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that the capabilities of LLMs have progressed dramatically in the last few years, but it’s hard to quantify just how good they’ve become.

Now I'll count squares by size: 1×1 squares: These are formed by any 4 dots that make a unit square.

The total should be: 10 (1×1 squares) + 7 (2×2 squares) = 17 squares I apologize for the error in my initial count.

================================================== SUMMARY OF SQUARES FOUND ================================================== Side length 1.0: 9 square(s) Side length 1.41: 4 square(s) Side length 2.24: 2 square(s) Side length 2.83: 4 square(s) Side length 3.61: 2 square(s) Total squares: 21 ==================================================…

Specific context engineering tipsShortening/summarizing the contextTool usageWhy care about agentic context engineeringThis infographic highlights the main contents of this article.

Then I’ll move to specific topics within agentic context engineering, such as shortening the context, context engineering short tips, and tool usage.

Before diving deeper into the specifics of context engineering, I’ll cover why agentic context engineering is important.

Why agents need context engineeringSo we now know why we need agents, but why do agents need context engineering?

Specific context engineering tipsAgentic context engineering builds on top of traditional context engineering.

In the past, I wrote about how recent AI developments are changing data science interview loops from a hiring manager’s perspective.

Mock Interviews: Conduct realistic mock interviews with feedback and scoring.

For example, DataInterview charges $247 for a one-hour mock interview, and an InterviewQuery premium subscription, which includes mock interview service, costs $79 per month.

Interview preparation : Brainstorm business metrics and data science use cases with ChatGPT and conduct mock interviews using voice mode to simulate conversation.

: Brainstorm business metrics and data science use cases with ChatGPT and conduct mock interviews using voice mode to simulate conversation.

Top technical guardrails at different layers of AI applicationGuardrails are created at the input, model, and output layers.

Each serves a unique purpose:Data layer: Guardrails at the data layer ensure that any sensitive, problematic, or incorrect data doesn’t enter the system.

Guardrails at the data layer ensure that any sensitive, problematic, or incorrect data doesn’t enter the system.

Model layer: It’s good to build guardrails at this layer to make sure the model is working as expected.

Output layer: Output layer guardrails assure the model doesn’t provide incorrect answers with high confidence — a common threat with AI systems.

This article presents a sensible, and fully functional, multi-file project structure, containing all the essentials to get started.

It therefore becomes necessary to start breaking up the single file to create a logical project structure to make project management easier.

However, guidance on how to approach a structured multi-page app, specifically with Dash, are few and far between.

AimWith the above in mind, this article is primarily concerned with four items in relation to creating a Dash dashboard:Multi-page Logical project structure (i.e.

The framework included in this article includes example plotly graphs, and the associated code to switch the graphs between the dark and light theme.

The surface on which the Sudoku grid is printed needs to be flat, but can be captured from an angle and appear skewed or rotated.

def find_sudoku_grid( image: np.ndarray, ) -> np.ndarray | None: """ Finds the largest square-like contour in an image, likely the Sudoku grid.

def find_sudoku_grid( image: np.ndarray, canny_threshold_1: int = 100, canny_threshold_2: int = 255, ) -> np.ndarray | None: """ Finds the largest square-like contour in an image, likely the Sudoku grid.

def order_points_simplified(pts: np.ndarray) -> np.ndarray: """ Orders a set of points to best match a target set of corner points.

To get this flat top-down view of our Sudoku grid, we can apply this perspective transfor…

That’s what inspired me to create this project, a command-line quiz application in R, right inside the terminal.

In my quest to seek feedback, I shared this with a friend who suggested adding categories (like “Geography” or “R Programming”), which could actually be a good improvement for later.

for (q in questions) { score <- score + ask_question(q$question, q$answer) } cat("🎉 Final score:", score, "out of", total, "") } # Uncomment to test # run_quiz(quiz_questions)At this point, the app felt complete.

Sample RunHere’s what it looked like when I played it in the R console:👋 Welcome to the R Quiz Game!

A command-line quiz game in R might sound trivial, but trust me, it is a powerful exercis…

a stream enables you to add a KQL database as a destination of the stream, and then open the KQL Database and execute queries against the database.

In this section, we’ll explore Microsoft Fabric items used to store the data within the Real-Time Intelligence workload.

Let’s now examine how to leverage the KQL database in Microsoft Fabric to store and query real-time analytics data.

In this section, we specifically focus on examining how Power BI works in synergy with the Real-Time Intelligence workload in Microsoft Fabric.

ConclusionReal-Time Intelligence — something that started as a part of the “Synapse experience” in Microsoft Fabric, is now a separate, dedicated workload.

Table of contentsWhy build a deep research system?

How to build a deep research systemYou can naturally utilize the deep research system from providers such as OpenAI, which provides a Deep Research API.

Anthropic wrote a very good article on their Multi Agent Research System (which is deep research), which I recommend reading to understand more details about the topic.

You’ve probably experienced this if you used any deep research system from a frontier lab, where the deep research system always starts off by asking a clarifying question.

ConclusionIn this article, I have discussed how to build a deep research system.

Revenue Over Time (line chart), Revenue by Category (bar chart), Top Products by Revenue (horizontal bar chart)Revenue Over Time (line chart), Revenue by Category (bar chart), Top Products by Revenue (horizontal bar chart) Filtering.

By date and categoryBy date and category Data Table.

totalRevenue / totalOrders : 0; // Calculate total revenue per category to find top category const revenueByCategory = data.reduce((acc, item) => { const category = item.categories || "Uncategorized"; acc[category] = (acc[category] || 0) + parseFloat(item.total); return acc; }, {}); // Determine category with highest total revenue const topCategory = Object.keys(revenueByCategory).reduce( (a, b) => (revenueBy…

The Complete MobileNetV2 ArchitectureNow let’s take a look at the complete MobileNetV2 architecture in Figure 5 below.

# Codeblock 7 Output original : torch.Size([1, 16, 112, 112]) after pwconv0 : torch.Size([1, 96, 112, 112]) #(1) after bn0_pwconv0 : torch.Size([1, 96, 112, 112]) after relu : torch.Size([1, 96, 112, 112]) after dwconv : torch.Size([1, 96, 56, 56]) #(2) after bn_dwconv : torch.Size([1, 96, 56, 56]) after relu : torch.Size([1, 96, 56, 56]) after pwconv1 : torch.Size([1, 24, 56, 56]) #(3) after bn_pwconv1 : torch.Size([1, 24, 56, 56])Inverted Residual Block for Stride 1The code used for implementing the inverted residual block with stride 1 is mostly similar to the one with s…

As data scientists, we’ve become extremely focused on building algorithms, causal/predictive models, and recommendation systems (and now genAI).

In this framework, each loan represents a three-way negotiation between the borrower, bank, and platform.

Since we modeled the borrower as leaving the market after the loan is rejected, this doesn’t put any downward pressure on the loan rate.

However, increasing the number of partner banks also decreases each banks’ profit per time (since per-bank profit falls with the number of banks).

Mechanism Design: Instead of take-it-or-leave-it offers and randomizing borrowers to the matched banks, platforms could run auctions where banks bid on borrowers.

As a software engineer in the AI space, my work has been a hybrid of software engineering, AI engineering, product intuition, and doses of user empathy.

One thing stood out: AI engineering is often more software than AI.

In other words, AI engineering isn’t replacing software engineering — it’s layering new complexity on top of it.

The three layers of an AI application stackThink of an AI app as being built on three layers: 1) Application development 2) Model development 3) Infrastructure.

As O’Reilly puts it, “AI engineering is just software engineering with AI models thrown into the stack.”Why evals matter and why they’re toughIn software, one of the biggest headaches for fast-moving team…

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

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

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

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

“Language Models,…

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

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

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

Will we see a transformer for AI interpretability?

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

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

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

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

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

Reinforcement learning (RL) is the part of AI that learns by doing.

Not from a teacher with answer keys (supervised learning), and not by free-associating the web (self-supervised pretraining), but by poking the world, seeing what happens, and tweaking itself to do better next time.

It’s simultaneously powerful and annoying: powerful because it can discover strategies nobody wrote down; annoying because it’s sample-hungry, finicky, and loves to “hack” whatever score you give it.

Origins: trial-and-error with a feedback loopEarly psychology noticed a simple rule: actions that lead to good outcomes get repeated.

Control theory made it operational: if you can estimate how “good” a situation is…

Created Using GPT-5The AI of week title this week belongs to Alibaba’s latest flagship model!

Qwen‑Max sits at the top of a lineage that has steadily expanded capability, context length, and serving sophistication across Qwen‑2.x and Qwen‑3.

The Max tier represents a turning point for the program: a production‑oriented mixture‑of‑experts (MoE) system that pushes model capacity into the trillion‑parameter regime while preserving practical per‑token compute and latency.

What makes Qwen‑Max notable is not only raw scale but the engineering discipline around routing, long‑context memory, test‑time compute for hard reasoning, and a post‑training stack tuned for instruction fidelity, multilingual…

Created Using GPT-5Today we will Discuss:An introduction to circuit tracing.

An overview of Anthropic’s circuit tracing technique for AI interpretability.

💡 AI Concept of the Day: An Introduction to Circuit TracingIn a previous edition of this series, we introduced the notion of circuits as a key component of mechanistic interpretability.

Circuit tracing has emerged as one of the most promising methods in mechanistic interpretability, offering a systematic way to uncover the internal “wiring diagrams” of neural networks.

Circuit tracing extends this approach, scaling it into a rigorous framework for analyzing how modern AI systems compute.

Created Using GPT-5Next Week in The Sequence:The Sequence Knowledge: Dives into the world if circuit tracing for mechanistic interpretability.

The Sequence AI of the Week: Let’s dive into Qwen-Max.

Subscribe Now to Not Miss Anything:📝 Editorial: Qwen’s One‑Week GauntletThe AI world has never seen a release spree like this.

AI Lab: Scale AISummary: Presents a contamination-resistant, enterprise-grade benchmark of 1,865 multi-file, long-horizon issues (avg ~107 LOC across ~4 files) from 41 repos, including private commercial codebases.

AI Lab: Meta FAIR CodeGen TeamSummary: Introduces CWM, a 32B parameter dense decoder-only LLM mid-trained on Python execution traces and agentic Docker environ…

Created Using GPT-5Recently, we published an essay explaining the rationale behind the US Goverment involvement with Intel.

In case you missed it, last week, Nvidia announced a $5 billion investment in Intel alongside plans to co‑design chips for PCs and data centers.

Technically and strategically, however, the move follows a clear logic: Nvidia wants more control over supply, packaging, and platform standardization; Intel wants validation, volume, and renewed platform influence.

This essay unpacks the architectural rationale for the alliance and its implications for Intel, AMD, and Arm, then pivots to Huawei’s newly expanded AI hardware push.

The through‑line is simple: the center of gravi…

Created Using GPT-5Alibaba’s Tongyi Lab is the research group behind a line of agent‑native systems—models, training curricula, and runtimes—built specifically for long‑horizon web research and tool‑use.

Tongyi DeepResearch is its flagship open‑weights agentic model and stack.

By contrast, Qwen is Alibaba Cloud’s broad, general‑purpose model family aimed at foundation capabilities—coding, math, multilingual tasks—delivered across multiple sizes and productized endpoints.

In short: Tongyi Lab optimizes agent behavior and research workflows end‑to‑end (model + controller + iterative consolidation), while Qwen targets versatile base models that power many applications.

This separation of conce…

created using GPT-5Today we will Discuss:An overview of the different types of mechanistic interpretability.

A research paper from Texas University that details a taxonomy for mechanistic interpretability methods.

💡 AI Concept of the Day: Types of Mechanistic InterpretabilityMechanistic interpretability seeks to reverse-engineer the internal computations of machine learning models, particularly large neural networks, to understand how and why they produce specific outputs.

While post-hoc interpretability methods provide correlations or approximations, mechanistic approaches aim for a causal, circuit-level understanding—analogous to reading and comprehending an algorithm’s source code.

This …

The claim is that it systematically outperforms existing proprietary and open‑source “deep research” agents in the reported settings.

🔎 AI ResearchAI Lab: University of Cambridge; Institute for AI, University of Stuttgart; Max Planck Institute for Intelligent Systems; ELLIS Institute; University of Southampton; Tübingen AI Center.

AI Lab: Tongyi Lab, Alibaba Group.

AI Lab: Tongyi Lab, Alibaba Group.

🤖 AI Tech ReleasesTongyi DeepResearchAlibaba Tongyi open sourced a new autonomous research agent.

Created Using GPT-5Building a transformer-based model for robotics holds great promise for generalizing across multiple tasks and robot embodiments.

In recent years, researchers have begun applying the same Transformer architecture that revolutionized NLP and vision to robotics, aiming to create foundation models for robots.

Such models would learn from large-scale, diverse data and potentially perform many tasks (and even work with different types of robots) without retraining from scratch for each new skill.

This essay surveys the opportunities and challenges in that direction, with a focus on how transformer models can generalize across tasks, what makes that hard (data, training, safety…

Created Using GPT-5Large language models should feel deterministic when we ask them to be.

Set temperature to zero, fix the seed, and you expect the same bytes back for the same prompt.

Yet many practitioners have watched a production endpoint return slightly different completions across requests even under greedy decoding.

This isn’t a ghost in the GPU so much as a systems property: modern inference servers perform dynamic batching, kernels make shape‑dependent choices, and tiny numeric nudges can flip a token early in generation and cascade into a visibly different output.

The result is a practical path to “same input, same output” under real production load.

Created Using GPT-5Today we will Discuss:An intro to sparse autoencoders.

OpenAI’s research about scaling sparse autoencoders.

💡 AI Concept of the Day: An Introduction to Sparse AutoencodersToday, we are going to discuss one of the most interesting architectures in the world of mechanistic interpretability.

Sparse autoencoders are a class of neural network models designed to learn compact, high-level representations of input data by enforcing a sparsity constraint on the hidden units.

This sparse activation pattern not only promotes efficient coding but also lays the foundation for interpretability by making individual hidden units more selective.

The Sequence AI of the Week: Reviews Thinking Machines’ opening work on non-deterministic foundation modelsSubscribe Now to Not Miss Anything:📝 Editorial: Oracle’s Quiet AI Decade, Loud WeekOracle just had the kind of AI week that forces a narrative rewrite.

Experiments with Llama-3.2-3B show that LSP matches or surpasses data-driven RL baselines, demonstrating the feasibility of perpetual self-improvement without additional training data.

AI Lab: Google DeepMind, Google ResearchSummary: This work introduces SimpleQA Verified, a rigorously filtered 1,000-prompt benchmark that addresses noise, redundancy, and biases in OpenAI’s SimpleQA dataset.

AI Lab: MiniMax, HKUST, University of Waterloo…

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

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

Continue reading on SyncedReview »

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Можно и фразу.

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

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

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

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

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

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

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

Imbalanced datasets are a common challenge in machine learning.

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

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

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

Choosing the right text representation is a critical first step in any natural language processing (NLP) project.

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Introduction In machine learning, no single model is perfect.

Let's face it: keeping up with new research, tools, and industry shifts in machine learning can be down-right overwhelming.

The increasing sophistication of cyber threats calls for a systemic change in the way we defend ourselves against them.

When building machine learning models to classify imbalanced data — i.

Validating machine learning models requires careful testing on unseen data to ensure robust, unbiased estimates of their performance.

Deep neural networks have drastically evolved over the years, overcoming common challenges that arise when training these complex models.

This article provides a summary of and commentary on the recent paper

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This has spoilers for MIT Mystery Hunt 2025.

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

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

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

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

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

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

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

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

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

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

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

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

Point processes and Markov ChainsBack to Markov chains.

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

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

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

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

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

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

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

The authoring experience is much more convenient for me there.

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

And check out our How Transformer LLMs Work course!

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

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

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

By learning geometry and motion jointly from …

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This approach effectively integrate…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

PDFAbstract

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

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

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

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

Second, inspired b…

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

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

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

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

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Additional troubleshooting information here.

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

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

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

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

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Additional troubleshooting information here.

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

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Additional troubleshooting information here.

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

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

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

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

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

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

Gemini Robotics 1.5 is currently available to select partners.

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

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

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

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

Gemini Robotics 1.5 is currently available to select partners.

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

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

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

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

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

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

W…

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

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

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

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

W…

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

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

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

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

With our novel AI methods, we pre…

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

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

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

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

With our novel AI methods, we pre…

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

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

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

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

With our novel AI methods, we pre…

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

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

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

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

Gemini solved …

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

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

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

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

Gemini solved …

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

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

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

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

Gemini solved …

Applying the scaling laws to build VaultGemmaThe Gemma models are designed with responsibility and safety at their core.

This makes them a natural foundation for developing a production-quality, DP-trained model like VaultGemma.

Algorithmic advancements: Training at scaleThe scaling laws we derived above represent an important first step towards training a useful Gemma model with DP.

One prominent gap between the research underlying the scaling laws and the actual training of VaultGemma was our handling of Poisson sampling, which is a central component of DP-SGD.

This method posed two main challenges: it created batches of different sizes, and it required a specific, randomized order for pr…

Applying the scaling laws to build VaultGemmaThe Gemma models are designed with responsibility and safety at their core.

This makes them a natural foundation for developing a production-quality, DP-trained model like VaultGemma.

Algorithmic advancements: Training at scaleThe scaling laws we derived above represent an important first step towards training a useful Gemma model with DP.

One prominent gap between the research underlying the scaling laws and the actual training of VaultGemma was our handling of Poisson sampling, which is a central component of DP-SGD.

This method posed two main challenges: it created batches of different sizes, and it required a specific, randomized order for pr…

Our novel Deep Loop Shaping method improves control of gravitational wave observatories, helping astronomers better understand the dynamics and formation of the universe.

In a paper published today in Science, we introduce Deep Loop Shaping, a novel AI method that will unlock next-generation gravitational-wave science.

Deep Loop Shaping reduces the noise level in the most unstable and difficult feedback loop at LIGO by 30 to 100 times, improving the stability of its highly-sensitive interferometer mirrors.

In the future, Deep Loop Shaping could also be applied to many other engineering problems involving vibration suppression, noise cancellation and highly dynamic or unstable systems import…

Still, charting the optimal path forward — especially with the integration of AI agents — often presents significant technical complexityTo help startups navigate this new landscape, we’re launching our Startup technical guide: AI agents.

AI agents combine the intelligence of advanced AI models with access to tools so they can take actions on your behalf, under your control.

Unlike traditional AI, agentic AI can break down intricate tasks, refine plans, and dynamically utilize external resources and tools.

The key takeaway is that AI agents can tackle complex, multi-step problems, ultimately transforming from a passive tool into a proactive problem-solver.

If your startup is looking to get …

In recent years, we have been able to take that to an entirely new level, making our leading generative AI technology available to even the youngest startups.

During the AI Builders Forum, we showcased the work of dozens of startups who have taken that technology in new and exciting directions.

There’s so many more hard at work with Google Cloud that we wanted to highlight as many as we could.

And when it comes to the especially hot segment of AI agents, we’ve just introduced our Startup Technical Guide: AI Agents.

All it takes is the right AI, the right partners, a good team, and a great idea.

You can deploy these models — including closed-source models and those with restricted commercial licenses — directly into your own Virtual Private Cloud (VPC).

You will find all of these models in the Vertex AI Model Garden, our central gateway to over 200 foundation models, including Google’s versatile Gemini family, leading open models, and third-party models.

Announcing self-deployable proprietary models in your VPCFor organizations that require maximum control over their data and infrastructure, you can now self-deploy powerful proprietary models from leading AI model builders directly within your VPC.

This is just the beginning, and you’ll see us continue to expand our catalog with th…

Many data science teams rely on Apache Spark running on Dataproc managed clusters for powerful, large-scale data preparation.

But running inference on a Spark DataFrame using a model from Vertex AI typically requires custom development, making it complex to build a single, end-to-end workflow.

To solve this problem, we are developing a new open-source Python library designed to simplify AI/ML inference for Dataproc.

This library connects your Apache Spark jobs to use popular ML frameworks and Vertex AI features, starting with model inference.

Apply Gemini models to your Spark dataYou can apply generative AI models, like Gemini, to columns in your Spark DataFrame.

That’s why we’re thrilled to announce major updates across our suite of generative media models—including Gemini 2.5 Flash Image (now GA!

), Veo, Imagen, and Gemini 2.5 Text-To-Speech — on Vertex AI.

Gemini 2.5 Flash Image is Generally Available (GA) on Vertex AIWe are excited to announce the General Availability of Gemini 2.5 Flash Image.

We’re already seeing incredible adoption of Gemini 2.5 Flash Image.

Here’s an example of how companies are putting the pushing the creative boundaries of Gemini 2.5 Flash Image:

To solve these challenges, DZ BANK and Google Cloud built an architectural solution for trustworthy data delivery to blockchain applications.

Building trustworthy oracle architectureOff-chain data is supplied to DLT systems via oracles –- interfaces that deliver external information to smart contracts.

The combination of Google Cloud’s secure, highly available infrastructure with DZ BANK’s vision for standardized, deterministic financial protocols meets these requirements.

This architectural pattern provides a blueprint for other institutions facing similar challenges, creating reusable components for trustworthy data delivery across different financial smart contracts and blockchain networ…

One minute you're writing code, the next you're switching to the PostgreSQL database client to run a query, and then it's over to the console to check on your instances.

This can mean adding the right extensions to your PostgreSQL database and learning how to use it.

The recently announced Gemini CLI extension for PostgreSQL is here to do just that.

Let's see it in action: The fuzzy search challengeImagine you want to add a "fuzzy search" feature to your app — you know, so users can find a "t-shirt" even if they type "tshirt".

But with the Gemini CLI, it's a conversation:

For AI agents to be really useful, they need to be able to securely interact with enterprise data. In July, we introduced a toolset to help AI agents interact with and analyze business data in BigQuery through natural language, and with just a few lines of code. Today, we’re taking the next step, with “Ask data insights” for Conversational Analytics and the “BigQuery Forecast” for time-series predictions, going beyond fetching metadata and executing queries to full-scale data analysis and predictions. Both tools are available today in the MCP Toolbox as well as Agent Development Kit's built-in toolset.

Let's dive into what you can do with these new tools.ask_data_insights: Converse with Big…

How customers are building with Claude on Vertex AILeading organizations are already leveraging the powerful combination of Claude and Google Cloud to drive significant business impact.

Augment Code is powering its AI coding assistant, which helps developers navigate and contribute to production-grade codebases, with Claude on Vertex AI.

“Customers tell us that our platform, powered by Claude models and Google Cloud, enables one person to create applications in one to two hours that previously took up to three months.” - Amitay Gilboa, CEO, spring.newTELUS built its generative AI platform, Fuel iX™, on Google Cloud to give its team members a choice of curated AI models, like Claude, inspiri…

While it’s important to be fluent in business strategy, boards should also work with security leaders towards integrating cybersecurity into their overall roadmap.

Boards can encourage a collaborative approach to align cybersecurity with critical business services, which can help strengthen security posture, protect critical assets, and enhance resilience against evolving and emerging threats.

Develop a framework for cybersecurity investmentsBoards should ask questions to ensure cybersecurity investments deliver real business value — beyond compliance.

Here’s where boards should encourage third-party assessments, running simulations, and tabletop exercises to help prepare an organization fo…

Available through the Compute Engine instance API, gcloud CLI, and the Google Cloud console, Flex-start VMs provide a simple and direct way to create single VM instances that can wait for in-demand GPUs.

What are Flex-start VMs?

Flex-start VMs are ideal for defined-duration tasks such as AI model fine-tuning, batch inference, HPC, and research experiments that don’t need to start immediately.

Cost-effective pricing: Flex-start VM SKUs offer significant discounts compared to standard on-demand pricing, making cutting-edge accelerators more accessible.

Flex-start VMs can run uninterrupted for a maximum of seven days and consume preemptible quota.

The market now demands that data scientists build the future by designing and deploying intelligent, autonomous agents that can reason, act, and learn on behalf of the enterprise.

These capabilities help data scientists move beyond analysis to action by enabling them to:Stop wasting time context-switching.

We're providing a complete 'Build-Deploy-Connect' toolkit to move your logic from a single notebook into a secure, production-grade fleet of autonomous agents.

Unifying the environment for data scienceThe greatest challenge of data science productivity is friction.

This integration breaks the barrier between SQL, Python, and visualization, transforming the notebook into an integrated deve…

In June, Google introduced Gemini CLI, an open-source AI agent that brings the power of Gemini directly into your terminal.

And today, we’re excited to announce open-source Gemini CLI extensions for Google Data Cloud services.

Using a Data Cloud Gemini CLI extensionBefore you get started, make sure you have enabled the APIs and configured the IAM permissions required to access specific services.

Before starting the Gemini CLI, you’ll need to configure the extension to connect with your Google Cloud project by adding the required environment variables.

ConfigurationNow, you can start the Gemini CLI using command gemini.

An evaluation framework for gen AIEvaluating gen AI applications and agents like DB Lumina requires a custom framework due to the complexity and variability of model outputs.

Currently, DB Lumina is already in the hands of around 5,000 users across Deutsche Bank Research, specifically in divisions like Investment Bank Origination & Advisory and Fixed Income & Currencies.

For example, the U.S. and European Economics teams use DB Lumina to score central bank communications to assess hawkishness and dovishness over time.

Increased accuracy: Analysts have also started using DB Lumina as part of their editing process.

DB Lumina has proved the value of combining retrieval, gen AI, and conversatio…

To make this work, they embraced the JAX ecosystem, even translating models from PyTorch themselves — a prime example being their JAX translation of the Boltz-2 structure prediction model.

This approach gives what April called an "expressive language for protein design," where models can be composed, added, and transformed to define a final objective.

They then calculate a gradient of this final score with respect to the input sequence via backpropagation.

To build their system, they rely on key libraries within the JAX ecosystem like Equinox and Optax.

We are excited to see community contributions like Escalante's Mosaic library, which contains the tools for their protein design work and i…

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

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

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

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

But I feel like broadly…

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

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

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

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

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

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

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

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

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

If interested in our other related re…

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

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

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

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

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

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

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

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

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

3D animation sequence r…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Implications for science and trustworthy discoveryT…

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

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

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

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

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

To verify its findings, Project Ire can invoke a validator tool that cross-checks claims in the report against the chain of evidence.

This tool draws on expert statements from malware reverse engineers on the Project Ire team.

For each file it analyzes, Project Ire generates a report that includes an evidence section, summaries of all examined code functions, and other technical artifacts.

Project Ire correctly identified the code that locates and disables antivirus programs, providing evidence that the file was malicious.

The issue was later resolved by updating decompiler rules, but this example illustrates how Project Ire navigates uncertainty during analysis.

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

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

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

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

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

MIT engineers have developed a printable aluminum alloy that can withstand high temperatures and is five times stronger than traditionally manufactured aluminum.

When they printed the alloy and tested the resulting material, the team confirmed that, as predicted, the aluminum alloy was as strong as the strongest aluminum alloys that are manufactured today using traditional casting methods.

Olson challenged the class to design an aluminum alloy that would be stronger than the strongest printable aluminum alloy designed to date.

The researchers showed that 3D printing, broadly also known as additive manufacturing, can be a faster way to cool and solidify the aluminum alloy.

“My dream is that …

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

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

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

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

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

The molecule, called enterololin, suppresses a group of bacteria linked to Crohn’s disease flare-ups while leaving the rest of the microbiome largely intact.

Using a generative AI model, the team mapped how the compound works, a process that usually takes years but was accelerated here to just months.

“The problem isn’t finding molecules that kill bacteria in a dish — we’ve been able to do that for a long time.

Here, the team turned to DiffDock, a generative AI model developed at CSAIL by MIT PhD student Gabriele Corso and MIT Professor Regina Barzilay.

“A lot of AI use in drug discovery has been about searching chemical space, identifying new molecules that might be active,” she says.

The Martin Trust Center for MIT Entrepreneurship announced that Ana Bakshi has been named its new executive director.

Bakshi joins the Trust Center at an exciting time in its history.

“I am truly honored to join the Trust Center at such a pivotal moment,” Bakshi says.

Now, with AI-powered tools like Orbit and JetPack, the Trust Center is changing the way that entrepreneurship is taught and practiced.

This approach of leveraging proven evidence-based methodology, emerging technology, the ingenuity of MIT students, and responding to industry shifts is similar to how MIT established the field of chemical engineering in the 1890s.

The new TX-Generative AI Next (TX-GAIN) computing system at the Lincoln Laboratory Supercomputing Center (LLSC) is the most powerful AI supercomputer at any U.S. university.

Whereas traditional AI focuses on categorization tasks, like identifying whether a photo depicts a dog or cat, generative AI produces entirely new outputs.

Today, generative AI is widely known for its use of large language models to create human-like responses to user prompts.

At Lincoln Laboratory, teams are applying generative AI to various domains beyond large language models.

Beyond supporting programs solely at Lincoln Laboratory, TX-GAIN is enhancing research collaborations with MIT's campus.

In fact, the environmental impact of building data centers is one reason companies like Meta and Google are exploring more sustainable building materials.

Reducing operational carbon emissionsWhen it comes to reducing operational carbon emissions of AI data centers, there are many parallels with home energy-saving measures.

Gadepally’s group found that about half the electricity used for training an AI model is spent to get the last 2 or 3 percentage points in accuracy.

Location can have a big impact on reducing a data center’s carbon footprint.

For instance, a generative AI model could streamline interconnection studies that determine how a new project will impact the power grid, a step th…

Cameras and visual language models also allow the system to monitor experiments, detect issues, and suggest corrections.

Machine-learning models can speed up the discovery of new materials by making predictions and suggesting experiments.

The researchers used CRESt to develop an electrode material for an advanced type of high-density fuel cell known as a direct formate fuel cell.

The researchers incorporated some of the model’s suggestions, leading to improved consistency, suggesting the models already make good experimental assistants.

“CREST is an assistant, not a replacement, for human researchers,” Li says.

Unlike other medical image segmentation models, this system allows the user to segment an entire dataset without repeating their work for each image.

In the long run, this tool could accelerate studies of new treatment methods and reduce the cost of clinical trials and medical research.

With interactive segmentation, they input an image into an AI system and use an interface to mark areas of interest.

This new system, MultiverSeg, combines the best of each approach.

Compared to the researchers’ previous system, MultiverSeg reached 90 percent accuracy with roughly 2/3 the number of scribbles and 3/4 the number of clicks.

A collaboration with economics doctoral student Shakked Noy yielded the 2023 study investigating ChatGPT as a tool to improve productivity.

Their research found it substantially increased workers’ productivity on writing tasks, most so for workers who initially performed the worst on the tasks.

“We’ve seen a relationship between higher turnover and inconsistent, inadequate schedules, which suggests workers dis-prefer these kinds of schedules,” Zhang says.

By conducting this research, Zhang hopes to better understand whether or not scheduling regulations can improve affected employees’ quality of life, while also considering potential unintended consequences.

“I’ve become the kind of well-ro…

MIT Department of Mathematics researchers David Roe ’06 and Andrew Sutherland ’90, PhD ’07 are among the inaugural recipients of the Renaissance Philanthropy and XTX Markets’ AI for Math grants.

With AI technologies such as large language models (LLMs), the barrier to entry for these formal tools is dropping rapidly, making formal verification frameworks accessible to working mathematicians.

Mathlib is a large, community-driven mathematical library for the Lean theorem prover, a formal system that verifies the correctness of every step in a proof.

Mathlib currently contains on the order of 105 mathematical results (such as lemmas, propositions, and theorems).

This bridge will benefit both h…

But when it comes to designing materials with exotic quantum properties like superconductivity or unique magnetic states, those models struggle.

Now, MIT researchers have developed a technique that lets popular generative materials models create promising quantum materials by following specific design rules.

The rules, or constraints, steer models to create materials with unique structures that give rise to quantum properties.

Steering models toward impactA material’s properties are determined by its structure, and quantum materials are no different.

With SCIGEN, users can give any generative AI diffusion model geometric structural rules to follow as it generates materials.

The Martin Trust Center for MIT Entrepreneurship strives to teach students the craft of entrepreneurship.

Students in this year’s cohort used AI tools to accelerate their coding, draft presentations, learn about new industries, and brainstorm ideas.

The Trust Center is encouraging students to use AI as they see fit while also staying mindful of the technology’s limitations.

“You need to verify everything when you are using AI to build a business,” says Kenney, who is also a lecturer at MIT Sloan and MIT D-Lab.

“Some AI tools can increase your speed by doing things like automatically sorting your email or helping you vibe code apps, but many AI tools are built off averages, and those can be …

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

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

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

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

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

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

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

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

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

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

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

Smoothing behavior of RL AVs.

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

Amazon QuickSight data stories support global customers by transforming complex data into interactive narratives for faster decisions.

In this post, we demonstrate how Amazon Nova Act automates QuickSight data story creation, saving time so you can focus on making critical, data-driven business decisions.

Using browser automation, Amazon Nova Act seamlessly interacts with QuickSight to create customized data narratives.

To view your data story, choose Data stories in the navigation pane and choose your data story.

ConclusionIn this post, we demonstrated how to create a QuickSight data story using Amazon Nova Act prompts.

This makes batch inference particularly valuable for handling extensive data to get inference from Amazon Bedrock FMs.

This solution demonstrates how to implement automated monitoring for Amazon Bedrock batch inference jobs using AWS serverless services such as AWS Lambda, Amazon DynamoDB, and Amazon EventBridge, reducing operational overhead while maintaining reliable processing of large-scale batch inference workloads.

The same Lambda function triggers a new Amazon Bedrock batch inference job using this JSONL file.

This DynamoDB table functions as a state manager for Amazon Bedrock batch inference jobs, tracking the lifecycle of each request.

We also showed how to implement an automated m…

Incremental training capability : The ability to continually improve our content filtering model with new examples of problematic content was essential.

: The ability to continually improve our content filtering model with new examples of problematic content was essential.

We used the Faster autoscaling on Amazon SageMaker realtime endpoints notebook to set up autoscaling on SageMaker AI endpoints.

Fine-tuned model metrics compared to out-of-the-box content filtering solutionsThe fine-tuned content filtering model demonstrated higher performance than generic, out-of-the-box filtering solutions in key safety metrics.

He specializes in building enterprise-grade Generative AI applications usin…

Now, with cross-Region inference, you can choose either a geography-specific inference profile or a global inference profile.

Additionally, global CRIS supports key Amazon Bedrock features, including prompt caching, batch inference, Amazon Bedrock Guardrails, Amazon Bedrock Knowledge Bases, and more.

Implement global cross-Region inferenceTo use global cross-Region inference with Anthropic’s Claude Sonnet 4.5, developers must complete the following key steps:Use the global inference profile ID – When making API calls to Amazon Bedrock, specify the global Anthropic’s Claude Sonnet 4.5 inference profile ID ( global.anthropic.claude-sonnet-4-5-20250929-v1:0 ) instead of a Region-specific model…

In this post, we demonstrate how to access AgentCore Gateway through a VPC interface endpoint from an Amazon Elastic Compute Cloud (Amazon EC2) instance in a VPC.

Create a security group for the interface VPC endpointTo create a security group for the interface VPC endpoint, follow these steps:Create a second security group named vpce-agentcore-sg that will be attached to the AgentCore Gateway interface VPC endpoint using similar steps to the preceding instructions and selecting the same VPC.

Create an interface VPC endpointCreate an interface VPC endpoint using Amazon Virtual Private Cloud (Amazon VPC) that automatically uses AWS PrivateLink technology, enabling secure communication from y…

Solution overviewThe Amazon Q Business data accessor provides a secure interface that integrates Kore.ai’s AI for Work platform with Amazon Q index.

Add Kore.ai as a data accessorAfter creating an Amazon Q Business application with AWS IAM Identity Center, administrators can configure Kore.ai as a data accessor through the Amazon Q Business console.

Note down the following information for the next step: Amazon Q Business application ID AWS Region of the Amazon Q Business application Amazon Q Business retriever ID Region for IAM Identity Center instanceConfigure Amazon Q index in Kore.ai’s AI for WorkKore.ai’s AI for Work supports flexible integration with Amazon Q index based on your enterp…

Today, we’re excited to announce the Amazon Bedrock AgentCore Model Context Protocol (MCP) Server.

In this post we introduce the new AgentCore MCP server and walk through the installation steps so you can get started.

AgentCore MCP server capabilitiesThe AgentCore MCP server brings a new agentic development experience to AWS, providing specialized tools that automate the complete agent lifecycle, eliminate the steep learning curve, and reduce development friction that can slow innovation cycles.

Layer 2: AWS service documentation – Install the AWS Documentation MCP Server for comprehensive AWS service documentation, including context about Bedrock AgentCore.

InstallationTo get started with …

For Hapag-Lloyd, accurate vessel schedule predictions are crucial for maintaining schedule reliability, where schedule reliability is defined as percentage of vessels arriving within 1 calendar day (earlier or later) of their estimated arrival time, communicated around 3 to 4 weeks before arrival.

Prior to developing the new ML solution, Hapag-Lloyd relied on simple rule-based and statistical calculations, based on historical transit patterns for vessel schedule predictions.

Each model has a dedicated training pipeline in SageMaker Pipelines, handling data preprocessing steps and model training.

The individual workflow begins with a data processing pipeline that prepares the input data (ves…

Rox addresses this by providing a revenue operating system: a unified layer that brings these signals together and equips AI agents to execute go-to-market (GTM) workflows.

Today, we’re excited to announce that Rox is generally available, with Rox infrastructure built on AWS and delivered across web, Slack, macOS, and iOS.

In this post, we share how Rox accelerates sales productivity with AI agents powered by Amazon Bedrock.

Solution overviewAs noted in Rox is transforming revenue teams with AI-driven integration powered by AWS, modern GTM teams need more than a static database.

Anthropic’s Claude Sonnet 4 has consistently demonstrated unmatched tool-calling and reasoning performance, allow…

In this post, we show you how to overcome these challenges using GraphStorm, particularly the new real-time inference capabilities of GraphStorm v0.5.

GraphStorm v0.5 makes this possible through native real-time inference support through Amazon SageMaker AI.

If you’re interested in implementing GNN-based models for real-time fraud prevention or similar business cases, you can adapt the approaches presented here to create your own solutions.

Step 3 is GraphStorm v0.5’s simplified deployment process that creates SageMaker real-time inference endpoints with one command.

Step 3: Real-time endpoint deploymentIn the Notebook 3-GraphStorm-Endpoint-Deployment, you deploy the real-time endpoint thro…

The integration of agentic AI is ushering in a transformative era in health care, marking a significant departure from traditional AI systems.

Deploy, enhance, and monitor AI agents at scale using Amazon Bedrock AgentCoreBy using Amazon Bedrock AgentCore, you can deploy and operate highly capable AI agents securely at scale.

Tools exposed using AgentCore Gateway: AgentCore Gateway provides secure access to the necessary tools required for the Strands or LangGraph agent using standard MCP clients.

In this solution, REST APIs are hosted on Amazon API Gateway and exposed as MCP tools using AgentCore Gateway.

Ingress authentication for AgentCore Gateway: AgentCore Gateway is protected with oAut…

In this post, we demonstrate how to build a multi-agent SRE assistant using Amazon Bedrock AgentCore, LangGraph, and the Model Context Protocol (MCP).

We walk you through the complete implementation, from setting up the demo environment to deploying on Amazon Bedrock AgentCore Runtime for production use.

Implement persistent intelligence with Amazon Bedrock AgentCore MemoryWhereas Amazon Bedrock AgentCore Gateway provides seamless API access, Amazon Bedrock AgentCore Memory transforms the SRE agent from a stateless system into an intelligent, learning assistant.

Initialize memory strategiesThe SRE agent memory component is built on Amazon Bedrock AgentCore Memory’s event-based model with au…

With this powerful new capability, you can create iterative, condition-based workflows directly within your Amazon Bedrock flows, using Prompt nodes, AWS Lambda functions, Amazon Bedrock Agents, Amazon Bedrock Flows inline code, Amazon Bedrock Knowledge Bases, Amazon Simple Storage Service (Amazon S3), and other Amazon Bedrock nodes within the loop structure.

DoWhile loop workflowA DoWhile loop consists of two parts: the loop and the loop controller.

DoWhile loops in Amazon Bedrock Flows are now available in all the AWS Regions where Amazon Bedrock Flows is supported, except for the AWS Gov Cloud (US) Region.

To get started, open the Amazon Bedrock console or Amazon Bedrock APIs to begin bu…

To address these challenges, NVIDIA introduced the hardware Decompression Engine (DE) in the NVIDIA Blackwell architecture—and paired it with the nvCOMP library.

How the Decompression Engine worksThe new DE in the Blackwell architecture is a fixed-function hardware block designed to accelerate decompression of Snappy, LZ4, and Deflate-based streams.

When the DE is available, nvCOMP will make use of it without changes to user code.

Comparing the performance of streaming multiprocessors to the Decompression Engine, as shown in six examples.

Get startedThe Decompression Engine in Blackwell makes it much easier to deal with one of the biggest challenges in data-heavy workloads: fast, efficient …

The growing availability of GPU nodes and the broad feature coverage of GPU algorithms makes GPU data processing more accessible than ever before.

To support the increasing demand, IBM and NVIDIA are working together to bring NVIDIA cuDF to the Velox execution engine, enabling GPU-native query execution for widely used platforms like Presto and Apache Spark.

For more details, see Extending Velox – GPU Acceleration with cuDF.

The team collected query runtime data using benchmarks in Presto tpch (derived from TPC-H) using Parquet data sources with both the Presto C++ and Presto-on-GPU worker types.

Integration of GPU exchange into the cuDF backend for Velox is in progress, and the implementat…

These functions are inherently risky, and when combined with prompt injection, they can make RCE almost trivial.

The other serious vulnerability we commonly see is broad access to write to the RAG data store.

This opens the door to indirect prompt injection, which in some cases can be very precisely and narrowly targeted, making detection extremely difficult.

In the case of workspace documents (e.g., SharePoint, Google Workspace), enabling a user to select between only their documents, documents only from people in their organization, and all documents may help limit the impact of maliciously shared documents.

ConclusionThe NVIDIA AI Red Team has assessed dozens of AI-powered applications a…

From thrilling adventures to spine‑tingling scares, the cloud gaming lineup is packed with 17 new games, including the highly anticipated shooter Battlefield 6, launching on GeForce NOW this month.

But first, catch the six games coming this week.

Miami and Warsaw, Poland, are the latest regions to get GeForce RTX 5080-class power, with Portland and Ashburn coming up next.

This week, inZOI and Total War: Warhammer III join the lineup of GeForce RTX 5080-ready titles, both already available on the service.

Look for the “GeForce RTX 5080 Ready” row in the app or check out the full list.

Last week, over 900 attendees joined NVIDIA AI Day Tokyo to learn about sovereign AI — including two-dozen breakout sections on agentic and physical AI, quantum computing and AI factories.

NVIDIA Cloud Partners SoftBank, GMO Internet and KDDI each introduced the latest advancements to their AI factories and showcased how they’re supporting developers to build AI models and services.

“Japan will see a 320x increase from 2020 in demand for AI computing power by 2030,” Kuniyoshi Suzuki, senior director of the cloud AI service division at SoftBank Corp., said at the event.

“To ensure transparency and safety as AI adoption expands, it is crucial to build a foundation of domestic technologies — h…

NVIDIA RTX PCs accelerate these experiences, delivering fast and snappy AI to users.

Getting Started With Local LLMs Optimized for RTX PCsNVIDIA has worked to optimize top LLM applications for RTX PCs, extracting maximum performance of Tensor Cores in RTX GPUs.

NVIDIA has worked with llama.cpp to optimize performance on NVIDIA RTX GPUs.

By loading syllabi, assignments and textbooks into AnythingLLM on RTX PCs, students can gain an adaptive, interactive study companion.

Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter.

Quantum computing promises to reshape industries — but progress hinges on solving key problems.

Error correction.

The parallel processing of accelerated computing offers the power needed to make the quantum computing breakthroughs of today and tomorrow possible.

Accelerating Quantum Error Correction Decoders With NVIDIA CUDA-Q QEC and cuDNNQuantum error correction (QEC) is a key technique for working with unavoidable noise in quantum processors.

Explore quantum computing sessions at NVIDIA GTC Washington, D.C, running Oct. 27-29.

The Newton physics engine and enhanced NVIDIA Isaac GR00T models enable developers to accelerate robot learning through unified OpenUSD simulation workflows.

Codeveloped by Google DeepMind, Disney Research and NVIDIA, and managed by the Linux Foundation, Newton is an open-source, GPU-accelerated physics engine to advance robot learning.

NVIDIA Isaac Lab: The latest version of Isaac Lab, an open-source, modular robot learning framework built on NVIDIA Isaac Sim and OpenUSD, is now available as an early developer release.

OpenUSD’s interoperability ensures these advanced physics simulations, foundation models and learning frameworks work together seamlessly, enabling developers to build unifi…

AI is making advancements across the globe and across industries, creating impacts that will last across time.

To help foster AI innovation for everyone, everywhere, NVIDIA hosts specialized events — including AI Days — for and in different pockets of the world.

These events draw in hundreds of enthusiasts, developers, researchers and startups to explore the latest technologies making AI breakthroughs possible.

The latest stop: Ho Chi Minh City, Vietnam.

Why tree-based models perform well in manufacturingData from semiconductor fabrication and chip testing is typically highly structured and tabular.

Accelerated training workflows for tree-based modelsAmong tree-based algorithms, XGBoost, LightGBM, and CatBoost consistently dominate data science competitions for tabular data.

While training gets a lot of attention, inference speed is what matters in production.

For a deep dive, check out Supercharge Tree-Based Model Inference with Forest Inference Library in NVIDIA cuML.

If you’re new to accelerated data science, check out the hands-on workshops, Accelerate Data Science Workflows with Zero Code Changes and Accelerating End-to-End Data Scienc…

Stream the 10 new games in the cloud, including the beloved ‘Phoenix Wright: Ace Attorney Trilogy.’Suit up and head for the cloud.

Mecha BREAK, the popular third-person shooter, is now available to stream on GeForce NOW with NVIDIA DLSS 4 technology.

Mecha BREAK is now available for streaming on GeForce NOW, making it easy to jump into the action wherever, whenever.

In Capcom’s iconic Phoenix Wright: Ace Attorney Trilogy, players become Phoenix Wright and experience the thrill of battle in the fight to save their innocent clients in a court of law.

Stream the Phoenix Wright: Ace Attorney Trilogy across devices instantly, with crisp visuals and smooth performance that lets every “Hold it!” l…

That’s why the NVIDIA Nemotron family of multimodal AI models, datasets and techniques is openly available.

NVIDIA Nemotron is a collection of open-source AI technologies designed for efficient AI development at every stage.

, a pioneer in AI reasoning, led to the development of Nemotron math, code and reasoning open datasets that can be used to teach models how to think.

Start training and customizing AI models and agents with NVIDIA Nemotron models and data on Hugging Face, or try models for free on OpenRouter.

Stay up to date on agentic AI, Nemotron and more by subscribing to NVIDIA developer news, joining the developer community and following NVIDIA AI on LinkedIn, Instagram, X and Face…

NVIDIA’s Kari Briski joins Canada’s AI Minister Evan Solomon and Cohere’s Aiden Gomez in a panel this week, as TELUS unveils Canada’s first fully sovereign AI factory in Rimouski and RBC Capital Markets creates AI agents for financial services.

Canada’s role as a leader in artificial intelligence was on full display at this week’s All In Canada AI Ecosystem event.

Accenture will develop and deploy industry-specific solutions on the TELUS sovereign AI platform, accelerating AI adoption across its Canadian clients.

RBC Capital Markets’ work with NVIDIA software to build enterprise-grade AI agents for capital markets research reflects this trend, enabling Canadian institutions to deploy intell…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Models and the Primary AI Workloads Are Rapidly Evolving.

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

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

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

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

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

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

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

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

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

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

Expansions for the Federa…

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

Production risk was one of the areas we tackled first.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What is LLM grounding?

LLM grounding is analogous.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What gradient issues occur during foundation model training?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Best practices for ethical LLM developmentNavigating the regulatory landscape r…

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

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

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

Performance: All adaptation methods for Closed LLMs ach…

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

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

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

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

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

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

This is the key idea behind Mixture of Expert LLMs.

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

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

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

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

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

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

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

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

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

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

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

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

…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Sam Zimmerman, Kelley Rivoire, Thom…

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

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

https://ykilcher.com/discord Links:

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Twitter: https://twitter.com/ykilcher

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BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):

SubscribeStar: https:/…

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

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

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

What role were ruler and compass constructions really serving?

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

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

Additional editing: Jack Saxon

3d Blender model: Jan-Hendrik Müller

Additional Blender help: Thibaut Modrzyk (@Deepia)

Illustrations: Alex Zepherin/DonDada Studio

Drums: Jeremy Gustin

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

Visual edition of Book 1: htt…

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

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

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

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

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

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

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

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

https://www.youtube.com/w…

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

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

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

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

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

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

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

How AlphaGeometry combines logic and intuition.

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

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

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

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

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

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

0:00 - What's surprising

1:33 - Solve without AI

7:10 - Where AI comes in

12:48 - Grant's comments ------------------…

Diffusion models, CLIP, and the math of turning text into images

Welch Labs Book: https://www.welchlabs.com/resources/imaginary-numbers-book Sections

0:00 - Intro

3:37 - CLIP

6:25 - Shared Embedding Space

8:16 - Diffusion Models & DDPM

11:44 - Learning Vector Fields

22:00 - DDIM

25:25 Dall E 2

26:37 - Conditioning

30:02 - Guidance

33:39 - Negative Prompts

34:27 - Outro

35:32 - About guest videos + Grant’s Reaction Special Thanks to:

Jonathan Ho - Jonathan is the Author of the DDPM paper and the Classifier Free Guidance Paper.

https://arxiv.org/pdf/2006.11239

https://arxiv.org/pdf/2207.12598 Preetum Nakkiran - Preetum has an excellent introductory diffusion tutorial:

https://arxiv.org/pdf/24…

Make a math explainer, get feedback, and receive prizes: https://some.3b1b.co

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

An equally valuable form of support is to simply share the videos. ------------------ These animations are largely made using a custom Python library, manim. See the FAQ comments here:

https://3b1b.co/faq#manim

https://github.com/3b1b/manim

https://github.com/ManimCommunity/manim/ All code for specific videos is visible here:

https://github.com/3b1b/videos/ The music is by Vincent Rubinetti.

https://www.vincentrubinetti.com

https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown

https://open.spotify.com/…

Addressing viewer questions from the last video.

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

An equally valuable form of support is to share the videos. ------------------ These animations are largely made using a custom Python library, manim. See the FAQ comments here:

https://3b1b.co/faq#manim

https://github.com/3b1b/manim

https://github.com/ManimCommunity/manim/ All code for specific videos is visible here:

https://github.com/3b1b/videos/ The music is by Vincent Rubinetti.

https://www.vincentrubinetti.com

https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown

https://open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u ------------------ 3blue1brown is a ch…

Qubits, state vectors, and Grover's algorithm for search.

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

An equally valuable form of support is to share the videos. The subtitles on this video were done using AI, and are likely imperfect, but they are open for community corrections at https://criblate.com/ Adam Brown's paper on the connection between Grover's Algorithm and block collisions:

https://arxiv.org/pdf/1912.02207 If you want to learn the relevant underlying quantum mechanics here, a very friendly resource is the course Mithuna at Looking Glass Universe is currently putting together. See, for instance, this explainer of a qubit:…

Full video: https://youtu.be/RQWpF2Gb-gU

From this video: https://youtu.be/hFMaT9oRbs4

From this video with Terry Tao: https://youtu.be/hFMaT9oRbs4

From this video with Terry Tao: https://youtu.be/hFMaT9oRbs4

❤️ Check out the Fully Connected Conference by Weights & Biases - https://wandb.me/fclon2025-2min

20% discount code: FCLON2025-2MIN 📝 The paper is available here:

https://www.cemyuksel.com/research/hairmesh_rendering/ Try the demo and try to break it, it is super fun:

https://www.cemyuksel.com/research/hairmesh_rendering/demo/ 📝 My paper on simulations that look almost like reality is available for free here:

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

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

Benji Rabhan, B Shang, Christian Ahlin, Gordon Child…

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

Rent one of their GPUs with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here:

https://graphics.cs.utah.edu/research/projects/ogc/ Sources:

https://www.youtube.com/watch?v=CfEg7fucVYg 📝 My paper on simulations that look almost like reality is available for free here:

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

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

❤️ Check out Vast.ai and run DeepSeek or any AI project: https://vast.ai/papers 📝 Magica 2 is available here:

https://blog.dynamicslab.ai/ Try it out:

https://demo.dynamicslab.ai/chaos 📝 My paper on simulations that look almost like reality is available for free here:

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

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

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

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

Rent one of their GPUs with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here:

https://ge.in.tum.de/publications/very-large-scale-two-phase-flip/ 📝 My paper on simulations that look almost like reality is available for free here:

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

https://www.nature.com/articles/s41567-022-01788-5 Sources:

https://www.youtube.com/watch?v=ielqS1hkoLc

https://www.youtube.com/wat…

❤️ Check out the Fully Connected Conference by Weights & Biases - https://wandb.me/fclon2025-2min

20% discount code: FCLON2025-2MIN 📝 The paper is available here:

https://github.com/Lightricks/LTX-Video 📝 My paper on simulations that look almost like reality is available for free here:

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

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

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

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

Rent one of their GPU's with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here:

https://www.sdiolatz.info/publications/00ImageGS.html Genetic algorithm for the Mona Lisa:

https://users.cg.tuwien.ac.at/zsolnai/gfx/mona_lisa_parallel_genetic_algorithm/ 📝 My paper on simulations that look almost like reality is available for free here:

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

https://www.nature.com/art…

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

Rent one of their GPU's with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here:

https://nadmag.github.io/LightLab/ 📝 My paper on simulations that look almost like reality is available for free here:

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

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

Benji Rabhan, B S…

❤️ Check out DeepInfra and run DeepSeek or many other AI projects: https://deepinfra.com/papers 📝 The paper is available here:

https://graphics.stanford.edu/papers/waveblender/ 📝 My paper on simulations that look almost like reality is available for free here:

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

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

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

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

Rent one of their GPUs with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here:

https://diffusecloc.github.io/website/ 📝 My paper on simulations that look almost like reality is available for free here:

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

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

Benji Rabhan, …

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

https://arxiv.org/abs/2405.14595 📝 My paper on simulations that look almost like reality is available for free here:

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

https://www.nature.com/articles/s41567-022-01788-5 Sources:

https://www.youtube.com/shorts/Mq7zzK-ZiWI

https://www.youtube.com/watch?v=A_Cdz-QBlT4 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

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

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

Rent one of their GPU's with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b Genie 3:

https://deepmind.google/discover/blog/genie-3-a-new-frontier-for-world-models/ Sources:

https://x.com/amoufarek/status/1955776162447102238

https://x.com/amoufarek/status/1955299375548076382

https://x.com/holynski_/status/1953882726656094622

https://x.com/holynski_/status/1953879983535141043

https://x.com/RuiHuang_art/status/1954716703340048877

https://x.com/mattmcgill_/status/1953827141700…

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

Rent one of their GPU's with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper "Physically Controllable Relighting of Photographs" is available here:

https://yaksoy.github.io/PhysicalRelighting/ 📝 My paper on simulations that look almost like reality is available for free here:

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

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

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

Rent one of their GPU's with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b Try it online:

https://gpt-oss.com/ 📝 My paper on simulations that look almost like reality is available for free here:

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

https://www.nature.com/articles/s41567-022-01788-5 Humanity's Last Exam:

https://agi.safe.ai/ Sources:

https://x.com/flavioad/status/1952792389636198489

https://x.com/kwindla/status/1952947685…

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

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

https://hunyuan-gamecraft.github.io/ 📝 My paper on simulations that look almost like reality is available for free here:

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

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

Benji Rabhan, B Shang, Christian Ahlin,…

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

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

https://graphics.cs.utah.edu/research/projects/merging-and-splitting/ 📝 My paper on simulations that look almost like reality is available for free here:

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

https://www.nature.com/articles/s41567-022-01788-5 Video game glitch: https://www.youtube.com/watch?v=fZgRVatBXTE 🙏 We would like to thank our generous…

Генерация видео — это творческая и интересная, но одновременно сложная задача, требующая большого количества ресурсов. ‎ ‎Расскажу, как в команде Kandinsky обучают большие трансформеры для генерации видео: какие техники используют для эффективной утилизации кластера из огромного количества GPU. Обсудим DDP, FSDP, activation checkpointing, tensor & sequence parallel и другие алгоритмы. ‎‎

https://github.com/ai-forever/Kandinsky-5/tree/pml_conf На практической части мастер-класса покажу, как ускорить инференс и генерацию видео, распараллелив трансформер через библиотеку pytorch с помощью алгоритма tensor parallel.

После нашумевшего анонса Sora прошло чуть больше года, а генеративные модели видео уже умеют создавать впечатляющие клипы длиной 2–8 секунд.

Такая ограниченность не мешает в простых задачах, но становится серьёзным барьером, когда речь идёт о множестве согласованных генераций. Например, о создании длинных роликов или сюжетных историй. На мастер-классе мы разберём, какие подходы могут быть полезны, как они реализованы в опенсорсе и на закрытых платформах. А ещё — используя опенсорс-модели, попробуем собрать собственный пайплайн для генерации сюжетного видео.

Это фрагмент из доклада Александра Исакова, руководителя группы прогнозирования в Яндекс Лавке, который он прочитал на конференции AHA!25. Александр рассказал о создании и внедрении системы среднесрочного прогнозирования для Яндекс Лавки. Такое решение точно прогнозирует заказы на полугодовой период и помогает планировать ресурсы в условиях быстрого роста сервиса и высокой волатильности спроса. А больше подробностей вы найдёте в полной записи выступления — смотрите её на нашем канале. #яндекс #яндекславка #прогнозирование #datascience #machinelearning #ai #bigdata #аналитика #aha25 #ритейл

Это фрагмент из доклада Александра Исакова, руководителя группы прогнозирования в Яндекс Лавке, который он прочитал на конференции AHA!25. Александр рассказал о создании и внедрении системы среднесрочного прогнозирования для Яндекс Лавки. Такое решение точно прогнозирует заказы на полугодовой период и помогает планировать ресурсы в условиях быстрого роста сервиса и высокой волатильности спроса. А больше подробностей вы найдёте в полной записи выступления — смотрите её на нашем канале. #яндекс #яндекславка #prophet #прогнозирование #datascience #machinelearning #ai #bigdata #аналитика #aha25

Это фрагмент из доклада Александра Исакова, руководителя группы прогнозирования в Яндекс Лавке, который он прочитал на конференции AHA!25. Александр рассказал о создании и внедрении системы среднесрочного прогнозирования для Яндекс Лавки. Такое решение точно прогнозирует заказы на полугодовой период и помогает планировать ресурсы в условиях быстрого роста сервиса и высокой волатильности спроса. А больше подробностей вы найдёте в полной записи выступления — смотрите её на нашем канале. #яндекс #яндекславка #aha25 #прогнозирование #datascience #ml #ai #bigdata #аналитика

Это фрагмент из доклада Александра Исакова, руководителя группы прогнозирования в Яндекс Лавке, который он прочитал на конференции AHA!25. Александр рассказал о создании и внедрении системы среднесрочного прогнозирования для Яндекс Лавки. Такое решение точно прогнозирует заказы на полугодовой период и помогает планировать ресурсы в условиях быстрого роста сервиса и высокой волатильности спроса. А больше подробностей вы найдёте в полной записи выступления — смотрите её на нашем канале. #яндекс #яндекславка #aha25 #datascience #ml #ai #прогнозирование #аналитика #bigdata #прогнозспроса

Это фрагмент доклада Николая Савушкина, руководителя службы рекомендательных технологий в Яндексе, с конференции AHA!25. Николай рассказал, как устроена современная персонализация, как большие генеративные модели внедряют в рекомендательные системы Яндекса и что ждёт нас в будущем. Полное выступление смотрите на нашем канале. #яндекс #aha25 #рекомендательныесистемы #рекомендации #персонализация #ai #ml #искусственныйинтеллект #нейросети #машиннообучение #generativeai #bigmodels #recommendations #datascience #технологиибудущего #conference

Это фрагмент доклада Николая Савушкина, руководителя службы рекомендательных технологий в Яндексе, с конференции AHA!25. Николай рассказал, как устроена современная персонализация, как большие генеративные модели внедряют в рекомендательные системы Яндекса и что ждёт нас в будущем. Полное выступление смотрите на нашем канале. #яндекс #aha25 #рекомендательныесистемы #рекомендации #персонализация #ai #ml #искусственныйинтеллект #нейросети #машиннообучение #generativeai #bigmodels #recommendations #datascience #технологиибудущего #conference

Это фрагмент доклада Николая Савушкина, руководителя службы рекомендательных технологий в Яндексе, с конференции AHA!25. Николай рассказал, как устроена современная персонализация, как большие генеративные модели внедряют в рекомендательные системы Яндекса и что ждёт нас в будущем. Полное выступление смотрите на нашем канале. #яндекс #aha25 #рекомендательныесистемы #рекомендации #персонализация #ai #ml #искусственныйинтеллект #нейросети #машиннообучение #generativeai #bigmodels #recommendations #datascience #технологиибудущего #conference

Data Dojo — это сообщество ML-экспертов. Здесь обсуждают тренды, разбирают реальные задачи, делятся опытом и практикуются. Додзё в японской культуре — место Пути, где совершенствуют не только мастерство, но и дух. Мы перенесли этот принцип в мир данных. Программа:

17:00 Приветственное слово | Владислав Офицеров, модератор встречи, руководитель команды развития нейронных технологий международного поиска, и Пётр Ермаков, ML-бренд-директор 17:05 Лекция: R&D в Яндексе | Алексей Колесов, руководитель команды NLP 17:35 Лекция: Актуальные технологии в рекомендательных системах | Николай Савушкин, руководитель команды рекомендательных технологий в Поиске 19:05 Разбор ML-задачи 19:50 Лекция: Как ML-…

Спикер: Владислав Тимофеев, Dev Tools Engineer, Russian Research Institute Data Fest 2025: https://ods.ai/events/datafest2025

Презентацию к докладу можно скачать в треке секции Code Generation (AI4SE): https://ods.ai/tracks/df25-ai4se

______

Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

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

Спикер: Владимир Скурихин, Иннотех, ML-инженер Тема: Retrieval-Augmented Fill-in-the-Middle: архитектура генерации структурированных документов с LLM. Data Fest 2025: https://ods.ai/events/datafest2025 Презентацию к докладу Вы можете скачать в треке секции Data Fusion: https://ods.ai/tracks/df25-data-fusion ______

Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

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

Спикер: Александр Мелехин, Инженер лаборатории интеллектуального транспорта, Центр когнитивного моделирования, МФТИ Data Fest 2025: https://ods.ai/events/datafest2025

Презентацию к докладу Вы можете скачать в треке секции Robotics: https://ods.ai/tracks/df25-robotics

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Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

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

Спикер: Максим Куркин, Младший научный сотрудник лаборатории FusionBrain, AIRI

Тема: MMReD: A Cross-Modal Benchmkar for Dense Context Reasoning и почему нельзя замерять только NIAH Data Fest 2025: https://ods.ai/events/datafest2025

Презентацию к докладу Вы можете скачать в треке секции DL Frontier: https://ods.ai/tracks/df25-dl-frontier

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Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

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

Спикер: Андрей Савченко, Д.т.н., Директор по науке, Лаборатория ИИ, Сбер Data Fest 2025: https://ods.ai/events/datafest2025

Трек-секция GenAI от СБЕР: https://ods.ai/tracks/df25_sber_genai

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Спикер: Максим Смоляков, Управление экспериментальных систем машинного обучения, Исполнительный директор, Сбер Data Fest 2025: https://ods.ai/events/datafest2025 Презентацию к докладу Вы можете скачать в треке секции ML in Music: https://ods.ai/tracks/df25-ml-in-music ______

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Спикер: Егор Швецов, Skoltech, инженер-исследователь Тема: Рассказ про методы оптимизации обучения моделей за счет квантизации, спарсификации и прунинга на примере рекомендательных систем. Data Fest 2025: https://ods.ai/events/datafest2025

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Четырнадцатый выпуск подкаста Капитанский мостик, обсуждение новостей из мира ИИ за прошедшую неделю и не только. Оставляйте новости к следующему выпуску в нашем канале в Mattermost. ИИ-саммари: В этом выпуске обсуждаются актуальные темы, такие как искусственные актеры в кино, влияние социальных сетей на видео-контент, изменения в кинематографе с переходом к сериалам, а также вопросы о пузыре инвестиций в ИИ и состоянии космической индустрии. В этом разговоре обсуждаются различные темы, включая юбилейные события, финансовые спекуляции, кризисы роста, криптовалюты в Казахстане, инвестиции в искусственный интеллект, инициативы JetBrains, кодирование с помощью ИИ и новые технологии в области г…

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Patreon:

https://www.patreon.com/primerlearning Helpful resources if you want to learn more about neural networks

https://www.youtube.com/@AndrejKarpathy

https://course.fast.ai/

https://www.youtube.com/@WelchLabsVideo

https://www.youtube.com/@3blue1brown Early papers. These probably aren't helpful for understanding the concepts in this video, but if you're interested in history.

The Perceptron – A perceiving and recognizing automaton: https://bpb-us-e2.wpmucdn.com/websites.umass.edu/dist/a/27637/files/2016/03/rosenblatt-1957.pdf

The Perceptron: A probabilistic model for information storage and organization in the brain: https://www.ling.upenn.edu/courses/cogs501/Rosenblatt1958.pdf A Logical…

Patreon: https://www.patreon.com/primerlearning Ageless book: https://www.amazon.com/Ageless-Science-Getting-Older-Without/dp/0525566317/ Papers and other further reading:

Diversity of aging across the tree of life: https://pmc.ncbi.nlm.nih.gov/articles/PMC4157354/

Antagonistic pleiotropy and p53: https://pmc.ncbi.nlm.nih.gov/articles/PMC2771578/

An unsolved problem of biology (Medawar): https://ia903408.us.archive.org/31/items/medawar-1952-unsolved-problem/Medawar1952-Unsolved-Problem.pdf

Evolution of the mutation rate: https://pmc.ncbi.nlm.nih.gov/articles/PMC2910838/

Our World in Data Life Expectancy explainer: https://ourworldindata.org/life-expectancy-how-is-it-calculated-and-how-shoul…

Pavel Durov is the founder and CEO of Telegram.

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Transcript:https://lexfridman.com/pavel-durov-transcriptCONTACT LEX:Feedback – give feedback to Lex: https://lexfridman.com/surveyAMA – submit questions, videos or call-in: https://lexfridman.com/amaHiring – join our team: https://lexfridman.com/hiringOther – other ways to get in touch: https://lexfridman.com/contactEPISODE LINKS:Pavel’s Telegram: https://t.me/durovPavel’s X: https://x.com/durovTelegram: https://telegram.org/Telegram Contests: https://contest.c…

Norman Ohler is a historian and author of “Blitzed: Drugs in the Third Reich,” a book that investigates the role of psychoactive drugs, particularly stimulants such as methamphetamine, in the military history of World War II.

It is a book that two legendary historians Ian Kershaw and Antony Beevor give very high praise for its depth of research.

Norman also wrote “Tripped: Nazi Germany, the CIA, and the Dawn of the Psychedelic Age”, and he is working on a new book “Stoned Sapiens” looking at the history of human civilization through the lens of drugs.

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Dave Hone is a paleontologist, expert on dinosaurs, co-host of the Terrible Lizards podcast, and author of numerous scientific papers and books on the behavior and ecology of dinosaurs.

He lectures at Queen Mary University of London on topics of Ecology, Zoology, Biology, and Evolution.

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Dave Plummer is a programmer, former Microsoft software engineer (Windows 95, NT, XP), creator of Task Manager, author of two books on autism, and host of the Dave’s Garage YouTube channel, where he shares stories from his career, insights on software development, and deep dives into technology.

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Scott Horton is the director of the Libertarian Institute, editorial director of Antiwar.com, host of The Scott Horton Show, co-host of Provoked, and for the past three decades a staunch critic of U.S. military interventionism.

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Keyu Jin is an economist specializing in China’s economy, international macroeconomics, global trade imbalances, and financial policy.

She is the author of The New China Playbook: Beyond Socialism and Capitalism.

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Jack Weatherford is an anthropologist and historian specializing in Genghis Khan and the Mongol Empire.

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Demis Hassabis is the CEO of Google DeepMind and Nobel Prize winner for his groundbreaking work in protein structure prediction using AI.

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David Heinemeier Hansson (aka DHH) is a legendary programmer, creator of Ruby on Rails, co-owner & CTO of 37signals that created Basecamp, HEY, & ONCE, and is a NYT-best-selling author (with Jason Fried) of 4 books: REWORK, REMOTE, Getting Real, and It Doesn’t Have To Be Crazy At Work.

He is also a race car driver, including a class-winning performance at the 24 hour Le Mans race.

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Debate on Iran war between Scott Horton and Mark Dubowitz.

Scott Horton is the author and director of the Libertarian Institute, editorial director of Antiwar.com, host of The Scott Horton Show, and for the past three decades, a staunch critic of U.S. foreign policy and military interventionism.

Mark Dubowitz is the chief executive of the Foundation for Defense of Democracies, host of the Iran Breakdown podcast, and a leading expert on Iran and its nuclear program for over 20 years.

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Terence Tao is widely considered to be one of the greatest mathematicians in history.

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Sundar Pichai is CEO of Google and Alphabet.

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James Holland is a historian specializing in World War II.

He hosts a podcast called WW2 Pod: We Have Ways of Making You Talk.

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Go to https://drinkag1.com/lexNotion: Note-taking and team collaboration.

Oliver Anthony is singer-songwriter who first gained worldwide fame with his viral hit Rich Men North of Richmond.

He became a voice for many who are voiceless, with many of his songs speaking to the struggle of the working class in modern American life.

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Janna Levin is a theoretical physicist and cosmologist specializing in black holes, cosmology of extra dimensions, topology of the universe, and gravitational waves.

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

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

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

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

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

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Prior to med school, Daniel pursued experiences that cultivated his interest in the application of AI in medical practice and education.

Really, really looking forward to this chat.

There’s AI before ChatGPT and before, you know, generative AI really became a big thing, and then afterwards.

And then after we talk about what’s really happening, what do you think should happen in medical education given the reality of generative AI?

And I do agree [that] AI really gives us real hope that we can make it true.

Our goal is to learn from their successes and their stumbles to move the science and practice of AI testing forward.

We have examples, like the pharmaceutical or medical device industry experts with whom you spoke, that’s really, you know, testing … there is a pre-deployment requirement.

And the third is just how rigid versus adaptive these testing and evaluation regimes or frameworks are in these different domains.

I really agree that there has been a lot of emphasis to date on, sort of, testing models upstream, the AI model evaluation.

You know, I think there’s been real progress already in the AI evaluation and testing ecosystem in the public-private partnership context.

Absolutely, I really, really was.

As a principal director on the Microsoft AI Red Team, Tori leads all AI security and safety red team operations, as well as dangerous capability testing, to directly inform C-suite decision-makers.

This year, we’ve pulled a lot of those assets and insights into the Azure [AI] Foundry AI Red Teaming Agent (opens in new tab).

So you can get a little taste of what we do day to day in the AI Red Teaming Agent.

WESTERHOFF: I think the most important takeaway from those lessons is that AI security is truly a team sport.

Dr. Eric Topol is the executive vice president of the biomedical research non-profit Scripps Research, where he founded and now directs the Scripps Research Translational Institute.

Let’s continue our deep dive on AI and biomedical research with this conversation with Noubar Afeyan:LEE: Noubar, thanks so much for joining.

And there’s the origin story of contact with AI, you know, before the emergence of generative AI and afterwards.

What is going on today with respect to AI really being used for something meaningful in the design and development of drugs?

TOPOL: You would read about how, you know, data is the new oil and, you know, gold and whatnot.

Our goal is to learn from their successes and their stumbles to move the science and practice of AI testing forward.

During the pre-market phase, medical testing establishes baseline safety and effectiveness metrics through bench testing, performance standards, and clinical studies.

SULLIVAN: So medical devices face a pretty prescriptive multi-level testing path before they hit the market.

We are looking into medical devices, as well, obviously, but also other technologies in advanced medical computing.

So we see Phase 3 trials as something that occurs in the medical devices and pharmaceuticals field.

As generative AI continues to advance, Microsoft has gathered a range of experts—from genome editing to cybersecurity—to share how their fields approach evaluation and risk assessment.

CHARO: Well, you know, genome editing is both very old and very new.

Now the earliest forms of genome editing were very inefficient, and so we didn’t worry that much.

But the bottom-line thing to remember, the way to really think about it is, we don’t regulate genome editing; we regulate the things that use genome editing.

And she said, you know, we don’t regulate genome editing; we regulate the things that use genome editing.

Our goal is to learn from their successes and their stumbles to move the science and practice of AI testing forward.

And I think, really, there are two reasons why tech is so, kind of, representative of that kind of challenge that I’ve always found fascinating.

Continues to be a really important topic in the AI policy conversation right now, I think, for really good reason.

Testing is an important component for governance and AI and, of course, in all of these other domains, as well.

I think about almost, like, in the near to mid-term, like three issues that we need to address in the AI, kind of, policy and testing context.

LEE: Yeah, yeah.

It cannot—as, you know, Bill was saying—it cannot learn from your document.

And I don’t know if the two of you remember, but I ended up doing a lot of tests.

I don’t know if you know, but just recently, there was a paper that was published on a scientific discovery using o3- mini (opens in new tab).

Like, if you have a human trained for one task and you put them into another task, then you don’t … you often don’t know.

So I don’t think we should be surprised that business schools matter on this because we care about management.

That’s really going to change the way, like, middle school works, was my thinking at the time.

We’ve gone from AI being highly discriminative to AI that’s able to explore the world in particular ways.

The symptoms that they’re showing are quite different, and also their compliance is really, really different.

LEE: Yeah, really, really interesting.

And single-cell foundation models claim to be capable of unraveling deeper insights than ever before.

Basically, we showed that single-cell foundation models perform worse in settings that are fundamental to biological discovery than much simpler machine learning and statistical methods that were used in the field before single-cell foundation models emerged and are the go-to standard for unpacking meaning from these complicated experiments.

And the way to understand this is because single-cell foundation models are trained in a way that tries to expose these models to millions of single-cells.

But let’s also talk about the impact for methodologists, people who are trying to improve these s…

This is such exciting work about environmental forecasting, so we’re happy to have the two of you join us today.

Mostly because AI weather forecasting models are computationally much more efficient and can even be more accurate.

What’s unfortunate though, about this big step forward, is that these developments are mostly limited to the setting of weather forecasting.

Weather forecasting is very important, obviously, but there are many other important environmental forecasting problems out there, such as air pollution forecasting or ocean wave forecasting.

STANLEY: Current approaches have really focused training very specifically on weather forecasting models.

LUNGREN: And now it really feels like this collaborative effort, you know, really can help start to extend that mission.

I think, you know, Will and Smitha, that we definitely feel the passion and the innovation.

Again, you know, in text, you refer to that earlier and certainly off the shelf, there’s really powerful applications.

LUNGREN: So, I think AI has always been thought of as a savior kind of technology.

And I guess for my part, I think really what we’re going to see is a massive unleash of creativity.

In this episode of Data Skeptic's Recommender Systems series, host Kyle Polich interviews Dr. Kunal Mukherjee, a postdoctoral research associate at Virginia Tech, about the paper "Z-REx: Human-Interpretable GNN Explanations for Real Estate Recommendations" The discussion explores how the post-COVID real estate landscape has created a need for better recommendation systems that can introduce home buyers to emerging neighborhoods they might not know about. Dr. Mukherjee, explains how his team developed a graph neural network approach that not only recommends properties but provides human-interpretable explanations for why certain regions are suggested. The conversation covers the advantages o…

In this episode of Data Skeptic, we explore the challenges of studying social media recommender systems when exposure data isn't accessible. Our guests Sabrina Guidotti, Gregor Donabauer, and Dimitri Ognibene introduce their innovative "recommender neutral user model" for inferring the influence of opaque algorithms.

In this episode of Data Skeptic, we dive into eco-friendly AI with Antonio Purificato, a PhD student from Sapienza University of Rome. Antonio discusses his research on "EcoAware Graph Neural Networks for Sustainable Recommendations" and explores how we can measure and reduce the environmental impact of recommender systems without sacrificing performance.

Kyle reveals the next season's topic will be "Recommender Systems". Asaf shares insights on how network science contributes to the recommender system field.

Kyle and Asaf discuss a project in which we link former guests of the podcast based on their co-authorship of academic papers.

In this episode, Professor Pål Grønås Drange from the University of Bergen, introduces the field of Parameterized Complexity - a powerful framework for tackling hard computational problems by focusing on specific structural aspects of the input. This framework allows researchers to solve NP-complete problems more efficiently when certain parameters, like the structure of the graph, are "well-behaved". At the center of the discussion is the network diversion problem, where the goal isn’t to block all routes between two points in a network, but to force flow - such as traffic, electricity, or data - through a specific path. While this problem appears deceptively similar to the classic "Min.Cu…

In this episode, we learn why simply analyzing the structure of a network is not enough, and how the dynamics - the actual mechanisms of interaction between components - can drastically change how information or influence spreads. Our guest, Professor Baruch Barzel of Bar-Ilan University, is a leading researcher in network dynamics and complex systems ranging from biology to infrastructure and beyond. BarzelLab BarzelLab on Youtube Paper in focus: Universality in network dynamics, 2013

In this episode, Kyle does an overview of the intersection of graph theory and computational complexity theory. In complexity theory, we are about the runtime of an algorithm based on its input size. For many graph problems, the interesting questions we want to ask take longer and longer to answer! This episode provides the fundamental vocabulary and signposts along the path of exploring the intersection of graph theory and computational complexity theory.

In this episode, listeners will learn about Actantial Networks—graph-based representations of narratives where nodes are actors (such as people, institutions, or abstract entities) and edges represent the actions or relationships between them. The one who will present these networks is our guest Armin Pournaki, a joint PhD candidate at the Max Planck Institute and Sciences, who specializes in computational social science, where he develops methods to extract and analyze political narratives using natural language processing and network science. Armin explains how these methods can expose conflicting narratives around the same events, as seen in debates on COVID-19, climate change, or the wa…

How to build artificial intelligence systems that understand cause and effect, moving beyond simple correlations? As we all know, correlation is not causation. "Spurious correlations" can show, for example, how rising ice cream sales might statistically link to more drownings, not because one causes the other, but due to an unobserved common cause like warm weather. Our guest, Utkarshani Jaimini, a researcher from the University of South Carolina's Artificial Intelligence Institute, tries to tackle this problem by using knowledge graphs that incorporate domain expertise. Knowledge graphs (structured representations of information) are combined with neural networks in the field of neurosymbo…

In this episode we talk with Manita Pote, a PhD student at Indiana University Bloomington, specializing in online trust and safety, with a focus on detecting coordinated manipulation campaigns on social media. Key insights include how coordinated reply attacks target influential figures like journalists and politicians, how machine learning models can detect these inauthentic campaigns using structural and behavioral features, and how deletion patterns reveal efforts to evade moderation or manipulate engagement metrics. Follow our guest X/Twitter Google Scholar Papers in focus Coordinated Reply Attacks in Influence Operations: Characterization and Detection ,2025 Manipulating Twitter throug…

Kyle discusses the history and proof for the small world hypothesis.

Breaking news: Jon Krohn welcomes Adrian Kosowski to the show to talk about the groundbreaking research happening at Pathway. Adrian and his team demonstrate how they have brought attention in AI closer to the way the brain functions, creating, in essence, a “massively parallel system of [artificial] neurons” that communicate with one another and exhibit properties similar to natural neurons. The goal is to move beyond the current limitations of transformers, where reasoning can be generalized across more complex and extended reasoning patterns, approximating a more human-like approach to problem-solving. This episode is brought to you by the Trainium2, the latest AI chip from AWS, by ⁠Dell…

Prompt injections, malicious code, and AI agents: In this week’s Five-Minute Friday, Jon Krohn looks into the current security weaknesses found in AI systems. A structural vulnerability that The Economist dubs a “lethal trifecta” could cause havoc for AI users, unless we take the necessary steps to contain our systems. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/928⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Earlier this year, David Loker joined CodeRabbit as their Director of AI. As more people come to write code with the help of large language models, David believes CodeRabbit will become a helpful assistant for code reviewing and pull requests. He tells Jon Krohn how CodeRabbit assists developers with real-time feedback, as well as the reality of vibe coding, the optimization challenges of agentic AI, and other pressing questions in AI and tech. This episode is brought to you by the ⁠Dell⁠, by ⁠Intel⁠, by ⁠Gurobi⁠ and by ⁠ODSC, the Open Data Science Conference⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/927⁠⁠ In this episode you will learn: (01:26) How CodeRabbit helps …

In this Five-Minute Friday, Jon Krohn explores how AI is reshaping the legal industry. He investigates how AI tools are helping lawyers make conclusions faster, how paralegals are being retrained, and the latest in-demand role in law (hint: It concerns AI). Listen to hear how Harvey AI and Thomson Reuters’ CoCounsel are using AI to help lawyers get ahead. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/926⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Tech innovation’s dependence on economic systems, trust in technology throughout history, and job displacement through AI: The Dieter Schwartz Associate Professor of AI and work at the University of Oxford, Carl Benedikt Frey, talks to Jon Krohn about his latest book, How Progress Ends, as well as how different economic systems deal with innovation and scaling, dealing with the homogeneity of generative AI output, and how to stay afloat in the new wave of job automation. This episode is brought to you by the ⁠Dell⁠, by ⁠Intel⁠, by ⁠ODSC, the Open Data Science Conference⁠ and by ⁠Gurobi⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/925⁠⁠⁠⁠⁠ Interested in sponsoring a Super…

MIT lab NANDA (“Networked AI Agents in Decentralized Architecture”) reveals less than promising results for the future of AI adoption in businesses. According to “The GenAI Divide: State of AI in Business 2025”, a whopping 95% of enterprise AI projects “are getting zero return” on their $30-40 billion investment. Jon Krohn takes this Five-Minute Friday to look into why this has happened, with help from a critical response to the report written by Futuriom’s R. Scott Raynovich. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/924⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Graphs, but not as you would expect them: Graph analytics guru Amy Hodler speaks to Jon Krohn about the graph data structure and graph applications, graph algorithms, graph RAG, and graphs as memory systems for AI agents. We can use graphs in a surprising number of ways. Money laundering and fraud, as well as supply-chain crime, leave breadcrumbs at multiple “touch-points” over time, behaviors that graphs are better suited to reveal than rows and tables. Amy sees that most interest in graphs has been in the cybersecurity space. But this work isn’t only restricted to fighting crime! Listen to the episode to hear more case examples and how to get into graph work. This episode is brought to yo…

Hugo Dozois-Caouette speaks to Jon Krohn about his startup MaintainX and how he secured $100 million in venture capital. MaintainX manages and maintains computerized maintenance management systems (CMSs), or work-execution software, for the industrial and manufacturing industries. This “digitized version of a clipboard” with the help of web and mobile applications, provide a list of procedures, guidelines and regulations to help increase worker productivity and give a company the data-driven insights it needs to refine its processes. Listen to the episode to hear Hugo’s thoughts on the gaps in the manufacturing industry that technology can fill, the tech stack used by MaintainX, and the dis…

Using Windows for AI development and the bleeding edge of NPUs: Shirish Gupta and Ish Shah from Dell Technologies speak to Jon Krohn about the latest products from Dell, the future of neural-processing units (NPUs), and how AI developers can make sound hardware investments. This episode is brought to you by the Trainium2, the latest AI chip from AWS, by ODSC, the Open Data Science Conference and by Gurobi. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/921⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (04:18) Why Windows still outranks other operating systems (20…

This month’s episode of In Case You Missed It gives us reasons to be cautiously optimistic about the future of large language models (LLMs), with guests discussing what to do about recent reports that found AI agents blackmailed human users when threatened, the importance of post-training LLMs, and the training we have available for data and AI engineers to create robust, secure, and useful AI. Jon Krohn includes clips from his interviews with Akshay Agrawal (Episode 911), Julien Launay (Episode 913), Michelle Yi (Episode 915), and Kirill Eremenko (Episode 917). Additional materials: ⁠⁠⁠⁠⁠www.superdatascience.com/920⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natali…

PyTorch, AGI, and the future of alignment research: Aurélien Géron joins Jon Krohn in this live interview to talk about the fourth edition of his bestselling Hands-On Machine Learning as well as what superintelligence makes him hopeful for, as well as what concerns him about machines surpassing human intelligence. This episode is brought to you by Gurobi and by the Dell AI Factory with NVIDIA⁠ Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/919⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (02:04) Why Aurélien wrote Hands-On Machine Learning (20:54) How Aurélien …

In this Five-Minute Friday, Jon Krohn introduces listeners to CrewAI, an open-source Python framework that can create and manage multi-agent teams. The clue is in the title: CrewAI assembles specialized agents into single “crews” that achieve complex goals between them. CrewAI’s agent teams can also learn and iterate, meaning that after the crew has achieved its goals for the first time, they can refine and tailor their approach to future goals. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/918⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Founder of SuperDataScience, Kirill Eremenko, talks to Jon Krohn about how he found the best tools and approaches to help launch his 8-week AI engineering bootcamp. He breaks down the topics participants cover each week, and he also shares his tips with listeners who might want to start their own tech bootcamp or sign up for SuperDataScience’s September 2025 cohort. This episode is brought to you by the Dell AI Factory with NVIDIA and by ODSC, the Open Data Science Conference Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/917⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will…

GPT-5 has just been released, but with not very much fanfare. In this Five-Minute Friday, Jon Krohn asks if GPT-5 deserves the community’s underwhelmed response to its release. He outlines five features of the model and explains why people might be feeling less than enthusiastic in the broader context of LLM development. Which LLMs are leading the way, and which are still playing the game of catch-up? Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/916⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Tech leader, investor, and Generationship cofounder Michelle Yi talks to Jon Krohn about finding ways to trust and secure AI systems, the methods that hackers use to jailbreak code, and what users can do to build their own trustworthy AI systems. Learn all about “red teaming” and how tech teams can handle other key technical terms like data poisoning, prompt stealing, jailbreaking and slop squatting. This episode is brought to you by ⁠Trainium2, the latest AI chip from AWS⁠ and by the ⁠Dell AI Factory with NVIDIA⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/915⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship…

ReferencesWar On The Rocks: https://warontherocks.com/2025/08/ukraine-isnt-the-model-for-winning-the-innovation-war/LinkedIn: https://www.linkedin.com/in/jonasrsinger/Spotify: https://tr.ee/Omy_1X8k1UApple Podcast: https://podcasts.apple.com/us/podcast/defence-innovation-podcast/id1797131332YouTube: https://youtube.com/@DefenceInnovationpodcast?si=cu2WlnVgL5XKnM0pSponsorsThis episode is proudly sponsored by Amethix Technologies.

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

Discover more at https://amethix.comThis episode is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers…

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

Discover more at amethix.comDSH is brought to you by Intrepid AI.

🐦 Twitter: @DataScienceAtHome📘 LinkedIn: https://www.linkedin.com/in/fragadaleta/Instagram: https://www.instagram.com/datascienceathome/Facebook: https://www.facebook.com/datascienceAHLinkedIn: https://www.linkedin.com/company/data-science-at-home-podcastDiscord Channel: https://discord.gg/4UNKGf3NEW TO DATA SCIENCE AT HOME?

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

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

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

Discover more at amethix.comDSH is brought to you by Intrepid AI.

🐦 Twitter: @DataScienceAtHome📘 LinkedIn: https://www.linkedin.com/in/fragadaleta/Instagram: https://www.instagram.com/datascienceathome/Facebook: https://www.facebook.com/datascienceAHLinkedIn: https://www.linkedin.com/company/data-science-at-home-podcastDiscord Channel: https://discord.gg/4UNKGf3NEW TO DATA SCIENCE AT HOME?

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

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

The brutal truth about why Silicon Valley is blowing billions on glorified autocomplete while pretending it’s the next iPhone.

We’re diving deep into the AI investment circus where VCs who can’t code are funding companies that barely understand their own technology.

From blockchain déjà vu to the “ChatGPT wrapper” economy—this episode will make you question every AI valuation you’ve ever seen.

Fair warning: We’re naming names and calling out the hype.

Don’t listen if you work at a “revolutionary AI startup” that’s just OpenAI’s API with a pretty interface.

From the viral article “Tech’s Dumbest Mistake: Why Firing Programmers for AI Will Destroy Everything” on my newsletter at https://defragzone.substack.com/p/techs-dumbest-mistake-why-firinghere are my thoughts about AI replacing programmers…🎙️ Sponsors AGNTCY — The open source collective building the Internet of Agents🌐 https://www.agntcy.org✨ Connect with us!

🐦 Twitter: @DataScienceAtHome📘 LinkedIn: https://www.linkedin.com/in/fragadaleta/Instagram: https://www.instagram.com/datascienceathome/Facebook: https://www.facebook.com/datascienceAHLinkedIn: https://www.linkedin.com/company/data-science-at-home-podcastDiscord Channel: https://discord.gg/4UNKGf3NEW TO DATA SCIENCE AT HOME?

Data Scie…

In this episode of Data Science at Home, we explore the fascinating world of neuromorphic computing — a brain-inspired approach to computation that could reshape the future of AI and robotics.

The episode breaks down how neuromorphic systems differ from conventional AI architectures like transformers and LLMs, diving into spiking neural networks (SNNs), their benefits in energy efficiency and real-time processing, and their limitations in training and scalability.

Real-world applications are highlighted, including low-power drones, hearing aids, and event-based cameras.

Francesco closes with a vision of hybrid systems where neuromorphic chips and LLMs coexist, blending biological inspiratio…

This episode explores the invisible battlespace of cyber and electronic warfare, where AI takes center stage.

SponsorsBuilding multi-agent software is hard — agent-to-agent and agent-to-tool communication is still the wild west.

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

Discover more at amethix.comWarcoded is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers and defense innovators the tools to prototype, simulate, and deploy autonomous systems with confidence.

Swarming the Battlefield explores how artificial intelligence is revolutionizing combat through coordinated drone swarms.

This episode uncovers how these intelligent agents turn the chaos of the battlefield into a synchronized dance of machine warfare.

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

Discover more at amethix.comWarcoded is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers and defense innovators the tools to prototype, simulate, and deploy autonomous systems with confidence.

In this gripping follow-up, we dive into how AI is transforming kinetic operations—from identifying a threat to executing a strike.

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

Discover more at amethix.comWarcoded is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers and defense innovators the tools to prototype, simulate, and deploy autonomous systems with confidence.

Whether it’s in the sky, on the ground, or in orbit—if it’s intelligent and mobile, Intrepid helps you build it.

Welcome to DSH/WarcodedWe explore how AI is transforming ISR (Intelligence, Surveillance, Reconnaissance)—from satellite imagery to drone feeds.

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

Discover more at amethix.com.”Warcoded is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers and defense innovators the tools to prototype, simulate, and deploy autonomous systems with confidence.

Learn more at intrepid.ai.”#AI #defensetech #ISR #LLM #Warcoded #DataScienceAtHome #OSINT #SIGINT #dronewarfare

🎙️ In this episode of Data Science at Home, we sit down with Kenny Vaneetvelde, the mastermind behind Atomic Agents, a groundbreaking framework redefining AI development.

🔍 Discover how atomicity simplifies complex AI systems, why modularity matters more than ever, and how Atomic Agents is eliminating hidden assumptions and redundant complexity in AI workflows.

💡 From real-world applications to the tech stack behind the framework, Kenny takes us on a deep dive into this lightweight, powerful tool for creating consistent and brand-aligned AI.

📌 Timestamps:0:00 – Intro2:30 – Kenny’s journey in AI5:00 – What are Atomic Agents?

10:45 – Why atomicity matters in AI18:20 – The tech behind Atomic A…

This episode explores how to break down barriers by running massive AI models on “crappy machines”—affordable, low-spec devices.

🐦 Twitter: @DataScienceAtHome📘 LinkedIn: https://www.linkedin.com/in/fragadaleta/Instagram: https://www.instagram.com/datascienceathome/Facebook: https://www.facebook.com/datascienceAHLinkedIn: https://www.linkedin.com/company/data-science-at-home-podcastDiscord Channel: https://discord.gg/4UNKGf3NEW TO DATA SCIENCE AT HOME?

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

Whether you’re a data professional, tech enthusiast, or just curious about the field, our podcast delivers insights, interviews, and discussions.

Send us mail …

Enter WeightWatcher—the AI detective tool that peeks inside neural networks without needing their data.

🐦 Twitter: @DataScienceAtHome📘 LinkedIn: https://www.linkedin.com/in/fragadaleta/Instagram: https://www.instagram.com/datascienceathome/Facebook: https://www.facebook.com/datascienceAHLinkedIn: https://www.linkedin.com/company/data-science-at-home-podcastDiscord Channel: https://discord.gg/4UNKGf3NEW TO DATA SCIENCE AT HOME?

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

Whether you’re a data professional, tech enthusiast, or just curious about the field, our podcast delivers insights, interviews, and discussions.

Send us mail at:hello@datascienceathom…

From the viral article “Tech’s Dumbest Mistake: Why Firing Programmers for AI Will Destroy Everything” on my newsletter at https://defragzone.substack.com/p/techs-dumbest-mistake-why-firinghere are my thoughts about AI replacing programmers…✨ Connect with us!

🐦 Twitter: @DataScienceAtHome📘 LinkedIn: https://www.linkedin.com/in/fragadaleta/Instagram: https://www.instagram.com/datascienceathome/Facebook: https://www.facebook.com/datascienceAHLinkedIn: https://www.linkedin.com/company/data-science-at-home-podcastDiscord Channel: https://discord.gg/4UNKGf3NEW TO DATA SCIENCE AT HOME?

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

Whether you’re a data profes…

In this episode, we dive into the transformative world of AI, data analytics, and cloud infrastructure with Josh Miramant, CEO of Blue Orange Digital.

As a seasoned entrepreneur with over $25 million raised across ventures and two successful exits, Josh shares invaluable insights on scaling data-driven businesses, integrating machine learning frameworks, and navigating the rapidly evolving landscape of cloud data architecture.

From generative AI to large language models, Josh explores cutting-edge trends shaping financial services, real estate, and consumer goods.

Tune in for a masterclass in leveraging data for impact and innovation!

Linkshttps://blueorange.digital/https://blueorange.digit…