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The idea was simple: analyze my daily habits — sleep, study hours, screen time, exercise, and mood — and see how they affect my productivity and general well-being.

# Make sure values are reasonable (no negative sleep) data[:, 1] < 0Output:array([False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False])This means no negatives.

# TODO: Display weekly results clearly print(f”Week 1 — Average sleep: {week1_avg_sleep:.2f} hrs, Study: {week1_avg_study:.2f} hrs, “ f”Screen time: {week1_avg_screen:.2f} hrs, Mood score: {week1_avg_mood:.2f}”) print…

This is Reinforcement Learning (RL), and it’s fundamentally different from other machine learning approaches.

Reinforcement learning: OverviewA lot of the idea can be related to Pavlov’s dog and Skinner’s rat experiments.

For this post, I have written a bespoke video game that anyone can access and use to train their own machine learning agent to play the game.

“Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning.” Machine Learning.

“Deep Reinforcement Learning: Pong from Pixels.” Blog post: http://karpathy.github.io/2016/05/31/rl/Influential educational resource Spinning Up in Deep RL by OpenAI Educational resource: https://spinningup.openai.com/Excell…

Individual skills are organized folders containing instructions, scripts, and resources that Claude can load dynamically to improve its performance on specialized tasks.

Levels of Claude Skills and how they fit into agentic ecosystem.

After a few iterations, I developed the following Claude Skill.

For this example, the skill focuses on generating modern Neo4j Cypher queries.

I’m planning to write a blog post soon on how to add a Neo4j skill for Python code execution.

That’s why it’s essential to understand what current AI models actually are and what they can (and can’t) do.

Nonetheless, this is not (yet) the case, and today’s AI models, as impressive and advanced as they are, remain limited by the principles they operate on.

On the flip side, all existing English text in books and the internet are a few hundreds billions of words words (around 10^12).

In particular, such reasoning models can break down the user’s query into a sequence of step-by-step, smaller queries, resulting in better answers.

Neural networks and AI models inherently operate on inductive-style reasoning, even if they many times sound like performing deduction.

Please go to the Microsoft documentation to learn more about creating a new UDF in Power BI Desktop.

Now, I create a measure to use this Function:Get Inflation rate = ReturnRate([Inflation rate Value])The measure [Inflation rate Value] was created when I created the parameter to select the Inflation rate.

UDF can be directly called like any other DAX function.

ReferencesHere, the Microsoft documentation for user-defined functions: Using DAX user-defined functions (preview) – Power BI | Microsoft Learn.

A collection of free to use model-independent UDF: Extend Power BI with DAX Lib.

I’ll discuss why we need AI audio models, and different application areas such as speech-to-text, text-to-speech, and speech-to-speech.

Why we need audio modelsWe already have extremely powerful LLMs that can deal with a lot of human interactions, so it’s important to highlight why there’s a need for audio models.

Modality in this case refers to a type of data, such asTextVisionAudioMy second point also highlights an important need for audio models.

Audio model typesIn this section, I’ll go through the main audio model types that you’ll encounter when working with audio models.

Audio models are important because audio is an important modality to understanding the world, just like text and v…

Two to three years ago, I started a daily habit of writing down lessons that I learned from my ML work.

In looking back through some of the lessons from this month, I found three practical lessons that stand out:Using README documents for yourself Requesting MIG slices instead of full GPUs Sprinkling movements throughout the dayKeep a README — for your future selfMost READMEs are written with other people in mind.

But most day-to-day ML work doesn’t require LLM-scale models.

I relearned this the hard way this month: I was training a 4-layer MLP and waiting ages to be scheduled.

And for many workloads, those small slices are more than enough.

We either need people constantly watching dozens or even hundreds of metrics, or we need an automated alerting and monitoring system.

So, in this article, I’ll walk you through a practical approach to building an effective monitoring system for your KPIs.

You’ll learn about different monitoring approaches, how to build your first statistical monitoring system, and what challenges you’ll likely encounter when deploying it in production.

Setting up monitoringLet’s start with the big picture of how to architect your monitoring system, then we’ll dive into the technical details.

Plus, it gives you a valuable dataset for evaluating the real impact on your monitoring system when you make changes …

While the previous article devoted more space to a discussion of framework types, will place the spotlight on framework dimensions.

A Primer on Framework DimensionsWhereas the framework type defines the structure of what you are trying to represent, the framework dimensions determine the content.

The following sections examine this classification of framework dimensions in more detail and go over some aspects that you should consider when including multiple dimensions in a framework.

It is worth understanding what makes the sales reps in the top-left quadrant so efficient and what the other sales reps can learn from them.

The WrapWhile the framework type determines how the framework will sa…

As AI systems begin to master complex reasoning we *must* confront a profound question: What is the next step?

Then a more balanced system emerges, where AI brainstorms with a decentralized human governance to maximally balance progress with prudence.

We have already moved well beyond the initial excitement of chatbots and image generators to much more complex AI systems that have penetrated all of science, technology, and entertainment.

Further demonstrating this is the development of AI systems that can discover “their own” learning algorithms, achieving state-of-the-art performance on tasks it has never encountered before.

There’s also the question of accountability: who is responsible w…

See the following:Up to this point in my data visualization series, I have covered the foundational elements of visualization design.

To ensure you are ready for this next step, this article will consist of a brief review of Python essentials, followed by a discussion of their relevance to coding data visualizations.

The Basics—Expressions, Variables, FunctionsExpressions, variables, and functions are the primary building blocks of all Python code—and indeed, code in any language.

Python and Data VisualizationNow then, let me address the question you may be asking yourself: Why all this Python review to begin with?

You’ll often need to store specific values or sets of values for later incor…

The gold layer is then the geospatial presentation layer where the output of geospatial analytics such as journey time or density calculations can be stored.

Geospatial Data PreparationIn addition to the typical data quality challenges faced when unifying many individual data sources in a data lake architecture (missing data, variable recording practices etc), geospatial data has unique data quality and preparation challenges.

Some organisations may prefer that geospatial outputs be written to downstream systems such as CRMs or other geospatial databases.

Curated geospatial data and its aggregations are also frequently used as input features to ML models and this works seamlessly with geosp…

Reflection in frameworksSince there’s no doubt that reflection brings value to AI agents, it’s widely used in popular frameworks.

Specify the format (Tab Separated With Names) in the SQL query output to ensure that column names are included in the output.

llm_judge_system_prompt = ''' You are a senior analyst and your task is to compare two SQL query results and determine if they are equivalent.

simple_reflection_user_prompt_template = ''' Your task is to assess the SQL query generated by another analyst and propose improvements if necessary.

feedback_reflection_user_prompt_template = ''' Your task is to assess the SQL query generated by another analyst and propose improvements if necessary.

In this article, I’ll discuss how to consistently extract metadata from your documents, considering approaches to metadata extraction and challenges you’ll face along the way.

I’ll first discuss why we need to extract document metadata, and how it’s useful for downstream tasks.

Continuing, I’ll discuss approaches to extract metadata, with Regex, OCR + LLM, and vision LLMs.

Why extract document metadataFirst, it’s important to clarify why we need to extract metadata from documents.

ConclusionIn this article, I’ve discussed how you can consistently extract metadata from your documents.

Such a constraint leads to a fundamental trade-off:Imagine that if you fix a compute budget, increasing the model size means that you must reduce the model size you can train on, and vice versa.

The optimal model size does not grow linearly with compute budget.

But we can still see that the growth of computing leads to an increase in optimal model size, but at a diminishing rate.

Optimal model size and data size grow with compute.

ConclusionIn this blog post, we provide a study of the trade-off between model size and data under a fixed compute budget for LLMs with a toy case.

Despite this, scale maximalists have implicitly suggested that multimodal models can be a structure-agnostic framework for AGI.

While structure-agnostic scale maximalism has succeeded in producing LLMs and LVMs that pass Turing tests, a multimodal scale maximalist approach to AGI will not bear similar fruit.

CitationFor attribution in academic contexts or books, please cite this work asBenjamin A. Spiegel, "AGI Is Not Multimodal", The Gradient, 2025.

@article{spiegel2025agi, author = {Benjamin A. Spiegel}, title = {AGI Is Not Multimodal}, journal = {The Gradient}, year = {2025}, howpublished = {\url{https://thegradient.pub/agi-is-not-multimodal}, }ReferencesAndreas, Jacob.

“Language Models,…

Created Using GPT-5💡 AI Concept of the Day: A Summary About Our Series About Interpretability in AI Foundation ModelsToday, we are closing our series about AI interpretability with a summary of what we have published in the last few weeks.

This series went deep into some of the most recent trends and research about interpretability in foundation models.

Before that, let’s recap everything we covered in terms of AI interpretability which we truly hope have broaden your understanding of the space.

This might be the deepest compilation of AI interpretability topics for the new generation of AI models.

The research frontier then becomes making these components robust, composable, and cheap—so i…

Subscribe Now to Not Miss Anything:📝 Editorial: Last Week in AI: Browsers, Coders, Context—and LangChain’s Agent StackThis week’s AI releases feel less like isolated features and more like a quiet re‑wiring of the software stack around agents.

By living where reviews and tickets already happen, the agent can open PRs, attach artifacts, and report progress without everyone spelunking into terminals.

If your roadmap still isolates browsing automations, code assistance, and context scaling, this week is your nudge to converge.

🤖 AI Tech ReleasesOpenAI AtlasOpenAI officially entered the AI browser race with the release of Atlas.

Mistral AI StudioMistral launched its AI Studio platform to build …

Created Using GPT-5Fine-tuning has long been the workhorse for adapting large AI models to specific tasks and domains.

Enter reinforcement learning (RL) – particularly techniques like RLHF (Reinforcement Learning from Human Feedback) and its cousins – which are now emerging as powerful alternatives to traditional supervised fine-tuning.

We’ll dive into the history of fine-tuning, the rise of RL-based methods, why RL offers more control at scale, and real case studies (from GPT-4 to robotics) of this paradigm shift.

So buckle up for a journey from the fine-tuning era into the reinforcement learning future of AI.

From Rigid Models to Fine-Tuning: A Brief History

Created Using GPT-5Agentic systems have crossed a threshold: LLMs can reason well enough to orchestrate tools, write and run code, and manipulate files.

Claude’s new Agent Skills formalize that know-how as first-class, modular units you can compose into robust agents.

Think of Skills as “folders of expertise” your agent can discover, load, and execute on demand—without you endlessly re-prompting or stuffing a giant system message.

This essay walks through how Agent Skills work under the hood, why they’re different from prompts and classic tool plugins, how to design and test them, and how to ship them via the Claude API and Claude Code.

Along the way, we’ll sketch practical patterns, tradeo…

Created Using GPT-5Today we will Discuss:The core arguments in favor and against the viability of solving AI interpretability.

💡 AI Concept of the Day: Is Interpretability Solvable?

To conclude our series of AI interpretability, I wanted to debate a controversial idea.

Is AI interpretability for frontier models even solvable?

Whether AI interpretability for frontier models is “solvable” depends on what we mean by solving it.

We will be releasing a long piece about fine-tuning vs. reinforcement learning that you cannot miss and will dive into Anthropic’s new Agent Skills.

Subscribe Now to Not Miss Anything:📝 Editorial: Last Week in AI: From Vibes to Verbs: Agent Skills, Haiku 4.5, Veo 3.1, and nanochatThis week in AI was just a lot of fun: the frontier is racing, but the tooling is finally congealing into something you can depend on.

Anthropic’s Agent Skills shift agents from “one giant brain” to a set of precisely scoped capabilities you can load on demand.

🤖 AI Tech ReleasesClaude Haiku 4.5Anthropic released Claude Haiku 4.5, its latest small model that showcases performance comparable to Sonnet 4.

Agent Skill…

Created Using GPT-5OpenAI’s multi‑year partnership with AMD to deploy large fleets of Instinct accelerators is more than a procurement decision—it’s a strategic signal.

For the first time in the current AI cycle, a top‑tier model lab is committing to build at hyperscale on non‑NVIDIA silicon.

For AMD, the endorsement validates its GPU roadmap and software stack, and it opens the door to tens of billions in potential revenue.

For OpenAI, it diversifies supply, improves negotiating leverage, and ensures headroom to scale.

Hardware Architectures: Instinct vs. NVIDIA in Real AI Workloads

Created Using GPT-5There’s a quiet but important trend in reasoning research: instead of making models bigger, we’re making them loop.

Call this family Tiny Recursion Models (TRMs).

If you’ve ever solved Sudoku by penciling in candidates, updating constraints, and iterating until the board stabilizes, you already understand TRMs.

They explicitly separate two pieces of state: a proposal (the current best answer) and a scratchpad (internal reasoning features).

Then, in a small loop, they first improve the scratchpad using the input and the current proposal, and next refresh the proposal using the improved scratchpad.

Image Created GPT-5Today we will Discuss:How can chain of thought monitoring(CoT) influence AI interpretability.

💡 AI Concept of the Day: Chain of Thought and InterpretabilityChain-of-thought (CoT) monitoring sits at the intersection of interpretability and oversight: it promises a window into a model’s intermediate reasoning while giving us a handle for detecting misbehavior.

The catch is faithfulness—whether the text a model writes as its “thoughts” actually reflects the causal path to its answer.

More recently, large-scale tests on modern reasoning models report that CoTs often omit the very cues that drove a solution, especially under optimization pressure.

Together, these results frame…

Google, for its part, drew the enterprise map with Gemini Enterprise: a single front door for Gemini models, prebuilt agents, and no/low‑code tooling.

AI Lab: FAIR at Meta.

AI Lab: NVIDIA.

AI Lab: ETH Zürich & Max Planck Institute for Intelligent Systems.

AI Lab: Google DeepMind & Google.

Created Using GPT-5Two weeks ago, a bold new venture called Periodic Labs emerged from stealth with an audacious goal: to build a universal AI scientist.

After AI systems beating humans at games and writing code, here was the next frontier: applying AI to the very process of scientific discovery.

A new wave of initiatives (from well-funded startups to academic consortia) is racing to create foundation models for science – AI systems that can reason across biology, chemistry, physics, and more.

The premise is straightforward but profound: a sufficiently advanced AI, fed all of human scientific knowledge and equipped with the ability to experiment, could dramatically accelerate innovation.

Th…

Created Using GPT-5DeepSeek’s “3.2” release is not a wholesale reinvention of its V‑series so much as a deliberate, experimental branch designed to de‑risk a set of architectural ideas before they migrate into the next production generation.

The public artifact—often referred to as DeepSeek 3.2—centers on a new DeepSeek Sparse Attention (DSA) mechanism that aggressively lowers compute and memory overhead for long‑context prefill and decode while aiming to preserve quality.

Around that nucleus, the release also pushes on platform pragmatism: first‑class support for Chinese accelerators and vendor stacks, together with runtime integrations that make those hardware choices deployable in mainst…

Created Using GPT-5Today we will Discuss:A powerful idea: a transformer for AI interpretability.

Anthropic’s famous paper about the biology of language models.

💡 AI Concept of the Day: : A Transformer for AI InterpretabilityToday we would like to challenge with an interesting hypothesis.

Will we see a transformer for AI interpretability?

Text models learn syntax and semantics, vision models learn objectness and spatial composition, audio models learn pitch and rhythm, and world models learn latent dynamics—mostly from predicting what comes next or what was masked out.

Created Using GPT-5Next Week in The Sequence:The Sequence Knowledge: We explore the idea of a transformer model for AI interpretability.

Subscribe Now to Not Miss Anything:📝 Editorial: Last Week in AI: Rails, Windows, and Shots — Tinker, DeepSeek V3.2, Sora 2, and Periodic’s $300MI wanted to try a different format for today’s editorial.

AI Lab: DeepSeekSummary: Introduces DeepSeek Sparse Attention (with a light “indexer” + top-k token selection) to speed long-context training/inference while largely matching V3.1-Terminus on core benchmarks.

AI Lab: AppleSummary: Introduces a 3B end-to-end on-device GUI agent trained with a unified action space, synthetic/real GUI data, CoT, zoom-in visual …

DeepSeek AI, a prominent player in the large language model arena, has recently published a research paper detailing a new technique aimed…Continue reading on SyncedReview »

Однако все задачи в MERA CODE, как впрочем и в SWE-bench и других бенчмарках подобного назначения, следуют классической парадигме, когда у нас есть фиксированный обучающий набор данных и, что более важно, фиксированный проверочный набор.

Но большие языковые модели для кодинга, которые мы и пытаемся оценивать нашим набором, также учатся на GitHub – со времен еще первой модели LLaMa.

Кажется, что 700 задач немного, но это уже очень приличное количество, и что самое важное — это новые задачи.

Current behavior: from sympy import ask, Q, Symbol x = Symbol('x') print(ask(Q.finite(x**-1), Q.real(x))) # Output: True Expected behavior: The function should return None to indicate uncertainty, as x**-…

Ответ: Кэисукэ ТибаSPARQL-запрос SimpleSELECT DISTINCT ?s ?r ?o WHERE { { SELECT ?s ?r ?o WHERE { ?s ?r ?o . }

GROUP BY ?s ?r HAVING(count(?o) = 1) } { SELECT ?s ?r ?o WHERE { ?s ?r ?o . }

Ответ: Национальная система платежных карт (НСПК) Центр биометрических технологий (ЦБТ) ЕБСSELECT ?s ?r ?o ?len WHERE { { SELECT ?s ?r (COUNT(?o1) as ?len) (GROUP_CONCAT(DISTINCT(STR(?o1));separator="|") AS ?o) WHERE { ?s ?r ?o1 . }

FILTER(?o != ?o1) } GROUP BY ?o ?o1 ?r ?r1 HAVING(COUNT(?s) = 1) } UNION { SELECT ?s ?r ?o ?r1 ?s1 WHERE { ?s ?r ?o .

FILTER(?o != ?o1) } GROUP BY ?o ?o1 ?r ?r1 HAVING(COUNT(?s) = 1) } UNION { SELECT ?s ?r ?o ?r1 ?s1 WHERE { ?s ?r ?o .

В этой статье я хочу поделиться своим опытом по конвертации нейросети в формат rknn с помощью библиотеки rknn-toolkit2.

Вот как выглядят веса pytorch модели в Netron:веса pytorch модели в NetronВажно!

Конвертация onnx модели в rknnДалее создается объект RKNN , который управляет процессом конвертации и инференса модели на платформе Rockchip.

На этом этапе происходит подготавка модели к конвертации в формат RKNN и последующему запуску на NPU Rockchip.

Создание и экспорт rknn моделиНа этом этапе происходит конвертация ONNX-модели во внутренний формат RKNN, оптимизация графа и подготовка к запуску на NPU Rockchip.

🔗MERA Code🔗GitHub с кодом и данными🔗Коллекция на Hugging Face🔗Статья на arxiv🔗Репозиторий проекта на GitVerseЧто такое MERA Code?

Современные кодовые языковые модели и модели общего назначения (ChatGPT, Claude, Qwen, YandexGPT, GigaChat и др.)

Список текущих задач MERA Code и их характеристикКаталог задач MERA Code и их подробное описание представлено на сайте.

В MERA Code промпты строго подобраны под задачу и корректный выбор ответа.

В заключениеMERA Code — это попытка закрыть важный пробел в тестировании LLM: насколько они действительно полезны в реальной, локализованной разработке.

", подумал я. И, к счастью, у меня как раз под рукой оказался идеальный подопытный.

Стандартное арифметическое среднее между 360° и 0° даст нам 180°, несмотря на то, что и 360°, и 0° указывают в одном направлении.

Нулевая гипотеза утверждает, что данные распределены равномерно по кругу, альтернативная — что это не так.

from pingouin import circ_vtest v, pval = circ_vtest(data['radians'], dir=np.pi) print(f"V-statistics: {v:.3f}; p-value: {pval:.6f}")>> V-statistics: 24.127; p-value: 0.002904Вот мы и подобрались к чему-то интересному!

Априорное распределение и функция правдоподобияПредположим, что у нас есть:Априорное распределение с параметрамиФункция правдоподобия для нового наблюдения с п…

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.

Share Post ShareIntroductionTime series data normally requires an in-depth understanding in order to build effective and insightful forecasting models.

This article presents 10 simple Python one-liners for generating time series features based on different characteristics and properties underlying raw time series data.

shift ( 1 )For daily time series data, if you wanted to capture previous values for a given day of the week, e.g.

It calculates the difference between consecutive observations (current and previous) of a target attribute:df['diff_1'] = df['value'].diff() 1 df [ 'diff_1' ] = df [ 'value' ] .

If it were the index, you may need to use this instead:df['hour'], df['dayofweek'] = d…

Share Post ShareIn this article, you will learn how to use Pydantic to validate, parse, and serialize structured data in Python using type hints.

This article covers the basics of using Pydantic for data validation using type hints.

Basic Pydantic ModelsUnlike manual data validation approaches that require writing extensive if-statements and type checks, Pydantic integrates well with your existing Python code.

Each error includes the field location, error type, and a human-readable message.

datetime ( 2024 , 3 , 15 , 18 , 30 ) , 'attendees' : 45 , 'is_public' : True } { "name" : "Python Meetup" , "date" : "2024-03-15T18:30:00" , "attendees" : 45 , "is_public" : true } { 'name' : 'Python Mee…

Instead of training models from scratch, you’re adapting pretrained models to specialized tasks using far less data and compute.

Essential Parameter-Efficient Fine-Tuning MethodsFull fine-tuning updates all model parameters, requiring massive compute and memory.

As error rates in training data increase linearly, downstream model error can rise superlinearly—making data curation your highest-leverage activity.

Avoiding Critical PitfallsOverfitting occurs when models memorize training data instead of learning generalizable patterns.

Your Learning PathFor machine learning practitioners new to fine-tuning, adopt a progressive learning approach that builds skills systematically.

IntroductionIn the epoch of LLMs, it may seem like the most classical machine learning concepts, methods, and techniques like feature engineering are no longer in the spotlight.

Feature engineering can be extremely valuable on raw text data used as input to LLMs.

This article presents five advanced feature engineering techniques through which LLMs can incorporate valuable information from (and into) fully structured, tabular data into their workflows.

Semantic Feature Generation Via Textual ContextsLLMs can be utilized to describe or summarize rows, columns, or values of categorical attributes in a tabular dataset, generating text-based embeddings as a result.

A promising way to alleviate t…

Share Post ShareIn this article, you will learn seven proven agentic AI design patterns, when to use each, and how to choose the right one for your production workload.

This article explains seven design patterns that separate effective agents from expensive experiments.

Sequential Workflow: The Predictable PipelineSequential patterns organize agent systems as fixed-order pipelines.

I hope you found this overview of agentic AI design patterns useful.

However, here’s what you should pay attention to: all successful agent systems evolve.

Share Post ShareIn this article, you will learn how to future-proof your AI engineering career for 2026 by deepening core fundamentals, embracing system-level automation, and aligning your work with open source and evolving policy.

But here’s the uncomfortable truth: the skills that made AI engineers successful five years ago might not hold up much longer.

Future-proofing your AI engineering career isn’t just about chasing the latest tools — it’s about adapting faster than the industry itself.

Building Cross-Disciplinary FluencyThe next generation of AI engineering will be less about isolated model performance and more about integration.

AI systems are leaking into every corner of the enter…

Share Post ShareIn this article, you will learn how the enhanced BigQuery ML UI streamlines end-to-end model creation, management, and prediction directly in the BigQuery console.

There are significant enhancements to the BigQuery ML UI, designed to streamline your machine learning workflows directly within the BigQuery console.

Streamlined Model Creation FlowThe updated UI significantly improves the model creation process.

census_adult_incomeCreating the ML Model in BigQuery UIFrom the BigQuery Home screen, you can click on “ML Model” to begin the model creation process.

Explore the new BigQuery ML UI and experience a streamlined MLOps workflow.

Share Post ShareIn this article, you will learn how vector databases power fast, scalable similarity search for modern machine learning applications and when to use them effectively.

What Makes Vector Databases DifferentVector databases are purpose-built for similarity search.

How Vector Databases Handle ScaleModern vector databases combine multiple techniques to handle billions of vectors efficiently.

Production Vector Database OptionsThe vector database landscape has exploded over the past few years.

Vector databases that handle multiple vector types per item enable this.

Share Post ShareIn this article, you will learn three proven ways to speed up model training by optimizing precision, memory, and data flow — without adding any new GPUs.

3 Ways to Speed Up Model Training Without More GPUsImage by EditorIntroductionTraining large models can be painfully slow, and the first instinct is often to ask for more GPUs.

Method 1: Mixed Precision and Memory OptimizationsOne of the easiest ways to speed up training without new GPUs is to use mixed precision.

Method 2: Gradient Accumulation and Effective Batch Size TricksSometimes the biggest barrier to faster training isn’t compute, it’s GPU memory.

Frameworks like DeepSpeed and Hugging Face Accelerate implement this…

Time series data have the added complexity of temporal dependencies, seasonality, and possible non-stationarity.

Imbalanced datasets are a common challenge in machine learning.

You've loaded your dataset and the distribution plots look rough.

Selecting the right model is one of the most critical decisions in any machine learning project.

Usually shrouded in mystery at first glance, Python decorators are, at their core, functions wrapped around other functions to provide extra functionality without altering the key logic in the function being "decorated".

Every now and then, someone asks me why I blog, and I don’t know really know what to tell them.

That’s another reason I’m not celebrating 10 years with more gusto, I know I’ve been writing less.

Indiana Jones and the Great Circle: I don’t know how they did it, but Indiana Jones and the Great Circle was just fun all the way through.

My one complaint is that the hand-to-hand combat feels like the worst part of the game, so of course they put a bunch of upgrades behind learning parry timings you’ll never use later.

I have not tried Peak, but Another Crab’s Treasure was really good and is worth playing if you’re interested in a Souls-like.

A music concert in the evenings, typically set up as a rave with EDM or rock music made by brony musicians.

She has been involved in organizing pony music concerts for over a decade, for both BABSCon and other pony conventions.

Thank you, BABSCon ChairsThe brony musicians immediately jump into an emergency Discord call with Pinkaboo, to get her side of the story.

Other conventions start tweeting in support of the brony musicians, with no one taking BABSCon’s side.

It’s hard for me to explain why I like MLP fan music, because brony music really isn’t accessible.

I think the easy answer to this question is that right now, AI is for the AI developers.

Code is useful, it makes money, it is a testbed for AI speeding up the development of AI, and it is easy.

I’m working in AI because it pays well and is potentially really good for the world.

The artists did not know what AI was, but when they learned, they quickly decided they did not want it.

It feels like the most likely outcome is that people go all-in on pushing raw intelligence, in the way that AI developers can measure it, leaving behind those that are not like AI developers.

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.

We differentiate Markov chains on values the index $t$ can take: if $t$ is an integer, we call it a discrete-time Markov chain, and when $t$ is real, we call the process a continuous-time Markov chain.

Discrete Markov chains are fully described by a collection of state transition matrices $P_t = [P(X_{t+1} = i\vert X_{t}=j)]{i,j}$.

The geometric distribution is the only discrete distribution with the memory-less property, stated as $\mathbb{P}[T=s+t\vert T>s] = \mathbb{P}[T = t]$.

Point processes and Markov ChainsBack to Markov chains.

We then discussed different representations of discrete point processes, and noted (although did not prove) their equivalence.

At Google, in addition to the “Data Scientist - Research” (DS-R) role mentioned above, there are other data science roles including “Business Data Scientist”.

At Google, in addition to the “Data Scientist - Research” (DS-R) role mentioned above, there are other data science roles including “Business Data Scientist”.

As a result, the range of statistical skills that a data scientist may possess has become quite wide.

Furthermore, DS-R is just one of several data science roles at Google (e.g., Data Scientist - Product, and Data Scientist - Marketing).

Furthermore, DS-R is just one of several data science roles at Google (e.g., Data Scientist - Product, and Data Scientist - Marketing).

I’m freezing this blog and starting to post on my Substack instead.

The authoring experience is much more convenient for me there.

Please follow me there, and check out The Illustrated DeepSeek R-1 if you haven’t yet.

And check out our How Transformer LLMs Work course!

We present SpatialTrackerV2, a feed-forward 3D point tracking method for monocular videos.

Going beyond modular pipelines built on off-the-shelf components for 3D tracking, our approach unifies the intrinsic connections between point tracking, monocular depth, and camera pose estimation into a high-performing and feedforward 3D point tracker.

It decomposes world-space 3D motion into scene geometry, camera ego-motion, and pixel-wise object motion, with a fully differentiable and end-to-end architecture, allowing scalable training across a wide range of datasets, including synthetic sequences, posed RGB-D videos, and unlabeled in-the-wild footage.

By learning geometry and motion jointly from …

Calisthenics skill classification is the computer vision task of inferring the skill performed by an athlete from images, enabling automatic performance assessment and personalized analytics.

Traditional methods for calisthenics skill recognition are based on pose estimation methods to determine the position of skeletal data from images, which is later fed to a classification algorithm to infer the performed skill.

This work proposes a direct approach to calisthenics skill recognition, which leverages depth estimation and athlete patch retrieval to avoid the computationally expensive human pose estimation module.

Using Depth Anything V2 for depth estimation and YOLOv10 for athlete localizat…

Inference is now the dominant AI workload, yet existing systems force trade-offs between latency, throughput, and cost.

Arctic Inference, an open-source vLLM plugin from Snowflake AI Research, introduces Shift Parallelism, a dynamic parallelism strategy that adapts to real-world traffic while integrating speculative decoding, SwiftKV compute reduction, and optimized embedding inference.

It achieves up to 3.4 times faster request completion, 1.75 times faster generation, and 1.6M tokens/sec per GPU for embeddings, outperforming both latency- and throughput-optimized deployments.

Already powering Snowflake Cortex AI, Arctic Inference delivers state-of-the-art, cost-effective inference for ent…

FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting.

The approach is designed to respect spherical geometry and to accurately model the spatially correlated probabilistic nature of the problem, resulting in stable spectra and realistic dynamics across multiple scales.

FourCastNet 3 delivers forecasting accuracy that surpasses leading conventional ensemble models and rivals the best diffusion-based methods, while producing forecasts 8 to 60 times faster than these approaches.

In contrast to other ML approaches, FourCastNet 3 demonstrates excellent probabilistic calibration and retains realis…

We study A/B experiments that are designed to compare the performance of two recommendation algorithms.

The bias arising from this type of data sharing is known as "symbiosis bias".

In this paper, we highlight that, for decision-making purposes, the sign of the GTE often matters more than its precise magnitude when selecting the better algorithm.

We formalize this insight under a multi-armed bandit framework and theoretically characterize when the sign of the expected GTE estimate under data sharing aligns with or contradicts the sign of the true GTE.

Our analysis identifies the level of exploration versus exploitation as a key determinant of how symbiosis bias impacts algorithm selection.

Task-agnostic prompt compression leverages the redundancy in natural language to reduce computational overhead and enhance information density within prompts, especially in long-context scenarios.

Existing methods predominantly rely on information entropy as the metric to compress lexical units, aiming to achieve minimal information loss.

However, these approaches overlook two critical aspects: (i) the importance of attention-critical tokens at the algorithmic level, and (ii) shifts in information entropy during the compression process.

Motivated by these challenges, we propose a dynamic attention-aware approach for task-agnostic prompt compression (DAC).

This approach effectively integrate…

Large Language Models (LLMs) can provide accurate word definitions and explanations for any context.

However, the scope of the definition changes for different target groups, like children or language learners.

We investigate how simplification impacts homonym definition quality across three target groups: Normal, Simple, and ELI5.

Our results show that simplification drastically degrades definition completeness by neglecting polysemy, increasing the risk of misunderstanding.

Fine-tuning Llama 3.1 8B with Direct Preference Optimization substantially improves homonym response quality across all prompt types.

Multimodal large language models (MLLMs) have revolutionized cross-modal understanding but continue to struggle with hallucinations - fabricated content contradicting visual inputs.

Existing hallucination mitigation methods either incur prohibitive computational costs or introduce distribution mismatches between training data and model outputs.

We identify a critical insight: hallucinations predominantly emerge at the early stages of text generation and propagate through subsequent outputs.

To address this, we propose **SENTINEL** (**S**entence-level **E**arly i**N**tervention **T**hrough **IN**-domain pr**E**ference **L**earning), a framework that eliminates dependency on human annotations…

Language models (LMs) are challenging to adapt to new data distributions by simple finetuning.

This is due to the rigidity of their subword tokenizers, which typically remain unchanged during adaptation.

This inflexibility often leads to inefficient tokenization, causing overfragmentation of out-of-distribution domains, unseen languages, or scripts.

In this work, we develop byte-level LMs with learnable tokenizers to make tokenization adaptive.

Our models include a submodule that learns to predict boundaries between the input byte sequence, encoding it into variable-length segments.

To address the challenges posed by cascading reactions caused by component failures in autonomous cargo ships (ACS) and the uncertainties in emergency decision-making, this paper proposes a novel hybrid feature fusion framework for constructing a graph-structured dataset of failure modes.

A hierarchical feature fusion framework is constructed, using Word2Vec encoding to encode subsystem/component features, BERT-KPCA to process failure modes/reasons, and Sentence-BERT to quantify the semantic association between failure impact and emergency decision-making.

The dataset covers 12 systems, 1,262 failure modes, and 6,150 propagation paths.

In the label prediction results, the Shore-based Meteor…

In recent years, models based on Graph Convolutional Networks (GCN) have made significant strides in the field of graph data analysis.

Although the Graph Transformer architecture has mitigated some of these issues, its performance is still limited when processing heterogeneous graph data.

To address these challenges, this study proposes a novel deep clustering framework that comprising GCN, Autoencoder (AE), and Graph Transformer, termed the Tri-Learn Graph Fusion Network (Tri-GFN).

The tri-learning mechanism allows mutual learning among these modules, while the feature fusion strategy enables the model to capture complex relationships, yielding highly discriminative representations for gra…

We propose the APTx Neuron, a novel, unified neural computation unit that integrates non-linear activation and linear transformation into a single trainable expression.

The APTx Neuron is derived from the APTx activation function, thereby eliminating the need for separate activation layers and making the architecture both computationally efficient and elegant.

The proposed neuron follows the functional form $y = \sum_{i=1}^{n} ((\alpha_i + \tanh(\beta_i x_i)) \cdot \gamma_i x_i) + \delta$, where all parameters $\alpha_i$, $\beta_i$, $\gamma_i$, and $\delta$ are trainable.

We validate our APTx Neuron-based architecture on the MNIST dataset, achieving up to 96.69\% test accuracy in just 20 ep…

While this problem has been widely studied in traditional recommendation settings, its implications for bundle recommendation (BR) remain largely unexplored.

Existing fairness frameworks and metrics designed for traditional recommender systems may not directly translate to this multi-layered setting.

In this paper, we conduct a comprehensive reproducibility study of product-side fairness in BR across three real-world datasets using four state-of-the-art BR methods.

We analyze exposure disparities at both the bundle and item levels using multiple fairness metrics, uncovering important patterns.

Overall, our findings offer actionable insights for building fairer bundle recommender systems and…

However, the lack of tools that provide effective visualization is still a significant challenge.

In this paper, we present OntView, an ontology viewer that is designed to provide users with an intuitive visual representation of ontology concepts and their formal definitions through a user-friendly interface.

Building on the use of a DL reasoner, OntView follows a "What you see is what you meant" paradigm, showing the actual inferred knowledge.

One key aspect for this is its ability to visualize General Concept Inclusions (GCI), a feature absent in existing visualization tools.

OntView has been released with an open-source license for the whole community.

These approaches fail to capture elaborate relations hidden in real-world bundle structures, resulting in suboptimal bundle representations.

To overcome this limitation, we propose RaMen, a novel method that provides a holistic multi-strategy approach for bundle construction.

RaMen utilizes both intrinsic (characteristics) and extrinsic (collaborative signals) information to model bundle structures through Explicit Strategy-aware Learning (ESL) and Implicit Strategy-aware Learning (ISL).

Integrating diverse strategies enables RaMen to learn more comprehensive and robust bundle representations.

Meanwhile, Multi-strategy Alignment & Discrimination module is employed to facilitate knowledge tr…

The rise of Large Reasoning Models (LRMs) promises a significant leap forward in language model capabilities, aiming to tackle increasingly sophisticated tasks with unprecedented efficiency and accuracy.

However, despite their impressive performance, recent studies have highlighted how current reasoning models frequently fail to generalize to novel, unseen problems, often resorting to memorized solutions rather than genuine inferential reasoning.

In this paper, we introduce Nexus Architect, an enhanced iteration of our multi-agent system framework, Nexus, equipped with a novel automated workflow synthesis mechanism.

Given a user's prompt and a small set of representative examples, the Archi…

One of the most tested ways to establish such a communication is through the use of sign based languages.

However, not many people are aware of the smaller intricacies involved with sign language.

Sign language recognition using computer vision aims at eliminating the communication barrier between deaf-mute and ordinary people so that they can properly communicate with others.

In recent studies, it has been found that people with hearing disabilities prefer to sign over typing during these video calls.

In this paper, we are proposing a browser extension that will automatically translate sign language to subtitles for everyone else in the video call.

In this paper, we present our submission to the MM-ArgFallacy2025 shared task, which aims to advance research in multimodal argument mining, focusing on logical fallacies in political debates.

Our approach uses pretrained Transformer-based models and proposes several ways to leverage context.

In the fallacy classification subtask, our models achieved macro F1-scores of 0.4444 (text), 0.3559 (audio), and 0.4403 (multimodal).

Our multimodal model showed performance comparable to the text-only model, suggesting potential for improvements.

PDFAbstract

On-demand ride-sharing platforms face the fundamental challenge of dynamically bundling passengers with diverse origins and destinations and matching them with vehicles in real time, all under significant uncertainty.

However, conventional MARL-based ride-sharing approaches heavily rely on the accurate estimation of Q-values or V-values, which becomes problematic in large-scale, highly uncertain environments.

To address these challenges, we propose two novel alternative methods that bypass value function estimation.

First, we adapt GRPO to ride-sharing, replacing the PPO baseline with the group average reward to eliminate critic estimation errors and reduce training bias.

Second, inspired b…

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

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

We introduce a novel classification framework, ZClassifier, that replaces conventional deterministic logits with diagonal Gaussian-distributed logits. Code: https://github.com/ShimSoonYong/ZClassifier

In this paper, we fill this gap and propose a family of novel gradual semantics for equipping assumptions, which are the core components in ABA frameworks, with dialectical strengths. Code: https://github.com/briziorusso/GradualABA

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Despite rapid progress in end-to-end AI music generation, AI-driven modeling of professional Digital Signal Processing (DSP) workflows remains challenging. Code: https://github.com/IsaacYQH/WildFX

This paper explores the application of a simple weighted loss function to Transformer-based models for multi-label emotion detection in SemEval-2025 Shared Task 11. Code: https://github.com/summer1278/semeval2025-task11

We present Step-wise Policy for Rare-tool Knowledge (SPaRK), a novel reinforcement learning framework that teaches large language models to explore diverse tool usage patterns beyond conventional high-temperature sampling. Code: https://github.com/gabrielkmbo/explore-rl

Service robots are increasingly deployed in diverse and dynamic environments, where both physical layouts and social contexts change over time and across locations. Code: https://github.com/Cavendish518/LE-Nav

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The frequent data swaps between the systolic array and external vector units result in low systolic array utilization. Code: https://github.com/VCA-EPFL/FSA

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Additional troubleshooting information here.

We’re partnering with Commonwealth Fusion Systems (CFS) to bring clean, safe, limitless fusion energy closer to reality.

Today, we’re announcing our research partnership with Commonwealth Fusion Systems (CFS), a global leader in fusion energy.

SPARC leverages powerful high-temperature superconducting magnets and aims to be the first magnetic fusion machine in history to generate net fusion energy — more power from fusion than it takes to sustain it.

That landmark achievement is known as crossing “breakeven,” and a critical milestone on the path to viable fusion energy.

Now, we’re bringing that work to CFS to accelerate the timeline to deliver fusion energy to the grid.

The virtual screen involved two stages:Immune-Context-Positive: We provided the model with real-world patient samples with intact tumor-immune interactions and low-level interferon signaling.

The model predicted a strong increase in antigen presentation when silmitasertib was applied in the “immune-context-positive” setting, but little to no effect in the “immune-context-neutral” one.

The experiments demonstrated:Treating the cells with silmitasertib alone had no effect on antigen presentation (MHC-I).

Treating the cells with both silmitasertib and low-dose interferon produced a marked, synergistic amplification of antigen presentation.

Getting started with C2S-Scale 27BThe new C2S-Scale 27…

Five months ago, we introduced Flow, our AI filmmaking tool powered by Veo, and have been inspired by the creativity it has sparked with over 275 million videos generated in Flow .

We're always listening to your feedback, and we've heard that you want more artistic control within Flow, with increased support for audio across all features.

Today, we’re introducing new and enhanced creative capabilities to edit your clips, giving you more granular control over your final scene.

For the first time, we’re also bringing audio to existing capabilities like “Ingredients to Video,” “Frames to Video” and “Extend.”We’re also introducing Veo 3.1, which brings richer audio, more narrative control, and …

Earlier this year, we mentioned that we're bringing computer use capabilities to developers via the Gemini API.

Today, we are releasing the Gemini 2.5 Computer Use model, our new specialized model built on Gemini 2.5 Pro’s visual understanding and reasoning capabilities that powers agents capable of interacting with user interfaces (UIs).

Developers can access these capabilities via the Gemini API in Google AI Studio and Vertex AI.

To complete these tasks, agents must navigate web pages and applications just as humans do: by clicking, typing and scrolling.

How it worksThe model’s core capabilities are exposed through the new `computer_use` tool in the Gemini API and should be operated withi…

Responsibility & Safety Introducing CodeMender: an AI agent for code security ShareCopy link ×Using advanced AI to fix critical software vulnerabilities Today, we’re sharing early results from our research on CodeMender, a new AI-powered agent that improves code security automatically.

Pause video Play videoWhile large language models are rapidly improving, mistakes in code security could be costly.

Example #2: Agent is able to create non-trivial patches In this example, the CodeMender agent was able to come up with a non-trivial patch that deals with a complex object lifetime issue.

Pause video Play video Pause video Play videoProactively rewriting existing code for better security We also…

Earlier this year, we made incredible progress bringing Gemini's multimodal understanding into the physical world, starting with the Gemini Robotics family of models.

Starting today, we’re making Gemini Robotics-ER 1.5 available to developers via the Gemini API in Google AI Studio.

Gemini Robotics 1.5 is currently available to select partners.

Gemini Robotics 1.5: Unlocking agentic experiences for physical tasksMost daily tasks require contextual information and multiple steps to complete, making them notoriously challenging for robots today.

Gemini Robotics-ER 1.5 then gives Gemini Robotics 1.5 natural language instructions for each step, which uses its vision and language understanding to…

Earlier this year, we made incredible progress bringing Gemini's multimodal understanding into the physical world, starting with the Gemini Robotics family of models.

Starting today, we’re making Gemini Robotics-ER 1.5 available to developers via the Gemini API in Google AI Studio.

Gemini Robotics 1.5 is currently available to select partners.

Gemini Robotics 1.5: Unlocking agentic experiences for physical tasksMost daily tasks require contextual information and multiple steps to complete, making them notoriously challenging for robots today.

Gemini Robotics-ER 1.5 then gives Gemini Robotics 1.5 natural language instructions for each step, which uses its vision and language understanding to…

Today, we’re publishing the third iteration of our Frontier Safety Framework (FSF) — our most comprehensive approach yet to identifying and mitigating severe risks from advanced AI models.

We’ve also incorporated lessons learned from implementing previous versions and evolving best practices in frontier AI safety.

To address risks posed by CCLs, we conduct safety case reviews prior to external launches when relevant CCLs are reached.

Advancing our commitment to frontier safetyThis latest update to our Frontier Safety Framework represents our continued commitment to taking a scientific and evidence-based approach to tracking and staying ahead of AI risks as capabilities advance toward AGI.

W…

Today, we’re publishing the third iteration of our Frontier Safety Framework (FSF) — our most comprehensive approach yet to identifying and mitigating severe risks from advanced AI models.

We’ve also incorporated lessons learned from implementing previous versions and evolving best practices in frontier AI safety.

To address risks posed by CCLs, we conduct safety case reviews prior to external launches when relevant CCLs are reached.

Advancing our commitment to frontier safetyThis latest update to our Frontier Safety Framework represents our continued commitment to taking a scientific and evidence-based approach to tracking and staying ahead of AI risks as capabilities advance toward AGI.

W…

Our new method could help mathematicians leverage AI techniques to tackle long-standing challenges in mathematics, physics and engineering.

For centuries, mathematicians have developed complex equations to describe the fundamental physics involved in fluid dynamics.

They help mathematicians identify fundamental limitations in the equations of fluid dynamics, and help improve our understanding of how the physical world functions.

It’s expected that unstable singularities play a major role in foundational questions in fluid dynamics because mathematicians believe no stable singularities exist for the complex boundary-free 3D Euler and Navier-Stokes equations.

With our novel AI methods, we pre…

Our new method could help mathematicians leverage AI techniques to tackle long-standing challenges in mathematics, physics and engineering.

For centuries, mathematicians have developed complex equations to describe the fundamental physics involved in fluid dynamics.

They help mathematicians identify fundamental limitations in the equations of fluid dynamics, and help improve our understanding of how the physical world functions.

It’s expected that unstable singularities play a major role in foundational questions in fluid dynamics because mathematicians believe no stable singularities exist for the complex boundary-free 3D Euler and Navier-Stokes equations.

With our novel AI methods, we pre…

Our new method could help mathematicians leverage AI techniques to tackle long-standing challenges in mathematics, physics and engineering.

For centuries, mathematicians have developed complex equations to describe the fundamental physics involved in fluid dynamics.

They help mathematicians identify fundamental limitations in the equations of fluid dynamics, and help improve our understanding of how the physical world functions.

It’s expected that unstable singularities play a major role in foundational questions in fluid dynamics because mathematicians believe no stable singularities exist for the complex boundary-free 3D Euler and Navier-Stokes equations.

With our novel AI methods, we pre…

Gemini 2.5 Deep Think achieves breakthrough performance at the world’s most prestigious computer programming competition, demonstrating a profound leap in abstract problem solving.

An advanced version of Gemini 2.5 Deep Think has achieved gold-medal level performance at the 2025 International Collegiate Programming Contest (ICPC) World Finals.

This milestone builds directly on Gemini 2.5 Deep Think's gold-medal win at the International Mathematical Olympiad (IMO) just two months ago.

Innovations from these efforts will continue to be integrated into future versions of Gemini Deep Think, expanding the frontier of advanced AI capabilities accessible to students and researchers.

Gemini solved …

Gemini 2.5 Deep Think achieves breakthrough performance at the world’s most prestigious computer programming competition, demonstrating a profound leap in abstract problem solving.

An advanced version of Gemini 2.5 Deep Think has achieved gold-medal level performance at the 2025 International Collegiate Programming Contest (ICPC) World Finals.

This milestone builds directly on Gemini 2.5 Deep Think's gold-medal win at the International Mathematical Olympiad (IMO) just two months ago.

Innovations from these efforts will continue to be integrated into future versions of Gemini Deep Think, expanding the frontier of advanced AI capabilities accessible to students and researchers.

Gemini solved …

Today, we're announcing expanded capabilities in Vertex AI Training that simplify and accelerate the path to developing large, highly differentiated models.

The Vertex AI training capabilities are organized around three areas:1.

Flexible, self-healing infrastructureWith Vertex AI Training, you can create a production-ready environment in minutes.

How customers are seeing impact with Vertex AI TrainingSalesforce: The Salesforce AI Research team leveraged Vertex AI Training to expand the capabilities of their large action models.

Vertex AI and its managed training clusters were instrumental in our development of SEA-LION v4.

The conversation around generative AI in the enterprise is getting creative.

Since launching our popular Nano Banana model, consumers have created 13 billion images and 230 million videos1.

Enterprises can combine Gemini 2.5 Pro with our generative media models – Lyria, Chirp, Imagen, and Veo – to bring their ideas to life.

To us, generative media is a canvas to explore ideas that were previously constrained by time, budget, or the limits of conventional production.

To test this, we briefed several top agencies to use Google's AI to create an “impossible” ad — a campaign that pushes the boundaries of what’s creatively and technically feasible.

To address this challenge, researchers at the Max Planck Institute of Biochemistry collaborated with Google Cloud to build a Proteomics Lab Agent that assists scientists with their experiments.

This agent simplifies performing complex scientific procedures through personalized AI guidance, making them easier to execute, while automatically documenting the process.

By making it easier to spot mistakes and offering personalized guidance, the agent can reduce troubleshooting time and build towards a future where real-time AI guidance can help prevent errors from happening.

The potential of the Proteomics AI agent goes beyond life sciences, addressing a universal challenge in specialized fields…

Earlier this year we introduced Model Armor, a model-agnostic advanced screening solution that can help safeguard gen AI prompts and responses, and agent interactions.

Model Armor offers a comprehensive suite of integration options, including direct API integration for developers, and inline integrations with Apigee, Vertex AI, Agentspace, and network service extensions.

By integrating with Model Armor, Apigee can become a critical security layer for generative AI interactions.

Today, we’re explaining how to get started using Model Armor with Apigee to secure your AI apps.

How to use Model Armor for AI app protectionModel Armor has five main capabilities.

We put Axion processors to the test: running Google production services.

Now that our clusters contain both x86 and Axion Arm-based machines, Google's production services are able to run tasks simultaneously on multiple instruction-set architectures (ISAs).

To make a long story short, the paper describes the combination of hard work, automation, and AI we used to get to where we are today.

Automation toolsWe had many sources of automation to help us, some of which we already used widely at Google before we started the multiarch migration.

Continuous Health Monitoring Platform (CHAMP), which is a new automated framework for rolling out and monitoring multiarch jobs.

When you use Dataplex Universal Catalog, Google Cloud’s unified data governance platform, the metadata that describes your data is no longer static — it’s where your AI applications can go to know where to find data and what to trust.

To solve this, we are extending Dataplex lineage capabilities from object-level to column-level, starting with support for BigQuery.

Column-level lineage provides that.

It's the foundation for governing our data responsibly and confidently.” - Latheef Syed - AVP, Data & AI Governance Engineering at VerizonWhile object-level lineage tracks the top-level connections between entire tables, column-level lineage charts the specific, granular path of a single data c…

Unlocking real value with AI in the enterprise calls for more than just intelligence.

This is the core of our strategy at Google Cloud: combining the most powerful models with the scale and security required for production.

Today, we are excited to announce that Google has been recognized as a Leader for our Gemini model family in the 2025 IDC MarketScape for Worldwide GenAI Life-Cycle Foundation Model Software (doc # US53007225, October 2025) report.

We believe the result validates our multi-year commitment to building the most capable, multimodal AI and delivering it to the enterprise through the Vertex AI platform.

It is this combined approach that leads organizations, from innovative st…

AI Agents are now a reality, moving beyond chatbots to understand intent, collaborate, and execute complex workflows.

In this dynamic environment, AI agents offer the necessary paradigm shift to overcome these persistent limitations.

Deloitte leveraged Google Cloud AI Agents and Gemini Enterprise to create a solution that generates insights, identifies discrepancies, and offers actionable recommendations based on inventory data.

Let's examine the scalable architecture patterns employed by Google Cloud SIs in the field to tackle Agentic AI challenges.

To comprehend Agentic AI architectures, it's crucial to first understand what an AI agent is.

Today, we announced the general availability of the G4 VM, powered by NVIDIA’s RTX PRO 6000 Blackwell Server Edition GPUs.

The addition of the G4 expands our comprehensive NVIDIA GPU portfolio, complementing the specialized scale of the A-series VMs, and the cost-efficiency of G2 VMs.

The G4 currently comes in 1, 2, 4, and 8 NVIDIA RTX PRO 6000 Blackwell GPU options, with fractional GPU options coming soon.

Dataproc: G4 VMs are fully supported on the Dataproc managed analytics platform, letting you accelerate large-scale Spark and Hadoop workloads.

What customers are sayingHere’s how customers are using G4 to innovate and accelerate within their businesses:“The combination of NVIDIA Omniver…

Today, we announced the general availability of the G4 VM family based on NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs.

Collective communications performance mattersLarge language models (LLMs) vary significantly in size, as characterized by their number of parameters: small (~7B), medium (~70B), and large (~350B+).

How G4 accelerates multi-GPU performanceMulti-GPU G4 VM shapes get their significantly enhanced PCIe P2P capabilities from a combination of both custom hardware and software.

The result is a low-latency data path that delivers a substantial performance increase for critical workloads like multi-GPU inference and fine-tuning.

In fact, across all major collectives, the enhanc…

This matters because monitoring tasks are everywhere.

To address this, we are introducing SentinelStep (opens in new tab), a mechanism that enables agents to complete long-running monitoring tasks.

Most real-world monitoring tasks share this limitation, making systematic bench marking very challenging.

In response, we are developing SentinelBench, a suite of synthetic web environments for evaluating monitoring tasks.

By embedding patience into plans, agents can responsibly monitor conditions and act when it matters—staying proactive without wasting resources.

Today, I’m excited to talk about the Paraphrase Project, an effort I co-led exploring how advances in AI tools for protein design might impact biosecurity.

These “patches,” akin to those in cybersecurity, have now been shared with organizations globally to strengthen biosecurity screening.

The project highlights that the same AI tools capable of incredible good can also be misused, requiring us to be vigilant, thoughtful, and creative so we continue to get the most benefit out of AI tools while working to ensure that we avoid costly misuses.

So things like, how similar is this to that template, wild-type protein structure that we used as our conditioning information?

But I feel like broadly…

In computer-based studies, we found that AI protein design (AIPD) tools could generate modified versions of proteins of concern, such as ricin.

Azure AI Foundry Labs Get a glimpse of potential future directions for AI, with these experimental technologies from Microsoft Research.

Stratified tiers of information : Data and code are classified into several tiers according to their potential hazard, from low-risk summaries through sensitive technical data to critical software pipelines.

The Age of AI in the Life Sciences: Benefits and Biosecurity Considerations, National Academies of Science, Engineering, and Medicine, 2025.

(opens in new tab)Protecting scientific integrity in an age of genera…

​​Genetic professionals use bioinformatics tools such as seqr, an open-source, web-based tool for rare disease case analysis and project management to assist them in filtering and prioritizing > 1 million variants to determine their potential role in disease.

They also envisioned collaborating with other genetic professionals to interpret, edit, and verify artifacts generated by the AI assistant.

Sensemaking Process with AI AssistantFigure: Sensemaking process when interpreting variants with the introduction of prototype AI assistant.

ConclusionWe explored the potential of generative AI to support​​ genetic professionals​ ​in diagnosing rare diseases​​.

If interested in our other related re…

Hugging Face is now serving many Spaces apps over MCP (opens in new tab), and Shopify has enabled MCP for millions of storefronts (opens in new tab).

Figure 1: We can extend Magentic-One by adding an agent that equips the GitHub MCP server.

Likewise, Docker MCP Hub is a registry that distributes MCP servers as Docker images, and we manually collected popular entries.

To automate the inspection of running MCP servers, we developed an MCP Interviewer tool.

One-size fits all (but some more than others)So, what does our survey of MCP servers tell us about the MCP ecosystem?

This shift is giving rise to a new field known as neural rendering.

Neural rendering combines deep learning with traditional graphics techniques, allowing models to simulate complex light transport without explicitly modeling physical optics.

RenderFormer: Toward a general-purpose neural rendering modelTo overcome these limitations, researchers at Microsoft Research have developed RenderFormer, a new neural architecture designed to support full-featured 3D rendering using only ML—no traditional graphics computation required.

View-independent rendering effects decoded directly from the view-independent transformer, including diffuse lighting and coarse shadow effects.

3D animation sequence r…

Traditional copper links are power-efficient and reliable, but limited to very short distances (< 2 meters) that restrict their use to within a single GPU rack.

With copper links, higher channel speeds lead to greater signal integrity challenges, which limits their reach.

With optical links, high-speed transmission is inherently inefficient, requiring power-hungry laser drivers and complex electronics to compensate for transmission impairments.

This imbalance ultimately erects a networking wall akin to the memory wall, in which CPU speeds have outstripped memory speeds, creating performance bottlenecks.

Operating at low speed improves power efficiency by eliminating the need for complex ele…

To address this, we have released Crescent (opens in new tab), a cryptographic library that adds unlinkability to widely used identity formats, protecting privacy.

These include JSON Web Tokens (the authentication standard behind many app logins) and mobile driver’s licenses.

Some digital identity systems already offer selective disclosure, allowing users to share only specific pieces of information in each interaction.

SetupA Crescent service pre-generates the zero-knowledge parameters for creating and verifying proofs from JSON Web Tokens and mobile driver’s licenses.

Age verification: The user presents their mobile driver’s license to a social network, proving they are over 18.

Recently, we released a paper (Working with AI: Measuring the Occupational Implications of Generative AI) that studied what occupations might find AI chatbots useful, and to what degree.

We set out to better understand how people are using AI, highlighting where AI might be useful in different occupations.

So, to summarize, our paper is about identifying the occupations where AI may be most useful, by assisting or performing subtasks.

Finally, we only evaluated AI chatbot usage, so this study does not evaluate the impact or applicability of other forms of AI.

The public interest in our research is based, in large part, on the topic of AI and job displacement.

KOHANE: So I think you’ve “nerd sniped” me because you [LAUGHTER]—which is all too easy—but I think there’s a central issue here.

But I actually think this is dark matter of human organizational technology that is not well understood.

AZEEM AZHAR: We didn’t talk about, you know, AI in its ability to potentially do this, which is to extend the clinician’s presence throughout the week.

And so I think there’s always going to be an opening for either differences of opinion or agreeing with you too much.

And this gets into whether AI is really going to get almost to the ab initio understanding of human biology.

A new research framework helps AI agents explore three-dimensional spaces they can’t directly detect.

Called MindJourney, the approach addresses a key limitation in vision-language models (VLMs), which give AI agents their ability to interpret and describe visual scenes.

MindJourney applies the same process to AI agents, letting them roam a virtual space before answering spatial questions.

Given a spatial reasoning query, MindJourney searches through the imagined 3D space using a world model and improves the VLM’s spatial interpretation through generated observations when encountering new challenges.

This enhancement could enable agents more accurately interpret spatial relationships and ph…

And yet, last December, a new optimizer called Muon (opens in new tab) showed serious promise by powering a nanoGPT speedrun (opens in new tab).

Going back to the inspiration for Muon, the key idea is an orthonormal update, which sparked the search for more scalable alternative linear algebras realizing the same goal.

This is achieved by enforcing orthonormality (opens in new tab) on the update matrix, thereby equalizing its effect across all input directions.

Wall-clock time speedup of Dion for 3B model trainingWhy would adding a constraint improve the update rule?

Using Dion with rank fraction 1/16 or lower offers an order-of-magnitude speedup over Muon.

All this is critical for maintaining good public health, as well as aligning better health and financial outcomes.

Umair and Gianrico are CEO-level leaders representing some of the best of the worlds of public health, healthcare delivery, medical research, and medical education.

So there’s so many use cases that AI has for population health or public health.

LEE: I find it really interesting that you are using the terms “public health” and “population health” …SHAH: Yeah.

As Umair explained in our discussion, the core of public health is the idea of population health, the idea of extracting new health insights from signals from population-scale data.

Recent approaches show promise in the utilization of intrinsic rewards for training reasoning models (RLIR).

While today’s reasoning models require additional data in the training phase and limit user control during the reasoning generation process, CLIO’s approach enables users to steer reasoning from scratch without additional data.

Through this open architecture approach to reasoning, we alleviate the necessity for further model post-training to achieve desired reasoning behavior.

Capabilities such as explaining the outcomes of internal reasoning are standard in the scientific field and are present in current reasoning model approaches.

Implications for science and trustworthy discoveryT…

Instead, an LM was prompted to classify the claim as “Fully Supported,” “Not Fully Supported,” or “Inconclusive” based on the evidence.

In this case, the verdict was “Fully Supported.”Since the verdict in Iteration 1 was “Fully Supported,” VeriTrail proceeded to Iteration 2.

Right: VeriTrail’s hallucination detection process for a “Not Fully Supported” claim, where the maximum number of consecutive “Not Fully Supported” verdicts was set to 2.

Once again, the verdict was “Not Fully Supported.” Since this was the second consecutive “Not Fully Supported” verdict, verification terminated and the verdict was deemed final.

A higher maximum increases confidence that a flagged claim is truly halluc…

That same curiosity about materials properties and performance drives her research on the high energy cost of computing, especially for artificial intelligence.

Schwacke develops new materials and devices for neuromorphic computing, which mimics the brain by processing and storing information in the same place.

And if you compare that to the amount of energy that we consume as humans when we’re learning things, the brain consumes a lot less energy,” Schwacke says.

“I wanted to try a totally new place,” she says, where she studied materials science, including nanostructured materials thousands of times thinner than a human hair.

She focused on materials properties and microstructure — the ti…

On Oct. 20 during its annual meeting, the National Academy of Medicine announced the election of 100 new members, including MIT faculty members Dina Katabi and Facundo Batista, along with three additional MIT alumni.

She is also a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), where she leads the Networks at MIT Research Group.

She is a MacArthur Fellow; a member of the American Academy of Arts and Sciences, National Academy of Sciences, and National Academy of Engineering; and a recipient of the ACM Computing Prize.

Established originally as the Institute of Medicine in 1970 by the National Academy of Sciences, the National Academy of Medicine addresses crit…

Today, collaborations like the MIT-IBM Watson AI Lab, which launched eight years ago, are continuing to deliver expertise for the promise of tomorrow’s AI technology.

At the MIT-IBM Watson AI Lab, success takes the form of 54 patent disclosures, an excess of 128,000 citations with an h-index of 162, and more than 50 industry-driven use cases.

The last of these comprise large language models, AI hardware, and foundation models, including multi-modal, bio-medical, and geo-spatial ones.

“In order to unlock the full economic and societal potential of AI, we need to foster ‘useful and efficient intelligence,’” says Sriram Raghavan, IBM Research VP for AI and IBM chair of the lab.

Academic-indust…

But what if we could simply reassemble our clothes into whatever outfits we wanted, adapting to trends and the ways our bodies change?

A team of researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Adobe are attempting to bring eco-friendly, versatile garments to life.

Their new “Refashion” software system breaks down fashion design into modules — essentially, smaller building blocks — by allowing users to draw, plan, and visualize each element of a clothing item.

As a user designs their clothing piece, the system automatically creates a simplified diagram of how it can be assembled.

She’s exploring how to design clothing by patchwork — essentially, cutti…

But if someone wants to use a generative AI model like GPT-5 to monitor their pet while they are at work, the model could fail at this basic task.

Vision-language models like GPT-5 often excel at recognizing general objects, like a dog, but they perform poorly at locating personalized objects, like Bowser the French Bulldog.

To address this shortcoming, researchers from MIT and the MIT-IBM Watson AI Lab have introduced a new training method that teaches vision-language models to localize personalized objects in a scene.

Their method uses carefully prepared video-tracking data in which the same object is tracked across multiple frames.

In the end, finetuning VLMs with this new dataset improv…

For three decades at the Institute, Bamberger found ways to use computers to engage students and help them learn music.

While at MIT, Bamberger became the first woman to earn tenure in the Music and Theater Arts Section.

She was know for pioneering the use of computer languages to teach children to learn music.

A child prodigy turned music philosopher, Jeanne was a pioneer in music and AI long before it was fashionable.

In 2002, Bamberger became professor emerita at MIT and moved to Berkeley, California, continuing to teach in the Music Department at the University of California at Berkeley.

“For most of my life, writing and playing music was the clearest way I had to express myself,” says Lecamwasam.

“I got to see firsthand not only the ways that audiences reacted to music, but also how much value music had for musicians,” she says.

Brown recommended that she look into the graduate programs at the MIT Media Lab within the Program in Media Arts and Sciences (MAS), which turned out to be an ideal fit.

The overarching theme of Lecamwasam’s research is exploring the various impacts of music and affective computing — physically, mentally, and emotionally.

She is also working to clinically validate music listening, composition, and performance as health interventions, in combination…

World Food Day calls on not only world governments, but business, academia, the media, and even the youth to take action to promote resilient food systems and combat hunger.

This year, the Abdul Latif Jameel Water and Food Systems Laboratory (J-WAFS) is spotlighting an MIT researcher who is working toward this goal by studying food and water systems in the Global South.

Before starting his PhD at MIT, Aouad worked on projects that looked at subsidies for smallholder farmers in low- and middle-income countries.

His seed grant project, Optimal subsidy design: Application to food assistance programs, aims to leverage data on preferences and purchasing habits from local grocery stores in India …

Now, a new AI tool developed by MIT engineers could help clear the quality-control bottleneck, offering a faster and cheaper option for certain materials-driven industries.

In a study appearing today in the journal Matter, the researchers present “SpectroGen,” a generative AI tool that turbocharges scanning capabilities by serving as a virtual spectrometer.

Certain spectroscopic modalities reveal specific properties in a material: Infrared reveals a material’s molecular groups, while X-ray diffraction visualizes the material’s crystal structures, and Raman scattering illuminates a material’s molecular vibrations.

And as it turns out, for most materials infrared spectra characteristically co…

Unfortunately, scientists hoping to go from data to new cures often require help from someone with experience in software engineering.

Now, Watershed Bio is helping scientists and bioinformaticians run experiments and get insights with a platform that lets users analyze complex datasets regardless of their computational skills.

“Scientists want to learn about the software and data science parts of the field, but they don’t want to become software engineers writing code just to understand their data,” co-founder and CEO Jonathan Wang ’13, SM ’15 says.

“The data in biology is growing exponentially, and the sequencing technologies generating this data are only getting better and cheaper,” Wang…

Innovator, futurist, and author Ray Kurzweil ’70 emphasized his optimism about artificial intelligence, and technological progress generally, in a lecture on Wednesday while accepting MIT’s Robert A. Muh Alumni Award from the School of Humanities, Arts, and Social Sciences (SHASS).

“People do not appreciate that the rate of progress is accelerating,” Kurzweil said, forecasting “incredible breakthroughs” over the next two decades.

The Muh Award was founded and endowed by Robert A. Muh ’59 and his wife Berit, and is one of the leading alumni honors granted by SHASS and MIT.

Robert and Berit Muh were both present at the lecture, along with their daughter Carrie Muh ’96, ’97, SM ’97.

He is also…

“Our collaboration with MBZUAI reflects a shared commitment to advancing AI in ways that are responsible, inclusive, and globally impactful.

At MIT, Philip Isola, the Class of 1948 Career Development Professor in the Department of Electrical Engineering and Computer Science, will serve as program lead.

At MBZUAI, Le Song, professor of machine learning, will take on the role.

Supported by MBZUAI — the first university dedicated entirely to advancing science through AI, and based in Abu Dhabi, U.A.E.

— the collaboration will fund a number of joint research projects per year.

You can think of robot training data as a collection of how-to videos that walk the systems through each motion of a task.

How exactly steerable scene generation guides its creation toward realism, however, depends on the strategy you choose.

Then, steerable scene generation can bring your requests to life with precision.

Each simulation appeared fluid and realistic, resembling the real-world, adaptable robots steerable scene generation could help train, one day.

In the future, they’d like to use generative AI to create entirely new objects and scenes, instead of using a fixed library of assets.

“It was very clear to me the extent to which inequity is a rampant issue around the world,” Donti says.

“I wanted to have a hand in developing those algorithms and tool kits by creating new machine learning techniques grounded in computer science,” she says.

While Donti was working on her PhD, she co-founded a nonprofit called Climate Change AI.

“We’re really thinking about where technology has a much longer-horizon impact and how technology, society, and policy all have to work together,” Donti says.

Another technology she is developing works to provide data that can be used in training machine learning systems for power system optimization.

If tokamaks can operate safely and efficiently, the machines could one day provide clean and limitless fusion energy.

The researchers trained and tested the new model on plasma data from an experimental tokamak in Switzerland.

The new model, which the team highlights this week in an open-access Nature Communications paper, could improve the safety and reliability of future fusion power plants.

“A delicate balance”Tokamaks are experimental fusion devices that were first built in the Soviet Union in the 1950s.

The TCV is a small experimental fusion experimental device that is used for research purposes, often as test bed for next-generation device solutions.

Defending against Prompt Injection with Structured Queries (StruQ) and Preference Optimization (SecAlign)Recent advances in Large Language Models (LLMs) enable exciting LLM-integrated applications.

To mitigate the imminent prompt injection threat, we propose two fine-tuning-defenses, StruQ and SecAlign.

Prompt Injection Attack: CausesBelow is the threat model of prompt injection attacks.

Prompt injection threat model in LLM-integrated applicationsWe propose that prompt injection has two causes.

Below are resources to learn more and keep updated on prompt injection attacks and defenses.

Repurposing Protein Folding Models for Generation with Latent DiffusionPLAID is a multimodal generative model that simultaneously generates protein 1D sequence and 3D structure, by learning the latent space of protein folding models.

In PLAID, we develop a method that learns to sample from the latent space of protein folding models to generate new proteins.

Unlike many previous protein structure generative models, PLAID addresses the multimodal co-generation problem setting: simultaneously generating both discrete sequence and continuous all-atom structural coordinates.

In this way, we can use structural understanding information in the weights of pretrained protein folding models for the p…

Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway DeploymentTraining Diffusion Models with Reinforcement LearningWe deployed 100 reinforcement learning (RL)-controlled cars into rush-hour highway traffic to smooth congestion and reduce fuel consumption for everyone.

The challenges of phantom jamsA stop-and-go wave moving backwards through highway traffic.

Smoothing behavior of RL AVs.

Overall, the steps towards deployment involved:Training in data-driven simulations: We used highway traffic data from I-24 to create a training environment with realistic wave dynamics, then validate the trained agent’s performance and robustness in a variety of new traffic scenarios.…

The goal is to promote and encourage certain responsible AI functionalities of AI systems.

The risks associated with generative AI can differ from or even amplify existing AI risks.

CBRN Information or Capabilities; Harmful Bias and Homogenization GV-3.2-002 Consider adjustment of organizational roles and components across lifecycle stages of large or complex generative AI systems, including: test and evaluation, validation, and red-teaming of generative AI systems; generative AI content moderation; generative AI system development and engineering; increased accessibility of generative AI tools, interfaces, and systems; and incident response and containment.

Also, at AWS we provide detailed…

As Sri Elaprolu, Director of the Generative AI Innovation Center, explains: “Effective validation creates alignment between vision and execution.

The Generative AI Innovation Center partnered with SparkXGlobal, an AI-driven marketing-technology company, to validate their new solution through comprehensive testing.

Certified partners, including Generative AI Partner Innovation Alliance members who receive direct enablement training from the Generative AI Innovation Center team, extend this expertise across industries.

Sabine Khan is a Strategic Initiatives Leader with the AWS Generative AI Innovation Center, where she implements delivery and strategy initiatives focused on scaling enterprise…

To address this challenge, we explore an approach that converts natural language to Gremlin queries, using Amazon Bedrock models such as Amazon Nova Pro.

In this post, we outline our methodology for generating Gremlin queries from natural language, comparing different techniques and demonstrating how to evaluate the effectiveness of these generated queries using large language models (LLMs) as judges.

Query similarityIn the query evaluation case, we propose two metrics: query exact match and query overall rating.

Execution accuracyTo calculate accuracy, we compare the results of the LLM-generated Gremlin queries against the results of ground truth queries.

Query generation latency and costF…

Over the past two years, companies have seen an increasing need to develop a project prioritization methodology for generative AI.

One new concern for generative AI compared to other domains is considering issues like hallucination, generative AI agents making incorrect decisions and then acting on those decisions through tool calls to downstream systems, and dealing with the rapidly changing regulatory landscape.

Responsible AI overviewThe AWS Well-Architected Framework defines responsible AI as “the practice of designing, developing, and using AI technology with the goal of maximizing benefits and minimizing risks.” The AWS responsible AI framework begins by defining eight dimensions of r…

This post demonstrates how to use AWS services, particularly Amazon Bedrock, to transform your creative processes through generative AI.

By using Amazon Bedrock and Amazon Nova models, the team can transform its creative process.

The Lambda function sends the contextual information from the knowledge base to Amazon Bedrock, along with the user’s creative briefs.

In the Amazon Bedrock console, find and select Model access from the navigation menu on the left.

Amazon Bedrock Knowledge BasesWith Amazon Bedrock Knowledge Bases, you can provide foundation models (FMs) and agents with contextual information from your organization’s private data sources, to deliver more relevant, accurate, and tai…

In Part 1 of our series, we introduced a proactive cost management solution for Amazon Bedrock, featuring a robust cost sentry mechanism designed to enforce real-time token usage limits.

Additional Amazon Bedrock analyticsIn addition to the custom metrics dashboard, CloudWatch provides automatic dashboards for monitoring Amazon Bedrock performance and usage.

These tags integrate with AWS cost management tools including AWS Cost Explorer, AWS Budgets, and AWS Cost Anomaly Detection, enabling detailed cost analysis and budget control.

Cost Explorer reportingTo create an Amazon Bedrock usage report in Cost Explorer based on your tag, complete the following steps:On the Billing and Cost Managem…

In this two-part series, we introduce a comprehensive solution for proactively managing Amazon Bedrock inference costs.

The goal is to deliver a predictable, cost-effective approach to Amazon Bedrock deployments that aligns with organizational financial constraints.

Amazon Bedrock model router – A separate Step Functions state machine that acts as a centralized gateway for invoking various Amazon Bedrock models.

Amazon Bedrock model router workflowThe Amazon Bedrock model router workflow is a separate Step Functions state machine responsible for invoking the appropriate Amazon Bedrock model based on the request parameters.

Token usage tracking with CloudWatch metricsThe Amazon Bedrock cost …

Solution overviewThe solution employs the Amazon Bedrock Converse API with Amazon Nova Premier to convert legacy C code to modern Java/Spring framework code through a systematic agentic workflow.

Individual file conversion:Conversion agent – Performs code migration on individual files based on the information from the code analysis agent.

– Performs code migration on individual files based on the information from the code analysis agent.

This framework-specific implementation facilitates reliable, scalable code conversion while maintaining the flexibility to handle diverse C code base structures and complexities.

ConclusionOur solution demonstrates that the Amazon Bedrock Converse API with …

To implement Progen2 on EC2 Inf2 instances, we traced custom Progen2 checkpoints trained on proprietary enzymes using the bucketing technique.

Custom docker containers are stored on the Amazon Amazon Elastic Container Registry (Amazon ECR).

To compare the cost of generating sequences using both services, we generated 10,000 sequences based on prompts derived from 10 common sequences in UniProtKB, using a temperature of 1.0.

These cost estimates include expected Amazon EC2 Spot interruption frequencies of 20% for Amazon EC2 g6e.xlarge instances powered by NVIDIA L40S Tensor Core GPUs and 5% for EC2 inf2.xlarge instances.

Natural, characterized enzymes used as prompts shown in red, generative…

In this post, we walk through how to take an ML model built in SageMaker Canvas and deploy it using SageMaker Serverless Inference.

Solution overviewTo demonstrate serverless endpoint creation for a SageMaker Canvas trained model, let’s explore an example workflow:Add the trained model to the Amazon SageMaker Model Registry.

PrerequisitesAs a prerequisite, you must have access to Amazon Simple Storage Service (Amazon S3) and Amazon SageMaker AI.

Save your model to the SageMaker Model RegistryComplete the following steps to save your model to the SageMaker Model Registry:On the SageMaker AI console, choose Studio to launch Amazon SageMaker Studio.

In this post, we showed how to deploy a Sage…

This blog post explores Amazon Nova Sonic voice agent applications and demonstrates how they integrate with Strands Agents framework sub-agents while leveraging Amazon Bedrock AgentCore to create an effective multi-agent system.

This simplifies the reasoning logic in Nova Sonic while keeping business logic encapsulated, similar to the software engineering modular design patterns.

Integrate Nova Sonic with AgentCore through tool use eventsAmazon Nova Sonic relies on tool use to integrate with agentic workflows.

If you’re exploring ways to enhance your AI applications, multi-agent patterns with Nova Sonic and AgentCore are a powerful approach worth testing.

Learn more about Amazon Nova Sonic …

The Amazon SageMaker HyperPod training operator further enhances training resilience for Kubernetes workloads through pinpoint recovery and customizable monitoring capabilities.

Amazon SageMaker HyperPod training operatorThe Amazon SageMaker HyperPod training operator helps you accelerate generative AI model development by efficiently managing distributed training across large GPU clusters.

It orchestrates lifecycles of training workers on each container and communicates with the Amazon SageMaker HyperPod training operator.

Training operator setupThis section walks through installing the Amazon SageMaker HyperPod training operator as an Amazon EKS add-on.

He specializes in Generative AI wor…

This post shows how TP ICAP used Amazon Bedrock Knowledge Bases and Amazon Bedrock Evaluations to build ClientIQ, an enterprise-grade solution with enhanced security features for extracting CRM insights using AI, delivering immediate business value.

They specifically used the following Amazon Bedrock managed capabilities:Amazon Bedrock foundation models – Amazon Bedrock provides a range of foundation models (FMs) from providers, including Anthropic, Meta, Mistral AI, and Amazon, accessible through a single API.

The Lambda functions use Amazon Bedrock FMs to determine whether a question is best answered by querying structured data in Amazon Athena or by retrieving information from an Amazon …

In the post Principal Financial Group increases Voice Virtual Assistant performance using Genesys, Amazon Lex, and Amazon QuickSight, we discussed the overall Principal Virtual Assistant solution using Genesys Cloud, Amazon Lex V2, multiple AWS services, and a custom reporting and analytics solution using Amazon QuickSight.

Solution overviewThe solution uses the services described in Principal Financial Group increases Voice Virtual Assistant performance using Genesys, Amazon Lex, and Amazon QuickSight.

Deploy the Amazon Lex V2 CDK stack from their local environment.

To maintain consistent, repeatable evaluations of your Amazon Lex V2 bots, it’s essential to manage and organize your test da…

At the ROSCon robotics conference, NVIDIA announced contributions to the ROS 2 robotics framework and the Open Source Robotics Alliance’s new Physical AI Special Interest Group, as well as the latest release of NVIDIA Isaac ROS.

At the conference, running through Wednesday, Oct. 29, NVIDIA announced collaborations with partners and the Open Source Robotics Alliance (OSRA), as well as new robotics software to advance open standards and accelerate robotics development.

In addition, NVIDIA announced that NVIDIA Isaac ROS 4.0 — a new collection of ROS-compatible, GPU-accelerated libraries and AI models — is now available on the NVIDIA Jetson Thor platform for deploying physical AI and robotics.…

NVIDIA DGX Spark provides an alternative to cloud instances and data-center queues.

The Blackwell-powered, compact supercomputer contains 1 petaflop of FP4 AI computer performance, 128 GB of coherent unified system memory, memory bandwidth of 273 GB/second, and the NVIDIA AI software stack preinstalled.

With DGX Spark, you can work with large, compute intensive tasks locally, without moving to the cloud or data center.

To show how DGX Spark performs at this workload, we ran three tuning tasks using different methodologies: full fine-tuning, LoRA, and QLoRA.

And get your DGX Spark, join the DGX Spark developer community, and start your AI-building journey today.

At Meta, we are constantly pushing the boundaries of LLM inference systems to power applications such as the Meta AI App.

These metrics highlight the distinct computational demands of LLM inference: Prefill is compute-intensive, while decoding is memory bandwidth-intensive.

Communication: Communication latency increases when parallelizing across multiple hosts.

In EP-based inference, we utilize a two-shot, all-to-all communication pattern to exchange tokens between data parallelism and expert parallelism ranks based on routing.

We are committed to continuous innovation to ensure efficient and scalable LLM inference for millions of users worldwide.

We leveraged AI to help us improve this database and understand our Scope 3 emissions associated with IT hardware by:Identifying similar components and applying existing PCFs to similar components that lack these carbon estimates.

Understanding the carbon footprint of IT racks and applying generative AI (GenAI) as a categorization algorithm to create a new and standard taxonomy .

If these similar components are not identified their carbon footprint estimates will remain at a lower data quality.

These similar components can be mapped to a representative proxy PCF, allowing us to use high-quality PCF data in similar components.

For example, we can scale the carbon footprint calculation for a …

At Open Compute Project Summit (OCP) 2025, we’re sharing details about the direction of next-generation network fabrics for our AI training clusters.

At Meta, we believe that open hardware is a catalyst for innovation — especially as data center infrastructure increasingly supports new and emerging AI technologies.

Open hardware plays a crucial role in enabling disaggregation, allowing us to break down traditional data center technologies into their core components.

Today, through OCP, we continue to advance open network technologies for the next generation of AI applications.

Ethernet for Scale-Up Networking in OCP: Meta’s Industry LeadershipAt Meta, we recognize that the future of AI and …

By leveraging LLMs we’ve been able to overcome the barriers that have prevented mutation testing from being efficiently deployed at scale.

Our presentations shared insights into how we’ve used LLMs to solve the major barriers that have prevented mutation testing at scale and highlighted new areas in automated software testing where LLMs can have a significant impact.

Mutation Testing Isn’t ScalableTraditional mutation testing generates a very large number of mutants, making it computationally expensive and difficult to scale to large industrial codebases.

Mutation Testing Requires a Lot of Computational ResourcesMutation testing is costly in terms of computational resources and developer ef…

Imagine being able to use AI to create 3D virtual worlds using prompts as easily as you can generate images.

In his keynote, Mark Zuckerberg shared his vision of a future where anyone can create virtual worlds using AI-powered tools like the ones available in the upcoming Meta Horizon Studio.

But AI is already making it easier than ever to create 3D assets.

On this episode of the Meta Tech Podcast, Pascal Hartig is joined by Mahima and Rakesh from Meta’s XR Tech team to discuss AssetGen, a new foundation model for 3D assets.

They talk about how they built and trained AssetGen, the important role LLMs have to play in the future of VR, and how they’re tackling the ambitious goal of generating…

As our user base grew globally, we scaled beyond single data center buildings and into data center regions consisting of multiple buildings.

Enter AI Workloads (2020)While we were navigating the challenges of scaling, we were also seeing glimpses of how AI workloads would impact our infrastructure.

To build out our AI infrastructure, we’ve leveraged solutions from partners like AMD and NVIDIA as well as our own custom silicon.

Constructing Prometheus has been a monumental engineering feat, with infrastructure spanning five or more data center buildings in a single data center region.

We are still early in the evolution and adoption of AI workloads.

AI is everywhere and, as network engineers, we are right in the thick of it: building the network infrastructure for AI.

Setting Context: Rapid Changes and EvolutionGiven AI continues to drive so much innovation in networking and general infrastructure, we once again focused @Scale: Networking on AI networking, sharing the new insights and progress in the field.

The Models and the Primary AI Workloads Are Rapidly Evolving.

More from @Scale:Networking 2025Please visit the @Scale YouTube channel to check out all the talks from this year’s Networking @Scale.

We look forward to what promises to be another rapid year of network and AI innovation that we’ll cover at the next @Scale: Networking in…

We’ve introduced a diversity-aware notification ranking framework to reduce uniformity and deliver a more varied and engaging mix of notifications.

Instagram leverages machine learning (ML) models to decide who should get a notification, when to send it, and what content to include.

To tackle this, we’ve introduced a diversity-aware notification ranking framework that helps deliver more diverse, better curated, and less repetitive notifications.

Introducing Instagram’s Diversity-Aware Notification Ranking FrameworkInstagram’s diversity-aware notification ranking framework is designed to enhance the notification experience by balancing the predicted potential for user engagement with the nee…

We’re exploring Meta’s Federation Platform, a scalable set of tools for managing compliance-related tasks, along with Privacy Waves, our method for batching these tasks and ensuring accountability.

To facilitate this, we developed the Federation Platform and Privacy Waves program:The Federation Platform breaks down large compliance-related initiatives into smaller, manageable workstreams.

Internal surveys reveal significantly higher positive sentiment for Privacy Waves tasks compared to ad-hoc tasks.

Step 6: Reporting and recognitionThe centralized distribution of tasks via Federation Platform and Privacy Waves streamline operational effectiveness and verification.

Expansions for the Federa…

Built on a fine-tuned Llama LLM, DRS evaluates code changes and metadata to produce a risk score and highlight potentially risky code snippets.

Production risk was one of the areas we tackled first.

The demand to build such features also led us to build the Risk Awareness Platform to provide risk analysis APIs and tool integrations.

We believe code risk can play a significant role in improving this tradeoff, so we will build more risk-aware features while improving their quality.

While code changes cause the plurality of SEVs at Meta, configuration changes are another large category.

What if you could control any device using only subtle hand movements?

New research from Meta’s Reality Labs is pointing even more firmly toward wrist-worn devices using surface electromyography (sEMG) becoming the future of human-computer interaction.

Generalization has been one of the most significant challenges in the field of human-computer interaction (HCI).

They discuss the road to creating a first-of-its-kind, generic human-computer neuromotor interface, what happens when software and hardware engineering meet neuroscience, and more!

And if you’re interested in learning more about career opportunities at Meta visit the Meta Careers page.

But concrete suppliers can utilize AI to develop and scale innovative concrete mixes as drop-in replacements, accelerating the discovery and integration of sustainable materials for large-scale use.

Meta’s AI model for green concreteDesigning concrete formulas is a complex, multi-objective problem.

To accelerate the concrete mix design process, Meta developed an AI model for sustainable concrete using BoTorch and Ax, Meta’s open-source software for Bayesian optimization and adaptive experimentation, respectively.

Our AI pipeline consists of the workflow of generating baseline data, training an AI model, using it to develop and validate new hypotheses, and then improving the baseline data an…

Meta and NVIDIA collaborated to accelerate vector search on GPUs by integrating NVIDIA cuVS into Faiss v1.10 , Meta’s open source library for similarity search.

In its latest release, Faiss 1.10.0 officially includes these algorithms from the NVIDIA cuVS library.

Faiss 1.10.0 also includes a new conda package that unlocks the ability to choose between the classic Faiss GPU implementations and the newer NVIDIA cuVS algorithms, making it easy for users to switch between GPU and CPU.

Build time (95% recall@10)Index Embeddings100M x 96(seconds) Embeddings5M x 1536(seconds) Faiss Classic Faiss cuVS Faiss Classic Faiss cuVS Faiss Classic Faiss cuVS IVF Flat IVF Flat 101.4 37.9 (2.7x) 24.4 15.2 (1…

We are introducing AutoPatchBench, a benchmark for the automated repair of vulnerabilities identified through fuzzing.

As illustrated, fixing a fuzzing crash involves:Analyzing the crash stack trace and the target code.

Inside AutoPatchBenchWe’re making AutoPatchBench publicly available as part of CyberSecEval 4 to encourage community collaboration in tackling the challenge of automating fuzzing crash repairs.

Then we find the lowest common ancestor (LCA) across all pairs of stacktraces offered by the groundtruth patch and the LLM patch.

As an experienced Security Engineer at Meta, your task is to address the following security-critical fuzzing crash.

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.

In the first part of this series, we covered the fundamentals of instruction fine-tuning (IFT).

def calculate_irs(instruction, output, reference_model): evaluation_prompt = f""" Instruction: {instruction} Model Output: {output} Rate how well the output follows the instruction on these criteria: 1.

| SourceHINT addresses a computational inefficiency in standard instruction fine-tuning: repeatedly reprocessing the same task instruction with every input example.

Read more about foundation model training infrastructure and other topics in Neptune’s 2025 State of Foundation Model Training Report.

First, during initial instruction fine-tuning across multiple diverse tasks, the model learns genera…

Large Language Model (LLM) inference at scale is challenging as it involves transferring massive amounts of model parameters and data and performing computations on large tensors.

In the following, we’ll use the Llama model family architecture as a specific example to understand the LLM workload at inference.

For a far more detailed analysis of the LLM workload at inference, see the chapter All About Transformer Inference in the book How to Scale Your Model, published by Google DeepMind.

See also How to Run LLMs Locally Read moreA quick primer on hardware for LLM inferenceA typical LLM inference cluster consists of several nodes, each with a multi-core CPU and multiple accelerator devices, …

In this article, we’ll explore the fundamental concepts of LLM grounding as well as strategies for optimally grounding models.

What is LLM grounding?

LLM grounding is analogous.

If relevant knowledge cannot be inferred from the data, then LLM grounding cannot yield more relevant responses.

When grounding LLMs using RAG, consider retaining only a few of the top hits (i.e., top-k) for your retrieval queries.

TL;DR Instruction fine-tuning (IFT) refines pre-trained large language models (LLMs) to follow specific task instructions by training on prompt-response pairs.

Instruction fine-tuning in a nutshellIFT tailors LLMs to follow user instructions by bridging their inherent next-word prediction with human-defined objectives.

Related LLM Fine-Tuning and Model Selection Using Neptune and Transformers Read moreParameter-efficient instruction fine-tuningWhile major foundation models like GPT-4 or Llama-2 undergo full parameter instruction fine-tuning during development, parameter-efficient fine-tuning (PEFT) methods have become widely adopted for instruction fine-tuning since the LoRA paper was publi…

Prompt injection 101: When prompts go rogueThe term ‘Prompt Injection’ comes from SQL injection attacks.

There is another claim of the independent discovery of prompt injection attacks, which suggests that Riley Goodside publicly exhibited a prompt injection in a tweet back in September 2022.

The indirect prompt injection attacks are classified into active, passive, user-driven and virtual prompt attacks.

Virtual prompt injection attacksThis injection type is closely related to passive injection attacks previously described.

Prompt injection: current challenges & lessons learnedThe arms race between prompt injection attacks and defenses is a challenge for researchers, developers, and users.

This simple idea avoids computing loss on input prompt tokens the model already knows.

Prompt tokens are (too) expensive in low-resource settingsDuring pre-training, LLMs are trained in causal language modeling through a next-token prediction task.

=> Mo fẹ́ràn ìrẹsì,” the model is trained to predict every token, from the prompt to the actual answer:Step Prompt Next token 1 Translate English Static prompt 2 Translate English to Static prompt 3 Translate English to Yoruba: Static prompt 4 Translate English to Yoruba: I 5 Translate English to Yoruba: I love 6 Translate English to Yoruba: I love rice.

This is straightforward to implement in PyTorch by masking out the prompt tokens in the label …

What gradient issues occur during foundation model training?

During training, gradient descent updates model parameters by computing the gradients of the loss function via forward and backward passes.

The green line corresponds to a learning rate of 10, while the orange line has a learning rate of 0.1.

The gradient norm for the orange line with LR = 0.1 is very high in the first steps, while the gradient norm of the green line with LR = 10 diverges to NaN after a few steps.

Techniques for gradient stabilizationMonitoring gradient norms and training loss provides insights into the learning dynamics of the foundation models.

Unstructured pruning removes individual weights, while structured pruning removes entire model components.

In the context of MoEs, as expert structures from training MoEs correspond to such patterns, pruning experts is a natural fit for structured pruning.

Thus, structured pruning does not significantly decrease kurtosis, leaving plenty of margin for unstructured pruning.

Since structured pruning primarily reduces architectural redundancy rather than reshaping the underlying weight distribution, our two-phase approach—leveraging unstructured pruning after structured pruning—outperforms unstructured-only pruning.

Since STUN does not make any assumption about base MoE models, it is generaliza…

Related Building LLM Applications With Vector Databases Read moreDimensions of RAG evaluationEvaluating a RAG pipeline means assessing its behavior across three dimensions:1.

The evaluation of the RAG pipeline is a multi-step process, starting with creating an evaluation dataset, then evaluating the individual components (retriever, generator, etc.

Curating an evaluation datasetThe first step in the RAG evaluation process is the creation of a ground truth dataset.

MAP considers both the presence and rank of relevant chunks but fails to consider the relative position of relevant chunks.

However, not all retrieved chunks are equally relevant and sometimes, the most relevant chunks might not b…

The efficiency of an LLM agent depends on the selection of the right LLM model.

In this article, we’ll introduce the fundamental building blocks of LLM agents and then walk through the process of building an LLM agent step by step.

Building an LLM agent from scratchIn the following, we’ll build a trip-planning LLM agent from scratch.

Using AutoGen’s OpenAI Assistant Agent, we instantiate a prompt that the LLM agent will follow throughout its interactions.

Related Ethical Considerations and Best Practices in LLM Development Read moreEnhancing LLM agent performanceWhile architecting an LLM agent, you have to keep in mind opportunities to improve the performance of the LLM agent.

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…

Theo's Video: https://www.youtube.com/watch?v=bAYZjVAodoo

Cloudflare article: https://blog.cloudflare.com/code-mode/ Links:

Homepage: https://ykilcher.com

Merch: https://ykilcher.com/merch

YouTube: https://www.youtube.com/c/yannickilcher

Twitter: https://twitter.com/ykilcher

Discord: https://ykilcher.com/discord

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

SubscribeStar: https://www.subscribestar.com/yannickilcher

Patreon: https://www.patreon.com/yannickilcher

Bitcoin (BTC): bc1q49lsw3q325tr58ygf8…

Paper: https://arxiv.org/abs/2508.21038 Abstract:

Vector embeddings have been tasked with an ever-increasing set of retrieval tasks over the years, with a nascent rise in using them for reasoning, instruction-following, coding, and more. These new benchmarks push embeddings to work for any query and any notion of relevance that could be given. While prior works have pointed out theoretical limitations of vector embeddings, there is a common assumption that these difficulties are exclusively due to unrealistic queries, and those that are not can be overcome with better training data and larger models. In this work, we demonstrate that we may encounter these theoretical limitations in realist…

jack Morris's investigation into GPT-OSS training data https://x.com/jxmnop/status/1953899426075816164?t=3YRhVQDwQLk2gouTSACoqA&s=09

Paper: https://research.trychroma.com/context-rot Abstract:

Large Language Models (LLMs) are typically presumed to process context uniformly—that is, the model should handle the 10,000th token just as reliably as the 100th. However, in practice, this assumption does not hold. We observe that model performance varies significantly as input length changes, even on simple tasks.

In this report, we evaluate 18 LLMs, including the state-of-the-art GPT-4.1, Claude 4, Gemini 2.5, and Qwen3 models. Our results reveal that models do not use their context uniformly; instead, their performance grows increasingly unreliable as input length grows. Authors: Kelly Hong, Anton Troynikov, Jeff Huber Links:

…

Paper: https://arxiv.org/abs/2507.02092

Code: https://github.com/alexiglad/EBT

Website: https://energy-based-transformers.github.io/ Abstract:

Inference-time computation techniques, analogous to human System 2 Thinking, have recently become popular for improving model performances. However, most existing approaches suffer from several limitations: they are modality-specific (e.g., working only in text), problem-specific (e.g., verifiable domains like math and coding), or require additional supervision/training on top of unsupervised pretraining (e.g., verifiers or verifiable rewards). In this paper, we ask the question "Is it possible to generalize these System 2 Thinking approaches, and de…

An in-depth look at Anthropic's Transformer Circuit Blog Post

Part 1 here: https://youtu.be/mU3g2YPKlsA

Discord here: https;//ykilcher.com/discord https://transformer-circuits.pub/2025/attribution-graphs/biology.html Abstract:

We investigate the internal mechanisms used by Claude 3.5 Haiku — Anthropic's lightweight production model — in a variety of contexts, using our circuit tracing methodology. Authors:

Jack Lindsey†, Wes Gurnee*, Emmanuel Ameisen*, Brian Chen*, Adam Pearce*, Nicholas L. Turner*, Craig Citro*,

David Abrahams, Shan Carter, Basil Hosmer, Jonathan Marcus, Michael Sklar, Adly Templeton,

Trenton Bricken, Callum McDougall◊, Hoagy Cunningham, Thomas Henighan, Adam Jermyn, Andy …

An in-depth look at Anthropic's Transformer Circuit Blog Post https://transformer-circuits.pub/2025/attribution-graphs/biology.html Abstract:

We investigate the internal mechanisms used by Claude 3.5 Haiku — Anthropic's lightweight production model — in a variety of contexts, using our circuit tracing methodology. Authors:

Jack Lindsey†, Wes Gurnee*, Emmanuel Ameisen*, Brian Chen*, Adam Pearce*, Nicholas L. Turner*, Craig Citro*,

David Abrahams, Shan Carter, Basil Hosmer, Jonathan Marcus, Michael Sklar, Adly Templeton,

Trenton Bricken, Callum McDougall◊, Hoagy Cunningham, Thomas Henighan, Adam Jermyn, Andy Jones, Andrew Persic, Zhenyi Qi, T. Ben Thompson,

Sam Zimmerman, Kelley Rivoire, Thom…

#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…

A fuller version of this explanation, also including the reason we care about complex exponents in the first place: https://youtu.be/-j8PzkZ70Lg

Visualizing the most important tool for differential equations.

Previous chapter: https://youtu.be/-j8PzkZ70Lg

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.

Home page: https://www.3blue1brown.com Artwork by Kurt Bruns Engine animation borrowed with permission from this (excellent) blog: https://ciechanow.ski/internal-combustion-engine/ Timestamps:

0:00 - Understanding the engine

1:16 - Key background ideas

5:41 - Definition and intuition

10:43 - Complex integration

20:43 - Analytic continuation

23:52 - The transform of exponentials

26:15 - A deep look at cos(t)

32:59 - W…

A fuller version of this explanation, also including the reason we care about complex exponents in the first place: https://youtu.be/-j8PzkZ70Lg

How dynamics explain Euler's formula, and vice versa.

Early view of the Laplace Transform video: https://www.patreon.com/posts/laplace-early-140428165

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.

Home page: https://www.3blue1brown.com Timestamps:

0:00 - Intro

1:51 - Euler's formula explained dynamically

9:27 - The harmonic oscillator

21:08 - General linear equations

22:47 - Motivating the Laplace Transform ------------------ These animations are largely made using a custom Python library, manim. See the FAQ comments here:

https://3b1b.co/faq#manim Music by Vincent Rubin…

What role were ruler and compass constructions really serving?

Check out Ben's channel: @bensyversen Interview with the author of this video: https://youtu.be/VohYM99j8e0

Supporters get early views of new videos: https://3b1b.co/support Written, produced, edited, and animated by Ben Syversen

Additional editing: Jack Saxon

3d Blender model: Jan-Hendrik Müller

Additional Blender help: Thibaut Modrzyk (@Deepia)

Illustrations: Alex Zepherin/DonDada Studio

Drums: Jeremy Gustin

Additional music from Epidemic Sound Special thanks to Viktor Blåsjö: https://intellectualmathematics.com/opinionated-history-of-mathematics/ References/Recommended reading: Euclid’s Elements:

Visual edition of Book 1: htt…

Full video: https://youtu.be/_BrFKp-U8GI

Sol Lewitt's "Incomplete Open Cubes" and rediscovering Burnside's lemma in group theory

This is a guest video by Paul Dancstep: https://youtu.be/JEeM2ABUMoo

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

An equally valuable form of support is to share the videos.

Home page: https://www.3blue1brown.com Thanks to the Wadsworth Atheneum for granting permission to use LeWitt's notebooks. Talks by Paul you can find online: What is Category Theory:

https://www.youtube.com/watch?app=desktop&v=eXBwU9ieLL0 How to Predict Eclipses:

https://www.exploratorium.edu/eclipse/video/how-predict-eclipses Theo Jansen's Strandbeests

https://www.youtube.com/w…

Deriving the Boltzmann formula, defining temperature, and simulating liquid/vapor.

@SpectralCollective has the second part: https://youtu.be/yEcysu5xZH0

You can play with a simulation of this model here: https://vilas.us/simulations/liquidvapor/

These lessons are funded directly by viewers: https://3b1b.co/support

Home page: https://www.3blue1brown.com Notes from Vilas:

1) This open problem is to prove the ergodicity of the deterministic dynamical systems that are used to model the molecule-level physics. A good example of such a dynamical system is the box with particles evolving according to Newton's laws with elastic collisions, like in the video. 2) This video assumes that all probabili…

From this guest video by @WelchLabsVideo on how diffusion models work: https://youtu.be/iv-5mZ_9CPY

How AlphaGeometry combines logic and intuition.

Share stories about AI in math research for an upcoming video: https://forms.gle/gr9aZVdUrW5T3yDg9

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.

Home page: https://www.3blue1brown.com AlphaGeometry announcement:

https://deepmind.google/discover/blog/alphageometry-an-olympiad-level-ai-system-for-geometry/ Similar open-source model, Newclid, by Harmonic:

https://harmonic.fun/news#blog-post-geometry Timestamps:

0:00 - What's surprising

1:33 - Solve without AI

7:10 - Where AI comes in

12:48 - Grant's comments ------------------…

Diffusion models, CLIP, and the math of turning text into images

Welch Labs Book: https://www.welchlabs.com/resources/imaginary-numbers-book Sections

0:00 - Intro

3:37 - CLIP

6:25 - Shared Embedding Space

8:16 - Diffusion Models & DDPM

11:44 - Learning Vector Fields

22:00 - DDIM

25:25 Dall E 2

26:37 - Conditioning

30:02 - Guidance

33:39 - Negative Prompts

34:27 - Outro

35:32 - About guest videos + Grant’s Reaction Special Thanks to:

Jonathan Ho - Jonathan is the Author of the DDPM paper and the Classifier Free Guidance Paper.

https://arxiv.org/pdf/2006.11239

https://arxiv.org/pdf/2207.12598 Preetum Nakkiran - Preetum has an excellent introductory diffusion tutorial:

https://arxiv.org/pdf/24…

Make a math explainer, get feedback, and receive prizes: https://some.3b1b.co

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. ------------------ These animations are largely made using a custom Python library, manim. See the FAQ comments here:

https://3b1b.co/faq#manim

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

https://github.com/ManimCommunity/manim/ All code for specific videos is visible here:

https://github.com/3b1b/videos/ The music is by Vincent Rubinetti.

https://www.vincentrubinetti.com

https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown

https://open.spotify.com/…

Addressing viewer questions from the last video.

These lessons are funded directly by viewers: https://3b1b.co/support

An equally valuable form of support is to share the videos. ------------------ These animations are largely made using a custom Python library, manim. See the FAQ comments here:

https://3b1b.co/faq#manim

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

https://github.com/ManimCommunity/manim/ All code for specific videos is visible here:

https://github.com/3b1b/videos/ The music is by Vincent Rubinetti.

https://www.vincentrubinetti.com

https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown

https://open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u ------------------ 3blue1brown is a ch…

Qubits, state vectors, and Grover's algorithm for search.

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

An equally valuable form of support is to share the videos. The subtitles on this video were done using AI, and are likely imperfect, but they are open for community corrections at https://criblate.com/ Adam Brown's paper on the connection between Grover's Algorithm and block collisions:

https://arxiv.org/pdf/1912.02207 If you want to learn the relevant underlying quantum mechanics here, a very friendly resource is the course Mithuna at Looking Glass Universe is currently putting together. See, for instance, this explainer of a qubit:…

Full video: https://youtu.be/RQWpF2Gb-gU

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide:

Rent one of their GPUs with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here:

https://add-moo.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, Chr…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide:

Rent one of their GPUs with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝Paper: https://drive.google.com/file/d/1OrOKJH_im1L4j1cJB18sfvNHEbZVSqjL/view

Code and examples are available here: https://github.com/st-tech/ppf-contact-solver

Guide on how to try it: https://drive.google.com/file/d/1n068Ai_hlfgapf2xkAutOHo3PkLpJXA4/view Sources:

https://www.youtube.com/watch?v=5GDIoshj9Rw

https://www.youtube.com/watch?v=X53VuYLP0VY

https://www.youtube.com/shorts/x0WjJgotCXU

http…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide:

Rent one of their GPUs with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here:

https://video-zero-shot.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 Sh…

❤️ Check out the Fully Connected Conference by Weights & Biases - https://wandb.me/fclon2025-2min

20% discount code: FCLON2025-2MIN 📝 The paper is available here:

https://www.cemyuksel.com/research/hairmesh_rendering/ Try the demo and try to break it, it is super fun:

https://www.cemyuksel.com/research/hairmesh_rendering/demo/ 📝 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…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide:

Rent one of their GPUs with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here:

https://graphics.cs.utah.edu/research/projects/ogc/ Sources:

https://www.youtube.com/watch?v=CfEg7fucVYg 📝 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 Patr…

❤️ Check out Vast.ai and run DeepSeek or any AI project: https://vast.ai/papers 📝 Magica 2 is available here:

https://blog.dynamicslab.ai/ Try it out:

https://demo.dynamicslab.ai/chaos 📝 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, Sven Pfiffner, Taras…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide:

Rent one of their GPUs with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here:

https://ge.in.tum.de/publications/very-large-scale-two-phase-flip/ 📝 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 Sources:

https://www.youtube.com/watch?v=ielqS1hkoLc

https://www.youtube.com/wat…

❤️ Check out the Fully Connected Conference by Weights & Biases - https://wandb.me/fclon2025-2min

20% discount code: FCLON2025-2MIN 📝 The paper is available here:

https://github.com/Lightricks/LTX-Video 📝 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, Sv…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide:

Rent one of their GPU's with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here:

https://www.sdiolatz.info/publications/00ImageGS.html Genetic algorithm for the Mona Lisa:

https://users.cg.tuwien.ac.at/zsolnai/gfx/mona_lisa_parallel_genetic_algorithm/ 📝 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/art…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide:

Rent one of their GPU's with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here:

https://nadmag.github.io/LightLab/ 📝 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 S…

❤️ Check out DeepInfra and run DeepSeek or many other AI projects: https://deepinfra.com/papers 📝 The paper is available here:

https://graphics.stanford.edu/papers/waveblender/ 📝 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, Sven Pfiffner, Taras Bobrovy…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide:

Rent one of their GPUs with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here:

https://diffusecloc.github.io/website/ 📝 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, …

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

https://arxiv.org/abs/2405.14595 📝 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 Sources:

https://www.youtube.com/shorts/Mq7zzK-ZiWI

https://www.youtube.com/watch?v=A_Cdz-QBlT4 🙏 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…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide:

Rent one of their GPU's with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b Genie 3:

https://deepmind.google/discover/blog/genie-3-a-new-frontier-for-world-models/ Sources:

https://x.com/amoufarek/status/1955776162447102238

https://x.com/amoufarek/status/1955299375548076382

https://x.com/holynski_/status/1953882726656094622

https://x.com/holynski_/status/1953879983535141043

https://x.com/RuiHuang_art/status/1954716703340048877

https://x.com/mattmcgill_/status/1953827141700…

Математики говорят на собственном языке, который чем-то напоминает естественные, но всё-таки очень сильно от них отличается. Возможные сложности и путаница только усугубляются в математической физике, где такие слова как «энергия» или «сила» имеют не только устоявшийся обиходный смысл, но и свой отдельный смысл в различных физических контекстах и теориях. Размышления о языке математики имеют прямое прикладное значение в эпоху больших языковых моделей, и я постараюсь поговорить как о том, чего бы математикам от этих моделей хотелось, так и о том, с какими сложностями мы можем столкнуться на пути осуществления мечты. Больше материалов про ML в канале: https://t.me/+Ug9D4CjJrJxmZGRi #ML #AI #M…

В последние годы диффузионные модели были основным драйвером развития генеративного моделирования изображений, а область Image Understanding резко продвинулась вперёд за счёт Visual Language Models. Сейчас мы видим много работ, связанных с объединением дискриминативного и генеративного моделирования в одной архитектуре. Сергей Овчаренко, руководитель отдела мультимодальных анализа и генерации в Яндекс R&D, предложил обсудить, насколько это практически оправданно и станут ли такие модели новой парадигмой. Презентацию смотрите тут: https://disk.yandex.ru/d/8kvpK-u_lFBxkg Больше материалов про ML в канале: https://t.me/+Ug9D4CjJrJxmZGRi #ML #AI #MachineLearning #DeepLearning #LLM #VLM #NeuralN…

Андрей Татаринов, CEO и CTO в Epoch8, рассказал о системе компьютерного зрения для приложения Brickit, которое сканирует множество деталей LEGO и подсказывает, что из них можно собрать. В докладе Андрей объяснил, как ребята боролись с редкими классами и оптимизировали пайплайн под мобильные устройства. А ещё он поделился MLOps-решениями для масштабируемого дообучения и поддержки модели для динамичных данных. Больше материалов про ML в канале: https://t.me/+Ug9D4CjJrJxmZGRi #ML #AI #MachineLearning #DeepLearning #LLM #VLM #NeuralNetworks #Transformers #GenerativeAI #NLP #ComputerVision #DataScience #BigData #MLOps #ModelTraining #AIResearch #ArtificialIntelligence #AIDevelopment #AIFuture #T…

Карина Романова, руководитель команды CoreLLM: user behavior в Wildberries & Russ, рассказала об эволюции модели UniSRec: от классической задачи рекомендаций к универсальным поведенческим эмбеддингам, которые можно применять в разных ML-системах. А ещё Карина разобрала, как архитектура из статьи Towards Universal Sequence Representation Learning for Recommender Systems была адаптирована под индустриальные требования, какие улучшения дали наибольший прирост качества и как эти решения интегрированы в масштабную продакшен-инфраструктуру. Больше материалов про ML в канале: https://t.me/+Ug9D4CjJrJxmZGRi #ML #AI #MachineLearning #DeepLearning #LLM #VLM #NeuralNetworks #Transformers #GenerativeAI…

Пётр Зайдель, старший ML-инженер в Яндекс Музыке, рассказал, как ребята внедряли в сервис target-aware реалтайм-трансформер с ранним связыванием. Всё это — с деталями архитектуры модели, тонкостями пайплайна обучения, особенностями продакшен-инфраструктуры инференса и собственными инсайтами. Больше материалов про ML в канале: https://t.me/+Ug9D4CjJrJxmZGRi #ML #AI #MachineLearning #DeepLearning #LLM #VLM #NeuralNetworks #Transformers #GenerativeAI #NLP #ComputerVision #DataScience #BigData #MLOps #ModelTraining #AIResearch #ArtificialIntelligence #AIDevelopment #AIFuture #Tech #Engineering #Yandex #SberAI #AvitoTech #TBank #AIConference #YandexML #DataEngineering #RecommenderSystems #AITalk…

Данил Кашин, руководитель команды претрейна VLM в Яндекс R&D, рассказал, как создаются визуально-языковые модели: от концепции и архитектуры до оценки качества. В докладе он разобрал, почему именно предобучение определяет итоговые возможности модели, какие данные нужны, как их отбирать, а также какие ловушки подстерегают разработчиков на этом пути. Больше материалов про ML в канале: https://t.me/+Ug9D4CjJrJxmZGRi #ML #AI #MachineLearning #DeepLearning #LLM #VLM #NeuralNetworks #Transformers #GenerativeAI #NLP #ComputerVision #DataScience #BigData #MLOps #ModelTraining #AIResearch #ArtificialIntelligence #AIDevelopment #AIFuture #Tech #Engineering #Yandex #SberAI #AvitoTech #TBank #AIConfere…

Прогресс в русскоязычном синтезе речи замедляется. Нужны масштабные публичные датасеты. Чтобы восполнить этот пробел, группа энтузиастов опубликовала самый большой на сегодня корпус чистой русской речи, который содержит 4700 часов аудио из открытых источников. Денис Петров, старший аудио-ML-инженер в Audio2Midi, разобрал пайплайн датасета: нормализацию аудио, разделение речи и шума, диаризацию, сегментацию, автоматическую фильтрацию качества и транскрипцию. Больше материалов про ML в канале: https://t.me/+Ug9D4CjJrJxmZGRi #ML #AI #MachineLearning #DeepLearning #LLM #VLM #NeuralNetworks #Transformers #GenerativeAI #NLP #ComputerVision #DataScience #BigData #MLOps #ModelTraining #AIResearch #…

Андрей Бабкин, ведущий исследователь-разработчик в Т-Банке, рассказал, как ребятам удалось объединить данные из разных сервисов, чтобы получить последовательности действий клиентов. А ещё Андрей объяснил, как трансформеры над этими последовательностями помогли улучшить клиентский опыт. Больше материалов про ML в канале: https://t.me/+Ug9D4CjJrJxmZGRi #ML #AI #MachineLearning #DeepLearning #LLM #VLM #NeuralNetworks #Transformers #GenerativeAI #NLP #ComputerVision #DataScience #BigData #MLOps #ModelTraining #AIResearch #ArtificialIntelligence #AIDevelopment #AIFuture #Tech #Engineering #Yandex #SberAI #AvitoTech #TBank #AIConference #YandexML #DataEngineering #RecommenderSystems #AITalks #MLT…

Добро пожаловать на вечерний митап для ML-инженеров от Яндекса. В этот раз поговорим про VLM, оценку и сложности релизов LLM, а также про кэш. ML Party пройдет в Белграде. Какие доклады услышим: 19:00 (21:00 Мск) — «Кэш для товарного поиска Лавки на основе LLM» (Евгений Комаров, Руководитель команды ML Поиска, Яндекс Лавка) 19:30 (21:30 Мск) — «Релиз: что может пойти не так?» (Алексей Колесов, CTO, Яндекс R&D) 20:15 (22:15 Мск) — «Как найти лучшую генеративную модель для своей задачи» (Кирилл Власов, PO AI Studio, Yandex Cloud) 20:45 (22:45 Мск) — «Визуально-языковые модели (VLM) в Яндексе: подходы, данные, подводные камни» (Сергей Овчаренко, Руководитель отдела мультимодальных анализа и ге…

Николай Савушкин, руководитель службы рекомендательных технологий в Яндекс R&D, поделился опытом адаптации ARGUS к различным продуктам в Яндексе. А ещё Николай рассказал, какие изменения претерпели архитектура и процесс обучения, где нам удалось существенно повысить качество, а где получилось упростить модель. Больше материалов про ML в канале: https://t.me/+Ug9D4CjJrJxmZGRi #ML #AI #MachineLearning #DeepLearning #LLM #VLM #NeuralNetworks #Transformers #GenerativeAI #NLP #ComputerVision #DataScience #BigData #MLOps #ModelTraining #AIResearch #ArtificialIntelligence #AIDevelopment #AIFuture #Tech #Engineering #Yandex #SberAI #AvitoTech #TBank #AIConference #YandexML #DataEngineering #Recomme…

Когда в интернете появляются новые мемы и инфоповоды, пользователи тут же заваливают нейросети вопросами о них. Чтобы голосовой помощник мог поддержать диалог на трендовую тему, нужно узнавать о новых терминах хотя бы за несколько недель до пика их популярности. Иными словами, тренды и мемы приходится предугадывать. Арсений Нестюк, руководитель команды аналитики голосового ввода в Яндекс R&D, рассказал, как автоматически вытаскивать их из потока Алисы и успевать подготовить продукт к актуальным темам. Больше материалов про ML в канале: https://t.me/+Ug9D4CjJrJxmZGRi #ML #AI #MachineLearning #DeepLearning #LLM #VLM #NeuralNetworks #Transformers #GenerativeAI #NLP #ComputerVision #DataScience…

Антон Семенистый, старший DS-инженер в департаменте монетизации AvitoTech, рассказал, как Авито изучает нейросетевые модели для предсказания CTR в поиске. А ещё Антон поделился итогами исследований и экспериментов, в результате которых команда получила устойчивый прирост по ML-метрикам. Больше материалов про ML в канале: https://t.me/+Ug9D4CjJrJxmZGRi #ML #AI #MachineLearning #DeepLearning #LLM #VLM #NeuralNetworks #Transformers #GenerativeAI #NLP #ComputerVision #DataScience #BigData #MLOps #ModelTraining #AIResearch #ArtificialIntelligence #AIDevelopment #AIFuture #Tech #Engineering #Yandex #SberAI #AvitoTech #TBank #AIConference #YandexML #DataEngineering #RecommenderSystems #AITalks #ML…

Трансформеры уже пишут код и рисуют картины, но могут ли они управлять автомобилем? Максим Спорышев, руководитель службы поведения и предсказания в Яндекс Автономном транспорте, рассказал об уникальных архитектурах моделей, которые используются для решения этой задачи. Внутри: путь команды от первых ML-экспериментов с генеративными нейросетями до регулярных испытаний автопилота на реальных автомобилях. Больше материалов про ML в канале: https://t.me/+Ug9D4CjJrJxmZGRi #ML #AI #MachineLearning #DeepLearning #LLM #VLM #NeuralNetworks #Transformers #GenerativeAI #NLP #ComputerVision #DataScience #BigData #MLOps #ModelTraining #AIResearch #ArtificialIntelligence #AIDevelopment #AIFuture #Tech #E…

В Городских сервисах Яндекса мы уже несколько лет используем алгоритмы для снижения аварийности. С ними сложные маршруты достаются опытным водителям, а простые — новичкам. За этой идеей стоит длинный путь развития: от первых версий с ошибками и «детскими болезнями» до стабильной системы. В докладе Филипп Ульянкин, руководитель группы технологий безопасности поездки в Техплатформе Городских сервисов Яндекса, рассказал, как мы улучшали технологию и какие инсайты получили в процессе. Больше материалов про ML в канале: https://t.me/+Ug9D4CjJrJxmZGRi #ML #AI #MachineLearning #DeepLearning #LLM #VLM #NeuralNetworks #Transformers #GenerativeAI #NLP #ComputerVision #DataScience #BigData #MLOps #Mod…

0:00:00 введение

0:00:50 Вафли для NVIDIA

0:03:40 Intel для Microsoft

0:07:20 Google для Anthropic

0:10:17 Неэффективные GPU

0:15:40 АЭС для Amazon

0:19:46 ChatGPT торгует криптой

0:22:59 Железо и ПО для ИИ в реестре

0:28:41 Липецкий суперкомпьютер

0:34:17 Письмо 800

0:55:08 ChatGPT и вежливость

1:01:11 Perplexity учит язык 1:05:46 Робот по цене iPhone

1:15:58 Amazon не дает спать ИИ-саммари:

В этом выпуске подкаста обсуждаются актуальные темы в области технологий, включая импортозамещение в производстве чипов, диверсификацию цепочек поставок, сотрудничество между Google и Anthropic, а также эффективность использования GPU и развитие малых атомных электростанций. Также рассматриваются вопро…

Спикеры: Антон Воронов, Валентин Малых, Тимур Мерлин Data Fest 2025: https://ods.ai/events/datafest2025

Трек-секция Avito ML Challenge: https://ods.ai/tracks/df25_challengeavitotech

______

Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

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

Канал с апдейтами по курсам: https://t.me/odscourses

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

В этот раз Капитанский мостик проводился вживую, как часть Сибирского ДатаФеста, были живые вопросы из аудитории.

0:00:00 введение

0:00:04 Альтман и тупые

0:03:57 OpenAI и Аргентина

0:13:18 Илон Маск и игры

0:22:11 В Китае заменили женщину

0:28:41 250 плохих документов

0:41:18 Нидерланды украли чипы

0:58:02 OpenAI и Broadcom

1:01:23 nVIDIA и AMD

1:05:29 Плохой консенсус в Долине

1:10:18 Соцсети заканчиваются

1:18:28 Альтман обещал взрослый контент

1:27:30 Т-банк и Китай

1:29:44 Доклад State of AI

1:34:43 Книги и люди Канал в mattermost: https://mm.ods.ai/ods/channels/data_captain (авторизуйтесь через ODS.ai) 1. https://fortune.com/2025/10/03/sam-altman-on-ai-bubble-people-make-some-dumb-cap…

В этот раз Капитанский мостик проводился вживую, как часть Сибирского ДатаФеста, были живые вопросы из аудитории. 0:00:04 Альтман и тупые

0:03:57 OpenAI и Аргентина

0:13:18 Илон Маск и игры

0:22:11 В Китае заменили женщину

0:28:41 250 плохих документов

0:41:18 Нидерланды украли чипы

0:58:02 OpenAI и Broadcom

1:01:23 nVIDIA и AMD

1:05:29 Плохой консенсус в Долине

1:10:18 Соцсети заканчиваются

1:18:28 Альтман обещал взрослый контент

1:27:30 Т-банк и Китай

1:29:44 Доклад State of AI

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Спикер: Дмитрий Федотов Тема: Мечта из бегущего по лезвию или разработка проекционной системы на базе робота Unitree Go2 Data Fest 2025: https://ods.ai/events/datafest2025

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Спикер: Александр Квашнин

Доклад про поиск и исследование новых функциональных материалов методами искусственного интеллекта Data Fest 2025: https://ods.ai/events/datafest2025

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Спикер: Даниил Васильев, Технологический предприниматель с 2015 года, основатель 2-х действующих IT-компаний. Общий стаж работы в сфере IT - 12 лет. Эксперт по построению продуктовых и проектных команд, запуску технологических стартапов. Запускаю ai medtech стартап "Я здоров". Выступил на более чем 20 IT и бизнес-ивентах. Спикер российских IT-конференций, ведущих международных отраслевых мероприятий, бизнес-завтраков. Выступал на обучающих семинарах, организованных «Опорой России», «Бизнес-инкубатором», и др. Проблема разрозненной структуры данных и форм бланков.

Сбор базы данных биомаркеров, единиц измерений и референсных значений.

Подходы к решению: OCR + векторный поиск, LLM, обучение мо…

Спикер: Владислав Плотников, главный программист, УНМЦ "Гидронавтика", МГТУ им. Н.Э. Баумана Data Fest 2025: https://ods.ai/events/datafest2025

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Спикеры: Алексей Масютин, руководитель Центра ИИ ВШЭ;

Александр Гущин, главный тренер на межнар по AI из Центрального Университета;

Ренат Исказиев, руководитель программы «Цифровые навыки и компетенции» Благотворительного фонда Сбербанка «Вклад в будущее»;

Полина Полунина, Руководитель по эффективности и методологии внедрения ИИ Альфабанк;

Ульяна Артамошина, AI Education Lead Газпромбанк;

Андрей Кармацкий, CPO Яндекс Учебник. Data Fest 2025: https://ods.ai/events/datafest2025

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Спикер: Андрей Терещенко Data Fest 2025: https://ods.ai/events/datafest2025

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Спикер: Сергей Линок, MIRT Data Fest 2025: https://ods.ai/events/datafest2025

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Спикер: Павел Денисенко, X5 Tech

Тема: Дата-платформа нового поколения на основе принципов Data Mesh и Lakehouse. Highload. Composable. All-in-One Data Fest 2025: https://ods.ai/events/datafest2025 Презентацию к докладу Вы можете скачать в треке секции ML in Retail: https://ods.ai/tracks/df25-ml-in-retail ______

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Спикеры: Вячеслав Васильев, Мария Ковалёва; Sber AI, руководитель направления по исследованию данных; Sber AI, ведущий специалист по исследованию данных Data Fest 2025: https://ods.ai/events/datafest2025

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

Patreon:

https://www.patreon.com/primerlearning Helpful resources if you want to learn more about neural networks

https://www.youtube.com/@AndrejKarpathy

https://course.fast.ai/

https://www.youtube.com/@WelchLabsVideo

https://www.youtube.com/@3blue1brown Early papers. These probably aren't helpful for understanding the concepts in this video, but if you're interested in history.

The Perceptron – A perceiving and recognizing automaton: https://bpb-us-e2.wpmucdn.com/websites.umass.edu/dist/a/27637/files/2016/03/rosenblatt-1957.pdf

The Perceptron: A probabilistic model for information storage and organization in the brain: https://www.ling.upenn.edu/courses/cogs501/Rosenblatt1958.pdf A Logical…

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…

Julia Shaw is a criminal psychologist and author who in her books explores human nature, including psychopathy, violent crime, the psychology of evil, police interrogation, false memory manipulation, deception detection, and human sexuality.

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Pavel Durov is the founder and CEO of Telegram.

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Transcript:https://lexfridman.com/pavel-durov-transcriptCONTACT LEX:Feedback – give feedback to Lex: https://lexfridman.com/surveyAMA – submit questions, videos or call-in: https://lexfridman.com/amaHiring – join our team: https://lexfridman.com/hiringOther – other ways to get in touch: https://lexfridman.com/contactEPISODE LINKS:Pavel’s Telegram: https://t.me/durovPavel’s X: https://x.com/durovTelegram: https://telegram.org/Telegram Contests: https://contest.c…

Norman Ohler is a historian and author of “Blitzed: Drugs in the Third Reich,” a book that investigates the role of psychoactive drugs, particularly stimulants such as methamphetamine, in the military history of World War II.

It is a book that two legendary historians Ian Kershaw and Antony Beevor give very high praise for its depth of research.

Norman also wrote “Tripped: Nazi Germany, the CIA, and the Dawn of the Psychedelic Age”, and he is working on a new book “Stoned Sapiens” looking at the history of human civilization through the lens of drugs.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep481-scSee below for timestamps, transcript, and to give f…

Dave Hone is a paleontologist, expert on dinosaurs, co-host of the Terrible Lizards podcast, and author of numerous scientific papers and books on the behavior and ecology of dinosaurs.

He lectures at Queen Mary University of London on topics of Ecology, Zoology, Biology, and Evolution.

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Dave Plummer is a programmer, former Microsoft software engineer (Windows 95, NT, XP), creator of Task Manager, author of two books on autism, and host of the Dave’s Garage YouTube channel, where he shares stories from his career, insights on software development, and deep dives into technology.

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Scott Horton is the director of the Libertarian Institute, editorial director of Antiwar.com, host of The Scott Horton Show, co-host of Provoked, and for the past three decades a staunch critic of U.S. military interventionism.

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Keyu Jin is an economist specializing in China’s economy, international macroeconomics, global trade imbalances, and financial policy.

She is the author of The New China Playbook: Beyond Socialism and Capitalism.

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Jack Weatherford is an anthropologist and historian specializing in Genghis Khan and the Mongol Empire.

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Demis Hassabis is the CEO of Google DeepMind and Nobel Prize winner for his groundbreaking work in protein structure prediction using AI.

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David Heinemeier Hansson (aka DHH) is a legendary programmer, creator of Ruby on Rails, co-owner & CTO of 37signals that created Basecamp, HEY, & ONCE, and is a NYT-best-selling author (with Jason Fried) of 4 books: REWORK, REMOTE, Getting Real, and It Doesn’t Have To Be Crazy At Work.

He is also a race car driver, including a class-winning performance at the 24 hour Le Mans race.

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Debate on Iran war between Scott Horton and Mark Dubowitz.

Scott Horton is the author and director of the Libertarian Institute, editorial director of Antiwar.com, host of The Scott Horton Show, and for the past three decades, a staunch critic of U.S. foreign policy and military interventionism.

Mark Dubowitz is the chief executive of the Foundation for Defense of Democracies, host of the Iran Breakdown podcast, and a leading expert on Iran and its nuclear program for over 20 years.

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Terence Tao is widely considered to be one of the greatest mathematicians in history.

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Sundar Pichai is CEO of Google and Alphabet.

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See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript:

https://lexfridman.com/sundar-pichai-transcript CONTACT LEX:

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James Holland is a historian specializing in World War II.

He hosts a podcast called WW2 Pod: We Have Ways of Making You Talk.

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Go to https://drinkag1.com/lexNotion: Note-taking and team collaboration.

Oliver Anthony is singer-songwriter who first gained worldwide fame with his viral hit Rich Men North of Richmond.

He became a voice for many who are voiceless, with many of his songs speaking to the struggle of the working class in modern American life.

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Go to https://drinkLMNT.com/lexOUTLINE:(00:00) – Introduction(09:00) – Open mics(13:03) – Mainstream country music(22:10) – Fame(28:06) – Music vs politics(36:56) – Rich Men North of Richmon…

Today, I’m excited to talk about the Paraphrase Project, an effort I co-led exploring how advances in AI tools for protein design might impact biosecurity.

These “patches,” akin to those in cybersecurity, have now been shared with organizations globally to strengthen biosecurity screening.

The project highlights that the same AI tools capable of incredible good can also be misused, requiring us to be vigilant, thoughtful, and creative so we continue to get the most benefit out of AI tools while working to ensure that we avoid costly misuses.

So things like, how similar is this to that template, wild-type protein structure that we used as our conditioning information?

But I feel like broadly…

KOHANE: So I think you’ve “nerd sniped” me because you [LAUGHTER]—which is all too easy—but I think there’s a central issue here.

But I actually think this is dark matter of human organizational technology that is not well understood.

AZEEM AZHAR: We didn’t talk about, you know, AI in its ability to potentially do this, which is to extend the clinician’s presence throughout the week.

And so I think there’s always going to be an opening for either differences of opinion or agreeing with you too much.

And this gets into whether AI is really going to get almost to the ab initio understanding of human biology.

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Prior to med school, Daniel pursued experiences that cultivated his interest in the application of AI in medical practice and education.

Really, really looking forward to this chat.

There’s AI before ChatGPT and before, you know, generative AI really became a big thing, and then afterwards.

And then after we talk about what’s really happening, what do you think should happen in medical education given the reality of generative AI?

And I do agree [that] AI really gives us real hope that we can make it true.

Our goal is to learn from their successes and their stumbles to move the science and practice of AI testing forward.

We have examples, like the pharmaceutical or medical device industry experts with whom you spoke, that’s really, you know, testing … there is a pre-deployment requirement.

And the third is just how rigid versus adaptive these testing and evaluation regimes or frameworks are in these different domains.

I really agree that there has been a lot of emphasis to date on, sort of, testing models upstream, the AI model evaluation.

You know, I think there’s been real progress already in the AI evaluation and testing ecosystem in the public-private partnership context.

Absolutely, I really, really was.

As a principal director on the Microsoft AI Red Team, Tori leads all AI security and safety red team operations, as well as dangerous capability testing, to directly inform C-suite decision-makers.

This year, we’ve pulled a lot of those assets and insights into the Azure [AI] Foundry AI Red Teaming Agent (opens in new tab).

So you can get a little taste of what we do day to day in the AI Red Teaming Agent.

WESTERHOFF: I think the most important takeaway from those lessons is that AI security is truly a team sport.

Dr. Eric Topol is the executive vice president of the biomedical research non-profit Scripps Research, where he founded and now directs the Scripps Research Translational Institute.

Let’s continue our deep dive on AI and biomedical research with this conversation with Noubar Afeyan:LEE: Noubar, thanks so much for joining.

And there’s the origin story of contact with AI, you know, before the emergence of generative AI and afterwards.

What is going on today with respect to AI really being used for something meaningful in the design and development of drugs?

TOPOL: You would read about how, you know, data is the new oil and, you know, gold and whatnot.

Our goal is to learn from their successes and their stumbles to move the science and practice of AI testing forward.

During the pre-market phase, medical testing establishes baseline safety and effectiveness metrics through bench testing, performance standards, and clinical studies.

SULLIVAN: So medical devices face a pretty prescriptive multi-level testing path before they hit the market.

We are looking into medical devices, as well, obviously, but also other technologies in advanced medical computing.

So we see Phase 3 trials as something that occurs in the medical devices and pharmaceuticals field.

As generative AI continues to advance, Microsoft has gathered a range of experts—from genome editing to cybersecurity—to share how their fields approach evaluation and risk assessment.

CHARO: Well, you know, genome editing is both very old and very new.

Now the earliest forms of genome editing were very inefficient, and so we didn’t worry that much.

But the bottom-line thing to remember, the way to really think about it is, we don’t regulate genome editing; we regulate the things that use genome editing.

And she said, you know, we don’t regulate genome editing; we regulate the things that use genome editing.

Our goal is to learn from their successes and their stumbles to move the science and practice of AI testing forward.

And I think, really, there are two reasons why tech is so, kind of, representative of that kind of challenge that I’ve always found fascinating.

Continues to be a really important topic in the AI policy conversation right now, I think, for really good reason.

Testing is an important component for governance and AI and, of course, in all of these other domains, as well.

I think about almost, like, in the near to mid-term, like three issues that we need to address in the AI, kind of, policy and testing context.

LEE: Yeah, yeah.

It cannot—as, you know, Bill was saying—it cannot learn from your document.

And I don’t know if the two of you remember, but I ended up doing a lot of tests.

I don’t know if you know, but just recently, there was a paper that was published on a scientific discovery using o3- mini (opens in new tab).

Like, if you have a human trained for one task and you put them into another task, then you don’t … you often don’t know.

So I don’t think we should be surprised that business schools matter on this because we care about management.

That’s really going to change the way, like, middle school works, was my thinking at the time.

We’ve gone from AI being highly discriminative to AI that’s able to explore the world in particular ways.

The symptoms that they’re showing are quite different, and also their compliance is really, really different.

LEE: Yeah, really, really interesting.

And single-cell foundation models claim to be capable of unraveling deeper insights than ever before.

Basically, we showed that single-cell foundation models perform worse in settings that are fundamental to biological discovery than much simpler machine learning and statistical methods that were used in the field before single-cell foundation models emerged and are the go-to standard for unpacking meaning from these complicated experiments.

And the way to understand this is because single-cell foundation models are trained in a way that tries to expose these models to millions of single-cells.

But let’s also talk about the impact for methodologists, people who are trying to improve these s…

This is such exciting work about environmental forecasting, so we’re happy to have the two of you join us today.

Mostly because AI weather forecasting models are computationally much more efficient and can even be more accurate.

What’s unfortunate though, about this big step forward, is that these developments are mostly limited to the setting of weather forecasting.

Weather forecasting is very important, obviously, but there are many other important environmental forecasting problems out there, such as air pollution forecasting or ocean wave forecasting.

STANLEY: Current approaches have really focused training very specifically on weather forecasting models.

LUNGREN: And now it really feels like this collaborative effort, you know, really can help start to extend that mission.

I think, you know, Will and Smitha, that we definitely feel the passion and the innovation.

Again, you know, in text, you refer to that earlier and certainly off the shelf, there’s really powerful applications.

LUNGREN: So, I think AI has always been thought of as a savior kind of technology.

And I guess for my part, I think really what we’re going to see is a massive unleash of creativity.

In this episode, we speak with Ashmi Banerjee, a doctoral candidate at the Technical University of Munich, about her pioneering research on AI-powered recommender systems in tourism. Ashmi illuminates how these systems can address exposure bias while promoting more sustainable tourism practices through innovative approaches to data acquisition and algorithm design. Key highlights include leveraging large language models for synthetic data generation, developing recommendation architectures that balance user satisfaction with environmental concerns, and creating frameworks that distribute tourism more equitably across destinations. Ashmi's insights offer valuable perspectives for both AI res…

In this episode of Data Skeptic's Recommender Systems series, host Kyle Polich interviews Dr. Kunal Mukherjee, a postdoctoral research associate at Virginia Tech, about the paper "Z-REx: Human-Interpretable GNN Explanations for Real Estate Recommendations" The discussion explores how the post-COVID real estate landscape has created a need for better recommendation systems that can introduce home buyers to emerging neighborhoods they might not know about. Dr. Mukherjee, explains how his team developed a graph neural network approach that not only recommends properties but provides human-interpretable explanations for why certain regions are suggested. The conversation covers the advantages o…

In this episode of Data Skeptic, we explore the challenges of studying social media recommender systems when exposure data isn't accessible. Our guests Sabrina Guidotti, Gregor Donabauer, and Dimitri Ognibene introduce their innovative "recommender neutral user model" for inferring the influence of opaque algorithms.

In this episode of Data Skeptic, we dive into eco-friendly AI with Antonio Purificato, a PhD student from Sapienza University of Rome. Antonio discusses his research on "EcoAware Graph Neural Networks for Sustainable Recommendations" and explores how we can measure and reduce the environmental impact of recommender systems without sacrificing performance.

Kyle reveals the next season's topic will be "Recommender Systems". Asaf shares insights on how network science contributes to the recommender system field.

Kyle and Asaf discuss a project in which we link former guests of the podcast based on their co-authorship of academic papers.

In this episode, Professor Pål Grønås Drange from the University of Bergen, introduces the field of Parameterized Complexity - a powerful framework for tackling hard computational problems by focusing on specific structural aspects of the input. This framework allows researchers to solve NP-complete problems more efficiently when certain parameters, like the structure of the graph, are "well-behaved". At the center of the discussion is the network diversion problem, where the goal isn’t to block all routes between two points in a network, but to force flow - such as traffic, electricity, or data - through a specific path. While this problem appears deceptively similar to the classic "Min.Cu…

In this episode, we learn why simply analyzing the structure of a network is not enough, and how the dynamics - the actual mechanisms of interaction between components - can drastically change how information or influence spreads. Our guest, Professor Baruch Barzel of Bar-Ilan University, is a leading researcher in network dynamics and complex systems ranging from biology to infrastructure and beyond. BarzelLab BarzelLab on Youtube Paper in focus: Universality in network dynamics, 2013

In this episode, Kyle does an overview of the intersection of graph theory and computational complexity theory. In complexity theory, we are about the runtime of an algorithm based on its input size. For many graph problems, the interesting questions we want to ask take longer and longer to answer! This episode provides the fundamental vocabulary and signposts along the path of exploring the intersection of graph theory and computational complexity theory.

In this episode, listeners will learn about Actantial Networks—graph-based representations of narratives where nodes are actors (such as people, institutions, or abstract entities) and edges represent the actions or relationships between them. The one who will present these networks is our guest Armin Pournaki, a joint PhD candidate at the Max Planck Institute and Sciences, who specializes in computational social science, where he develops methods to extract and analyze political narratives using natural language processing and network science. Armin explains how these methods can expose conflicting narratives around the same events, as seen in debates on COVID-19, climate change, or the wa…

How to build artificial intelligence systems that understand cause and effect, moving beyond simple correlations? As we all know, correlation is not causation. "Spurious correlations" can show, for example, how rising ice cream sales might statistically link to more drownings, not because one causes the other, but due to an unobserved common cause like warm weather. Our guest, Utkarshani Jaimini, a researcher from the University of South Carolina's Artificial Intelligence Institute, tries to tackle this problem by using knowledge graphs that incorporate domain expertise. Knowledge graphs (structured representations of information) are combined with neural networks in the field of neurosymbo…

Jon Krohn speaks to researcher, broadcaster and author Stephanie Hare about how the Hippocratic Oath might apply to artificial intelligence, and a guiding ethos for pushing innovation while protecting users from harm. A code of conduct, she says, could be one approach to ensuring that people are using technology more mindfully and ethically, as well as an opportunity for users to feel that they belong to a wider, global community. Although she sympathizes with people concerned by overregulation undermining innovation, Stephanie also notes that we expect certain standards to be met elsewhere, such as vehicle and drug safety, as well as fair journalistic practices. As Stephanie explains, we n…

With the number of jobs dramatically slowing in the last year, many question if this decline is down to companies turning to AI for completing entry-level tasks in particular. Research published earlier this month by Yale University shows no major difference in the types of roles and tasks in so-called `white-collar jobs` since late 2022, an auspicious date that coincides with the launch of ChatGPT. In this week‘s Five-Minute Friday, host Jon Krohn discusses if and when AI will undercut junior-level jobs, particularly in the US. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/934⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience…

Sheamus McGovern, CEO of Open Data Science, takes Jon Krohn and his listeners on a journey to launching his popular data science and AI conference, now in its tenth year, as well as the great shifts to the fields that he has seen on the way. For Seamus, the growth of his Open Data Science Conference has shown him that an AI engineer is just the beginning of several roles that will emerge from the industry. He asks Jon to consider the breadth of tasks demanded of today’s engineers, from data profiling and transformation to feature engineering, hyper-parameter tuning, and model deployments. Just as the AI engineer emerged from the data scientist role, Seamus expects the industry to respond to…

Larissa Schneider speaks to Jon Krohn in this Feature Friday about finding the right time to invest in AI solutions, and when it’s better to build them yourself. She discusses her work leading global strategy and operations at Unframe, and how they raised $50 million in venture capital since the company’s launch in March 2025. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/932⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

AI predictions, and how to act on them: Data Science Strategist at Gurobi, Jerry Yurchisin, speaks to Jon Krohn about how mathematical optimization helps enterprises automate decisions for business success and where to find the resources to make it happen. This episode is brought to you by the ⁠ODSC, the Open Data Science Conference, by Fabi, by ⁠Dell⁠, and by ⁠Intel⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/931⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (02:34) What mathematical optimization is (13:58) How to get started with mathematical optimizat…

Jon Krohn’s highlights from this month of interviews focus on ways to future-proof your career, looking at the hardware that will get you the most mileage, the emerging roles that are well worth a look, and the developments in AI that will endure in a field constantly testing the durability of its own breakthroughs. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/930⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Breaking news: Jon Krohn welcomes Adrian Kosowski to the show to talk about the groundbreaking research happening at Pathway. Adrian and his team demonstrate how they have brought attention in AI closer to the way the brain functions, creating, in essence, a “massively parallel system of [artificial] neurons” that communicate with one another and exhibit properties similar to natural neurons. The goal is to move beyond the current limitations of transformers, where reasoning can be generalized across more complex and extended reasoning patterns, approximating a more human-like approach to problem-solving. This episode is brought to you by the Trainium2, the latest AI chip from AWS, by ⁠Dell…

Prompt injections, malicious code, and AI agents: In this week’s Five-Minute Friday, Jon Krohn looks into the current security weaknesses found in AI systems. A structural vulnerability that The Economist dubs a “lethal trifecta” could cause havoc for AI users, unless we take the necessary steps to contain our systems. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/928⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Earlier this year, David Loker joined CodeRabbit as their Director of AI. As more people come to write code with the help of large language models, David believes CodeRabbit will become a helpful assistant for code reviewing and pull requests. He tells Jon Krohn how CodeRabbit assists developers with real-time feedback, as well as the reality of vibe coding, the optimization challenges of agentic AI, and other pressing questions in AI and tech. This episode is brought to you by the ⁠Dell⁠, by ⁠Intel⁠, by ⁠Gurobi⁠ and by ⁠ODSC, the Open Data Science Conference⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/927⁠⁠ In this episode you will learn: (01:26) How CodeRabbit helps …

In this Five-Minute Friday, Jon Krohn explores how AI is reshaping the legal industry. He investigates how AI tools are helping lawyers make conclusions faster, how paralegals are being retrained, and the latest in-demand role in law (hint: It concerns AI). Listen to hear how Harvey AI and Thomson Reuters’ CoCounsel are using AI to help lawyers get ahead. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/926⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Tech innovation’s dependence on economic systems, trust in technology throughout history, and job displacement through AI: The Dieter Schwartz Associate Professor of AI and work at the University of Oxford, Carl Benedikt Frey, talks to Jon Krohn about his latest book, How Progress Ends, as well as how different economic systems deal with innovation and scaling, dealing with the homogeneity of generative AI output, and how to stay afloat in the new wave of job automation. This episode is brought to you by the ⁠Dell⁠, by ⁠Intel⁠, by ⁠ODSC, the Open Data Science Conference⁠ and by ⁠Gurobi⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/925⁠⁠⁠⁠⁠ Interested in sponsoring a Super…

MIT lab NANDA (“Networked AI Agents in Decentralized Architecture”) reveals less than promising results for the future of AI adoption in businesses. According to “The GenAI Divide: State of AI in Business 2025”, a whopping 95% of enterprise AI projects “are getting zero return” on their $30-40 billion investment. Jon Krohn takes this Five-Minute Friday to look into why this has happened, with help from a critical response to the report written by Futuriom’s R. Scott Raynovich. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/924⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Graphs, but not as you would expect them: Graph analytics guru Amy Hodler speaks to Jon Krohn about the graph data structure and graph applications, graph algorithms, graph RAG, and graphs as memory systems for AI agents. We can use graphs in a surprising number of ways. Money laundering and fraud, as well as supply-chain crime, leave breadcrumbs at multiple “touch-points” over time, behaviors that graphs are better suited to reveal than rows and tables. Amy sees that most interest in graphs has been in the cybersecurity space. But this work isn’t only restricted to fighting crime! Listen to the episode to hear more case examples and how to get into graph work. This episode is brought to yo…

Hugo Dozois-Caouette speaks to Jon Krohn about his startup MaintainX and how he secured $100 million in venture capital. MaintainX manages and maintains computerized maintenance management systems (CMSs), or work-execution software, for the industrial and manufacturing industries. This “digitized version of a clipboard” with the help of web and mobile applications, provide a list of procedures, guidelines and regulations to help increase worker productivity and give a company the data-driven insights it needs to refine its processes. Listen to the episode to hear Hugo’s thoughts on the gaps in the manufacturing industry that technology can fill, the tech stack used by MaintainX, and the dis…

Using Windows for AI development and the bleeding edge of NPUs: Shirish Gupta and Ish Shah from Dell Technologies speak to Jon Krohn about the latest products from Dell, the future of neural-processing units (NPUs), and how AI developers can make sound hardware investments. This episode is brought to you by the Trainium2, the latest AI chip from AWS, by ODSC, the Open Data Science Conference and by Gurobi. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/921⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (04:18) Why Windows still outranks other operating systems (20…

Sanjoy Chowdhury reveals AI’s hidden weakness: while systems can see objects and hear sounds perfectly, they can’t reason across senses like humans do.

At the intersection of ethics and engineering, Amethix creates AI systems that don’t just function—they adapt, learn, and serve.

Discover more at https://amethix.comThis episode is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers and defense innovators the tools to prototype, simulate, and deploy autonomous systems with confidence.

Whether it’s in the sky, on the ground, or in orbit—if it’s intelligent and mobile, Intrepid helps you build it.

ReferencesWar On The Rocks: https://warontherocks.com/2025/08/ukraine-isnt-the-model-for-winning-the-innovation-war/LinkedIn: https://www.linkedin.com/in/jonasrsinger/Spotify: https://tr.ee/Omy_1X8k1UApple Podcast: https://podcasts.apple.com/us/podcast/defence-innovation-podcast/id1797131332YouTube: https://youtube.com/@DefenceInnovationpodcast?si=cu2WlnVgL5XKnM0pSponsorsThis episode is proudly sponsored by Amethix Technologies.

At the intersection of ethics and engineering, Amethix creates AI systems that don’t just function—they adapt, learn, and serve.

Discover more at https://amethix.comThis episode is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers…

At the intersection of ethics and engineering, Amethix creates AI systems that don’t just function—they adapt, learn, and serve.

Discover more at amethix.comDSH is brought to you by Intrepid AI.

🐦 Twitter: @DataScienceAtHome📘 LinkedIn: https://www.linkedin.com/in/fragadaleta/Instagram: https://www.instagram.com/datascienceathome/Facebook: 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 machine learning.

Send us mail at:[email protected]’t forget to like, subscribe, and hit the 🔔 for…

At the intersection of ethics and engineering, Amethix creates AI systems that don’t just function—they adapt, learn, and serve.

Discover more at amethix.comDSH is brought to you by Intrepid AI.

🐦 Twitter: @DataScienceAtHome📘 LinkedIn: https://www.linkedin.com/in/fragadaleta/Instagram: https://www.instagram.com/datascienceathome/Facebook: 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 machine learning.

Send us mail at:[email protected]’t forget to like, subscribe, and hit the 🔔 for…

The brutal truth about why Silicon Valley is blowing billions on glorified autocomplete while pretending it’s the next iPhone.

We’re diving deep into the AI investment circus where VCs who can’t code are funding companies that barely understand their own technology.

From blockchain déjà vu to the “ChatGPT wrapper” economy—this episode will make you question every AI valuation you’ve ever seen.

Fair warning: We’re naming names and calling out the hype.

Don’t listen if you work at a “revolutionary AI startup” that’s just OpenAI’s API with a pretty interface.

From the viral article “Tech’s Dumbest Mistake: Why Firing Programmers for AI Will Destroy Everything” on my newsletter at https://defragzone.substack.com/p/techs-dumbest-mistake-why-firinghere are my thoughts about AI replacing programmers…🎙️ Sponsors AGNTCY — The open source collective building the Internet of Agents🌐 https://www.agntcy.org✨ Connect with us!

🐦 Twitter: @DataScienceAtHome📘 LinkedIn: https://www.linkedin.com/in/fragadaleta/Instagram: https://www.instagram.com/datascienceathome/Facebook: 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 Scie…

In this episode of Data Science at Home, we explore the fascinating world of neuromorphic computing — a brain-inspired approach to computation that could reshape the future of AI and robotics.

The episode breaks down how neuromorphic systems differ from conventional AI architectures like transformers and LLMs, diving into spiking neural networks (SNNs), their benefits in energy efficiency and real-time processing, and their limitations in training and scalability.

Real-world applications are highlighted, including low-power drones, hearing aids, and event-based cameras.

Francesco closes with a vision of hybrid systems where neuromorphic chips and LLMs coexist, blending biological inspiratio…

This episode explores the invisible battlespace of cyber and electronic warfare, where AI takes center stage.

SponsorsBuilding multi-agent software is hard — agent-to-agent and agent-to-tool communication is still the wild west.

At the intersection of ethics and engineering, Amethix creates AI systems that don’t just function—they adapt, learn, and serve.

Discover more at amethix.comWarcoded is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers and defense innovators the tools to prototype, simulate, and deploy autonomous systems with confidence.

Swarming the Battlefield explores how artificial intelligence is revolutionizing combat through coordinated drone swarms.

This episode uncovers how these intelligent agents turn the chaos of the battlefield into a synchronized dance of machine warfare.

At the intersection of ethics and engineering, Amethix creates AI systems that don’t just function—they adapt, learn, and serve.

Discover more at amethix.comWarcoded is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers and defense innovators the tools to prototype, simulate, and deploy autonomous systems with confidence.

In this gripping follow-up, we dive into how AI is transforming kinetic operations—from identifying a threat to executing a strike.

At the intersection of ethics and engineering, Amethix creates AI systems that don’t just function—they adapt, learn, and serve.

Discover more at amethix.comWarcoded is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers and defense innovators the tools to prototype, simulate, and deploy autonomous systems with confidence.

Whether it’s in the sky, on the ground, or in orbit—if it’s intelligent and mobile, Intrepid helps you build it.

Welcome to DSH/WarcodedWe explore how AI is transforming ISR (Intelligence, Surveillance, Reconnaissance)—from satellite imagery to drone feeds.

At the intersection of ethics and engineering, Amethix creates AI systems that don’t just function—they adapt, learn, and serve.

Discover more at amethix.com.”Warcoded is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers and defense innovators the tools to prototype, simulate, and deploy autonomous systems with confidence.

Learn more at intrepid.ai.”#AI #defensetech #ISR #LLM #Warcoded #DataScienceAtHome #OSINT #SIGINT #dronewarfare

🎙️ In this episode of Data Science at Home, we sit down with Kenny Vaneetvelde, the mastermind behind Atomic Agents, a groundbreaking framework redefining AI development.

🔍 Discover how atomicity simplifies complex AI systems, why modularity matters more than ever, and how Atomic Agents is eliminating hidden assumptions and redundant complexity in AI workflows.

💡 From real-world applications to the tech stack behind the framework, Kenny takes us on a deep dive into this lightweight, powerful tool for creating consistent and brand-aligned AI.

📌 Timestamps:0:00 – Intro2:30 – Kenny’s journey in AI5:00 – What are Atomic Agents?

10:45 – Why atomicity matters in AI18:20 – The tech behind Atomic A…

This episode explores how to break down barriers by running massive AI models on “crappy machines”—affordable, low-spec devices.

🐦 Twitter: @DataScienceAtHome📘 LinkedIn: https://www.linkedin.com/in/fragadaleta/Instagram: https://www.instagram.com/datascienceathome/Facebook: 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 machine learning.

Whether you’re a data professional, tech enthusiast, or just curious about the field, our podcast delivers insights, interviews, and discussions.

Send us mail …

Enter WeightWatcher—the AI detective tool that peeks inside neural networks without needing their data.

🐦 Twitter: @DataScienceAtHome📘 LinkedIn: https://www.linkedin.com/in/fragadaleta/Instagram: https://www.instagram.com/datascienceathome/Facebook: 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 machine learning.

Whether you’re a data professional, tech enthusiast, or just curious about the field, our podcast delivers insights, interviews, and discussions.

Send us mail at:hello@datascienceathom…