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All four time-series foundation models scale, patch, and predict quantiles; the real split is how series share information.

The post Timestral, t0, Tabby, and Morpheus-2.0 Compared on GIFT-Eval appeared first on Towards Data Science.

Bellwether joins support tickets, store reviews, sales calls, and NPS to CRM accounts, then stress-tests the ranking on 6,693 synthetic signals.

The post How to Prioritize Product Feedback by Revenue at Risk appeared first on Towards Data Science.

Includes a worked example with 50 users and $110,000 in first-year cost, plus baselines, targets, and ownership rules for every AI initiative.

The post How to Measure AI Adoption, Performance, and ROI With 12 KPIs appeared first on Towards Data Science.

A side-by-side walkthrough of venv plus pip and uv init, uv add, uv run, and uv sync, with a lockfile that recreates the project on another machine.

The post How to Manage Python Environments with uv Instead of venv and pip appeared first on Towards Data Science.

AI Agents for Beginners, Part 2: The agent picks between three database tools, uses each result to choose its next call, and stops at an eight-turn circuit-breaker if it gets stuck.

The post Build Your First Multi-Tool AI Agent with Ollama and Python appeared first on Towards Data Science.

Three Python classes, an OpenRouter key, and gpt-4.1-mini give the agent a calculator, a stub web search, and conversation recall that survives restarts.

The post Give Your Python AI Agent Persistent Memory with One JSON File appeared first on Towards Data Science.

Configuration-driven reporting: one semantic model, one metric catalog, and a few lines of configuration per audience

The post How to Report for 100+ Clients With One Semantic Model appeared first on Towards Data Science.

We trained one model on methane. Without retraining, it also maps carbon monoxide, ammonia and nitrogen dioxide plumes, including the first automated CO detections from NASA’s EMIT.

The post How We Built NASA's Zero-Shot Detector for Gas Leaks from Space appeared first on Towards Data Science.

The internet of the future will look much different than the one today. Businesses are already transitioning.

The post How GEO, WebMCP, and Bot Paywalls Are Building the AI-Native Internet appeared first on Towards Data Science.

A tiny GPT memorized every fact I gave it, then couldn’t answer a single question that needed two of them. I tested the one-line fix from a recent paper, and it only half worked.

The post Why a Tiny GPT Can Know Two Facts and Still Not Connect Them appeared first on Towards Data Science.

Using 600,546 digitized newspaper pages to measure how suffrage coverage changed from 1890 to 1920

The post A Computational Analysis of Women’s Suffrage in Historical Newspapers appeared first on Towards Data Science.

Sometimes we need to calculate results based on contradictory filters. Without any action, these filters will lead to an empty result. Let's see how we can improve the data model to make it work.

The post How to Combine Contradictory Filters in DAX appeared first on Towards Data Science.

GPT-Live does the talking. A decision model tracks what’s confirmed and picks what to ask next.

The post Let Decision Models Guide Your Voice Assistant 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 …

TLDR: AI companies fear rising GPU rental prices while GPU operators fear falling ones, and GPU derivatives are an attempt to turn that disagreement into a market that makes compute costs and revenues predictable.

The company still has to secure capacity, and rental prices could move before the code is ready.

Across the table, a GPU operator has already purchased the machines and needs future rental income to recover its investment.

One business fears rising prices.

The ambition is to make compute expenses and revenues predictable enough to support better investment decisions.

TLDR: Useful AI increasingly depends on matching the model, its inputs, and its feedback to the job.

Mistral’s Timestral brings pretrained, zero-shot forecasting to numerical series, with quantile outputs that express uncertainty.

We close with a proposed design that pairs a numerical forecaster with a generalist agent, using clear, checkable interfaces between them.

That is what makes Google’s latest Gemini developments and Mistral’s Timestral interesting together.

My reading of the week is that useful AI increasingly depends on matching the model, its inputs, and its feedback to the job.

TLDR: A fixed dataset quietly decides what a model gets to practice, so the next step in recursive self-improvement is letting models generate their own curriculum.

A student who can solve every exercise in a workbook needs a different workbook.

Between those extremes lies the useful part of education: problems that connect what the student knows to what the student could learn next.

Absolute Zero and R-Zero let models generate the problems used in their own improvement.

An adaptive curriculum can respond to a learner’s changing capabilities, making the selection of future experience part of the learning process.

Microsoft’s September quarter produced $58 billion in operating cash flow against $39.4 billion of cash capital expenditure.

AI Lab: Mistral AISummary: This paper introduces Timestral, a family of four time series foundation models for probabilistic forecasting, from 5M to 209M parameters.

AI Lab: University of CambridgeSummary: This paper tackles how poorly LLMs gather information adaptively in experimental design tasks by building sequential Bayesian experimental design into the model.

AI Lab: Anthropic, Anthropic Fellows ProgramSummary: This paper tests whether experts defer to AI agents on judgment calls with no single correct answer.

AI Lab: OpenAISummary: This post describes OpenAI’s …

TL;DR Rhoda AI is betting that predicting short sensory futures can organize robot behavior, with a separate inverse dynamics model turning each predicted future into movements.

Its September scaling report ties better video prediction to stronger bearing-unpacking performance, and its October tactile report extends the loop to touch.

The open question is whether these company-reported results, from one task and embodiment, carry over to other workflows, repeated training runs, and sustained factory operation.

For a robot, however, recognizing that sequence leaves a rather inconvenient problem: which motors should move, and what happens when the bag snags?

Its bet is that predicting short s…

TLDR: AI companies pay for GPUs, buildings, and electricity long before revenue covers the bill, so credit bridges the gap, and lenders are becoming choosier about which promises they finance.

It then uses Meta’s mix of joint-venture and equity funding and SpaceX’s xAI GPU lease commitment as two live tests.

Companies pay for GPUs, buildings and electricity long before they collect enough revenue to cover the bill.

When lenders demand higher returns or more protection, the bridge gets narrower.

The question is how much uncertainty lenders will tolerate between buying a chip and getting their money back.

An AI demo compresses a small industrial operation into a blinking cursor.

DeepSeek released DeepInfer, Google introduced Agent Compute Engine, and NVIDIA used GTC Berlin to expand its infrastructure for agents.

My read: the next useful gains in AI will increasingly depend on how efficiently we run models, preserve their working environments, and control their actions.

Intelligence becomes a product only when you can afford to run it repeatedly and trust the environment in which it operates.

DeepSeek reveals how the inference engine works

TLDR: A model can use new knowledge in context and still lose it the moment the session ends, so persistent learning depends on deciding how experience should change the model, and who makes that decision.

We separate remembering from learning across context, external memory, and parameter updates, look at why a training example is really a choice about computation, and at what LoRA does and doesn’t solve.

Work such as SEAL gives the model a role in constructing its own updates, while Nested Learning examines how adaptation can operate across different timescales.

Together, they provide useful ways to think about persistent learning and the controls that make it beneficial.

Remembering and …

📝 Editorial: Last Week in AI: Cheap Intelligence, Expensive AmbitionsArtificial intelligence is getting more efficient, which apparently means everyone needs to spend more money on it.

This week, DeepSeek released inference software, NVIDIA offered faster AI factory construction, Meta and SpaceX reported enormous investments, and Legora collected another $550 million to help lawyers work.

Quarterly revenue rose 26% to $64.6 billion; management credited AI with improving advertising through better engagement and recommendations.

Its AI division, now called SpaceXSI, generated $7.56 billion in quarterly revenue, exceeding Starlink’s $5.25 billion.

You can run a rocket company and discover tha…

TL;DR Generalist is betting that large-scale physical pretraining can give robots a reusable foundation, so teaching a new task takes a short demonstration instead of a bespoke dataset.

Across GEN-0, GEN-1, and GEN-1.5, its models move from scaling evidence to roughly 99% success on selected tasks to in-context physical prompting.

The open question is whether those company-reported gains hold up across complete, long-horizon jobs in real deployments.

Its central bet is that enough physical experience can produce a reusable foundation for robot behavior.

The useful question is how much teaching a new task requires once that foundation exists.

TLDR: AI environments are becoming a core ingredient of model capability: executable worlds that generate trajectories and feedback, not just exams.

This essay traces their path from Atari, OpenAI Gym, and AlphaZero through MuJoCo to the RLVR-driven post-training behind Tulu 3 and DeepSeek-R1, then surveys the new generation of agent environments, including SWE-Gym, WebArena, OSWorld, TheAgentCompany, Tau2-bench, and AgentMercury.

This is a useful way to think about AI environments.

My thesis is that environments are becoming a major ingredient in AI capability.

They are generators of training data: executable worlds that produce trajectories and feedback.

Last week, the AI industry advanced two different ideas about what a useful model should do.

Meta wants an agent that remembers your constraints and keeps working when the first attempt fails.

TypeSafe and Liquid want models that make a bounded decision without writing an essay about it.

A model that spends minutes repairing a codebase might be valuable.

A model that spends minutes deciding which queue receives a support ticket has confused its job description.

TLDR: Most AI systems get better at tasks without getting any better at learning.

DeepMind’s DiscoRL goes after that machinery itself: a neural network represents the update rule, and a population of agents supplies the experience used to discover rules that produce better learners.

An AI system can become a better chess player without becoming any better at learning chess.

A neural network represents an update rule, and a population of agents supplies experience for discovering rules that produce better learners.

This is a useful entry point into a deeper idea: intelligence can be applied to the procedure that creates intelligence.

📝 Editorial: Last Week in AI: The Future Has Payment TermsAn AI model can be several businesses at once.

This week it released Muse Wonder and Muse Wonder Full for agentic tasks, with features it calls Active Memory and Active Focus: remembering relevant information, persisting through tasks, and recovering from errors.

It also released Muse Spark Open, an open-weight multimodal model with 816 billion parameters, 42 billion active per token.

It ships under the Muse Model License, which allows free use for many developers with limited commercial terms.

Jev 1.14TypeSafe AI released Jev 1.14.0 in preview, with small improvements across the board and the biggest gains in multilingual support.

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.

Unsloth’s FastDecisionModel converts a pretrained LLM into a decision model by adding a specialized decision head.

json : 100 % 76.2k / 76.2k [ 00 : 00 < 00 : 00 , 6.13MB / s ] Loading weights : 100 % 723 / 723 [ 00 : 35 < 00 : 00 , 86.48it / s , Materializing param = model .

weight ] Download complete : 0.00B Reconstruction complete : 0.00B / 0.00B Fetching 13 files : 100 % 13 / 13 [ 00 : 00 < 00 : 00 , 415.24it / s ]Step 3: Configuring LoRARather than updating all the model’s parameters, we’ll use LoRA.

Testing Our Decision Model with New ExamplesLet’s see how the trained model behaves when we give it a new user message.

save_pretrained ( "qwen-clinc150-decision" )If you want a merged mod…

Share Post ShareIn this article, you will learn how to match an LLM inference optimization technique to the actual bottleneck in your workload by working through a six-question decision tree.

Topics we will cover include:What each major inference optimization technique, from quantization to prefill-decode disaggregation, assumes about your workload.

What Each Inference Optimization Technique AssumesLLM inference has many optimization techniques, but each one addresses a different type of bottleneck.

Wrapping UpChoosing an inference optimization technique starts with identifying your bottleneck.

If you’re interested in learning more, you may find The Roadmap to Mastering LLM Inference Optimi…

The Building Blocks of Agent MemoryBefore looking at memory frameworks and implementation patterns, it helps to define what agent memory needs to store.

Agent Memory Design StrategiesKnowing the different memory types is only the starting point.

For a broader look at the agent memory framework landscape, see our agent memory frameworks round-up.

How Memory Frameworks DifferThese frameworks sit at different points between flat, text-based memory and structured knowledge graphs.

This article covered the main memory types, practical design patterns, common failure modes, and the architectural tradeoffs across different memory frameworks.

Context Window Accumulation (The Append-Only Trap)The most widespread mistake in agent design is treating the prompt array as a database.

Static Tool Schema BloatMost agent frameworks define tool schemas once and inject all of them on every LLM call.

Keep in mind the tension this creates with section four: dynamically pruning tool schemas moves the cache boundary and reduces cache-hit rates.

The catch: Small models are brittle when confronted with complex or overlapping tool schemas.

Each copy costs tokens, and when one copy is updated while another isn’t, the agent must reconcile conflicting versions.

Topics we will cover include:How frozen embeddings work as a lightweight alternative to full model fine-tuning, and when this approach is appropriate.

This article presents a practical comparison of fine-tuning a local LLM with LoRA (Low-Rank Adaptation, an algorithm for efficient weight updating) against training only a downstream scikit-learn classifier on frozen embeddings.

pad_token_idStrategy A: Frozen Embeddings & Scikit-learnWe start by trying out the alternative to fine-tuning our model.

Attaching LoRA Adapters to Base Model...") model = prepare_model_for_kbit_training(model) peft_config = LoraConfig( task_type="SEQ_CLS", r=16, lora_alpha=32, lora_dropout=0.05, target_modules=["q_pr…

How to build custom context managers using a class, the @contextmanager decorator, and standard library tools like TemporaryDirectory and patch.dict() .

INFO )Most Python developers meet context managers through with open(path) as f , learn that the file gets closed, and stop there.

Setup happens at the top, cleanup at the bottom, and the cleanup runs whether the middle succeeded or threw.

Whatever __enter__() returned is bound to the name after as , the block runs, and then __exit__() is called.

The previous level is captured before the change, the change is made, the block runs at the yield , and the finally puts the level back.

Share Post ShareIn this article, you will learn how to build a robust JSON serialization pipeline in Python that handles custom data types, graceful error recovery, and schema migrations.

Topics we will cover include:How to define a custom JSON encoder to handle Python types like datetime , set , Path , and Enum .

Before going deep into the three-step solution, let’s look at the root problem: JSON limitations when encountering certain data types like datetime , set , Path , or Enum .

JSONEncoder ) : def default ( self , obj ) : # Handling Datetimes if isinstance ( obj , datetime ) : return { "__type__" : "datetime" , "value" : obj .

These steps can be combined to form a robust serialization…

How the draft-and-verify loop works and why it preserves the target model’s output distribution exactly.

Modern variants of speculative decoding — including Medusa, Eagle, and Prompt Lookup Decoding — and the practical trade-offs involved in enabling the technique in production.

Speculative decoding sidesteps this problem without changing the model, retraining on new data, or sacrificing output quality.

With speculative decoding, the target model’s weights are loaded once per speculative step and then applied across multiple accepted tokens.

The actual speedup depends primarily on the acceptance rate, meaning how often the draft model’s candidates pass the target model’s verification.

Share Post ShareIn this article, you will learn how to build a multi-modal RAG pipeline in Python that retrieves and reasons over both text and images extracted from complex documents.

documents import Document embeddings = OpenAIEmbeddings ( model = "text-embedding-3-small" ) vectorstore = Chroma ( embedding_function = embeddings ) docstore = InMemoryStore ( ) retriever = MultiVectorRetriever ( vectorstore = vectorstore , docstore = docstore ) text_ids = [ str ( uuid .

Each ID links an embedded summary in the vector store to its corresponding raw Document in the document store.

append ( { "type" : "text" , "text" : doc .

The four stages are: parse with unstructured , summarize images with …

Topics we will cover include:What pgvector and dedicated vector databases are, and how they differ architecturally.

Six months later, a vendor pitch lands in their inbox insisting they need to migrate to a “real” vector database before they hit a wall.

What a Dedicated Vector Database IsA dedicated vector database is a separate, purpose-built service, run as its own process with its own storage engine, designed from the ground up around vector search rather than having it added onto a general-purpose database.

models import PointStruct batch_size = 1000 for start in range ( 0 , NUM_VECTORS , batch_size ) : batch = vectors [ start : start + batch_size ] points = [ PointStruct ( id = start + …

Topics we will cover include:How to build a validated attribute descriptor that enforces type and range constraints on class attributes.

We don’t intend to go that deep in this article, but we will show you how to use Python to build three types of controlled attributes through descriptors: computed, validated, and lazily loaded attributes.

private_name = f "_{name}" def __get__ ( self , obj , objtype = None ) : if obj is None : return self return getattr ( obj , self .

Building Computed Attributes through DescriptorsComputed attribute descriptors are useful in situations like dynamically calculating an attribute’s value based on another object’s state.

Wrapping UpWe have shown how to make …

Share Post ShareIn this article, you will learn how Python’s iterators and generators can be used to build memory-efficient data processing pipelines that handle large files without loading them into memory all at once.

Iterators and Generators for Memory-Efficient ProcessingThe script that summarizes the payments log ran fine for a year.

Iterators, generator functions, and generator expressions let a pipeline pull one record through every stage and release it before the next one arrives.

*)$" ) def read_lines ( paths ) : for path in paths : with open ( path , encoding = "utf-8" ) as f : yield from f def parse_records ( lines ) : for line in lines : match = LINE .

get ( "latency_ms" , 0 ) )…

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 , …

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…

Today, we’re rolling out Gemini Omni 1.2 Flash, and making it generally available across the Gemini API and Gemini for business.

Gemini Omni 1.2 Flash introduces native 15-second video creation, multi-turn conversational editing capabilities, and enhanced reference following.

We have dropped the price 30% from Gemini Omni 1.1 making it available at $0.07 per second for 720p video.

Longer clips up to 15 secondsYou can now generate and edit videos natively up to 15 seconds long, up from 10 seconds with Omni 1.1.

Better reference adherenceYou can guide generation combining character, product, or style images, short motion clips (up to three seconds), and audio cues — and Omni 1.2 Flash holds o…

Advanced cheminformatics systems must be able to unify these sources and deal with the many ways errors can arise in them.

To let scientists focus on hypotheses rather than data wrangling, we built a suite of Cheminformatics Skills for Google Antigravity, turning the surface into a collaborative science workbench with full access to local tooling for both computational chemists and experimental bench chemists.

Two substructures linked by a single rotatable bond can twist into many conformations, but adding a second bond to tie them together sharply limits how they can move in three dimensional space.

In crizotinib, there are five major rotatable bonds.

Lorlatinib is a third-generation, smal…

PlanAssist has shown it can cut the time planning officers spend assessing householder applications by up to 58%.

PlanAssist - an AI planning tool that handles the heavy lifting of routine applications so officers can focus their expertise where it matters most - is to be made available to an additional 12 local planning authorities across England.

In early pilot testing across Barnet, Camden, and Dorset, PlanAssist showed it could bring the average time a planning officer spent on householder assessment tasks down by 58%.

Designed to support human judgement, the tool speeds up routine checks while ensuring planning officers retain the final say on every application.

Built with Gemini, Plan…

Today, we’re announcing the next frontier model: Gemini 4 Carbon, our strongest model with state-of-the-art cybersecurity and long horizon capabilities.

Building on Gemini 4 Argon, which was launched two weeks ago, Carbon illustrates the pace at which we’re pushing the frontier forward.

Our researchers and models are working hand in hand to advance capabilities faster, while strengthening our safeguards in tandem.

We're taking a deliberate approach to rollout and are actively engaged in the U.S. government’s voluntary process for pre-release model testing.

Gemini 4 Carbon will launch at the same introductory price as Argon at $2 per million input tokens and $10 per million output tokens, wi…

To understand how interactive world models can support real-world workflows—and where they still need to grow—we’ve been working with cohorts of trusted testers across creative industries, journalism, education, gaming, and engineering.

: Creatives and technologists are testing Genie 4 as a rapid "draft mode" for pre-visualization and ideation.

Simulation: Aside from our robotics research, enterprise partners across industries are evaluating Genie 4 as a tool for rapid "zero-to-one" scenario generation, and identifying gaps to further improve Genie to best fulfill that simulation need.

Future Work: Towards hybrid world modelsA promising path for future progress lies in hybrid world mo…

Alongside it, we're sharing an early time series that tracks how these forests changed each year from 2017 to 2025, including where natural forests were replaced by plantations or farms.

Why "tree cover" isn't enoughAll forests store carbon and, together, forests are home to most of the world's land-based plant and animal species.

That's a challenge for those trying to protect natural forests.

Similarly, wood products, such as furniture, must also be free of forest degradation: they can't come from primary forests that have since been converted into plantation or planted forests, or from naturally regenerating forests that have since been converted into plantation forests.

Last year, we joi…

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…

AI is rapidly transforming video production at virtually every step of the process, from ideation and storyboarding to filming, editing, and release.

Rendering latency: Standard AI video rendering takes four to five minutes per short clip.

We run our API, AI Director, and background workers as Java services on Cloud Run, which scales with request volume.

In order to address message routing challenges, we decoupled job execution using Google Cloud Pub/Sub as our central event bus.

To minimize latency, media assets are stored in Google Cloud Storage and delivered via Cloud CDN.

Choosing between connectors and data agentsIn addition to using direct Data Cloud connectors to query data in plain language, you can also use Conversational Analytics agents to enable these experiences.

While Data Cloud connectors allow you to provide exploratory and ad hoc analysis for users, conversational analytics agents are scoped agents that are built and certified directly by data teams within your organization.

These can be built across Data Cloud products such as BigQuery, Looker, and operational databases including Spanner, AlloyDB, and Cloud SQL.

These data agents can also be published in Gemini for consumption or made available via API for building your own conversational apps …

Today, Gemini Omni 1.2 Flash is generally available on Google Cloud.

Here's what's new:A lower price: $0.07 per second of 720p video, 30% less than Gemini Omni 1.1 Flash, with similar savings at other resolutions.

Gemini Omni 1.2 Flash is ready for productionGemini Omni 1.2 Flash is production-ready and backed by Google Cloud’s enterprise-grade infrastructure and security, offering Provisioned Throughput.

Video creation inside the tools people already useDesign and creative platforms are building Gemini Omni 1.2 Flash directly into their products, so their users can create and edit videos where they already work.

Here’s how customers are already using Gemini Omni 1.2 Flash:

To solve these long-standing challenges, fintech innovator Nammu21 partnered with Google Cloud on a new proof-of-concept.

Using its AI-powered platform to structure legal agreements and instantiate them as smart contracts on the Google Cloud Universal Ledger (GCUL), Nammu21 automates the entire loan lifecycle, paving the way for improved settlement times and greater market efficiency.

Digitizing the loan marketWhile loan markets provide billions in capital to businesses worldwide, their underlying infrastructure has not kept pace with the speed of digital finance.

Nammu21 transforms bespoke credit documents into programmable assets at scale by rendering each facility's governing terms as a …

Today, the Google AI & Infrastructure team is announcing Physical-Engineering's Last Exam (PLE), a new benchmark and open competition, developed in collaboration with Mercor, and built to test a frontier model’s ability to complete tasks in mechanical, electrical, civil and structural engineering.

To help the research community measure and improve how agents handle physical engineering work, we are releasing 29 public tasks, their grading tools, and an open competition leaderboard at ple-bench.com.

Agents' Last Exam measures a model’s ability to complete digital professional work.

Physical-Engineering's Last Exam (PLE) measures a model’s ability to complete work that helps build the physica…

Today, we’re announcing that spend caps on Google Cloud Budgets are generally available.

How the new spend caps can help your businessWe have significantly enhanced the usability and power of spend caps in three ways:1.

Automation through spend caps API: Managing guardrails manually across hundreds of projects is a major operational challenge.

To address this, we have launched the spend caps API, which can be accessed from the existing Cloud Billing Budgets API.

Real-time visibility with live cost reports: Spend caps for AI services trigger within minutes of usage using estimated, live costs.

At the core of this breakthrough is Gemini Robotics Embodied Reasoning 2 (ER 2), a specialized Gemini model that serves as a robot’s high-level brain.

Today, we are bringing this same frontier embodied reasoning model to enterprise production environments: Gemini Robotics ER 2 is now available in public preview on the Gemini platform.

Train smart : Use Gemini Robotics ER 2's spatial pointing, moment finding, and progress classification to curate and label demonstration videos in Cloud Storage and BigQuery.

Operate easily: Deploy Gemini Robotics ER 2 workflows on the Gemini platform with enterprise IAM, security controls, and built-in function calling.

How customers are building with Gemini …

Every day, countless financial transactions are interrupted by slow, expensive, and inflexible conventional payment systems.

As a result, public commercial stablecoins like Tether, USDC, and PayPal USD have become increasingly popular by making it easy to move money efficiently with instant, 24/7 blockchain settlement.

Rather than channeling public stablecoins, GCUL provides the secure infrastructure banks need to issue and settle tokenized deposits.

Pain points in today's payments landscapeIn recent years, there’s been a marked proliferation of tokenized payment methods.

Still, commercial banks have been reluctant to offer popular reserve-backed stablecoins from permissionless blockchains …

Rather than operating as isolated utilities, IAM Helper Agent Skills guide agents through the cloud access lifecycle.

IAM Helper Agent Skills are designed for maximum token and cost efficiency, allowing agents to execute multi-step administrative operations autonomously.

While IAM Helper Agent Skills supply the high-level workflow logic, they are all based on a common set of foundational MCP Tools.

Powered by Google Cloud IAM MCP toolsGoogle Cloud IAM Helper MCP tools provide the backend connection through atomic, read-only functions exposed via Google-managed MCP servers across core IAM endpoints (Policy Troubleshooter, Policy Analyzer, Policy Simulator, and IAM v1/v2 APIs).

Start building…

At Google Cloud, we are committed to giving developers model choice and architectural flexibility to run workloads however they need.

Today, we’re announcing the general availability of an Anthropic-managed deployment option for Claude Models on Google Cloud.

Anthropic operates the stack as the data processor on Google Cloud infrastructure under Anthropic’s terms of service, while organizations authenticate with Google Cloud IAM, maintain Cloud Audit Logging, and consolidate spend under their existing Google Cloud billing and eligible commitments.

Claude Models on Google Cloud (Google managed)Continues to deliver day-zero access to the latest Claude models with strict governance, compliance…

Today, we are expanding that foundation by launching OpenAI models in Google Cloud’s model garden.

Because OpenAI models are transacted directly through Google Cloud Marketplace, all usage appears on a single Google Cloud invoice, draws down existing cloud commitments, and counts toward Flexible Savings Plans (FSP) across model providers.

Isolated access boundaries : OpenAI models run on dedicated Google Cloud infrastructure with Confidential Compute coming soon.

: OpenAI models run on dedicated Google Cloud infrastructure with Confidential Compute coming soon.

Coming soon, OpenAI models will be available in Google Cloud’s Gemini for business and Google Antigravity, providing enterprise cus…

In keeping with Google Cloud's commitment to an open ecosystem and to give developers the right model for every job, Claude Opus 5.5 and Claude Sonnet 5.5 are now available in Google Antigravity for Google Cloud customers.

Claude Opus 5.5 is built for complex work while Claude Sonnet 5.5 is a faster, lower-cost complement.

Once enabled by an administrator, developers can select Claude Opus 5.5 and Sonnet 5.5 directly from the model picker.

Administrators can also select Google’s Gemini models, including Gemini 3.8 Flash and Gemini 4 Argon.

Subagents configured with model: flash offload high-speed search, classification, and triage work to Gemini Flash models.

Partner agents: Skills and connectors come together in agents, including pre-built agents from public sector software providers that agencies can deploy right away.

System integrators and public sector ISVs help agencies customize Gemini, connect it to what they already have, and scale it without lock-in.

Where federal and state rules both apply, Gemini shows which one governs to empower agency decision makers.

Salesforce: Connects constituent case records, service requests, and interaction history, letting agents work a case in context without exporting it.

Connects constituent case records, service requests, and interaction history, letting agents work a case in context without exporting …

Imagining traditional gearheads getting their hands on software-defined vehicles is an amusing thought experiment.

With Horizon, our agentic innovation platform for developing software-defined vehicles, our vision is to help manufacturers avoid these inefficiencies by working with partners like RemotiveLabs.

Android Automotive OS (AAOS) runs inside Google’s official virtual testing environment for Android, Cuttlefish, which itself runs comfortably on Arm-based Google Cloud instances.

That’s because these are the parts that have traditionally been wired to hardware, and as a result are the biggest blocker to fully software-defined vehicles.

For those building software-defined vehicles whose …

Just like human reviewers, they can carry biases:Ultimately, LLM judges are not objective arbiters of truth.

They are subjective models with their own distinct biases.

Three helpful ways to audit your LLM judgesPillar 1: Measure internal stabilityBefore you can trust an AI judge’s score, you need to know that it isn't just guessing.

To prove your LLM judge is catching real errors, you need chance-corrected metrics.

Instead of asking one LLM judge to score an entire response, assign a specialized judge to evaluate a single criteria, like factual accuracy or tone.

How PDD Kit worksPDD Kit is to formal methods roughly what Spec Kit (opens in new tab) is to Spec-Driven Development.

PDD Kit makes that line explicit and asks you to sign off on each piece.

The capstone: a full-stack web appTo show PDD Kit integrating with existing codebases, we built Pet Store, a full-stack web app written entirely in Rust (opens in new tab).

PDD Kit recommends starting with the most sensitive parts of your application and incrementally verifying your application.

For login, PDD Kit drew a ticket desk (Figure 3).

WALKER: So I have a definite fascination with the rules, [LAUGHTER] but the thing you have to understand about the rules is that rules are a social construct.

LIU: OK. I’m going to have to ask you then about a game that we played recently called No Vehicles in the Park (opens in new tab).

WALKER: Well, there’s, there’s two ways to answer that.

So there’s, there’s this, sort of, blurring of offense and defense where computer security capabilities at their most atomic basic level aren’t offensive or defensive.

[LAUGHTER]LIU: OK, OK.

At a glance Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances.

Synthetic enterprise environmentsEvaluating enterprise AI requires test data that captures the full complexity of real organizations: reporting structures, project histories, communication patterns, and domain specific workflows.

But real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances.

Grounding in real world signalsTo ensure our synthetic environments are realistic, we grounded them …

Whether you read, watch, or listen to it, Research Focus is your source for research news at Microsoft.

Developed by the Microsoft Research AI for Science team, BioEmu2 builds on Biomolecular Emulator, a deep-learning model designed to help scientists better understand how proteins work.

Go to the BioEmu2 Microsoft Research GitHub repository (opens in new tab) to explore the model and learn more.

Explore the latest research, discover the people behind it, and keep tabs on the ideas shaping what’s next by following Microsoft Research on major social platforms.

We’ll see you in the next Microsoft Research Focus.

AI agents make it easy to analyze data and generate reports with data visualizations.

This requires agents to be able to create interactive data visualizations that communicate information while letting readers explore beyond the author’s chosen view, ideally with the quality of interactive stories in sophisticated data journalism.

Interactive visualizations are challenging enough that expressive interactive charts introduce poor layouts or broken interactions easily, and we risk introducing new problems when trying to fix them.

Listen now Opens in a new tabFlint-interactiveFlint-interactive (opens in new tab) is a language for creating interactive data visualizations on top of Flint.

Build…

Researchers resolved the Kannan-Lovász-Simonovits (KLS) conjecture, a 31-year-old problem widely regarded as one of the central open questions in high-dimensional geometry, with implications for statistics, optimization, algorithms, and computer science.

This would establish a dimension-free geometric principle with important consequences for high-dimensional geometry, probability, and theoretical computer science.

Now, two researchers, including Xinzhi Zhang from the Machine Learning and Optimization group at Microsoft Research, have proved the KLS conjecture (opens in new tab).

Foundry Labs Opens in a new tabThe importance of the KLS conjecture was recognized across the field long before …

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).

“Most of today’s deep-learning models require more data than traditional methods,” says Zalte.

Some successful models use extensive data from the published literature in chemical journals, he says.

Comparing its performance across 58 test cases, “we see that our model comes out on top.”Green adds, “The CheMeleon foundation model drastically reduces the number of molecules that need to be tested experimentally to accurately predict the properties of new molecules.

This is a huge advantage, since synthesizing and testing a large number of molecules is slow and expensive."

Besides the MIT members, the team also included Jochen Sieg, Christian Feldmann, and Miriam Mathea, researchers at BASF SE…

UQ-DAAAM integrates uncertainty quantification into the DAAAM framework.

It does this using a heuristic, or simple rule, that determines which additional viewpoints will reduce the robot’s uncertainty about the object.

This refinement step is able to resolve ambiguities for about 92 percent of the objects in the robot’s environment, while only requiring two additional captions per object.

Next, the researchers want to use UQ-DAAAM to guide different types of robots, like mobile robotic manipulators, and apply their uncertainty quantification techniques to other domains.

They are also exploring uncertainty quantification in systems that predict the future state of a robot’s environment.

Sponsored by the Northeast Microelectronics Coalition (NEMC) hub, the meeting focused on sharing approaches to lab management and building connections among facilities.

Universities and research organizations operate specialized facilities, each with unique equipment, expertise, and capabilities.

The result is a compilation built from the ground truths of actively-used facility management systems.

“The focus is to make the distributed infrastructure supporting research more discoverable and usable,” Osherov said.

Building infrastructure that can changeThe approach reflects a broader philosophy: Research infrastructure needs to evolve as research itself changes.

From keynote speakers to panelists, seed grant innovators to startup entrepreneurs, the conference featured exemplars in all these categories.

“We can absolutely develop new technologies that provide the exact same service, but at zero carbon,” he said.

“It’s an existential requirement.”Early-stage research and basic scienceThe conference also offered a bounty of promising early research on technologies and approaches for cutting carbon emissions.

A poster session revealed the next generation of energy researchers in such fields as chemistry, architecture, aeronautics and astronautics, and computer science.

The conference highlighted the importance of fundamental science in feeding the pipe…

Regina Barzilay, MIT’s School of Engineering Distinguished Professor of AI and Health, and James Collins, MIT’s Termeer Professor of Medical Engineering and Science, are the recipients of the 2026 Pritzker Prize for AI in Science Research Excellence.

“Historically, antibiotic discovery has depended on laborious, trial-and-error strategies,” says Collins.

With generative AI, we're now seeing a shift from AI being used as a tool for discovery to AI being used as a design tool.

This shift opens new frontiers in antibiotic discovery that could bring us the next generation of lifesaving drugs.”In Barzilay’s view, “despite significant progress in molecular modeling, we are still in early stages o…

Five MIT faculty members and two additional alumni were recently named to the 2026 cohort of AI2050 Fellows.

His research focuses on the mathematical foundations of deep learning, with an emphasis on optimization dynamics and representation learning.

She received her PhD in biology from Harvard University and BS in computer science from Stanford University.

Raghavan received his bachelor’s degree in electrical engineering and computer science from the University of California at Berkeley, and PhD from Cornell University.

MIT alumni Irene Chen PhD '22 and Allison Z. Koenecke '14 as well as former postdoc Juan Carlos Perdomo were also named AI2050 Early Career Fellows.

Podimata: People are increasingly using AI chatbots to find and make sense of political and election-related information.

Q: What sorts of questions and AI responses are you tracking through the dashboard?

Q: What do you wish voters across the country knew about AI chatbots when it comes to searching for election information?

Q: Professor Berinsky, your research has also shown that in some cases AI chatbots can be harnessed to help refute misinformation.

Instead, we hope to shed some light onto one part of this transformation: what information major AI systems are actually giving people about an election as it unfolds.

A: The revolution in information technology and digital transformation over the past 30 years has made infrastructure systems more interconnected.

Infrastructure systems that were once managed separately are increasingly dependent on one another.

Infrastructure value chain is too interconnected, and the challenges are too large.

This is particularly important because the biggest opportunities for new infrastructure development are not necessarily in the United States or other countries with mature infrastructure systems.

We're trying to create an ecosystem rather than just a forum, a mechanism for continuing the conversation among the different stakeholders across the infrastructure value c…

Now I really wanted to make computers that could understand sound,” he says.

Tuning in to unexplored domainsAt that time, many students in the Media Lab were using signal processing techniques to help machines handle sound.

He found that training machine-learning models to understand sound offered advantages that couldn’t be attained with traditional signal processing techniques.

“Over time, it became really clear to me that we need to teach computers how to listen.

One project in this area is enabling machines to understand how to listen to music, so they can provide constructive feedback to musicians.

As she looked for an educational opportunity to engage more deeply with AI, MIT quickly emerged as a natural choice.

It also provided her with invaluable professional relationships and the confidence to develop multiple AI solutions for Verizon.

Putting those news skills and relationships to work, she co-wrote, with some of her peers, “The AI Anthology,” a collection of AI insights designed to support other leaders navigating similar challenges with AI.

Learning alongside peers with different professional and cultural backgrounds gave her new perspectives and added another dimension to the experience.

Jha expressed to her peers an interest in capturing these stories and connections in a boo…

Each year, Climate Week NYC brings together more than 100,000 people in more than 1,000 events across the city to discuss climate challenges, explore solutions, and spur action.

“The MIT Climate Project is a whole-of-MIT mobilization, but no single institution can deliver the climate solutions the world needs — from reducing greenhouse gas emissions to adapting to a changing climate and building more-resilient communities and systems,” said Wang, who is also the Ford Professor of Engineering in the Department of Mechanical Engineering.

“Climate Week New York is important because it convenes the range of stakeholders and sectors that must work together to confront these challenges.

Tapping i…

“Many people genuinely wonder whether this nation’s system of government and rule of law, ignited 250 years ago, will survive,” Minow said.

Her remarks assessed the state of the U.S. in terms of what she called four key “ingredients” of democracy: education, reliable and accessible information, security from violence, and fairness.

Minow evaluated how AI has, among other things, influenced these areas of life — but also suggested ways people could use contemporary technology to help.

In this context, AI threatens to further weaken independent-thinking skills, Minow suggested, just when greater understanding of civic issues is absolutely vital.

In her talk, Minow also discussed the civic eff…

During a session on computing and society, panelists described different views and research into technology and how it affects humans.

“We project aspects of ourselves onto computational systems,” said MIT Professor D. Fox Harrell during his talk about seeing “selfness” in machines.

“I don’t think there’s a close analogy to a moment when everyone felt the world change so quickly,” he said.

Student and faculty musicians from a variety of styles and disciplines shared the stage, including Rambax MIT, MIT Concert Choir, and the MIT All-Star Jazz Combo.

“Clearly, whether it’s AI impact, the impact of climate change, or the impact of political and social change, SHASS is in the right place at th…

Q: Why did early computer engineers believe they needed to inject randomness and irrationality into AI systems designed for logic and calculation?

Many early neural networks were built by psychologists, who thought that by enabling scientists to build a model of the brain, artificial neural networks allowed scientists to observe what is happening inside, including how brains make productive mistakes.

A: When we think of AI systems, we think of ChatGPT or Claude.

Q: How did early neural networks that incorporated randomness to simulate human creativity evolve to become the dominant paradigm in AI?

Around 2012, neural networks started to outperform and outpace a statistical form of machine le…

Jiaji Li and lead author Xiang Chang, a TJU PhD student and visiting researcher at CSAIL, used X-Hinges to create many clever devices.

X-Hinges can even assemble devices that identify which objects have been placed on it.

Behind each of the scientists’ creations is an easy-to-use interface that helps you design interactive objects from scratch.

The researchers say that X-Hinges’ diverse motions are a novel feature compared to past attempts to 3D-print responsive everyday objects.

The researchers will present their work at the ACM Symposium on User Interface Software and Technology in early November.

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…

When you deploy a model to a GPU endpoint, the smallest unit of GPU you can request is one whole accelerator.

Today, Amazon SageMaker Inference supports GPU partitioning, which uses NVIDIA Multi-Instance GPU (MIG) to divide a physical GPU into independent partitions.

GPU partitioning on Amazon SageMaker AI removes that step: you pick a partition profile for your model, and SageMaker AI handles GPU configuration, placement and scaling.

NumberOfCpuCoresRequired and MinMemoryRequiredInMb still describe host CPU and host memory, because GPU memory comes from the partition type.

To try it yourself, run the GPU partitioning sample notebook, which deploys several models onto partitions of a single…

As the system evolved, one problem kept resurfacing: there was no shared definition of the primitives that agentic work is built from.

This post shares the taxonomy we converged on: one set of primitives found by decomposing agentic work into four plain questions: When?, What?, Who?, and How?

It’s the first post in a series, and upcoming posts cover our agentic operating system, background learning, proactive feeds, and much more.

ConclusionThe value of this agentic primitive taxonomy is that four plain questions, When, What, Who, and How, are enough to place any piece of agentic work, from a one-line chat request to a scheduled, compliance-grade process.

Next in the series: the agentic ope…

In the MCP server layer, an AI agent powered by Amazon Bedrock orchestrates tools built with FastMCP (a Python framework for building MCP servers).

MCP server layer: Stateless execution with conversation stateThe MCP server runs on its own ECS Fargate cluster, isolated from the client layer.

This architecture is available in the AWS Regions where Amazon Bedrock and Amazon Bedrock Knowledge Bases are available.

ConclusionA centralized MCP server on Amazon ECS Fargate provides a scalable, secure gateway between AI agents and enterprise data lakes.

To learn more, see Amazon ECS on AWS Fargate, Amazon Bedrock, the Amazon Bedrock Knowledge Bases user guide, Amazon Athena, and the Model Context P…

The agent runs on Amazon Bedrock AgentCore runtime, a capability of Amazon Bedrock AgentCore, and acts for signed-in users through Okta Cross App Access (XAA).

Okta decided which agent might act for which user, and Amazon Bedrock AgentCore Identity, a capability of Amazon Bedrock AgentCore, redeemed Okta’s grant inside the agent.

Amazon Bedrock AgentCore Gateway, a capability of Amazon Bedrock AgentCore, becomes the single front door and redeems Okta’s grant for each target.

And Amazon Bedrock AgentCore Observability, a capability of Amazon Bedrock AgentCore, with Amazon CloudWatch gives you one place to read every decision, including Okta’s.

For plain token exchange through a Gateway, read…

Amazon Bedrock AgentCore Identity, a capability of Amazon Bedrock AgentCore, redeems it for a short-lived access token with credentials your agent code doesn’t see, and keeps that token in its token vault.

In our tests, AgentCore Identity added about 0.6 seconds to a request that took about 30 seconds end to end.

AgentCore Identity reads the client secret from it, so you can rotate the secret without updating the provider (see Reference your own AWS Secrets Manager secrets in Amazon Bedrock AgentCore Identity).

Deploy the agent on AgentCore runtimeConfigure the agent runtime’s inbound authorizer to accept only ID-JAGs that Okta issued to this agent.

Its issuer is Okta’s org authorization se…

Greenlight Financial resolves more than 90 percent of up to 4,000 security and risk cases each month without human intervention.

This post explains how Greenlight Financial built an autonomous security operations center (SOC) solution on Amazon Bedrock AgentCore.

Architecture: Tiered multi-agent system on Amazon Bedrock AgentCoreGreenlight built Luna as a hierarchical multi-agent system on Amazon Bedrock AgentCore.

Expanding from SOC into financial risk triageGreenlight expanded Luna into financial risk assessment within two months of the SOC deployment.

The same agent that checks Zendesk tickets for security cases also checks them for risk cases.

Rather than build and operate the agent loop, conversation-state store, and session isolation ourselves, we chose Amazon Bedrock AgentCore for three reasons: its managed agent runtime removed infrastructure operations, built-in AgentCore memory, a capability of Amazon Bedrock AgentCore, eliminated a separate conversation-state store, and per-session isolation kept each user’s context contained.

Solution overviewTaxbit Assist is a Strands Agents SDK application running on AgentCore runtime, a capability of Amazon Bedrock AgentCore.

The agent retrieves candidate passages from an Amazon Bedrock Knowledge Bases knowledge base, and then reranks them server-side.

Amazon Bedrock Guardrails, which …

Why customers run Claude Code on Amazon BedrockCustomers choose Amazon Bedrock as Claude Code’s model provider for many reasons.

Amazon Bedrock doesn’t use your content to train models or share it with model providers, and providers can’t access your prompts, completions, or the Amazon Bedrock service logs.

Amazon Bedrock doesn’t use your content to train models or share it with model providers, and providers can’t access your prompts, completions, or the Amazon Bedrock service logs.

The same Amazon Bedrock, now for remote tasksIn each session, Claude Code uses Amazon Bedrock as its only model provider.

To learn more about the services and how they protect your data, see Amazon Bedrock Agen…

What Web Answer doesYour agent sends a question to your AgentCore Gateway, a capability of Amazon Bedrock AgentCore.

Web Answer is a managed AgentCore Gateway tool that takes a natural-language question and returns a synthesized answer in seconds grounded in current web sources.

For Web Answer, the role needs:bedrock-agentcore:InvokeWebAnswer , to call Web Answer.

Step 2: Create the Web Answer targetAdd Web Answer as a connector target.

Use Web Search on AgentCore when your own model should reason over raw results, and Web Answer when your agent needs a finished, cited answer.

A wide variety of brands, from established brands with large in-house design teams to bootstrapped brands with limited resources, use A+ Content, creating inconsistency in content quality.

Opportunities and challenges in VLM-based evaluationA vision language model (VLM) pairs an image encoder with a large language model (LLM).

Adaptive VLM content evaluation with expert annotationsA pretrained VLM prompted with a rubric scores content from its general knowledge, and those scores don’t always match ecommerce quality standards.

SageMaker AIOps provides the tool to track data quality, model quality, and feature-attribution drift.

Pages revised using the recommendations improved their quality s…

Alongside 15 built-in evaluators, AgentCore Evaluations supports custom evaluators that you define as either an LLM-as-a-judge or a code-based evaluator.

By the end of this post, you will know how to convert the AgentCore evaluator input into a decision model request, and the model’s probabilities back into an AgentCore evaluation result.

Decision modelsA decision model is a small, specialized model trained to select from a fixed set of options and return calibrated confidence scores rather than generating free-form text.

Jev, the hosted decision model from TypeSafe, and Strands Decider, an open-source, 2-billion-parameter decision model from the Strands Agents team, both support three type…

Smart Approvals in Amazon Quick addresses this challenge by reviewing eligible actions within your existing controls.

Audit evidence and investigationThe Quick permission service records permission updates associated with Smart Approvals in AWS CloudTrail in your account.

Quick also records permission-check verdicts, including denials from connector action permissions, and changes to a user’s Smart Approvals setting.

For security leaders, the case for expanding Smart Approvals should rest on evidence from the pilot.

For setup steps, see Smart Approvals in the governance section of the Amazon Quick User Guide.

When production AI agents handle tasks such as data analysis and report generation, context management can become a major bottleneck.

Part 2: Four layers of context engineering for production AI agents: How Deep Insight applies them in working code.

Deep Insight does this with a hierarchy of Coordinator, Planner, Plan Reviewer, Supervisor, and Tool Agents (Coder, Validator, Reporter, Tracker, Auditor).

“Only pointers in context, actual data in files”Layer 1 isolates context between agents, and Layer 2 limits output within agents.

Each of these layers is documented in Anthropic’s engineering posts, “Effective Context Engineering for AI Agents”, “Code Execution with MCP”, and “How We Built Ou…

Together, these capabilities help organizations improve data quality at enterprise scale while reducing the effort required to build and maintain data quality programs.

Why conventional data quality programs break downMost organizations have established data quality processes but keeping them effective is becoming increasingly difficult as enterprise data ecosystems evolve.

Rethinking data quality with generative AITraditional data quality tools often operate as standalone capabilities, disconnected from the broader enterprise data ecosystem.

NEXT UDP provides several standalone AI agents, such as the Data Ingestion agent, Data Quality agent, Orchestration agent, Data Catalog agent, and SQL…

Closing that gap takes an agentic architecture built on Amazon Bedrock AgentCore.

Architecture: Six layers, one interfaceAiDaH runs on a layered architecture built on Amazon Bedrock AgentCore, deployed in the Asia Pacific (Singapore) Region.

AgentCore memory, a capability of Amazon Bedrock AgentCore, maintains conversational context across turns, and Amazon DynamoDB holds the six-tier role-based access control model and session metadata.

Pre-flight check: The orchestrator resolves the caller against the six-tier role-based access control model held in Amazon DynamoDB, validates the token issued by AgentCore Identity, a capability of Amazon Bedrock AgentCore, and applies Amazon Bedrock Guard…

A year ago, NVIDIA open sourced its Aerial wireless software, joining Sionna and a broader suite of open source tools for accelerating next-generation 6G network development.

Over the past year, global developer adoption of NVIDIA AI Aerial and NVIDIA Sionna software has grown 10x, while NVIDIA’s open source 5G/6G tools have surpassed 1 million downloads.

But interference and changing radio conditions can make wireless signals harder to decode, causing errors and retransmissions that delay information from the field.

SoftBank demonstrated this by integrating a neural receiver into NVIDIA Aerial CUDA-Accelerated RAN (ACAR) and testing it with a commercial 5G smartphone.

NVIDIA AI Aerial and …

As businesses put more agents to work, AI factories need CPUs that can execute those steps quickly and support many agents at once, all within the limits of available power.

Why it matters: NVIDIA Vera delivers both fast agent performance and high capacity per watt within a single CPU.

The data that proves it: In a 100-megawatt data center comparison, NVIDIA found that a single Vera CPU performs better than the fastest x86 CPU and the most energy-efficient x86 CPU.

Vera delivers industry-leading agent speed while enabling 1.6x more agents per watt versus the fastest x86 CPU.

Vera delivers 2x faster agent speed with leading energy efficiency compared with the most energy-efficient x86 CPU.

Chris Alexiuk has a simple rule about open source AI: If you believe in it, be loud about it.

“Fully open source AI is necessary for the democratization of intelligence,” he said.

1 Nemotron Fan,” referring to the NVIDIA Nemotron family of open source, multimodal models built for long-running, self-evolving and specialized AI agents.

“I think Nemotron is the most important project that exists,” said Alexiuk, who also uses the pseudonym “Joe Nemotron” across social media.

“And then my whole life changed, because in that instance I realized it’s crazy what you could do with technology.”Since then, he’s been embedding himself in the open source AI community.

Larger models, longer contexts and agentic applications are increasing GPU memory pressure while supply tightens and prices rise — a trend expected to continue through the near term.

GPU high-bandwidth memory (HBM) is the fastest, most expensive and most contested tier — with model weights and KV cache competing for limited capacity.

Past that point, extra HBM capacity can’t be put to work, yet it still adds costs and draws power.

Maximizing the Value of Every Byte With Full-Stack CodesignNVIDIA’s full-stack codesign makes every byte of HBM count by relieving pressure from model weights, KV cache and RAG data.

NVIDIA CMX context memory storage partners include Cloudian , DDN , Dell Technolo…

Hogwarts Legacy receives an NVIDIA DLSS 5 upgrade with 3D-Guided Neural Rendering today, bringing lifelike lighting and materials to the wizarding world on GeForce NOW.

Blizzard Entertainment’s World of Warcraft: Forever also opens a new chapter in Azeroth, with a launch-week access reward available for GeForce NOW members.

Hogwarts in a New LightNVIDIA DLSS 5 with 3D-Guided Neural Rendering arrives in Hogwarts Legacy today, giving members a new reason to return to Hogwarts, streaming from Steam, Epic Games and Xbox with Play Anywhere support.

Hogwarts Legacy also includes support for the DLSS Multi Frame Generation and Ray Reconstruction features, accelerating and enhancing the ray-traced …

AI is transforming semiconductor manufacturing, impacting what engineers can do — and how fast.

Physics-based AI helps engineers predict process behavior without running a full simulation every time.

Together, these capabilities describe an AI-defined fab: a semiconductor manufacturing factory where AI helps engineers optimize processes, catch problems earlier and make better decisions across the full manufacturing chip lifecycle.

Using the NVIDIA cuDSS library, multiphysics software provider COMSOL is accelerating such simulations for semiconductor manufacturing by up to 10x.

As such, faster calculations can help researchers explore material behavior and reaction pathways that influence ne…

Building on initiatives announced at GTC Berlin, NVIDIA and its partners are expanding physical AI and AI agents to cities worldwide.

Agentic AI for CitiesFrom resident services to vision agents, cities are adopting NVIDIA agentic AI to deliver faster, more responsive public services.

At Dangote’s Lekki refinery in Nigeria, Honeywell Technologies’ Forge Plant Cognition uses Nemotron-powered AI agents to understand operational data.

Build a Broader Local AI EcosystemCities can extend AI to more services by building on infrastructure already in place and adding capacity.

Beyond city services, Barcelona Supercomputing Center’s AI Factory Accelerator Programme gives startups access to sovereign…

Some scientists have suggested that extraterrestrial civilizations, if they exist, could be billions of years older than humanity.

Breakthrough Listen and the SETI Institute are bringing agentic AI into the daily search for extraterrestrial intelligence.

The NVIDIA Holoscan platform processes the massive streams of sensor data, giving agents live data to act on.

The result is a faster, more adaptable search for potential signals from extraterrestrial life and technologies.

Learn more about NVIDIA AI for science.

S-GAME’s Phantom Blade Zero launches on GeForce NOW today, bringing a scarily good blend of martial arts and punk fantasy to the cloud.

Ultimate members can wield GeForce RTX 5080-class performance in the cloud across PCs, Macs, handhelds, mobile devices, browsers, compatible TVs and more.

Azeroth AwaitsGet ready for Blizzard Entertainment’s World of Warcraft: Forever, arriving Wednesday, Nov. 4, at 3 p.m. PT, on GeForce NOW.

A new reward will give eligible GeForce NOW members access to join beginning at launch and ending Wednesday, Nov. 11, at 11:59 p.m. PT.

Once available, members can sign in to their GeForce NOW account and head to the Account Portal to redeem.

NVIDIA RTX Spark powers thin and light laptops built for everything that developers, creators and gamers do.

RTX Remix Breathes New Life Into ClassicsNVIDIA RTX Remix, the modding platform for remastering classic games with path tracing and DLSS, released version 1.6 last week.

RTX Spark Laptops: Beautifully Designed, Built for EverythingRTX Spark laptops come in a range of designs, so users can choose one that fits how they work and play.

RTX Spark laptops are as thin as 14mm and as light as three pounds, with all-day battery life.

Follow NVIDIA RTX Spark on X, Instagram, TikTok and Facebook — and stay informed by subscribing to the NVIDIA Local AI newsletter.

The Consortium for Biomedical Research and AI in Neurodegeneration (C-BRAIN), working with NVIDIA, is using AI to change that.

He described their work as “AI tools built by scientists, for scientists.”The consortium is building a system that harnesses previously untapped Alzheimer’s research data to support scientists’ work.

Speeding Research From Weeks to HoursEssential assets in Alzheimer’s research are datasets that have never been publicly accessible to AI tools or human researchers.

Some dark data is directly deposited into secure computer systems for AI tools to access.

OpenScientist is an open source agentic AI co-scientist that takes a scientist’s research question and dataset, then…

The shift is visible across the full AI stack — from infrastructure to models to applications.

AI-Native Companies Scale With ComputeThe nation’s AI-native companies are also building out their compute capacity — and the applications that run atop them.

inDrive and Pony.ai are also collaborating to explore robotaxi services in Kazakhstan, with Astana as the first deployment city.

The companies will assess the feasibility of bringing Pony.ai’s seventh-generation robotaxis to the inDrive app, supported by NVIDIA AI infrastructure, built on the NVIDIA Hyperion sensor and compute platform with the NVIDIA Halos safety system.

Today’s announcements represent different layers of that stack — infra…

NVIDIA Magnum IO GPUDirect Storage (GDS) addresses this challenge by enabling a direct data path between supported storage and GPU memory.

The tutorial in this post demonstrates how to use the NVIDIA KvikIO Python API to configure storage I/O, read data into GPU memory, and confirm that the accelerated path within GDS was activated.

GDS is the broader technology that enables accelerated data movement between supported storage and GPU memory.

Step 6: cuFile configuration for a KvikIO readAfter KvikIO selects its cuFile path, cuFile applies another set of controls that determine how the request is executed.

Get started with NVIDIA KvikIOThis tutorial has demonstrated how to read data into GPU…

Launched today, NVIDIA Minerva is an AIOps platform built to help teams find problems earlier and keep AI factories productive.

Results That Maximize AI Factory YieldThe ability to correlate workload and infrastructure issues along with its agentic AI capabilities help simplify AI factory operations.

Minerva delivers value for the key metrics AI factories focus on:Longer time between interruptions : Minerva spots trouble before it happens, so jobs run longer without incident.

IREN is among the NVIDIA partners using Minerva to optimize its AI factories.

In addition to Dell Technologies, Lenovo and Supermicro will offer Minerva as part of their AI factory operations portfolios.

Call of Duty: Modern Warfare 4 arrives on GeForce NOW at launch, bringing a new campaign, multiplayer action and DMZ, high-stakes extraction mode to supported devices.

The campaign also gets an enhanced look: GeForce RTX 5080-class performance arrives for Ultimate members, bringing support for ray-tracing features in the cloud.

Activision and Infinity Ward’s Call of Duty: Modern Warfare 4 launches today on GeForce NOW.

Square Enix’s FINAL FANTASY RESONANCE launches today on GeForce NOW, bringing the series’ classic roots into a cinematic pixel-art world.

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

Our paper, “ Just-in-Time Catching Test Generation at Meta ,” fully details our initial deployment and testing with Catching JiTTests.

In the five months since we first unveiled this work, we’ve deployed our Catching JiTTests approach to determine its scalability and applicability to the wider industry.

Catching JiTTests is a fundamentally different approach (Table 1): It generates executable tests that demonstrate a concrete behavioral difference introduced by the change.

How We Implemented Catching JiTTests at MetaJiTTests are automatically generated and designed to fail, surfacing bugs in code changes (diffs) before they land in production.

Catching JiTTests is a solution that is scalabl…

This post describes the next generation of Meta Andromeda, Meta’s personalized ads retrieval engine, and how co-designing the model, system, and GPU hardware unlocks a new efficiency frontier for ads retrieval.

For advertisers, this means better performance automatically from the retrieval system matching ads across a larger candidate pool, within the same latency budget.

Meta Andromeda is Meta’s machine learning system for personalized ads retrieval, the first stage of the ads recommendation pipeline.

Higher Model Performance Requires a New ArchitectureAds retrieval has an outsized ROI on incremental quality: Small improvements in retrieval model performance can drive large gains in advert…

For internal AI agents, whose data access paths may not be known in advance, tool calls on supported paths carry agent context to PrivacyLib.

Engineering Challenges and Approaches of PrivacyLib at Meta’s ScalePrivacyLib operates across a broad range of data access paths at Meta:More than a hundred libraries used to access data services, from key/value stores to AI features.

Keeping Checks Computationally InexpensiveIn-process evaluation must also be computationally efficient because it runs on every data access.

Over time, Meta built new data and processing systems, services gained new ways to process data, and privacy requirements expanded.

The lesson was to integrate at a stable data acce…

Ethernet for scale-up : Tahan, the Ethernet scale-up switch for MTIA 300 based on Broadcom Tomahawk5; a preview of 72 accelerators per rack on 102.4T silicon; and ESUN progressing from 1.0 → 1.1.

We shared insights into key networking-related initiatives through talks from many of the Meta network and hardware teams.

Speaker: Santosh Janardhan, Head of Infrastructure, MetaBetting on the Network: Meta’s Case for Full-Stack, Open AI Networking (video)AI progress now depends on the network as much as on compute.

Speakers: Ashkan Nikravesh, Software Engineer, Meta; Yiorgos Nikolaidis, Software Engineer, MetaWatch Everything From the 2026 OCP Global SummitYou can watch all of the recorded talks …

Rather than maintaining a dedicated ID for every item, GenRecs Tokenization represents each item with a set of tokens from a stable semantic vocabulary.

Paired with Meta’s Generative Ads Model (GEM) and Meta’s Adaptive Ranking Model , GenRecs Tokenization has delivered a +0.35% lift in conversions across surfaces for advertisers.

Enabling cross-campaign transfer: Semantic tokenization creates a stable vocabulary that allows the models to carry insights across campaigns for advertisers.

In production, this yields:Prediction accuracy improvements in production: Paired with Meta’s GEM and Adaptive Ranking Model , GenRecs Tokenization delivered a +0.35% lift 1 in conversions.

Looking further ou…

We’re sharing our own process and insights, plus how any team can apply this to their production AI agents.

We built a 1,300-line orchestrator that routes 20+ workflows to 14 specialized AI agents.

We call this the thin orchestrator pattern: a router that owns sequencing and reliability checks, and leaves every domain decision to specialists.

The Approach: A Thin Router With Specialist WorkersWe split the system into two layers:A deterministic orchestrator that performs zero domain work.

It gets there by trading generality for deep domain knowledge, a deliberate architectural choice.

The first phase of Prometheus, our gigawatt-scale supercluster, is already complete and we’re working toward scaling it to comprise ~1 million GPUs.

The Prometheus supercluster is housed in Meta’s New Albany, Ohio, data center.

Multiple Generations of AI HardwarePrometheus currently deploys two NVIDIA GPU platforms, with a third (based on NVIDIA’s Vera Rubin NVL72) arriving soon.

We also work to extend our sustainability work by sharing designs for systems like IcePack, our liquid-cooled network rack, with the wider industry through OCP.

Hyperion, our upcoming supercluster in Richland Parish, Louisiana, is being designed from the ground up with insights from Prometheus in mind.

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…

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Ethereu…

Part of a series of monthly puzzles

Early view of some other solutions:

https://members.3blue1brown.com/posts/solution-to-10-158885046

https://members.3blue1brown.com/posts/solution-to-sum-156593740

Sharing a few thoughts on the future of math

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

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

Home page: https://www.3blue1brown.com If you want a finger on the pulse of how mathematicians are feeling right now, take a look at this post on Proofs and Prompt: https://proofsandprompts.com/2026/10/08/100-reactions-to-100-solutions/ Timestamps

0:00 - Math student angst

3:10 - How does math work?

11:24 - A proof machine

25:12 - A conjecture machine

26:59 - A theory machine

31:31 - If AI is better at everything

34:58 - The best time to enter math Secret Endscreen Vlog: https://youtu.be/9j7IZFKP96o -------------…

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

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 🙏 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 📝 OpenAI's paper is available here:

https://github.com/openai/math/blob/main/preprints/The-Exponential-Time-Hypothesis-is-False-October-25-2026/paper.pdf Our paper with the cherry splash ray tracing simulation at 0:17 https://users.cg.tuwien.ac.at/zsolnai/gfx/adaptive_metropolis/ 🙏 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 v4.1 Flash 1017 is available here:

https://api-docs.deepseek.com/news/news261017/ Sources:

https://x.com/marcthecreatorr/status/2111427716142571554/video/1

https://x.com/olezhavich/status/2112897791022264814/video/1

https://x.com/notjazii/status/2111432925313179689/video/1

https://x.com/izag82161/status/2113126108787089811/video/1

https://x.com/notjazii/status/2111414086349336841/photo/1

https://x.com/EnvolDev/status/2112304943076184535/video/1

https://x.com/loktar00/status/2111468860935127532/video/1

https://x.com/ItsmeAjayKV/status/2114036230354788581/video/1

https://x.com/SPAC89/status/211141392…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 Gemini 4 Argon + report are available here:

https://deepmind.google/models/model-cards/gemini-4-argon/

https://storage.googleapis.com/deepmind-media/gemini/gemini_4_safety_report.pdf 🙏 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 papers are available here:

https://openai.com/index/sharing-ai-progress-in-mathematics/

https://github.com/openai/math 🙏 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 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…

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…

Спикер: Арсен Кужамуратов, младший научный сотрудник группы «Генеративный ИИ для видео» Лаборатории FusionBrain, Институт AIRI Data Fest 2026: https://ods.ai/events/datafest2026

Презентацию к докладу Вы можете скачать в треке секции GenCV https://ods.ai/tracks/df26-gencv

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Спикер: Руслан Бабакян Data Fest 2026: https://ods.ai/events/datafest2026 Презентацию к докладу Вы можете скачать в треке секции Robotics

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Спикер: Иван Кириллов, ML-инженер, Сбер Data Fest 2026: https://ods.ai/events/datafest2026

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Спикер: Александр Гущин, "АНО ВО "Центральный Университет", Академический руководитель олимпиадного направления ИИ Data Fest 2026: https://ods.ai/events/datafest2026 Презентацию к докладу Вы можете скачать в треке секции ML & Education https://ods.ai/tracks/df26-ml-education

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0:00:00 Начало

0:02:33 ИИ-гуру благословил

0:09:55 ИИ-пузырь в Китае

0:16:03 Эксперты и агенты

0:20:34 SAP и маленькие модели

0:24:25 ИИ-экспорт Китая

0:28:17 ИИ и мейнфреймы

0:37:12 Gemini 4 Carbon и Terra 6.1

0:46:13 Швеция запрещает Claude

0:49:27 Claude запретил реверс

0:56:06 Роботы от Nvidia

1:02:07 Спящие модели

1:08:14 Cohere против Qwen

1:12:03 Робот-бензопила ИИ-саммари: Валентин Малых и Дмитрий Колодезев начинают с ИИ в религии и цифровизации в Индии, затем разбирают экономику: признаки ИИ-пузыря в Китае, состояние финансовых рынков и то, как агенты меняют работу экспертов. Большой блок о бизнесе: SAP и будущее маленьких языковых моделей, интерпретируемость и встраивание ML в про…

Спикер: Олеся Норицына, Cube | D'Innovate, Data Scientists Data Fest 2026: https://ods.ai/events/datafest2026 Презентацию к докладу Вы можете скачать в треке секции Analytical DS ______

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Alex Kontorovich is a mathematician at Rutgers specializing in number theory, who can speak to the Riemann hypothesis, Lean and machine-checked proofs.

Go to https://perplexity.ai/OUTLINE:(00:00) – Introduction(01:58) – Sponsors, Comments, and Reflections(09:53) – Crisis moment in mathematics(18:10) – Will AI “solve” mathematics?

(1:13:53) – Riemann hypothesis(1:37:45) – Navier-Stokes(2:03:42) – How nature computes(2:12:35) – Unique Games Conjecture and complexity theory(2:27:28) – How mathematicians think(2:41:05) – Is math invented or discovered?

(2:47:13) – Can AI solve P vs NP?

(2:57:28) – Why LLMs work so well(3:11:54) – Problems AI can’t solve yet(3:16:23) – Grigori Perelman(3:25:41) …

Andrew Scull is a historian of psychiatry.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep502-scSee below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

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DHH is the creator of Ruby on Rails, Omarchy Linux, CTO of 37signals, and a racecar driver.

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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).

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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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Go to https://upwork.com/lexLarridin: Measure AI adoption in your business.

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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.

Whether you read, watch, or listen to it, Research Focus is your source for research news at Microsoft.

Developed by the Microsoft Research AI for Science team, BioEmu2 builds on Biomolecular Emulator, a deep-learning model designed to help scientists better understand how proteins work.

Go to the BioEmu2 Microsoft Research GitHub repository (opens in new tab) to explore the model and learn more.

Explore the latest research, discover the people behind it, and keep tabs on the ideas shaping what’s next by following Microsoft Research on major social platforms.

We’ll see you in the next Microsoft Research Focus.

Like … or was it always kind of like a slow hunch that you were going to pursue research as a career?

You know, the kinds of things that you do, you know, once you finish degrees that have research in them.

But I didn’t know, you know, for example, that when you apply to PhD programs, that often you get paid to go.

But instead, you know, they sat us down; they said, “Look, you know, we think this can make a difference.

LIU: OK, OK, fine.

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 …

What if we could 3D print food with customized flavors and textures?

Yamato Miyatake joins us to explore how digital food fabrication, human-computer interaction, and AI could transform the future of cooking and dining.

The next evolution of wearable technology may not require new screens or controllers, but new ways to interact with the devices we already wear.

Shaikh Shawon explores how gesture-sensing earbuds could transform virtual reality and wearable computing through intuitive, user-centered interactions that make technology more accessible and adaptable.

His research has been published at leading HCI venues, including CHI, MobileHCI, IMWUT/UbiComp, and Graphics Interface.

He has also taught undergraduate computer science courses and supervised student research projects as a faculty member at North South University.

His research aims to advance the next generation of wearable and immersive computing…

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.

#Sponsored, SVP, Head of Product, AgentExchange and Partner Experience, Tyler Carlson, returns to talk to Jon Krohn about building for an agent-native app store. Since Tyler’s last appearance in Episode 956, Salesforce has gone headless, opening its data, workflow and permissions infrastructure through APIs and MCP servers so that customers and partners can build their own personalized interfaces on top. AgentExchange now brings Salesforce and Slack apps, agents, skills, plugins, and MCPs together in one unified marketplace, built to serve human buyers as well as the AI agents searching on their behalf. Hear Tyler’s framework for the new software stack, how Salesforce tests skills and plugi…

In Episode #1041, Dr. Vijoy Pandey (SVP and General Manager of Outshift, Cisco's incubation engine) returns for a third visit to explain why multi-agent systems fail without anyone noticing. Agents from different vendors, clouds and organisations can now exchange perfectly valid messages, but connection is not cognition: they do not agree on what those messages mean. Vijoy's team analyzed multi-agent sessions from its own production deployments and from public traces, and found a cognitive failure in every single one. In this episode, he lays out the kinds of failure he sees and how fast they compound, introduces the open-source observability platform and protocols Outshift has shipped to c…

In Episode #1040, Amy Blackshaw (SVP at Palo Alto Networks) joins Jon Krohn to explain what changes about cybersecurity when attackers can think for free. Nearly every breach, Amy argues, comes down to someone getting hold of an identity they should not have had, and frontier AI has not created new attack techniques so much as deleted the cost of the expert reasoning that used to chain them together. The recent Hugging Face intrusion is her case in point: agents sustained an action every 20 seconds for four and a half days, but what let them reach the crown jewels was ordinary privilege hygiene. In this episode, she explains why the line between privileged and ordinary users has dissolved, …

In Episode #1039, Prof. John Renne (Professor of Real Estate and Urban Planning at Tulane University) joins Jon Krohn to describe what happens when a tenured full professor goes back to school for a master’s in AI. John is a world-leading expert on transit-oriented development who spent decades pushing the limits of Excel and SPSS; learning Python and then vibe coding has let him scale that research by orders of magnitude, from 4,500 American train stations to more than 110,000 worldwide. In this episode, he traces how walkable communities went from academic heresy to the most profitable form of real estate development, explains how computer vision can now put a number on how attractive a s…

In Episode #1038, Jon Krohn unpacks the remarkable and remarkably contentious run of mathematical results AI has produced in recent weeks, led by OpenAI’s claim to have cracked part of a million-dollar Millennium Prize problem. He lays out what was actually proved, the three big caveats that temper it, the chaotic bulk release of 722 manuscripts that followed, and why 25 Fields medallists are alarmed. His throughline for anyone building with AI: when generation gets cheap, verification becomes the bottleneck. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1038⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email nat…

In Episode #1037, Dr. Richard Socher (CEO and Co-Founder of Recursive) joins Jon Krohn to make the case for automating the scientific method itself. Richard’s startup came out of stealth this year with over half a billion dollars raised to build recursively self-improving superintelligence, and his new book, The Eureka Machine, is already a national bestseller. He also co-founded You.com and the venture firm AIX Ventures, was chief scientist at Salesforce, taught the first university course anywhere on neural networks for NLP, and helped build the ImageNet dataset. In this episode, he traces how AI keeps replacing hand-crafted expertise with scale, lays out the four pillars of his Eureka Ma…

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…

I still can't play chess.

Developers who hand their coding to AI are heading the same way: they can build the machine but can't play the game.

Personal newsletter: https://defragzone.substack.com📩 Newsletter: https://datascienceathome.substack.com🎙 Podcast: Available on Spotify, Apple Podcasts, and more.

Data Science at Home explores the latest in AI, data science, and machine learning.

Send us mail at:[email protected]’t forget to like, subscribe, and hit the 🔔 for 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@…

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@…

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@…

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@…

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@…

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@…

What is “wrong” with the EU AI Act?

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.

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@…

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@…

This and much more is covered in this 1st episode of AI and videogames.

Check Check 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.

Send us mail at: [email protected]’t forget to like, subscribe, and hit the 🔔 for updates on the latest in AI and data science!

What is "wrong" with the EU AI Act?

Check Check 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.

Send us mail at: [email protected]’t forget to like, subscribe, and hit the 🔔 for updates on the latest in AI and data science!

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!