravanti/status/1881565759123702140

https://eu.jotform.com/tables/241831324457354

https://x.com/deepseek_ai/status/1881318130334814301

https://x.com/nobody_qwert/status/1881620406710452535

https://github.com/bytedance/UI-TARS

https://github.com/bytedance/UI-TARS-desktop

https://…

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

https://openai.com/index/openai-o1-system-card/ 📝 My paper on simulations that look almost like reality is available for free here:

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

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

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Martin, Michael Albrecht, Michael Tedder, Owen Skarpness, Richard Sund…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "CLoSD - Closing the Loop between Simulation and

Diffusion for multi-task character control" is available here:

https://guytevet.github.io/CLoSD-page/ 📝 My paper on simulations that look almost like reality is available for free here:

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

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

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, L…

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…

DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

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

Это фрагмент из доклада Екатерины Глазковой, тимлида команды элайнмента VLM службы компьютерного зрения. На Practical ML Conf 2024 Екатерина рассказала о продуктовых сценариях использования VLM — нейросетей, которые работают одновременно с изображением и текстом. В докладе показаны методы элайнмента под продуктовые требования на примере трёх реальных задач: мультимодального поиска, описания изображений и фантазийно-генеративных сценариев. Смотрите выступление целиком на нашем канале.

Это Антон Клочков, руководитель подгруппы распознавания текста в VLM в Поиске и рекламных технологиях. Антон продолжает серию рассказов про развитие картиночной мультимодальности в Яндексе. В докладе он показал, какие эксперименты над VLM ребята успели поставить, какие уроки из них извлекли и какое новогоднее чудо с ними приключилось! Узнать больше о мероприятиях для разработчиков можно тут: https://events.yandex.ru Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml

Это мастер-класс на Practical ML Conf. Его провели ребята из Yandex Cloud: Дмитрий Рыбалко, продуктовый архитектор ML-сервисов, и Дмитрий Сошников, консультант, доцент МАИ, НИУ ВШЭ и технический руководитель AI Lab Школы дизайна в ВШЭ. В сфере LLM набирают популярность агентские подходы, когда несколько моделей взаимодействуют друг с другом, чтобы достичь одной цели. В рамках мастер-класса мы показали, как применить этот подход для построения вопросо-ответной системы в транспортной компании. При этом сами методы могут пригодиться в любой индустрии. Для решения задачи поэкспериментировали с различными структурами данных для RAG, в том числе с текстовыми и графовыми. За основу взяли языковую …

Это мастер-класс на Practical ML Conf. Его провёл Михаил Каменщиков, руководитель юнита «Рекомендации» в Авито. Михаил объяснил, зачем нужны алгоритмы приближённого поиска соседей. А ещё рассмотрел реализацию популярных алгоритмов IVF, HNSW и показал, как на своих данных делать бенчмарк разных подходов с использованием библиотеки ann-benchmarks. Подписывайтесь на телеграм-канал Яндекса для ML-специалистов: https://t.me/yandexforml

Это мастер-класс на Practical ML Conf. Его провели: Лев Новицкий, ведущий специалист по исследованию данных Kandinsky Research в Sber AI, и Вера Соболева, младший научный сотрудник Института искусственного интеллекта AIRI. Современные диффузионные модели позволяют генерировать качественные и разнообразные изображения по текстовому описанию. Но что делать, если мы хотим создать не случайную картинку, а конкретный объект, например вазу, которую после продадим на маркетплейсе? Это задача персонализированной генерации: нужно в предобученную модель внедрить знания о конкретном объекте по нескольким фотографиям, чтобы показывать его в новых сценах. На мастер-классе разобрали основы диффузии и баз…

Это фрагмент из доклада Екатерины Глазковой, тимлида команды элайнмента VLM службы компьютерного зрения. На Practical ML Conf 2024 Екатерина рассказала о продуктовых сценариях использования VLM — нейросетей, которые работают одновременно с изображением и текстом. В докладе показаны методы элайнмента под продуктовые требования на примере трёх реальных задач: мультимодального поиска, описания изображений и фантазийно-генеративных сценариев. Смотрите выступление целиком на нашем канале.

Это фрагмент из доклада Екатерины Глазковой из Яндекс Поиска, с выступлении на Practical ML Conf 2024.

Добро пожаловать на вечерний митап для ML-инженеров от Яндекса. В этот раз поговорим про про развитие картиночной мультимодальности, книжном синтезе и не только. Вступайте в канал Yandex for ML, чтобы быть в курсе всех событий и мероприятий Яндекса https://t.me/+UohL-bYM25YyZDNi

Оставляйте свои вопросы спикерам в чате с тегом #вопрос: https://t.me/+OsKnLNG-7DE1ZTFi

Это фрагмент из доклада Екатерины Глазковой из Яндекс Поиска, с выступлении на Practical ML Conf 2024.

Это Эдуард Мартынов, выпускник факультета вычислительной математики и кибернетики МГУ. В докладе на Data Dojo в Санкт-Петербурге Никита рассказал, как применять трансформеры в соревнованиях на платформе Kaggle: обсудил технические трудности, кастомные архитектуры и показал, что нужно для победы в соревнованиях. Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml

Это Александр Перевалов из академии Лейпцигского университета прикладных наук (HTWK Leipzig). В докладе на Data Dojo в Санкт-Петербурге Александр рассказал, как решал задачу извлечения информации из финансовой отчётности. Авторский подход предполагает LLM-агент, использующий RAG, мультимодальность (vision) и графы знаний для хранения метаданных. Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml

Это Никита Синчинов, ML-разработчик в рекламной платформе Яндекс Маркета. В докладе на Data Dojo в Санкт-Петербурге Никита рассказал о соревновании по машинному обучению, в котором нужно было классифицировать ущерб фермерского поля по его изображению. Внутри: несколько подходов к решению задачи и история о том, как с помощью лика в данных удалось взять серебро. Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml

Это Андрей Мищенко, руководитель команды качества в Яндекс Рекламе. Андрей рассказал, как прошла ICML в этом году, как на инженерную конференцию попали работы об этике, и подробно остановился на самых важных статьях про рекомендательные системы. Например, на работе On Multi-Armed Bandit with Impatient Arms, которая даёт повод поразмышлять о рекламных инструментах с помощью метафоры однорукого бандита. Подписывайтесь на телеграм-канал «Яндекс для ML-инженеров»: https://t.me/yandexforml

Это ребята из Яндекс Поиска: руководитель команды нейросетевых технологий Сергей Овчаренко и ведущий разработчик генеративных моделей Александр Шишеня. Ребята рассказали, как правильно ездить на конференции и что нужно из них выносить. А ещё поделились полезными статьями на тему компьютерного зрения, которые нашли на ICML этого года. Например, Александр выделил работу Inferring 3D Structures with 2D Priors от Google DeepMind, которая посвящена восстановлению 3D-образа из малого количества плоских изображений. В пределе — всего из одной картинки. А Сергей рассказал о статье Genie: Generative Interactive Environments от DeepMind. Модель, о которой идёт речь в этой статье, обучена на записях г…

Ежегодная серия соревнований по машинному обучению Data Fusion Contest стартовала. Страница с задачами: https://ods.ai/tracks/data-fusion-2025-competitions Во вторник 25 февраля (19:00 - 20:00 по мск) мы провели первый митап по Data Fusion Contest 2025 в ODS спейсе Spatial.Chat. Поговорили о задачах, ответим на ваши вопросы участников. В программе: Анатолий Глушенко Разбор задачи 1 “LabelCraft”

Алексей Натёкин Обзор задачи 2 “4Cast” и задачи 3 “Distribution” Команда Data Fusion Contest 2025 Q&A и обсуждение вопросов с участниками

Спикер: Анастасия Вепрева - разработчик моделей в области прогнозирования физико-химических свойств и биологических активностей малых молекул, сотрудница Центра ИИ в химии ИТМО

Мероприятие 21.02.2025: https://ods.ai/events/ai_chemistrymk1 Наши соц.сети:

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Спикер: Антон Воронов, Газпром ИД, Заместитель директора департамента, Руководитель платформы Поиска Data Ёлка 2024 в гостях у VK: https://ods.ai/events/data-elka-24-vk-offline

Data Ёлка 2024: https://ods.ai/events/data-elka-2024

_____

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Спикер: Пётр Ермаков, ML Brand Director, Яндекс Data Ёлка 2024 в гостях у VK: https://ods.ai/events/data-elka-24-vk-offline

Data Ёлка 2024: https://ods.ai/events/data-elka-2024

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Спикер: Валентин Малых, руководитель группы, MTS AI Data Ёлка 2024 в гостях у VK: https://ods.ai/events/data-elka-24-vk-offline

Data Ёлка 2024: https://ods.ai/events/data-elka-2024

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Спикер: Николай Анохин, ведущий специалист по ML, AI VK Data Ёлка 2024 в гостях у VK: https://ods.ai/events/data-elka-24-vk-offline

Data Ёлка 2024: https://ods.ai/events/data-elka-2024

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Спикер: Ирина Голощапова, CDO Raiffeisenbank Operations Data Ёлка 2024 в гостях у VK: https://ods.ai/events/data-elka-24-vk-offline

Data Ёлка 2024: https://ods.ai/events/data-elka-2024

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Спикер: Дмитрий Колодезев, директор, Промсофт Data Ёлка 2024 в гостях у VK: https://ods.ai/events/data-elka-24-vk-offline

Data Ёлка 2024: https://ods.ai/events/data-elka-2024

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Спикер: Андрей Шадриков Data Ёлка 2024: https://ods.ai/events/data-elka-2024

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Спикер: Алексей Смирнов Data Ёлка 2024 в гостях у Ecom.tech: https://ods.ai/events/data-elka-24-ecomtech-offline

Data Ёлка 2024: https://ods.ai/events/data-elka-2024

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Спикер: Евгений Никитин Data Ёлка 2024 в гостях у Ecom.tech: https://ods.ai/events/data-elka-24-ecomtech-offline

Data Ёлка 2024: https://ods.ai/events/data-elka-2024 _____

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Patreon: https://www.patreon.com/primerlearning Ageless book: https://www.amazon.com/Ageless-Science-Getting-Older-Without/dp/0525566317/ Papers and other further reading:

Diversity of aging across the tree of life: https://pmc.ncbi.nlm.nih.gov/articles/PMC4157354/

Antagonistic pleiotropy and p53: https://pmc.ncbi.nlm.nih.gov/articles/PMC2771578/

An unsolved problem of biology (Medawar): https://ia903408.us.archive.org/31/items/medawar-1952-unsolved-problem/Medawar1952-Unsolved-Problem.pdf

Evolution of the mutation rate: https://pmc.ncbi.nlm.nih.gov/articles/PMC2910838/

Our World in Data Life Expectancy explainer: https://ourworldindata.org/life-expectancy-how-is-it-calculated-and-how-shoul…

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

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Narendra Modi is the Prime Minister of India.

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Dylan Patel is the founder of SemiAnalysis, a research & analysis company specializing in semiconductors, GPUs, CPUs, and AI hardware.

Nathan Lambert is a research scientist at the Allen Institute for AI (Ai2) and the author of a blog on AI called Interconnects.

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Marc Andreessen is an entrepreneur, investor, co-creator of Mosaic, co-founder of Netscape, and co-founder of the venture capital firm Andreessen Horowitz.

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Jennifer Burns is a historian of ideas, focusing on the evolution of economic, political, and social ideas in the United States in the 20th century.

She wrote two biographies, one on Milton Friedman, and the other on Ayn Rand.

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Volodymyr Zelenskyy is the President of Ukraine.

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Adam Frank is an astrophysicist studying star systems and the search for extraterrestrial life and alien civilizations.

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Saagar Enjeti is a political journalist & commentator, co-host of Breaking Points with Krystal and Saagar and The Realignment Podcast.

He is exceptionally well-read, and the books he recommends are always fascinating and eye-opening.

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Javier Milei is the President of Argentina.

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Dario Amodei is the CEO of Anthropic, the company that created Claude.

Amanda Askell is an AI researcher working on Claude’s character and personality.

Chris Olah is an AI researcher working on mechanistic interpretability.

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(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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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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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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About two years ago, with Carey Goldberg and Zak Kohane, we wrote a book, The AI Revolution in Medicine.

If you’re a patient, in what ways could AI change your experience as you try to navigate a complex healthcare system?

A strange and bizarre thought, I admit, but a natural one, I think, for any human being that’s encountering this amazing AI technology for the first time.

And since then, of course, I’ve come to learn that many people have had similar experiences in their first encounters with AI.

And in fact, I’ve come to think of this as, somewhat tongue in cheek, the nine stages of AI grief.

CHETAN NAYAK: People sometimes say, well, quantum computers are just going to be like classical computers but faster.

This idea of quantum, because you’ve mentioned Albert Einstein, there’s quantum physics, quantum mechanics, now quantum computing.

Well, let me …NAYAK: And that’s quantum mechanics!

HUIZINGA: OK.NAYAK: You’re probably going to say, well, how does quantum computing fit into this, you know?

[LAUGHS]NAYAK: And, you know, there are people out there who said, you know, quantum computers are decades away; don’t worry about it.

His work lies at the intersection of systems, AI, and machine learning with a focus on designing, deploying, and scaling AI systems to solve compelling real-world problems.

CHRIS STETKIEWICZ: You’re listening to Ideas, a Microsoft Research Podcast that dives deep into the world of technology research and the profound questions behind the code.

NAMBI: That’s right.

This represents a major step towards building AI systems that’s much more holistic personal tutors, which help student understanding and create more engaging, effective learning experience.

Are there some things that could go wrong, even if we get the technology right?

His work lies at the intersection of systems, AI, and machine learning with a focus on designing, deploying, and scaling AI systems to solve compelling real-world problems.

CHRIS STETKIEWICZ: You’re listening to Ideas, a Microsoft Research Podcast that dives deep into the world of technology research and the profound questions behind the code.

NAMBI: That’s right.

This represents a major step towards building AI systems that’s much more holistic personal tutors, which help student understanding and create more engaging, effective learning experience.

Are there some things that could go wrong, even if we get the technology right?

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

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

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

Really, really a lot.

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

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

BADANES: Right, right.

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

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

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

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

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

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

STRICKLAND: Yeah, yeah.

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

And then the second paradigm really emerged in the 17th century.

And so the third paradigm really began, I guess, sort of, in the ’50s and ’60s, the development of digital computers.

And when I think about AI for Science actually, the space of opportunity is colossal because science is, science is really just understanding more about the world around us.

And now the SMILES autoregressive model can now generate a molecule that’s an improvement on the starting molecule and knows about the protein binding.

But also if you think about [it], science is really about learning more about the world.

Today I’m talking to Jindong Wang, a senior researcher at Microsoft Research, and Steven Whang, a tenured associate professor at the Korea Advanced Institute of Science and Technology.

JINDONG WANG: OK, everybody knows that with the widespread usage of large language models, hallucination has become a crucial factor of concern.

So foreign key constraint basically requires that if there is some director mentioned in the movie table, it has to be one of the directors in the director table.

So now we can join the movie and director table and generate a bigger table.

HUIZINGA: Well, Jindong Wang and Steven Whang, thanks for joining us today, and to our listeners, thanks for tuning in.

The other one actually is some token actually is very, very hard to be predicted during the pretraining.

And the important thing for the data is about data filtering.

If we’re able to build a better model actually is able to benefit so many different kinds of application.

And definitely there’s a lot of things about how to build a better data [that] is unsolved yet in the literature.

And the other thing actually, we are working on something that’s very exciting.

The drawback of this approach is that it often misses the cultural nuances of local languages.

CHITALE: Now that we have created a benchmark, the next step is to evaluate how these multimodal models are performing on this benchmark.

So what we observed is there is a huge gap when it comes … in performance when we compare these proprietary offerings versus the open-source models.

These open-source models significantly lag behind the proprietary models.

CHITALE: CVQA is significant because it addresses a fundamental gap in how we evaluate vision-language and multimodal models today.

FOSTER: So this is a, kind of, a theoretical work on reinforcement learning, or RL.

FOSTER: Yeah, so if you look at these sort of RL problems with latent dynamics, this is something that’s actually received a lot of investigation in theory.

Like, can we take existing algorithms and use them to solve rich-observation RL problems in a modular fashion?

TINGLE: Dylan, I’d like to know—and I’m sure our audience would, too—what this work means when it comes to real-world application.

TINGLE: Well, Dylan Foster, thank you for joining us today to discuss your paper on reinforcement learning under latent dynamics.

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In this episode we talk with Justin Wang Ngai Yeung, a PhD candidate at the Network Science Institute at Northeastern University in London, who explores how network science helps uncover criminal networks. Justin is also a member of the organizing committee of the satellite conference dealing with criminal networks at the network science conference in The Netherlands in June 2025. Listeners will learn how graph-based models assist law enforcement in analyzing missing data, identifying key figures in criminal organizations, and improving intervention strategies. Key insights include the challenges of incomplete and inaccurate data in criminal network analysis, how law enforcement agencies us…

In this episode today’s guest is Celine Wüst, a master’s student at ETH Zurich specializing in secure and reliable systems, shares her work on automated software testing for graph databases. Celine shows how fuzzing—the process of automatically generating complex queries—helps uncover hidden bugs in graph database management systems like Neo4j, FalconDB, and Apache AGE. Key insights include how state-aware query generation can detect critical issues like buffer overflows and crashes, the challenges of debugging complex database behaviors, and the importance of security-focused software testing. We'll also find out which Graph DB company offers swag for finding bugs in its software and get C…

In this episode, Gabriel Petrescu, an organizational network analyst, discusses how network science can provide deep insights into organizational structures using OrgXO, a tool that maps companies as networks rather than rigid hierarchies. Listeners will learn how analyzing workplace collaboration networks can reveal hidden influencers, organizational bottlenecks, and engagement levels, offering a data-driven approach to improving effectiveness and resilience. Key insights include how companies can identify overburdened employees, address silos between departments, and detect vulnerabilities where too few individuals hold critical knowledge. Real-life applications range from mergers and acq…

Is it better to have your work team fully connected or sparsely connected? In this episode we'll try to answer this question and more with our guest Hiroki Sayama, a SUNY Distinguished Professor and director of the Center for Complex Systems at Binghamton University. Hiroki delves into the applications of network science in organizational structures and innovation dynamics by showing his recent work of extracting network structures from organizational charts to enable insights into decision-making and performance, He'll also cover how network connectivity impacts team creativity and innovation. Key insights include how the structure of organizational networks—such as the depth of hierarchy …

A man goes into a bar… This is the beginning of a riddle that our guest, Yoed Kennet, an assistant professor at the Technion's Faculty of Data and Decision Sciences, uses to measure creativity in subjects. In our talk, Yoed speaks about how to combine cognitive science and network science to explore the complexities and decode the mysteries of the human mind. The listeners will learn how network science provides tools to map and analyze human memory, revealing how problem-solving and creativity emerge from changes in semantic memory structures. Key insights include the role of memory restructuring during moments of insight, the connection between semantic networks and creative thinking, and…

In this episode, Garima Agrawal, a senior researcher and AI consultant, brings her years of experience in data science and artificial intelligence. Listeners will learn about the evolving role of knowledge graphs in augmenting large language models (LLMs) for domain-specific tasks and how these tools can mitigate issues like hallucination in AI systems. Key insights include how LLMs can leverage knowledge graphs to improve accuracy by integrating domain expertise, reducing hallucinations, and enabling better reasoning. Real-life applications discussed range from enhancing customer support systems with efficient FAQ retrieval to creating smarter AI-driven decision-making pipelines. Garima’s …

In this episode, Bnaya Gross, a Fulbright postdoctoral fellow at the Center for Complex Network Research at Northwestern University, explores the transformative applications of network science in fields ranging from infrastructure to medicine, by studying the interactions between networks ("a network of networks"). Listeners will learn how interdependent networks provide a framework for understanding cascading failures, such as power outages, and how these insights transfer to physical systems like superconducting materials and biological networks. Key takeaways include understanding how dependencies between networks can amplify vulnerabilities, applying these principles to create resilient…

Our guests, Erwan Le Merrer and Gilles Tredan, are long-time collaborators in graph theory and distributed systems. They share their expertise on applying graph-based approaches to understanding both large language model (LLM) hallucinations and shadow banning on social media platforms. In this episode, listeners will learn how graph structures and metrics can reveal patterns in algorithmic behavior and platform moderation practices. Key insights include the use of graph theory to evaluate LLM outputs, uncovering patterns in hallucinated graphs that might hint at the underlying structure and training data of the models, and applying epidemic models to analyze the uneven spread of shadow ban…

In this episode, Šimon Mandlík, a PhD candidate at the Czech Technical University will talk with us about leveraging machine learning and graph-based techniques for cybersecurity applications. We'll learn how graphs are used to detect malicious activity in networks, such as identifying harmful domains and executable files by analyzing their relationships within vast datasets. This will include the use of hierarchical multi-instance learning (HML) to represent JSON-based network activity as graphs and the advantages of analyzing connections between entities (like clients, domains etc.). Our guest shows that while other graph methods (such as GNN or Label Propagation) lack in scalability or h…

Thibaut Vidal, a professor at Polytechnique Montreal, specializes in leveraging advanced algorithms and machine learning to optimize supply chain operations. In this episode, listeners will learn how graph-based approaches can transform supply chains by enabling more efficient routing, districting, and decision-making in complex logistical networks. Key insights include the application of Graph Neural Networks to predict delivery costs, with potential to improve districting strategies for companies like UPS or Amazon and overcoming limitations of traditional heuristic methods. Thibaut’s work underscores the potential for GNN to reduce costs, enhance operational efficiency, and provide bette…

Our guest in this episode is David Tench, a Grace Hopper postdoctoral fellow at Lawrence Berkeley National Labs, who specializes in scalable graph algorithms and compression techniques to tackle massive datasets. In this episode, we will learn how his techniques enable real-time analysis of large datasets, such as particle tracking in physics experiments or social network analysis, by reducing storage requirements while preserving critical structural properties. David also challenges the common belief that giant graphs are sparse by pointing to a potential bias: Maybe because of the challenges that exist in analyzing large dense graphs, we only see datasets of sparse graphs? The truth is ou…

In this episode, Dave Bechberger, principal Graph Architect at AWS and author of "Graph Databases in Action", brings deep insights into the field of graph databases and their applications. Together we delve into specific scenarios in which Graph Databases provide unique solutions, such as in the fraud industry, and learn how to optimize our DB for questions around connections, such as "How are these entities related?" or "What patterns of interaction indicate anomalies?" This discussion sheds light on when organizations should consider adopting graph databases, particularly for cases that require scalable analysis of highly interconnected data and provides practical insights into leveraging…

In this episode, Adam Machowczyk, a PhD student at the University of Leicester, specializes in graph rewriting and its intersection with machine learning, particularly Graph Neural Networks. Adam explains how graph rewriting provides a formalized method to modify graphs using rule-based transformations, allowing for tasks like graph completion, attribute prediction, and structural evolution. Bridging the worlds of graph rewriting and machine learning, Adam's work aspire to open new possibilities for creating adaptive, scalable models capable of solving challenges that traditional methods struggle with, such as handling heterogeneous graphs or incorporating incremental updates efficiently. R…

In this episode, the data scientist Wentao Su shares his experience in AB testing on social media platforms like LinkedIn and TikTok. We talk about how network science can enhance AB testing by accounting for complex social interactions, especially in environments where users are both viewers and content creators. These interactions might cause a "spillover effect" meaning a possible influence across experimental groups, which can distort results. To mitigate this effect, our guest presents heuristics and algorithms they developed ("one-degree label propagation”) to allow for good results on big data with minimal running time and so optimize user experience and advertiser performance in soc…

Alex Bisberg, a PhD candidate at the University of Southern California, specializes in network science and game analytics, with a focus on understanding social and competitive success in multiplayer online games. In this episode, listeners can expect to learn from a network perspective about players interactions and patterns of behavior. Through his research on games, Alex sheds light on how network analysis and statistical tests might explain positive contagious behaviors, such as generosity, and explore the dynamics of collaboration and competition in gaming environments. These insights offer valuable lessons not only for game developers in enhancing player experience, engagement and rete…

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So I've been using deep research near daily as a part of that Pro subscription and have been continuously impressed.

And from there, deep research, spent three minutes and looked across eight different sources to come up with my results.

I hope that gives you a sense and kind of a deep dive into deep research with a specific example.

Like any LLM-based tool deep research could hallucinate or make incorrect references, although I haven't caught any of these myself yet, and OpenAI's internal evaluations apparently show markedly lower hallucination rates with deep research than any of their previous tools.

16:11In summary, OpenAI's deep research is transforming the research process by automati…

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Podcast TranscriptJon Krohn: 00:06This is episode number 868 our "In Case You Missed It" February episode.

And that was a really, really big thing that I love.

Jon Krohn: 05:10But yeah, but in DBT automatically creating a documentation for all the fields that you have in a data file.

So Tableau, well, I guess you can run it a few ways, but essentially with Sigma, it sits right on top of Snowflake and works really, really well with that.

LLMs, I think, are one start, but I think all models in general are going to be used long-term.

Jon Krohn: 00:00:00This is episode number 867 with Dr. Andriy Burkov, machine learning lead at TalentNeuron.

Andriy wrote the indispensable Hundred-page Machine Learning Book that seems to be on every data scientist and ML engineer's bookshelf.

They put some installations like sculptures from ice illuminated with different colors, so it's really, really like a postcard.

You have over 15 years of hands-on experience in automated data analysis, machine learning, natural language processing, you're currently the machine learning lead at a company called TalentNeuron.

I don't know, do you want to dig into this a bit more?Andriy Burkov: 00:30:39Well, I have a couple of comments.

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Well, look no further, Super Data Science community is the perfect place to connect, interact, and exchange ideas with over 600 professionals in data science, machine learning, and AI.

Today’s episode is brought to you by ODSC, the Open Data Science Conference.00:16Welcome to the SuperDataScience Podcast, the most listened to podcast in the data science industry.

Cal is head of AI and data science at Further, a data and AI company based in Atlanta that has hundreds of employees.

But I found early on with Pandata that when I was selling data science, most people didn't know what data science was.

This was really, really, really bad results.

OpenAI's o3-mini is a reasoning model like DeepSeek's-R-1 model, which I detailed two weeks ago in episode number 860.

There are two reasons why this new o3-mini reasoning model is such an important release.

02:13To be more explicit, this means that o3-mini high outperforms not only o1-mini, but also DeepSeek-R1 and even OpenAI's much more expensive to run full size o1 model.

So how can you access this powerful new o3-mini model?

It gets an ELO rating of over 2700 while the next closest models are o3-mini high with about 2100 and DeepSeek-R1 at about 2000.

Tabular data, I'm sure you're familiar with them once I describe them, are data stored in a table format, tabular.

Frank Hutter: 00:06:22So, what is tabular data, and why is it so different than, yeah, vision data or speech data or text data and so on?

That's really, really cool.

We can't publish in Nature, but let's go for a next version, and let's make this really, really strong," because the first version, all that that did is ...

00:55:57In the university, I will keep an academic co-affiliation, and in the university, I will focus very much on tabular data as well then and research about the tabular data, things like interpretability.

If it's growing linearly, I think you're making incredibly extreme assumptions based on what evidence has shown us.

13:26Earlier I talked about how large language models are a subset of all the foundation models out there.

Even two years ago there was no such thing as only just starting foundation models and LLMs and so on.

So it's one of the foundation models by Amazon, which by the way just released their brand new foundation models called Nova.

Jon Krohn: 31:27All right, that's it for today's In Case You Missed It episode.

00:09:34I actually got to coach some of the girls that were my teammates in college, which was really, really cool.

01:08:17So we did work with big businesses too, but we were really, really passionate about helping local small businesses continue to grow, which was really, really fun.

And that was a really, really big thing that I love.

But there's so many moments in between where there's opportunities to make it really, really fun.

And just stuff that honestly, I was getting really, really passionate about doing and I was seeing really good results without being in the gym for hours on end.

This is episode number 860 on the DeepSeek R1.

At the time of writing, DeepSeek's R1 reasoning model is statistically, so within a 95 % confidence interval tied for first place on the overall LM Arena leaderboard with the top models.

But speaking in rough approximations, training a single DeepSeek V3 or DeepSeek R1 model appears to cost on the order of millions of dollars.

I'm not going to go further into the technical details of the DeepSeek models in this episode.

I've got a link in the show notes to the R1 model from DeepSeek provided by Olama, so you can do just that.

In this week’s guest interview, Vaibhav Gupta talks to Jon Krohn about creating a programming language, BAML, that helps companies save up to 30% on their AI costs. He explains how he started tailoring BAML to facilitate natural language generation interactions with AI models, how BAML helps companies optimize their outputs, and he also lets listeners into Boundary’s hiring process. This episode is brought to you by ODSC, the Open Data Science Conference. Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (04:53) What BAML stands for (14:33) Making a prompt engineering a serious practic…

In this episode, we dive into the transformative world of AI, data analytics, and cloud infrastructure with Josh Miramant, CEO of Blue Orange Digital.

As a seasoned entrepreneur with over $25 million raised across ventures and two successful exits, Josh shares invaluable insights on scaling data-driven businesses, integrating machine learning frameworks, and navigating the rapidly evolving landscape of cloud data architecture.

From generative AI to large language models, Josh explores cutting-edge trends shaping financial services, real estate, and consumer goods.

Tune in for a masterclass in leveraging data for impact and innovation!

Linkshttps://blueorange.digital/https://blueorange.digit…

Here’s the updated text with links to the websites included:AI is revolutionizing the military with autonomous drones, surveillance tech, and decision-making systems.

In this episode of Data Science at Home, we expose the cutting-edge tech reshaping defense—and the chilling ethical questions that follow.

🐦 Twitter: @DataScienceAtHome📘 LinkedIn: Francesco Gad📷 Instagram: https://www.instagram.com/datascienceathome/📘 Facebook: https://www.facebook.com/datascienceAH💼 LinkedIn: https://www.linkedin.com/company/data-science-at-home-podcast💬 Discord Channel: https://discord.gg/4UNKGf3NEW TO DATA SCIENCE AT HOME?

Data Science at Home explores the latest in AI, data science, and machine learning.

S…

In this episode of Data Science at Home, we’re diving deep into the powerful strategies that top AI companies, like OpenAI, use to scale their systems to handle millions of requests every minute!

From stateless services and caching to the secrets of async processing, discover 8 essential strategies to make your AI and machine learning systems unstoppable.

Instagram: https://www.instagram.com/datascienceathome/Twitter: @datascienceathomeFacebook: https://www.facebook.com/datascienceAHLinkedIn: https://www.linkedin.com/company/data-science-at-home-podcastDiscord Channel: https://discord.gg/4UNKGf3NEW TO DATA SCIENCE AT HOME?

Data Science at Home explores the latest in AI, data science, and ma…

Together, they explore the growing importance of distinguishing human-written from AI-generated text, discussing real-world examples from social media to news.

How reliable are current detection tools like DetectGPT?

What are the ethical and technical challenges ahead as AI continues to advance?

And is the balance between innovation and regulation tipping in the right direction?

Tune in for insights on the future of AI text detection and the broader implications for media, academia, and policy.

Welcome to Data Science at Home, where we don’t just drink the AI Kool-Aid.

Today, we’re dissecting Sam Altman’s “AI manifesto”—a magical journey where, apparently, AI will fix everything from climate change to your grandma’s back pain.

In this episode, I’ll break down the bold (and often bizarre) claims in Altman’s grand speech for the Intelligence Age.

I’ll give you the real scoop on what’s realistic, what’s nonsense, and why some tech billionaires just can’t resist overselling.

Chapters00:00 – Intro00:18 – CEO of Baidu Statement on AI Bubble03:47 – News On Sam Altman Open AI06:43 – Online Manifesto “The Intelleigent Age”13:14 – Deep Learning16:26 – AI gets Better With Scale17:45 – Conclu…

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?