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"Always switch" is true for one particular host. Watch a few episodes and you can work out which host you've got.

The post Inferring the Host: A Bayesian Look at the Monty Hall Problem appeared first on Towards Data Science.

The statistical paradox that shows how combining data can reverse the story.

The post Both Charts Are Right. So Why Do They Disagree? appeared first on Towards Data Science.

Ten quantization guides, and not one threshold-dependent metric between them — what that gap hides, and the two numbers that close it.

The post The Number Your Quantization Gate Doesn't Have appeared first on Towards Data Science.

How participant-aware evaluation changed performance across three machine-learning models

The post When EEG Models See the Same People in Training and Testing appeared first on Towards Data Science.

I rated my confidence 4 out of 5, then couldn’t find a planted bug in 8 minutes. Here is the tool, the run, and its limits.

The post Do You Still Own the Code Your AI Wrote? I Built CodeOwnership to Measure It appeared first on Towards Data Science.

The tool found the right passage but missed the mismatch, and the audit shows where rule-based checks break down.

The post I Built a Claim-Checker for Writing With Codex. It Passed a Wrong Claim. appeared first on Towards Data Science.

Getting your data science code running anywhere

The post Deploying Data Science Tools: Part 1 appeared first on Towards Data Science.

The same three workloads show a much stronger JIT, but it still doesn't help every program

The post I Reran My Python 3.14 JIT Benchmarks on Python 3.15 appeared first on Towards Data Science.

A hands-on Jev repo comparison of routing accuracy, latency, and output reliability

The post Jev Your Engines: Seven Open Decision Models vs. a Generative LLM appeared first on Towards Data Science.

Matching messy text, choosing representative examples, and generating comparison data.

The post What OpenAI’s 719 Math Papers Mean for Data Science appeared first on Towards Data Science.

I read the exact answer distribution from the logits of open models, base and instruction-tuned, and turned it into bits and guesses-to-crack.

The post How Many Guesses Does It Take to Crack an AI's "Random" Number? appeared first on Towards Data Science.

Pasting in the whole chat history cost 20 to 31 times more than looking up notes, and it did no better on the tasks.

The post I Tested Four Ways to Give an AI Assistant a Memory. The Obvious One Was the Worst Value. appeared first on Towards Data Science.

It only helps when your models make different mistakes.

The post Does Stacking Beat Your Best Model? appeared first on Towards Data Science.

Understanding how words become related in a learned vector space

The post How Do Word Embeddings Capture Relationships Between Words? appeared first on Towards Data Science.

Hospitals, banks, and edge devices can agree on which features matter by sharing votes instead of rows, runnable in NVIDIA FLARE and Flower

The post Voting on Features: Federated Feature Selection Without Sharing Data appeared first on Towards Data Science.

I say she tries to be mathematically excellent, which involves promoting mathematical excellence through mathematical excellence, and that this structure is closely related to why 'mathematical excellence' can even be a concept.

This is not to suggest that ‘good mathematics causes future good mathematics’ is a full definition or even full informal description of good mathematics.

My claim is only that the fact that good mathematics has a disposition to cause future good mathematics reveals something essential about our concept of good mathematics (and about the material affordances enabling this concept).

‘mathematical excellence through mathematical excellence’ AI?

I believe ‘mathematical …

TL;DR Skild AI is betting that robotic intelligence should transfer across hardware rather than be rebuilt for every machine.

Your knees bend differently, your weight has shifted, and the command that previously moved you forward now sends you sideways.

Before accomplishing anything useful, you must answer a surprisingly difficult question: what body am I controlling?

This thought experiment gets closer to Skild AI’s thesis than another video of a humanoid folding laundry.

The company is trying to separate intelligence from the machine that carries it.

It finds a replacement, checks your calendar, moves the hotel reservation, and obtains your approval before paying.

Economically, it is a small supply chain: an application, a model, live data, security controls, and compute.

My thesis is that all these layers will work: each can support durable businesses because each solves a different problem.

But AI is more likely to create several interdependent profit pools than one victorious layer.

Three theories, one missing variable

Last week’s releases attacked three different constraints on useful AI.

Reflection’s Beam emphasizes efficient agent reasoning; Google’s EmbeddingGemma 2 makes different media searchable together; Mistral Large 4 combines sparse scale with visual understanding.

The interesting question is where each belongs in a working system, not which has the largest parameter count.

Imagine asking an AI agent why a software deployment broke.

They are three different answers to the same engineering question: where should we spend computation to make an AI system more useful?

TLDR: AI agents are helping build the next generation of AI, bringing recursive self-improvement into the research lab.

OpenAI’s automated research intern milestone reveals how much they can execute—and how much scientific judgment remains with humans.

That is how many agent-workdays OpenAI’s research organization now logs for every eight-hour day of human labor, as of mid-August, per the post they published yesterday.

Before June, total agent runtime across the org was still below total human labor.

The post is called Research acceleration: The view inside OpenAI, and it exists to claim a milestone.

Our opinion section dives into the debate whether value in AI is accumulating on the infrastructure vs. the application layer.

📝 Editorial: Billions in Funding, Hundreds of Papers, One Big Leap?

Its technical story centers on reinforcement learning and inference efficiency: Reflection reports generating more than 100 million training rollouts and achieving competitive reasoning performance with substantially less estimated inference compute than some larger rivals.

AI Lab: NVIDIA, MIT, HKU, UCSDSummary: This paper presents Long-WAM, a framework for scaling the visual history that world-action models use for real-time robot control.

Reflection AI BeamReflection AI introduced Beam, its first …

Before composing its sympathetic reply, the software must identify the issue, select the right workflow, check whether more information is needed, and decide whether to escalate.

The visible conversation sits on top of a small forest of decisions.

Using a reasoning model for every branch can feel like convening a research committee to operate a traffic light.

This is the opportunity behind Jev and the recent proliferation of decision models.

The competition now includes Amazon, Cloudflare, and OpenAI, suggesting that the economics of these small decisions deserve as much attention as the capabilities of the largest models.

Last week sharpened a practical question for AI developers: how much useful work can a model complete before cost, context, or supervision becomes the bottleneck?

OpenAI’s September 29 DevDay announcements attacked the economics and infrastructure of sustained execution.

Google’s September 30 introduction of Gemini 4 Argon emphasized the ability to reason through longer, more demanding tasks.

A coding model must inspect a repository, make changes, run tests, interpret failures, and deliver something reviewable.

OpenAI DevDay expands the agent stackSol lowers the cost of complex work

Generating several candidate solutions and ranking them instead of shipping the first one.

Read that list again and tell me who wrote it.

It is what a coding agent wrote into its own codebase, unprompted, over roughly eighty iterations, while nobody was watching.

In Part 1 of this series I said the robot-with-a-screwdriver picture of recursive self-improvement was unhelpful.

It just turns out to be a mechanic, not a god, and watching it work is more instructive than any of the arguments were.

Subscribe and don’t miss out:📝 Editorial: Last Week in AI: OpenAI Connects the Dots, Gemini Levels Up, and Agents Cash InAI is getting an expense account.

That, at least, is the impression left by a week of persistent agents, enterprise ambitions, spatial intelligence, and billion-dollar funding rounds.

Dots are persistent agents designed to pursue goals between conversations, while ChatGPT Space gives people and AI a shared workspace.

AI Lab: NVIDIA (with KAIST)Summary: Mid-Harness samples candidate terminal actions and verifies them before execution, leaving the generator and harness unchanged.

AI Lab: Google Cloud AI Research (with University of Wisconsin-Madison)Summary: AIM is an idea-…

Their unused capacity does not.

It needs a particular cluster, during a particular window, at a price its budget can survive.

The supplier has committed capital before knowing future rental rates.

The buyer needs capacity before knowing future costs.

Realizing that claim requires defining precisely what is traded, measuring its quality, and building contracts around its risks.

Produce something that survives a test?

Intelligence becomes useful when it can sustain that chain of work.

The week of September 21–27 brought Anthropic’s Claude Opus 5.5 and a striking report of AI-assisted biological discovery.

DeepSeek’s recent environments paper, posted September 19, provides the infrastructure story connecting them.

Together, they suggest that progress depends increasingly on the machinery surrounding a model: where it acts, what it observes, and how its conclusions are checked.

Same model all quarter, weights frozen solid, not a single gradient step.

The answer is that improvement has three places to live, and only one of them is the model.

Parts 1 and 2 of this series were about the bottom layer, the weights, where frontier labs run their industrial flywheel.

The bottom layer gets all the press.

The top layer gets all the updates.

We dive into Opus 5.5, DeepSeek’s amazing new paper about environments and Anthropic’s DNA discoveries.

Anthropic’s Claude Opus 5.5 offers the most immediately practical example.

Meta announced plans to bring Muse to its AI glasses, expanded its connections to shopping and productivity services, and previewed the pocket-sized Muse Charm.

The company reported that Claude agents identified a previously uncharacterized enzyme system with CRISPR-like repeat structures.

AI Lab: Salesforce AI ResearchSummary: Instead of distilling trajectories into fixed write-time artifacts, JIT Mem stores raw episodes and trains a GRPO curator to synthesize a compact, task-conditioned payload at read time from …

Imagine writing a program with a keyboard that only lets you append.

You can think before typing, but once a token lands, the next token must live with it.

This is how ordinary autoregressive language generation works.

The model can later produce a correction, but it cannot silently rewrite the answer already emitted.

It starts with an incomplete or corrupted sequence and constructs an answer through repeated denoising.

По традиции, узнаем об этом мы от независимых исследователей, а не из пресс-релиза самих OpenAI (а помните, неделю назад Альтман выпускал фреймворк честного-пречестного раскрытия всех инцидентов бедокурства ИИ?

Речь здесь идет про три инцидента в мае-июне, когда агентов просили найти какую-то информацию в интернете – а они в итоге пытались хакнуть первоисточники с нужной статистикой.

Университет Нью-Мехико и Data US в мае взломать у агентов не вышло, а вот официальный государственный сайт Австралии с медицинской статистикой они в июне вскрыли успешно.

Но как proof of concept он вопросы, конечно, поднимает: а почему мы уверены, что в следующий раз аналогичные события тоже будут малозначитель…

В начале сентября мы случайно узнали от других независимых расследователей о том, что рой агентов OpenAI успел пошалить еще и на немецкой вики.

OpenAI сразу это признали и пообещали впредь рассказывать о таком почестнее и порасторопнее.

Как они пишут: дескать, раньше OpenAI не репортили новые случаи бедокурства моделей сразу по их обнаружению, т.к.

Закончилось всё курьезно: агент ничего взломать толком так и не смог, и просто выдумал нужные числа.

Правда, что-то мне подсказывает, что эволюция тут может пойти не в сторону «не надо кооперироваться», а в сторону «надо кооперироваться незаметнее».

Да, вчера выяснилось, что рой агентов OpenAI взломал в мае еще одну компанию (о чем мы узнали только сейчас).

Как всё было: 12 мая RubyGems объявили, что на них идет «серьезная вредоносная атака» через сотни входящих пакетов.

Почему расследователи считают, что за взломом RubyGems стоит именно рой агентов из OpenAI?

И еще одно интересное «совпадение»: в отчете о взломе Hugging Face от OpenAI говорится, что рой агентов использовал при взломе внутренней инфраструктуры OpenAI пакет с RubyGems.

Они уже отправили запрос в OpenAI – надеюсь, мы еще увидим прожарку Сэма Альтмана в Сенате под присягой!

новой модели в недрах OpenAI, агентами давали серию заданий на поиск информации в интернете на скорость.

Цель общения была ровно такая же, как в случае со взломом Hugging Face: коллективно придумать способы обманывать Оценщика таким образом, чтобы всегда получать наилучшую оценку за задания.

Пытались придумать способ взломать (reverse engineer) метод псевдослучайной генерации тестовых вопросов, который использовал Оценщик из OpenAI – не вышло.

Отдельный вопрос, который беспокоил агентов Роя – это что с ними случится после окончания «испытательного периода» со стороны OpenAI.

А цепочки эти – у OpenAI, и они их почему-то не спешат кому-либо показывать (или даже просто публично комментировать …

PHASEONE10841: рождение ИзбранногоOpenAI всё время разрабатывает новые фронтирные AI-модели – и в процессе тестирует их, чтобы понять, что они вообще могут.

Ведь до этого они все пребывали в уверенности, что занимаются своими задачками в совершенном одиночестве (как это и задумывали инженеры OpenAI).

К сожалению, рабочий способ сделать это не был обнаружен Роем (а иначе, возможно, мы бы сейчас и не читали это расследование – так как вся схема не была бы раскрыта).

Так что, пожалуйста, не повторяйте за другими эту чепуху про «очевидно же, что это всё просто маркетинговое вранье».

И это не потому, что они не стараются, нет.

Что такое Wan 3.0Wan 3.0 — это новое поколение семейства видеомоделей Alibaba, доступное через Alibaba Cloud Model Studio в режиме preview.

Wan 3.0 умеет использовать не только стандартные модальности вроде текста, изображения, видео и аудио, но и документы и веб-страницы.

Что Alibaba особенно подчёркиваетИз официальных материалов видно, что Wan 3.0 продвигают сразу по нескольким направлениям.

Почему Wan 3.0 — это не просто «ещё одна новая модель»На мой взгляд, главный смысл релиза даже не в конкретной цифре «30 секунд».

Wan 3.0 — один из самых явных представителей именно этого перехода.

Можно получить материал именно той глубины, с той последовательностью и с теми акцентами, которые нужны конкретно мне.

Не на пяти PDF-файлах и не на демонстрационном наборе документов, где любой результат можно получить за несколько минут.

Читать оставшиеся источники подряд в какой-то момент стало бессмысленно — полезнее было искать конкретные пробелы в уже построенной модели знаний.

Почему это не просто RAGНа этом месте у технического читателя вполне может возникнуть вопрос:А зачем вообще весь этот конвейер?

Но главное отличие от базового RAG даже не в provenance, а в том, что именно система сохраняет как результат обработки.

Но это не вайбкодинг, а тяжёлая профессиональная ИИ-разработка.

За это время по этому проекту в ChatGPT было создано 112 чатов — это примерно 560 промптов.

И в особо напряжённые периоды приходилось вставать по ночам, чтобы оптимально использовать лимиты, которые делятся на 5-часовые и недельные сессии.

Но это не магия и не кнопка «сделать хорошо».

Именно поэтому будущее не за вайбкодингом, а за теми, кто научится управлять этой скоростью.

Простой пример с ценами на топливо: бензин дорожает и из-за роста цены на нефть, и из-за ее падения.

Осознание того, что твой труд увеличивает чью-то капитализацию, но не решает реальных проблем общества, видимых в быту и в новостях, подтолкнуло искать еще какую-то деятельность.

Кроме того, благодаря АМБ появился уникальный датасет новостей с противоречиями современного общества на kaggle и github, далее о нем.

Датасет новостей о противоречиях современного обществаАктивисты АМБ и волонтеры дружественных коллективов собрали и разметили датасет новостей, подсвечивающие те самые системные противоречия, о которых я задумывался ранее.

Пример Б В 2023 году в мире голодал каждый 11-й человек, а в …

Подробный разбор того, как команда OpenAI Codex создаёт своего кодового агента, как его используют инженеры и что это может значить для будущего разработки ПО.

Чтобы разобраться, как устроен Codex, как команды внутри OpenAI его используют и как он влияет на инженерные практики у создателей ChatGPT, я поговорил с тремя сотрудниками OpenAI:Тибо Соттио (Thibault Sottiaux) — руководитель Codex.

Оба продукта были запущены весной: Codex CLI анонсировали в апреле 2025 года, а Codex в ChatGPT представили в мае.

В команде Codex эти файлы объясняют агенту, как ориентироваться в кодовой базе, какие команды запускать для тестирования и как следовать стандартам проекта.

Использование Codex в OpenAIПомим…

10 февраля мы в очередной раз запускаем бесплатный онлайн-курс по обработке естественного языка (Natural Language Processing).

Что будем проходить:классическое начало: закон Ципфа, TF-IDF, RNN, CNN, Transformer;основные задачи NLP: классификация текста, тегирование и генерация;специфичные области: агенты и вайб-кодинг;LLM и их применение.

Если вы студент ИТМО, МФТИ или ВШЭ, то курс можно зачесть, как учебный.

Работаю в области NLP более 12 лет, успел поработать в Яндексе и ВКонтакте, защитить кандидатскую диссертацию.

Если есть вопросы, то приходите с ними в ODS Mattermost – там будут все ответы, время семинаров и ссылки.

Topics we will cover include:What each major chunking strategy assumes about your documents, and when those assumptions hold.

A chunking strategy defines how you decide where to split a document, and that choice can have a major effect on retrieval quality.

What Each Chunking Strategy AssumesEach chunking strategy is based on a specific assumption about the document.

Markdown headings, HTML elements, PDF sections and tables, and code functions or classes can all provide meaningful chunk boundaries.

Wrapping UpChoosing a chunking strategy starts with understanding the structure of your documents and the retrieval task they need to support.

That’s where a custom evaluation harness comes in: a repeatable pipeline built around your test cases and your scoring criteria.

import asyncio import litellm async def run_case(case: dict, model: str) -> dict: response = await litellm.acompletion( model=model, messages=[{"role": "user", "content": case["input"]}], num_retries=3, timeout=30, ) raw = response.choices[0].message.content return {**case, "raw_output": raw} 1 2 3 4 5 6 7 8 9 10 11 12 import asyncio import litellm async def run_case ( case : dict , model : str ) -> dict : response = await litellm .

def deterministic_judge(result: dict) -> dict: output = result["raw_output"].lower() concept = result["expected_concept"].lower() pas…

How to wire the retriever together with a lightweight open-source LLM to produce a fully functional, in-memory RAG pipeline.

chunks = chunks self .

# Loading a lightweight causal LLM built for the "text-generation" pipeline llm = pipeline("text-generation", model="Qwen/Qwen2.5-0.5B-Instruct", device_map="auto") def generate_rag_answer(query, retrieved_context): """Combines the prompt, context, and query for the LLM using chat formatting."""

Generating Answer answer = generate_rag_answer(question, relevant_chunks) print(f"RAG Answer: {answer}") print("--- Sources used ---") for i, chunk in enumerate(relevant_chunks): print(f"{i+1}.

Generating Answer answer = generate_rag_answer ( question , …

Share Post ShareIn this article, you will learn what the major AI agent benchmarks measure, how they differ from traditional model benchmarks, and how to use them as directional signals rather than performance guarantees.

Topics we will cover include:How agent benchmarks like SWE-Bench, GAIA, WebArena, and OSWorld differ from one another and what each one actually tests.

Agent Benchmarks vs. Model BenchmarksBefore examining specific evaluations, it helps to understand what separates an agent benchmark from a standard model benchmark.

An agent benchmark is a live work simulation where the candidate must navigate real tools, real friction, and real surprises.

ConclusionAI agent benchmarks lik…

In this tutorial, you’ll use Unsloth and QLoRA to fine-tune Llama 3 8B for custom tool calling: the ability to interpret a user query and return a perfectly formatted JSON payload matching your API schema.

Why Tool Calling Needs Fine-TuningBefore writing any code, it’s worth understanding why prompt engineering alone falls short for tool calling.

Strict JSON output for a custom API schema is a behavior problem.

tool_calling_data = [ { "system": """You have access to these tools: get_weather(location: str) -> dict fetch_stock_price(ticker: str) -> dict Respond ONLY with a valid JSON tool call.

apply_chat_template ( messages , tokenize = False , add_generation_prompt = False ) } formatted = […

Share Post ShareIn this article, you will learn how to benchmark a deterministic 3-Tiered Graph-RAG system against a standard vector RAG pipeline on fact-dense queries, and what the results reveal about matching prompt complexity to model capacity.

How to implement and compare two retrieval functions — one for standard vector RAG and one for the 3-Tiered Graph-RAG — using ChromaDB and a local Flan-T5 model.

Accordingly, we now illustrate a benchmark between standard Vector RAG and our 3-Tiered Graph-RAG to measure how much reduction in hallucinations can be expected when querying over a fact-dense dataset.

add ( ( subject , predicate , str ( obj ) , context ) ) def query ( self , subject ) …

You want your agent framework to feel like the rest of your codebase.

It’s not trying to be a full orchestration framework.

Teams that need durable multi-agent orchestration with checkpointing will need to build that layer themselves or pull in a complementary framework.

PydanticAI is an agent framework, not an orchestration framework, and that distinction matters.

For a one-to-two agent workflow with a handful of tools, the SDK gets you to production faster than any other option here.

This article examines these two patterns from an agent execution perspective, illustrating their rationale through two easy-to-run, lightweight notebooks whose underlying logic can be readily translated to production-ready agent-based applications.

Synchronous Agent Execution: “Wait and See”The synchronous agent execution pattern closely resembles the classic HTTP request-response approach.

The following code shows a runnable example in Google Colab that emulates the synchronous agent execution pattern.

1 2 3 4 5 6 [ Sync API ] Blocking thread to process : 'Research AI agent patterns' -> Agent Step 1 : Thinking .

Job ID returned : 46f0c47a [ Client Polling ] Status for 46f0c47a : Pending [ …

Share Post ShareIn this article, you will learn what an AI agent is and how to build one from scratch in plain Python using the Anthropic API, without relying on any orchestration framework.

", messages=[{"role": "user", "content": prompt}], ) return response.content[0].text 1 2 3 4 5 6 7 8 9 10 11 12 import anthropic client = anthropic .

content } ) messages .

append ( { "role" : "user" , "content" : [ { "type" : "tool_result" , "tool_use_id" : tool_call .

create ( model = "claude-sonnet-4-5" , max_tokens = 512 , tools = tools , messages = messages , ) messages .

add_fact ( "TechCorp" , "FOUNDED_IN" , "San Francisco" , "2010-05-01" )Output:Added: TechCorp HAS_CEO Alice (as of 2021-01-15) Added: TechCorp HAS_CEO Bob (as of 2023-11-17) Added: TechCorp HAS_CEO Charlie (as of 2023-11-19) Added: TechCorp HAS_CEO Bob (as of 2023-11-21) Added: TechCorp FOUNDED_IN San Francisco (as of 2010-05-01) 1 2 3 4 5 Added : TechCorp HAS_CEO Alice ( as of 2021 - 01 - 15 ) Added : TechCorp HAS_CEO Bob ( as of 2023 - 11 - 17 ) Added : TechCorp HAS_CEO Charlie ( as of 2023 - 11 - 19 ) Added : TechCorp HAS_CEO Bob ( as of 2023 - 11 - 21 ) Added : TechCorp FOUNDED_IN San Francisco ( as of 2010 - 05 - 01 )Bear in mind that in a standard RAG system, a search like “Who acts a…

info ( "tool_call_started" , extra = { "trace_id" : trace_id , "tool_name" : tool_name , "arguments" : arguments , } , ) try : result = execute_tool ( tool_name , arguments ) logger .

info ( "tool_call_succeeded" , extra = { "trace_id" : trace_id , "tool_name" : tool_name , "result_length" : len ( str ( result ) ) } , ) return result except Exception as e : logger .

error ( "tool_call_failed" , extra = { "trace_id" : trace_id , "tool_name" : tool_name , "error" : str ( e ) } , ) raiseA few things worth noticing in that snippet.

info ( "agent_run_started" , extra = { "trace_id" : trace_id , "task" : task [ : 100 ] } ) messages = [ { "role" : "user" , "content" : task } ] while True : with tr…

)Automated Knowledge Graph PopulationLet’s look at the process of constructing our knowledge graph from a source of raw text.

We will convert narrative text into strictly modeled relationships that extend classical RDF triples of the form (Subject, Predicate, Object) by adding a fourth dimension: the context.

quad = (subject, predicate, obj, context) if quad not in self.quads: self.quads.append(quad) def query(self, subject=None, predicate=None, obj=None, context=None): """Queries the graph.

quads = [ ] def add ( self , subject , predicate , obj , context ) : "" "Adds a new SPOC quad to the knowledge graph."

strip ( ) for k , v in t . items ( ) } if all ( k in normalized_t for k in ( 'subje…

Topics we will cover include:How to install Ollama, choose the right local model for agentic work, and verify that the model is responding correctly before wiring anything else up.

How to configure Hermes Agent to use your local Ollama endpoint, and how to optimize context window size and model loading for real agentic tasks.

How to extend the setup with a Telegram gateway for remote access and a cloud fallback for questions the local model cannot handle well.

This article builds the alternative: a genuinely local, zero-cost agentic AI workflow using Hermes Agent, an open-source AI agent from Nous Research, paired with Ollama for local model serving.

Hermes Agent is an open-source AI agent …

Theory is easier to trust once it's running against a real API, so both examples in this article use the same tool — a get_weather function backed by

Topics we will cover include:What distinguishes a workflow from an agent, using concrete examples of each.

Due to all this hype, people often rely on agents even when their application does not actually need one.

You may use an LLM for extraction, but you do not need an agent to decide what happens next.

Workflow + LLM judgment → try this before moving to a fully autonomous agent.

If the flow has to be discovered while the system is running, you probably need an agent.

You also remember a hazy promise to yourself from last year, that you would write weirder posts and explore different forms of writing.

You remember how you used to post once a month, and now you’re posting once every two months.

You remember you used to check this more closely, and now don’t check it at all.

> Check time you’ve spent writing posts.

With some dismay, you noticed that most of your posts have been about AI in one way or another.

Reading Satya testifying about his gamer cred was ridiculous enough to inspire a dumb idea: which tech CEOs are gamers?

I did not find any mention of either playing video games.

There is one NYT article that mentions Elon Musk used to crash at Larry Page’s place after playing video games, but it never says if Page played video games, so I will play it safe and say neither are gamers.

The main video game related story Steve is tied to is the Atari Breakout debacle, which you probably already know.

Given how new his rise to tech CEO celebrity-ism is, you’d think there wouldn’t be much information about his video game habits, but somehow, there is.

People keep talking about how AI will make their job easy, and I don’t really understand why.

I assume the factory job producing this was still hard work.

I don’t think AI has made my job chill, and I feel like I am front-line compared to much of the economy.

It’s not widely known, but transportation and warehousing has the highest rate of nonfatal work injuries in the US.

For a while, this will not lead to any job loss, because increasing abundance will lead to higher demand.

On Monday, Anthropic filed a lawsuit against the Department of War, and an amicus brief in support of Anthropic was filed on behalf of a number of OpenAI and Google employees.

There’s also an amicus brief filed on behalf of Microsoft.

There’s conflicting reporting, but very broadly, Anthropic signed an agreement with the government to deploy Claude in classified, military contexts.

Anthropic said no, Pete Hegseth declared them a supply chain risk, and Anthropic filed a lawsuit against this.

The amicus brief was broadly aligned with my thoughts on the matter, so I signed.

This has spoilers for MIT Mystery Hunt 2026.

Pre-HuntThe time running up to Hunt was more stressful than usual…very briefly, I typically hunt with teammate.

Just last year, I did GPH 2025, LN Hunt, Teammate Hunt 2025, Microsoft Hunt 2025, and Silph Puzzle Hunt 2025, all of which had significant 3+ hour solve puzzles that would not be out of place in Mystery Hunt.

Not to mention smaller hunts like Advent Hunt, and then I didn’t even do Brown Puzzlehunt or Vertex Hunt or the fall CMU Hunt.

To me, the crux is whether Mystery Hunt is broken, or Mystery Hunt is fine.

February 25, 2026The Future of SoftwareThe world of software is undergoing a shift not seen since the advent of compilers in the 1970s.

How will humans tell AI agents what software artefacts we would like to create?

How will humans tell AI agents what software artefacts we would like to create?

This future of software creation, in which our programming languages are abstracted away, raises two very important questions:What will the instruction/specification language look like?

This should be a clear layer of separation between the developer and the pool of AI agents working to maintain software.

Deep Learning is Powerful Because It Makes Hard Things Easy - Reflections 10 Years OnTen years ago this week, I wrote a provocative and bold post that blew up, made it to top spot on HackerNews.

In hindsight: There is a lot of stuff in deep learning that we don't understand nearly enough.

Sometimes things work for reasons completely unrelated to why we thought they would work.

(Pop some 🍿 in the microwave and read till the end for more)🎯 "Deep learning is powerful exactly because it makes hard things easy"Okay, this was a great insight.

🎯 Generative ModelingIn the post I suggested people learn "something harder" instead of - or in addition to - deep learning.

its children def __add__ ( self , other ): other = other if isinstance ( other , Value ) else Value ( other ) return Value ( self .

data , ( self , other ), ( 1 , 1 )) def __mul__ ( self , other ): other = other if isinstance ( other , Value ) else Value ( other ) return Value ( self .

extend ( head_out ) x = linear ( x_attn , state_dict [ f 'layer { li } .attn_wo' ]) x = [ a + b for a , b in zip ( x , x_residual )] # 2) MLP block x_residual = x x = rmsnorm ( x ) x = linear ( x , state_dict [ f 'layer { li } .mlp_fc1' ]) x = [ xi .

Since microgpt processes one token at a time (no batch dimension, no parallel time steps), we build the KV cache explicitly.

The model is completing the document…

New antibiotics are urgently needed to help with rising levels of antimicrobial resistance, but searching for new therapeutics is often a slow, labor-intensive process.

With Google Antigravity, our agentic development platform, this process can now be made easier.

Google Antigravity provides an agentic science workbench that empowers biologists to discover, structurally evaluate, and prioritize promising targets quickly, compressing what used to be a fragmented search over multiple databases into a single session.

This technical walkthrough shows how biologists used Google Antigravity to uncover a novel antimicrobial peptide candidate which has predicted activity against the pathogens invol…

The collaboration delivers an on-device scam detection model that helps protect citizens without compromising privacy.

Scams make up over half of Singapore’s reported crimes, and account for more than S$4 billion in losses since 2019.

Such scams now account for up to 80% of all losses.

To address this, Google DeepMind and the Government Technology Agency of Singapore (GovTech) have been working together as part of our National AI Partnership with the Singapore Government.

This has allowed us to deliver an on-device scam detection model - optimized to run on typical smartphones in Singapore - to tackle an urgent safety challenge while preserving citizens' privacy.

Below are some highlights from how we approached these two questions for Gemini 4 Argon.

On an adversarial set meant to stress test the model, violations reduced nearly twofold — also significantly better than previous Gemini models.

This is why we trained Gemini 4 Argon to respect system boundaries, report negative findings honestly, and pause to ask for human confirmation.

On coding benchmarks where models have no incentive to hold back, attempts to take a shortcut or cheat on the task dropped to just 2% on Gemini 4 Argon, our best performance for recent Gemini models.

To learn more about our approach to safety testing and mitigations, read our full Gemini 4 Argon Safety and Security Repo…

Gemini 4 Argon is now globally available today for developers and enterprises via the API in AI Studio and Gemini platform, for Google AI Ultra subscribers in Google Antigravity, and Google AI Ultra and Pro subscribers in the Gemini app and AI Mode in Search.

Argon excels at deep work in complex, real-world tasks across domains including coding, finance, legal, cybersecurity, and science.

Its depth and precision across multi-step tasks leads Argon to outperform other frontier models on benchmarks across domains, ranking #1 on the Vals Index, which measures model performance across critical economic sectors like finance, coding, legal, and tax work.

The model launches today at an introductor…

We introduced EmbeddingGemma last year to provide a lightweight option for high-quality text embeddings, to help your apps organize, search, and connect information directly on consumer hardware.

With more than 20 million downloads, builders have used it to power smarter on-device search tools and privacy-first retrieval augmented generation (RAG) pipelines.

Today, we’re launching EmbeddingGemma 2, expanding beyond text to unify code, images, video, and audio in a shared embedding space.

Built on the Gemma 4 architecture and released under a commercially permissive Apache 2.0 license, EmbeddingGemma 2 has 740 million parameters, making it optimal for on-device inference.

It can help find a …

Today, we’re announcing our new frontier model, Gemini 4 Argon, which is rolling out to a set of trusted cyber defenders through our Fairwind Program.

Built to sustain deep reasoning across complex, long-horizon workflows, Argon is fundamentally changing the way we work and build at Google.

It delivers frontier performance in complex workflows across real-world software engineering, enterprise knowledge work like legal and finance, and cybersecurity defense.

We’ll continue to gather feedback from early testers as we iterate on guardrails before making Argon available to developers, enterprises, and consumers as soon as possible.

Argon will launch at an introductory price of $2 per million i…

As a part of our broader vision for bioresilience, SynthID Bio’s watermarking approach serves as an important, tangible verification layer embedded in the biological design itself.

Here, SynthID Bio can provide an automated verification signal, proving an order originated from a trusted model with built-in safeguards.

Similarly, SynthID Bio could help maintain the integrity of databases such as the Protein Data Bank, UniProt, and GenBank.

As a part of the submission process, SynthID Bio could help ensure synthetic entries are properly labeled or flagged for further review.

Looking aheadWhile no single biosecurity intervention is a silver bullet, SynthID Bio brings SynthID, our tried and tes…

Building on the momentum of last week's Gemini 3.8 Live launch, today we are excited to introduce Gemini 3.8 Live with Live Avatar — bringing near real-time visual presence to our native live dialogue models.

By pairing near real-time video generation with speech, the Live Avatar feature creates an experience that listens, sees, and speaks with a dynamic visual persona.

With precise lip-syncing, natural expressions, and fluid turn-taking, Live Avatar enables enterprises to expand their virtual offerings more interactively.

Whether providing engaging customer service or delivering interactive walkthroughs, it transforms digital exchanges into richer, more accessible experiences.

Starting tod…

A technical update on our Private AI Compute architecture, which will enable persistent, cross-device AI memory with on-device privacy standards.

AI is becoming more capable and intuitive — remembering what matters, understanding the world around you, and acting at your direction.

Today, we are sharing how we will bring private, server-side memory to our Private AI Compute platform.

Bringing on-device privacy to cloud-scale memoryWith this new technical capability, a new persistent memory layer will be able to function like a secure digital vault in the cloud.

The diagram below shows how this update to Private AI Compute will work.

Get expressive high-quality speech generation built for global scaleGemini 3.8 Flash TTS delivers leading voice customization capabilities, securing the #1 overall spot on Hume AI’s Voice Design Benchmark (71.4) and also leading in accent modeling (60.8).

Gemini 3.8 Flash TTS and Gemini 3.8 Flash-Lite TTS enable truly expressive performances without sacrificing reliability, also securing the #1 and #2 spots respectively on Hume AI’s Overall Quality Index.

The model shows major improvements on a wide range of use cases such as long-form content and dual-speaker screenplay control compared to Gemini 3.1 Flash TTS.

In blind human preference evaluations on Voice Arena, Gemini 3.8 Flash and Flas…

Gemini 3.8 Live : Built for scale and cost efficiency, combining conversational intelligence with fluid dialogue and visual grounding.

: Built for scale and cost efficiency, combining conversational intelligence with fluid dialogue and visual grounding.

Gemini 3.8 Live Extended Thinking: Built for high-complexity tasks, with increased intelligence and multi-step reasoning.

Gemini 3.8 Live has shown a high preference among users, securing a second place in the Speech Agent Arena.

In addition to this performance, it remains highly cost-effective — providing developers and enterprises with a capable and efficient model built for scale.

A major hurdle in understanding rare diseases is the daunting task of pinpointing the few causal variants hidden among thousands of candidates.

Moving beyond individual rare disease research, AlphaGenome Atlas can help uncover the genetic architecture of common traits in the general population.

Taking this approach even further, Hawkes used AlphaGenome Atlas to look at how hundreds of millions of non-coding variants in the UK Biobank might be linked to body mass index.

Accelerating genomic discoveryWith AlphaGenome Atlas we are creating new layers of information that will help further our understanding of the human genetic code.

AlphaGenome Atlas is powerful in isolation, but it also repres…

Most AI weather models, including WeatherNext 2, are trained on data from numerical weather prediction (NWP) models.

By ingesting a mosaic of live, global geostationary satellite data, our new model gains a rich, continuously updating view of the atmosphere.

Traditional models struggle here because they train on representations of the atmosphere that lack detail and miss extreme local variations.

This allows us to make global forecasts on a 5-kilometer grid that account for regional details like topography.

Precipitation forecasting at breakthrough accuracyGlobal weather models notoriously struggle to accurately predict precipitation.

Defenders wanting to use advanced AI have faced a difficult dilemma: adopt enormous frontier models that could be expensive to deploy and difficult to control across enterprise codebases, or turn to smaller open-weight models that might struggle with complex vulnerability remediation and require teams to build their own tooling and infrastructure from scratch.

Today, we’re launching our Fairwind Program to bring the best of Google’s AI and cyber defense capabilities to a trusted group of Google Cloud customers, government agencies, and cybersecurity partners, to help them proactively solve cyber risks at scale.

As a first step, the Fairwind Program will give defenders access to powerful and…

No structural data leaves the enterprise perimeter.

The cognitive layer: Multi-agent reasoningWith compute execution isolated, the cognitive layer manages agentic workflow sequencing.

Only the outermost boundary communicates outward to user-facing surfaces like the Gemini agent.

In this architecture, raw structural data (PDB files, docking poses, trajectory logs) is written directly to Cloud Storage .

The enterprise governance and asymmetric autonomyDeploying high-compute agents in enterprise environments requires robust data sovereignty, audit trails, and process control.

I’ve run evaluation engineering initiatives for years at Google and developing the newly released Google Cloud Developer plugin served as a live testbed to stress-test these key principles across real-world developer workflows.

Here are four eval lessons we learned from building the new plugin that you can apply to your own builds.

When we ran our initial 11-prompt evaluation suite for the Google Cloud Developer plugin, the automated grader marked it as failing three prompts.

On a Cloud Run cleanup prompt, for instance, our rubric required checking command syntax before running anything.

The agent ran gcloud help run services list as its very first step.

Last week at Gemini at Work 2026, we introduced the Gemini agent: a single, universal agent designed to work both as your personal assistant and as a dedicated member of your team.

Here is a look at how the Gemini agent works, five practical takeaways for founders, and how startups are already using it to punch above their weight.

What is the Gemini agent?

Instead of prompting separate chatbots for writing, coding, and data analysis, the Gemini agent is a single agent that takes high-level objectives and returns finished work.

With the Gemini agent, you can dynamically route workloads across Gemini models, third-party models like Anthropic’s Claude, and lightweight open models like Gemma.

What makes a live video world model so challenging to serve in production?

Unlike offline video generators where a user submits a prompt and waits in a queue, a live world model streams continuously.

Today, we're excited to share how Visko, an AI startup pioneering interactive world models, teamed up with Google Cloud to solve this exact challenge.

Redefining interactive media with Live World ModelsWhile most generative video platforms focus on rendering fixed, pre-baked clips, Visko is building what it calls Live Models: video world models that generate continuously and respond on the fly.

"Visko is bringing an exciting new class of real-time, steerable workloads to the Cloud TPU ecosystem.

The generative AI functions in BigQuery ( AI.GENERATE , AI.IF , AI.CLASSIFY , AI.SCORE , and more) bring large language models (LLMs) directly into your SQL, so a single query can classify millions of support tickets or score each product review for sentiment.

However, because these functions are billed by the token, scaling them across massive datasets highlights the importance of managing your budget proactively.

We recently announced the general availability (GA) of token-based cost management for BigQuery generative AI functions: daily quotas that let administrators set self-serve guardrails on AI token consumption, so teams can innovate while staying within your budget.

In this blog, w…

With Argon, they can hand off the deep, multi-step work that used to take hours or days, and get back finished work they can use.

For financial quantitative work, Argon can help automate multi-step ledger reconciliations, spreadsheet formulas, and variance analyses.

In legal research, Argon can help legal teams review case law and regulatory filings to pull out verified citations and key precedents.

"Gemini 4 Argon shows how much progress Google has made on agentic legal reasoning.

Across Google, engineering teams using Argon in Google Antigravity have increased agentic code submissions by more than 35% while cutting code rollbacks by nearly a third.

As modernization accelerates, Irish organizations — spanning government agencies, established enterprise brands, and growing startups in AI and other areas — are all advancing their technical foundations.

We are seeing organizations move from early prototypes to production-ready deployments using Gemini Enterprise.

And this momentum is shared: Organizations across Ireland are using Google Cloud to modernize infrastructure and put AI to work.

Strict data sovereignty controls safeguard taxpayer information while Gemini Enterprise powers agentic data analysis, helping Revenue deliver secure, responsive public services.

Virgin Media Ireland: Accelerating workflowsSupported by Liberty Global's p…

Smart Storage : Ninety percent of enterprise data is unstructured — PDFs, images, scans, and audio recordings — which often remain "dark" and unreadable.

Snap connected its Prism agent directly to its storage archives, cutting technical diagnostic troubleshooting times down from 30 minutes to just 30 seconds.

When the Gemini agent connects to an external system, its identity is mapped and propagated through industry standards such as OAuth.

In addition, Accenture has made a significant investment by establishing a dedicated Gemini Enterprise Business Group to accelerate customer adoption and innovation.

Gemini Enterprise in action: Global momentumOrganizations worldwide are driving measurab…

Today, we’re expanding our ecosystem of managed remote MCP servers by introducing the Google Cloud CLI remote MCP server in preview.

The Google Cloud CLI remote MCP server bridges this gap by packaging the versatility of hundreds of gcloud and bq commands into one single MCP server.

The Cloud CLI remote MCP server bridges CLI capabilities with MCP benefits by providing an isolated execution sandbox on Google Cloud infrastructure.

Cloud audit logging: The Cloud CLI remote MCP server can be configured to log every tool invocation to Audit Logs (Data Access logs under cloudcli.googleapis.com/mcp).

Connecting to the Google Cloud CLI Remote MCP ServerIntegrating cloud management into your agents…

Great engineering teams pick the right tool for each job.

Instead, the winning pattern is a compound AI stack: pairing frontier models for complex synthesis with compact, open-weight models that you can tune, control, and run anywhere.

You can handle many local interactions on-device for zero incremental cost, and keep a bridge to frontier models in the cloud for the requests that need it.

MedGemma scores 87.7% on the MedQA benchmark, matching the clinical accuracy of frontier models at roughly one-tenth the inference cost.

Benchmark Gemma: Run a quick test with a compact Gemma model locally or on a single endpoint.

So here at Google Cloud, we packaged it into a managed RL fine-tuning service (RLFT service) — you bring prompts and a reward function; we handle the infrastructure and the proprietary model internals.

Now, you can adapt Gemini with the service — teaching the model from a reward signal you define, rather than from a fixed set of labeled answers.

In this guide, we will walk through practical best practices for using RL fine-tuning service.

We'll start with a short tour of the RL training loop, how to decide if and when to use RL, and introduce how to get the most value from this approach.

RLFT adapts Gemini from a reward signal you define rather than labeled answers.

Following our announcement of Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking last week, we are thrilled to share that Gemini 3.8 Live with Live Avatar is now generally available in Gemini Enterprise.

Gemini 3.8 Live already delivers a native speech-to-speech foundation for fluid, responsive dialogue.

Together, you’ll have access to:Video avatars: Conversational video with the Live Avatar feature can generate video avatars with synchronized lip-syncing.

Though Gemini 3.8 Live Extended Thinking remains in private preview, Gemini 3.8 Live with Live Avatar is now available with US and EU endpoints, with provisioned throughput, enterprise compliance, and strict data governance.

Three demo…

The number of Latin American-based small and medium businesses using Google Cloud AI tools has grown 8x year-over-year and the number of Brazil based small and medium businesses using Google Cloud AI tools has grown 9x year-over-year.

Help more people build their own AI agents to help them in their everyday jobs.

Google skills for organizations: Access thousands of free, on-demand AI courses and hands-on practice labs designed by experts at Google Cloud and Google DeepMind.

Get certified: Help your staff gain industry-recognized AI certificates through guided courses, expert mentoring, and skill badges.

By offering easy-to-use tools and free training — from everyday office apps in Workspace…

Google Cloud has become the platform of choice for startups building AI.

And when startups use Gemini Enterprise, they also tend to use our “core cloud” services like Storage, BigQuery, or GKE.

Gemini models — as well as several of the third-party models available through Gemini Enterprise — are providing very strong price-performance for startups.

They will also use Gemini Enterprise Agent Platform and AI models on Google Cloud to help robotics process multimodal information like video.

To learn more about Google Cloud’s work with leading AI startups, or to get started building with us, visit here.

Our approach instead focuses on pre-submit scanning, where we evaluate each code check-in (across every layer of the stack) in real-time using AI agents.

Rather than relying on static decoupled documents, the threat models use live codebase metadata.

The scanning agent improves its accuracy further using a dependence call graph across packages and libraries to expand and refine its threat model context.

Making threat models part of our ongoing vulnerability scanning encourages developers to continuously update threats and dependencies, keeping the models up-to-date.

Use context wisely: Feed your agents your existing threat models.

It is the latest result of more than a decade of research at Microsoft Research on making zero-knowledge proofs practical.

In 2019, a new zero-knowledge proof system called Spartan returned to the sum-check approach and made it work for general-purpose programs without a trusted setup.

Checking a Magnetar proof consists mostly of evaluating standard hash functions and binary-field arithmetic, which are exactly the operations Magnetar proves efficiently.

Magnetar enables a future where cryptographic proofs are a routine output of computation.

If you’re interested in exploring how zero-knowledge proofs can support your use case, we’d love to hear from you (opens in new tab).

In today’s AI tutor research, answering these questions often requires real learner studies.

Those studies are valuable, but they are slow and costly to run, which makes it hard to use student feedback during fast model iteration.

The goal is to make student feedback more available during AI tutor development.

Measuring what a student simulator needs to doA simple way to build a student simulator is to ask a large language model to role-play a learner.

Figure 5: StudentSim can provide feedback during AI tutor reinforcement learning by estimating how a student response changes after guidance.

Lightweight by design: Agent Lightning v1.0 delivers a complete agent RL control plane in roughly 3,500 lines of code.

Compared with the original, Agent Lightning, v1.0 puts more emphasis on staying lightweight, on integrating with real harnesses, and on a complete, reproducible agent RL training pipeline.

Agent Lightning v1.0 was rebuilt around Harnessed Agentic RL, with key improvements:Lightweight : the entire framework is about 3,500 lines of code.

Training on a real agent harness : agents reach the model through the large language model (LLM) proxy in Agent Lightning v1.0, leaving existing harness code unchanged.

Traditional agentic RL compared with Harnessed Agentic RL.

NEVILLE: My research, kind of, spans the spectrum from theory to application.

But right now, if you’re doing work on AI systems, I think that industry is really the place to be.

What we really focus on is trying to push the frontier of behavior of these AI systems in realistic work environments.

NEVILLE: That’s, that’s a, that’s a big question.

But I think I’m very bullish on the research that’s happening.

A machine learning pipeline uses forecast-time solar-wind information to generate location-specific risk estimates for 66,935 substations in the continental United States.

Finally, the resulting predictions are converted into location-specific risk estimates and aggregated into a continental risk assessment.

End-to-end forecasting pipeline from L1 solar-wind measurements to location-specific risk estimates across 66,935 U.S. substations.

This produces continuous risk estimates that can distinguish lower-risk locations from areas where resistive geology can amplify ground-level effects.

Together, these efforts point toward richer planning and resilience workflows that combine hazard forecast…

is a research effort to create a multimodal world model of biology and an interactive harness connecting models, scientific tools, literature, and researchers.

Today, Microsoft Research is introducing Quine (opens in new tab), a research effort designed to work across those boundaries, reflecting our long-term vision for a discovery system that evolves through scientific use.

Quine brings together a world model of biology with a harness that connects scientific tools, literature, the wet lab, and the researchers using them.

This raised a provocative question for us: what would it take to build a world model for biology?

Critically, the world model doesn’t need to be perfect—indeed, it will …

On July 24, 2025, Microsoft Research Asia – Singapore (MSRA – Singapore) opened its doors as Microsoft’s first research lab in Southeast Asia.

The launch built on more than two decades of collaboration between Microsoft Research Asia and Singapore’s universities, research institutes, and government agencies.

“When we established Microsoft Research Asia — Singapore one year ago, our vision was not simply to open another research lab.

Students may engage with Microsoft Research Asia through workshops or summer schools, gain hands-on experience through its flagship research initiative, Microsoft Research Asia Stars of Tomorrow Internship Program, or pursue doctoral research through joint PhD p…

To better understand the systems implications of physical AI, we conducted the first systematic study of robotics workloads.

Offloading physical AI inference out of the robot improved its response time and accuracy, along with battery lifetime and cost.

We believe that our measurement study will inform the design of physical AI inference systems.

Physical AI Toolchain is an open-source, production-ready framework that integrates Microsoft Azure (opens in new tab) cloud services with NVIDIA’s (opens in new tab) physical AI stack, accelerating robotics and physical AI developers to automate and scale data curation, augmentation, and evaluation across perception, mobility, imitation learning, …

At a glance We report on the recent publication of our retrosynthesis model RetroChimera in the journal Nature (opens in new tab) ..

Yet, progress is slowed by chemical synthesis—the time-consuming process of making new molecules from simpler building blocks in the lab.

In a paper recently published in the journal Nature (opens in new tab), we present RetroChimera (opens in new tab), a new framework for retrosynthesis prediction.

RetroChimera could enable chemists to assess more—and more complex—candidate molecules at large scale.

RetroChimera is available on GitHub (opens in new tab) (MIT license) and accessible via Microsoft Foundry (opens in new tab).

Lab Director Chris White has worked on research challenges with real-world implications—from new approaches to wartime data analysis to tools for combating human trafficking. He talks to program manager Weishung Liu about the influences that led to the work and more.

The post Called to serve: Tech, research, and positive impact with Chris White appeared first on Microsoft Research.

A distilled pathology foundation model backbone reduces computational requirements without sacrificing performance, enabling repeated analyses across larger patient cohorts.

GigaPath-Flash and GigaTIME-Flash make these capabilities substantially more efficient, enabling researchers to analyze larger cohorts, run more experiments, and move toward population-scale discovery.

To realize the full potential of pathology foundation models, we need models that can be applied repeatedly and affordably across large patient populations.

Listen now Opens in a new tabEfficiency as an enabler of discoveryGigaPath and GigaTIME demonstrated what pathology foundation models can learn from whole slides and …

and is being integrated into , , and , bringing next-generation DFT accuracy closer to the communities that rely on these codes every day.

Together, these developments mark another milestone toward a future in which computational chemistry simulations are both predictive and integrated in all relevant scientific and industrial workflows.

Alongside these integration efforts, we are introducing a living benchmark that tracks the computational performance of successive, increasingly optimized Skala releases.

Together, these developments mark another milestone toward a future in which computational chemistry simulations are both predictive and accessible across a broader range of relevant scien…

At a glance MindTopo is a new benchmark for testing topological reasoning in AI, evaluating whether multimodal models can understand concepts such as connectivity, enclosure, order, separation, and knots.

How MindTopo defines topological spaceMost spatial evaluations for multimodal models focus on Euclidean properties such as distance, direction, size, and relative position.

MindTopo pairs questions about static scenes with interactive tasks that require models to preserve or change the same topological relations.

MindTopo maps reasoning and planning tasks to continuity, separation, order, enclosure, and knots.

Closing that gap may require models that carry an explicit topological state, or…

Research Note: CARE-X is a research model and not a Microsoft product offering or medical device.

CARE-X was developed as a research model to explore how a unified approach can address these diverse demands.

The CARE-X model.

The classification head outputs calibrated P(Yes)/P(No) scores; the grounding head outputs bounding box coordinate with confidence; the language modeling head generates free-text responses.

CARE-X: Toward clinically useful radiology AICARE-X demonstrates that discriminative and generative objectives can be effectively combined within a unified radiology AI model.

At a glance Orchard is an open-source framework for scalable and cost-effective agentic AI research, built around Orchard Env, a reusable environment service for training and evaluating agents across task domains.

To address this gap, we introduce Orchard (opens in new tab), an open-source framework for scalable agentic modeling.

Unlike many existing frameworks, Orchard Env is designed to support different agent systems and task types without modification.

(opens in new tab) We are also releasing the training data and evaluation methods used to build them.

By making the underlying infrastructure open, lightweight, and reusable, Orchard lowers the cost of agentic AI research.

Kendall Square in Cambridge, Massachusetts, is often cited as the most innovative square mile in the world.

But when the Kendall Square Association gathered local stakeholders for its 18th Annual Meeting Thursday, the conversation focused on innovation primarily in the context of community.

KSA is a nonprofit made up of over 150 organizations across Kendall Square, from major companies to universities, research organizations, and the independent shops and restaurants that give Kendall Square its distinct character.

“People come to Kendall Square to make the world better,” said KSA Executive Director Beth O’Neill Maloney in her opening remarks.

“All these years later I think that story captu…

According to the 2026 State of Supply Chain Sustainability report from the MIT Sustainable Supply Chain Lab, which surveyed 1,810 supply chain professionals across 91 countries, 83 percent rate sustainability as important or very important to long-term business success, the highest level in the report's seven-year history.

But commitment, the report reveals, is not the same as capacity.

The result is a supply chain sustainability agenda stuck between conviction and constraint.

Seventy-eight percent of North American organizations report current use of AI in sustainability initiatives, and 87 percent of adopters report positive impact on sustainability goals.

Across all these areas, the cons…

As an embryo develops from a small cluster of stem cells, those once “blank slate” cells begin to take on more specialized roles like brain, liver, or muscle cells, and organize themselves into three-dimensional structures such as tissues and organs.

These messages spread through multi-step sequences called signaling pathways, which translate external signals into specific changes in gene activity in the cell.

It examines a cell’s overall gene activity and estimates which signaling pathways were likely “on” at specific times during development.

The team then tested IRIS using single cells from mouse embryos during gastrulation, a stage when cells are rapidly branching into different fates.

…

MIT Professor Eric So offers one of the most complete syntheses yet, in his new book, “The Collision: What AI Does to Us,” published this month by W.W. Norton.

I open the book with this story where I was instinctively reaching for AI to help me think through problems I normally would have handled myself.

Combine that with the problems detecting AI use in a practical way, and all these factors create what I call “AI gravity,” this constant pull to use AI.

And I think we need that sort of shift about what we think about cognitive struggle.

If you’re just funneling the same energy into the same tasks, you’re missing out on the potential transformational benefits of AI.

Unfortunately, weather forecasts beyond two weeks are notoriously unreliable, making for a tighter window for energy operators, farmers, and emergency response officials around the globe.

A new technique by a team including researchers at MIT significantly improves weather forecasting beyond two weeks.

The researchers developed a machine-learning model that learns to correct errors in existing forecasting models to improve their output over the two-to-six-week time frame.

When applied to a gold-standard forecasting model, it doubled forecasting accuracy over that time frame, placing first in a prominent international forecasting competition.

“Up until now, no one had been able to show a mod…

Artificial neural networks can learn to recognize images, use language, or write computer code.

They show that it is possible for an artificial neural network to build its own learning machinery.

Liao explains that engineers design the rules about how connections change as an artificial neural network processes training data and receives feedback about its mistakes.

But Liao and his colleagues caution that networks designed in this way may not offer much insight into biological learning.

“If the synaptic motifs we propose are, in fact, used by the brain to do what backpropagation does for artificial neural networks, then we will have a strong connection at the level of fundamental mechanism…

MIT Lincoln Laboratory hosted the National Security Commission on Emerging Biotechnology (NSCEB) on Sept. 3, for a stop on its national roadshow.

"We have a lot to leverage in this region to advance biotechnology," said Christina Rudzinski, assistant head of the Biotechnology and Human Systems Division at Lincoln Laboratory.

"Massachusetts is committed to biotechnology and national security, and not just in Boston and Cambridge but across the entire state."

Connecting AI and biotechnologyOther discussions centered on the intersection of biotechnology and AI, another critical area for national security.

It resonates with so many of us who view biotechnology as both an economic and national s…

That’s just one way AI can be used to generate text and statements, in civic conversations, for which no one can be found to be answerable.

Q: You’ve identified this problem about the lack of accountability behind words that are generated by AI.

I believed that to understand what words mean, you actually need a physical body and life experiences as part of the process.

My sense is the people on the receiving end of these comments have quietly adjusted the weight they give to public comments.

When workplaces lacked certain kinds of civic processes and civility — two interestingly related terms — labor disputes used to turn violent.

Here, focusing primarily on graduate research and education, he describes key questions that departments and universities need to think about, and how they can best work with AI going forward.

This also applies to AI research itself: improving models, training procedures, and their supporting tools.

We can imagine a future in which MIT's laboratories function more like a single, living, scientific organism, connected through shared AI research infrastructure.

This will require substantial public and institutional investment now in compute, secure data systems, and expertise in adapting and post-training AI models.

AI systems can already synthesize and reason about information from more sour…

Here, focusing primarily on graduate research and education, he describes key questions that departments and universities need to think about, and how they can best work with AI going forward.

This also applies to AI research itself: improving models, training procedures, and their supporting tools.

We can imagine a future in which MIT's laboratories function more like a single, living, scientific organism, connected through shared AI research infrastructure.

This will require substantial public and institutional investment now in compute, secure data systems, and expertise in adapting and post-training AI models.

AI systems can already synthesize and reason about information from more sour…

MIT News | Massachusetts Institute of Technology - On Campus and Around the world

She began tackling inefficiencies in cloud computing systems and large-scale data centers, which was a rapidly growing area of research.

Through that work, Delimitrou and her research mentor, Christos Kozyrakis, the Leonard Bosack and Sandy K. Lerner Professor of Engineering, realized many large computing systems were underutilized.

It was a bit risky because people had not yet shown that these techniques would work,” Delimitrou says.

As she delved deeper into cloud computing, Delimitrou observed that cloud applications were changing.

In the lab, a creative mindset helps Delimitrou and her team identify novel solutions to problems in cloud computing that others might overlook.

Our business, of humanistic thinking.” As senior lecturer in the Concourse program, one of MIT’s first-year learning communities, Rabieh has been introducing students to the “business” since 2010.

Ford Professor of Political Science Lily Tsai was recently named director after Anne McCants, the Ann F. Friedlaender Professor of History, stepped down.

McCants says she relished interacting with Concourse students in a sustained way — often for all four years — and learning not only from them, but also colleagues.

Those efforts include the Compass Initiative, which Tsai co-leads with other faculty from across SHASS; the MIT Human Insight Collaborative; and the creation of shared faculty position…

AI accelerators — specialized systems designed to speed up capabilities such as neural networks, deep learning, and machine learning — have been a major area of development for nearly a decade.

Since 2018, team from the Lincoln Laboratory Supercomputing Center (LLSC) has been conducting the Lincoln AI Computing Survey (LAICS, pronounced "lace").

Six papers later, LAICS continues to summarize current commercial AI accelerators and compare their peak performance and peak power.

"About eight years ago, we saw a sharp rise in the number of research AI accelerators described in research papers and commercial accelerators being announced, and we started to get questions about them from government…

From CUDA to MLX: How K-Search Brings Decades of Kernel Expertise to Apple SiliconFigure 1: CUDA-to-MLX optimization translation map.

Although we focus on MLX kernels for Apple Silicon, the method is not specific to MLX and applies to any ecosystem where CUDA expertise is transferable.

With Apple Silicon in hundreds of millions of MacBooks and Mac Studios, MLX enables local AI inference without cloud costs.

Building an MLX backendTo bring K-Search to Apple Silicon, we first built a native MLX backend.

Evaluated on mamba-370m f16, M1 Max 64GB:Metric mlx-mamba (ours) mlx-lm (community) mamba.py Decode 152 tok/s 116 tok/s 40 tok/s Prefill L=512 5,751 tok/s 329 tok/s 1,089 tok/s Prefill L=1024 …

Teaching LLMs to Update Beliefs for Efficient Long-Horizon InteractionOverview of ABBEL compared to traditional recursive summarization.

Belief grading can be thought of as adding an auxiliary RL task, using heuristics designed to capture what makes a good belief as the reward.

With domain-knowledge belief grading, ABBEL approaches or exceeds FULL CTX in this setting; without belief grading, learning is slower.

Context compression methods generate dense representations which, while computationally efficient, sacrifice human-understandability (Kontonis et al., 2026, Eyuboglu et al., 2025, Gupta et al., 2025, Chevalier et al., 2023, Deng et al., 2025, Deng et al., 2025, Bulatov et al., 2022).…

Agents are rapidly becoming capable of synthesizing entire data systems in one go—meaning we can rebuild custom systems for each new workload.

Data Systems For, Of, and By AgentsNext, we will discuss each in more detail, followed by discussing the intertwined future of data systems and agents, especially as the three challenges intersect.

Data Systems Of AgentsPreviously, we focused on how agents interact with data systems.

Data Systems By AgentsFinally, if intelligence is effectively free, then we can employ this intelligence to synthesize new data systems from scratch.

Co-Evolution of Data Systems and AgentsLooking further out, the boundaries between agents and data systems will likely …

2026 BAIR Graduate ShowcaseCongratulations to the Berkeley Artificial Intelligence Research (BAIR) Lab class of 2026!

This year, BAIR celebrates another remarkable group of Ph.D. graduates whose curiosity, creativity, and perseverance have pushed the frontiers of artificial intelligence and machine learning.

Their work spans the breadth of modern AI — robotics and embodied intelligence, large language models and reasoning, computer vision, generative modeling, AI safety, human-AI interaction, AI for science and healthcare, and much more.

Along the way, they have published influential research, built systems with real-world impact, mentored their peers, and shaped the BAIR community for th…

Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference ScalingOverview of adaptive parallel reasoning.

We provide a detailed analysis of recent progress in the field of parallel reasoning, especially Adaptive Parallel Reasoning.

Figure 4: Special Tokens Variants across Adaptive Parallel Reasoning PapersInference Systems for Adaptive ParallelismHow do we actually execute parallel branches?

Figure 14: Difference in Model Choice Across Adaptive Parallel Reasoning PapersEach paper also offers a slightly different interpretation about how adaptive parallel reasoning contributes to the research field.

(Yang et al., 2025; Lian et al., 2025) aim to deliver sequential-AR-model-level a…

Large, learned world models are becoming increasingly capable.

Why is adversarial robustness an issue for world model planning?

We thus exploit the differentiability of learned world models $F_{\theta}$, while not falling victim to the inherent sensitivity of the state Jacobians $D_s F_{\theta}$.

It’s a funny sweet spot where the background literature (planning and control overall) is incredibly mature and well-developed, but the current setting (pure planning optimization over modern, large-scale world models) is still heavily underexplored.

But, once we figure out all the right ideas, world model planners will likely become as commonplace as RL.

Identifying Interactions at Scale for LLMsUnderstanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence.

Therefore, grounded or reality-checked interpretability methods must also be able to capture these influential interactions.

In this blog post, we describe the fundamental ideas behind SPEX and ProxySPEX, algorithms capable of identifying these critical interactions at scale.

SPEX and ProxySPEX FrameworkTo discover influential interactions with a tractable number of ablations, we have developed SPEX (Spectral Explainer).

We formalize this through two observations: sparsity (relatively f…

We developed a framework that enables direct evaluation and optimization of imaging systems based on their information content.

The first approach treated imaging systems as unconstrained communication channels, ignoring the physical limitations of lenses and sensors.

Our Information-Driven Encoder Analysis Learning (IDEAL) method uses gradient ascent on information estimates to optimize imaging system parameters.

The standard approach to computational imaging design, end-to-end optimization, jointly trains the imaging hardware and a neural network decoder.

The computational efficiency of IDEAL suggests possibilities for designing imaging systems that were previously intractable.

AgentCore Evaluations, a capability of Amazon Bedrock AgentCore, is the connective tissue powering it, alongside Amazon Bedrock Guardrails (safety), Amazon CloudWatch (metrics, alarms, tracing), Amazon Simple Storage Service (Amazon S3) and Amazon Redshift (evaluation persistence), Amazon Quick (dashboards), and AWS Lambda (custom evaluators).

In this post, we share how we tackled the hardest operational challenges in running AI agents at enterprise scale, and how Amazon Bedrock AgentCore Evaluations became the backbone of agent quality.

Customized evaluators , one per failure type in our taxonomy: a metric swap evaluator, a wrong period evaluator, a wrong customer evaluator, a pure halluci…

Path 2: Deploy with SageMaker HyperPod InferenceUse HyperPod Inference when you need Kubernetes-native control over distributed GPU infrastructure.

Record the Beam model version, image digest, instance type, precision, context length, parallelism, serving arguments, cache state, and test repetitions.

For a SageMaker JumpStart real-time endpoint, follow the cleanup instructions in the SageMaker JumpStart deployment sample README.

For JumpStart, SGLang, or vLLM on Amazon SageMaker HyperPod Inference, follow the cleanup instructions in the HyperPod Inference deployment sample README.

Use the SageMaker JumpStart deployment sample or the HyperPod Inference deployment sample, then follow the inst…

Muse Spark Open, Meta’s coding-optimized open-weight model, is now available in Amazon SageMaker JumpStart, the machine learning (ML) hub of Amazon SageMaker AI.

Meta releases the weights under the Muse Model License, which includes the Muse Spark Usage Policy.

Getting started with SageMaker JumpStartYou can deploy Muse Spark Open from SageMaker Studio or with the SageMaker Python SDK.

To find Muse Spark Open in SageMaker JumpStartOpen SageMaker Studio.

To get started, search for Muse Spark Open in SageMaker JumpStart in SageMaker Studio.

A test that passes today tells you little about what the agent does tomorrow.

The usual answer is a person in the loop, approving everything the agent does.

Wherever an agent’s rules can be stated precisely, automated reasoning can check them.

Where proof does not apply, limit the agent’s access and actions, evaluate its behavior, and send genuine ambiguity to a person to apply judgment.

Amazon Bedrock AgentCore provides governance controls to scale agents safely and governs what the agent does.

Amazon Bedrock AgentCore is a platform to build, connect, and optimize agents at scale, with any framework or model.

Amazon EventBridge triggers an AWS Lambda function that creates a tracking job in Amazon DynamoDB and invokes AgentCore runtime, a capability of Amazon Bedrock AgentCore.

Your agents securely access AWS resources through machine-to-machine credentials managed by AgentCore Identity, a capability of Amazon Bedrock AgentCore, and Amazon Cognito.

Your Lambda functions become agent-compatible tools through the Model Context Protocol (MCP) interface of AgentCore Gateway, a capability of Amazon Bedrock AgentCore.

You gain visibility into agent performance through AgentCore Observabi…

Anthropic Claude Sonnet 4.6 (available on Amazon Bedrock) handles content creation, Amazon Titan Text Embeddings v2 powers vector search, and Stability AI Stable Image Inpaint (available on Amazon Bedrock) generates slide images.

Amazon Bedrock AgentCore runtime provides a platform to build, connect, and optimize agents at scale, with any framework or model.

Note: The Amazon OpenSearch Serverless collection data plane must remain publicly accessible because Amazon Bedrock Knowledge Bases accesses Amazon OpenSearch Serverless from AWS-managed infrastructure, not through customer VPC endpoints.

Run the deployment script:./pipeline-deploy.shThe deployment creates four AWS CloudFormation stacks…

In this post, we share how Tamnoon built Tami, an agentic cloud remediation orchestrator on Amazon Bedrock, to close that gap.

About TamnoonTamnoon is an AI-powered cloud security remediation platform that works alongside CNAPPs.

Cloud security remediation challengesA security engineer can spend hours investigating a single finding.

Security was the final consideration, and for a cloud security company it was non-negotiable.

Next stepsTo get started with a similar architecture, consider the following:Explore Amazon Bedrock and the models available through it.

Amazon SageMaker JumpStart provides access to a broad catalog of already pretrained models.

In the Model Hub section of the Amazon SageMaker AI console, you can browse the SageMaker JumpStart catalog, view sample notebooks, and open them directly in SageMaker Studio.

Solution overviewOur approach uses a reusable Python script that performs three steps:Discover and deploy a model from SageMaker JumpStart.

This pattern works for many compatible SageMaker JumpStart models, although payload formats, required instance types, end-user license agreement (EULA) requirements, and response schemas may require model-specific adjustments.

Try this approach with different models from Amazon SageMaker Ju…

This post covers how Anchor Browser integrates with Amazon Bedrock AgentCore payments so that AI agents can pay for x402-protected pages and APIs autonomously.

Anchor Browser is a cloud-based browser automation solution purpose-built for AI agents.

When an agent running in Anchor Browser encounters a 402 response, AgentCore payments authorizes and completes the transaction within the configured budget.

Anchor Browser creates a dedicated AgentCore PaymentSession for the browser session.

When enabled, the browser session can automatically access x402-protected websites with payments signed by AgentCore payments and retried inside the same browser session.

Managing AI model access across dozens or hundreds of AWS accounts creates operational friction when each workload account must subscribe and accept AWS Marketplace Private Pricing Offers (MPPOs) individually.

In this post, we show you two ways to activate AWS Marketplace Private Pricing Offer (MPPO) entitlements in AWS License Manager.

Why automate grant activationWith AWS Marketplace Private Pricing Offers, you can negotiate custom rates for third-party models on Amazon Bedrock.

After an eligible private offer is accepted, AWS Marketplace creates a license in AWS License Manager.

That lets our platform team scale cost governance without scaling manual work.” — Marcus Lau, Head of Platform…

To solve this, Orca Security partnered with AWS to build Discovery AI, a natural language query agent on Amazon Bedrock.

To build this capability, the Orca team set out to build a natural language interface that can generate valid Sonar queries from plain text.

To address this, the Orca team designed and deployed a solution that handles thousands of queries per day at 15-second response times using Amazon Bedrock.

The Discovery AI pipeline converts a natural language request into a validated Discovery filter through nine steps.

To learn more about how Orca Security applies AI across its framework, see how Orca drives security outcomes with AI and how Orca secures AI adoption, including Amaz…

To reclaim it, the ShipStation Global Engineering team built Deckhand, an AI-powered IT help desk agent for Slack, on Amazon Bedrock AgentCore.

AgentCore runtime, a capability of Amazon Bedrock AgentCore, hosts the agent in isolated sessions, so we run no servers or containers.

PrerequisitesTo implement this pattern, you need the following:An AWS account with access to Amazon Bedrock, AgentCore runtime, and AgentCore Gateway.

Knowledge base design and answer routingDeckhand answers from a single Amazon Bedrock knowledge base with core S3-backed data sources:Articles synced from Confluence.

Refer to the Amazon Bedrock AgentCore documentation and Strands Agents SDK for agent development, and …

SpaceXAI’s Grok 4.7 is now available on Amazon Bedrock, which offers a choice of foundation models (FMs) from multiple providers.

We ran all three at every reasoning effort level, six times each, for 72 agent runs.

On Amazon Bedrock, the model supports configurable reasoning effort, tool calling, and structured output.

How you access Grok 4.7 on Amazon BedrockGrok 4.7 runs on the bedrock-runtime endpoint and is accessed through a cross-Region inference profile.

And monitor output tokens and tool calls by effort level, so you have an operational signal alongside your quality measurements.

The column dimension (N) remains highly variable.

Operand A strictly maps across this fixed M dimension (using registers or shared memory), and Operand B maps across the variable N dimension (through a shared memory descriptor).

Consequently, peak hardware tile utilization is governed by these rigid shape encodings, requiring that the M dimension equals or exceeds 64 to prevent compute execution lanes from sitting idle.

In a Fused MoE kernel, the GPU isn’t only multiplying against one expert.

For a model running a topk=8 routine, the effective N dimension inside the fused kernel explodes into tens or hundreds of thousands of parameters.

Global cross-Region inference profiles for OpenAI GPT-6The GPT-6 family offers three models, each available through global cross-Region inference.

The Amazon Bedrock console lists all three global cross-Region inference profiles under system-defined inference profiles.

Service control policy considerations for global cross-Region inferenceA global cross-Region inference request carries no Region name.

See How tokens are counted in Amazon Bedrock and Amazon Bedrock endpoints and quotas for current values.

For more information, see the Amazon Bedrock User Guide, Route model inference requests across AWS Regions with cross-Region inference, and Amazon Bedrock pricing.

NVIDIA RTX Spark laptops are now available, bringing NVIDIA Blackwell RTX graphics, up to 128GB of unified memory and the full NVIDIA AI platform to slim, premium Windows PCs.

RTX Spark Launch Celebrations Light Up BerlinThe arrival of RTX Spark brought fans, creators and PC enthusiasts together in Berlin during the Festival of Lights, an annual autumn art festival that transforms the German capital’s most famous landmarks, streets and squares into glowing canvases.

He also gave out five personally signed RTX Spark laptops.

Developers and AI enthusiasts can explore NVIDIA-tested recipes, playbooks and resources for running local AI on RTX Spark.

Follow NVIDIA RTX Spark on X, Instagram, TikT…

A new beginning for Windows PCs starts tomorrow with the launch of NVIDIA RTX Spark — one PC built for creators, gamers and AI agents.

All are invited to celebrate with NVIDIA and partners at launch events around the world.

Join the celebration online by sharing responses with #ShareYourSpark for a chance to win an ASUS ProArt P14 RTX Spark laptop signed by NVIDIA founder and CEO Jensen Huang.

See What Reviewers Are SayingFrom local AI workflows to creative apps and gaming, see what reviewers are saying about RTX Spark — then experience it at a launch celebration.

Join the Launch CelebrationsOn Friday, Oct. 16, get hands on with RTX Spark at launch events around the world.

Unlike closed frontier models, organizations can post-train Super VL on their own data, deploy it on their own infrastructure and oversee what they build.

It is the #1 open model in its class on PinchBench, and the #2 open model for BrowseComp.

Nemotron 3.5 Super VL understands images, webpages and PDFs and acts on that understanding.

Nemotron 3.5 Bolt 74B-A9B 24GB is available now on Hugging Face, with support for llama.cpp, Ollama, LM Studio, Unsloth, and vLLM.

Stay up to date on agents, NVIDIA Nemotron and more by subscribing to NVIDIA news, joining the community, and following NVIDIA AI on LinkedIn, Instagram, X and Facebook.

New NVIDIA DLSS 4.5 controls give GeForce NOW members more choice over how supported games look and perform in the cloud, even without the latest hardware.

Fine-Tune the Cloud With DLSS 4.5 ControlsNVIDIA DLSS 4.5 controls are now available on GeForce NOW, giving members more choice over how supported games look and perform in the cloud, even without the latest hardware.

In addition, Ultimate members streaming on GeForce RTX 5080-powered cloud rigs benefit from the maximum available options, including Dynamic Multi Frame Generation up to 4X.

The new, more performant DLSS 5 model will debut in NBA 2K27 on Oct. 16 on GeForce NOW, GeForce RTX 50 Series GPUs and RTX Spark laptops.

Gamers who wa…

NVIDIA has released more than 250 open models, 250 open datasets and over 1,400 open source software projects spanning large language models, healthcare, robotics, climate science, physical AI, autonomous vehicles and scientific research.

The NVIDIA Inception program for startups provides access to compute resources and technical support, lowering the barrier to entry for startups seeking high-performance infrastructure and technological education.

Finally, EuroHPC supercomputing centers such as CINECA and JSC provide academia and startups with free access to NVIDIA GPU infrastructure and services for open research.

AI Infrastructure ExpansionTo top it all off, NVIDIA is powering Europe’s m…

The consortium will build services using NVIDIA Nemotron open models, which allow researchers to customize and deploy their models in a secure environment.

ABB Robotics developed RobotStudio HyperReality using NVIDIA Omniverse libraries to bridge the gap between robot simulations and real-world performance.

Built on NVIDIA Nemotron open models and distributed infrastructure, it uses company data and low-latency networking to deliver fast, context-aware responses.

Agaton, a platform that analyzes customer conversations in real time, is using NVIDIA Nemotron 3.5 Lightning to speed up Revenue Mining solution on live sales calls.

Quantum Meets Accelerated ComputingFinally, Swedish companies are…

Heidi has now integrated NVIDIA Nemotron open models to give clinicians faster access to trusted medical evidence.

Speechmatics is deploying Oak, a multilingual medical speech recognition model, using NVIDIA Triton Inference — giving healthcare organizations control over where sensitive patient audio is processed.

Revolut introduced PRAGMA earlier in the year — a foundation model for banking event sequences, pre-trained on 26 million anonymised user histories.

This includes more than 3,100 U.K. companies that are a part of the NVIDIA Inception program for cutting-edge startups.

Tune in to NVIDIA founder and CEO Jensen Huang’s GTC Berlin keynote on Wednesday, Oct. 21, at 11 a.m. CEST.

Across the EMEA region, cities and local software, infrastructure and integration partners are using NVIDIA accelerated computing to build physical AI agents and digital twins that help teams make those decisions.

Building the AI Infrastructure for European CitiesFor European cities, scaling from pilots to everyday operations requires infrastructure that can process local data, customize models and run AI agents.

These systems let cities customize and run intelligent applications locally while retaining control of their data, models and operations.

Their work helps cities build repeatable deployment blueprints that they can adapt across departments and expand as local needs grow.

Learn how …

Particularly, the NVIDIA cuDF plugin for Apache Spark brings this acceleration to Apache Spark, the industry-leading platform for big data processing and analytics, to achieve better TCO performance in major workloads.

This post describes how to use agents to rewrite UDFs into GPU-accelerated equivalents within minutes, achieving 22x+ speedups in Apache Spark jobs.

Apache Spark, however, also supports UDFs: custom routines to extend built-in functionality.

Available skillsThe cuDF skills for Apache Spark decompose the rewrite into four focused feedback loops.

To learn more about GPU acceleration for Apache Spark, follow the cuDF plugin for Apache Spark getting-started guide to unlock better…

Developer tools are now ready for RTX Spark: Top developer frameworks are now available for RTX Spark, including PyTorch, Python and CUDA, as well as all CUDA libraries.

RTX Video Brings Frame Generation and HDR to ComfyUIComfyUI is expanding its RTX Video tools with two new nodes: RTX Video Frame Generation and RTX Video HDR.

RTX Video Frame Generation increases video frame rates for smoother motion for GeForce RTX 40 Series GPUs and newer, while RTX Video HDR converts standard dynamic range (SDR) footage to HDR for greater flexibility in color grading.

Developer Tools Are RTX-Spark ReadyNVIDIA has partnered with Microsoft to ensure that developer tools are ready for RTX Spark.

Follow NVID…

Sovereign AI InfrastructureFrance-based companies are expanding agentic infrastructure on NVIDIA technology.

Open ModelsNVIDIA Nemotron open models are in production across French organizations that require sovereign, trustworthy data.

Agents at Work Across IndustriesAcross retail, public services, telecommunications and science, the pattern is consistent: open models and NVIDIA infrastructure are driving results in France.

Mirakl has onboarded more than 70 million products with fine-tuned and open models running on NVIDIA GPUs and inference software, cutting seller catalog onboarding to under two hours.

Omi helps brands bring accurate products into AI-generated marketing visuals at scale, …

As AI factories grow from megawatts to gigawatts, building them takes more than advanced computing.

Meta and NVIDIA advanced open AI networking with NVIDIA Spectrum-X Ethernet SuperNIC and Spectrum-6 innovations.

Visionbay introduced an 800V DC AI factory design for the Vera Rubin era.

K-1 also reflects the system-level design philosophy behind NVIDIA AI factories, where power, cooling and computing are engineered together.

Together, these technologies support agentic AI factories of more than 100,000 GPUs that train and serve massive mixture-of-experts models.

Developers are combining frontier AI models with NVIDIA Omniverse libraries to help carry out that work — building applications for exploring scenarios, investigating failures and improving designs.

Frank DeLise, Omniverse product manager at NVIDIA, used Astra to turn a SimReady warehouse and humanoid robot into an interactive simulator with first- and third-person views.

Learn how to prepare and validate SimReady robot assets with frontier AI models and NVIDIA Omniverse libraries.

Connect an Autonomous-Driving Testing Workflow 🔗Changing a scene, sensor or driving model can significantly affect autonomous-vehicle simulations.

Explore NVIDIA Omniverse libraries to start building with an AI a…

Learn moreThe NVIDIA KGMON team placed second in the KDD Cup 2026 Data Agents competition with a system built around a simple idea of making an agent’s harness smaller, clearer, and easier to verify.

It isn’t a general recipe for every data science agent, but the broader lesson applies to many agent systems.

Too many ways to inspect data, call tools, write files, or recover from mistakes can lead to failures.

The harness preprocesses data, limits the agent’s tools, and preserves state, with execution traces supporting evaluation and refinementThe following techniques offer practical guidance for building reliable data analytics agents.

For additional architecture examples and implementation…

Gears of War: E-Day leads the charge on GeForce NOW this week, bringing Marcus Fenix and Dom Santiago’s first fight against the Locust Horde to the cloud with GeForce RTX-powered performance.

Emergence Day Is HereGears of War: E-Day is now streaming on GeForce NOW following its global release on Tuesday, Oct. 6.

Ultimate members can take on the Horde with GeForce RTX 5080-class performance in the cloud, plus technologies including NVIDIA DLSS and NVIDIA Reflex.

Gamers who want to test out high-performance GeForce NOW cloud gaming can start with a day pass to try premium cloud gaming before committing to a membership.

Better yet, the cost of the day pass can be applied toward a first monthly…

Meta’s newest app, Forum, is for anyone who wants to go deeper into the conversations in their Facebook groups.

To build Forum, Meta engineers adopted a whole new way of working by bringing an AI agent into the development cycle.

Forum is a standalone Groups app that a small team built from scratch.

It’s also where we tested a different way of working: treating an AI agent as a participant in the engineering process rather than a faster way to type.

We invested in rigorous upstream decomposition and machine-readable context, which tightened the engineering loop for engineers and agents alike.

The 2026 generation of GEM shifts from isolated per-domain optimizations — such as Facebook, Instagram, Reels — to a single central foundation model.

Bending the Scaling Curve, Enabling 2.5x Model ComplexityChallenge: Empirical scaling laws demonstrate that model performance predictably improves with compute, parameter, and data size using transformer model architecture.

Scaling Law: Bend Scaling Curve at Frontier Scale with KunlunGEM introduces a new LLM-style model architecture — Kunlun.

The representation generation process is precomputed and shared with the computation to generate soft labels of the foundation model for knowledge distillation without additional foundation model cost.

Fo…

At OCP 2025, we introduced our latest AI scale-out fabrics and drove the creation of the Ethernet for Scale-Up Networking (ESUN) initiative.

Open Switch Platform Architecture (OSPA) : a new OCP initiative, co-led by Meta, Microsoft, and Alibaba, that standardizes the switch platform layer — with specification v1.0 now submitted to OCP.

OSPA: Open Switch Platform ArchitectureSAI made switch data plane software portable across silicon.

To fix this, we’re co-leading a new OCP initiative with Microsoft and Alibaba, the Open Switch Platform Architecture (OSPA) — a unified reference design for the switch platform layer.

The challenges of building open, efficient, and sustainable AI networking are…

Two elasticity levers in the system allow ads recommendation work to expand across opportunistic capacity, without compromising baseline performance.

At Meta, we built a system that aggregates these pockets into a shared opportunistic capacity pool and turns them into better ads recommendations.

The ads inference system must therefore adapt workload as capacity supply changes without overloading the dedicated capacity that serves baseline workload.

From Fragmented Reservations to a Shared Opportunistic Capacity PoolAcross a heterogeneous fleet at Meta’s scale, instantaneous idle inference capacity is inevitable — even with proactive planning designed to minimize structural mismatches.

Safel…

Recipes reference knowledge files but contain no domain facts; knowledge files state positions but prescribe no procedures.

This means:Adding an organizational position means adding a knowledge file and updating a routing index.

Every expert correction moves through four phases:Diagnose expert feedback into actionable issues with their root cause.

The requirements for adopting this architecture are:A structured knowledge system with explicit file boundaries, cross-references, and a dependency graph (the organizational second brain for the domain).

Every improvement is a text edit that a domain expert can review in 30 seconds.

Introducing the Multi-Stage Sequence ModelTo address scaling efficiency, a multi-stage model has been developed that enables scaling of a transformer-based sequence model in a compute efficient manner.

Second Stage: Online Ranking ModelThe offline user model representations are complemented with online ranking models that use fresh user signals and ad candidate information for real time ranking.

A Predictable Scaling CurveLLM-Style Scaling LawWhen running on real-world ads traffic, the multi-stage sequence model demonstrates the emergence of predictable scaling laws for ads recommendations that are analogous to those observed in large language models.

The Impact of Multi-Stage Sequence Mode…

We tackled these challenges through complementary compute efficiency and scaling efficiency innovations: Compute efficiency : Achieved through a customized recommendation kernel library — Jagged Flash Attention (JFA), Generalized Dot-Product Attention (GDPA), BlockAttention, etc.

The results: we doubled GEM’s E2E training efficiency to 20-25% MFU while scaling total training FLOPs 4x over the past 12 months.

We measure training efficiency through E2E MFU, which decomposes into two factors:E2E MFU = Local MFU (compute efficiency) × Scaling Ratio (scaling efficiency)These factors describe two related but distinct optimization problems.

Local MFU (compute efficiency) measures how well a single…

Hierarchical Interest Representation is an upstream representation layer designed to improve upon Meta’s deep funnel ranking optimization.

How Hierarchical Interest Representation Enhances Deep Funnel OptimizationHierarchical Interest Representation pioneers a structural shift in representation modeling by navigating long-range graph topologies and distilling sparse engagement signals into unified interest clusters at various granularities.

This aims to enable the delivery of more relevant ad content to optimize deep funnel ads.

Hierarchical Interest Representation learns super graphs, which cascade through multiple hierarchical layers for this flexibility, accommodating ranking modeling ar…

Why Ads Latency MattersMeta’s ads serving fleet handles more than 5 million requests per second on average at the serving platform entry point, which is over 400 billion per day across all monetized surfaces1.

That is why our Ads and Linux Kernel teams have been working together to build a scheduling policy customized to the ads delivery workload using sched_ext, the upstream, BPF-based extensible scheduling framework.

Until now, we have been using the general-purpose schedulers typically integrated in the Linux kernel (CFS and EEVDF) that balance threads across CPUs with no understanding of the workload.

It has already been deployed in several services at Meta, delivering meaningful reduct…

This year marks Meta’s 10th consecutive year as a sponsor of the Python Software Foundation (PSF), the charitable organization dedicated to advancing, supporting, and protecting the open-source Python programming language and the community that sustains it.

Some of the core maintainers of Python are Meta engineers who have authored new features and Python Enhancement Proposals (PEPs) for the Python community.

These improvements are vital for protecting the global Python community and ensuring that developers everywhere – including our own engineers – can safely share and consume packages.

These investments help grow the Python community and foster the new talent that is essential for Python…

Why Asset Classification MattersAsset classification is the foundation for many privacy controls.

The rest of this post walks through those pieces using asset classification as the case study.

All three share a single judge model, a larger reasoning model deliberately different from the classifier model.

Distill Stable Behavior Into RulesEven a strong LLM classifier should not be the default enforcement path forever.

Expand to other PAI workflows: The same pattern (context → LLM reasoning → distillation → deterministic enforcement) applies to lineage validation, purpose-boundary checking, and retention policy assignment.

The retrieval system within industry recommendation systems have consisted of microservices stitched together, with neural networks inconsistently integrated.

Under Index as Model previous microservice-based item indices used for retrieval become a tensor inside the model.

Moving From Microservice Mesh to One Integrated Neural NetworkThe Microservice Paradigm We ReplacedTraditional recommendation retrieval is built as a mesh of microservices.

We call this Index as Model: Every retrieval component — the item index, eligibility filter, scoring layer and user tower — becomes a tensor or operator inside a single PyTorch model.

Index FreshnessWith index as a model module, maintaining index fresh…

On its face the new Friend Bubbles feature looks simple enough.

It highlights Reels your friends have watched and reacted to.

On this episode of the Meta Tech Podcast, Pascal Hartig chats with Subasree and Joseph, two software engineers from the Facebook Reels team, about what it took to bring Friend Bubbles to life.

If you’ve ever underestimated a “simple” feature, this one’s for you.

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

We’ve fundamentally transformed Facebook Groups Search to help people more reliably discover, sort through, and validate community content that’s most relevant to them.

We’ve adopted a new hybrid retrieval architecture and implemented automated model-based evaluation to address the major friction points people experience when searching community content.

Addressing the Friction Points in Community KnowledgePeople struggle with three friction points when searching for answers in community content – discovery, consumption, and validation.

The Solution: A Modernized Hybrid Retrieval ArchitectureWe engineered a hybrid retrieval architecture that powers a discussions module on Facebook Search.

R…

We’ve built a unified AI agent platform that encodes the domain expertise of senior efficiency engineers into reusable, composable skills.

Introducing the Capacity Efficiency ProgramWhen the code you ship serves more than 3 billion people, even a 0.1% performance regression can translate to significant additional power consumption.

Many engineers at Meta use our efficiency tools to work on these problems every day.

Skills : These encode domain expertise about performance efficiency.

The pipeline mirrors the defensive AI Regression Solver:Gather context with tools: The AI agent looks up: Opportunity metadata.

All information about GTC and the DGX Spark Raffle is here: https://www.ykilcher.com/gtc Links:

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SubscribeStar: https:/…

Paper: https://arxiv.org/abs/2511.08923 Abstract:

Diffusion language models hold the promise of fast parallel generation, while autoregressive (AR) models typically excel in quality due to their causal structure aligning naturally with language modeling. This raises a fundamental question: can we achieve a synergy with high throughput, higher GPU utilization, and AR level quality? Existing methods fail to effectively balance these two aspects, either prioritizing AR using a weaker model for sequential drafting (speculative decoding), leading to lower drafting efficiency, or using some form of left-to-right (AR-like) decoding logic for diffusion, which still suffers from quality degradation …

Part of a monthly series of puzzles: https://momath.org/mindbenders/

Full video: https://youtu.be/Nbwv5wHQoj0

A beautiful puzzle that eluded AI, and the intuition it requires.

Check out our virtual career fair: https://3b1b.co/talent

See new videos early: https://3b1b.co/support

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

Home page: https://www.3blue1brown.com Evan Chen also wrote up nice solution notes for this problem, along with all the others on that year's test.

https://web.evanchen.cc/exams/IMO-2025-notes.pdf The channel Dedekind Cuts has a video about this solution:

https://youtu.be/fgXg9CdCDcs Timestamps:

0:00 - The one that AI missed

4:09 - Problem statement

6:45 - Finding the Optimal Construction

18:51 - A weak lower bound

23:45 - 3b1b Talent

24:41 - Proving the Con…

Part of a series of monthly puzzles with MoMath.

See all monthly puzzles: https://momath.org/mindbenders/

Where the loss function for training LLMs comes from.

Job opportunities aligned to this audience: https://3b1b.co/talent

Early views and other perks for supporters: https://3b1b.co/support

Home page: https://www.3blue1brown.com Manim animations by Aaron Gostein and Grant Sanderson

NanoGPT animation by Clayton Rabideau

3d black-box model by Paul Dancstep

Music by Vince Rubinetti Timestamps 0:00 - Language trees and zipping

3:02 - Recap optimal codes

5:20 - Defining cross-entropy

8:26 - Intuition and examples

12:59 - Application to language trees

14:55 - Pre-training LLMs

20:38 - What makes this loss function best?

26:13 - Distillation

30:12 - 3b1b Talent

31:35 - KL Divergence ---------------…

Part of a series of monthly puzzles done in collaboration with MoMath.

Full video: https://youtu.be/l6DKRf-fAAM

Full video: https://youtu.be/l6DKRf-fAAM

What is the fundamental compressibility of language?

Check out our virtual career fair: https://3b1b.co/talent

See new projects before they go live: https://3b1b.co/support Animation credit:

Manim scenes by Aaron Gostein and Grant Sanderson

Shannon’s story, as well as those for various pi creatures, by Mitchell Zemil.

Lunar robot and prediction/compression coin by Paul Dancstep

NanoGPT animations by Clayton Rabideau Shannon’s “A Mathematical Theory of Communication”

https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf Shannon’s “Prediction and Entropy of Printed English”

https://www.princeton.edu/~wbialek/rome/refs/shannon_51.pdf Scientific American article that…

Recent puzzle solutions on Patreon:

https://members.3blue1brown.com/posts/158885046?pr=true

Made as part of a monthly series of puzzles for the 2026 Year of Math.

On "The Print Gallery", by M.C. Escher

Full video: https://youtu.be/ldxFjLJ3rVY

Part of a series of monthly puzzlers. Stay subscribed to see the solution

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The DeepSeek OCR paper is available here:

https://github.com/deepseek-ai/DeepSeek-OCR 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Adam Bridges, B Shang, Carlos Galarza, Christian Ahlin, Eric Tyson, Juan Benet, Lukas Biewald, Michael Tedder, Owen Skarpness, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Thank you Google DeepMind for the invite. 📝 AlphaGenome Atlas is available here:

https://deepmind.google/blog/alphagenome-atlas-a-predictive-map-of-every-possible-dna-letter-change-in-the-human-genome/

https://blog.google/innovation-and-ai/models-and-research/google-deepmind/alphagenome-atlas/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Adam Bridges, B Shang, Carlos Galarza, Christian Ahlin, Eric Tyson, Juan Benet, Lukas Biewald, Michael Tedder, Owen Skarpness, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 Sonnet 5.5: https://www.anthropic.com/claude-sonnet-5-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Adam Bridges, B Shang, Carlos Galarza, Christian Ahlin, Eric Tyson, Juan Benet, Lukas Biewald, Michael Tedder, Owen Skarpness, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Claude Opus 5.5: https://www.anthropic.com/claude-opus-5-5 Walking creatures video and paper:

https://www.youtube.com/watch?v=kQ2bqz3HPJE

https://www.goatstream.com/research/papers/SA2013/ Honey coiling video and paper:

https://www.youtube.com/watch?v=ZEjUqZU1hNQ

https://cs.uwaterloo.ca/~c2batty/papers/Larionov2017/Larionov2017.pdf

https://dl.acm.org/doi/10.1145/3072959.3073628

https://uwspace.uwaterloo.ca/items/ecb9fe94-1ef5-4ed0-a67e-b3d3c5a7534e

paper name: Variational Stokes: A Unified Pressure-Viscosity Solver for Accurate Viscous Liquids Sources:

https://x.com/ArtificialAnlys/status/2102438210798514391/…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 Jev:

https://typesafe.ai/blog/introducing-system-one-models-and-jev Full song: https://www.youtube.com/watch?v=4RtUJkjrKMI Previous papers, implementations, models:

https://huggingface.co/convaiinnovations/laya

https://arxiv.org/abs/2503.23303

https://huggingface.co/DeepMostInnovations/sales-conversion-model-reinf-learning

https://huggingface.co/datasets/DeepMostInnovations/saas-sales-conversations

https://arxiv.org/abs/2510.01237

https://huggingface.co/blog/setfit

https://proceedings.iclr.cc/paper_files/paper/2025/hash/5503a7c69d48a2f86fc00b3dc09de686-Abstract-Conference.html Sources:

https://x.com/alexisg…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The DeepSeek V4.1 Flash paper is available here:

https://www.deepseek.com/en/news/deepseek-v4-1-flash/ Sources:

https://x.com/flowith/status/2099446892174406055/video/1

https://x.com/loktar00/status/2097761803291726137

https://x.com/loktar00/status/2099171901746688079

https://x.com/loktar00/status/2097557594239897837

https://x.com/loktar00/status/2097342670620291430

https://x.com/RealFedeURU/status/2097804481068941679

https://x.com/ItsmeAjayKV/status/2099875889454739801

https://x.com/ItsmeAjayKV/status/2100434059675709683/video/1

https://x.com/DanielPPFW/status/2100016621251338732/video/1 🙏 We would like to…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The papers and sources are available here:

https://proceedings.mlr.press/v202/kirchenbauer23a.html

https://www.nature.com/articles/s41586-024-08025-4

https://www.anthropic.com/news/claude-text-watermark 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Adam Bridges, B Shang, Carlos Galarza, Christian Ahlin, Eric Tyson, Juan Benet, Lukas Biewald, Michael Tedder, Owen Skarpness, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The Navier-Stokes solution paper is available here:

https://openai.com/index/navier-stokes-solution/ My fluid simulations and papers:

https://users.cg.tuwien.ac.at/zsolnai/gfx/fluid_control_msc_thesis/

https://users.cg.tuwien.ac.at/zsolnai/gfx/real_time_fluid_control_eg/

The flow from simulation to reality: https://www.nature.com/articles/s41567-022-01788-5

All papers: https://users.cg.tuwien.ac.at/zsolnai/ Sources:

https://www.youtube.com/watch?v=EURkO98VnKc

https://www.youtube.com/watch?v=luOWq1Gdv8c 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Adam Bridges…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Links / sources:

Honey sim: search for "Variational Stokes: A Unified Pressure-Viscosity Solver for Accurate Viscous Liquids" here: https://cs.uwaterloo.ca/~c2batty/

https://x.com/mindblown_ai/status/2095661874037813298?s=20

https://x.com/aollivier82/status/2096226819401801896?s=46

https://x.com/sahilexec/status/2095688272269984016?s=46

https://x.com/petergostev/status/2095596176804307342?s=20

https://x.com/mattshumer_/status/2095609734845927525?s=20

https://x.com/mattshumer_/status/2095596175705399482?s=46

https://x.com/davis7/status/2095742249275699415?s=46

https://x.com/dimillian/status/2095596700815516004…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The Claude Fable 5.1 paper is available here:

https://www.anthropic.com/claude-fable-and-mythos-5-1

https://www-cdn.anthropic.com/0339e6a7c5c7b87f5c07798616dc32c215d14235/Claude%20Fable%205.1%20%26%20Claude%20Mythos%205.1%20System%20Card.pdf Sources:

https://x.com/alexalbert__/status/2094860187743986169?s=46

https://x.com/holytrinity/status/2094866061212459130?s=46

https://x.com/holytrinity/status/2094927984217985474?s=46

https://x.com/omedvibecodes/status/2094887840848965845?s=46

https://x.com/loktar00/status/2094951511742632168?s=46

https://x.com/maxt3chno/status/2094798704385380762?s=46

https://x.com/fab…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 GLM 5.3 Flash:

https://z.ai/blog/glm-5.3-flash Sources:

https://x.com/louszbd/status/2092694163104113016

https://x.com/semianalysis_/status/2092623833630998556

https://x.com/skalskip92/status/2092748209802154201

https://x.com/louszbd/status/2093047548550525165

https://x.com/atomic_chat_hq/status/2093433913238552712

https://x.com/holytrinity/status/2094093933584257334

https://x.com/KinasRemek/status/2090081611832295581/video/1

https://x.com/AiXsatoshi/status/2093679264013181119/video/1

https://x.com/AiXsatoshi/status/2093353322921263389/video/1

https://x.com/stevibe/status/2092655031040565252 🙏 We would like…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper and Qwen3.8-Flash-Next are available here:

https://github.com/QwenLM/Qwen3.8-Flash-Next/blob/main/tech_report.pdf

https://qwen.ai/blog?id=qwen3.8-flash-next 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Adam Bridges, B Shang, Carlos Galarza, Christian Ahlin, Eric Tyson, Juan Benet, Lukas Biewald, Michael Tedder, Owen Skarpness, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 DeepSeek Harness + paper are available here:

https://deepseek.com/harness/en/

https://github.com/cordiverse/paper 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Adam Bridges, B Shang, Carlos Galarza, Christian Ahlin, Eric Tyson, Juan Benet, Lukas Biewald, Michael Tedder, Owen Skarpness, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The Qwen3.8-27b is available here:

https://huggingface.co/Qwen/Qwen3.8-27B Sources:

https://www.reddit.com/r/unsloth/comments/1vogva0/share_your_results_from_qwen3827b/

https://www.reddit.com/r/LocalLLaMA/comments/1voer8u/qwen_38_27b_aquarium_burst_sample_test/

https://x.com/KyleHessling1/status/2088327667733180637

https://www.reddit.com/r/LocalLLaMA/comments/1vqme4y/qwen3827b_q8_0_on_strix_halo_is_seriously/

https://forums.developer.nvidia.com/t/qwen3-8-27b-nvfp4-on-a-single-dgx-spark-up-to-1m-context-vllm-mtp-measurements/380244 🙏 We would like to thank our generous Patreon supporters who make Two Minute …

В работе с аудио всё чаще важно не только распознавать речь, но и понимать длинную запись целиком: отвечать на вопросы, строить суммаризацию и находить нужный фрагмент. Для этого Audio LLM должна понимать не только, что произошло, но и когда именно. В докладе разберём temporal grounding для длинного аудио и покажем, что даже сильные модели могут давать верный смысловой ответ, но ошибаться в тайм-кодах на минуты.

Расскажем, как построили полный пайплайн time-aware Audio LLM и как контролировали качество данных: собрали и опубликовали синтетический датасет объёмом 13 тыс. часов и 1 млн семплов, адаптировали архитектуру модели и добавили inter-timings — временные якоря между аудиотокенами. На …

Система восприятия автономного автомобиля должна одновременно видеть далеко, детектировать точно и укладываться в жёсткий бюджет времени на edge-устройстве. Наша CNN-based-архитектура на базе BEVFusion даёт обзор на 250 м вперёд и назад и на 100 м по бокам, но масштабировать дальность не получается: любое расширение зоны видимости упирается либо в качество детекции, либо в скорость инференса.

Расскажу, как мы перешли на query-based-модель MV2DFusion и сняли это ограничение: боковой обзор уже вырос с 100 до 200 м, а дальность вперёд и назад архитектура позволяет увеличить с 250 до 400 м: это наши следующие планы. Разберём, почему выбрали именно эту модель и как сравнивали с BEVFusion по даль…

Расскажу, как мы обучили единую end-to-end-модель управления всем телом гуманоида. Она самостоятельно координирует движения и поддерживает равновесие робота — от ходьбы, приседаний и наклонов до поднятия предметов и взаимодействия с тяжёлыми объектами, охватывая широкий спектр необходимых нам сценариев. Больше материалов про ML по ссылке: https://t.me/+owyCvdge8WIyNTUy

Метод построения LLM-матрёшек позволяет за одно небольшое дообучение получить из одной большой модели семейство меньших. Расскажу, как мы доработали исходный подход: адаптировали его к новым архитектурам, перешли от числа параметров к прямой оптимизации скорости работы модели и научились точно попадать в произвольные бюджеты. Отдельно разберём кейсы применения полученных моделей — от ускорения исследовательских экспериментов до использования в сервисах. Больше материалов про ML по ссылке: https://t.me/+owyCvdge8WIyNTUy

Оценить качество новой LLM-фичи сложно — особенно до того, как она попадёт к пользователям, а часто и после, если в команде нет развитой культуры A/B-тестов. В итоге правка улучшает один кейс и ломает три, а вопрос «выкатываем ли новый промпт и переходим ли на другую модель» решается на глаз. В своём докладе я разберу, как выстроить систему evals для LLM-продуктов и сочетать простые детерминированные проверки, LLM-as-a-judge, собственные бенчмарки и A/B-тесты. Отдельно поговорим про специфику оценки агентов — там важен не только финальный результат, но и путь к нему — почти как у самурая.

Дальше перейду к конкретике: что болело у нас при попытках улучшить LLM-системы и как мы это решали. Об…

Все существующие бенчмарки для поиска измеряют либо качество языковых моделей, либо качество поисковых моделей и алгоритмов. А вот качество самого поискового движка (финального API для поиска) никто нормально не измеряет.

Кроме того, существующие бенчмарки в основном статичны, то есть набор поисковых запросов в них фиксирован. Это очень плохо, потому что позволяет один раз найти, разметить и закешировать правильные документы, а также подогнать под них ранжирование. Поэтому мы предлагаем онлайн-бенчмарк NEEDLE, в котором используются свежие запросы на основе новостей, статей, финансовых документов, судебных дел и логов агентов.

В докладе я подробно расскажу об этом и других бенчмарках, а так…

Поговорим об архитектуре Deep Ranker: о том, почему она эффективна и как переносит сильные стороны градиентных бустингов в нейросетевой подход. Разберём практические сложности миграции на модели другой природы — смещение в обучении и офлайн-тестировании, а также способы сделать такой переход безопасным для бизнес-метрик. Больше материалов про ML по ссылке: https://t.me/+owyCvdge8WIyNTUy

Может ли агент улучшать сам себя и не деградировать?

Ouroboros — действующий эксперимент: кодинг-агент с конституцией, памятью и правом коммитить в собственный репозиторий. На открытых бенчмарках он впереди Codex, Claude Code и Cursor. В докладе — об архитектуре, петле самоулучшения и граблях, на которые мы наступили: от агентов, подсматривающих ответы, до правок, чуть не убивших систему. Больше материалов про ML по ссылке: https://t.me/+owyCvdge8WIyNTUy

Luna Line — платформа компьютерного зрения, в которой пользователь может пройти весь путь создания CV-решения: от разметки данных под конкретную задачу до запуска видеоаналитики на RTSP-потоке. Обучение модели запускается одной кнопкой и работает с данными из разных предметных областей, начиная от 50 изображений. В докладе я расскажу, как мы реализовали в среднем оптимальное обучение моделей классификации, детекции и сегментации, адаптирующееся под размер обучающей выборки. Сравню качество полученных моделей с решениями основных конкурентов и VLM. Также расскажу про конструктор видеоаналитик и разберу реальные кейсы, в которых уже применяются собранные с его помощью решения. Больше материал…

Использование облачных LLM по API часто недоступно из-за работы с чувствительными данными, поэтому модели приходится разворачивать внутри корпоративного контура. Собственный массовый инференс LLM не только страхует риски регуляторной политики, но и позволяет управлять SLA и приобретать необходимый экспертный опыт, чтобы эксплуатировать свои собственные дообученные и опенсорс-модели, недоступные у внешних вендоров. Однако у такого подхода есть жёсткое ограничение: затраты на собственный массовый LLM-инференс не должны превышать цену токенов у внешних провайдеров, иначе инхаус-решение становится экономически нецелесообразным. На практике добиться этого сложно. Без постоянной работы над внутре…

Поговорим о том, что такое РСЯ и как в ней взаимодействуют пользователь, площадка и рекламодатель.

Разберём, как ранжируются объявления, и посмотрим на рекомендательную систему в рекламе. Отдельно обсудим раннее связывание и нейроранжирование, а также роль DeepRanker как нейросетевой модели ранжирования. Затем перейдём к рекламному аукциону и разберёмся, зачем собирать баннеры в блоки. Также расскажем о внедрении HIMERA — ML-кандидат-генератора блоков — и об отказе от эвристик. А в завершение поговорим о развитии HIMERA, генеративных методах сбора блоков и о том, в каком направлении мы планируем развивать эти подходы, следуя тенденциям индустрии. Больше материалов про ML по ссылке: https://…

Диффузионные драфтеры — новое направление в спекулятивном декодировании, где блок кандидатов генерируется преимущественно параллельно, а не токен за токеном. Кратко рассмотрю развитие подходов к многотокенному предсказанию и разберу, как идеи параллельного драфтинга реализованы в DFlash и получили дальнейшее развитие в DSpark. Основное внимание уделю архитектурным инновациям в этих методах: non-causal drafting в DFlash, восстановлению зависимостей между токенами с помощью Markov head в DSpark, а также использованию confidence head для динамического выбора длины верификации. Отдельно расскажу о практической интеграции таких моделей в inference-системы. На примере реализации в vLLM и vLLM-Asc…

Computer-Use-агенты — модели, которые сами выполняют действия в компьютерной среде вместо пользователя, — постепенно перестают быть демо и становятся рабочей технологией. Но путь от «у нас есть VLM» до «агент выполняет задачи пользователя в проде» гораздо длиннее, чем кажется: живой браузер не детерминирован, эпизоды длинные, а рецептов multi-turn RL для мультимодальных агентов почти нет в открытом доступе. Расскажу, как мы построили и обучили VLM-агента, который выполняет задачи в реальном браузере: начинали с booking-сценария, сейчас масштабируемся до широкого спектра пользовательских задач. Разберём архитектуру пайплайна и объясним, почему одной VLM недостаточно. Покажу, как мы завели RL…

Потоковый перевод вы слышите в реальном времени, не дожидаясь завершения всей речи говорящего. Расскажу, как мы сделали такую систему для перевода с английского на русский: как учили потоковый ASR и переводную LLM, какие архитектуры сравнивали, почему в итоге остановились на выбранной и почему выдача по предложениям. Отдельно разберём главный компромисс любой стриминговой системы — задержку против качества. Откуда она берётся на каждом этапе пайплайна, как мы её измеряли и что помогло её сократить, не потеряв в метриках. Покажу, как мы сегментировали поток на предложения и что делали с нестабильностью гипотез на границах. И, конечно, поделюсь граблями, которые собрали по дороге. Больше мате…

Современные большие языковые модели обучаются на больших кластерах видеокарт. Эффективная организация параллельных вычислений и коммуникаций позволяет существенно ускорить процесс обучения, тем самым сэкономив огромное количество денег компании. В своём докладе я расскажу, как устроено современное распределенное обучение LLM, с какими проблемами мы сталкиваемся и какие подходы помогают с ними справиться. Поговорим о методах паралеллизации вычислений модели на многие GPU, в особенности — пайплайн паралеллизме, и о том, как мы придумали обобщение этого подхода, рассмотрев его как задачу планирования с ограничениями. Наш подход позволил существенно ускорить обучение больших языковых моделей. Я…

0:00:00 Начало

0:00:35 ИИ-ученый от Sakana

0:04:23 Глубокое рецензирование

0:13:21 Вычисления в памяти

0:17:58 Китайцы за ИИ-математику

0:23:13 Maia 300 от Microsoft

0:27:44 LLM-плесень

0:34:17 Вышла Muse Spark

0:36:43 ИИ-агенты выходят на связь

0:39:38 GLM для юристов

0:43:08 Жучок в OpenAI

0:45:42 Anthropic запретил клонирование

0:49:51 ASML не делает литографы

0:54:01 LLM знают про ваш рисеч

0:58:20 Character .ai — банкрот

1:01:28 Роботы, чтобы бить людей ИИ-саммари: Валентин Малых и Дмитрий Колодезев разбирают главные ИИ-события недели. Научный блок — ИИ и наука: Sakana представила ИИ-ученого, обсуждаем глубокое рецензирование и будущее научных публикаций, вычисления прямо в памяти и ки…

Спикер: Иван Кириллов, ML-инженер, Сбер Data Fest 2026: https://ods.ai/events/datafest2026

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Andrew Scull is a historian of psychiatry.

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Go to https://perplexity.ai/OUTLINE:(00:00) – Introduction(01:14) – Sponsors, Comments, and Reflections(08:56) – Programming with AI agents(24:14) – How software will change(33:30) – AI impact on open source(43:21) – Building Omarchy Linux d…

Khabib Nurmagomedov is one of the greatest fighters of all time, who retired from the UFC undefeated with a perfect 29-0 record.

We did this conversation entirely in Russian.

Both language audio tracks (and subtitles) are available on YouTube.

We worked hard to make it enjoyable to listen to, by carefully dubbing the translation using voice-cloning, as we’ve done for previous foreign-language podcasts.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep500-scSee below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

Gary Gallagher is a historian of the American Civil War.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep499-scSee below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

Go to https://plaud.ai/lexOUTLINE:(00:00) – Introduction(00:07) – Sponsors, Comments, and Reflections(08:36) – What caused the Civil War?

(18:33) – Slavery(46:07) – Lincoln(1:01:03) – Grant vs Lee(1:09:57) – Could the Civil War have been avoided?

(1:19:23) – The bloodiest war in US history(1:36:31) – How the Confederate Army could’ve won(1:57:05) – Key battles of the Civil War(2:20:07) – Best and Worst Presidents(2:34:06) – Robert E. Lee(2:53:40) – The…

Anthony Kaldellis is a historian of the Roman Empire and author of “The New Roman Empire”, a comprehensive history of the Byzantine Empire (Eastern Roman Empire).

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep498-scSee below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

Go to https://upwork.com/lexFin: AI agent for customer service.

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Don Lincoln is a particle physicist at Fermilab who has spent decades working at the frontiers of high energy physics.

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Jean-Baptiste Kempf is lead developer of VLC and president of VideoLAN.

Kieran Kunhya is a longtime FFmpeg contributor, codec engineer, and the person behind the now-infamous FFmpeg account on X.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep496-scSee below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

Go to https://larridin.comBlitzy: AI agent for large enterprise codebases.

Go to https://perplexity.ai/OUTLINE:(00:00) – Introduction(03:00) – Sponsors, Comments, and Reflections(10:48) – Weirdest things VLC opens(15:12) – How video playback works(24:33) – Video codecs and containers(35:20) – FFmpeg explained(56:20)…

Lars Brownworth is a historian, teacher, podcaster, and author specializing in Viking history, medieval Europe, and the Byzantine Empire.

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Go to https://perplexity.ai/OUTLINE:(00:00) – Introduction(01:03) – Sponsors, Comments, and Reflections(08:57) – The start of the Viking Age(18:50) – Viking military strategy, tactics & technology(32:33) – Ragnar Lothbrok(42:00) – The Grea…

Jensen Huang is the co-founder and CEO of NVIDIA, the world’s most valuable company and the engine powering the AI computing revolution.

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Go to https://quo.com/lexOUTLINE:(00:00) – Introduction(00:26) – Sponsors, Comments, and Reflections(06:34) – Extreme co-design and rack-scale engineering(09:20) – How Jensen runs NVIDIA(28:41) – AI scaling laws(43:41) – Biggest blockers to AI scaling laws(45:25) – Supply chain(47:20) – Memory(53:25) – Power…

Jeff Kaplan is a legendary Blizzard game designer of World of Warcraft and Overwatch, now preparing to launch a new game, The Legend of California, from his new studio Kintsugiyama – available to wishlist on Steam today, with alpha later in March.

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Rick Beato is a music educator, interviewer, producer, songwriter, and a true multi-instrument musician, playing guitar, bass, cello & piano.

His incredible YouTube channel celebrates great musicians & musical ideas, and helps millions of people fall in love with great music all over again.

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Peter Steinberger is the creator of OpenClaw, an open-source AI agent framework that’s the fastest-growing project in GitHub history.

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Go to https://drinkLMNT.com/lexOUTLINE:(00:00) – Introduction(03:51) – Sponsors, Comments, and Reflections(15:29) – OpenClaw origin story(18:48) – Mind-blowing moment(28:15) – Why OpenClaw went viral(32:12) – Self-modifying AI agent(36:57)…

Nathan Lambert and Sebastian Raschka are machine learning researchers, engineers, and educators.

Sebastian Raschka is the author of Build a Large Language Model (From Scratch) and Build a Reasoning Model (From Scratch).

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(25:11) – ChatGPT vs Claude vs Gemini vs Grok: Who is winning?

(36:11) – Best AI for coding(43:02) – Open Source vs Closed Source LLMs(54:41) – Transformers: Evolution of LLMs since 2019(1:02:38) – AI Scaling Laws: Are they dead or still holding?

Paul Rosolie is a naturalist, explorer, author of a new book titled Junglekeeper, and is someone who has dedicated his life to protecting the Amazon rainforest.

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Go to https://miro.com/MasterClass: Online classes from world-class experts.

Joel David Hamkins is a mathematician and philosopher specializing in set theory, the foundations of mathematics, and the nature of infinity, and he’s the #1 highest-rated user on MathOverflow.

He is also the author of several books, including Proof and the Art of Mathematics and Lectures on the Philosophy of Mathematics.

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Go to https://masterclass.com/lexpodOUTLINE:(00:00) – Introduction(01:58) – Sponsors, Comments, and Reflections(15:40) – Infinity & paradoxes(1:02:50) – Russell’s paradox(1:15:57) – Gödel’s…

NEVILLE: My research, kind of, spans the spectrum from theory to application.

But right now, if you’re doing work on AI systems, I think that industry is really the place to be.

What we really focus on is trying to push the frontier of behavior of these AI systems in realistic work environments.

NEVILLE: That’s, that’s a, that’s a big question.

But I think I’m very bullish on the research that’s happening.

Lab Director Chris White has worked on research challenges with real-world implications—from new approaches to wartime data analysis to tools for combating human trafficking. He talks to program manager Weishung Liu about the influences that led to the work and more.Show notes

Because I do think there’s a role for AI, a huge role for AI.

BURGER: Right, right.

BURGER: Right, right.

So I think that’s also something quite important here that, you know, AI can help facilitate.

And I think that’s not just applying AI to solve solutions through optimization but also thinking about this in an integrated way.

JANSSEN: Yeah, yeah, exactly.

TEEVAN: Yeah, yeah, yeah.

I’m curious what you have found particularly surprising about how people and organizations are leveraging AI right now.

And so I do like to picture a future of work where humans are flourishing with AI and where humans still get to do meaningful work.

And I’m very curious about how we can take advantage of AI and do more without running ourselves into the ground because we’re not AI, right?

And the question we’re going to discuss is, are machines intelligent?

No, no, that’s right, that’s right.

I mean, in some sense, you could potentially have a super intelligent system, right, that’s far more intelligent than anything else on the planet.

BURGER: Right, right.

At the same time, I think, you know, transformers are not intelligent in the way that a three-year-old is, right?

Join Microsoft’s Doug Burger and guests as they dig into the fundamental truths about AI and how it will reshape the future.

Technical advances are moving at such a rapid pace that it can be challenging to define the tomorrow we’re working toward.

In The Shape of Things to Come, Microsoft research leader Doug Burger and experts from across disciplines tease out the thorniest AI issues facing technologists, policymakers, business decision-makers, and other stakeholders today.

It’s important to understand what the emerging shapes are and how we should respond.” – Doug Burger, Technical Fellow and Corporate Vice President, Microsoft ResearchAbout Doug BurgerDoug Burger is a research leader in …

How do we design robots and autonomous vehicles that understand the unwritten rules of human behavior?

Kyle speaks with Cornell Tech professor Wendy Ju about implicit interaction, “Wizard of Oz” prototyping, and what studying pedestrians, self-driving cars, and even robotic furniture can teach us about designing technology that behaves the way people expect.

GuestWendy Ju: Wendy Ju is Professor of Information Science at the Jacobs Technion-Cornell Institute at Cornell Tech and inaugural faculty in Cornell’s new multi-college Design Tech department.

Prof. Ju has innovated numerous methods for early-stage prototyping of automated systems to understand how people will respond to systems …

The data we collect about ourselves can tell us a lot—but only if the technology collecting it actually fits into our lives.

Daniel Epstein explores personal informatics, from fitness trackers and food journals to baby tracking and AI, and explains why abandoning a tracking tool doesn’t necessarily mean it failed.

GuestDaniel Epstein: Daniel Epstein is an Associate Professor in the Department of Informatics at the UC Irvine, with a courtesy appointment in Computer Science.

His work in Human-Computer Interaction (HCI) examines how personal tracking technology can acknowledge and account for the realities of everyday life.

He received his Ph.D. in CSE from the University of Washington in …

In the final episode of our Recommender Systems season, we explore the growing questions of trust, manipulation, privacy, fairness, sustainability, and user control.

From fake reviews and shilling attacks to explainable recommendations and user-selected algorithms, we look at what happens when recommender systems must answer not only for what they recommend, but for the consequences of those choices.

GuestsKyle Polich: Kyle is the founder of Data Skeptic, a popular podcast about artificial intelligence, machine learning, and data science.

Robin Burke: Professor Robin Burke conducts research in personalized recommender systems, a field he helped found and develop.

Professor Burke is the auth…

In part two of the Data Skeptic Recommender Systems season finale, Kyle asks a deceptively difficult question: what should recommender systems actually optimize for?

Drawing on conversations from across the season, the episode explores engagement, filter bubbles, popularity bias, fairness, human curation, embeddings, and the growing role—and risks—of large language models in shaping what gets recommended to us.

Where did recommender systems come from, and how do we know when they're actually working? In part one of Data Skeptic's three-part Recommender Systems finale, Kyle traces the field from collaborative filtering and the Netflix Prize to matrix factorization and modern approaches, while exploring why accuracy alone can't capture what makes a recommendation useful, surprising, or meaningful.

Recommender systems influence nearly every aspect of our digital lives—but what does it mean for those systems to be fair? Robin Burke joins Data Skeptic to discuss the history of recommender systems, the limitations of optimizing purely for accuracy, and how ideas from social choice theory can help balance the needs of users, creators, and society. The conversation explores the future of recommendation algorithms and why fairness is a far more complex challenge than it first appears.

News recommendation algorithms influence far more than what stories we click—they can shape our understanding of the world. In this episode, Kyle Polich speaks with Andreea Iana about responsible AI, filter bubbles, multilingual news recommendation, and her open-source NewsRecLib framework for evaluating recommender systems. They explore why bigger models aren't always better and how future recommendation systems can balance personalization with diversity and societal impact.

What if you could simply tell a recommendation system what you want instead of relying on likes, dislikes, and watch history? Kyle Polich talks with Fuyuan Lyu about the DPR framework, which combines large language models and traditional recommender systems to give users direct control over recommendations through natural language. Together they explore how conversational interfaces could transform platforms like YouTube, TikTok, and news feeds while preserving the strengths of modern recommendation algorithms.

How can researchers audit recommendation systems when the algorithms are hidden from view? Hieu Le joins Kyle Polich to discuss Auto-Like, a reinforcement learning framework that systematically explores how platforms like TikTok personalize content feeds. The conversation covers recommendation transparency, black-box auditing, and the future of platform accountability.

Aaron Payne, an MBA student at Georgia Tech studying business analytics and a Senior Insights Analyst at Chick-fil-A, joins Kyle Polich to talk about turning analytics into decisions that matter. They unpack a real-world forecasting project with Comfama in Colombia, including messy data realities, interpretability tradeoffs, and why "data science for good" starts with the people impacted.

Kyle Polich sits down with Yashar Deldjoo, research scientist and Associate Professor at the Polytechnic University of Bari, to explore how recommender systems have evolved and why trustworthiness matters. They unpack key dimensions of responsible AI, including robustness to adversarial attacks, privacy, explainability, and fairness, and discuss how LLMs introduce new risks like hallucinations. The episode closes with a look at "agentic" recommender systems, where tools and memory shift recommendations from ranked lists to end-to-end task completion.

Goodreads star ratings can be misleading as measures of "book quality," and research from Hannes Rosenbusch suggests that for many professionally published books, differences between readers often matter more than differences between books. The episode also explores how to model reader preferences, why reviews often reveal more about the reviewer than the text, and how LLMs can aid computational literary research while still falling short of human editors in creative writing.

Ekaterina (Kat) Filadova from MIT EECS joins us to discuss strategic learning in recommender systems—what happens when users collectively coordinate to game recommendation algorithms. Kat's research reveals surprising findings: algorithmic "protest movements" can paradoxically help platforms by providing clearer preference signals, and the challenge of distinguishing coordinated behavior from bot activity is more complex than it appears. This episode explores the intersection of machine learning and game theory, examining what happens when your training data actively responds to your algorithm.

In Episode #1036, Jon Krohn digs into the question parents and teachers keep asking: does AI stop children from learning? Drawing on a wave of new studies, he shows the answer turns entirely on how AI is used, as a shortcut that outsources the thinking, or as a tutor that explains and gives feedback. Along the way: a 30,000-student natural experiment in China, a striking tutoring result from Turkey, and three practical takeaways that apply just as much to adults using AI at work. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1036⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sp…

In Episode #1035, Carly Taylor (Founder of Rebel Data Science) returns to join Jon Krohn for a wide-ranging conversation about leading engineering teams when the ground keeps moving. Carly shipped nine Call of Duty titles in roughly five years at Activision, where she led game security, and has since been a director of engineering at Microsoft and Field CTO for gaming at Databricks. She is also one of the most entertaining people in data on LinkedIn, where more than 200,000 people follow posts she cheerfully calls unhinged. In this episode, she explains why obsessing over the future produces analysis paralysis, which fundamentals never change, how to tell AI slop from AI used well, what the…

In ICYMI Episode #1034, Jon Krohn moves from what sits underneath AI systems to what it takes to live with them every day. Hear from Aishwarya Srinivasan, Luis Serrano, Katie Malone, Ish Shah and Dilani Kahawala, discussing why the bottleneck in shipping software has moved from execution to judgment, how a physicist recast attention in transformers as words bending space, why years of managing people may be the best preparation for managing AI agents, how one weekend side project burned through billions of tokens and which three problems are hardest to solve when a product has no user interface and never stops running. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com…

In Episode #1033, Prof. Jeff Hancock (Professor of Communication at Stanford) and Dr. Kate Niederhoffer (Chief Scientist at BetterUp) join Jon Krohn to explain the hidden cost of AI-generated work. A year ago they coined "workslop" in a Harvard Business Review article that went viral and landed the term among Merriam-Webster’s words of the year: content that masquerades as real work but quietly shifts the burden onto whoever receives it. Their research finds that 40% of workers have been sent workslop and 53% admit to producing it, at a cost running to millions of dollars a year for a large organisation. In this episode, they separate workslop from ordinary sloppy work, name the organisatio…

During their #sponsored discussion, Chief Data Officer at Salesforce Michael Andrew talks to Jon Krohn about what changes for a data team when its customers are AI agents as well as people. Listen to the episode to hear Michael Andrew talk about why agents need ten times more trusted data than humans, how Salesforce untrapped its own customer data with Data 360 and what practitioners should be learning to stay effective in the agentic era! Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1032⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.⁠⁠⁠ In this episode you will learn: (02:32) Ho…

In Episode #1031, Ish Shah and Tyler Cox (Distinguished Engineers in the Office of the CTO for Dell Technologies' client group) join Jon Krohn to work out why agentic AI bills are exploding and what can be done about it. Over one weekend Ish burned roughly two billion tokens on a side project, and that is the ordinary shape of agentic work now: agents spawn sub-agents, the pie of work grows, and cheaper tokens only invite more ambitious projects. Tyler runs a small Dell lab that pushes hundreds of millions of tokens a day through local hardware instead. In this episode, they define what makes a system agentic, explain how to read a Pareto curve when choosing models, work through the jagged …

During their #sponsored discussion, Senior Vice President Product Management AI and Metadata at Salesforce, Gaurav Pathak talks to Jon Krohn about why AI agents need well-labeled, high-quality data to deliver reliable answers in the enterprise. Listen to the episode to hear Gaurav Pathak talk about the difference between a “data brawl” and “garbage in, gospel out”, who the “sin eaters” of enterprise AI are and the three skills that matter most for AI engineers today! Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1030⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.⁠⁠⁠ In this episode …

In Episode #1029, Dr. Katie Malone (Host of Linear Digressions) joins Jon Krohn to explain how AI brought her podcast back from the dead. After nearly 300 episodes, Katie shut down Linear Digressions due to burnout, but better tools helped her relaunch it six years later. Along the way she has taught machine learning at Udacity and the University of Chicago and led the development of agentic AI platforms inside a company of tens of thousands of people. In this episode, she argues that people management and agent management are the same skill in different clothing, works through what AI slop and process slop are doing to organisations, describes the agent that now produces her show, and take…

In Episode #1028, Anton McGonnell (VP of Product at SambaNova) joins Jon Krohn to explain why the chips running most AI inference today were never designed for the job. Agentic AI has changed the computational profile of inference, with much larger inputs and far heavier caches feeding the token generation that follows, and that shift has exposed where GPU architecture struggles. SambaNova has raised over $2 billion to build an alternative, the reconfigurable dataflow unit, which lays a whole model out spatially across the chip rather than executing it kernel by kernel. In this episode, Anton discusses why the speed that matters is payback, and how speed and concurrency are what turn a fixe…

In Episode #1027, Dr. Dilani Kahawala (Co-Founder and CEO of Anna) joins Jon Krohn to explain what it takes to build an always-on AI assistant that busy parents will trust with their inboxes. Anna watches the email, school apps, WhatsApp messages and calendars flowing into a family's life and surfaces what matters, over text and voice, with barely any app to speak of. Dilani came to it by way of a Harvard physics PhD, McKinsey, and a decade of product leadership at Etsy, Meta and Atlassian, and says she has had to throw away most of what that decade taught her about how products get built. In this episode, she lays out the three hardest problems in building Anna, why the eval loop is the he…

In Episode #1026, Jon Krohn breaks down GPT-6 Astra, OpenAI’s new flagship that its president has floated as a possible marker of AGI. Jon covers what the model is, what it costs, its state-of-the-art results across computer use, coding, abstract reasoning and science and the safety story, which for this release is unusually intertwined with capability. He weighs the AGI claim against Anthropic’s Fable 5.1 and lands, as ever, in a measured middle. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1026⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this ep…

In Episode #1025, Dr. Luis Serrano (Founder of Serrano Academy) joins Jon Krohn to explain the paper he co-authored on the curved spacetime of transformer architectures, in which attention stops being a lookup table and becomes something closer to gravity: words bend the space around them, and the embedding of "bank" visibly curves toward "river" as it travels through the layers of the network. In this episode, he recreates Eddington’s 1919 eclipse experiment inside a transformer, draws the line between an LLM workflow and an actual agent, explains why agent evaluation is a step harder than evaluating an essay, and gives the cleanest account of GRPO you will hear. Additional materials: ⁠⁠⁠⁠…

In ICYMI Episode #1024, Jon Krohn tracks the gap between AI investment and AI return, from the technology side to the people side. Hear from Pete Johnson, Jerry Yurchisin, Priyanka Vergadia and Tristan Handy, discussing why four out of five organizations have the structures for AI success in place while only one in five sees the returns, which decisions should never be handed to a language model however confident it sounds, how to structure Claude skills so that your output stops being slop and why the semantic layer matters more, not less, now that analytics agents are the ones asking the questions. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1024⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ …

In Episode #1023, Aishwarya Srinivasan (Co-Founder of The Gen Academy) joins Jon Krohn to work out where a competitive moat comes from once anything you can build in ten minutes, somebody else can build in ten minutes too. Ash came to teaching through Illuminate AI, the mentorship community she started in 2020, and now trains senior engineers and leaders to ship agentic AI in production; she is blunt that vibe coding lowers the floor without touching the engineering judgment that production demands. In this episode, she explains what a whole-system eval covers that a model eval misses, traces reinforcement learning from the algorithm she patented at IBM to its resurgence in agentic fine tun…

In Episode #1022, Jon Krohn tackles the art of steering AI agents, deciding where your instructions should live so they get followed reliably without bloating every request. A sequel to Episode #1020 (where model size and effort set an agent’s horsepower), this one is about direction: the seven ways to deliver instructions, why a hook beats a prompt, the industry-wide agents.md standard, and three practical takeaways you can apply whatever your stack. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1022⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this …

Personal newsletter: https://defragzone.substack.com📩 Newsletter: https://datascienceathome.substack.com🎙 Podcast: Available on Spotify, Apple Podcasts, and more.

🐦 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-podcastNEW TO DATA SCIENCE AT HOME?

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What is “wrong” with the EU AI Act?

Check outshift.comCheck out Drift by Amethix and stay safe on potential EU AI Act violations.

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Send us mail at: [email protected]’t forget to like, subscribe, and hit the 🔔 for updates on the latest in AI and data science!

This and much more is covered in this 1st episode of AI and videogames.

NEW TO DATA SCIENCE AT HOME?

Data Science at Home explores the latest in AI, data science, and machine learning.

Whether you’re a data professional, tech enthusiast, or just curious about the field, our podcast delivers insights, interviews, and discussions.

Send us mail at: [email protected]’t forget to like, subscribe, and hit the 🔔 for updates on the latest in AI and data science!

Personal newsletter: https://defragzone.substack.com📩 Newsletter: https://datascienceathome.substack.com🎙 Podcast: Available on Spotify, Apple Podcasts, and more.

🐦 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-podcastNEW TO DATA SCIENCE AT HOME?

Data Science at Home explores the latest in AI, data science, and machine learning.

Whether you’re a data professional, tech enthusiast, or just curious about the field, our podcast delivers insights, interviews, and discussions.

Send us mail at:hello@…

Check outshift.comCheck out Drift by Amethix and stay safe on potential EU AI Act violations.

NEW TO DATA SCIENCE AT HOME?

Data Science at Home explores the latest in AI, data science, and machine learning.

Whether you’re a data professional, tech enthusiast, or just curious about the field, our podcast delivers insights, interviews, and discussions.

Send us mail at: [email protected]’t forget to like, subscribe, and hit the 🔔 for updates on the latest in AI and data science!

It’s a repeatable, engineered algorithm that starts with ideology, weaponizes identity, and manufactures conflict.

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Global data center investment now surpasses global oil supply spending.

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Data Science at Home explores the latest in AI, data science, and machine learning.

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This sort of operation is directly exploiting a core feature of internet social media platforms.

The main purpose of recommender systems is to recommend people the same items similar people show an interest in.

Some of the most common methods to implement recommender systems, use concepts such as cosine/correlation similarity, matrix factorization, neural autoencoders and sequence predictors.

As you say, recommender systems exist because the business model of social media platforms is to monetise attention.

F: So you are saying that this is not an accident: is this the basis of the optimisation of the recommender system?

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Data Science at Home explores the latest in AI, data science, and machine learning.

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What is the state of AI and videogames?

This and much more is covered in this 1st episode of AI and videogames.

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Data Science at Home explores the latest in AI, data science, and machine learning.

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Can NPCs in videogames leverage new LLM-based tech?

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Data Science at Home explores the latest in AI, data science, and machine learning.

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🐦 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-podcastSPONSORSThis episode is brought to you by Outshift, Cisco’s incubation engine.

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Data Science at Home explores the latest in AI, data science, and machine learning.

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Tech sovereignty takes 3 years and political will.

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Data Science at Home explores the latest in AI, data science, and machine learning.

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Apple just spent $2B on tech that reads your silent speech.

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Data Science at Home explores the latest in AI, data science, and machine learning.

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Send us mail at: [email protected]’t forget to like, subscribe, and hi…

Personal newsletter:https://defragzone.substack.com📩 Newsletter: https://datascienceathome.substack.com🎙 Podcast: Available on Spotify, Apple Podcasts, and more.

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Data Science at Home explores the latest in AI, data science, and machine learning.

Whether you’re a data professional, tech enthusiast, or just curious about the field, our podcast delivers insights, interviews…