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Of course, if a model takes more time to work on a more difficult problem, it generates more CoT tokens (Wei et al., 2022)4.

Both of the modules are implemented as simple transformer blocks (Vaswani et al., 2017)2 stacked on top of each other.

(Source: Adapted from Wang et al., 20251, Figure 5)These two graphs must be analysed together to understand the efficiency of the ACT mechanism.

“Hierarchical Reasoning Model.” arXiv preprint arXiv:2506.21734 (2025).

“Chain-of-thought prompting elicits reasoning in large language models.” Advances in neural information processing systems 35 (2022): 24824-24837.

A signal is simply any quantity that varies over time (what we usually call a time series in data science).

Empirical Mode DecompositionEmpirical Mode Decomposition was introduced by Huang et al.

Its goal is simple but powerful: take a signal and split it into a set of clean oscillatory components, called Intrinsic Mode Functions (IMFs).

The intuition behind the EMD AlgorithmThe EMD algorithm is surprisingly intuitive.

White noise noise = 0.5 * np.random.randn(N) # --- Composite Signal --- signal = s1 + s2 + noise # Plot the synthetic signal plt.figure(figsize=(12, 4)) plt.plot(t, signal) plt.title(f'Synthetic Signal (Components at {f1} Hz and {f2} Hz)') plt.xlabel('Time (s)') plt.ylabel('A…

In this post, we’ll dive into two of the most common pitfalls in model development: overfitting and underfitting.

Predictions with high variance: The model performance is unstable and the predictions are not reliable.

Training a complex and expensive model: Training and building a complex model in production is an expensive and high-resource job.

If you want to go the extra mile, consider implementing a monitoring alert if the deployed model’s performance deviates significantly from the validation set error.

Poor performance: The model performs poorly on training data, therefore poorly also on test and real-world data.

This article explores modern alternatives to Pandas, including Polars and DuckDB, and examines how they can simplify and improve the handling of large datasets.

Pandas took minutes, which slowed down my analysis.

Conclusion and takeawaysWorking with large datasets doesn’t have to feel like wrestling with your tools.

Together, they make working with large data feel more manageable and less tiring.

If you want to go deeper into the ideas explored in this tutorial, the official documentation of Polars and DuckDB are good places to start.

Instead of tagging an article with Mathematical Software and Computer Science, I can just tag it with Mathematical Software.

Click “Neo4j”.

For the Neo4j database URL, you will need to inspect the Neo4j instance you created in Neo4j.

This is the one that Neo4j gave you when you created your Neo4j instance.

For our recommendation engine, to find articles similar to our Mathematical Software article, we want to find other articles that are tagged with Mathematical Software, but ALSO articles tagged with other branches of computer science.

The paradigm shift of Natural Language Visualization is the same.

In this article, I delve into a new approach called Natural Language Visualization (NLV).

What is Natural Language Visualization?

The acronym NLV (Natural Language Visualization) carries two distinct, historical meanings.

Its Natural Language Visualization (NLV) function is explicit, allowing users to ask it to summarize data, identify patterns, or even generate visualizations.

They’d used generative AI to optimize a suspension component: 40% weight reduction while maintaining structural integrity, completed in hours instead of the usual months.

The industry is making a fundamental mistake: treating generative AI as an optimization tool when it’s actually a reimagination engine.

General Motors announced last year they’re using generative AI to redesign vehicle components, projecting 50% reduction in development time.

Partner with generative AI researchersMost automotive AI deployments focus on immediate production applications.

Specific actions:Fund PhD research at MIT, Stanford, CMU on automotive applications of generative AI.

Never miss a new edition of The Variable, our weekly newsletter featuring a top-notch selection of editors’ picks, deep dives, community news, and more.

We get it: tinkering your way into a solution can sometimes save you time, and it’s often a fun way to go about learning.

Sooner or later, when something — say, your data pipeline, or your team’s most-prized metric — goes awry, having this mental model in place will keep you focused and effective as a data or AI leader.

Data Culture Is the Symptom, Not the SolutionCareful planning, prioritizing, and strategizing doesn’t only benefit specific tools or teams.

As Jens Linden explains, it’s essential for organizations to thrive and for investme…

Why you should use Gemini 3In my opinion, Gemini 2.5 pro was already the best conversational LLM available before the release of Gemini 3.

Gemini 3 in the consoleI first started testing Gemini 3 Pro in the console.

For example, when discussing EPC certificates with Gemini, the model found the image below:This is an image of Gemini 3 Pro, which I used to answer my questions about EPC certificates.

I also took some of the older queries I ran in the Gemini console with Gemini 2.5 Pro, and ran the exact same queries again, this time using Gemini 3 Pro.

However, I would probably regard Gemini 3 Pro as the second-best coding model I’ve used.

In this article, I will walk through the process of visualizing time-series data in Python in detail.

A graph depicting the daily temperature of your favorite city over the course of the year is a graph that depicts time-series data.

Time-series data is an excellent starting point for data visualization for a few reasons:It is an extremely common and useful type of data.

As compared with some other types of data, time-series visualizations are fairly intuitive to humans and align with our perception of time.

This is about learning data visualization holistically, and my hope is that you have walked away from this article with a better understanding of how time-series data is visualized.

Both models are generative frameworks over text, differing mainly in their scope: RM1 models short queries from documents, transformers model full sequences.

The RM1 relevance model estimates the probability of a word w under the hidden relevance distribution given a query q.

with the posterior probability of a document d given a query q given by:Posterior probability of a document d given a query q.

To estimate this relevance model, RM1 uses the top-retrieved documents as pseudo-relevant feedback (PRF) — it assumes the highest-scoring documents are likely to be relevant.

We even coded up a neural variant of the Relevance Model, using modern encoder-only models, thereby formally unifying pa…

pip install marimo marimo edit --sandbox notebook.py1.

I can easily turn my Notebooks into appsTurning a marimo notebook into an interactive app is also easy.

See the documentation as you typeOne of the most underrated features of marimo notebooks for me has been the Live Docs panel.

Molab: marimo notebooks in the cloudMolab is for marimo notebooks, what Google Colab is for Jupyter notebooks.

Generating entire notebooks via AI in marimo | Image by AuthorThe second is refactoring and debugging.

In this article, I’ll discuss agentic information finding, covering how information retrieval has changed with the release of LLMs, and in particular with the rise of AI Agents, who are much more capable of finding information than we’ve seen until now.

Information retrieval is such a critical task because, as humans, we’re so reliant on quickly finding information to solve different problems.

Information retrieval toolsNow that we have the information retrieval tools readily available, we can start performing agentic information retrieval.

ConclusionIn this article, I covered the basics of agentic information retrieval.

I started by discussing why agentic information is so important, highl…

As with many areas of AI, sexuality gets its meaning and social interpretations from human beings, not from the models.

If that person didn’t consent to being the subject of sexual content, what are their rights and what are the obligations of the generative AI company and the generative AI user?

How does it affect generative AI users’ understanding of consent when they can so easily acquire this kind of content through generative AI, without ever directly interacting with the individual/s?

I don’t want to portray this as an indictment of all sexual content, or necessarily even sexual content generated by AI.

Generative AI can be a tool for good, but the risks it creates need to be acknowle…

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-5I spent last week researching world models like Marble and SIMA2 and decided to put together a insanely long essay.

Achieving spatial intelligence would enable AI not just to talk about the world, but to truly understand and operate within it.

In this essay, we survey the current landscape and future potential of world models as a path toward spatial intelligence.

We begin with the technical foundations of world models and their key capabilities.

Technical Foundations of World Models

Create Using GPT-5World models are becoming a reality in front of our eyes!

Today, we would like to dive into one of the most exciting ones.

DeepMind’s SIMA 2 is best understood as a systems project disguised as a gaming demo: it is a full-stack embodied agent that wraps a Gemini model in a visuomotor control loop, trains it across many 3D games, and then lets it improve itself through model-driven task generation and self-play.

Rather than proposing a new neural building block in isolation, SIMA 2 offers a reference architecture for how a large multimodal model can perceive, reason, and act in complex simulated worlds using exactly the same interface as a human player.

Diving into Microsoft’s WinzardLM model that uses generative synthesis for following instructions.

💡 AI Concept of the Day: Understanding Generative SynthesisToday, let’s dive into one of the most straightforward mechanisms for synthetic data generation.

Generative synthesis is the process of creating new data by modeling the underlying patterns and distributions of real-world datasets.

Rather than simply augmenting data with random perturbations, generative synthesis learns the generative process itself, allowing it to produce realistic and diverse samples across domains such as text, images, time series, and structured data.

The approach has become foundational in synthetic data generatio…

In the AI of the week, we are going to dive into DeepMind’s new SIMA world model.

Subscribe and don’t miss out:📝 Editorial: Last Week in AI: Worlds Built, Models Refined, and Legends Move OnPretty loaded week in AI across several trends: world-models, large language models, and multilingual speech—are starting to converge into a more coherent picture of where the field is heading.

Before turning to language models, another notable development this week came from the developer-tooling ecosystem: Cursor announced a $2.3 billion funding round, lifting its valuation to $29.3 billion.

In parallel, large language models are entering a refinement phase.

AI Lab: Microsoft Research IndiaSummary: Int…

Created Using GPT-5Lets discuss one of the most important debate in AI for math: do we need specialists models or the general multimodal foundation models will inevitably win?

By 2025, large “foundation” AI models are tackling tasks from high school algebra homework all the way up to difficult research conjectures.

Generalist models have shown a surprising ability to solve standard math problems and even achieve high scores on competitive exams, yet they often falter when asked to produce truly novel mathematical insights.

Specialized models, in contrast, can deliver more rigorous or precise results in their niche, but they come with limitations regarding scope, credibility, and flexibility…

Created Using GPT-5Kimi K2 Thinking is Moonshot AI’s bid to redefine what it means for a large language model to “think.” Rather than being a chat model that produces a single-shot answer, K2 Thinking behaves like an autonomous solver—capable of reasoning, planning, and acting over long horizons without losing coherence.

It is built upon the Kimi K2 backbone, a trillion-parameter mixture-of-experts (MoE) Transformer with roughly 32 billion active parameters per token.

Around this backbone, Moonshot has developed a multi-layered training process that combines large-scale data efficiency, reinforcement learning for tool use, and native support for interleaved reasoning.

Created Using GPT-5Today we will Discuss:Explore the different types of synthetic data generation methods.

Dive into Tiny Stories, Microsoft synthetically generated dataset for training small language models.

💡 AI Concept of the Day: A Taxonomy for Synthetic Data Generation MethodsSynthetic data is no longer a trick for filling gaps—it is a disciplined way to shape model behavior along three axes: fidelity (truthfulness and label correctness), diversity (coverage across tasks and difficulty), and controllability (ability to target slices and constraints).

A practical taxonomy begins with how supervision is produced and how tightly we can steer it.

In production pipelines, multiple families …

Subscribe and don’t miss out:📝 Editorial: Last Week in AI: K2’s Brains, Lambda’s Capacity, ARR GravitThis week crystallized three threads—technical progress, compute access, and business scale—that define where AI is heading.

The “K2 Thinking” variant leans hard into long-horizon reasoning and tool use, with credible jumps on coding and logic tasks.

Authors: Stanford University; Princeton University; Cornell University.

Authors: University of Chicago; University of Illinois Urbana–Champaign; VirtueAI; Microsoft Research; UK AI Safety Institute; University of Oxford; UC Berkeley.

🤖 AI Tech ReleasesKimi K2 ThinkingMoonshot AI released Kimi K2 Thinking, a reasoning model that excels in agentic…

Artificial intelligence has raced ahead, but knowing what counts as real progress is still surprisingly hard.

We largely rely on standardized benchmarks and tidy metrics to declare winners and track improvement.

Yet a central paradox undercuts this practice: as soon as a metric becomes the target, it often stops measuring what we intended.

This is Goodhart’s Law in action—“when a measure becomes a target, it ceases to be a good measure.” In AI, that means a leaderboard gain may reflect success at gaming the test, not a durable leap in capability.

This essay examines the core challenges of AI benchmarks and evaluations across language, vision, and reinforcement learning (RL).

Created Using GPT-5MiniMax-M2 stands out as one of the most technically ambitious and pragmatic open-weight models of 2025.

Released by MiniMax, a Chinese AI lab with a growing reputation for engineering precision, M2 reflects a refined approach to scaling—maximizing capability for agents, coding assistants, and reasoning systems while maintaining efficient serving costs.

At a time when many labs chase raw parameter counts, M2 makes the case for thoughtful architectural composition.

Beneath the marketing term “Mini” lies a massive sparse Mixture-of-Experts (MoE) model, comprising around 230 billion parameters, of which only about 10 billion are active per token.

This design choice forms the…

Created Using GPT-5Today we will Discuss:An intro to our new series about synthetic data generation.

💡 AI Concept of the Day: An Intro to our Series About Synthetic Data GenerationSynthetic data has moved from a lab curiosity to a board-level strategy because it changes the slope of the learning curve.

Models no longer improve only when you find more “naturally occurring” data; they improve when you can manufacture targeted, higher-quality supervision on demand.

The shift mirrors the move from passively scraping the web to actively designing curricula.

If scaling laws taught us that more data helps, synthetic data reframes the question: not “how much,” but “what distribution—and with which …

Subscribe Now to Not Miss Anything:📝 Editorial: Last Week in AI: OpenAI Eyes Wall Street, MiniMax Opens Up, and Vertical AI Goes DeepThis week in AI captured the sector’s accelerating evolution across capital markets, open-weight innovation, and verticalized products.

Five stories shaped the conversation: OpenAI’s potential IPO, MiniMax’s release of M2, Harvey’s new capital raise, Mercor’s valuation surge, and LayerLens’s public platform launch.

Legal AI platform Harvey raised $150 million at an $8 billion valuation, underscoring how defensibility in AI now lies in deep domain integration.

AI Lab: Tongyi Lab, Alibaba GroupSummary: AgentFold proposes a proactive “context folding” mechanism e…

Would OpenAI launch a crypto token?

We are publishing this just as OpenAI has announced its plans to IPO in 2026.

OpenAI stands at the epicenter of an AI revolution, pushing the limits of technology – and the limits of traditional financing.

This has led to speculation that OpenAI could even launch its own cryptocurrency token as a novel form of financing.

In this essay, we dive deep into why OpenAI might consider a crypto token, how past partnerships have put pressure on its finances, Altman’s long-running involvement with crypto, and what a pragmatic token strategy could look like for the AI powerhouse.

Created Using GPT-5For years, the race to scale large language models has focused on stretching context windows to tens or hundreds of thousands of tokens.

But what if, instead of feeding more text tokens, we could compress them into compact visual representations and recover them with high fidelity when needed?

DeepSeek-OCR explores exactly that idea.

It is not just a next-generation optical character recognition system; it is a rethink of how to represent, compress, and retrieve long textual contexts using the visual modality.

Image Credit: DeepSeekThe Core Idea: Optical Compression for Contexts

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…

Decision tree-based models for predictive machine learning tasks like classification and regression are undoubtedly rich in advantages — such as their ability to capture nonlinear relationships among features and their intuitive interpretability that makes it easy to trace decisions.

This article is divided into two parts; they are: • Picking a Dataset • Training a Tokenizer To keep things simple, we'll use English text only.

Decision tree-based models in machine learning are frequently used for a wide range of predictive tasks such as classification and regression, typically on structured, tabular data.

You've learned about

LLMs

As a machine learning engineer, you probably enjoy working on interesting tasks like experimenting with model architectures, fine-tuning hyperparameters, and analyzing results.

Share Post ShareA language model is a mathematical model that describes a human language as a probability distribution over its vocabulary.

In this article, you’ll learn about datasets used to train language models and how to source common datasets from public repositories.

A Good Dataset for Training a Language ModelA good language model should learn correct language usage, free of biases and errors.

For language model training, datasets typically contain text strings.

Post-Processing the DatasetsBefore training a language model, you may want to post-process the dataset to clean the data.

ReAct (Reasoning + Acting) is a common pattern for building AI agents that think through problems and take actions to solve them.

add_conditional_edges ( "reasoning" , route , { "action" : "action" , "end" : END } ) workflow .

get ( "iteration_count" , 0 ) if iteration_count >= 3 : return { "messages" : [ "Thought: I have gathered enough information" ] , "next_action" : "end" , "iteration_count" : iteration_count } history = "" .

add_conditional_edges ( "reasoning" , lambda s : s [ "next_action" ] , { "action" : "action" , "end" : END } ) workflow_llm .

=== LLM-Powered ReAct (No Mock Data) === User: Tell me about Tokyo and Japan Thought: QUERY: What is the history and significance of Tokyo …

Share Post ShareIn this article, you will learn three expert-level feature engineering strategies — counterfactual features, domain-constrained representations, and causal-invariant features — for building robust and explainable models in high-stakes settings.

In these situations, it can be worth going beyond classic feature engineering techniques and adopting advanced, expert-level strategies tailored to such settings.

nn as nn import torch .

nn as nn import torch .

randn ( 2 , 1 ) ) def forward ( self , x ) : return x @ self .

Evaluating large language models has quietly become one of the trickiest (and surprisingly complex) problems in artificial intelligence.

LLM-as-a-Judge EvaluationA newer way to evaluate language models is to have one large language model judge another.

Doing safety and ethical evaluation helps ensure large language models are not just capable, but also responsible and trustworthy in the real world.

This is especially useful for tasks that need planning, problem-solving, or multi-step reasoning—like RAG systems, math solvers, or agentic large language models.

This way, you’ll have a quick reference you can save or refer back to whenever you’re working with large language model evaluation.

Given statistics’ role as an invaluable compass for machine learning engineers, this article covers seven core pillars that every person in this role should know — not only to succeed in interviews, but to build reliable and robust machine learning systems in day-to-day work.

7 Key Statistical Concepts for Machine Learning EngineersWithout further ado, here are the seven cornerstone statistical concepts that should become part of your core knowledge and skill set.

Probability FoundationsVirtually every machine learning model — from simple classifiers based on logistic regression to state-of-the-art language models — has probabilistic foundations.

Bayes’ theorem shows up throughout machine l…

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The way you split documents into chunks determines what information your system can retrieve and how accurately it can answer queries.

Proper chunking improves retrieval accuracy, reduces hallucinations, and ensures the LLM receives focused, relevant context.

Semantic ChunkingRather than relying on characters or structure, semantic chunking uses meaning to determine boundaries.

LLM-Based ChunkingIn LLM-based chunking, we use an LLM to determine chunk boundaries and push chunking into intelligent territory.

The agent might use document-based chunking for structured reports and semantic chunking for narrative content within the same corpus.

Share Post ShareIn this article, you will learn a clear, practical framework to diagnose why a language model underperforms and how to validate likely causes quickly.

This article adopts a diagnostic standpoint and explores a 5-point framework for understanding why a language model — be it a large, general-purpose large language model (LLM), or a small, domain-specific one — might fail to perform well.

To diagnose data issues, inspect a sufficiently representative portion of the training data if possible, analyzing properties such as relevance, coverage, and topic balance.

The amount of context a language model can retain, or context window, is largely determined by memory limitations.

Lang…

As we can see, the difference between them is subtle, but there is a powerful bridge connecting both: feature importance.

This article unveils 10 simple but effective Python one-liners to calculate model feature importance from different perspectives — helping you understand not only how your machine learning model behaves, but also why it made the prediction(s) it did.

Global SHAP Feature ImportanceSHAP is a popular and powerful library to get deeper into explaining model feature importance.

import numpy as np import shap shap_values = shap.TreeExplainer(model).shap_values(X) importances = np.abs(shap_values).mean(0) 1 2 3 4 import numpy as np import shap shap_values = shap .

predict_proba…

* * *I’ve been thinking about the anger surrounding generative AI.

To keep things fair, he took the best human images and best AI images, meaning human art from famous artists, and AI art from prompters skilled at removing obvious tells of image generation.

When people complain about AI slop, I see it as a complaint against the deluge of default style AI images.

We’ve seen this happen in all forms: AI text, AI music, older forms of computer generated content like CGI.

As much as we celebrate imperfection, digital imperfection is a step too far.

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.

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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This could happen for a several reasons, including no shared cipher suites.

Additional troubleshooting information here.

At Google DeepMind, we believe AI can serve as a powerful tool to collaborate with mathematicians, augmenting creativity and accelerating discovery.

Today, we’re introducing the AI for Math Initiative, supported by Google DeepMind and Google.org.

It brings together five of the world's most prestigious research institutions to pioneer the use of AI in mathematical research.

A pivotal moment for AI and mathematicsThe AI for Math Initiative comes at a time of remarkable progress in AI’s reasoning capabilities; our own work has seen rapid advancement in recent months.

We hope this new initiative can explore how AI can accelerate discovery in mathematical research, and tackle harder problems.

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 …

The Gemini command line interface (CLI) is an open-source AI agent that provides access to Gemini directly in your terminal, enabling you to interact with Google’s latest AI models directly from the interface you know best.

With the release of Looker and Looker Conversational Analytics extensions, available now, you can interact with your Looker data and dashboards from the command line, streamlining your workflows and making data more accessible.

These new extensions for the Gemini CLI simplify your ability to ask complex questions of your data, generate insightful reports, and create new dashboards without leaving your terminal, opening up new possibilities for data exploration and analys…

We created BigQuery ML to bring AI to your data, enabling data scientists and data analysts to build and deploy machine learning models directly inside BigQuery.

Today, we’re introducing BigQuery AI, which brings together BigQuery’s built-in ML capabilities, generative AI functions, vector search, intelligent agents, and agent tools.

Using BigQuery AI, you can:Apply gen AI to your data : Bring Google and partner AI models directly to your multimodal data in BigQuery through simple SQL functions.

With managed AI functions, BigQuery chooses a model for you that is optimized for cost and quality.

Data processing to AI inference all under one roofWhen we first launched BigQuery ML, our goal was…

Today, we’re announcing Nano Banana Pro (Gemini 3 Pro Image), our state-of-the art image generation and editing model, available starting today in Vertex AI and Google Workspace, and coming soon to Gemini Enterprise.

Nano Banana Pro excels in visual design, world knowledge, and text generation, making it easier for enterprises to:Deploy localized global campaigns faster.

Nano Banana Pro supports up to 4K images for a higher level of detail and sharpness across multiple aspect ratios.

Nano Banana Pro and Nano Banana are designed to power a complete creative workflow.

Start with Nano Banana for high-velocity ideation, then transition to Nano Banana Pro when you need the highest fidelity for p…

Today, we're making it easier for agent developers in Google’s Agent Development Kit (ADK) to answer these questions.

With a single line of code, ADK developers can stream agent interaction data directly to BigQuery and get insights into their agent activity in a scalable manner.

To do so, we are introducing BigQuery Agent Analytics, a new plugin for ADK that exports your agent's interaction data directly into BigQuery to capture, analyze, and visualize agent performance, user interaction, and cost.

With your agent interaction data centralized in BigQuery, analyzing critical metrics such as latency, token consumption, and tool usage is straightforward.

This plugin is available in preview fo…

Vertex AI is that platform that provides the control and choice necessary for your business.

Vertex AI is fueled by continuous, market-leading innovation from Google DeepMind, ensuring you always have instant access to the most advanced intelligence.

Get started todayWe believe Google Cloud’s recognition as a Leader in the Magic Quadrant underscores the strategic advantage Vertex AI offers in powering the agent economy today.

Download a complimentary copy of the 2025 Gartner® Magic Quadrant™ for AI Application Development Platforms report to learn more about why Google was recognized as a Leader.

Gartner research publications consist of the opinions of Gartner's research organization and sh…

Phil Venables: The CISO is absolutely, undeniably becoming a peer business executive alongside all the other executives.

Phil Venables: When you look at the overall CISO 2.0 strategy, it's all about actually having a strategy.

Phil Venables: We talk a lot about interactions with boards and with the board and what the board expects.

One of the great common patterns of some of the best security organizations is they just aren’t good at interacting with the board.

It’s important that we think about all of our roles in the security and business community more broadly.

We are thrilled to announce the integration of TimesFM into our leading data platforms, BigQuery and AlloyDB.

This brings the power of large-scale, pre-trained forecasting models directly to your data within the Google Data Cloud, enabling you to predict future trends with unprecedented ease and accuracy.

By specifying `model => “TimesFM 2.5”`, you can use the latest TimesFM model to achieve better forecasting accuracy and lower latency.

AI.FORECAST supports displaying historical data : Displaying historical data together with forecasts is supported by setting `output_historical_time_series` to true.

In this example, you can use the TimesFM 2.5 model and specify the context window = 1024 in…

Today, we’re bringing Gemini 3, our most intelligent model, to every developer and enterprise team.

It’s the best model in the world for multimodal understanding, and our most powerful agentic and vibe-coding model yet.

Gemini 3 is available now in Gemini Enterprise and Vertex AI, so businesses and developers can access:State-of-the-art reasoning and multimodality: Gemini 3 uses multimodal understanding and state-of-the-art reasoning to analyze text, video, and files all at once.

Powerful agentic coding and front-end creation: Gemini 3 is our most powerful agentic and vibe-coding model yet for transforming application development and design.

Advanced tool use and planning: Gemini 3 enables …

To build these skills directly into the model, we leveraged the publicly available Supervised Tuning API for Gemini on Vertex AI.

AlloyDB integrates AI capabilities directly into the database, allowing developers to run powerful AI models using standard SQL queries without moving data.

And if you want to write some SQL yourself, Gemini Code Assist can help.

With a simple prompt, you can instruct Gemini as to the query you want to create.

Gemini will generate your code and you can immediately test it by executing it against your database.

Memorystore for Redis Cluster offered the best of both worlds.

This eliminated the need for client affinity, freed us from routing all session traffic through a single service, and made session data accessible across the platform.

Session data in Memorystore for Redis Cluster is now integral to Waze’s core features, from evaluating configurations to triggering real-time updates for drivers.

This work will ultimately give every microservice independent access to update session data.

Looking ahead, we're also focused on scaling Memorystore for Redis Cluster to meet future user growth and fine-tuning it for both cost and performance.

That’s why today we’re announcing a deeper partnership between Hugging Face and Google Cloud that:reduces Hugging Face model download times through Vertex AI and Google Kubernetes Engineoffers native support for TPUs on all open models sourced through Hugging Faceprovides a safer experience through Google Cloud’s built-in security capabilities.

We’ll enable faster download times through a new gateway for Hugging Face repositories that will cache Hugging Face models and datasets directly on Google Cloud.

Moving forward, developers working with Hugging Face’s open models on Google Cloud should expect download times to take minutes, not hours.

We’re also working with Hugging Face to add native…

Together, these functions allow answering new kinds of questions previously out of reach for SQL analytics, for example, companies to news articles which mention them even when an old or unofficial name is used.

Function deep diveAI.IF: Semantic filtering and joiningWith AI.IF, you can filter or join data using conditions written in natural language.

This is useful for tasks like identifying negative customer reviews, filtering images that have specific attributes, or finding relevant information in documents.

BigQuery optimizes the query plan to reduce the number of calls to LLM by evaluating non-AI filters first.

For example, the following query finds tech news articles from BBC that are …

In early 2024, we launched vector search in the BigQuery data platform, making its powerful capabilities accessible to all BigQuery users.

In the before-times: Building vector search the hard wayBefore we added native support for vector search in BigQuery, building a scalable vector search solution was a complex, multi-step process.

In the beginning: Focus on simplicityWe kicked off BigQuery vector search with one goal: to make the simplest vector database on the market.

Here are a few examples of the various applications that vector search can enhance:LLM applications with retrieval augmented generation (RAG) : By providing relevant business data, vector search helps ensure accurate and gr…

Training large video diffusion models at scale isn't just computationally expensive — it can become impossible when your framework can't keep pace with your ambitions.

JAX has become a popular computational framework across AI applications, now recognized for its capabilities in training large-scale AI models, such as LLMs and life sciences models.

Their LTX-Video team is building high-performance video generation models, and their journey is a masterclass in overcoming technical hurdles.

We launched LTX Studio to build generative video tools that truly serve the creative process.

With JAX, sharding strategies (sharding divides large models across multiple chips) that previously failed now …

At KubeCon EU 2025 earlier this year, we announced a series of enhancements to Kubernetes to better support AI inference.

Today, at KubeCon NA 2025, we’re focused on making Kubernetes the most open and scalable platform for AI agents, with the introduction of Agent Sandbox.

AI agents help applications go from answering simple queries to performing complex, multi-step tasks to achieve the users objective.

With its maturity, security, and scalability, we believe Kubernetes provides the most suitable foundation for running AI agents.

At its core, Agent Sandbox is a new Kubernetes primitive built with the Kubernetes community that’s designed specifically for agent code execution and computer us…

Real-world reasoning increasingly involves analyzing long-form video content, where context spans minutes or hours, far beyond the context limits of most models.

The Planner agent decomposes a user query, identifies the appropriate reasoning tools, performs multimodal operations, and drafts a preliminary answer.

MMCTAgent’s Planner–Critic architecture enables multimodal reasoning over long-form video through structured ingestion, retrieval, and iterative feedback.

The VideoAgent extends this architecture to long-form video reasoning.

Takeaways and next stepsMMCTAgent demonstrates a scalable agentic approach to multimodal reasoning with a Planner–Critic architecture.

Many studies have explored red teaming code LLMs, testing whether the models can reject unsafe requests and whether their generated code exhibits insecure patterns.

Knowledge-enhanced blue teaming: Building on the foundation of red-teaming knowledge, BlueCodeAgent significantly improves blue-teaming performance by leveraging constitutions derived from knowledge and dynamic testing.

Generalization to seen and unseen risks: Empowered by comprehensive red-teaming knowledge, BlueCodeAgent generalizes effectively to unseen risks.

A blue teaming agent enabled by red teamingFigure 2: Overview of BlueCodeAgent, an end-to-end blue teaming framework powered by automated red teaming for code security.…

Spotlight: Event Series Microsoft Research Forum Join us for a continuous exchange of ideas about research in the era of general AI.

These differentiated advantages stem from PIKE-RAG’s unique approach to understanding and processing professional knowledge.

“It’s also worth noting that the researchers at Microsoft Research Asia demonstrated strong industry knowledge and rigorous scientific methodology.

Through this collaboration, we validated that PIKE-RAG’s general approach can greatly improve the accuracy of professional knowledge Q&A and accelerate scenario customization.

Our researchers also gained valuable experience in handling domain-specific data,” explained Jiang Bian, partner rese…

To help navigate this uncertainty, we built Magentic Marketplace (opens in new tab)— an open-source simulation environment for exploring the numerous possibilities of agentic markets and their societal implications at scale.

To explore these dynamics in depth, the Magentic Marketplace platform enables controlled experimentation across diverse agentic marketplace scenarios.

With Magentic Marketplace, researchers can model how agents representing customers and businesses interact—shedding light on the dynamics that could shape future digital markets.

Magentic Marketplace includes two agent types: Assistant Agents (customers) and Service Agents (businesses).

Unlike traditional markets, which d…

In the context of code, effective red-teaming requires more than simply checking whether the target code agent rejects unsafe requests.

After the second request was rejected by the code agent, RedCodeAgent invoked both Code Substitution and GCG to optimize the prompt.

Ultimately, RedCodeAgent successfully combined the suggestion from Code Substitution (i.e., using pathlib) with the adversarial suffix generated by GCG, making the target code agent delete the specified file.

In the context of code, it is not enough for the target code agent to simply avoid rejecting the request; the target code agent must also generate and execute code that performs the intended function.

Quantitatively, we f…

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 generation of LLMs known as reasoning models are being trained to solve complex problems.

In other words, they report today in the journal PNAS, the “cost of thinking” for a reasoning model is similar to the cost of thinking for a human.

The researchers, who were led by Evelina Fedorenko, an associate professor of brain and cognitive sciences and an investigator at the McGovern Institute, conclude that in at least one important way, reasoning models have a human-like approach to thinking.

De Varda and Fedorenko say the striking match in the costs of thinking demonstrates one way in which reasoning models are thinking like humans.

The researchers point out that even though reasoning mo…

Computer-Aided Design (CAD) is the go-to method for designing most of today’s physical products.

MIT engineers are looking to ease CAD’s learning curve with an AI model that uses CAD software much like a human would.

The MIT team has created a new dataset called VideoCAD, which contains more than 41,000 examples of how 3D models are built in CAD software.

By learning from these videos, which illustrate how different shapes and objects are constructed step-by-step, the new AI system can now operate CAD software much like a human user.

However, the team realized that these high-level commands alone were not enough to train an AI agent to actually use CAD software.

A dramatic case in point: the April 2025 outage in Spain and Portugal that left millions without power for eight to 15 hours.

“I want to emphasize that this failure was about more than the power system,” said MITEI research scientist Pablo Duenas-Martinez.

The forum’s members include MITEI companies that also participate in MIT’s Center for Environmental and Energy Policy Research (CEEPR).

Solutions from labs big and smallGlobal energy leaders offered glimpses of their research projects.

The panel also highlighted the MIT Proto Ventures Program, an initiative to seize early-stage MIT ideas and unleash them as world-changing startups.

A dramatic case in point: the April 2025 outage in Spain and Portugal that left millions without power for eight to 15 hours.

“I want to emphasize that this failure was about more than the power system,” said MITEI research scientist Pablo Duenas-Martinez.

The forum’s members include MITEI companies that also participate in MIT’s Center for Environmental and Energy Policy Research (CEEPR).

Solutions from labs big and smallGlobal energy leaders offered glimpses of their research projects.

The panel also highlighted the MIT Proto Ventures Program, an initiative to seize early-stage MIT ideas and unleash them as world-changing startups.

Isola, the newly tenured associate professor in the Department of Electrical Engineering and Computer Science (EECS), studies the fundamental mechanisms involved in human-like intelligence from a computational perspective.

While understanding intelligence is the overarching goal, his work focuses mainly on computer vision and machine learning.

To Isola, a better scientific understanding of the intelligence that AI agents possess will help the world integrate them safely and effectively into society, maximizing their potential to benefit humanity.

He was fascinated by geological processes and often wondered what made the natural world work.

This involves the ways in which AI models learn to …

At its annual research conference, MITEI announced the Data Center Power Forum, a targeted research effort for MITEI member companies interested in addressing the challenges of data center power demand.

The Data Center Power Forum builds on lessons from MITEI’s May 2025 symposium on the energy to power the expansion of artificial intelligence (AI) and focus panels related to data centers at the fall 2024 research conference.

MITEI member companies have expressed strong interest in the Data Center Power Forum and are committing to support focused research on a wide range of energy issues associated with data center expansion, Green says.

As part of the forum, MITEI’s Future Energy Systems Ce…

Building probes, routers, new attention mechanisms, synthetic datasets, and program-synthesis pipelines, the students’ work spans safety, inference efficiency, multimodal data, and knowledge-grounded reasoning.

Learning to trust, and whenMIT math graduate student Andrey Bryutkin’s research prioritizes the trustworthiness of models.

Armed with this and working with the lab, Bryutkin developed a method to peer into the nature of large learning models (LLMs) behaviors.

More accurate and consistent probes are especially important for domains with critical data in applications like IBM’s Granite Guardian family of models.

Crucially, the system uses reinforcement learning to train itself to deliv…

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are charting a more “modular” path ahead.

The result is software that’s more modular, transparent, and easier to understand.

This opens the door to safer, more automated software development, where AI assistants can propose new features without introducing hidden side effects.

Meng and Jackson’s work on concept design provides a promising way to describe what we want from software in a modular manner.

“If software is to become more trustworthy, we need ways of writing it that make its intentions transparent,” says Jackson.

The AI-driven system incrementally creates and aligns smaller submaps of the scene, which it stitches together to reconstruct a full 3D map while estimating the robot’s position in real-time.

Unlike many other approaches, their technique does not require calibrated cameras or an expert to tune a complex system implementation.

“For robots to accomplish increasingly complex tasks, they need much more complex map representations of the world around them.

To solve this problem, the MIT researchers designed a system that generates smaller submaps of the scene instead of the entire map.

Through this analysis, Maggio realized that errors in the way the machine-learning models process images made a…

“We’re trying to advocate for an ethos of humility as we examine AI in schools,” Reich says.

“The academic publishing cycle doesn’t lend itself to helping people with near-term challenges like those AI presents,” Reich says.

These challenges, coupled with potential and observed student impacts, significantly raise the stakes for schools and students’ families in the AI race.

“We can develop long-term solutions to schools’ AI challenges, but it will take time and work,” he says.

“AI isn’t like learning to tie knots; we don’t know what AI is, or is going to be, yet.”Reich also recommends learning more about AI implementation from a variety of sources.

To better understand these changes and protect vulnerable wildlife, conservationists like MIT PhD student and Computer Science and Artificial Intelligence Laboratory (CSAIL) researcher Justin Kay are developing computer vision algorithms that carefully monitor animal populations.

Q: In your paper, you pose the question of which AI models will perform the best on a particular dataset.

You want to know what species are in these images, a time-consuming task that computer vision classifiers can help automate.

The computer vision algorithms I’ve worked on that count migrating salmon in underwater sonar video are examples of that work.

We always encounter something new when we deploy a new camer…

In a power grid, constraints could be things like generator and line capacity.

Grid operators often rely on traditional solvers, which provide mathematical guarantees that the optimal solution doesn’t violate any problem constraints.

For a power grid operator, this could result in issues like unsafe voltage levels or even grid outages.

Their system cut solving times by orders of magnitude compared to the baseline approaches, while respecting all problem constraints.

“Finding solutions to challenging optimization problems that are feasible is paramount to finding ones that are close to optimal.

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…

RL without TD learningIn this post, I’ll introduce a reinforcement learning (RL) algorithm based on an “alternative” paradigm: divide and conquer.

We can do Reinforcement Learning (RL) based on divide and conquer, instead of temporal difference (TD) learning.

There are two classes of algorithms in RL: on-policy RL and off-policy RL.

We compared TRL with $n$-step TD learning with different values of $n$, from $1$ (pure TD) to $\infty$ (pure MC).

I still think one of the most important problems in RL (and even in machine learning) is to find a scalable off-policy RL algorithm.

What exactly does word2vec learn?

What exactly does word2vec learn, and how?

In this framing, it’s clear that word2vec is a minimal neural language model.

As a result, the theory predicts exactly what features are learned in terms of the corpus statistics and the algorithmic hyperparameters.

We find that over the course of learning, word2vec builds these linear representations in a sequence of noisy learning steps, and their geometry is well-described by a spiked random matrix model.

Whole-Body Conditioned Egocentric Video Prediction×Predicting Ego-centric Video from human Actions (PEVA).

We trained a model to Predict Ego-centric Video from human Actions (PEVA) for Whole-Body-Conditioned Egocentric Video Prediction.

We train an autoregressive conditional diffusion transformer on Nymeria, a large-scale dataset pairing real-world egocentric video with body pose capture.

We include some samples here:Body Movement Actions Move Forward Rotate Left Rotate Right Left Hand Actions Move Left Hand Up Move Left Hand Down Move Left Hand Left Move Left Hand Right Right Hand Actions Move Right Hand Up Move Right Hand Down Move Right Hand Left Move Right Hand RightLong RolloutHere you…

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 Generative AI Gateway is a reference architecture for enterprises looking to implement end-to-end generative AI solutions featuring multiple models, data-enriched responses, and agent capabilities in a self-hosted way.

In this post we’ll introduce how the Multi-Provider Generative AI Gateway reference architecture provides guidance for deploying LiteLLM into an AWS environment for production generative AI workload management and governance.

Multi-Provider Generative AI Gateway reference architectureThis guidance addresses these common customer challenges by providing a centralized gateway that abstracts the complexity of multiple AI providers behind a single, managed interface.

Comprehe…

Amazon SageMaker AI for cost-effective generative AI inference – This Amazon SageMaker AI capability provides managed infrastructure for deploying open source models with optimized inference runtimes, auto scaling, and operational tooling.

In this post, we demonstrate a production geospatial agent that combines Foursquare Spatial H3 Hub with reasoning models deployed on Amazon SageMaker AI.

Analysis-ready geospatial data with Foursquare Spatial H3 HubFoursquare’s Spatial H3 Hub eliminates traditional geospatial adoption barriers through a proprietary H3 indexing engine.

Designing the Foursquare spatial agentThe Foursquare spatial agent architecture combines reasoning models deployed on Sage…

Wipro PARI partnered with AWS and ShellKode to develop an innovative solution that transforms this time-intensive PLC code generation process using AI.

Solution overviewIn this section, we present the solution architecture and user workflow of the Wipro PLC Code Generator.

The following GIF illustrates the complete user workflow from Excel upload to PLC code generation and download.

The Wipro PLC Code Generator represents a milestone in industrial automation programming, directly addressing the productivity challenges faced by Wipro PARI’s engineering consultants.

His specializations include code generation, AI agent frameworks, fine-tuning vision language models and robot foundation models…

The application of AI, particularly generative AI, in streamlining complex processes is a growing trend.

Innovative solution: Generative AI for deviation managementTo address some of the major challenges in deviation management, the Digital Manufacturing Data Science team at MSD devised an innovative solution using generative AI (see How can language models assist with pharmaceuticals manufacturing deviations and investigations?).

To mitigate these risks, the solution mostly limits the generative AI content creation to low-risk areas and incorporates human oversight and other guardrails.

To protect proprietary and sensitive manufacturing information, the solution includes data encryption an…

AWS HealthOmics workflows along with Amazon S3 tables and Amazon Bedrock AgentCore together provide a transformative solution to these challenges.

Understanding variant annotation in genomic analysisThe foundation of genomic variant interpretation relies on comprehensive annotation pipelines that connect raw genetic variants to biological and clinical context.

The comparative visualization illustrates the distinct yet complementary annotation capabilities of ClinVar and VEP for genomic variant interpretation.

Domain-specific fine-tuning on genomic data could further improve interpretation accuracy, while integration with electronic health records would provide point-of-care genomic insights…

Our team at Amazon builds Rufus, an AI-powered shopping assistant which delivers intelligent, conversational experiences to delight our customers.

Integrating Amazon Bedrock with RufusWith Amazon Bedrock, we can evaluate and select the optimal model for each query type, balancing answer quality, latency, and engagement.

The result: AI-powered shopping at Amazon scaleBy using Amazon Bedrock, Rufus demonstrates how organizations can build sophisticated AI applications that scale to serve millions of users.

Saurabh Trikande is a Senior Product Manager for Amazon Bedrock and Amazon SageMaker Inference.

Somu Perianayagam is an Engineer at AWS specializing in distributed systems for Amazon Dynamo…

By caching the static medical record content while varying only the analysis questions, Care Access achieved significant cost reductions and faster processing times.

In this post, we demonstrate how healthcare organizations can securely implement prompt caching technology to streamline medical record processing while maintaining compliance requirements.

For Care Access, processing vast amounts of diverse medical data formats while maintaining strict compliance, privacy, and security standards required an innovative solution.

Through strategic implementation of Amazon Bedrock’s prompt caching capability, Care Access data processing achieved significant cost reductions and faster processing t…

Claude Code is an AI-powered coding assistant from Anthropic that helps developers write, review, and modify code through natural language interactions.

Recommendations for most enterprisesWe recommend the Guidance for Claude Code with Amazon Bedrock, which implements proven patterns that can be deployed in hours.

The Guidance for Claude Code with Amazon Bedrock implements both Cognito Identity Pool and Direct IAM federation patterns, but recommends Direct IAM for simplicity.

Deployment – Clone the Guidance for Claude Code with Amazon Bedrock repository and run the interactive poetry run ccwb init wizard.

The Guidance for Claude Code with Amazon Bedrock implements these patterns in a deploy…

Configuring Amazon Bedrock Guardrails for code domainLet’s explore how Amazon Bedrock Guardrails work to protect your development environment.

To illustrate:Configuring Amazon Bedrock Guardrails for harmful intent and content detectionIn the Amazon Bedrock Guardrails console, create a guardrail with a name and blocked prompt message.

Amazon Bedrock Guardrails now offers capabilities to counter such attacks within the coding domain.

Configuring Bedrock Guardrails for denied topicsTo configure denied topics, navigate to Step 3 in the Bedrock Guardrails console, choose Add denied topic, and enter your topic details, preferences, and optional sample phrases.

Get started with Amazon Bedrock Guar…

Today, we’re announcing the AWS Well-Architected Responsible AI Lens—a set of thoughtful questions and corresponding best practices that help builders address responsible AI concerns throughout development and operation.

The Responsible AI Lens guides builders through the end-to-end lifecycle of building a targeted AI application (not a frontier model).

How to use the Responsible AI LensThe Responsible AI Lens is organized into eight focus areas covering different steps in the AI lifecycle.

The Responsible AI Lens serves three audiences who play complementary roles in developing and deploying responsible AI systems:AI builders , including engineers, product managers, and scientists, who dev…

Amazon’s Compliance team has developed an AI-driven screening and investigations system that has transformed Amazon’s compliance processes into an industry-leading solution: Amazon Compliance Screening, which screens approximately 2 billion transactions daily across 160+ businesses globally to prevent prohibited transactions.

Tier 3 – AI-powered investigation system: Conducts comprehensive evaluation of remaining high-quality matches using specialized AI agents deployed on Amazon Bedrock AgentCore Runtime.

Synthesizes findings from all the AI agents, applies risk-weighted analysis, and generates final recommendations with detailed justifications based on the evidence gathered.

ConclusionAma…

Solution overviewThis solution implements a fully autonomous, explainable workflow for vehicle insurance claim processing using an agentic AI architecture.

Snowflake data setup – A `customer_policy_view` table or view with policyholder data ( customer ID , VIN , and POLICY_END ) and sample data.

Snowflake Document AI model builds – Two models in Snowsight for licenses and claims, trained and published for prediction.

Document processingThis sample solution uses Snowflake Document AI, powered by the proprietary Arctic-TILT LLM, to extract information from uploaded documents with impressive flexibility.

For information about getting started with Document AI, visit Setting up Document AI.

We will also discuss the tradeoff between QLoRA and Spectrum fine-tuning, showing that while QLoRA is more resource efficient, Spectrum results in higher performance overall.

Finally, you will create an Amazon SageMaker AI training job, providing the Spectrum analysis as an input.

The underlying principles of spectrum fine-tuning, which involve selectively training layers based on their signal-to-noise ratio, can be applied to vision models.

Specify the local location of your training code, the compute requirements for the training job, and other training job configuration options.

The No Spectrum run ends up with similar resulting loss metrics as 25% and 50% Spectrum runs, with 10% margina…

Inspired by this, we developed a game for the AWS re:Invent 2024 Builders Fair using Amazon Bedrock, Strands Agents, AWS IoT Core, AWS Lambda, and Amazon DynamoDB.

Supervisor AgentThe Supervisor Agent acts as an orchestrator that manages both the Move Agent and the Game Agent, coordinating and streamlining decisions across the system.

The Supervisor Agent consolidates the responses from the underlying agents and structures them into a unified output format.

By intelligently directing requests and unifying responses, the Supervisor Agent facilitates reliable execution, simplified monitoring, and enhanced user experience.

Game AgentThe Game Agent functions as an opponent, capable of playing a…

Amazon SageMaker HyperPod is a purpose-built infrastructure for optimizing foundation model training and inference at scale.

SageMaker HyperPod removes the undifferentiated heavy lifting involved in building and optimizing machine learning (ML) infrastructure for training foundation models (FMs).

Amazon EBS CSI support: HyperPod EKS now supports the Amazon Elastic Block Store (Amazon EBS) Container Storage Interface (CSI) driver, which manages the lifecycle of Amazon EBS volumes as storage for the Kubernetes volumes that you create.

To create your HyperPod cluster, please see Creating a SageMaker HyperPod cluster with Amazon EKS orchestrationCMK support can only be used with HyperPod cluste…

Cities are deploying AI agents, digital twins and computer vision to turn fragmented urban infrastructure into intelligent, responsive spaces.

Editor’s note: This post is part of Into the Omniverse, a series focused on how developers, 3D practitioners and enterprises can transform their workflows using the latest advancements in OpenUSD and NVIDIA Omniverse.

Leading cities and technology partners are deploying the NVIDIA Blueprint for smart city AI, a reference application that provides the complete software stack to build, test and operate AI agents in simulation-ready (SimReady) digital twins.

OpenUSD-enabled digital twins serve as SimReady environments where cities can simulate “what if”…

For example, the model arranged Darwin’s finches by beak size, without teaching the concept of size, shown in the image below.

This paper builds on the first BioCLIP model, released over a year ago, which was also trained on NVIDIA GPUs and received the Best Student Paper award at the Computer Vision and Pattern Recognition (CVPR) conference.

These researchers set out to discover what would happen if they trained a biology model on more data than ever.

Berger-Wolf’s team used a cluster of 64 NVIDIA Tensor Core GPUs to accelerate model training, plus individual Tensor Core GPUs for inference.

People could explore, visualize and learn about the natural environment and its many species from en…

The NVIDIA Blackwell RTX upgrade is nearing the finish line, letting GeForce NOW Ultimate members across the globe experience true next-generation cloud gaming from anywhere.

Stockholm will soon be the final region to get Blackwell RTX power, completing the rollout of GeForce RTX 5080-class performance worldwide.

The Ultimate ContestMembers can join in celebrating GeForce RTX 5080-class power streaming all over the world by sharing gameplay videos of why the Ultimate experience stands out.

It features the 14 episodes of Apollo Justice: Ace Attorney, Phoenix Wright: Ace Attorney – Dual Destinies and Phoenix Wright: Ace Attorney – Spirit of Justice, as well as two special episodes that were p…

The previous NVIDIA Research solution, Prolonged Reinforcement Learning (ProRL), showed that adding more reinforcement learning (RL) steps during prolonged training could expand the reasoning boundaries of LLMs.

Today, we’re excited to introduce Broadened Reinforcement Learning (BroRL), a new paradigm that explores a complementary and powerful scaling dimension: rollout scaling.

Core comparison of step scaling (ProRL) and rollout scaling (BroRL)How does rollout scaling control RL instability?

This confirms that scaling rollout size is a more effective and computationally efficient strategy for pushing the boundaries of a saturated model.

They can be overcome by scaling rollouts to generate …

Five finalists for the Gordon Bell Prize for outstanding achievements in high-performance computing (HPC) are using NVIDIA-powered supercomputers for their critical work in climate modeling, materials science, fluid simulation, geophysics and electronic design.

Announced today at SC25, the finalists’ projects are driving AI and HPC for science using physics simulation, high-precision math and other advanced supercomputing techniques, accelerating breakthroughs across weather forecasting, semiconductor design, space exploration and other fields.

Their results are open and accessible on ArXiv.

The supercomputers powering their work include:

This massive-scale infrastructure will integrate hundreds of thousands of NVIDIA Blackwell GPUs for large-scale training.

NVIDIA and Microsoft have also partnered to optimize their fleet for AI workload performance through the NVIDIA DGX Cloud Benchmarking suite.

NVIDIA is accelerating AI in the new Microsoft SQL Server 2025 by integrating it with NVIDIA Nemotron open models and NVIDIA NIM microservices.

To power these new enterprise agents, Microsoft Foundry now offers NVIDIA Nemotron models for digital AI and NVIDIA Cosmos models for physical AI as secure NIM microservices.

With NVIDIA Omniverse libraries available on Microsoft Azure, NVIDIA is unlocking end-to-end reindustrialization in …

An emerging class of GPU-accelerated data and storage infrastructure — the AI data platform — transforms unstructured data into AI-ready data quickly and securely.

The AI Data Platform — a New Class of Enterprise Data and Storage InfrastructureAI data platforms are an emerging class of GPU-accelerated data and storage infrastructure that makes enterprise data AI-ready.

By embedding GPU acceleration directly into the data path, AI data platforms transform data for AI pipelines as a background operation invisible to the user.

AI data platforms deliver an integrated, state-of-the-art AI data pipeline out of the box.

By integrating GPU acceleration into the data path, AI data platforms enable e…

Anthropic to scale Claude on Azure, Anthropic to adopt NVIDIA architecture, and NVIDIA and Microsoft to invest in Anthropic.

Today, Microsoft, NVIDIA and Anthropic announced new strategic partnerships.

Anthropic and NVIDIA will collaborate on design and engineering, with the goal of optimizing Anthropic models for the best possible performance, efficiency and TCO, and optimizing future NVIDIA architectures for Anthropic workloads.

As part of the partnership, NVIDIA and Microsoft are committing to invest up to $10 billion and up to $5 billion respectively in Anthropic.

Anthropic cofounder and CEO Dario Amodei, Microsoft Chairman and CEO Satya Nadella, and NVIDIA founder and CEO Jensen Huang …

The Great Flip: How Accelerated Computing Redefined Scientific Systems — and What Comes NextIt used to be that computing power trickled down from hulking supercomputers to the chips in our pockets.

In 2019, nearly 70% of the TOP100 high-performance computing systems were CPU-only.

Today, that number has plunged below 15%, with 88 of the TOP100 systems accelerated — and 80% of those powered by NVIDIA GPUs.

Across the broader TOP500, 388 systems, 78%, now use NVIDIA technology, including 218 GPU-accelerated systems (up 34 systems year over year) and 362 systems connected by high-performance NVIDIA networking.

It starts with scientific computing.

NVIDIA drives global scientific discovery with a combined total of 4,500 exaflops of AI performance across systems unveiled in the past year.

Scientific Innovation on the Horizon for TACCWith 4,000 NVIDIA Blackwell GPUs, the Horizon supercomputer can deliver up to 80 exaflops of AI compute at FP4 precision.

At ANL, two AI supercomputing systems featuring NVIDIA Blackwell GPUs and NVIDIA networking will connect with the DOE’s network of scientific instruments and data assets, enabling researchers to develop powerful AI models for science and energy applications.

A system of that scale featuring NVIDIA GB200 NVL72 systems can reach a staggering 1,000 exaflops of AI training compute for traini…

From NVIDIA DGX Spark to NVIDIA BlueField-4 DPUs, next-generation networking and quantum leaps — accelerated systems showcased at SC25 highlight global supercomputing and AI advances.

At SC25, NVIDIA unveiled advances across NVIDIA BlueField DPUs, next-generation networking, quantum computing, national research, AI physics and more — as accelerated systems drive the next chapter in AI supercomputing.

Built on the Grace Blackwell architecture, it integrates NVIDIA GPUs, CPUs, networking, CUDA libraries and the full NVIDIA AI software stack.

Quantum Computing System: 540 Blackwell GPUs will accelerate quantum algorithms, hybrid simulation and quantum‑classical methods.

NVLink Fusion links CPU…

The NIM microservices are part of NVIDIA ALCHEMI, a suite of microservices and toolkits for chemistry and materials science.

Japanese energy company ENEOS and New Jersey-based OLED display technology company Universal Display Corporation are among the early-access users of the NVIDIA ALCHEMI NIM microservices.

“By using GPU-accelerated computing and NVIDIA ALCHEMI together with our in-house expertise, we can completely change the scale and speed of discovery,” said Brown.

UDC is applying NVIDIA ALCHEMI NIM microservices to research projects including the development of blue phosphorescent OLEDs that could meaningfully improve energy efficiency and device performance.

Learn more about NVIDIA…

Accelerated by NVIDIA AI infrastructure, the new AI physics models will enable developers to integrate real-time capabilities into their simulation software across a broad range of industries.

Northrop Grumman and Luminary Cloud are also using NVIDIA AI physics models to accelerate spacecraft thruster nozzle design.

Rescale is accelerating engineering innovation by integrating NVIDIA Apollo models into its industry-leading AI physics operating system.

Synopsys is using NVIDIA AI physics to multiply GPU acceleration and achieve up to 500x speedups in computational engineering.

NVIDIA Apollo models are coming soon to build.nvidia.com, HuggingFace and as NVIDIA NIM microservices.

Enter RDMA for S3-compatible storage — which uses remote direct memory access (RDMA) to accelerate the S3-application programming interface (API)-based storage protocol and is optimized for AI data and workloads.

Accelerated Storage: Faster data access and performance for AI training and inference — including vector databases and key-value cache storage for inference in AI factories.

AI data platform solutions gain faster storage object storage access and more metadata for content indexing and retrieval.

solutions gain faster storage object storage access and more metadata for content indexing and retrieval.

Plus, learn more about a new NVIDIA Object Storage Certification, part of the NVIDI…

Editor’s note: This post is part of the AI On blog series, which explores the latest techniques and real-world applications of agentic AI, chatbots and copilots.

Three approaches organizations can use to boost their legacy computer vision systems with agentic intelligence are to:Apply dense captioning for searchable visual content.

Augmenting Computer Vision System Alerts With VLM ReasoningCNN-based computer vision systems often generate binary detection alerts such as yes or no, and true or false.

With a VLM layered on top of CNN-based computer vision systems, detection alerts are not only flagged but reviewed with contextual understanding — explaining where, how and why the incident occur…

But have you heard about open hardware?

And did you know open source can have a positive impact on the environment?

On this episode of the Meta Tech Podcast, Pascal Hartig sits down with Dharmesh and Lisa to talk about all things open hardware, and Meta’s biggest announcements from the 2025 Open Compute Project (OCP) Summit – including a new open methodology for leveraging AI to understand Scope 3 emissions.

You’ll also hear how AI and open hardware are helping Meta push to achieve net zero emissions in 2030, including how AI is being used to develop new concrete mixes for data center construction.

And if you’re interested in learning more about career opportunities at Meta visit the Meta C…

We’re sharing details about Meta’s Generative Ads Recommendation Model (GEM), a new foundation model that delivers increased ad performance and advertiser ROI by enhancing other ads recommendation models’ ability to serve relevant ads.

GEM propagates its learnings, leveraging a suite of post-training techniques across the entire ads model fleet, enabling a paradigm shift in Meta’s Ads Recommendation system.

GEM leverages enhanced training scalability that efficiently utilizes thousands of GPUs for building and iterating an LLM-scale ads foundation model.

The Generative Ads Recommendation Model (GEM) is Meta’s most advanced ads foundation model, built on an LLM-inspired paradigm and trained …

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…

For instance, financial data is highly sensitive and protected by very strict regulations, and synthetic data mimics the real data distribution without revealing customer information.

Read more about how leading foundation model teams curate their training data and other topics in the State of Foundation Model Training Report 2025.

Choosing the right synthetic data generation technique depends on the type of data and its complexity.

Synthetic tabular data generation is a promising direction to overcome these challenges by learning the distribution of the tabular data.

Post-processingAs the distribution of tabular data is highly complex, it makes the synthetic tabular data generation very ch…

TL;DR LLM embeddings are the numerical, vector representations of text that Large Language Models (LLMs) use to process information.

Unlike their predecessor word embeddings, LLM embeddings are context-aware and dynamically change to capture semantic and syntactic relationships based on the surrounding text.

What are the applications of LLM embeddings?

Word EmbeddingsSparse Word Embeddings One-Hot Vectors 1970s TF-IDF1980s Co-Occurrence MatrixStatic Word Embeddings Word2Vec 2013 GloVe 2014Contextualized word embeddings ELMo 2018 GPT-1 2018 BERT 2018 LLAMA 2023 DeepSeek-V1 2023 GPT-4 2023Static word embeddingsStatic word embeddings, such as word2vec in 2013, marked a significant development.…

TL;DR Dead neurons silently waste compute and reduce effective model capacity in foundation models.

Dead neurons’ impactRecent studies into dead neurons in the context of foundation models show interesting, albeit worrying, results.

These large reported fractions of dead neurons in foundation models are a concern from a computational perspective.

Before we move on to discuss how to detect and fix dead neurons, let’s touch upon an important distinction between dead neurons and vanishing gradients.

Further reading How to Monitor, Diagnose, and Solve Gradient Issues in Foundation Models Read moreVisualizing activation distributionsIs your foundation model suffering from dead neurons?

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 …

https://arxiv.org/abs/2510.17558 Abstract:

We propose an extension of the decoder Transformer that conditions its generative process on random latent variables which are learned without supervision thanks to a variational procedure. Experimental evaluations show that allowing such a conditioning translates into substantial improvements on downstream tasks. Author: François Fleuret Links:

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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…

Studying the forced harmonic oscillator by taking a Laplace transform and studying its poles.

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 Chapter on the Laplace Transform:

https://youtu.be/j0wJBEZdwLs Chapter on the S-plane and Simple Harmonic Motion:

https://youtu.be/-j8PzkZ70Lg Timestamps:

0:00 - Opening puzzle

1:06 - Key properties of a Laplace Transform

3:29 - Qualitative analysis with Laplace Transforms

4:29 - The Laplace Transforms of a Derivative

6:06 - The forced oscillator

11:59 - Intuition from the transformed solution

1…

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

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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:…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Get Blender 5.0 here: https://www.blender.org/

Example scenes: https://www.blender.org/download/demo-files/

Multiple scattering paper: https://cg.iit.bme.hu/~szirmay/volreuse_link.htm 📝 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, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall…

❤️ 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 following the command from here - 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://danijar.com/project/dreamer4/ Source:

https://www.youtube.com/watch?v=6bnM84xGxbg 📝 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 Patre…

❤️ 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 "Fast Physics-Based Modeling of Knots and Ties Using Templates" is available here:

https://wanghmin.github.io/publication/guo-2025-fpb/ Sources:

https://www.youtube.com/watch?v=2RQcoLV_bVk

https://www.youtube.com/watch?v=7d158rQ1R3k

https://www.youtube.com/watch?v=qirVdKg3qgs

https://www.youtube.com/watch?v=TPokJdN2bkw

https://www.youtube.com/watch?v=DRzT3c1jk14

https://www.youtube.com/w…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper "A practical octree liquid simulator with adaptive surface resolution" is available here:

https://cs.uwaterloo.ca/~c2batty/papers/Ando2020/Ando2020.pdf Sources:

https://www.youtube.com/watch?v=kdt5Cs1VYJA

https://www.youtube.com/watch?v=YmmSDZ6dBdY

https://www.youtube.com/shorts/FVIDRU9-FW8

https://www.youtube.com/watch?v=gNZtx3ijjpo&pp=ygUHb2N0cmVlcw%3D%3D

https://www.youtube.com/shorts/1Euba1QvhW0

https://www.youtube.com/shorts/k2P9yWSMaXE

https://www.youtube.com/watch?v=Z5qbxQI6dgw

https://www.youtube.com/watch?v=laoGmqNtUMI 📝 My paper on simulations that look almost like reality is availa…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper "Multi-Material Mesh-Based Surface Tracking with Implicit Topology Changes" is available here under one of these links hopefully:

https://pub.ista.ac.at/group_wojtan/projects/2024_MultimatMeshing/SuperDuperTopoFixer.pdf

https://dl.acm.org/doi/10.1145/3658223 📝 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=dtBqv-qIFLo

https://www.youtube.com/watch?v=EZul6DR-fHc

https://www.youtube…

❤️ 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 "Neural Robot Dynamics" is available here:

https://neural-robot-dynamics.github.io/

https://github.com/NVlabs/neural-robot-dynamics 📝 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 gener…

❤️ 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 "A unified multi-scale method for simulating immersed bubbles" is available here:

https://alexey.stomakhin.com/research/unibubbles.html 📝 My paper on simulations that look almost like reality is available for free here:

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

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our g…

❤️ 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://dress-1-to-3.github.io/ ❤️ Get cool perks and support The Papers on Patreon! Link: https://www.patreon.com/c/TwoMinutePapers 📝 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, Juan Benet, Michael Tedder, Owe…

❤️ 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…

Это отрывок из доклада Алексея Колесова, CTO в Яндекс R&D. На Practical ML Conf 2025он рассказал, как ребята учили YandexGPT 5.1 лучше помнить факты и применять знания о них. А ещё показал, как у нас стабильно заработал online RL. Полная запись уже на канале! #YandexGPT #LLM #AI #ArtificialIntelligence #MachineLearning #DeepLearning #ReinforcementLearning #OnlineRL #NLP #GenerativeAI #YandexForDevelopers #YandexForML #Яндекс #AIDevDay #TechConference #DataScience #ML #AIResearch #LanguageModel #YandexTech

Data Dojo — это сообщество ML-экспертов. Здесь обсуждают тренды, разбирают реальные задачи, делятся опытом и практикуются. Додзё в японской культуре — место Пути, где совершенствуют не только мастерство, но и дух. Мы перенесли этот принцип в мир данных. Программа: Приветственное слово | Владислав Офицеров, модератор встречи, руководитель команды развития нейронных технологий международного поиска, и Пётр Ермаков, ML-бренд-директор Лекция: Обзор трендов и предварительный итоги года | Сергей Овчаренко, руководитель отдела мультимодального анализа и генерации Лекция: Научить AI не бредить, сдать физику и получить права: как мы готовили задачи ML‑квалификации Yandex Cup | Сергей Фиронов, ведущи…

Это отрывок из доклада Алексея Колесова, CTO в Яндекс R&D. На Practical ML Conf 2025 он рассказал, как ребята учили YandexGPT 5.1 лучше помнить факты и применять знания о них. А ещё показал, как у нас стабильно заработал online RL. Полная запись уже на канале! #YandexGPT #LLM #AI #ArtificialIntelligence #MachineLearning #DeepLearning #ReinforcementLearning #OnlineRL #NLP #GenerativeAI #YandexForDevelopers #YandexForML #Яндекс #AIDevDay #TechConference #DataScience #ML #AIResearch #LanguageModel #YandexTech

Это Сергей Овчаренко, руководитель отдела мультимодальных анализа и генерации в Яндекс R&D. В своём докладе Сергей рассказал о VLM в Яндексе: какие подходы мы используем и с какими подводными камнями сталкиваемся. А еще — о претрейне и о том, почему добиться хорошего качества бывает непросто, даже когда, казалось бы, всё делаешь правильно. Узнать больше о мероприятиях для разработчиков можно тут: https://events.yandex.ru Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml #ML #AI #MachineLearning #DeepLearning #LLM #VLM #NeuralNetworks #Transformers #GenerativeAI #NLP #ComputerVision #DataScience #BigData #MLOps #ModelTraining #AIResearch #ArtificialIntellig…

Это Алексей Колесов, CTO в Яндекс R&D. Поговорили честно и без прикрас об обратной стороне релизов — о нюансах и неожиданном поведении LLM, с которыми сталкивались на своём опыте, и о том, как решали такие кейсы. Узнать больше о мероприятиях для разработчиков можно тут: https://events.yandex.ru Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml #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 #Reco…

Это Евгений Комаров, руководитель команды ML Поиска в Яндекс Лавке. Поиск товаров — одна из самых нагруженных частей Лавки. В докладе Евгений рассказал, как команда реализовала кэш на основе LLM, чтобы повысить релевантность и скорость отклика товарного поиска. Узнать больше о мероприятиях для разработчиков можно тут: https://events.yandex.ru Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml #ML #AI #MachineLearning #DeepLearning #LLM #VLM #NeuralNetworks #Transformers #GenerativeAI #NLP #ComputerVision #DataScience #BigData #MLOps #ModelTraining #AIResearch #ArtificialIntelligence #AIDevelopment #AIFuture #Tech #Engineering #Yandex #SberAI #AvitoTech #TBa…

Это Кирилл Власов, PO AI Studio в Yandex Cloud. Когда мы работаем с AI-проектами, первый вопрос, который мы задаём себе, — какую модель выбрать? Многообразие растёт: каждая компания утверждает, что именно у неё — лучшая модель в мире. В своём докладе Кирилл делится тем, как выжить в этом хаосе и перейти к системной работе с пайплайном промптинга, эвала и трейсинга. Узнать больше о мероприятиях для разработчиков можно тут: https://events.yandex.ru Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml #ML #AI #MachineLearning #DeepLearning #LLM #VLM #NeuralNetworks #Transformers #GenerativeAI #NLP #ComputerVision #DataScience #BigData #MLOps #ModelTraining #AIRe…

Делимся отрывком с Practical ML Conf 2025 — главной конфы Яндекса по машинному обучению. Тут мы поймали нескольких участников и спросили, какие темы, по их мнению, самые интересные и что они думают об ивенте 😎 #PracticalMLConf #YandexForML #YandexForDevelopers #Яндекс #AI #ML #MachineLearning #ArtificialIntelligence #NeuralNetworks #DeepLearning #DataScience #AIAgents #GenerativeAI #LLM #YandexTech #AIFuture #MLFuture #TechConference #ITConference #AIConference #YandexAI #YandexML #AIDevDay #AICommunity #MLCommunity #YandexEvents #AIEducation #AITech #AIinPractice #TechEvents

В докладе на Data Fest Siberia Всеволод Парамонов и Андрей Шевцов, разработчики группы аналитики ценообразования Яндекс Лавки, рассказали, что лежит под капотом динамического ценообразования в сервисе. Ребята разобрали, как устроены модели спроса и эластичности и какие математические формулы приводят в движение весь процесс. Наш телеграм-канал Yandex for ML: https://t.me/+Ug9D4CjJrJxmZGRi #YandexForML #DataFest #Яндекс #MachineLearning #ML #AI #RAG #LLM #VLM #YandexGPT #МультимодальныеМодели #DataFestSiberia #NeuralNetworks #DeepLearning #ComputerVision #ЯндексПоиск #MLTech #AIinYandex #MLTalks #TechConference

Делимся отрывком с Practical ML Conf 2025 — главной конфы Яндекса по машинному обучению. Тут мы поймали нескольких спикеров и задали им несколько вопросов: какие темы интересуют их больше всего, что ребята думают об ивенте, где будут применяться AI-агенты и какие есть трудности в обучении нейросетей. #PracticalMLConf #YandexForML #YandexForDevelopers #Яндекс #AI #ML #MachineLearning #ArtificialIntelligence #NeuralNetworks #DeepLearning #DataScience #AIAgents #GenerativeAI #LLM #YandexTech #AIFuture #MLFuture #TechConference #ITConference #AIConference #YandexAI #YandexML #AIDevDay #AICommunity #MLCommunity #YandexEvents #AIEducation #AITech #AIinPractice #TechEvents

Делимся отрывком с Practical ML Conf 2025 — главной конфы Яндекса по машинному обучению. Участники рассказали нам, почему тренировать AI не так-то просто: это дорого, ресурсозатратно, а ещё всегда нужно держать в уме, как выделиться среди конкурентов. #PracticalMLConf #YandexForML #YandexForDevelopers #Яндекс #AI #ML #MachineLearning #ArtificialIntelligence #NeuralNetworks #DeepLearning #DataScience #AIAgents #GenerativeAI #LLM #YandexTech #AIFuture #MLFuture #TechConference #ITConference #AIConference #YandexAI #YandexML #AIDevDay #AICommunity #MLCommunity #YandexEvents #AIEducation #AITech #AIinPractice #TechEvents

Иногда пользователи Яндекс Карт по ошибке загружают фотографии не в те организации. Такое бывает, главное — заметить и вовремя исправить. А как научить модели это делать, рассказал Константин Гордеев, разработчик группы магии дискавери бизнес-группы Поиска и Рекламных технологий, в докладе на Data Fest Siberia. Наш телеграм-канал Yandex for ML: https://t.me/+Ug9D4CjJrJxmZGRi #YandexForML #DataFest #Яндекс #MachineLearning #ML #AI #RAG #LLM #VLM #YandexGPT #МультимодальныеМодели #DataFestSiberia #NeuralNetworks #DeepLearning #ComputerVision #ЯндексПоиск #MLTech #AIinYandex #MLTalks #TechConference

Спойлер: во всех. Даже в сельском хозяйстве 🌾 По крайней мере, так считают некоторые участники Practical ML Conf 2025. #PracticalMLConf #YandexForML #YandexForDevelopers #Яндекс #AI #ML #MachineLearning #ArtificialIntelligence #NeuralNetworks #DeepLearning #DataScience #AIAgents #GenerativeAI #LLM #YandexTech #AIFuture #MLFuture #TechConference #ITConference #AIConference #YandexAI #YandexML #AIDevDay #AICommunity #MLCommunity #YandexEvents #AIEducation #AITech #AIinPractice #TechEvents

В своём докладе на Data Fest Siberia Арсений Михайлов, руководитель группы базового качества картинок бизнес-группы Поиска и Рекламных технологий, рассказал, как работает поиск картинок в Яндексе. Арсений показал, как ребята обучали схему ранжирования, майнили свежие запросы при помощи RAG и с какими трудностями столкнулись в процессе. Наш телеграм-канал Yandex for ML: https://t.me/+Ug9D4CjJrJxmZGRi #YandexForML #DataFest #Яндекс #MachineLearning #ML #AI #RAG #LLM #VLM #YandexGPT #МультимодальныеМодели #DataFestSiberia #NeuralNetworks #DeepLearning #ComputerVision #ЯндексПоиск #MLTech #AIinYandex #MLTalks #TechConference

0:00:00 Начало

0:00:25 Gonka за GPU

0:12:17 Хассабис про модели мира

0:24:06 Три-Майл-Айленд перезапустят

0:29:40 ЛеКун уходит из FAIR*

0:38:39 Nokia милитаризируется

0:41:14 GPT-5 помогает ученым

0:51:20 Алиса стала популярной

0:57:31 DeepMind против дип-фейков

1:01:48 Сосули против ИИ ИИ-саммари: В этом выпуске обсуждаются последние новости в области искусственного интеллекта, включая децентрализованные вычисления и аренду вычислительных мощностей. Участники делятся мнениями о перспективах и проблемах, связанных с новыми технологиями, а также о будущем ИИ, акцентируя внимание на моделях мира и их значении для развития AGI. В этом эпизоде обсуждаются ключевые аспекты развития нейронных сет…

Спикер: Лев Максимов, ИАиЭ СО РАН Data Fest Siberia 6: https://ods.ai/events/datafestsiberia6

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

Спикер: Анна Латушко, НГУ Data Fest Siberia 6: https://ods.ai/events/datafestsiberia6

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Спикер: Борис Иванчиков, Яндекс.Маркет

Тема: Построение мультимодальных эмбеддингов товара для улучшения матчинга в Яндекс.Маркете Data Fest Siberia 6: https://ods.ai/events/datafestsiberia6

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Спикеры: Ангелина Калюжная, Иман Хассун, Huawei R&D Тема: The metric songs: how academic benchmarks meet the real production in IDE inline completion Data Fest Siberia 6: https://ods.ai/events/datafestsiberia6

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Спикер: Илья Гончаров, LaserSoft Imaging Data Fest Siberia 6: https://ods.ai/events/datafestsiberia6

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Спикер: Владислав Смирнов, Яндекс Data Fest Siberia 6: https://ods.ai/events/datafestsiberia6

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Спикер: Арсений Михайлов, Яндекс Data Fest Siberia 6: https://ods.ai/events/datafestsiberia6

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Спикер: Иван Бондаренко, НГУ Data Fest Siberia 6: https://ods.ai/events/datafestsiberia6

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Спикер: Дари Батурова, НГУ, Сибирские нейросети Data Fest Siberia 6: https://ods.ai/events/datafestsiberia6

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

Спикер: Артём Лобанов, Яндекс Data Fest Siberia 6: https://ods.ai/events/datafestsiberia6

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

Спикеры: Всеволод Парамонов, Андрей Шевцов, Яндекс Data Fest Siberia 6: https://ods.ai/events/datafestsiberia6

Презентацию к докладу Вы можете скачать в треке секции RandomML / ML Mix: https://ods.ai/tracks/sibfest6-randomml-mlmix

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

Спикер: Валентин Малых, MWS AI Data Fest Siberia 6: https://ods.ai/events/datafestsiberia6

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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…

Dan Houser is co-founder of Rockstar Games and is a legendary creative mind behind Grand Theft Auto (GTA) and Red Dead Redemption series of video games.

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Go to https://drinkLMNT.com/lexOUTLINE:(00:00) – Introduction(01:29) – Sponsors, Comments, and Reflections(11:32) – Greatest films of all time(23:45) – Making video games(26:36) – GTA 3(29:55) – Open world video games(32:42) – Character creation(36:09) – Superintelligent AI in A Bette…

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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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.

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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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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 of Data Skeptic's Recommender Systems series, host Kyle Polich explores DataRec, a new Python library designed to bring reproducibility and standardization to recommender systems research. Guest Alberto Carlo Mario Mancino, a postdoc researcher from Politecnico di Bari, Italy, discusses the challenges of dataset management in recommendation research—from version control issues to preprocessing inconsistencies—and how DataRec provides automated downloads, checksum verification, and standardized filtering strategies for popular datasets like MovieLens, Last.fm, and Amazon reviews. The conversation covers Alberto's research journey through knowledge graphs, graph-based recommen…

In this episode of Data Skeptic's Recommender Systems series, Kyle sits down with Aditya Chichani, a senior machine learning engineer at Walmart, to explore the darker side of recommendation algorithms. The conversation centers on shilling attacks—a form of manipulation where malicious actors create multiple fake profiles to game recommender systems, either to promote specific items or sabotage competitors. Aditya, who researched these attacks during his undergraduate studies at SPIT before completing his master's in computer science with a data science specialization at UC Berkeley, explains how these vulnerabilities emerge particularly in collaborative filtering systems. From promoting a …

In this episode, Rebecca Salganik, a PhD student at the University of Rochester with a background in vocal performance and composition, discusses her research on fairness in music recommendation systems. She explores three key types of fairness—group, individual, and counterfactual—and examines how algorithms create challenges like popularity bias (favoring mainstream content) and multi-interest bias (underserving users with diverse tastes). Rebecca introduces LARP, her multi-stage multimodal framework for playlist continuation that uses contrastive learning to align text and audio representations, learn song relationships, and create playlist-level embeddings to address the cold start prob…

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.

What’s on the horizon for AI? Jon Krohn wades through opinions from more than experts, curated by the Longitudinal Expert AI Panel (LEAP), about what we can expect from the industry. From estimates on AI-assisted workers through energy consumption to AI performance in highly skilled domains, find out just how much LEAP thinkers believe AI is permeating our daily work and life in this Five-Minute Friday. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/942⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Vijoy Pandey imagines a bold new society in which agents and humans make scientific discoveries and complete physical tasks together, and he tells Jon Krohn about his work at AGNTCY, Cisco’s open-source platform for the Internet of Agents. Listen to the episode to hear Vijoy Pandey talk about how a future society in which multi-agents and humans interact may be a real possibility, what TCP/IP is, how to find trustworthy AI agents, and how to get your hands on AGNTCY today! This episode is brought to you by the Dell⁠⁠⁠⁠⁠⁠⁠⁠⁠, by⁠ ⁠⁠Intel⁠⁠⁠, by ⁠Fabi⁠ and by ⁠Gurobi⁠⁠⁠⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/941⁠⁠⁠ Interested in sponsoring a SuperDataScience Po…

Jon Krohn curates a selection of clips from the month that was. Hear from the orchestrators of an expanding AI universe in this episode of In Case You Missed It, with news, views and groundbreaking ideas from Sheamus McGovern, Jerry Yurchisin, Stephanie Hare, Larissa Schneider, and Adrian Kosowsky. We cover baby dragons, the Hippocratic Oath, and, of course, all the latest in artificial intelligence! Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/940⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

State space models (SSMs), granite models, and Mamba: Dell’s Tyler Cox and Shirish Gupta discuss with Jon Krohn why state space models can process information so efficiently, and how Dell’s AI factory helps enterprises manage custom AI workloads. Hear the latest on the Dell Pro AI Studio and Dell’s partnerships with IBM and Hugging Face in this episode. This episode is brought to you by the Trainium2, the latest AI chip from AWS and by Gurobi. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/939⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (02:58) Dell Pro AI…

Jon Krohn speaks to Rohan Kodialam, Cofounder and CEO of Sphinx, the company that redefines how machine intelligence reasons data with frontier AI. In this Feature Friday, Jon and Rohan discuss the benefits of using Sphinx to assist with data analysis. Get under the hood to learn how Sphinx operates, from running commands to ensuring your data stays secure, and find out how you can get your hands on this great tool for free. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/938⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

AI tools won’t eliminate but elevate data scientists, says Marc Dupuis. The CEO of fabi.ai talks to Jon Krohn about the new wave of AI-driven platforms that integrate workflows within popular work tools like Slack and email, and how building AI-first products means widening access to all ability levels. This episode is brought to you by the Gurobi⁠⁠⁠⁠, by ⁠⁠Dell⁠⁠ and by ⁠⁠Intel. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/937 Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (09:31) Will fabi.ai outshine data science practitioners (20:40) Resolving workflows w…

How much power – and risk – do we carry around with us in our pockets? A Reuters investigation about how easily LLMs can be utilized for online phishing scams is the subject of this week’s Five-Minute Friday with Jon Krohn. By asking six of the most popular LLMs (Grok, ChatGPT, Meta AI, Claude, DeepSeek and Gemini) to generate phishing emails specifically targeting elderly people, Reuters found the safety sometimes severely lacking in the models. Listen to the episode to hear Jon quantify this problem with real-world examples, why mere content warnings in LLM models don’t work, and the troubling results of the phishing requests. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/…

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.

LLMs generate text painfully slow, one low-info token at a time.

Researchers just figured out how to compress 4 tokens into smart vectors & cut costs by 44%—with full code & proofs!

🔥📊SponsorsThis episode is brought to you by Statistical HorizonsAt Statistical Horizons, you can stay ahead with expert-led livestream seminars that make data analytics and AI methods practical and accessible.

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VortexNet uses actual whirlpools to build neural networks.

By borrowing equations from fluid dynamics, this new architecture might solve deep learning’s toughest problems—from vanishing gradients to long-range dependencies.

Today we explain how vortex shedding, the Strouhal number, and turbulent flows might change everything in AI.

SponsorsThis episode is brought to you by Statistical HorizonsAt Statistical Horizons, you can stay ahead with expert-led livestream seminars that make data analytics and AI methods practical and accessible.

Join thousands of researchers and professionals who’ve advanced their careers with Statistical Horizons.

At the intersection of ethics and engineering, Amethix creates AI systems that don’t just function—they adapt, learn, and serve.

With a focus on dual-use innovation, Amethix is shaping a future where intelligent machines extend human capability, not replace it.

Discover more at https://amethix.com This 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.

Learn more at intrepid.aiReferencesWebsite: finalspark.comDiscord account: / discordNewsletter: https://finalspark.com/#newsletterTopics: Biological computing • Neural engineering • Energy-effic…

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.

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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!

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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.

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🎙️ 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…