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NVIDIA Nsight vs. PyTorch ProfilerReaders familiar with our work may be surprised at the mention of NVIDIA Nsight profiler rather than PyTorch Profiler.

In our previous posts we have advocated strongly for the use of PyTorch Profiler in AI/ML model development as a tool for identifying and optimizing runtime performance.

Optimization 1: Multi-Process Data LoadingThe first step is to modify the data input pipeline to use multi-process data loading.

Multiproc Dataloading PyTorch Profiler Trace (by Author)With our finding of the data-copy performance bottleneck in hand, we proceed to our next optimization.

The main difference is the placement of the yellow “copy data” block in the NVTX row of …

MCPsMCP stands for Model Context Protocol and today it is a standard way of providing tools to the majority of the agentic pipelines.

MCPs provide description for each tool and description for each argument of a tool so these values are just used blindly in the agentic pipelines as an LLM API calling parameters.

Dataset and benchmarkTo prove that different tools descriptions can change model’s behavior I used NVidia’s “When2Call” dataset.

Tool description adjustments.

It exposes a method with the simple UI to edit this list (JSON-schema), so the user can experiment with different tools’ descriptions.

We’ll break down the two main types of drift: data drift and concept drift.

The Main Types of Drift: Data Drift and Concept DriftGenerally, drift occurs when the joint probability P(X, y) changes over time.

Concept and data drift can take different forms, and these forms may have varying implications for drift detection and drift handling strategies.

These methods monitor the performance of a model (concept drift detection) or directly analyse incoming data (data drift detection).

Finally, one last note: While the article introduces several increasingly more complex methods and concepts, bear in mind that any drift detection is always better than no drift detection.

intelligence (AI), the future of data goes beyond the traditional data analyst or data scientist roles.

Data is everywhere around us and yet, many industries have not seen an influx of data professionals to their maximum potential.

Renewable energy employs data professionals under roles such as:Energy systems analystForecasting and optimization data scientistGrid analytics engineerEnergy policy data analystThese roles exist across utilities, energy startups, government agencies, and research labs.

In looking at these fields, we see a broader trend that data careers are becoming less centralized and more contextual.

Rashi is a data wiz from Chicago who loves to analyze data and create data s…

If you’ve ever simplified a dashboard only to watch it snowball back into chaos, trust me, this one’s for you.

When you add too many charts, your dashboard becomes overfit to individual stakeholder requests and loses its narrative focus.

When you simplify a dashboard, you’re making judgment calls about what matters and what doesn’t.

Then later, I’d track how stakeholders actually used them.

If you’re stuck between what you know works and what your organization will accept, don’t worry, you’re not alone.

How to build, score, and interpret RFM segments step by step

The post EDA in Public (Part 3): RFM Analysis for Customer Segmentation in Pandas appeared first on Towards Data Science.

The Fix: Reward State Transitions, Not SnapshotsThe fix: reward based on state transitions r(s, s') , not just current state r(s') .

Chapters 6-7 cover TD learning and Actor-Critic methods.

“Asynchronous Methods for Deep Reinforcement Learning.” International Conference on Machine Learning.

Original TD learning paperPrevious Posts in This SeriesJumle, V. (2025).

“Deep Reinforcement Learning: 0 to 100 – Policy Gradients (REINFORCE).” Part 0: REINFORCE ImplementationCode Repository & ImplementationJumle, V. (2025).

had launched its own LLM agent framework, the NeMo Agent Toolkit (or NAT), I got really excited.

Image by authorThe NeMo Agent Toolkit is configured via YAML files that define both the workflow and the underlying LLMs.

------------------------------ [AGENT] Agent input: Is Denmark happier than Finland?

# **Explanation:** # - Finland's happiness score: 7.73 # - United Kingdom's happiness score: 6.73 # - Absolute difference: 7.73 - 6.73 = 1.00 # - Percentage calculation: (1.00 ÷ 6.73) × 100 = 14.86% # This means Finland's happiness score is approximately 14.86% higher than # the United Kingdom's happiness score.

The nice thing about the NeMo Agent Toolkit is that we don’t need to rewrite this…

In this article, I reflect on five learnings that I got from following the Advent of Code challenge this year and how they translate to data science.

Naturally, this is equally as important in data science projects where assumptions about data (implicit or explicit) can lead to serious issues if they remain unchecked.

In data science, similar principles can be observed.

The parallel to data science is also given here as solutions may work well on sample data or limited datasets but are prone to fail when faced with “production-level” sizes.

Skill development in data science rarely comes from one-off projects or isolated deep dives either.

Table of Contents1 – Why Chunk Size Is More Than Just a Parameter2 – How Does Chunk Size Influence the Stability of Retrieval Results in Small RAG Systems?

As a result, chunk size determines which parts of the meaning are actually compared when a query is matched against a chunk.

2 – How Does Chunk Size Influence the Retrieval Results in Small RAG Systems?

I therefore asked myself the following questions:How does chunk size change retrieval results in a small, controlled RAG system?

Based on this small RAG system experiment, it is not really possible to derive a “best chunk size” conclusion.

use gradient descent to find the optimal values of their weights.

In the previous articles, we used simple gradient descent because it is easier to show and easier to understand.

Modern systems use variants of gradient descent that improve speed, stability, or convergence.

It changes the size of the learning rate across iterations so that the optimisation becomes more stable near the minimum.

Adam combines these ideas, and Learning Rate Decay adjusts the step size over time.

One might encounter a number of frustrating difficulties when trying to numerically solve a difficult nonlinear and nonconvex optimal control problem.

Then, I’ll state the optimal control problem in all its detail.

4.3 Control chatteringControl chattering is the rapid jumping/oscillation/switching of the optimal control signal.

“Globally Convergent Homotopies for Discrete-Time Optimal Control.” SIAM Journal on Control and Optimization 63 (4): 2686–2711.

Applied Optimal Control: Optimization, Estimation and Control.

But we start with a confusion matrix, because that is where everyone begins in practice.

Why a confusion matrix is not enough1.1 Scores from modelsA classifier will usually give us scores, not final decisions.

Then we typically compute ratios such as:Precision = TP / (TP + FP)= TP / (TP + FP) Recall (TPR) = TP / (TP + FN)= TP / (TP + FN) Specificity = TN / (TN + FP)= TN / (TN + FP) FPR = FP / (FP + TN)= FP / (FP + TN) Accuracy = (TP + TN) / TotalSo far, everything is clean and intuitive.

3.1 Computing the areaOnce the ROC curve exists as a list of points (FPR, TPR), the AUC is pure geometry.

Seen this way, ROC AUC is an intuitive metric, and a spreadsheet is enough to make every step explic…

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,…

As the first post of 2026, I wanted to share some of the research trends that I think might be super influential in this year frontier model breakthroughs.

We built these incredible “stochastic parrots” that could complete your code, write poetry, and pass the bar exam, all by just really, really wanting to predict the next token.

But if you look at the research papers dropping recently, the vibe has shifted.

We don’t just want models that can talk smooth; we want models that can think straight.

As we look toward 2026, we are moving from the era of Generative AI (making things that look real) to Verifiable AI (making things that are correct).

In the frenetic closing weeks of 2025, the artificial intelligence landscape was dominated by the usual titans.

Google’s Gemini 3 Pro dazzled with its multimodal fluidity, and the rumor mill surrounding OpenAI’s GPT-5.2 reached a fever pitch.

Yet, amidst this cacophony of frontier model releases, a quieter, perhaps more consequential shift occurred on December 22.

Zhipu AI (Z.ai) released GLM-4.7, a model that signals a definitive transition in the open-weight ecosystem: a move away from “conversational competency” toward “agentic reliability.”GLM-4.7 is not designed to be a chat partner; it is designed to be an employee.

By aggressively optimizing for long-context loops, terminal error rec…

Today we will Discuss:Key concepts of synthetic data generation for image models.

💡 AI Concept of the Day: Synthetic Data Generation for Image ModelsSynthetic image data has moved from a niche trick to a core ingredient in modern vision systems.

When real images are scarce, private, or unbalanced, synthetic pipelines let you generate pixels with known labels, push coverage into rare and long-tail cases, and iterate quickly on edge conditions.

The key is choosing the right generator, the right control signals, and a rigorous quality-control loop so that synthetic variety actually translates into downstream gains.

Diffusion models (text-to-image, image-to-image, inpainting) and GANs can produ…

In the AI of the Week section we are going to cover the newly released GLM 4.7.

In the opinion section, we will discuss three non-trivial AI areas that I am excited about fro 2026.

For Nvidia, this isn’t just about removing a rival; it’s about acknowledging that the next phase of the AI cycle is inference.

AI Lab: Princeton University, ByteDance Seed, Columbia University, University of Michigan, University of ChicagoSummary: The authors propose GenEnv, a framework that trains agents via a co-evolutionary game where a generative simulator creates dynamic tasks aligned with the agent’s current difficulty level.

🤖 AI Tech ReleasesGLM 4.7Z.ai released GLM 4.7, a new version of its marquee model…

Created Using GPT-5For the last few years, AI progress has felt almost… procedural.

That mood is what Ilya Sutskever calls the “age of scaling”—a period where one word (“scaling”) basically told an entire industry what to do next.

What are the techniques that convert compute into genuine generalization—models that learn faster, adapt better, and make fewer weird mistakes?

Below is a map of the most promising technique-clusters that could plausibly unlock the next wave of frontier innovation.

It’s not a list of “one weird trick.” It’s more like a toolbox for the post-pretraining world.

Last week, NVIDIA quietly redefined the baseline for open-weight intelligence with the release of the Nemotron 3 family.

The headline isn’t just the benchmarks (though the Nemotron 3 Nano creates a new state-of-the-art for the 30B parameter class); it is the architecture.

We are seeing the first major industrial-scale deployment of a Hybrid Mamba-Transformer Mixture-of-Experts (MoE).

Image Credit: NVIDIAFor the enterprise, this is a signal that the “brute force” era of dense Transformers is ending.

The Architecture: A Hybrid “Frankenstein” (in the best way)

Created Using Gemini 3Today we will Discuss:The idea of using reinforcement learning(RL) environments to generate synthetic data.

The famous Reflexion paper about improving AI agents using RL data generation.

💡 AI Concept of the Day: Synthetic Data Generation with RL EnvironmentsWhen real-world data is scarce or privacy-restricted, reinforcement learning (RL) environments become a force multiplier for synthetic data.

This is especially potent for domains where outcomes are verifiable but logs are limited (coding sandboxes, web automation, spreadsheets/SQL, robotics-in-sim).

By executing tasks rather than describing them, RL pipelines mint trajectories that teach models how to act under cons…

In the AI of the week edition, we discuss NVIDIA’s amazing Nemotron 3 release.

Subscribe and don’t miss out:📝 Editorial: Last Week in AI: Tokens, Throughput, and TrillionsThis week’s AI story didn’t arrive as one dramatic demo; it arrived as a synchronized upgrade across the stack—capital, platforms, and product surfaces all moving in lockstep.

AI Lab: Tongyi Lab (Alibaba Group)Summary: QwenLong-L1.5 proposes an end-to-end post-training recipe for long-context reasoning, combining a scalable synthesis pipeline for multi-hop, globally-grounded tasks with stabilized long-context RL (including task-balanced sampling, task-specific advantage estimation, and AEPO).

AI Lab: NVIDIASummary: This wh…

From speech recognition and voice synthesis to music generation and environmental sound analysis, frontier AI models for audio are tackling challenges unique to sound.

The goal is to provide a comprehensive, engaging overview of cutting-edge audio AI for a technically savvy audience.

Audio’s Unique Challenges and OpportunitiesAudio data is fundamentally different from text or images, presenting unique challenges for AI models.

This means even a few seconds of audio involve very long sequences of data points.

Unlike text (which has discrete tokens like words) or images (2D grids of pixels), raw audio is high-frequency and high-dimension.

Created Using GPT 5.2Google’s agentic releases last week weren’t “yet another model drop.” They were Google quietly shipping an agent runtime—and then immediately proving it works by dropping a managed research agent that basically demands that runtime to function.

On December 11, 2025, the Gemini API changelog logged two entries that, together, mark a clean architectural pivot: the Interactions API (Beta) and the Gemini Deep Research Agent (Preview).

If you build agents for a living, you already know the pattern: most of your complexity isn’t “getting the model to answer.” It’s dealing with the messy reality that real agent workflows are stateful, tool-heavy, and often long-running.

You bu…

Created Using GPT-5.2Today we will Discuss:An introduction to multiturn data synthesis for data generation.

A review of the famous Reflexion paper that uses synthetic data to improve AI agents.

💡 AI Concept of the Day: What is Multiturn Data Synthesis?

Multi-turn synthesis and self-play are other important categories in synthetic data generation .

These methods treat data generation as an interactive process rather than a single shot.

Created using GPT-5Next Week in The Sequence:Learn more about synthetic data generation with a deep dive into multi-turn data synthetic.

Our AI of the Week section dives into Google’s new agentic releases.

AI Lab: Carnegie Mellon UniversitySummary: The authors build a controlled synthetic reasoning framework to disentangle how pre-training, mid-training, and RL each contribute to reasoning generalization in language models.

Gemini Deep Research AgentGoogle released a new Deep Research agent with advanced tool capabilities.

FACTS BenchmarkGoogle DeepMind released the FACTS Benchmark Suite, three benchmarks to evaluate factuality in AI models.

What got me thinking about this idea was the announcement of Unconventional AI which raised a considerable amount of money of work precisesly on this problem.

Recent events underscore this concern: a new startup called Unconventional AI made headlines by raising an unprecedented $475 million seed round to develop radically new computing hardware for AI.

The human brain performs extraordinary feats on only ~20 watts of power, whereas training a single large AI model can devour megawatt-hours.

The sheer gap suggests that AI might require a new form of computing to continue its trajectory.

The Reign of Matrix Multiplications and GPUs

Created Using GPT-5Gemini Deep Think is one of the most innovative architectures of recent times and, yet, we know so little about it.

Today, I would like to summarize some of the things I learned about Deep Think.

Gemini DeepThink made news when it score a gold medal at the 2025 international math olympiad using a parallel technique over the standard Gemini model.

It embodies the current frontier idea that how a model uses its compute at inference time matters as much as raw parameter count.

From chain-of-thought hacks to “thinking models”

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

device ( f "cuda:{local_rank}" ) print ( f "World size: {world_size}, Rank: {rank}, Local rank: {local_rank}.

save ( { "model" : model .

mlp = LlamaMLP ( config ) def forward ( self , hidden_states : Tensor , rope : RotaryPositionEncoding , attn_mask : Tensor ) -> Tensor : # First residual block: Self-attention residual = hidden_states hidden_states = self .

device ( f "cuda:{local_rank}" ) print ( f "World size: {world_size}, Rank: {rank}, Local rank: {local_rank}.

save ( { "model" : model .

It is not the same model object you created using nn.Module , but it shares the same tensors with the original model.

This is because the compiled model is an object that shares the same weights as the original model.

state_dict ( ) , "model.pth" )The original model can be accessed from the compiled model using model._orig_mod .

This can be achieved by increasing the batch size: with the same number of data samples, a larger batch size means fewer batches to process.

You also learned that gradient accumulation is a technique for training with a larger effective batch size by accumulating gradients from multiple mini-batches.

set_postfix ( loss = loss .

save ( { "model" : model .

state_dict ( ) , "optimizer" : optimizer .

state_dict ( ) , "scaler" : scaler .

If you still encounter memory issues, another technique trades time for memory: gradient checkpointing.

Share Post ShareIntroducing Google JulesIf you have an interest in agentic coding, there’s a pretty good chance you’ve heard of Google Jules by now.

Using Google Jules with an Existing GitHub RepositoryThe process for using Google Jules generally involves initial setup, task prompting, and subsequent human review and approval.

Step 1: Initial Access and AuthenticationVisit the official Google Jules website and click “Try Jules”.

These are just a few specific concerns to keep in mind while exploring Google Jules and agentic coding.

Wrapping UpThis has been an introduction to using Google Jules for agentic coding.

When you train a language model, you want to measure how accurately it predicts human language use.

Specifically, you will learn:What is perplexity, and how to compute itHow to evaluate the perplexity of a language model with sample dataLet’s get started.

OverviewThis article is divided into two parts; they are:What Is Perplexity and How to Compute ItEvaluate the Perplexity of a Language Model with HellaSwag DatasetWhat Is Perplexity and How to Compute ItPerplexity is a measure of how well a language model predicts a sample of text.

Evaluate the Perplexity of a Language Model with HellaSwag DatasetPerplexity is a dataset-dependent metric.

from_pretrained ( model ) model = transformers .

Share Post ShareIn this article, you will learn three reliable techniques — ordinal encoding, one-hot encoding, and target (mean) encoding — for turning categorical features into model-ready numbers while preserving their meaning.

Applying target (mean) encoding for high-cardinality features without leaking the target.

fit_transform ( data ) print ( "Original Data:" , data .

The answer lies in Target Encoding, also frequently called Mean Encoding.

Target (Mean) Encoding: This is the powerful answer for high-cardinality features that would overwhelm OHE.

Once trained, you can load it back with the following code:from tokenizers import Tokenizer tokenizer = Tokenizer.from_file("bpe_50k.json") 1 2 3 from tokenizers import Tokenizer tokenizer = Tokenizer .

tokenizer = tokenizer self .

device = device self .

save ( { "model" : model .

save ( { "model" : model .

dim = dim self .

cat ( ( inv_freq , inv_freq ) , dim = - 1 ) position = torch .

dim = dim self .

pi / inv_freq max_wavelen = base_length / low_factor min_wavelen = base_length / high_factor smooth_factor = ( base_length / wavelen - low_factor ) / ( high_factor - low_factor ) smoothed = ( 1 - smooth_factor ) * inv_freq / scale_factor + smooth_factor * inv_freq inv_freq = torch .

cat ( ( inv_freq , inv_freq ) , dim = - 1 ) position = torch .

In this tutorial, we’ll learn how to fine-tune two powerful open-source models, Mistral 7B and Llama 3 8B, using a customer support question-and-answer dataset.

print("Creating customer support Q&A dataset...") # Create realistic customer support data customer_support_data = [ { "instruction": "You are a helpful customer support agent.

decode ( outputs [ 0 ] , skip_special_tokens = True ) # Extract just the response text if "[/INST]" in response : response = response .

strip ( ) elif "assistant" in response : response = response .

strip ( ) elif "### Response:" in response : response = response .

A practical trick is a diff budget, an explicit limit on lines changed per iteration.

startswith ( ( "+++" , "---" ) ) ) changed = count_changed_lines ( diff ) if changed > MAX_CHANGED_LINES : raise ValueError ( f "Diff too large: {changed} changed lines" )For manual workflows, bake the constraint into your prompt:Output only a unified diffHard limit: 120 changed lines totalNo unrelated formatting or refactorsIf you need more, stop and ask for a second patchAgents respond well to constraints that are measurable.

ratelimit import SlidingWindowLimiter def test_allows_n_requests_per_window ( ) : lim = SlidingWindowLimiter ( limit = 3 , window_seconds = 1 ) assert lim .

allow ( "u1" ) assert li…

Vector databases solve this by storing embeddings and facilitating super-fast similarity searches across billions of vectors.

This article covers the top five vector databases for production LLM applications.

WeaviateWeaviate is an open-source vector database that works well for combining vector search with traditional database capabilities.

If you already use PostgreSQL and would like to explore a vector search extension, you can also consider pgvector.

To learn more about how vector databases work, read The Complete Guide to Vector Databases for Machine Learning.

Share Post ShareIntroductionBuilding newly trained machine learning models that work is a relatively straightforward endeavor, thanks to mature frameworks and accessible computing power.

Without further ado, here is the list of 10 machine learning engineer best practices I curated for you and your upcoming models to shine at their best in terms of long-term reliability.

Therefore, everything surrounding a machine learning model should be properly versioned.

Continuous Monitoring and ObservabilityThis is probably already in your checklist of best practices, but as an essential of machine learning engineering, it is worth pointing it out.

This article provided a checklist of 10 essential best…

IntroductionFrom daily weather measurements or traffic sensor readings to stock prices, time series data are present nearly everywhere.

lstm = nn .

transformer = nn .

Linear ( d_model , 1 ) def forward ( self , x ) : x = self .

embed ( x ) x = self .

This randomness is not a bug but a core feature of how the model samples its next token from a probability distribution.

Softmax transforms these raw scores into a probability distribution.

LLMs don’t always select the token with the highest probability; instead, they sample from the probability distribution to produce a different output each time.

Top-$p$ sampling (also known as nucleus sampling) addresses this issue by sampling tokens according to their cumulative probability rather than a fixed count.

You learned to select different values for the temperature, top-$k$, and top-$p$ sampling parameters for different LLM use cases.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Additional troubleshooting information here.

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

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

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

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

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Additional troubleshooting information here.

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

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Additional troubleshooting information here.

Google 2025 recap: Research breakthroughs of the year

Today, we're expanding the Gemini 3 model family with the release of Gemini 3 Flash, which offers frontier intelligence built for speed at a fraction of the cost.

Last month, we kicked off Gemini 3 with Gemini 3 Pro and Gemini 3 Deep Think mode, and the response has been incredible.

With Gemini 3, we introduced frontier performance across complex reasoning, multimodal and vision understanding and agentic and vibe coding tasks.

Gemini 3 Flash retains this foundation, combining Gemini 3's Pro-grade reasoning with Flash-level latency, efficiency and cost.

Starting today, Gemini 3 Flash is rolling out to millions of people globally:

Today, we are releasing Gemma Scope 2: a comprehensive, open suite of interpretability tools for all Gemma 3 model sizes, from 270M to 27B parameters.

Producing Gemma Scope 2 involved storing approximately 110 Petabytes of data, as well as training over 1 trillion total parameters.

What’s new in Gemma Scope 2Interpretability research aims to understand the internal workings and learned algorithms of AI models.

Like its predecessor, Gemma Scope 2 acts as a microscope for the Gemma family of language models.

While the original Gemma Scope enabled research in key areas of safety, such as model hallucination, identifying secrets known by a model, and training safer models, Gemma Scope 2 support…

Earlier this week, we introduced greater control over audio generation with an upgrade to our Gemini 2.5 Pro and Flash Text-to-Speech models.

Today, we’re releasing an updated Gemini 2.5 Flash Native Audio for live voice agents.

Gemini 2.5 Flash Native Audio is now available across Google products including Google AI Studio, Vertex AI, and has also started rolling out in Gemini Live and Search Live, bringing the naturalness of native audio to Search Live for the first time.

Beyond powering helpful agents, native audio unlocks new possibilities for global communication.

Live Voice Agents

Today, we're announcing an expanded partnership with the UK AI Security Institute (AISI) through a new Memorandum of Understanding focused on foundational security and safety research, to help ensure artificial intelligence is developed safely and benefits everyone.

The research partnership with AISI is an important part of our broader collaboration with the UK government on accelerating safe and beneficial AI progress.

This is why we have partnered with the UK AISI since its inception in November 2023 to test our most capable models.

We are actively working with AISI to build more robust evaluations for AI models, and our teams have collaborated on safety research to move the field forward…

The UK has already laid a strong foundation to seize this moment and is uniquely positioned to translate AI innovation into public benefit.

That’s why we are excited to deepen our collaboration with the UK government to accelerate this work and offer a blueprint for other countries.

Accelerating access to frontier AI in key sectors: Science & EducationOur partnership will center on providing access to frontier AI in two areas foundational to the UK’s long-term success: scientific discovery and education.

The UK has a rich history of applying new technologies to drive scientific progress, from Hooke’s microscope to Faraday’s electrical experiments.

Establishing Google DeepMind’s first automa…

The FACTS Benchmark SuiteToday, we’re teaming up with Kaggle to introduce the FACTS Benchmark Suite.

A Search Benchmark that tests a model’s ability to use Search as a tool to retrieve information and synthesize it correctly.

Similar to our previous release, we are following standard industry practice and keeping an evaluation set held-out as a private set.

The FACTS Benchmark Suite Score (or FACTS Score) is calculated as the average accuracy of both public and private sets across the four benchmarks.

Kaggle will oversee the management of the FACTS Benchmark Suite.

Scientists are using AlphaFold in their research to strengthen an enzyme that’s vital to photosynthesis, paving the way for more heat-tolerant crops.

As global warming accompanies more droughts and heatwaves, harvests of some staple crops are shrinking.

But less visible is what is happening inside these plants, where high heat can break down the molecular machinery that keeps them alive.

Plants use photosynthesis to produce the glucose that fuels their growth via an intricate choreography of enzymes inside plant cells.

"Our job is to learn from those examples and build that same resilience into the crops we depend on."

They used AlphaFold alongside comparative genomics to better understand how plants perceive changes in their environment, paving the way for more resilient crops.

AlphaFold has been cited in more than 35,000 papers and more than 200,000 papers incorporated elements of AlphaFold 2 in their methodology.

An independent analysis of AlphaFold 2’s impact, carried out by the Innovation Growth Lab, suggests that researchers using AlphaFold 2 see an increase of over 40% in their submission of novel experimental protein structures.

Those protein structures are more likely to be dissimilar to known structures, encouraging the exploration of uncharted areas of science.

The AlphaFold Server is empowerin…

Both have a family history of heart disease – a reminder of what’s at stake in their work to better understand and ultimately help treat this deadly condition.

That protein, apoB100, has defied mapping not only because it’s enormous (for a protein), but also because it connects to fats and other molecules in complicated ways.

ApoB100 forms the molecular scaffold of “bad cholesterol”, which is known to scientists as low-density lipoprotein (LDL).

Discovering the structure of its key protein promised to shed light on how bad cholesterol becomes harmful inside the body, giving scientists a better chance to develop ways to prevent and treat ASCVD.

The images weren’t sharp enough to map the stru…

We stand at an inflection point where the convergence of advanced AI and scientific research promises to unlock a new golden age of discovery.

There is perhaps no clearer expression of this than the application of AI within science.

Putting our advanced AI tools into the hands of American scientistsGoogle DeepMind will provide an accelerated access program for scientists at all 17 DOE National Laboratories to our frontier AI for Science models and agentic tools, starting today with AI co-scientist on Google Cloud.

We’re excited to see what America’s leading researchers will be able to do with our frontier AI models and agentic tools.

By combining human ingenuity with advanced AI capabilitie…

At Google, we’ve long invested in ways to provide you with helpful context about information you see online.

Now, as generative media becomes increasingly prevalent and high-fidelity, we are deploying tools to help you more easily determine whether the content you're interacting with was created or edited using AI.

Starting today, we’re making it easier for everyone to verify if an image was generated with or edited by Google AI right in the Gemini app, using SynthID, our digital watermarking technology that embeds imperceptible signals into AI-generated content.

Since then, over 20 billion AI-generated pieces of content have been watermarked using SynthID, and we have been testing our Synt…

Today, we’re releasing Nano Banana Pro (Gemini 3 Pro Image), a higher-fidelity model built on Gemini 3 Pro for developers to access studio-quality image generation.

This follows our release of Nano Banana (Gemini 2.5 Flash Image) just a few months ago.

Since then, we’ve loved seeing the community put its key features to work — from character consistency to photo restoration, and even using its capabilities to make local edits in an infinite canvas.

This state-of-the-art image generation and editing model is starting to roll out in paid preview to build a new wave of intelligent, multimodal applications with the Gemini API in Google AI Studio and Vertex AI for enterprises.

This model unlocks…

How Nano Banana Pro helps you bring any idea or design to lifeNano Banana Pro can help you visualize any idea and design anything — from prototypes, to representing data as infographics, to turning handwritten notes into diagrams.

With Nano Banana Pro, now you can:Generate more accurate, context-rich visuals based on enhanced reasoning, world knowledge and real-time informationWith Gemini 3’s advanced reasoning, Nano Banana Pro doesn’t just create beautiful images, it also helps you create more helpful content.

You can get accurate educational explainers to learn more about a new subject, like context-rich infographics and diagrams based on the content you provide or facts from the real wor…

Google AntigravityTo advance how the model and IDE work together, we’re introducing Google Antigravity to showcase what’s possible with Gemini 3.

It’s a faster way to develop: you act as the architect, collaborating with intelligent agents that operate autonomously across the editor, terminal, and browser.

These agents plan and execute complex software tasks, communicating their work with the user via detailed artifacts.

This elevates all aspects of development, from building features, UI iteration, and fixing bugs to researching and generating reports.

Visit the Google Antigravity website to download the public preview at no charge, now available for MacOS, Windows and Linux.

Building the most trusted cloudWe continued to enhance our security capabilities and controls on our cloud platform to help organizations secure their cloud environments and address evolving policy, compliance, and business objectives.

Our forecast for 2026As security professionals, we know that threat actors will continue to innovate to achieve their mission objectives.

To help defenders proactively prepare for the coming year, we publish our annual forecast report with insights from across Google.

We look forward to sharing more insights to help organizations strengthen their security posture in the new year.

For more leadership guidance from Google Cloud experts, please visit our CISO In…

Descriptive and prescriptive contextAs mentioned above, the AlloyDB AI natural language API relies on descriptive and prescriptive context to improve the accuracy of the SQL code it generates.

The AlloyDB AI natural language API facilitates the creation of descriptive and prescriptive context.

The value index clarifies what kind of entity “John Smith” is, and can be automatically created by AlloyDB AI for your application.

Natural language search over structured, unstructured and multimodal dataWhen it comes to applications that provide search over structured data, the AlloyDB AI natural language API enables a clean and powerful search experience.

Bringing the AlloyDB AI natural language AP…

At Google Cloud, we continue to make critical investments to Vertex AI Agent Builder, our comprehensive and open platform, enabling you to build faster, scale efficiently, and govern with enterprise-grade security.

Today, with the integration of the Cloud API Registry, we’re excited to bring enhanced tool governance capabilities to Vertex AI Agent Builder.

With this latest update, administrators can now manage available tools for developers across your organization directly in Vertex AI Agent Builder Console, and developers can leverage tools managed by the registry with a new ApiRegistry .

Govern your tools with confidenceBuilding a useful agent requires the agent to have access to the nec…

Today, we are delivering on those requirements with the General Availability (GA) of Cluster Director and the Preview of Cluster Director support for Slurm on Google Kubernetes Engine (GKE).

Cluster Director (GA) is a managed infrastructure service designed to meet the rigorous demands of modern supercomputing.

There's no extra charge to use Cluster Director.

How Cluster Director supports each phase of deploymentDay 0: PreparationStanding up a cluster typically involves weeks of planning, wrangling Terraform, and debugging the network.

Cluster Director changes the ‘Day 0’ experience entirely, with tools for designing infrastructure topology that’s optimized for your workload requirements.

Yesterday, Forrester released The Forrester Wave™: AI Infrastructure Solutions, Q4 2025 report, evaluating 13 vendors, and we believe their findings validate our commitment to solving these core challenges.

Access the full report: The Forrester Wave™: AI Infrastructure Solutions, Q4 2025Accelerating time-to-value with an integrated systemEnterprises don’t run AI in a vacuum.

Delivering continuous AI innovationWe are honored to be recognized as a Leader in The Forrester Wave™ report, which we believe validates decades of R&D and our approach to building ultra-scale AI infrastructure.

Access the full report: The Forrester Wave™: AI Infrastructure Solutions, Q4 20251.

IDC Business Value Snapsh…

Today, we’re expanding the Gemini 3 model family with Gemini 3 Flash, which offers frontier intelligence built for speed at a fraction of the cost.

Gemini 3 Flash builds on the model series that developers and enterprises already love, optimized for high-frequency workflows that demand speed, without sacrificing quality.

Gemini 3 Flash is built to be highly efficient, pushing the boundaries of quality at better price performance and faster speed.

It is available now in Gemini Enterprise, Vertex AI, and Gemini CLI, so businesses and developers can access:Advanced multimodal processing: Gemini 3 Flash enables enterprises to build applications capable of complex video analysis, data extraction…

Google Cloud is at the forefront of this shift, empowering you to build robust, data-driven applications quickly and accurately.

With Model Context Protocol (MCP) servers powered by MCP Toolbox for Databases now available within Antigravity, you can securely connect your AI agents to services like AlloyDB for PostgreSQL, BigQuery, Spanner, Cloud SQL, Looker and others within Google’s Data Cloud, all within your development workflow.

We designed Antigravity to keep you in the flow, but the power of an AI agent is limited by what it "knows."

By integrating pre-built MCP servers directly into Antigravity, you don’t need to perform any manual configuration.

Discover and launchYou can find MCP s…

Today, we announced the general availability of Gemini Live API on Vertex AI, which is powered by the latest Gemini 2.5 Flash Native Audio model.

In this post we'll look at two templates and three reference demos that help you understand how to best use Gemini Live API.

Gemini Live API fundamentally changes the engineering approach with a unified, low-latency, native audio architecture.

Native audio processing: Gemini 2.5 Flash Native Audio model processes raw audio natively through a single, low-latency model.

Next-generation conversation featuresGemini Live API gives you a suite of production-ready features that define a new standard for AI agents:

We can all agree that the quality of AI-driven answers relies on the consistency of the underlying data.

Building off the recent introduction of Looker’s Model Context Protocol (MCP) server, in this blog we take you through the process of creating an Agent Development Kit (ADK) agent that is connected to Looker via the MCP Toolbox for Databases and exposing it within Gemini Enterprise.

Instead of managing tool logic and authentication themselves, agents act as MCP clients and request tools from the Toolbox.

The MCP Toolbox handles all the underlying complexities, including secure connections to Looker, authentication and query execution.

The MCP Toolbox for Databases natively supports Looke…

Marina Kaganovich, executive trust leadThe heightened capability of agentic AI to take actions and execute tasks autonomously elevates the importance of cybersecurity basics.

Vesselin Tzvetkov, senior cybersecurity advisorAs Francis noted, agentic security operations are set to become the standard for modern SOCs, dramatically enhancing the speed and capabilities of security organizations.

Vinod D’Souza, director, manufacturing and industryIn 2026, agentic AI will help the manufacturing and industrial sector cross the critical threshold from static automation to true autonomy.

By rooting security strategies in data-centered Zero Trust, organizations stop treating security as a gatekeeper an…

Today, we are excited to announce that Gemini Live API, powered by the latest Gemini 2.5 Flash Native Audio model, is generally available on Vertex AI.

A new standard with real-time multimodal AI agentsGemini Live API represents a new standard for bringing AI to life.

Deploying on Vertex AI allows you to leverage our expanding global infrastructure across multiple regions, delivering reliability for your users.

Building real-world impact with Gemini Live APIThe true power of Gemini Live API is demonstrated by the companies who are using it today to redefine their customer experiences.

Shopify, the leading global commerce platform, developed Sidekick, a multimodal AI assistant powered by Gem…

In a recent IDC global survey of over 1,300 AI decision-makers, inference was already cited as the largest AI workload segment, accounting for 47% of all AI operations.

This surge in demand is exposing a critical vulnerability for many organizations: the AI efficiency gap.

The TCO crisis in an age of agentsThe AI efficiency gap is the difference between the theoretical performance of an AI stack and the actual, real-world performance achieved.

That is why we created AI Hypercomputer: an integrated supercomputer system designed to deliver exceptional performance and efficiency for demanding AI workloads.

Get your free copy of the whitepaper to learn more: The AI Efficiency Gap: From TCO Cris…

This innovative solution combines two powerful, specialized AI agents: a prediction agent built by Google Cloud and App Orchid’s Data Agent offering.

Google prediction agent - The forecasting powerhouseThe prediction agent, which is primarily the custom engineering work of Google Cloud, is the system’s window to the future.

App Orchid Data Agent - The enterprise intelligence data expertAccurate predictions depend on high-quality, AI-ready data, which is where App Orchid’s Data Agent excels.

The combined business forecasting agentAt the heart of the solution is a unified business forecasting agent, which brings together the capabilities of our unique prediction and data agents in a discrete …

When a tools/list or tools/call request is made to the MCP endpoint, Apigee uses the operations documented in the OpenAPI spec as the MCP tools list.

And, with the recent launch of Apigee API insights, you can also use the new “Insights” tab in Apigee API hub’s catalog to view traffic and performance metrics for your MCP endpoints.

Benefits of Apigee’s approach to MCP supportOur main goal with MCP support in Apigee is to make sure that you can secure, govern, and monitor usage of MCP tools with the same policies and workflows in Apigee that you’re already familiar with.

Centralized tool catalog: After you deploy an MCP proxy, Apigee automatically registers your MCP endpoint in Apigee API hu…

Today we’re announcing the release of fully-managed, remote MCP servers.

Google’s existing API infrastructure is now enhanced to support MCP, providing a unified layer across all Google and Google Cloud services.

Developers can now simply point their AI agents or standard MCP clients like Gemini CLI and AI Studio to a globally-consistent and enterprise-ready endpoint for Google and Google Cloud services.

With the new Cloud API Registry and Apigee API Hub, developers can find trusted MCP tools from Google and their own organizations, respectively.

We pair this ease of discovery with rigorous control: administrators can manage access via Google Cloud IAM, rely on audit logging for observabili…

To address this, a research team from Microsoft Research Asia – Shanghai has introduced Agent Lightning.

Whether it involves multiple collaborating agents or dynamic tool use, Agent Lightning breaks it down into a sequence of transitions.

Agent Lightning as middlewareAgent Lightning serves as middleware between RL algorithms and agent environments, providing with modular components that enable scalable RL through standardized protocols and well-defined interfaces.

In practice, developers can keep their existing agent frameworks and switch model calls to the Agent Lightning API without changing their agent code (Figure 5).

By bridging existing agentic systems with reinforcement learning, Age…

To address this, we are excited to introduce Promptions (prompt + options), a UI framework that helps developers build AI interfaces with more precise user control.

We compared the static design from the first study, called the “Static Prompt Refinement Control” (Static PRC), against a “Dynamic Prompt Refinement Control” (Dynamic PRC) with features that responded to participants’ feedback.

Comparison of user preferences for Static PRC versus Dynamic PRC across key evaluation criteria.

(1) The Option Module reads the user’s prompt and conversation history and (2) generates prompt options.

Key usability challenges include clarifying how dynamic options affect AI output and managing the comple…

C, Scatter plot comparing the subtype-level GigaTIME-translated virtual mIF activations between TCGA and Providence virtual populations.

To our knowledge, this is the first large-scale study exploring multimodal AI for scaling virtual mIF generation.

H, A case study showcasing the activation maps across different virtual mIF channels for a H&E slide in our virtual population, and virtual mIF of sample patches from this slide.

By applying GigaTIME to Providence real-world data, we generated a virtual population of 14,256 patients with virtual mIF and key clinical attributes.

G, Bar plot comparing pan-cancer patient stratification performance in terms of survival log rank p-values among virtu…

This week, machine learning researchers around the world will be attending the annual Conference on Neural Information Processing Systems, or NeurIPS.

In this series, we’ll explore the technologies that are shaping our future and the big ideas that propel them forward.

So around that time when I started my PhD at Penn, I was working in machine learning theory and algorithmic economics.

How had you experienced a lack of community or network of women in machine learning before the founding of WiML?

So particularly when working on topics related to fairness, I’ve ended up focusing a bunch on stuff to do with marginalized groups as part of my responsible AI work.

The theory of contextual integrity frames privacy as the appropriateness of information flow within specific social contexts.

Each tackles contextual integrity from a different angle, but both aim to build directly into AI systems a greater sensitivity to information-sharing norms.

Contextual Integrity in LLMs via Reasoning and Reinforcement Learning, accepted at NeurIPS 2025, takes a different approach to applying contextual integrity.

Contextual integrity through reasoning and reinforcement learningIn our second paper, we explore whether contextual integrity can be built into the model itself rather than enforced through external checks at inference time.

To address this trade-off, we int…

Today, we are pleased to announce Fara-7B, our first agentic SLM designed specifically for computer use.

Unlike traditional chat models that generate text-based responses, Computer Use Agent (CUA) models like Fara-7B leverage computer interfaces, such as a mouse and keyboard, to complete tasks on behalf of users.

This results in reduced latency and improved privacy, as user data remains local.

Fara-7B breaks ground on a new pareto frontier, showing that on-device computer use agents are approaching the capabilities of frontier models.

For guidance on how to use our model safely, and the security considerations to be mindful of when using our model, please refer to our Model card (opens in n…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Unlike traditional markets, which d…

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

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

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

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

Quantitatively, we f…

This matters because monitoring tasks are everywhere.

To address this, we are introducing SentinelStep (opens in new tab), a mechanism that enables agents to complete long-running monitoring tasks.

Most real-world monitoring tasks share this limitation, making systematic bench marking very challenging.

In response, we are developing SentinelBench, a suite of synthetic web environments for evaluating monitoring tasks.

By embedding patience into plans, agents can responsibly monitor conditions and act when it matters—staying proactive without wasting resources.

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

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

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

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

But I feel like broadly…

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

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

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

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

But I feel like broadly…

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

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

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

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

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

“At MIT, innovation ranges from awe-inspiring technology to down-to-Earth creativity,” noted Chronicle, during a campus visit this year for an episode of the program.

In 2025, MIT researchers made headlines across print publications, podcasts, and video platforms for key scientific advances, from breakthroughs in quantum and artificial intelligence to new efforts aimed at improving pediatric health care and cancer diagnosis.

Neha Narula, director of the MIT Digital Currency Initiative, examines the future of cash as the use of digital currencies expands.

Full story via Michigan Farm NewsBug-sized robots could help pollination on future farmsInsect-sized robots crafted by MIT researchers cou…

Remarkably, even untrained networks contain architectural biases that can be transferred, while trained guides additionally convey learned patterns.

This result underscores a key insight: Untrained networks already encode valuable architectural biases that can steer other networks toward effective learning.

By aligning with a guide network, it’s possible to separate the contributions of architectural biases from those of learned knowledge.

By revealing the hidden potential of even the most stubborn networks, guidance provides a powerful new tool for understanding — and hopefully shaping — the foundations of machine learning.

Remarkably, the authors show this can be done using small, untrain…

Kim’s co-authors include lead author Songlin Yang, an EECS graduate student and former MIT-IBM Watson AI Lab Summer Program intern; Kaiyue Wen of Stanford University; Liliang Ren of Microsoft; and Yikang Shen, Shawn Tan, Mayank Mishra, and Rameswar Panda of IBM Research and the MIT-IBM Watson AI Lab.

The cumulative effect lets the system model how the meaning changes along the path between words, not just how far apart they are.

PaTH Attention improved perplexity and outcompeted other methods on reasoning benchmarks it wasn’t trained on.

PaTH Attention consistently proved capable of content-awareness.

In this way, PaTH Attention extends the expressive power of transformer architectures.

This framework could enable scientists to probe “what-if” questions about vision systems that are difficult to study experimentally.

Building a scientific sandboxThe paper began as a conversation among the researchers about discovering new vision systems that could be useful in different fields, like robotics.

Over many generations, agents evolve different elements of vision systems that maximize rewards.

Testing hypothesesWhen the researchers set up experiments in this framework, they found that tasks had a major influence on the vision systems the agents evolved.

In the future, the researchers want to use this simulator to explore the best vision systems for specific applications, which c…

The researchers tackled these challenges using a vision-language model (VLM), a powerful generative AI model that has been pre-trained to understand images and text.

By serving as both the eyes and brain of the robot, the VLM enables the robot to do this,” Kyaw says.

A user prompts the system with text, perhaps by typing “make me a chair,” and gives it an AI-generated image of a chair to start.

For instance, the model can determine that the seat and backrest should have panels to have surfaces for someone sitting and leaning on the chair.

Then the VLM chooses the labels that correspond to the geometric parts of the chair that should receive panels on the 3D mesh to complete the design.

Q: What drew you to research microbes in extreme environments, and what are the challenges in studying them?

I wanted to be an astronaut growing up, and the closest thing to astrobiology is examining extreme environments on Earth.

And the only thing that lives in those extreme environments are microbes.

My latest work is genomic language modeling.

A genomic language model is technically a large language model, except the language is DNA as opposed to human language.

What if there were a way to solve one of the most significant obstacles to the use of nuclear energy — the disposal of high-level nuclear waste (HLW)?

Such a move would be especially important for the public’s acceptance of nuclear energy.

“We’re reframing the problem of nuclear waste, transforming it from a liability to an energy source,” Sarsenbayev says.

The nuances of nuclearSarsenbayev had to do a bit of reframing himself in how he perceived nuclear energy.

Removing the bottleneck for nuclear energy adoption by producing carbon-free power and ensuring the safe disposal of radioactive waste.

In a study appearing today in the journal Nature Methods, the team presents a new deep-learning model that learns, then predicts, how certain geometric properties of individual cells will change as a fruit fly develops.

The team applied the model to videos of developing fruit fly embryos, each of which starts as a cluster of about 5,000 cells.

As a proof of principle, the team trained the new model to “learn” how individual cells change over time during fruit fly gastrulation.

“The overall shape of the fruit fly at this stage is roughly an ellipsoid, but there are gigantic dynamics going on at the surface during gastrulation,” Guo says.

What’s more, the videos contain labels of individual c…

As language models (LMs) improve at tasks like image generation, trivia questions, and simple math, you might think that human-like reasoning is around the corner.

Small LMs can’t do this reliably on their own; large language models (LLMs) sometimes can, particularly if they’re optimized for reasoning tasks, but they take a while to respond, and they use a lot of computing power.

Then, the LLM relays these instructions and guidelines in a clear way to smaller models.

For instance, whereas existing reasoning models like OpenAI’s o1 perform reasoning in text, DisCIPL “reasons” by writing Python code, which is more compact.

DisCIPL’s efficiency gains stem partly from using small Llama models a…

Artificial intelligence can enhance decision-making and enable action with reduced risk and greater precision, making it a critical tool for national security.

“The potential for artificial intelligence is just starting to be fully realized.

The 2N6 curriculum is application focused, and the content is built to satisfy the U.S. Navy’s sub-specialty code for Applied Artificial Intelligence.

“The admiral made the connection, envisioning an applied AI program similar to 2N.”2N6 will run as a pilot program for at least two years.

The program’s first cohort will comprise only U.S. Navy officers, with plans to expand more broadly.

Let’s say an environmental scientist is studying whether exposure to air pollution is associated with lower birth weights in a particular county.

In simulations and experiments with real data, their method was the only technique that consistently generated accurate confidence intervals.

Finally, they assume the source data are similar to the target data where one wants to estimate.

A smooth solutionThe new method for generating confidence intervals explicitly accounts for this potential bias.

When they compared their method to other common techniques, they found it was the only one that could consistently produce reliable confidence intervals for spatial analyses.

MIT researchers have developed a new fabrication method that could enable the production of more energy efficient electronics by stacking multiple functional components on top of one existing circuit.

This new electronics integration platform allows scientists to fabricate transistors and memory devices in one compact stack on a semiconductor chip.

Stacking active components would reduce the distance data must travel and improve a chip’s energy efficiency.

These compact memory transistors demonstrated switching speeds of only 10 nanoseconds, hitting the limit of the team’s measurement instruments.

In the future, they want to build upon these demonstrations by integrating back-end memory tra…

Two current MIT affiliates and seven additional alumni are among those named to the 2025 cohort of AI2050 Fellows.

Zongyi Li, a postdoc in the MIT Computer Science and Artificial Intelligence Lab, and Tess Smidt ’12, an associate professor of electrical engineering and computer science (EECS), were both named as AI2050 Early Career Fellows.

He received his PhD in computing and mathematical sciences from Caltech, where he was advised by Anima Anandkumar and Andrew Stuart.

Li's work has been supported by a Kortschak Scholarship, PIMCO Fellowship, Amazon AI4Science Fellowship, Nvidia Fellowship, and MIT-Novo Nordisk AI Fellowship.

Besides the AI2050 fellowship, she has received an Air Force Yo…

“We’re connecting natural language processing, 3D generative AI, and robotic assembly,” says Alexander Htet Kyaw, an MIT graduate student and Morningside Academy for Design (MAD) fellow.

Generative AI and robotics are moving us ever closer to the day when we can ask for an object and have it created within a few minutes.

In fact, MIT researchers have developed a speech-to-reality system, an AI-driven workflow that allows them to provide input to a robotic arm and “speak objects into existence,” creating things like furniture in as little as five minutes.

This is followed by creation of a feasible assembly sequence and automated path planning for the robotic arm to assemble physical objects …

The company’s unloading robots combine generative AI and machine-learning algorithms with sensors, cameras, and machine-vision software to navigate new environments on day one and improve performance over time.

The Pickle Robot Company wants its machines to do the heavy lifting.

The robots can unload anywhere from 400 to 1,500 cases per hour depending on size and weight.

“Our immediate product roadmap is load and unload,” Meyer says.

What does it mean for the robot unloading a truck to talk to the robot palletizing, or for the forklift to talk to the inventory drone?

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

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

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

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

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

What exactly does word2vec learn?

What exactly does word2vec learn, and how?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Smoothing behavior of RL AVs.

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

You can apply the same approach to migrate existing SageMaker managed MLflow tracking servers to the new serverless MLflow capability on SageMaker.

Whichever environment you select must maintain connectivity to both your source tracking server and your target tracking server.

MLflow Export Import supports exports from both self-managed tracking servers and Amazon SageMaker MLflow tracking servers (from MLflow v2.16 onwards) to Amazon SageMaker Serverless MLflow.

To prepare for a successful migration:Verify the current MLflow version of your existing MLflow tracking server: mlflow --version Review the latest supported MLflow version in the Amazon SageMaker MLflow documentation.

For more info…

This post demonstrates how to solve this challenge by building an AI-powered website assistant using Amazon Bedrock and Amazon Bedrock Knowledge Bases.

Amazon Bedrock managed LLMs – A large language model (LLM) from Amazon Bedrock generates AI-powered responses to user questions.

Model access in Amazon Bedrock for Amazon Titan and Amazon Nova Lite.

Create knowledge base and ingest website dataThe first step is to build a knowledge base to ingest data from a website and operational documents from an S3 bucket.

For Data source name, enter a name for your data source.

Today, we explore how to programmatically create an IDP solution that uses Strands SDK, Amazon Bedrock AgentCore, Amazon Bedrock Knowledge Base, and Bedrock Data Automation (BDA).

Amazon Bedrock Data Automation can be used as a standalone feature or as a parser when setting up a knowledge base for Retrieval-Augmented Generation (RAG) workflows.

Amazon Bedrock AgentCore is a fully managed service that allows you to build and configure autonomous agents.

Here’s an overview of how you can setup Bedrock Knowledge Bases with data automation as a parser with Bedrock AgentCore.

With Amazon Bedrock Data Automation, we can enhance the RAG experience for more complex data formats including visual ric…

This workflow demonstrates the full capabilities of AI-powered browser automation, from initial navigation through complex decision-making to human-in-the-loop intervention.

Amazon Bedrock AgentCore Browser provides a secure, cloud-based browser that enables the AI agent (Amazon Nova Act and Strands agent in this case) to interact with websites.

ConclusionAI agent-driven browser automation represents a fundamental shift in how enterprises approach workflow management.

Veda Raman is a Sr Solutions Architect for Generative AI for Amazon Nova and Agentic AI at AWS.

She helps customers design and build Agentic AI solutions using Amazon Nova models and Bedrock AgentCore.

In this post, we explore how agentic QA automation addresses these challenges and walk through a practical example using Amazon Bedrock AgentCore Browser and Amazon Nova Act to automate testing for a sample retail application.

Benefits of agentic QA testingAgentic AI shifts QA testing from rule-based automation to intelligent, autonomous testing systems.

AgentCore Browser for large-scale agentic QA testingTo realize the potential of agentic AI testing at enterprise scale, organizations need robust infrastructure that can support intelligent, autonomous testing agents.

Agentic QA with the Amazon Nova Act SDKThe infrastructure capabilities of AgentCore Browser become truly powerful when combi…

This post illustrates this process by finding an optimal deployment for a Qwen-3-4B model on an Amazon SageMaker AI endpoint.

This makes deployment & inference straightforward, or an interactive development environment (IDE) such as PyCharm or Visual Studio Code.

For a complete end-to-end sample on deploying an LMI container for real time inference on SageMaker AI, refer to this example.

By combining BentoML’s LLM-Optimizer with Amazon SageMaker AI, organizations can now move from hypothesis to deployment through a data-driven, automated optimization loop.

With BentoML’s LLM-Optimizer and Amazon SageMaker AI, that balance can be discovered systematically, reproduced consistently, and deploy…

This has been central to our business from the start, and it was particularly in focus from the earliest days of Amazon Bedrock.

Model providers have no mechanism to access customer data, because inferencing is done only within the Amazon Bedrock-owned account that model providers don’t have access to.

Following the approach of the AWS Nitro System, we have designed Mantle from the ground up to be zero operator access (ZOA), where we have intentionally excluded any technical means for AWS operators to access customer data.

Interactive communication tools like Secure Shell (SSH), AWS Systems Manager Session Manager, and serial consoles aren’t installed anywhere in Mantle.

Throughout the enti…

The AWS AI League provides an innovative program to help enterprises overcome the challenges of building advanced AI capabilities through exciting competitions that drive innovation in agentic AI and model customization.

In 2025, the first AWS AI League competition captured the attention of developers, data scientists, and business leaders globally.

The AWS AI League experience begins with a hands-on, 2-hour workshop led by AWS experts, followed by self-paced experimentation.

ConclusionIn this post, we explored the new AWS AI League challenges and how they are transforming how organizations approach AI development.

To learn more about hosting an AWS AI League within your organization visit …

In this post, we demonstrate how to use Foundation Models (FMs) from Amazon Bedrock and the newly launched Amazon Bedrock AgentCore alongside W&B Weave to help build, evaluate, and monitor enterprise AI solutions.

Tracking Amazon Bedrock FMs with W&B Weave SDKW&B Weave integrates seamlessly with Amazon Bedrock through Python and TypeScript SDKs.

Experimenting with Amazon Bedrock FMs in W&B Weave PlaygroundThe W&B Weave Playground accelerates prompt engineering with an intuitive interface for testing and comparing Bedrock models.

When working with AgentCore and W&B Weave together, teams can use AgentCore’s built-in operational monitoring and security foundations while also using W&B Weave if…

dLocal decided to partner with AWS to implement Amazon Quick Automate, a capability of Amazon Quick Suite, as one of the service’s earliest adopters.

Using Quick Automate, dLocal automated its merchant compliance website review process, enabling large-scale, efficient, and consistent policy enforcement.

Through specialized AI agents, Quick Automate helps organizations automate complex processes across applications and departments while reducing operational costs through usage-based pricing.

Automated website analysis using the UI AgentThe UI Agent is a feature in Quick Automate to automate complex browser-based actions based on natural language instructions.

We integrated Amazon Quick Autom…

The assessment and diagnosis of attention deficit hyperactive disorder (ADHD) has traditionally relied on clinical observations and behavioral evaluations.

Qbtech developed and deployed a model that efficiently processes data from smartphone cameras, motion sensors, and test results.

Building the artificial intelligence (AI) model: From raw data to clinical insightsQbtech’s approach to mobile ADHD assessment utilizes machine learning techniques to process and analyze multiple data streams simultaneously.

The team selected Binary LightGBM as their primary algorithm for the ADHD assessment model.

Clinical impact: Comparative clinical performanceThe clinical validation of QbMobile against Qbte…

Composed of four models: Amazon Nova Micro, Amazon Nova Lite, Amazon Nova Pro, Amazon Nova Premier.

Composed of four models: Amazon Nova Micro, Amazon Nova Lite, Amazon Nova Pro, Amazon Nova Premier.

Integrated by two models: Amazon Nova Canvas (image generation) and Amazon Nova Reel (video generation).

Integrated by two models: Amazon Nova Canvas (image generation) and Amazon Nova Reel (video generation).

You can learn about prompt engineering for Amazon Nova Canvas and Amazon Nova Reel at Image and video prompt engineering for Amazon Nova Canvas and Amazon Nova Reel in the AWS Artificial Intelligence Blog.

Introducing Visa Intelligent Commerce on AWSVisa Intelligent Commerce empowers businesses and developers to build the next generation of agentic payment experiences.

How Amazon Bedrock AgentCore powers these solutionsBefore diving into the specific use cases, it’s important to understand the role Amazon Bedrock AgentCore plays as the foundational infrastructure enabling these agentic commerce experiences.

The value Amazon Bedrock AgentCore adds:The core of this solution is Amazon Bedrock AgentCore Runtime, a secure, serverless hosting environment purpose-built for AI agents and MCP servers.

Amazon Bedrock AgentCore Memory maintains long-duration context over extended, multistep journeys lik…

The key innovation of CoD lies in its constraint: each reasoning step is limited to five words or less.

For instance, when solving a mathematical word problem, instead of generating full sentences explaining each step, CoD produces concise numerical operations and key logical markers.

Implementation and evaluation on AWSTo evaluate the efficiency of CoD prompting techniques, we run a test in Amazon Bedrock and solve the “Red, Blue, and Green Balls” puzzle using an LLM.

Box 1 is labelled “Red Balls Only.” Box 2 is labelled “Blue Balls Only.” Box 3 is labelled “Red and Blue Balls Only.” The labels on the boxes are all incorrect.

Small language models : CoD underperformed on models with fewer …

Mistral AI’s Voxtral models combine text and audio processing capabilities in a single framework.

In this post, we demonstrate hosting Voxtral models on Amazon SageMaker AI endpoints using vLLM and the Bring Your Own Container (BYOC) approach.

Text-only processing uses the standard chat completion API for traditional conversational AI where audio processing isn’t required.

This post also demonstrates integrating the Voxtral model deployed on SageMaker with Strands Agents to build agentic applications with minimal code.

The following sections provide a complete implementation guide to get your Voxtral model running on SageMaker endpoints.

Holiday lights are twinkling, hot cocoa’s on the stove and gamers are settling in for a well-earned break.

Whether staying in or heading on a winter getaway, GeForce NOW makes it easy to keep gaming from anywhere.

With NVIDIA Blackwell RTX power everywhere, Ultimate members can stream even the most graphically demanding adventures at GeForce RTX 5080-power without needing the latest hardware.

For those who want to share their holiday adventures, ARC Raiders offers thrilling teamwork and dramatic battles under electric skies.

Seasonal festivals in Stardew Valley and the rotating holiday events in Fortnite help set a cozy mood in just a few clicks.

The lab in Massachusetts is studying molecular mechanisms of human memory function powered by NVIDIA RTX GPUs, HP Z Workstations and virtual-reality technology.

The works of Plato state that when humans have an experience, some level of change occurs in their brain, which is powered by memory — specifically long-term memory.

The team is studying a small portion of these “leaves” — representing protein markers: an incredibly tedious task due to their length, at about a micrometer each.

A researcher must search through the forest of brain cells to find the correct protein markers, which make up only about 1% of all protein markers in the hippocampus.

Collecting and analyzing enough 3D volumet…

NVIDIA and the US Department of Energy outline priorities for collaboration in support of accelerating scientific discovery.

NVIDIA will join the U.S. Department of Energy’s (DOE) Genesis Mission as a private industry partner to keep U.S. AI both the leader and the standard in technology around the world.

The Genesis Mission, which is part of an Executive Order recently signed by President Trump, aims to redefine American leadership in AI across three key areas: energy, scientific research and national security.

Together, these priorities focus on using advanced AI, robotics and high‑performance computing to transform energy, manufacturing and scientific discovery across the Department of E…

The NVIDIA RTX PRO 5000 72GB Blackwell GPU is now generally available, bringing robust agentic and generative AI capabilities powered by the NVIDIA Blackwell architecture to more desktops and professionals across the world.

Fueling the Next Generation of AI DevelopmentAs generative AI evolves into complex, multimodal agentic AI, more demand is placed on the hardware required to develop and deploy these technologies.

And for computer-aided engineering and product design, the RTX PRO 5000 72GB offers more than 2x graphics performance.

As industries race to integrate AI into every facet of operation — from generative design to coding copilots — RTX PRO 5000 72GB is equipped to meet the moment.…

The ‘Fallout’ series leads this week’s five new games, along with ‘Hogwarts Legacy’ and ‘LEGO Harry Potter,’ for a magical lineup this GFN Thursday.

To mark the occasion, GeForce NOW members can claim Fallout 3 and Fallout 4 as special rewards, completing a wasteland-ready trilogy in the cloud.

Have Yourself a Merry Little ‘Fallout’Bethesda’s Fallout: New Vegas is rolling onto GeForce NOW with a suitcase full of wasteland wit.

To sweeten the deal, GeForce NOW Ultimate members can claim a stack of classics — including Fallout 3 and Fallout 4 — while supplies last.

In Hogwarts Legacy, players can chart their own path as a fifth-year student at Hogwarts in the 1800s.

Users can use the services provided to migrate existing EMR CPU Spark workloads to GPUs.

Configure Aether for EMROnce the Aether package is installed, configure the Aether client for the EMR platform using the following commands:# Initialize and list config $ aether config init $ aether config list # Select EMR platform and region $ aether config set core.selected_platform emr $ aether config set platform.emr.region # Set required EMR s3 paths $ aether config set platform.emr.spark_event_log_dir $ aether config set platform.emr.cluster.artifacts_path $ aether config set platform.emr.cluster.log_path Example Aether EMR migration workflowThe Aether CLI tool provides several modular command…

Hybrid memory mode—blurring the line between CPU and GPUcuDSS hybrid memory mode is designed to overcome the memory limitations of a single GPU when solving extremely large sparse linear problems by using the GPU and CPU memories.

Hybrid memory mode is not on by default, so the first step to enable it is to call the function cudssConfigSet() to set CUDSS_CONFIG_HYBRID_MODE, which tells cuDSS to use hybrid memory mode.

The first cuDSS function, called cudssConfigSet() , enables hybrid memory mode before calling the first analysis step, symbolic factorization.

This is followed by using cudssDataGet() to find the minimal amount of device memory sufficient for hybrid memory mode.

NVIDIA cuDSS p…

New NVIDIA safety frameworks and technologies are advancing how developers build safe physical AI.

To align these advances with rigorous global standards, the NVIDIA Halos AI Systems Inspection Lab — accredited by ANAB — provides impartial inspection and certification of Halos elements across robotaxi fleets, AV stacks, sensors and manufacturer platforms through the Halos Certification Program.

AV Ecosystem Leaders Putting Physical AI Safety to WorkBosch, Nuro and Wayve are among the first participants in the NVIDIA Halos AI Systems Inspection Lab, which aims to accelerate the safe, large-scale deployment of robotaxi fleets.

Onsemi, which makes sensor systems for AVs, industrial automation …

The Hao AI Lab research team at the University of California San Diego — at the forefront of pioneering AI model innovation — recently received an NVIDIA DGX B200 system to elevate their critical work in large language model inference.

How Is Hao AI Lab Using the DGX B200?

With the DGX B200 now fully accessible to the Hao AI Lab and broader UC San Diego community at the School of Computing, Information and Data Sciences’ San Diego Supercomputer Center, the research opportunities are boundless.

The research phase of FastVideo taps into NVIDIA H200 GPUs in addition to the DGX B200 system.

Learn more about the NVIDIA DGX B200 system.

2025 was another milestone year for developers and researchers working with NVIDIA technologies.

Progress in data center power and compute design, AI infrastructure, model optimization, open models, AI agents, and physical AI redefined how intelligent systems are trained, deployed, and moved into the real world.

Looking aheadStay tuned for more transformative innovations in 2026.

Subscribe to the Developer Newsletter and stay in the loop on content tailored to your interests.

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One of the biggest challenges has been managing the binary size of our CUDA C++ libraries, which affects user experience as well as the ability to pip install from PyPI.

PyPI limits binary size to keep costs for the Python Software Foundation (PSF) under control and protect users from downloading unexpectedly large binaries.

The complexity of the cuML library has historically required a larger binary than PyPI could host, but we’ve worked closely with PSF to overcome this by reducing binary size.

This post walks you through the new pip install path for cuML and a tutorial on the steps the team used to drop the CUDA C++ library binary size, which enabled the availability of cuML wheels on Py…

NVIDIA is partnering with the University of Wisconsin-Madison to bring GPU-accelerated analytics to DuckDB through the open-source Sirius engine.

This blog post outlines the Sirius architecture and demonstrates how it achieved record-breaking performance on ClickBench, a widely used analytics benchmark.

Sirius Query on CPU and GPUsAs illustrated in Figure 2, the process begins when Sirius receives an already optimized query plan from DuckDB’s internal format, ensuring robust logical and physical optimizations are preserved.

In benchmarks like ClickBench, Sirius can cache frequently accessed tables on the GPU, accelerating repeated query execution.

ClickBench cost and relative runtimeFigure …

NVIDIA today announced it has acquired SchedMD — the leading developer of Slurm, an open-source workload management system for high-performance computing (HPC) and AI — to help strengthen the open-source software ecosystem and drive AI innovation for researchers, developers and enterprises.

NVIDIA will continue to develop and distribute Slurm as open-source, vendor-neutral software, making it widely available to and supported by the broader HPC and AI community across diverse hardware and software environments.

HPC and AI workloads involve complex computations running parallel tasks on clusters that require queuing, scheduling and allocating computational resources.

As HPC and AI clusters g…

Another powerful starting point for fine-tuning is the just-announced NVIDIA Nemotron 3 family of open models, data and libraries.

Teaching AI New TricksFine-tuning is like giving an AI model a focused training session.

Check out some of these Unsloth guides:Learn how to install Unsloth on NVIDIA DGX Spark.

#ICYMI — The Latest Advancements in NVIDIA RTX AI PCs🚀 FLUX.2 Image-Generation Models Now Released, Optimized for NVIDIA RTX GPUsThe new models from Black Forest Labs are available in FP8 quantizations that reduce VRAM and increase performance by 40%.

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

DrP’s key components include:Expressive SDK : The DrP SDK allows engineers to codify investigation workflows into analyzers.

Post-processing system : After an investigation, the post-processing system can take automated actions based on the analysis results.

Bootstrap code : The DrP SDK provides bootstrap code to create a template analyzer with pre-populated boilerplate code.

Data access and analysis : The SDK includes libraries for data access and analysis, such as dimension analysis and time series correlation.

This provides immediate analysis results to on-call engineers.

Generative AI and automation accelerate the adoption of secure frameworks at scale, enabling consistent security enforcement and efficient migration across Meta’s vast codebase.

How We Design Secure-by-Default Frameworks at MetaDesigning secure-by-default frameworks for use by a large number of developers shipping vastly different features across multiple apps is an interesting challenge.

There shouldn’t be one security framework that covers all security issues, and not every security issue is general enough to deserve its own framework.

Now that we’ve looked at the design philosophy behind our frameworks, let’s look at one of our most widely used Android security frameworks, SecureLinkLaun…

Zoomer has delivered training time reductions, and significant QPS improvements, making it the de-facto tool for AI performance optimization across Meta’s entire AI infrastructure.

Zoomer is Meta’s automated, one-stop-shop platform for performance profiling, debugging, analysis, and optimization of AI training and inference workloads.

AI Performance Optimization Using ZoomerZoomer is an automated debugging and optimization platform that works across all of our AI model types (ads recommendations, GenAI, computer vision, etc.)

Memory Analysis : Comprehensive analysis of GPU memory usage patterns, allocation tracking, and leak detection.

Realtime Memory Profiling : GPU memory allocation track…

But have you heard about open hardware?

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

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

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

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

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

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

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

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

At Meta, we are constantly pushing the boundaries of LLM inference systems to power applications such as the Meta AI App.

These metrics highlight the distinct computational demands of LLM inference: Prefill is compute-intensive, while decoding is memory bandwidth-intensive.

Communication: Communication latency increases when parallelizing across multiple hosts.

In EP-based inference, we utilize a two-shot, all-to-all communication pattern to exchange tokens between data parallelism and expert parallelism ranks based on routing.

We are committed to continuous innovation to ensure efficient and scalable LLM inference for millions of users worldwide.

We leveraged AI to help us improve this database and understand our Scope 3 emissions associated with IT hardware by:Identifying similar components and applying existing PCFs to similar components that lack these carbon estimates.

Understanding the carbon footprint of IT racks and applying generative AI (GenAI) as a categorization algorithm to create a new and standard taxonomy .

If these similar components are not identified their carbon footprint estimates will remain at a lower data quality.

These similar components can be mapped to a representative proxy PCF, allowing us to use high-quality PCF data in similar components.

For example, we can scale the carbon footprint calculation for a …

At Open Compute Project Summit (OCP) 2025, we’re sharing details about the direction of next-generation network fabrics for our AI training clusters.

At Meta, we believe that open hardware is a catalyst for innovation — especially as data center infrastructure increasingly supports new and emerging AI technologies.

Open hardware plays a crucial role in enabling disaggregation, allowing us to break down traditional data center technologies into their core components.

Today, through OCP, we continue to advance open network technologies for the next generation of AI applications.

Ethernet for Scale-Up Networking in OCP: Meta’s Industry LeadershipAt Meta, we recognize that the future of AI and …

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Models and the Primary AI Workloads Are Rapidly Evolving.

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

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

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

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

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

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

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

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

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

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

Expansions for the Federa…

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

Production risk was one of the areas we tackled first.

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

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

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

Piotr Niedźwiedź, CEO/CTO and founder of neptune.aiI’m excited to share that we’ve entered into a definitive agreement to be acquired by OpenAI, subject to closing conditions.

We are thrilled to join the OpenAI team and help their AI researchers build better models faster.

Neptune is a metrics dashboard company.”We’ve worked closely with OpenAI to create the metrics dashboard that helps teams building foundation models.

Our future with OpenAINeptune will join OpenAI and continue to support AI researchers with tools to monitor, debug, and evaluate frontier models.

We are looking forward to working with top AI researchers and supporting OpenAI’s mission of ensuring that AGI benefits all of hu…

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

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

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

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

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

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

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

What are the applications of LLM embeddings?

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

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

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

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

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

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

In the first part of this series, we covered the fundamentals of instruction fine-tuning (IFT).

def calculate_irs(instruction, output, reference_model): evaluation_prompt = f""" Instruction: {instruction} Model Output: {output} Rate how well the output follows the instruction on these criteria: 1.

| SourceHINT addresses a computational inefficiency in standard instruction fine-tuning: repeatedly reprocessing the same task instruction with every input example.

Read more about foundation model training infrastructure and other topics in Neptune’s 2025 State of Foundation Model Training Report.

First, during initial instruction fine-tuning across multiple diverse tasks, the model learns genera…

Large Language Model (LLM) inference at scale is challenging as it involves transferring massive amounts of model parameters and data and performing computations on large tensors.

In the following, we’ll use the Llama model family architecture as a specific example to understand the LLM workload at inference.

For a far more detailed analysis of the LLM workload at inference, see the chapter All About Transformer Inference in the book How to Scale Your Model, published by Google DeepMind.

See also How to Run LLMs Locally Read moreA quick primer on hardware for LLM inferenceA typical LLM inference cluster consists of several nodes, each with a multi-core CPU and multiple accelerator devices, …

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

What is LLM grounding?

LLM grounding is analogous.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What gradient issues occur during foundation model training?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Paper: https://arxiv.org/abs/2501.00663 Abstract:

Over more than a decade there has been an extensive research effort on how to effectively utilize recurrent models and attention. While recurrent models aim to compress the data into a fixed-size memory (called hidden state), attention allows attending to the entire context window, capturing the direct dependencies of all tokens. This more accurate modeling of dependencies, however, comes with a quadratic cost, limiting the model to a fixed-length context. We present a new neural long-term memory module that learns to memorize historical context and helps attention to attend to the current context while utilizing long past information. We sh…

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

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

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Paper: https://arxiv.org/abs/2508.21038 Abstract:

Vector embeddings have been tasked with an ever-increasing set of retrieval tasks over the years, with a nascent rise in using them for reasoning, instruction-following, coding, and more. These new benchmarks push embeddings to work for any query and any notion of relevance that could be given. While prior works have pointed out theoretical limitations of vector embeddings, there is a common assumption that these difficulties are exclusively due to unrealistic queries, and those that are not can be overcome with better training data and larger models. In this work, we demonstrate that we may encounter these theoretical limitations in realist…

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

Paper: https://research.trychroma.com/context-rot Abstract:

Large Language Models (LLMs) are typically presumed to process context uniformly—that is, the model should handle the 10,000th token just as reliably as the 100th. However, in practice, this assumption does not hold. We observe that model performance varies significantly as input length changes, even on simple tasks.

In this report, we evaluate 18 LLMs, including the state-of-the-art GPT-4.1, Claude 4, Gemini 2.5, and Qwen3 models. Our results reveal that models do not use their context uniformly; instead, their performance grows increasingly unreliable as input length grows. Authors: Kelly Hong, Anton Troynikov, Jeff Huber Links:

…

Paper: https://arxiv.org/abs/2507.02092

Code: https://github.com/alexiglad/EBT

Website: https://energy-based-transformers.github.io/ Abstract:

Inference-time computation techniques, analogous to human System 2 Thinking, have recently become popular for improving model performances. However, most existing approaches suffer from several limitations: they are modality-specific (e.g., working only in text), problem-specific (e.g., verifiable domains like math and coding), or require additional supervision/training on top of unsupervised pretraining (e.g., verifiers or verifiable rewards). In this paper, we ask the question "Is it possible to generalize these System 2 Thinking approaches, and de…

An in-depth look at Anthropic's Transformer Circuit Blog Post

Part 1 here: https://youtu.be/mU3g2YPKlsA

Discord here: https;//ykilcher.com/discord https://transformer-circuits.pub/2025/attribution-graphs/biology.html Abstract:

We investigate the internal mechanisms used by Claude 3.5 Haiku — Anthropic's lightweight production model — in a variety of contexts, using our circuit tracing methodology. Authors:

Jack Lindsey†, Wes Gurnee*, Emmanuel Ameisen*, Brian Chen*, Adam Pearce*, Nicholas L. Turner*, Craig Citro*,

David Abrahams, Shan Carter, Basil Hosmer, Jonathan Marcus, Michael Sklar, Adly Templeton,

Trenton Bricken, Callum McDougall◊, Hoagy Cunningham, Thomas Henighan, Adam Jermyn, Andy …

An in-depth look at Anthropic's Transformer Circuit Blog Post https://transformer-circuits.pub/2025/attribution-graphs/biology.html Abstract:

We investigate the internal mechanisms used by Claude 3.5 Haiku — Anthropic's lightweight production model — in a variety of contexts, using our circuit tracing methodology. Authors:

Jack Lindsey†, Wes Gurnee*, Emmanuel Ameisen*, Brian Chen*, Adam Pearce*, Nicholas L. Turner*, Craig Citro*,

David Abrahams, Shan Carter, Basil Hosmer, Jonathan Marcus, Michael Sklar, Adly Templeton,

Trenton Bricken, Callum McDougall◊, Hoagy Cunningham, Thomas Henighan, Adam Jermyn, Andy Jones, Andrew Persic, Zhenyi Qi, T. Ben Thompson,

Sam Zimmerman, Kelley Rivoire, Thom…

Because why not make a pi creature neck pillow?

Available at 3b1b.co/store

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

Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support

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

Home page: https://www.3blue1brown.com Chapter on the Laplace Transform:

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

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

0:00 - Opening puzzle

1:06 - Key properties of a Laplace Transform

3:29 - Qualitative analysis with Laplace Transforms

4:29 - The Laplace Transforms of a Derivative

6:06 - The forced oscillator

11:59 - Intuition from the transformed solution

1…

A fuller version of this explanation, also including the reason we care about complex exponents in the first place: https://youtu.be/-j8PzkZ70Lg

Visualizing the most important tool for differential equations.

Previous chapter: https://youtu.be/-j8PzkZ70Lg

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 Artwork by Kurt Bruns Engine animation borrowed with permission from this (excellent) blog: https://ciechanow.ski/internal-combustion-engine/ Timestamps:

0:00 - Understanding the engine

1:16 - Key background ideas

5:41 - Definition and intuition

10:43 - Complex integration

20:43 - Analytic continuation

23:52 - The transform of exponentials

26:15 - A deep look at cos(t)

32:59 - W…

A fuller version of this explanation, also including the reason we care about complex exponents in the first place: https://youtu.be/-j8PzkZ70Lg

How dynamics explain Euler's formula, and vice versa.

Early view of the Laplace Transform video: https://www.patreon.com/posts/laplace-early-140428165

Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support

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

Home page: https://www.3blue1brown.com Timestamps:

0:00 - Intro

1:51 - Euler's formula explained dynamically

9:27 - The harmonic oscillator

21:08 - General linear equations

22:47 - Motivating the Laplace Transform ------------------ These animations are largely made using a custom Python library, manim. See the FAQ comments here:

https://3b1b.co/faq#manim Music by Vincent Rubin…

What role were ruler and compass constructions really serving?

Check out Ben's channel: @bensyversen Interview with the author of this video: https://youtu.be/VohYM99j8e0

Supporters get early views of new videos: https://3b1b.co/support Written, produced, edited, and animated by Ben Syversen

Additional editing: Jack Saxon

3d Blender model: Jan-Hendrik Müller

Additional Blender help: Thibaut Modrzyk (@Deepia)

Illustrations: Alex Zepherin/DonDada Studio

Drums: Jeremy Gustin

Additional music from Epidemic Sound Special thanks to Viktor Blåsjö: https://intellectualmathematics.com/opinionated-history-of-mathematics/ References/Recommended reading: Euclid’s Elements:

Visual edition of Book 1: htt…

Full video: https://youtu.be/_BrFKp-U8GI

Sol Lewitt's "Incomplete Open Cubes" and rediscovering Burnside's lemma in group theory

This is a guest video by Paul Dancstep: https://youtu.be/JEeM2ABUMoo

Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support

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

Home page: https://www.3blue1brown.com Thanks to the Wadsworth Atheneum for granting permission to use LeWitt's notebooks. Talks by Paul you can find online: What is Category Theory:

https://www.youtube.com/watch?app=desktop&v=eXBwU9ieLL0 How to Predict Eclipses:

https://www.exploratorium.edu/eclipse/video/how-predict-eclipses Theo Jansen's Strandbeests

https://www.youtube.com/w…

Deriving the Boltzmann formula, defining temperature, and simulating liquid/vapor.

@SpectralCollective has the second part: https://youtu.be/yEcysu5xZH0

You can play with a simulation of this model here: https://vilas.us/simulations/liquidvapor/

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

Home page: https://www.3blue1brown.com Notes from Vilas:

1) This open problem is to prove the ergodicity of the deterministic dynamical systems that are used to model the molecule-level physics. A good example of such a dynamical system is the box with particles evolving according to Newton's laws with elastic collisions, like in the video. 2) This video assumes that all probabili…

From this guest video by @WelchLabsVideo on how diffusion models work: https://youtu.be/iv-5mZ_9CPY

How AlphaGeometry combines logic and intuition.

Share stories about AI in math research for an upcoming video: https://forms.gle/gr9aZVdUrW5T3yDg9

Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support

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

Home page: https://www.3blue1brown.com AlphaGeometry announcement:

https://deepmind.google/discover/blog/alphageometry-an-olympiad-level-ai-system-for-geometry/ Similar open-source model, Newclid, by Harmonic:

https://harmonic.fun/news#blog-post-geometry Timestamps:

0:00 - What's surprising

1:33 - Solve without AI

7:10 - Where AI comes in

12:48 - Grant's comments ------------------…

Diffusion models, CLIP, and the math of turning text into images

Welch Labs Book: https://www.welchlabs.com/resources/imaginary-numbers-book Sections

0:00 - Intro

3:37 - CLIP

6:25 - Shared Embedding Space

8:16 - Diffusion Models & DDPM

11:44 - Learning Vector Fields

22:00 - DDIM

25:25 Dall E 2

26:37 - Conditioning

30:02 - Guidance

33:39 - Negative Prompts

34:27 - Outro

35:32 - About guest videos + Grant’s Reaction Special Thanks to:

Jonathan Ho - Jonathan is the Author of the DDPM paper and the Classifier Free Guidance Paper.

https://arxiv.org/pdf/2006.11239

https://arxiv.org/pdf/2207.12598 Preetum Nakkiran - Preetum has an excellent introductory diffusion tutorial:

https://arxiv.org/pdf/24…

Make a math explainer, get feedback, and receive prizes: https://some.3b1b.co

Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support

An equally valuable form of support is to simply share the videos. ------------------ These animations are largely made using a custom Python library, manim. See the FAQ comments here:

https://3b1b.co/faq#manim

https://github.com/3b1b/manim

https://github.com/ManimCommunity/manim/ All code for specific videos is visible here:

https://github.com/3b1b/videos/ The music is by Vincent Rubinetti.

https://www.vincentrubinetti.com

https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown

https://open.spotify.com/…

Addressing viewer questions from the last video.

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

An equally valuable form of support is to share the videos. ------------------ These animations are largely made using a custom Python library, manim. See the FAQ comments here:

https://3b1b.co/faq#manim

https://github.com/3b1b/manim

https://github.com/ManimCommunity/manim/ All code for specific videos is visible here:

https://github.com/3b1b/videos/ The music is by Vincent Rubinetti.

https://www.vincentrubinetti.com

https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown

https://open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u ------------------ 3blue1brown is a ch…

Yup. My free course on how to write a light simulation program (ray tracing): https://users.cg.tuwien.ac.at/zsolnai/gfx/rendering-course/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Benji Rabhan, B Shang, Christian Ahlin, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi

If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/

Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Using DeepSeek on Lambda:

https://lambda.ai/inference-models/deepseek-r1 📝 The paper "A Stream Function Solver for Liquid Simulations" is available here:

https://pub.ista.ac.at/group_wojtan/projects/2015_Ando_ASFSfLS/download/vecpotential.pdf 📝 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, Fred R, Gordon …

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Using DeepSeek on Lambda:

https://lambda.ai/inference-models/deepseek-r1 📝 The paper is available here:

https://research.nvidia.com/labs/toronto-ai/trace-pace/ 📝 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, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras B…

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

https://wanghmin.github.io/publication/wu-2022-gbm/ 📝 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, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi

If you wish to appear here or …

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Using DeepSeek on Lambda:

https://lambda.ai/inference-models/deepseek-r1 📝 The paper is available here:

https://simworld.org/ 📝 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, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli G…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Using DeepSeek on Lambda:

https://lambda.ai/inference-models/deepseek-r1 📝 The SIMA 2 paper is available here:

https://deepmind.google/blog/sima-2-an-agent-that-plays-reasons-and-learns-with-you-in-virtual-3d-worlds/ 📝 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, Fred R, Gordon Child, Juan Benet, Michael…

Full interview: https://www.youtube.com/watch?v=Vhcwjzeukts

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Using DeepSeek on Lambda:

https://lambda.ai/inference-models/deepseek-r1 My hobby channel with guitars and labcoats 🥼:

https://www.youtube.com/watch?v=GjMMhn4pS38

https://www.youtube.com/watch?v=BxS62W6V48E 📝 The paper is available here:

https://arxiv.org/abs/2505.21946 📝 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, Chri…

Thank you so much to John for being so kind and insightful, and to the film crew as well - they all did an incredible job. To celebrate the 5th anniversary of #AlphaFold, I was invited by Google DeepMind to interview Nobel Prize Winner and Distinguished Scientist, John Jumper. Note that we have no business ties with them. AlphaFold: https://deepmind.google/science/alphafold/

The full Thinking Game Movie: https://www.youtube.com/watch?v=d95J8yzvjbQ My research: https://cg.tuwien.ac.at/~zsolnai/

Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The Unreal Engine 5.7 is available here:

https://www.unrealengine.com/en-US/news/unreal-engine-5-7-is-now-available Sources:

https://www.youtube.com/watch?v=Mj_-2SdsYLw

https://www.youtube.com/watch?v=ngzPTqtZWo4

https://advances.realtimerendering.com/s2023/2023%20Siggraph%20-%20Substrate.pdf 📝 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:

Be…

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

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

Multiple scattering paper: https://cg.iit.bme.hu/~szirmay/volreuse_link.htm 📝 My paper on simulations that look almost like reality is available for free here:

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

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall…

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

Rent one of their GPUs with over 16GB of VRAM

Open a terminal

Just get Ollama following the command from here - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here:

https://danijar.com/project/dreamer4/ Source:

https://www.youtube.com/watch?v=6bnM84xGxbg 📝 My paper on simulations that look almost like reality is available for free here:

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

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

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

Rent one of their GPUs with over 16GB of VRAM

Open a terminal

Just get Ollama with this command - https://ollama.com/download/linux

Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper "Fast Physics-Based Modeling of Knots and Ties Using Templates" is available here:

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

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

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

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

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

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

https://www.youtube.com/w…

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

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

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

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

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

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

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

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

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

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

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

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

https://dl.acm.org/doi/10.1145/3658223 📝 My paper on simulations that look almost like reality is available for free here:

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

https://www.nature.com/articles/s41567-022-01788-5 Sources:

https://www.youtube.com/watch?v=dtBqv-qIFLo

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

https://www.youtube…

ML Global Recap 2025 — митап для ML-сообщества, на котором мы рассказали о главных международных конференциях года и самых интересных трендах в рекомендательных технологиях, компьютерном зрении, распознавании речи и NLP. С докладом на ивенте выступил Роман Исаченко, руководитель команды анализа изображений в Яндекс R&D. Он рассказал про мультимодальный анализ изображений (VLM) и диффузионки — картиночную генерацию. Больше ML-контента по ссылке: https://t.me/+Ug9D4CjJrJxmZGRi #ML, #MachineLearning, #AI, #DataScience, #MLGlobalRecap2025, #нейросети, #искусственныйинтеллект, #рекомендательныесистемы, #NLP, #ComputerVision, #SpeechRecognition, #LLM, #DeepLearning, #MLтренды, #ITконференция, #Ya…

ML Global Recap 2025 — митап для ML-сообщества, на котором мы рассказали о главных международных конференциях года и самых интересных трендах в рекомендательных технологиях, компьютерном зрении, распознавании речи и NLP. С докладом на ивенте выступил Александр Юшкевич, руководитель команды развития моделей базового качества в Поисковых сервисах и ИИ. Он показал, как конференции отражают тренды в NLP: растёт закрытость топовых LLM-моделей, а также спрос на alignment & safety, инференс, интерпретируемость и оптимизацию. А ещё появляются новые бенчмарки (куда без них). Больше ML-контента по ссылке: https://t.me/+Ug9D4CjJrJxmZGRi #ML, #MachineLearning, #AI, #DataScience, #MLGlobalRecap2025, #не…

ML Global Recap 2025 — митап для ML-сообщества, на котором мы рассказали о главных международных конференциях года и самых интересных трендах в рекомендательных технологиях, компьютерном зрении, распознавании речи и NLP. С докладом на ивенте выступил Борис Шелудько, руководитель команды качества звука в Яндекс R&D. Он рассказал про итоги двух главных международных конференций по голосовым технологиям: Interspeech в Нидерландах и ICASSP в Индии. Больше ML-контента по ссылке: https://t.me/+Ug9D4CjJrJxmZGRi #ML, #MachineLearning, #AI, #DataScience, #MLGlobalRecap2025, #нейросети, #искусственныйинтеллект, #рекомендательныесистемы, #NLP, #ComputerVision, #SpeechRecognition, #LLM, #DeepLearning, …

ML Global Recap 2025 — митап для ML-сообщества, на котором мы рассказали о главных международных конференциях года и самых интересных трендах в рекомендательных технологиях, компьютерном зрении, распознавании речи и NLP. С докладом на ивенте выступил Николай Савушкин, руководитель команды рекомендательных технологий в Яндекс R&D. Он поделился инсайтами с CIKM и RecSys и рассказал про ключевые тренды в рекомендательных системах: фундаментальные и End2End-модели, масштабирование, мультимодальность, attention-based ranking и другие. Больше ML-контента по ссылке: https://t.me/+Ug9D4CjJrJxmZGRi #ML, #MachineLearning, #AI, #DataScience, #MLGlobalRecap2025, #нейросети, #искусственныйинтеллект, #ре…

ML Global Recap 2025 — митап для ML-сообщества, на котором мы рассказали о главных международных конференциях года и самых интересных трендах в рекомендательных технологиях, компьютерном зрении, распознавании речи и NLP. Ивент открыл Алексей Гусаков, CTO Поисковых сервисов и ИИ. В докладе — вступительное слово, краткий обзор NeurIPS и немного про кризис бенчмарков. Больше ML-контента по ссылке: https://t.me/+Ug9D4CjJrJxmZGRi #ML, #MachineLearning, #AI, #DataScience, #MLGlobalRecap2025, #нейросети, #искусственныйинтеллект, #рекомендательныесистемы, #NLP, #ComputerVision, #SpeechRecognition, #LLM, #DeepLearning, #MLтренды, #ITконференция, #Yandex, #MLсообщество

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

Митап Яндекса для ML-сообщества, на котором расскажем о шести международных конференциях и главных трендах в рекомендательных технологиях, компьютерном зрении, технологиях распознавания речи и NLP.

Мок-интервью: как проходит секция на базовые алгоритмы для ML-инженеров Подробнее: https://yandex.ru/jobs/interview/mldev

Вакансии: https://yandex.ru/jobs/vacancies?professions=ml-developer Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/+Ug9D4CjJrJxmZGRi #ml, #machinelearning, #mlengineer, #mlinterview, #datascience, #яндекс, #yandex, #itcareer, #mldeveloper, #techinterview, #algorithms, #машиннообучение, #вакансии

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

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

Это отрывок из доклада Алексея Колесова, CTO в Яндекс R&D. На Practical ML Conf 2025 он рассказал, как ребята учили YandexGPT 5.1 лучше помнить факты и применять знания о них. А ещё показал, как у нас стабильно заработал online RL. Полная запись уже на канале! #YandexGPT #LLM #AI #MachineLearning #GenerativeAI #YandexForML #YandexForDevelopers #Яндекс #AIDevDay #NeuralNetworks #ArtificialIntelligence #Football #Soccer #RealMadrid #Barcelona #ElClasico #LaLiga #Messi #Ronaldo #TechAndSports #AIInSports #FootballFans #SportsAnalytics #YandexTech #AIComparison

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

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

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

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

0:00:00 Начало

0:01:01 Корейцы продали секрет памяти

0:04:36 Карпаты не успевает

0:16:34 Живи быстро и не умирай

0:24:55 Бигтехи и вузы

0:35:51 Apple учит иcторию партии

0:43:32 Nvidia купила Groq

0:45:47 Япония покупает ИИ

0:53:48 Роботакси превратились в тыкву

1:00:28 Итоги года ИИ-саммари:

В этом выпуске обсуждаются ключевые события года, включая рост цен на технологии памяти, скорость изменений в индустрии, дефляцию технологий и трансформацию маркетинга. Также рассматриваются новые навыки, необходимые в программировании, и важность образования в технологической сфере России. В этом разговоре обсуждаются ключевые проблемы и перспективы агрономии, сравнение технологий в агропромышленности…

0:00:00 Начало

0:00:27 Альтман разочаровался в ИИ

0:15:27 Непонятный стандарт для ИИ

0:24:55 ИИ от Маска в 2026

0:32:20 Китайские статьи про ИИ

0:38:57 Microsoft доит программистов

0:41:27 ИИ пишет плохой код

0:47:22 VISA и платежи для агентов

0:52:35 Стор навыков для роботов

1:01:06 X5 про ИИ-риски

1:12:45 Крошечный компьютер для ИИ

1:19:28 Пьяный робот не падает ИИ-саммари:

В этом выпуске обсуждаются ключевые темы, связанные с искусственным интеллектом, включая концепцию сверхсильного интеллекта, проблемы взаимодействия с ИИ, необходимость разработки стандартов и регулирования, а также оптимизм Илона Маска в отношении будущего AGI. Участники подчеркивают важность терминологии и коммуникац…

Patreon:

https://www.patreon.com/primerlearning Helpful resources if you want to learn more about neural networks

https://www.youtube.com/@AndrejKarpathy

https://course.fast.ai/

https://www.youtube.com/@WelchLabsVideo

https://www.youtube.com/@3blue1brown Early papers. These probably aren't helpful for understanding the concepts in this video, but if you're interested in history.

The Perceptron – A perceiving and recognizing automaton: https://bpb-us-e2.wpmucdn.com/websites.umass.edu/dist/a/27637/files/2016/03/rosenblatt-1957.pdf

The Perceptron: A probabilistic model for information storage and organization in the brain: https://www.ling.upenn.edu/courses/cogs501/Rosenblatt1958.pdf A Logical…

Irving Finkel is a scholar of ancient languages and a longtime curator at the British Museum, renowned for his expertise in Mesopotamian history and cuneiform writing.

He specializes in reading and interpreting cuneiform inscriptions, including tablets from Sumerian, Akkadian, Babylonian, and Assyrian contexts.

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Michael Levin is a biologist at Tufts University working on novel ways to understand and control complex pattern formation in biological systems.

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(2:42:41) – Mind uploading(3:01:22) – Alien intelligence(3:16:17) – Advice for young people(3:22:46) – Questions for AGI

David Kirtley is a nuclear fusion engineer and CEO of Helion Energy, a company working on building the world's first commercial fusion power plant by 2028.

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

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Julia Shaw is a criminal psychologist and author who in her books explores human nature, including psychopathy, violent crime, the psychology of evil, police interrogation, false memory manipulation, deception detection, and human sexuality.

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Pavel Durov is the founder and CEO of Telegram.

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Transcript:https://lexfridman.com/pavel-durov-transcriptCONTACT LEX:Feedback – give feedback to Lex: https://lexfridman.com/surveyAMA – submit questions, videos or call-in: https://lexfridman.com/amaHiring – join our team: https://lexfridman.com/hiringOther – other ways to get in touch: https://lexfridman.com/contactEPISODE LINKS:Pavel’s Telegram: https://t.me/durovPavel’s X: https://x.com/durovTelegram: https://telegram.org/Telegram Contests: https://contest.c…

Norman Ohler is a historian and author of “Blitzed: Drugs in the Third Reich,” a book that investigates the role of psychoactive drugs, particularly stimulants such as methamphetamine, in the military history of World War II.

It is a book that two legendary historians Ian Kershaw and Antony Beevor give very high praise for its depth of research.

Norman also wrote “Tripped: Nazi Germany, the CIA, and the Dawn of the Psychedelic Age”, and he is working on a new book “Stoned Sapiens” looking at the history of human civilization through the lens of drugs.

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Dave Hone is a paleontologist, expert on dinosaurs, co-host of the Terrible Lizards podcast, and author of numerous scientific papers and books on the behavior and ecology of dinosaurs.

He lectures at Queen Mary University of London on topics of Ecology, Zoology, Biology, and Evolution.

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Dave Plummer is a programmer, former Microsoft software engineer (Windows 95, NT, XP), creator of Task Manager, author of two books on autism, and host of the Dave’s Garage YouTube channel, where he shares stories from his career, insights on software development, and deep dives into technology.

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Scott Horton is the director of the Libertarian Institute, editorial director of Antiwar.com, host of The Scott Horton Show, co-host of Provoked, and for the past three decades a staunch critic of U.S. military interventionism.

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Keyu Jin is an economist specializing in China’s economy, international macroeconomics, global trade imbalances, and financial policy.

She is the author of The New China Playbook: Beyond Socialism and Capitalism.

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Jack Weatherford is an anthropologist and historian specializing in Genghis Khan and the Mongol Empire.

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Demis Hassabis is the CEO of Google DeepMind and Nobel Prize winner for his groundbreaking work in protein structure prediction using AI.

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David Heinemeier Hansson (aka DHH) is a legendary programmer, creator of Ruby on Rails, co-owner & CTO of 37signals that created Basecamp, HEY, & ONCE, and is a NYT-best-selling author (with Jason Fried) of 4 books: REWORK, REMOTE, Getting Real, and It Doesn’t Have To Be Crazy At Work.

He is also a race car driver, including a class-winning performance at the 24 hour Le Mans race.

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This week, machine learning researchers around the world will be attending the annual Conference on Neural Information Processing Systems, or NeurIPS.

In this series, we’ll explore the technologies that are shaping our future and the big ideas that propel them forward.

So around that time when I started my PhD at Penn, I was working in machine learning theory and algorithmic economics.

How had you experienced a lack of community or network of women in machine learning before the founding of WiML?

So particularly when working on topics related to fairness, I’ve ended up focusing a bunch on stuff to do with marginalized groups as part of my responsible AI work.

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

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

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

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

But I feel like broadly…

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

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

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

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

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

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Prior to med school, Daniel pursued experiences that cultivated his interest in the application of AI in medical practice and education.

Really, really looking forward to this chat.

There’s AI before ChatGPT and before, you know, generative AI really became a big thing, and then afterwards.

And then after we talk about what’s really happening, what do you think should happen in medical education given the reality of generative AI?

And I do agree [that] AI really gives us real hope that we can make it true.

Our goal is to learn from their successes and their stumbles to move the science and practice of AI testing forward.

We have examples, like the pharmaceutical or medical device industry experts with whom you spoke, that’s really, you know, testing … there is a pre-deployment requirement.

And the third is just how rigid versus adaptive these testing and evaluation regimes or frameworks are in these different domains.

I really agree that there has been a lot of emphasis to date on, sort of, testing models upstream, the AI model evaluation.

You know, I think there’s been real progress already in the AI evaluation and testing ecosystem in the public-private partnership context.

Absolutely, I really, really was.

As a principal director on the Microsoft AI Red Team, Tori leads all AI security and safety red team operations, as well as dangerous capability testing, to directly inform C-suite decision-makers.

This year, we’ve pulled a lot of those assets and insights into the Azure [AI] Foundry AI Red Teaming Agent (opens in new tab).

So you can get a little taste of what we do day to day in the AI Red Teaming Agent.

WESTERHOFF: I think the most important takeaway from those lessons is that AI security is truly a team sport.

Dr. Eric Topol is the executive vice president of the biomedical research non-profit Scripps Research, where he founded and now directs the Scripps Research Translational Institute.

Let’s continue our deep dive on AI and biomedical research with this conversation with Noubar Afeyan:LEE: Noubar, thanks so much for joining.

And there’s the origin story of contact with AI, you know, before the emergence of generative AI and afterwards.

What is going on today with respect to AI really being used for something meaningful in the design and development of drugs?

TOPOL: You would read about how, you know, data is the new oil and, you know, gold and whatnot.

Our goal is to learn from their successes and their stumbles to move the science and practice of AI testing forward.

During the pre-market phase, medical testing establishes baseline safety and effectiveness metrics through bench testing, performance standards, and clinical studies.

SULLIVAN: So medical devices face a pretty prescriptive multi-level testing path before they hit the market.

We are looking into medical devices, as well, obviously, but also other technologies in advanced medical computing.

So we see Phase 3 trials as something that occurs in the medical devices and pharmaceuticals field.

As generative AI continues to advance, Microsoft has gathered a range of experts—from genome editing to cybersecurity—to share how their fields approach evaluation and risk assessment.

CHARO: Well, you know, genome editing is both very old and very new.

Now the earliest forms of genome editing were very inefficient, and so we didn’t worry that much.

But the bottom-line thing to remember, the way to really think about it is, we don’t regulate genome editing; we regulate the things that use genome editing.

And she said, you know, we don’t regulate genome editing; we regulate the things that use genome editing.

Our goal is to learn from their successes and their stumbles to move the science and practice of AI testing forward.

And I think, really, there are two reasons why tech is so, kind of, representative of that kind of challenge that I’ve always found fascinating.

Continues to be a really important topic in the AI policy conversation right now, I think, for really good reason.

Testing is an important component for governance and AI and, of course, in all of these other domains, as well.

I think about almost, like, in the near to mid-term, like three issues that we need to address in the AI, kind of, policy and testing context.

LEE: Yeah, yeah.

It cannot—as, you know, Bill was saying—it cannot learn from your document.

And I don’t know if the two of you remember, but I ended up doing a lot of tests.

I don’t know if you know, but just recently, there was a paper that was published on a scientific discovery using o3- mini (opens in new tab).

Like, if you have a human trained for one task and you put them into another task, then you don’t … you often don’t know.

So I don’t think we should be surprised that business schools matter on this because we care about management.

That’s really going to change the way, like, middle school works, was my thinking at the time.

We’ve gone from AI being highly discriminative to AI that’s able to explore the world in particular ways.

The symptoms that they’re showing are quite different, and also their compliance is really, really different.

LEE: Yeah, really, really interesting.

And single-cell foundation models claim to be capable of unraveling deeper insights than ever before.

Basically, we showed that single-cell foundation models perform worse in settings that are fundamental to biological discovery than much simpler machine learning and statistical methods that were used in the field before single-cell foundation models emerged and are the go-to standard for unpacking meaning from these complicated experiments.

And the way to understand this is because single-cell foundation models are trained in a way that tries to expose these models to millions of single-cells.

But let’s also talk about the impact for methodologists, people who are trying to improve these s…

This is such exciting work about environmental forecasting, so we’re happy to have the two of you join us today.

Mostly because AI weather forecasting models are computationally much more efficient and can even be more accurate.

What’s unfortunate though, about this big step forward, is that these developments are mostly limited to the setting of weather forecasting.

Weather forecasting is very important, obviously, but there are many other important environmental forecasting problems out there, such as air pollution forecasting or ocean wave forecasting.

STANLEY: Current approaches have really focused training very specifically on weather forecasting models.

In this episode, Kyle Polich sits down with Cory Zechmann, a content curator working in streaming television with 16 years of experience running the music blog "Silence Nogood." They explore the intersection of human curation and machine learning in content discovery, discussing the concept of "algatorial" curation—where algorithms and editorial expertise work together. Key topics include the cold start problem, why every metric is just a "proxy metric" for what users actually want, the challenge of filter bubbles, and the importance of balancing familiarity with discovery. Cory shares insights on why TikTok's algorithm works so well (clean data and massive interaction volume), the crucial …

In this episode, Santiago de Leon takes us deep into the world of eye tracking and its revolutionary applications in recommender systems. As a researcher at the Kempelin Institute and Brno University, Santiago explains the mechanics of eye tracking technology—how it captures gaze data and processes it into fixations and saccades to reveal user browsing patterns. He introduces the groundbreaking RecGaze dataset, the first eye tracking dataset specifically designed for recommender systems research, which opens new possibilities for understanding how users interact with carousel interfaces like Netflix. Through collaboration between psychologists and AI researchers, Santiago's work demonstrate…

In this episode of Data Skeptic, we dive deep into the technical foundations of building modern recommender systems. Unlike traditional machine learning classification problems where you can simply apply XGBoost to tabular data, recommender systems require sophisticated hybrid approaches that combine multiple techniques. Our guest, Boya Xu, an assistant professor of marketing at Virginia Tech, walks us through a cutting-edge method that integrates three key components: collaborative filtering for dimensionality reduction, embeddings to represent users and items in latent space, and bandit learning to balance exploration and exploitation when deploying new recommendations. Boya shares insigh…

In this episode of Data Skeptic, we explore the fascinating intersection of recommender systems and digital humanities with guest Florian Atzenhofer-Baumgartner, a PhD student at Graz University of Technology. Florian is working on Monasterium.net, Europe's largest online collection of historical charters, containing millions of medieval and early modern documents from across the continent. The conversation delves into why traditional recommender systems fall short in the digital humanities space, where users range from expert historians and genealogists to art historians and linguists, each with unique research needs and information-seeking behaviors. Florian explains the technical challen…

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

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

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

In this episode, we speak with Ashmi Banerjee, a doctoral candidate at the Technical University of Munich, about her pioneering research on AI-powered recommender systems in tourism. Ashmi illuminates how these systems can address exposure bias while promoting more sustainable tourism practices through innovative approaches to data acquisition and algorithm design. Key highlights include leveraging large language models for synthetic data generation, developing recommendation architectures that balance user satisfaction with environmental concerns, and creating frameworks that distribute tourism more equitably across destinations. Ashmi's insights offer valuable perspectives for both AI res…

In this episode of Data Skeptic's Recommender Systems series, host Kyle Polich interviews Dr. Kunal Mukherjee, a postdoctoral research associate at Virginia Tech, about the paper "Z-REx: Human-Interpretable GNN Explanations for Real Estate Recommendations" The discussion explores how the post-COVID real estate landscape has created a need for better recommendation systems that can introduce home buyers to emerging neighborhoods they might not know about. Dr. Mukherjee, explains how his team developed a graph neural network approach that not only recommends properties but provides human-interpretable explanations for why certain regions are suggested. The conversation covers the advantages o…

In this episode of Data Skeptic, we explore the challenges of studying social media recommender systems when exposure data isn't accessible. Our guests Sabrina Guidotti, Gregor Donabauer, and Dimitri Ognibene introduce their innovative "recommender neutral user model" for inferring the influence of opaque algorithms.

In this episode of Data Skeptic, we dive into eco-friendly AI with Antonio Purificato, a PhD student from Sapienza University of Rome. Antonio discusses his research on "EcoAware Graph Neural Networks for Sustainable Recommendations" and explores how we can measure and reduce the environmental impact of recommender systems without sacrificing performance.

Kyle reveals the next season's topic will be "Recommender Systems". Asaf shares insights on how network science contributes to the recommender system field.

Kyle and Asaf discuss a project in which we link former guests of the podcast based on their co-authorship of academic papers.

In this episode, Professor Pål Grønås Drange from the University of Bergen, introduces the field of Parameterized Complexity - a powerful framework for tackling hard computational problems by focusing on specific structural aspects of the input. This framework allows researchers to solve NP-complete problems more efficiently when certain parameters, like the structure of the graph, are "well-behaved". At the center of the discussion is the network diversion problem, where the goal isn’t to block all routes between two points in a network, but to force flow - such as traffic, electricity, or data - through a specific path. While this problem appears deceptively similar to the classic "Min.Cu…

Jon Krohn wraps up 2025 with his thoughts on how agentic AI has become as much a resounding success as an annoying buzzword for many in the tech industry, why such promising developments in generative AI mean that well-prepared, secured data will be ever more crucial, and Jon’s hopes for a better year for everyone across the world in 2026. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/954⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Dell Technologies’ John Roese talks to Jon Krohn about the phenomenon of “agent-washing”, his contribution to Dell’s incredible revenue boost in 2025, and why “knowledge layers” will be crucial to future tech. Hear also John’s predictions for where AI is going to lead us in 2026, from better, clearer governance, data management methods and definitions for agentic AI, to systems that keep AI tools and our data running and secure with the help of “AI factories” and “sovereign AI”. This episode is brought to you by MongoDB and by Y Carrot. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/953⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie…

“The Fit Data Scientist” newsletter author Pénélope Lafeuille talks to Jon Krohn about how to give your all at work, offering her top tips for a healthy body and a healthy mind. Learn why “The SuperDataScience Podcast” made it onto her top 3 data science podcasts, and why following your passion can pay off in dividends for your career. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/952⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

VP of Engineering at Dropbox Josh Clemm speaks to Jon Krohn about consolidating search tools across apps with the AI-powered workspace, Dropbox Dash, the new collaborative AI systems that enhance interoperability between team members and their projects, and how to avoid “context rot”. Dropbox Dash gives users the best of Dropbox’s cloud storage and search functions, plus a “universal search” ability to locate information across multimedia and apps. “AI really needs to understand you and your team, first and foremost, and all that connected data,” says Josh. This episode is brought to you by the ⁠⁠Dell⁠⁠, by ⁠⁠Intel⁠⁠, by Airia and by MongoDB. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www…

In this special holiday episode, the SuperDataScience Podcast team comes together to wish you happy holidays and thank you for listening throughout the year. Team members from around the world share warm greetings in their own voices and languages as we reflect on another year of learning, curiosity, and community. From all of us at SDS, we wish you a joyful holiday season and look forward to bringing you more data science, machine learning, and AI content in the year ahead. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/950⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Alex “Sandy” Pentland, Toshiba Professor of Media Arts & Science at MIT and Fellow at Stanford, speaks to Jon Krohn about his new book, Shared Wisdom, why he attributes AI to the collapse of the Soviet Union, and why those risks to society could still be relevant today. We can only achieve better system performance, Alex says, when we build tools that keep step with the way that people make decisions. Listen to the episode to hear Alex talk about how he is helping make AI agents work for individuals rather than the companies that develop them, and his work in making sure that systems operate consistently and fairly across the world. This episode is brought to you by the⁠ ⁠⁠⁠⁠Dell⁠⁠⁠, by⁠ ⁠⁠…

In this November episode of “In Case You Missed It” series, Jon Krohn selects his favorite clips from the month. Hear from Shirish Gupta and Tyler Cox (Episode 939), Vikoy Pandey (Episode 941), Marc Dupuis (Episode 937), and Maya Ackerman (Episode 943) on getting back to human motivation and the importance of evaluating the tools and data we use. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/948⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Jeff Li tells Jon Krohn what it's like to work at scale as a data scientist and a machine learning engineer at Netflix, Spotify and DoorDash, as well as how to get a foot in the door at these companies. Jeff also discusses how to run forecasts and trends, and how to read their results. Listen to hear Jeff Li discuss how Spotify became a podcast powerhouse, his startup move.ai, and the tools he uses every day. This episode is brought to you by the ⁠⁠Dell⁠⁠, by ⁠⁠Intel⁠⁠, by Fabi, and by Airia. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/947⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship informat…

Jon Krohn looks into the benefits of robotaxis, from safety to affordability, in this Five-Minute Friday. Hear about Waymo’s partnership with Jaguar Land Rover, the latest safety studies concerning driverless vehicles, and a case for robotaxis becoming the preferred method of transport in the US, where households spend roughly 15% of their budget on vehicle ownership. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/946⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Is there humor in data? Joel Beasley, host of Modern CTO, tells Jon Krohn how he used AI to turn his sights to stand-up comedy. He also shares his tips on tech leadership that he learned from his popular podcast, Modern CTO, and how he is using generative AI as a collaborative partner in his creative work. This episode is brought to you by the ⁠⁠Dell⁠⁠, by ⁠⁠Intel⁠⁠, by Fabi, and by Gurobi⁠⁠⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/945⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (02:14) Joel Beasley on his comedy career (19:04) Applying the ‘me…

Google is steaming ahead with launching its top-league new Gemini 3 Pro model across their product suite, from Google Search to Vertex AI cloud services. The multinational tech company is also letting eager early adopters like Wayfair and GitHub. Get all the detailed data, its performance across hard-to-game industry benchmarks, and what this all means for the way you use generative AI, in this week’s episode. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/944⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Creative human-AI partnerships and AI-generated music: WaveAI CEO and co-founder Maya Ackerman speaks with Jon Krohn about learning to see – and accept – AI’s potential as a creative partner in a human-centric, AI-forward future. Listen to the episode to hear Maya Ackerman discuss reframing hallucination as a creative force, her work at WaveAI, and how to push the boundaries of creativity using generative AI. This episode is brought to you by the ⁠⁠Dell⁠⁠, by ⁠⁠Intel⁠⁠, by Gurobi⁠⁠⁠ and by Airia. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/943⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship informat…

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

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

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

Mark Brocato built Mockaroo—the tool that taught millions of developers how to fake data.

Now, as Head of Engineering at Tonic.ai, he’s building the AI agent that’s making his own creation obsolete.

From the hidden failures of legacy mocks to the security implications of agent-driven synthesis, Mark reveals what happens when data generation becomes a conversation—not a pipeline.

SponsorsTonic.ai Synthetic data solutions for software and AI development.

Accelerate engineering velocity and ensure compliance with AI-powered data synthesisThis episode is brought to you by Statistical HorizonsAt Statistical Horizons, you can stay ahead with expert-led livestream seminars that make data analytics…

Most companies don’t have an AI problem.

In this conversation, he breaks down when AI actually makes sense, where AWS costs spiral out of control, and why your “cool demo” keeps dying before launch.

If you’re tired of AI hype and ready for straight answers, hit play.

Our Discord community is full of ML engineers, researchers, and AI enthusiasts discussing papers, sharing projects, and helping each other level up.

Whether you’re debugging your first neural net or training your tenth transformer, there’s a place for you.

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

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

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

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

Get $200 off any seminar with code DATA25 at https://statisticalhorizons.com

VortexNet uses actual whirlpools to build neural networks.

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

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

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

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

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

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

Discover more at https://amethix.com This episode is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers and defense innovators the tools to prototype, simulate, and deploy autonomous systems with confidence.

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

Sanjoy Chowdhury reveals AI’s hidden weakness: while systems can see objects and hear sounds perfectly, they can’t reason across senses like humans do.

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

Discover more at https://amethix.comThis episode is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers and defense innovators the tools to prototype, simulate, and deploy autonomous systems with confidence.

Whether it’s in the sky, on the ground, or in orbit—if it’s intelligent and mobile, Intrepid helps you build it.

ReferencesWar On The Rocks: https://warontherocks.com/2025/08/ukraine-isnt-the-model-for-winning-the-innovation-war/LinkedIn: https://www.linkedin.com/in/jonasrsinger/Spotify: https://tr.ee/Omy_1X8k1UApple Podcast: https://podcasts.apple.com/us/podcast/defence-innovation-podcast/id1797131332YouTube: https://youtube.com/@DefenceInnovationpodcast?si=cu2WlnVgL5XKnM0pSponsorsThis episode is proudly sponsored by Amethix Technologies.

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

Discover more at https://amethix.comThis episode is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers…

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

Discover more at amethix.comDSH is brought to you by Intrepid AI.

🐦 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.