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In this article, four of the expert panelists each tackle one common myth about AI trust and explain what you need to know to make your AI projects a trustworthy, safe success.

– Just as elevators require strong cables and pulleys to carry weight safely, AI systems rely on scalable, reliable data pipelines.

By measuring trust metrics and making them transparent to end-users, trustworthiness improves the adoption of AI systems.

Further ReadingAbout the AuthorsVrushali Channapattan is the Director of Engineering at Okta, where she leads Data and AI initiatives with a strong focus on Responsible AI.

She is a frequent speaker on topics such as responsible AI, data quality, and observability, an…

Now, in binary classification models, we have another way to evaluate the model, and that is ROC AUC.

We need to change the way we evaluate our model, and the best way to evaluate classification models with an imbalanced dataset is ROC AUC.

Now we know that there is another method to evaluate classification models, i.e., ROC AUC.

Now we need to interpret the ROC curve, and for that we will generate the ROC curve using Python on our dataset.

$$\text{Orange Rectangle: } l \times b = 0.33 \times 0.33 = 0.11$$$$\text{Green Rectangle: } l \times b = 0.34 \times 0.33 = 0.11$$$$\text{Red Rectangle: } l \times b = 0.33 \times 0.33 = 0.11$$$$\text{Total AUC} = 0.11 + 0.11 + 0.11 = 0.33$$This way we …

Yet across enterprise teams, intent recognition often happens in silos, each team building bespoke pipelines for different products, from troubleshooting assistants to chatbots and issue triage tools.

Wouldn’t it be better to create a modular system that different teams could configure for their own needs without starting from scratch?

That question set us on the path to what we now call the Unified Intent Recognition Engine (UIRE).

Preprocessing steps, summarization (if required), embedding and vector search tools (like MiniLM, SBERT, FAISS, Pinecone), similarity scoring logic, threshold tuning frameworks,.

Closing ThoughtsThe Unified Intent Recognition Engine is less a packaged product an…

Let’s utilize Python basics, including functions, conditional statements, dictionaries, and loops, to create our version of a calculator.

def add(num1, num2): return num1 + num2 def subtract(num1, num2): return num1 - num2 def multiply(num1, num2): return num1 * num2 def divide(num1, num2): return num1/num2Creating a Dictionary of OperationsLet us also create a dictionary of operations .

But now we want to restart the calculator, so for this purpose, we will use a Python function concept called recursion.

For the calculator to restart, we will define it as a function, and it will be called when we want to restart the calculator function without carrying on the result.

Recursion in Python al…

Why NotebookLM for EducationThe tagline for NotebookLM is understand anything, and it really lives up to that.

But it can be an effective tool for teaching as well, due to its features like mind maps, flashcards, quiz generator, customizable reports, video overviews, etc.

NotebookLM + Google AI StudioI love the mindmaps from NotebookLM because they capture the whole topic so well.

NotebookLM for learning a new languageI’ve been experimenting with NotebookLM for language learning, and the new Flashcards + Custom Reports feature fits really well.

ConclusionIn this article, I’ve shared some of my experiments in the AI for education space using NotebookLM.

with an enormous corpus of text data, where they, during their pre-training stage, essentially consume the entire internet.

Retrieving information beforehandThe easiest approach is to retrieve additional data by fetching it before processing any live requests.

This is essentially how Anthropic has set up their deep research system, where they create one orchestrator agent that can spawn sub-agents to fetch additional information.

LLM use cases for metadataUntil now, I’ve discussed why you should utilize additional data and how to get a hold of it.

ConclusionIn this article, I’ve discussed how to significantly enhance your LLM by providing it with additional data.

In today’s post I want to see how it affects and fools us in our A/B tests, by using a fictional example to illustrate better what I’m trying to share.

But he wants to prove it and he decides to run a typical A/B test.

The A/B test is simple: the squad is divided in two groups where one keeps on warming up as usual (group A) while the other is instructed a new warm-up routine (group B).

The Problem: Random Noise Looks Like a WinOn paper our coach’s test looks convincing.

Generalizing Beyond SportsThe warm-up story is just a vivid case study, but the same pitfalls show up in every single A/B test one performs.

There are a bunch of gradient boosted tree libraries, including XGBoost, CatBoost, and LightGBM.

Notable hyperparameters, which I’ll look into more later, include learning_rate (how steep the gradient is, default 0.1), and n_estimators (similar to random forest — the number of trees).

Fitting took 2.2s, predicting took 0.005s, and the results:Metric max_depth=None MAE 0.369 MAPE 0.216 MSE 0.289 RMSE 0.538 R² 0.779So, quicker than the default random forest, but slightly worse performance.

This is why a GBT only works as a combined ensemble, not as separate standalone trees like in a random forest.

It can also help with stakeholder management — execs prefer pretty pictures to tables of number…

Project WorkingIn my previous article on the Coffee Machine, I thoroughly explained the working of the project.

class MenuItem: def __init__(self, name, water, milk, coffee, cost): self.name = name self.cost = cost self.ingredients = { "water": water, "milk": milk, "coffee": coffee }Suppose we want to create an object menuitem of the class MenuItem .

In that case, we will need to give it the following parameters: name of the item, the amount of water required to make this menu item, the amount of milk required to make this menu item, the amount of coffee required to make this menu item, and the cost of this menu item.

Creating an Object from the “Menu” ClassTo put this class and its associa…

It also happened in the era of business intelligence, big data, data science, analytics, and machine learning.

Having a unified data platform, having a single source of data truth, having golden tables, data governance, etc.

As a late mover one can build a context platform instead of a pure data platform.

Below is one example of a largely open source stack that brings together both the Context Fabric and AgentOS.

Agent Builder Sprints: Teach and empower staff to build agents on approved infrastructure and own their efficiency goals.

So let’s learn how to use SpaCy with Hugging Face’s models!

RoBERTa model builds on top of BERT with some key differences: https://arxiv.org/abs/1907.11692.

If you have a dataset, you can further train the RoBERTa model using spaCy to fine-tune it on the specific downstream task you’re trying to solve.

Once converted and saved as .spacy files, these can be passed directly into spacy train , which is much faster than using plain JSON or text files.

python —m spacy train config.cfg --output ./output --gpu-id 0You will see the training starting with and you can monitor the loss of the TextCategorizer component.

As a machine learning engineer, you get to tackle fascinating problems, experiment with cutting-edge tools, and actually positively affect the world.

So in this article, I’ll give you a clear and simple learning roadmap to becoming a machine learning engineer, along with the best resources.

These are literally used inside every machine learning algorithm for training and learning.

Machine LearningTo everyone’s surprise, we need to learn machine learning to be a machine learning engineer!

Deep Learning (Adaptive Computation and Machine Learning series) — This book is written by one of the Godfathers of AI, Yoshua Bengio.

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: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…

Created Using GPT-5Modern “frontier” AI models – spanning language, vision, and multimodal systems – are now built in two major phases.

First comes pretraining, where a large model (often called a foundation model) is trained on broad data to acquire general knowledge.

In this essay, we explore the transition from pretraining to post-training for cutting-edge AI models across modalities.

A dedicated section delves into reinforcement learning in post-training (especially RLHF and the newer RLAIF), discussing benefits and current limitations.

We close with reflections on how post-training is shaping the future of deploying and researching frontier AI models.

Created Using GPT-5Alibaba’s newest release Qwen3-Max is one of the most impressive frontier AI models ever created boosting 1 trillion parameters!

Altough we are still learning about the details behind Qwen3-Max, I wanted to share some initial findings and impressions.

Qwen3‑Max sits at the intersection of three trends: very large‑scale pretraining, sparse (Mixture‑of‑Experts) computation for throughput and cost, and aggressive post‑training that pushes reasoning, coding, and long‑context behavior.

It’s delivered as a cloud model with an OpenAI‑compatible API and aims squarely at the front of the pack—reporting strong results on capability suites like Arena‑Hard, LiveBench, LiveCodeBench, …

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Subscribe Now to Not Miss Anything:📝 Editorial: The Trillion-Parameter MoE You Can Actually ShipAlibaba Qwen just released one of the most impressive AI models ever created.

Qwen‑Max is Alibaba Cloud’s flagship MoE language model, delivered through an OpenAI‑compatible API.

Under the hood, MoE gives Qwen‑Max high capacity without paying the full dense‑model cost for every token.

On capability, Qwen‑Max performs especially well on math, code, and hard multi‑step prompts—the stuff that actually blocks teams in daily workflows.

AI Lab: National University of Singapore, University of Oxford, Shanghai AI Lab, UCL, UIUC, Brown, Imperial College, CAS, CUHK, Fudan, Bristol, Georgia, UCSD, UCSB, Dal…

Created Using GPT-5The AI chip wars have been front and center of the public debates in AI recently.

Nowhere is this more evident than in the competition for AI accelerator chips – the high-performance graphics processing units (GPUs) and specialized AI chips needed to train and run advanced models.

NVIDIA has emerged as the dominant supplier of these AI chips worldwide, making it one of the world’s most valuable tech companies.

In response, China is racing to develop its own alternatives, with Huawei leading the charge to build high-end AI chips domestically.

NVIDIA, China, and the AI Chip Export Dilemma

Created Using GPT-5A year ago, AI lab Nous Research stunned the AI community by showing Hermes, a model completely pretrained in synthetic data.

The latest version of this endeavor came out last week.

Hermes 4 is Nous Research’s latest open‑weight family aimed at a question that hangs over today’s reasoning models: can we combine long‑form, structured thinking with crisp assistant behavior in a way that is both performant and reproducible?

Just as importantly, the team ships a transparent recipe—synthesized supervision with DataForge, verified trajectories with Atropos, and a standardized evaluation harness—that others can audit or extend.

For researchers and practitioners who care about co…

Created Using GPT-5Today we will Discuss:An overview of mechanistic interpretability.

Anthropic’s brekthought paper that dives into “Claude’s mind”.

💡 AI Concept of the Day: What is Mechanistic Interpretability?

Mechanistic interpretability is revolutionizing how we understand and trust modern AI systems.

Rather than treating neural networks as inscrutable black boxes, this approach aims to dissect models into meaningful components—circuits, neurons, and pathways—and trace how data flows and transforms through them.

Subscribe Now to Not Miss Anything:📝 Editorial: Flash, But Precise: Inside Gemini 2.5 Flash ImageGemini 2.5 Flash Image (internally nicknamed “nano-banana”) is Google’s new native image generation and editing model, designed to combine low-latency, cost-efficient inference with materially better visual quality and controllability than the Gemini 2.0 Flash image features.

It’s available now in Google AI Studio, the Gemini API, and Vertex AI.

Architecturally/operationally, Flash Image is positioned as a native image model rather than a multimodal text model with an image head.

AI Lab: Google ResearchSummary: This work introduces Adaptive Precise Boolean rubrics, a framework for evaluating hea…

Since the launch of ChatGPT in late 2022, a new class of startups has emerged to meet the exploding demand for running large AI models in real time.

These AI inference cloud providers – exemplified by companies like Together AI and Fireworks AI – offer hosted APIs to run generative models (large language models, image generators, etc.)

on their optimized GPU infrastructure.

In the past two years, they have proliferated rapidly, fueled by surging interest in AI applications and the need for cost-effective, customizable model deployment.

The race is on to provide the fastest, cheapest, and most flexible AI inference – but as the field matures, it may quickly commoditize, reshaping the landsca…

Created Using DeepSeek v3.1DeepSeek-V3.1 is not a new model family so much as a decisive unification of three capabilities that have usually lived apart: a high-throughput generalist LLM, an explicit “thinking” reasoner, and an execution-competent agent.

It sits on the DeepSeek-V3 base—an economical but large Mixture-of-Experts (MoE) transformer with Multi-Head Latent Attention (MLA)—and extends it along three axes: (1) hybrid inference that supports both “thinking” and “non-thinking” modes in a single checkpoint via chat-template control; (2) long-context and tool/agent upgrades driven by additional continued pretraining and post-training; and (3) operational polish (function-calling stric…

Created Using GPT-5Today we will Discuss:An introduction to intrinsic interpretability in frontier AI models.

A review of MIT’s famous network dissection and Intrinsic Interpretability paper.

💡 AI Concept of the Day: What is Intrinsic Interpretability?

Intrinsic interpretability refers to the characteristic of AI models whereby their decision-making processes and internal representations are inherently understandable to humans.

This emphasis on built‑in explainability is increasingly important for frontier models, whose massive scale and complexity can otherwise render them inscrutable and heighten risks in high‑stakes applications.

Subscribe Now to Not Miss Anything:📝 Editorial: Two Drops, One Direction: The Week Agentic AI Got PracticalTwo standout model releases this week—DeepSeek’s V3.1 and Cohere’s Command A Reasoning—move agentic AI from buzz to build.

It packages reasoning, tool use, and multilingual competence with the guardrails large organizations expect—strong controls, observability, and predictable throughput.

AI Lab: Ant GroupSummary: Atom-Searcher proposes a reinforcement learning framework that decomposes reasoning into "Atomic Thoughts," enabling fine-grained supervision through Reasoning Reward Models (RRMs).

AI Lab: MIT and Google ResearchSummary: This paper introduces MaxAdaptiveDegree (MAD) and MAD…

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 »

Continue reading on SyncedReview »

Navigation is a fundamental skill for any visually-capable organism, serving as a critical tool for survival. It enables agents to locate…Continue reading on SyncedReview »

The landscape of vision model pre-training has undergone significant evolution, especially with the rise of Large Language Models (LLMs)…Continue reading on SyncedReview »

A foundation model refers to a pre-trained model developed on extensive datasets, designed to be versatile and adaptable for a range of…Continue reading on SyncedReview »

Continue reading on SyncedReview »

While large language models (LLMs) dominate the AI landscape, Small-scale Large Language Models (SLMs) are gaining traction as…Continue reading on SyncedReview »

Consistency models (CMs) are a cutting-edge class of diffusion-based generative models designed for rapid and efficient sampling. However…Continue reading on SyncedReview »

The field of text-to-image synthesis has advanced rapidly, with state-of-the-art models now generating highly realistic and diverse images…Continue reading on SyncedReview »

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…

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

Developing machine learning systems entails a well-established lifecycle, consisting of a series of stages from data preparation and preprocessing to modeling, validation, deployment to production, and continuous maintenance.

Extreme gradient boosting ( XGBoost ) is one of the most prominent machine learning techniques used not only for experimentation and analysis but also in deployed predictive solutions in industry.

Experimenting, fine-tuning, scaling, and more are key aspects that machine learning development workflows thrive on.

Data merging is the process of combining data from different sources into a unified dataset.

When working with machine learning on structured data, two algorithms often rise to the top of the shortlist: random forests and gradient boosting .

In this article, you will learn: • The fundamental difference between traditional regression, which uses single fixed values for its parameters, and Bayesian regression, which models them as probability distributions.

Working with time series data often means wrestling with the same patterns over and over: calculating moving averages, detecting spikes, creating features for forecasting models.

When you have a small dataset, choosing the right machine learning model can make a big difference.

Perhaps one of the most underrated yet powerful features that scikit-learn has to offer, pipelines are a great ally for building effective and modular machine learning workflows.

In this article, you'll learn to: • Turn unstructured, raw image data into structured, informative features.

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.

I realize how late this is, but I didn’t get a post out while o1 was fresh, and still feel like writing one despite it being cold.

(Also, OpenAI just announced they’re going to ship new stuff starting tomorrow so it’s now or never to say something.)

OpenAI o1 is a model release widely believed (but not confirmed) to be a post-trained version of GPT-4o.

If true, that makes this video especially useful for understanding OpenAI o1.

Which I suppose is part of why I’m talking about o1 rather than building o1.

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

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

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

Point processes and Markov ChainsBack to Markov chains.

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

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

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

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

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

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

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

The authoring experience is much more convenient for me there.

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

And check out our How Transformer LLMs Work course!

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

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

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

By learning geometry and motion jointly from …

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This approach effectively integrate…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

PDFAbstract

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

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

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

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

Second, inspired b…

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

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

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

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

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

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

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

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

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

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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…

Today in the Gemini app, we're unveiling a new image editing model from Google DeepMind.

People have been going bananas over it already in early previews — it's the top-rated image editing model in the world.

Now, we're excited to share that it's integrated into the Gemini app so you have more control than ever to create the perfect picture.

Maintain your look as you editWe launched native image editing in the Gemini app earlier this year, and we’ve been working hard to improve it, with particular focus on maintaining a character's likeness from one image to the next.

Bring your vision to life with advanced editingHere are a few things to try as you explore this new image editing capability:

Gemma 3 270M brings strong instruction-following capabilities to a small-footprint model.

Internal tests on a Pixel 9 Pro SoC show the INT4-quantized model used just 0.75% of the battery for 25 conversations, making it our most power-efficient Gemma model.

While this model is not designed for complex conversational use cases, it’s a strong model that follows general instructions right out of the box.

The results were stunning: the specialized Gemma model not only met but exceeded the performance of much larger proprietary models on its specific task.

It's the perfect starting point for creating a fleet of small, specialized models, each an expert at its own task.

Gemma 3 270M brings strong instruction-following capabilities to a small-footprint model.

Internal tests on a Pixel 9 Pro SoC show the INT4-quantized model used just 0.75% of the battery for 25 conversations, making it our most power-efficient Gemma model.

While this model is not designed for complex conversational use cases, it’s a strong model that follows general instructions right out of the box.

The results were stunning: the specialized Gemma model not only met but exceeded the performance of much larger proprietary models on its specific task.

It's the perfect starting point for creating a fleet of small, specialized models, each an expert at its own task.

Gemma 3 270M brings strong instruction-following capabilities to a small-footprint model.

Internal tests on a Pixel 9 Pro SoC show the INT4-quantized model used just 0.75% of the battery for 25 conversations, making it our most power-efficient Gemma model.

While this model is not designed for complex conversational use cases, it’s a strong model that follows general instructions right out of the box.

The results were stunning: the specialized Gemma model not only met but exceeded the performance of much larger proprietary models on its specific task.

It's the perfect starting point for creating a fleet of small, specialized models, each an expert at its own task.

Our new Perch model helps conservationists analyze audio faster to protect endangered species, from Hawaiian honeycreepers to coral reefs.

These recordings can tell us a lot about the animals present in a given area, along with other clues about the health of that ecosystem.

Today, we are releasing an update to Perch, our AI model designed to help conservationists analyze bioacoustic data.

This new model has better state-of-the-art off-the-shelf bird species predictions than the previous model.

It can disentangle complex acoustic scenes over thousands or even millions of hours of audio data.

Our new Perch model helps conservationists analyze audio faster to protect endangered species, from Hawaiian honeycreepers to coral reefs.

These recordings can tell us a lot about the animals present in a given area, along with other clues about the health of that ecosystem.

Today, we are releasing an update to Perch, our AI model designed to help conservationists analyze bioacoustic data.

This new model has better state-of-the-art off-the-shelf bird species predictions than the previous model.

It can disentangle complex acoustic scenes over thousands or even millions of hours of audio data.

Our new Perch model helps conservationists analyze audio faster to protect endangered species, from Hawaiian honeycreepers to coral reefs.

These recordings can tell us a lot about the animals present in a given area, along with other clues about the health of that ecosystem.

Today, we are releasing an update to Perch, our AI model designed to help conservationists analyze bioacoustic data.

This new model has better state-of-the-art off-the-shelf bird species predictions than the previous model.

It can disentangle complex acoustic scenes over thousands or even millions of hours of audio data.

World models are also a key stepping stone on the path to AGI, since they make it possible to train AI agents in an unlimited curriculum of rich simulation environments.

Last year we introduced the first foundation world models with Genie 1 and Genie 2, which could generate new environments for agents.

We have also continued to push the state of the art in video generation with our models Veo 2 and Veo 3, which exhibit a deep understanding of intuitive physics.

Each of these models marks progress along different capabilities of world simulation.

Genie 3 is our first world model to allow interaction in real-time, while also improving consistency and realism compared to Genie 2.

World models are also a key stepping stone on the path to AGI, since they make it possible to train AI agents in an unlimited curriculum of rich simulation environments.

Last year we introduced the first foundation world models with Genie 1 and Genie 2, which could generate new environments for agents.

We have also continued to push the state of the art in video generation with our models Veo 2 and Veo 3, which exhibit a deep understanding of intuitive physics.

Each of these models marks progress along different capabilities of world simulation.

Genie 3 is our first world model to allow interaction in real-time, while also improving consistency and realism compared to Genie 2.

World models are also a key stepping stone on the path to AGI, since they make it possible to train AI agents in an unlimited curriculum of rich simulation environments.

Last year we introduced the first foundation world models with Genie 1 and Genie 2, which could generate new environments for agents.

We have also continued to push the state of the art in video generation with our models Veo 2 and Veo 3, which exhibit a deep understanding of intuitive physics.

Each of these models marks progress along different capabilities of world simulation.

Genie 3 is our first world model to allow interaction in real-time, while also improving consistency and realism compared to Genie 2.

Current AI benchmarks are struggling to keep pace with modern models.

As models reach closer to 100% on certain benchmarks, they also become less effective at revealing meaningful performance differences.

We continue to invest in new and more challenging benchmarks, but on the path to general intelligence, we need to continue to look for new ways to evaluate.

While we continue to evolve and pursue current AI benchmarks, we’re also consistently looking to test new approaches to evaluating models.

That’s why today, we're introducing the Kaggle Game Arena: a new, public AI benchmarking platform where AI models compete head-to-head in strategic games, providing a verifiable, and dynamic measure…

Current AI benchmarks are struggling to keep pace with modern models.

As models reach closer to 100% on certain benchmarks, they also become less effective at revealing meaningful performance differences.

We continue to invest in new and more challenging benchmarks, but on the path to general intelligence, we need to continue to look for new ways to evaluate.

While we continue to evolve and pursue current AI benchmarks, we’re also consistently looking to test new approaches to evaluating models.

That’s why today, we're introducing the Kaggle Game Arena: a new, public AI benchmarking platform where AI models compete head-to-head in strategic games, providing a verifiable, and dynamic measure…

Current AI benchmarks are struggling to keep pace with modern models.

As models reach closer to 100% on certain benchmarks, they also become less effective at revealing meaningful performance differences.

We continue to invest in new and more challenging benchmarks, but on the path to general intelligence, we need to continue to look for new ways to evaluate.

While we continue to evolve and pursue current AI benchmarks, we’re also consistently looking to test new approaches to evaluating models.

That’s why today, we're introducing the Kaggle Game Arena: a new, public AI benchmarking platform where AI models compete head-to-head in strategic games, providing a verifiable, and dynamic measure…

The Gemini embedding model utilizes a new quota control method known as Tokens Per Minute (TPM).

OSS embedding models in BigQuery MLThe OSS community is rapidly evolving the text-embedding model landscape, offering a wide spectrum of choices to fit any need.

Offerings range from top-ranking models like the recent Qwen3-Embedding & EmbeddingGemma to small, efficient, and cost-effective small models such as multilingual-e5-small .

You can now use any Hugging Face text embedding models (13K+ options) deployed to Vertex AI Model Garden in BigQuery ML.

To use an open-source text-embedding model, follow these steps.

Adyen’s collaboration on Google’s Agent Payments Protocol (AP2) is a natural extension of our mission to provide the merchants with the payments building blocks for tomorrow’s commerce.

- Ingo Uytdehaage, Co-CEO at AdyenAirwallex: "Airwallex is thrilled to support Google’s Agent Payments Protocol (AP2) .

Google's new Agent Payments Protocol (AP2) is a huge step forward, providing the foundational framework to make this possible.

Google's Agent Payments Protocol (AP2) combines programmable payments via modern blockchains like Sui with open protocols like A2A and MCP that are enjoying rapid growth.

We believe the Agent Payments Protocol (AP2) and extension to the Agent2Agent protocol represen…

CISOs are hopeful that bringing AI-driven automation to security operations workflows can help tip the scales towards defenders, said Franck Vervial, Regional CISO, APAC and MENA, L'Oreal.

As CISOs look for automated, agile defenses that scale beyond their existing security operations center (SOC) capacity, we’ve introduced our vision of an agentic SOC to help address the biggest security operations bottlenecks.

Agentic AI governance should follow the same guardrails for traditional AI systems, while implementing further measures for evolving security, privacy, and compliance risks, as appropriate.

Naturally, CISOs want to learn more about how AI can improve detection engineering, particula…

The re-computation of the prefill phase is very inefficient and adds unnecessary latency, with the user experiencing delays between each question.

With prefix-aware routing, the system intelligently reuses the data from the initial query by routing the request back to the same KV cache.

This bypasses the prefill phase, allowing the model to answer almost instantly.

Enhancements in GKE Inference Gateway, llm-d, and vLLM, work together to enable dynamic selection of prefill and decode nodes based on query size.

When a developer submits a completion request, the application must first process the input codebase; this is referred to as the prefill phase.

As generative AI becomes more widespread, it’s important for developers and ML engineers to be able to easily configure infrastructure that supports efficient AI inference, i.e., using a trained AI model to make predictions or decisions based on new, unseen data.

Further, deploying large generative AI models can be both complex and resource-intensive.

That's why we are excited to announce a new recipe for disaggregated inferencing with NVIDIA Dynamo, a high-performance, low-latency platform for a variety of AI models.

Specifically, this recipe makes it easy to deploy NVIDIA Dynamo on Google Cloud’s AI Hypercomputer, including Google Kubernetes Engine (GKE), vLLM inference engine, and A3 Ult…

Consumer search behavior is shifting, with users now entering longer, more complex questions into search bars in pursuit of more relevant results.

We are excited to announce the general availability of Google Cloud’s Conversational Commerce agent designed to engage shoppers in natural, human-like conversations to guide them from initial intent to a completed purchase.

Companies like Albertsons Cos., who was a marquee collaborator on this product and is using Conversational Commerce agent within their Ask AI tool, are already seeing an impact.

Early results show customers using Ask AI often add one or more additional items to their cart, uncovering products they might not have found otherwis…

Today, we’re closing the gap between your terminal and the cloud with a first look at the future of Gemini CLI, delivered through two new extensions: security extension and Cloud Run extension.

These commands are the first expression of a new extensibility framework for Gemini CLI.

While we'll be sharing more about the full Gemini CLI extension world soon, we couldn't wait to get these capabilities into your hands.

Security extension: automate security analysis with /security:analyzeTo help teams address software vulnerabilities early in the development lifecycle, we are launching the Gemini CLI Security extension.

And after the report is generated, you can also ask Gemini CLI to save it to…

We pioneered a multicloud data warehouse, enabling workloads to run across clouds.

We continue this open approach with the launch today of our new Data Transfer Essentials service for customers in the European Union and the United Kingdom.

Built in response to the principles of cloud interoperability and choice outlined in the EU Data Act, Data Transfer Essentials is a new, simple solution for data transfers between Google Cloud and other cloud service providers.

Although the Act allows cloud providers to pass through costs to customers, Data Transfer Essentials is available today at no cost to customers.

Read more about Data Transfer Essentials here.

The security operations centers of the future will use agentic AI to enable intelligent automation of routine tasks, augment human decision-making, and streamline workflows.

At Google Cloud, we want to help prepare today’s security professionals to get the most out of tomorrow’s AI agents.

As we build our agentic vision, we’re also excited to invite you to the first Agentic SOC Workshop: Practical AI for Today's Security Teams.

Ultimately, we believe that agentic AI will empower security professionals to focus more on complex investigations and strategic initiatives, and drive better security outcomes and operational efficiency.

We are building the next class of security experts empowered b…

Many of our partners are using Google Cloud's AI to build new defenses and automate security operations, transforming the security landscape.

Today, we’re excited to announce new security solutions from our partners, also available in the Google Cloud Marketplace.

CrowdStrike has enhanced security operations by embedding AI directly into its Falcon platform, and integrating platform data with Google Security Operations and Google Agentspace.

Fortinet has also unveiled how its product ecosystem can help secure AI workloads on Google Cloud.

Palo Alto Networks has designed Prisma AIRS to protect AI and agentic workloads on Google Cloud, including Vertex AI and Agentspace.

Introduced in 2024, this three-week, no-cost, virtual training program provides veterans with the foundational skills necessary to jump start rewarding careers with generative AI.

Become a leader in AI digital transformation with this virtual, no-cost programThe Gen AI Leader training, which does not require previous technical experience, kicks off with a two-day virtual event on November 13th and 14th.

After completing the program, you’ll receive a complimentary voucher to take the Gen AI Leader exam.

After successful completion, you’ll receive Google’s industry-recognized Gen AI Leader certification, a valuable credential to help you advance your career.

Learn more about Google Public Sec…

Hands-on learning with GKE: This is your shot to build the next evolution of applications by integrating agentic AI capabilities on GKE.

When you join the GKE Turns 10 Hackathon, your mission is to take pre-existing microservice applications (either Bank of Anthos or Online Boutique) and then integrate cutting-edge agentic AI capabilities.

Streamline maintenance and mitigation: Develop an agent that intelligently monitors microservice performance on GKE, suggests troubleshooting steps, and even automates remediation.

Crucial note: Your project must be built using GKE and Google AI models such as Gemini, focusing on how the agents interact with your chosen microservice application.

As long a…

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.

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.

In 2024, Xu joined Microsoft Research Asia where he began a new chapter focused on bridging between academic research and real-world AI applications.

“Microsoft Research Asia is committed to integrating scientific exploration with real-world applications, which creates a unique research environment,” Xu says.

Microsoft Research Asia – Singapore: Expanding global reach, connecting regional innovationRealizing AI’s full potential requires more than technical breakthroughs.

This is why Microsoft Research Asia continues to collaborate closely with Singapore’s top universities, research institutions, and industry partners.

– “Core knowledge in machine learning, linear algebra, and probability an…

From batching strategies and token optimization and orchestration, we’ll share what it takes to build a real-time LLM classification pipeline.

Engineering challenges & solutionsBuilding a high-throughput, LLM-powered classification pipeline at scale introduced a range of engineering challenges—from managing latency and token limits to ensuring system resilience.

Evolving LLM models & prompt alignmentChallenge: Each new LLM release—such as Phi, Mistral, DeepSeek, and successive generations of GPT (e.g., GPT-3.5, GPT-4, GPT-4 Turbo, GPT-4o)—brings improvements, but also subtle behavioral shifts.

Dynamic concurrency scaling for LLM callsChallenge: LLM endpoints frequently encounter rate limits…

According to Choshen, elucidating scaling laws not only enable better pre-training decisions, but also democratize the field by enabling researchers without vast resources to understand and build effective scaling laws.

In general, scaling laws aren’t new; however, in the field of AI, they emerged as models grew and costs skyrocketed.

“There hadn’t really been a lot of systematic meta-analysis, as everybody is individually training their own scaling laws.

With this, the team compared the scaling laws, and after analysis, distilled practical recommendations for AI practitioners about what makes effective scaling laws.

Unexpectedly, the researchers found that it’s possible to utilize the scal…

That is, until a new approach called “Fetal SMPL” from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Boston Children’s Hospital (BCH), and Harvard Medical School presented clinicians with a more detailed picture of fetal health.

Fetal SMPL was then trained on 20,000 MRI volumes to predict the location and size of a fetus and create sculpture-like 3D representations.

The extensive, real-world scans that Fetal SMPL learned from helped it develop pinpoint accuracy.

Fetal SMPL was only misaligned by an average of about 3.1 millimeters, a gap smaller than a single grain of rice.

Such upgrades would make the models more human-like, but the current version of Fetal SMPL al…

The U.S. Department of Energy’s National Nuclear Security Administration (DoE/NNSA) recently announced that it has selected MIT to establish a new research center dedicated to advancing the predictive simulation of extreme environments, such as those encountered in hypersonic flight and atmospheric re-entry.

The Center for the Exascale Simulation of Coupled High-Enthalpy Fluid–Solid Interactions (CHEFSI) — a joint effort of the MIT Center for Computational Science and Engineering, the MIT Schwarzman College of Computing, and the MIT Institute for Soldier Nanotechnologies (ISN) — plans to harness cutting-edge exascale supercomputers and next-generation algorithms to simulate with unprecedent…

In Ahmed’s course, 2.155/156 (AI and Machine Learning for Engineering Design), students use tools and techniques from artificial intelligence and machine learning for mechanical engineering design, focusing on the creation of new products and addressing engineering design challenges.

“When people think about mechanical engineering, they're thinking about basic mechanical tools like hammers and … hardware like cars, robots, cranes, but mechanical engineering is very broad,” says Faez Ahmed, the Doherty Chair in Ocean Utilization and associate professor of mechanical engineering at MIT.

“There’s a lot of reason for mechanical engineers to think about machine learning and AI to essentially exp…

3D printing has come a long way since its invention in 1983 by Chuck Hull, who pioneered stereolithography, a technique that solidifies liquid resin into solid objects using ultraviolet lasers.

The vast majority of consumer and industrial 3D printing still relies on petroleum-based plastic filament.

The rest of the part can be printed using greener, weaker filament, reducing plastic use while preserving structural integrity.

Using this ratio, SustainaPrint was able to recover up to 70 percent of the strength of an object printed entirely with high-performance plastic.

“It turns these abstract concepts into something tangible.”As 3D printing becomes more embedded in how we manufacture and pr…

Many attempts have been made to harness the power of new artificial intelligence and large language models (LLMs) to try to predict the outcomes of new chemical reactions.

Now, a team of researchers at MIT has come up with a way of incorporating these physical constraints on a reaction prediction model, and thus greatly improving the accuracy and reliability of its outputs.

So, this requires us to know what product is likely” to result from a given set of chemical inputs to a reaction.

This representation, he says, was one of the key elements to including mass conservation in their prediction system.

They say the model could potentially be relevant for predicting reactions for medicinal che…

Synthetic data are artificially generated by algorithms to mimic the statistical properties of actual data, without containing any information from real-world sources.

To unpack some pros and cons of using synthetic data, MIT News spoke with Kalyan Veeramachaneni, a principal research scientist in the Laboratory for Information and Decision Systems and co-founder of DataCebo whose open-core platform, the Synthetic Data Vault, helps users generate and test synthetic data.

Plus, the model creates synthetic data in a way that captures all the underlying rules and infinite patterns that exist in the real data.

Synthetic data provide data augmentation — additional data examples that are similar …

In this interview, she discusses machine learning in biology, areas that are ripe for problem-solving, and cutting-edge research coming out of the Schmidt Center.

What, within the current landscape of machine learning, makes now the right time to work on these specific problem classes?

Importantly, biology is poised to be not just a beneficiary of machine learning, but also a significant source of inspiration for new ML research.

A: Machine learning has demonstrated remarkable success in predictive tasks across domains such as image classification, natural language processing, and clinical risk modeling.

I believe that addressing these challenges will not only unlock new insights into the m…

But if the prediction is off, protection can drop significantly, leading to (potentially preventable) illness and strain on health care systems.

They created an AI system called VaxSeer, designed to predict dominant flu strains and identify the most protective vaccine candidates, months ahead of time.

For antigenicity, the system estimates how well a given vaccine strain will perform in a common lab test called the hemagglutination inhibition assay.

Applying the system to other viruses would also require large, high-quality datasets that track both viral evolution and immune responses — data that aren’t always publicly available.

The team, however is currently working on the methods that ca…

The team demonstrates that, in certain climate scenarios, much simpler, physics-based models can generate more accurate predictions than state-of-the-art deep-learning models.

This could lead someone to believe a deep-learning model makes more accurate predictions when that is not the case.

The researchers see their work as a “cautionary tale” about the risk of deploying large AI models for climate science.

Scientists often create climate emulators, simpler approximations of a state-of-the art climate model, which are faster and more accessible.

They found that the high amount of natural variability in climate model runs can cause the deep learning model to perform poorly on unpredictable l…

Cognitive readiness denotes a person's ability to respond and adapt to the changes around them.

For military service members, cognitive readiness is crucial for their health and safety, as well as mission success.

"Unfortunately, the cumulative effects of these exposures are often not well-documented during military service or after transition to Veterans Affairs, making it challenging to provide effective support."

Smalt is part of a team at the laboratory developing a suite of portable diagnostic tests that provide near-real-time screening for brain injury and cognitive health.

Both READY and MINDSCAPE are a response to a series of Congressional legislation mandates, military program requ…

“Humans all the time have been able to make this transition from good predictions to world models,” says Vafa, the study’s lead author.

So the question their team was addressing was, “have foundation models — has AI — been able to make that leap from predictions to world models?

“It would need to adapt, have a world model to adapt to any possible task.”Are AI systems anywhere near the ability to reach such generalizations?

To test the question, the team looked at different examples of predictive AI systems, at different levels of complexity.

The answer is yes: Predictive models do well at reconstructing the “world” in such a simple case.

Common organic solvents include ethanol and acetone, and there are hundreds of others that can also be used in chemical reactions.

The model could be particularly useful for identifying solvents that are less hazardous than some of the most commonly used industrial solvents, the researchers say.

Before Burns and Attia began working on their new model, the state-of-the-art model for predicting solubility was a model developed in Green’s lab in 2022.

Part of the reason that existing solubility models haven’t worked well is because there wasn’t a comprehensive dataset to train them on.

“ChemProp should always outperform any static embedding when you have sufficient data,” Burns says.

These models, which are based on large language models (LLMs), can make very accurate predictions of a protein’s suitability for a given application.

Opening the black boxIn 2018, Berger and former MIT graduate student Tristan Bepler PhD ’20 introduced the first protein language model.

Protein language models use a similar approach, but instead of analyzing words, they analyze amino acid sequences.

In the new study, the researchers wanted to dig into how protein language models make their predictions.

The new study from Berger’s lab is the first to use this algorithm on protein language models.

Using artificial intelligence, MIT researchers have come up with a new way to design nanoparticles that can more efficiently deliver RNA vaccines and other types of RNA therapies.

This approach could dramatically speed the process of developing new RNA vaccines, as well as therapies that could be used to treat obesity, diabetes, and other metabolic disorders, the researchers say.

Particle predictionsRNA vaccines, such as the vaccines for SARS-CoV-2, are usually packaged in lipid nanoparticles (LNPs) for delivery.

Creating particles that handle these jobs more efficiently could help researchers to develop even more effective vaccines.

“Most AI models in drug discovery focus on optimizing a s…

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.…

Verisk Rating Insights as a feature of ISO Electronic Rating Content (ERC) is a powerful tool designed to provide summaries of ISO Rating changes between two releases.

By integrating Anthropic’s Claude, available in Amazon Bedrock, and Amazon OpenSearch Service, Verisk created a sophisticated conversational platform where users can effortlessly access and analyze rating content changes.

Verisk’s generative AI solution is a comprehensive, secure, and flexible service for building generative AI applications and agents.

Business impact and opportunityBy integrating generative AI into Verisk Rating Insights, the business has seen a remarkable transformation.

Verisk Rating Insights, now powered …

Together, they developed a unified wrapper API framework that streamlines the deployment of Amazon Bedrock FMs on Quora’s Poe system.

Initially, integrating the diverse FMs available through Amazon Bedrock presented significant technical challenges for the Poe.com team.

Technical challenge: Bridging different systemsThe integration between Poe and Amazon Bedrock presented fundamental architectural challenges that required innovative solutions.

Solution overviewThe wrapper API framework provides a unified interface between Poe and Amazon Bedrock models.

– Converts responses from Amazon Bedrock format to Poe’s expected format and vice-versa, handling streaming chunks, image data, and video ou…

SageMaker HyperPod task governance streamlines resource allocation and facilitates efficient compute resource utilization across teams and projects on Amazon Elastic Kubernetes Service (Amazon EKS) clusters.

Refer to Best practices for Amazon SageMaker HyperPod task governance for more information.

In this post, we introduce topology-aware scheduling with SageMaker HyperPod task governance by submitting jobs that represent hierarchical network information.

In this post, we discussed how SageMaker HyperPod task governance helps schedule workloads to enable job efficiency by optimizing throughput and latency.

We also walked through how to schedule jobs using SageMaker HyperPod topology networ…

msg.ProfileMap is a cloud-based application that uses several AWS services, notably Amazon Neptune, Amazon DynamoDB, and Amazon Bedrock.

Amazon Bedrock met these needs by providing a fully managed, serverless interface to leading foundation models—without the need to manage infrastructure or deploy custom machine learning stacks.

Unlike hosting models on Amazon Elastic Compute Cloud (Amazon EC2) or Amazon SageMaker, Amazon Bedrock abstracts away provisioning, versioning, scaling, and model selection.

Conclusionmsg’s successful integration of Amazon Bedrock into msg.ProfileMap demonstrates that large-scale AI adoption doesn’t require complex infrastructure or specialized model training.

With…

Furthermore, management of high-performing RAG pipeline involves complex deployment, with teams often using manual RAG pipeline management, leading to inconsistent results, time-consuming troubleshooting, and difficulty in reproducing successful configurations.

The following diagram illustrates the architecture of a scalable RAG pipeline built on SageMaker AI, with MLflow experiment tracking seamlessly integrated at every stage and the RAG pipeline automated using SageMaker Pipelines.

A SageMaker Studio notebook for a development environment to experiment and automate the RAG pipelines with SageMaker managed MLflow and SageMaker Pipelines.

CI/CD for an agentic RAG pipelineNow we integrate t…

PrerequisitesTo use log probability support with custom model import in Amazon Bedrock, you need:An active AWS account with access to Amazon BedrockA custom model created in Amazon Bedrock using the Custom Model Import feature after July 31, 2025, when the log probabilities support was releasedAppropriate AWS Identity and Access Management (IAM) permissions to invoke models through the Amazon Bedrock RuntimeIntroducing log probabilities support in Amazon BedrockWith this release, Amazon Bedrock now allows models imported using the Custom Model Import feature to return token-level log probabilities as part of the inference response.

Let’s walk through an example of invoking a custom model on…

Anthropic’s Claude 4 Sonnet model has launched on Amazon Bedrock, marking a significant advancement in foundation model capabilities.

This post provides a systematic approach to migrating from Anthropic’s Claude 3.5 Sonnet to Claude 4 Sonnet on Amazon Bedrock.

The migration from Anthropic’s Claude Sonnet 3.5 Sonnet to Claude 4 Sonnet introduces capability and behavioral shifts that you can take advantage of:Increased context window – Anthropic’s Claude 4 Sonnet expands the context window from 200,000 tokens to 1 million tokens (beta).

PrerequisitesBefore you can start using Anthropic’s Claude 4 Sonnet model, you must enable access to these models in Amazon Bedrock.

For more details, refer t…

In this post, we explore how Amazon Bedrock Data Automation uses OSOD to enhance video understanding.

Amazon Bedrock Data Automation and video blueprints with OSODAmazon Bedrock Data Automation is a cloud-based service that extracts insights from unstructured content like documents, images, video and audio.

For more information about Amazon Bedrock Data Automation, see Automate video insights for contextual advertising using Amazon Bedrock Data Automation.

Example use casesIn this section, we explore some examples of different use cases for Amazon Bedrock Data Automation video blueprints using OSOD.

To learn more about Amazon Bedrock Data Automation video and audio analysis, see New Amazon …

In this post, we explain how Skello used Amazon Bedrock to create this AI assistant for end-users while maintaining customer data safety in a multi-tenant environment.

This led us to identify two key areas for improvement:Quick access to non-structured data – Our users needed to find specific information across various data types—employee records, scheduling data, attendance logs, and performance metrics.

Their ability to understand natural language and context, combined with their capability to generate structured outputs, made them perfectly suited for translating user questions into precise database queries.

The streamlined process makes sure your visualizations remain consistently clear…

Solution overviewThis solution demonstrates how to create a private workforce and a coupled Amazon Cognito user pool and its dependent resources.

Amazon Cognito user pool app client – The user pool app client configures the client application that will interact with the user pool.

Amazon Cognito user pool domain – The user pool domain defines the domain name for the managed login experience provided by the user pool.

Private workforce – Implemented using a custom resource backing the CreateWorkforce API call, the private workforce is the foundation to manage labeling activities.

ConclusionThis solution demonstrates how to programmatically create a private workforce on SageMaker Ground Truth…

We are excited to announce the availability of the Technology Innovation Institute (TII)’s Falcon-H1 models on Amazon Bedrock Marketplace and Amazon SageMaker JumpStart.

For help determining which deployment option—Amazon Bedrock Marketplace or SageMaker JumpStart—best suits your specific requirements, see Amazon Bedrock or Amazon SageMaker AI?

Deploy the model using the Amazon Bedrock Marketplace UITo deploy the model using Amazon Bedrock Marketplace, complete the following steps:On the Amazon Bedrock console, under Discover in the navigation pane, choose Model catalog.

To learn more about how IAM works with SageMaker AI, see Identity and Access Management for Amazon SageMaker AI.

To do so…

The company was processing 100,000–300,000 ship tickets per month across more than 200 facilities.

This post explores how Oldcastle partnered with AWS to transform their document processing workflow using Amazon Bedrock with Amazon Textract.

The end-to-end workflow uses Amazon Simple Email Service (Amazon SES) to receive ship tickets, which are sent directly from drivers in the field.

This strategic move towards AI-powered document processing is positioning Oldcastle for improved efficiency and scalability in its operations.

Review your current manual document processing procedures and identify where intelligent document processing can help you automate these workflows for your business.

Accordingly, regulators place great emphasis on the ability of market surveillance teams to keep pace with evolving risk profiles.

Effective monitoring must cover all MiFID asset classes, markets and jurisdictions to detect market abuse, while also giving weight to participant relationships, and market surveillance systems must scale with volumes and volatility.

In this post, we explore how LSEG used Amazon Bedrock and Anthropic’s Claude foundation models to build an automated system that significantly improves the efficiency and accuracy of market surveillance operations.

Powered by Amazon Bedrock, the solution improves efficiency and enhances the quality and consistency of market abuse de…

The AI Makers: NVIDIA Partners in UK Advance Physical and Agentic AI, Robotics, Life Sciences and MoreThe U.K. is driving investments in sovereign AI, using the technology to advance industries like manufacturing, life sciences and more.

ElevenLabs develops AI voice technology that generates natural, ultrarealistic speech in over 70 languages using NVIDIA software and NVIDIA DGX B200 systems.

PolyAI deployed advanced conversational AI agents using NVIDIA Riva automatic speech recognition NVIDIA NIM microservices.

Speechmatics developed speech-to-text software using NVIDIA Dynamo-Triton and NVIDIA cuDNN software.

Synthesia built an enterprise-focused AI video platform using NVIDIA Dynamo-Tri…

The UK-LLM development team has tapped the 49-billion-parameter Llama Nemotron Super model and 9-billion-parameter Nemotron Nano model, post-training them on Welsh-language data.

Bydd darparwr cwmwl Deallusrwydd Artiffisial yn y DU, Nscale, yn sicrhau bod y model newydd ar gael i ddatblygwyr trwy ei ryngwyneb rhaglennu rhaglenni (API).

O’i gymharu ag ieithoedd fel Saesneg neu Sbaeneg, mae llai o ddata ffynhonnell ar gael yn y Gymraeg ar gyfer hyfforddiant Deallusrwydd Artiffisial.

“Mae’n un peth cael y gallu Deallusrwydd Artiffisial hwn yn bodoli yn y Gymraeg, ond mae’n beth arall ei wneud yn agored ac yn hygyrch i bawb,” meddai Prys.

Dewch i ddechrau arni gyda NVIDIA Nemotron.

The new NVIDIA RTX PRO 6000 Blackwell Server Edition GPU fundamentally changes this.

The NVIDIA RTX PRO 6000 Blackwell Server Edition GPU sets a new benchmark for protein structure inferenceWhy do speed and scale matter in protein structure prediction?

Validated on 20 CASP14 protein targets, these benchmarks establish RTX PRO 6000 Blackwell as a breakthrough solution for end-to-end protein structure prediction.

The power of RTX PRO 6000 Blackwell Server Edition is available today in NVIDIA RTX PRO Servers from global system makers as well as in cloud instances from leading cloud service providers.

Find a partner for NVIDIA RTX PRO 6000 Blackwell Server Edition and experience protein folding…

GeForce NOW Blackwell RTX 5080-class SuperPODs are now rolling out, unlocking a new level of ultra high-performance, cinematic cloud gaming.

The new Install-to-Play feature is expanding the cloud library to nearly 4,500 games for Ultimate and Performance members.

With the NVIDIA Blackwell RTX upgrade, GeForce NOW brings GeForce RTX 5080-class performance to the cloud for the first time.

Look for the new “GeForce RTX 5080 Ready” row in the app for the full list of GeForce RTX 5080-optimized games, updated weekly with fresh additions.

Enjoy NVIDIA DLSS 4-fueled graphics and low-latency gameplay streaming from GeForce RTX 5080 gaming rigs in the cloud.

Expanding the Ecosystem: Automotive Leaders Embrace Cloud-to-Car AIAutomotive leaders are embracing NVIDIA’s cloud-to-car AI platform to transform their next-generation vehicles.

Lucid headlined the IAA showcase with its all-electric Lucid Gravity SUV, which is accelerated by the NVIDIA DRIVE AGX platform, uses the NVIDIA Blackwell architecture and operates on NVIDIA DriveOS.

Global Tech Leaders Accelerate Software-Defined Vehicles With NVIDIABeyond automakers, technology leaders across the globe are building on NVIDIA AI to accelerate the development of software-defined vehicles.

Cerence is presenting its xUI AI assistant at IAA, built on CaLLM models and running on NVIDIA DRIVE AGX with D…

Less than half a year since its debut at NVIDIA GTC, the NVIDIA GB300 NVL72 rack-scale system — powered by the NVIDIA Blackwell Ultra architecture — set records on the new reasoning inference benchmark in MLPerf Inference v5.1, delivering up to 1.4x more DeepSeek-R1 inference throughput compared with NVIDIA Blackwell-based GB200 NVL72 systems.

Blackwell Ultra builds on the success of the Blackwell architecture, with the Blackwell Ultra architecture featuring 1.5x more NVFP4 AI compute and 2x more attention-layer acceleration than Blackwell, as well as up to 288GB of HBM3e memory per GPU.

NVIDIA TensorRT Model Optimizer software quantized DeepSeek-R1, Llama 3.1 405B, Llama 2 70B and Llama 3.…

At the AI Infrastructure Summit, NVIDIA’s Ian Buck introduces a reference design and partner-driven strategy to transform global infrastructure for high-performance, energy-efficient AI.

At this week’s AI Infrastructure Summit in Silicon Valley, NVIDIA’s VP of Accelerated Computing Ian Buck unveiled a bold new vision: the transformation of traditional data centers into fully integrated AI factories.

Already, NVIDIA is collaborating with scores of companies across every layer of the stack, from building design and grid integration to power, cooling and orchestration.

NVIDIA, along with a deep bench of industrial and technology partners, is reactivating decades of infrastructure expertise to …

State-of-the-Art AI Models, Accelerated by RTXIncredible models for AI content creation have been released in the last weeks, all of which are now available in ComfyUI.

The Blender plug-in — featured in the NVIDIA AI Blueprint for 3D-guided generative AI — allows users to connect 2D and 3D workflows.

Run Hyper-Optimized Models for NVIDIA RTX GPUs in ComfyUIThe best way to use NVIDIA RTX GPUs is with the TensorRT library — a high-performance deep learning inference engine designed to squeeze maximum speed out of the Tensor Cores in NVIDIA RTX GPUs.

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

Join NVIDIA’s Discor…

“JUPITER marks the culmination of more than a decade of research and development,” said Thomas Lippert, director of the Jülich Supercomputing Centre.

“As the world’s most advanced and versatile exascale system, it represents a unique innovation, opening up completely new possibilities for science and industry in Europe.”“With JUPITER, Europe gains its most advanced AI supercomputer, built for large-scale simulation and AI, powered by NVIDIA Grace Hopper Superchips and Quantum-2 InfiniBand,” said Jensen Huang, founder and CEO of NVIDIA.

“JUPITER fuses high-performance computing and AI into a single architecture.

“Jupiter is the first European supercomputer, and the first outside the U.S., to…

NVIDIA today announced new AI education support for K-12 programs at a White House event to celebrate public-private partnerships that advance artificial intelligence education for America’s youth.

Pledging $25 million in support with AI education programs, NVIDIA is partnering with Study Fetch and CK-12 — two leading K-12 learning platforms — to tailor the NVIDIA Deep Learning Institute (DLI) and NVIDIA Academy content offerings to meet the instructional needs of U.S. K-12 classrooms.

The NVIDIA effort aligns with the White House executive order Advancing Artificial Intelligence Education for American Youth, announced in April.

Additionally, in support of the executive order, NVIDIA signed…

AI On: 6 Ways AI Agents Are Raising Team Performance — and How to Measure ItEditor’s note: This post is part of the AI On blog series, which explores the latest techniques and real-world applications of agentic AI, chatbots and copilots.

AI agents can help achieve and exceed efficiency goals as they learn, reason and adjust based on context and outcomes.

AI agents in IT operations offer:In fast-paced telco environments, agents can help manage networks by analyzing real-time performance indicators and predicting service failures.

Pegatron developed the PEGA AI Factory platform to accelerate the development of AI agents across the company by 400% in the last four years.

Stay up to date on age…

Don’t miss a special early GFN Thursday next Wednesday as GeForce NOW begins lighting up the globe with GeForce RTX 5080-class power streaming from the cloud.

With this upgrade, cloud gaming is about to level up.

NVIDIA Blackwell RTX brings GeForce RTX 5080-powered streaming to GeForce NOW, unlocking the highest resolutions and frame rates ever in the cloud.

Look out for Hell Is Us, announced at Gamescom and the first AAA title to hit the cloud at launch, along with the eagerly awaited Hollow Knight: Silksong, part of the five new games in the cloud available this week.

Play it on GeForce NOW and experience every tank clash in crystal-clear detail and fluid motion, streamed effortlessly to …

The new NVIDIA AI Blueprint for 3D object generation simplifies scene creation, and the new Microsoft TRELLIS NVIDIA NIM microservice accelerates performance by 20%, all powered by NVIDIA RTX.

NVIDIA AI Blueprints help address this challenge by providing sample workflows so users can skip the hypertechnical stages and quickly benefit from advanced generative AI techniques.

The new NVIDIA AI Blueprint for 3D object generation gives users a pipeline to automate the prototyping process.

Ready, Aim, DeployGet started with the NVIDIA AI Blueprint for 3D object generation by following these instructions:Load up the blueprint, including models.

Explore a variety of expertly curated AI Blueprints, …

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

Unlocking advertiser value through industry-leading ML innovationMeta Andromeda is a personalized ads retrieval engine that leverages the NVIDIA Grace Hopper Superchip, to enable cutting edge ML innovation in the Ads retrieval stage to drive efficiency and advertiser performance.

Its deployment across Instagram and Facebook applications has achieved +6% recall improvement to the retrieval system, delivering +8% ads quality improvement on selected segments.

Andromeda is designed to maximize ads performance by utilizing the exponential growth in volume of eligible ads available to the retrieval stage.

The design is optimized for AI hardware, minimizing memory bandwidth bottlenecks and enablin…

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…

TL;DR Multimodal Large Language Models (MLLMs) process data from different modalities like text, audio, image, and video.

This article explores Multimodal Large Language Models, exploring their core functionalities, challenges, and potential for various machine-learning domains.

Let’s break down the concept of Multimodal Large Language Models (MLLMs) by first understanding the terms “modal” and “multimodal:”“Modal” refers to a particular way of communicating or perceiving information.

| SourceGoogle: PaLM-EGoogle developed an embodied language model, PaLM-E, to incorporate continuous sensor modalities into language models and establish the link between words and perceptions.

Improving how t…

In this guide, we’ll show you how to build a RAG system using the LangChain framework, evaluate its performance using Ragas, and track your experiments with neptune.ai.

Part 1: Building a baseline RAG system with LangChainIn the first part of this guide, we’ll use LangChain to build a RAG system for the blog posts in the LLMOps category on Neptune’s blog.

Ragas works smoothly with LangChain, making it a great choice for evaluating our RAG system.

Step 1: Generate a RAG evaluation datasetAn evaluation set for RAG tasks is similar to a question-answering task dataset.

Step 2: Choose RAG evaluation metricsAs mentioned earlier, Ragas offers both LLM-based and non-LLM-based metrics for RAG syste…

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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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…

This paper demonstrates in a series of experiments that current safety alignment techniques of LLMs, as well as corresponding jailbreaking attacks, are in large part focusing on modulating the distribution of the first few tokens of the LLM response. Paper: https://openreview.net/forum?id=6Mxhg9PtDE&s=09 Abstract:

The safety alignment of current Large Language Models (LLMs) is vulnerable. Simple attacks, or even benign fine-tuning, can jailbreak aligned models. We note that many of these vulnerabilities are related to a shared underlying issue: safety alignment can take shortcuts, wherein the alignment adapts a model's generative distribution primarily over only its very first few output to…

A deep dive into the TokenFormer and an opinion about its impact, novelty, and relation to prior work. Paper: https://arxiv.org/abs/2410.23168 Abstract:

Transformers have become the predominant architecture in foundation models due to their excellent performance across various domains. However, the substantial cost of scaling these models remains a significant concern. This problem arises primarily from their dependence on a fixed number of parameters within linear projections. When architectural modifications (e.g., channel dimensions) are introduced, the entire model typically requires retraining from scratch. As model sizes continue growing, this strategy results in increasingly high com…

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

From this video: https://youtu.be/hFMaT9oRbs4 Artwork by Kurt Bruns

From this video: https://youtu.be/YdOXS_9_P4U

❤️ 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…

❤️ 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 📝 "Genesis: A Generative and Universal Physics Engine for Robotics and Beyond" is available here:

https://genesis-embodied-ai.github.io/ Tech report direct link: https://placid-walkover-0cc.notion.site/genesis-performance-benchmarking Criticism: https://stoneztao.substack.com/p/the-new-hyped-genesis-simulator-is

Their answer to criticism: https://placid-walkover-0cc.notion.site/genesis-performance-benchmarking (at 4.1 Res…

❤️ 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 My part at the official Blender 4.5 announcement video:

https://www.youtube.com/watch?v=wPhA0imjvVs&t=1474s Get Blender: https://www.blender.org/

Demo files: https://www.blender.org/download/demo-files/

Tutorial: https://www.youtube.com/watch?v=4haAdmHqGOw 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 supp…

❤️ 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 Kimi K2:

https://moonshotai.github.io/Kimi-K2/

API: https://platform.moonshot.ai Run it yourself locally: https://x.com/unslothai/status/1944780685409165589 Sources:

https://x.com/chetaslua/status/1943681568549052458

https://x.com/satvikps/status/1944861384573169929 📝 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 clicka…

❤️ Check out Vast.ai and run DeepSeek or any AI project: https://vast.ai/papers 📝 The paper is available here:

https://graphics.cs.utah.edu/research/projects/avbd/ Play with it!

https://graphics.cs.utah.edu/research/projects/avbd/avbd_demo2d.html 📝 My paper on simulations that look almost like reality is available for free here:

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

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

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-…

В этом году конференция Data Fest отмечает юбилей — 10 лет. За это время машинное обучение добралось до многих сфер нашей жизни. Нам стало интересно, как развитие ML изменит привычную реальность ещё через 10 лет. Вот мы и спросили об этом у гостей конференции! Полное видео ищите на нашем канале. #автоматизация #lowcode #nocode #будущееразработки #нейросети #AIвразработке #программирование #ИИ #разработчики #машинноеобучение #генерациякода #технологии

В этом году конференция Data Fest отмечает юбилей — 10 лет. За это время машинное обучение добралось до многих сфер нашей жизни. Нам стало интересно, как развитие ML изменит привычную реальность ещё через 10 лет. Вот мы и спросили об этом у гостей конференции! Полное видео ищите на нашем канале. #искусственныйинтеллект #нейросети #машинноеобучение #DataFest #будущеетехнологий #AI #ИИ #глубокообучение #технологиибудущего #GPT #этикаИИ #развитиеИИ #автоматизация

Это фрагмент выступления Дарьи Виноградовой, руководителя команды стримингового зрения в Яндексе. На Data Fest Дарья рассказала о VLM в Алисе. #Алиса #Яндекс #нейросети #ИИ #технологии #VLM #компьютерноезрение #голосовойассистент #визуальныйинтеллект #AI #умныеустройства #обучениеИИ #распознаваниеобъектов #мультимодальность

Это фрагмент выступления Дарьи Виноградовой, руководителя команды стримингового зрения в Яндексе. На Data Fest Дарья рассказала о VLM в Алисе. #Алиса #Яндекс #нейросети #искусственныйинтеллект #VLM #AI #технологии #умныеустройства #мультимодальность #АлисаЯндекс #голосовойассистент #визуальноеAI #ЯндексАлиса #VLMтехнологии #ИИ

Это фрагмент выступления Дарьи Виноградовой, руководителя команды стримингового зрения в Яндексе. На Data Fest Дарья рассказала о VLM в Алисе. #VLM #нейросети #ИИ #искусственныйинтеллект #мультимодальность #AI #визуальныйИИ #нейросетитексткартинка #компьютерноеЗрение #моделиЯндекса #умныеустройства #технологиибудущего #визуальноятехнология #machinelearning #deeplearning #языкитехнологии #AIЯндекс #модельпокартинке #LLM #NLP #CV

Это фрагмент выступления Дарьи Виноградовой, руководителя команды стримингового зрения в Яндексе. На Data Fest Дарья рассказала о VLM в Алисе. #Алиса #Муза #Яндекс #VLM #DataFest #ИИ #нейросети #AI #умныеустройства #визуальныемодели #визуальныйИИ #ЯндексТехнологии #визуальноятехнология #ассистентыбудущего #технологии #компьютерноеЗрение #LLM #интеллектуальныйассистент #стриминговоезрение #нейроассистент #техконференция

Александр Исаков, руководитель группы прогнозирования в Яндекс Лавке, прочитал этот доклад на конференции AHA!25. Александр рассказал о создании и внедрении системы среднесрочного прогнозирования для Яндекс Лавки. Эта система точно прогнозирует ключевую бизнес-метрику (заказы) на полугодовой период и помогает планировать ресурсы в условиях быстрого роста сервиса и высокой волатильности спроса. Александр подробно остановился на том, как ребята решили проблему волатильности временных рядов с помощью приведения данных к стабильному виду и выделения ключевых влияющих факторов, что позволило не только улучшить прогноз, но и объяснить отклонения от плана. А ещё рассказал, как они встроили прогноз…

Эльвира Морозова, ХХ в Яндексе, прочитала этот доклад на конференции AHA!25. Эльвира рассказала, как ребята внедрили YaGPT в формате подсказок оператора в одну из поддержек Яндекса. И в итоге получили доказанный в A/B-тестах экстраэффект как на скорости работы операторов, так и на качестве ответов. Эльвира показала, почему «экзоскелет» из GPT для оператора поддержки — это новая норма. Спойлер: применение GPT в саппорте сейчас приносит 15% чистой экономии в Яндекс Маркете. Больше классных материалов ищите в телеграм-канале Yandex for ML: https://t.me/yandexforml #GPT #Яндекс #поддержка #аналитика #AHA25 #YaGPT #искусственныйинтеллект #YandexForAnalytics #LLM #AI

Николай Савушкин, руководитель службы рекомендательных технологий в Яндексе, прочитал этот доклад на конференции AHA!25. Николай рассказал, как персонализация устроена сейчас, как большие генеративные модели внедряют в рекомендательные системы Яндекса и что ждёт нас в будущем. Доклад направлен на широкую аудиторию с техническим бэкграундом, знакомит с основными понятиями и показывает технологические тренды. Больше классных материалов ищите в телеграм-канале Yandex for ML: https://t.me/yandexforml #персонализация #рекомендации #машинноеобучение #генеративныемодели #Яндекс #AI #LLM #AHA25 #аналитика #YandexForAnalytics

Спикер: Александр Нозик Тема: Экосистема открытого научного ПО KScience и возможности развития открытого ПО в вузах Data Fest 2025: https://ods.ai/events/datafest2025

Презентацию к докладу Вы можете скачать в треке секции Open Source: https://ods.ai/tracks/df25-opensource 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: Екатерина Лемдясова, студентка ФГБОУ ВО "РОСБИОТЕХ" Тема: Алгоритм рекомендательной системы с учётом культурных факторов для решения проблем холодного старта. Data Fest 2025: https://ods.ai/events/datafest2025 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: Артём Земляк, Lead DS Поиска, ecom.tech Data Fest 2025: https://ods.ai/events/datafest2025

Презентацию к докладу можно скачать в треке секции Advanced LLM: https://ods.ai/tracks/df25-allm

______

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: Антон Воронов, Avito.tech Data Fest 2025: https://ods.ai/events/datafest2025

Презентацию к докладу Вы можете скачать в треке секции Career & TeamLead: https://ods.ai/tracks/df25-career

______

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: Петр Сокерин, Сколтех, МЛ инженер исследователь Data Fest 2025: https://ods.ai/events/datafest2025

Презентацию к докладу Вы можете скачать в треке секции Advanced LLM: https://ods.ai/tracks/df25-allm

______

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

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Спикер: Игорь Чурин Data Fest 2025: https://ods.ai/events/datafest2025

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Спикер: Сергей Абраменков, Full-stack seismologist, PhD Data Fest 2025: https://ods.ai/events/datafest2025

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Спикер: Михаил Козак, НПП "Югпромавтоматизация" Data Fest 2025: https://ods.ai/events/datafest2025

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Одиннадцатый выпуск подкаста "Капитанский мостик" традиционно ведут Валентин Малых и Дмитрий Колодезев. В этот раз снова вдвоем. Оставляйте новости к следующему выпуску в нашем канале в Mattermost. ИИ-саммари: В этом эпизоде подкаста обсуждаются последние новости в области технологий и инвестиций. Ведущие говорят о намерении компании SML стать крупным инвестором в Местраль, а также о том, как китайские компании, такие как Alibaba и Baidu, стремятся снизить зависимость от Nvidia, разрабатывая собственные чипы. В этом разговоре обсуждаются ключевые аспекты конкуренции и монополии в мире технологий, в частности, влияние компаний AMD и Intel на рынок процессоров, а также роль программного обесп…

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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…

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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Oliver Anthony is singer-songwriter who first gained worldwide fame with his viral hit Rich Men North of Richmond.

He became a voice for many who are voiceless, with many of his songs speaking to the struggle of the working class in modern American life.

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Go to https://drinkLMNT.com/lexOUTLINE:(00:00) – Introduction(09:00) – Open mics(13:03) – Mainstream country music(22:10) – Fame(28:06) – Music vs politics(36:56) – Rich Men North of Richmon…

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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Tim Sweeney is a legendary video game programmer, founder and CEO of Epic Games that created the Unreal Engine, Fortnite, Gears of War, Unreal Tournament, and many other groundbreaking and influential video games.

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Jeffrey Wasserstrom is a historian of modern China.

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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.

LEE: Yeah, yeah.

LEE: Yeah, yeah.

LEE: Yeah, yeah.

[LAUGHS]GOLDBERG: Right, right, right, yeah.

Yeah, yeah.

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.

Kyle asks Asaf questions about the new network science course he is now teaching. The conversation delves into topics such as contact tracing, tools for analyzing networks, example use cases, and the importance of thinking in networks.

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…

In this Five-Minute Friday, Cofounder and CTO of lakeFS Oz Katz talks to Jon Krohn about data warehouses, data lakes, and how companies can handle increasingly complex data infrastructures and formats. Hear about lakeFS’s collaboration with Legofest, lakeFS’s approach to helping users collaborate on data lakes, and how to overcome the challenges of working with multimodal data. Additional materials: ⁠www.superdatascience.com/914⁠ This episode is brought to you by the ⁠Dell AI Factory with NVIDIA⁠.

Julien Launay launched Adaptive to give data science teams in business enterprises their “RLOps tooling” to make reinforcement learning easier. Talking to Jon Krohn, Julien says, “Most of our users are data scientists who write Python codes to interface with the system”. Adaptive is also able to work with companies without data science teams, collaborating with partners like Deloitte to add the necessary personnel. Julien is currently working on making his platform more widely available. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/913⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

In this episode of In Case You Missed It, we look back on five great interview episodes from July. Hear from Lilith Bat-Leah (Episode 901), Sinan Ozdemir (Episode 903), Sebastian Gehrmann (Episode 905), Zohar Bronfman (Episode 907) and Robert Ness (Episode 909). They’ll tell you why data-centric machine learning is so important across disciplines, starting with law, and how we can use AI benchmarks and “red teaming” to refine our search for the best AI models. Additional materials: ⁠⁠⁠⁠www.superdatascience.com/912 Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Reproducibility, Python notebooks, and data science communities: Software developer Akshay Agrawal speaks to Jon Krohn about Marimo, the next-generation computational notebook for Python, how he built and fostered a thriving community around the product, and what makes this notebook so versatile and accessible for users. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/911⁠⁠⁠⁠⁠ This episode is brought to you by ⁠Trainium2, the latest AI chip from AWS ⁠and by the ⁠Dell AI Factory with NVIDIA⁠. Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

In this Five-Minute Friday, Jon Krohn looks into AI’s disruption of the journalism industry and how it has fundamentally reshaped news production. Multiple news outlets’ suing of ChatGPT over its use of copyrighted materials may have taken the most headlines to date, but this isn’t to say news media is rebuffing AI entirely. On the contrary, several outlets have launched summarization and analysis tools for both internal and external use, such as The New York Times’s Echo and The Washington Post’s Haystacker. This episode looks into the ways major news outlets are utilising AI, and what this means for journalists. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/910⁠⁠ Interested in sp…

Researcher at Microsoft Robert Usazuwa Ness talks to Jon Krohn about how to achieve causality in AI with correlation-based learning, the right libraries, and handling statistical inference. When dealing with causal AI, Robert notes how important it is to keep aware of variables in the data that may mislead us and force inaccurate assumptions. Not all variables will be useful. It is essential, then, that any assumptions are grounded in a deeper understanding of how the data were gathered, and not what appears in the dataset. Listen to the episode to hear how you can apply causal AI to your projects. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/907⁠⁠⁠⁠ This episode is brought to you…

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…