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Fast forward to 2020, the research paper on the Dimensions of Data Quality (DDQ), identified an astonishing number of data quality dimensions (around 65!!

), reflecting not just how data quality definition should be constantly evolving, but also how data itself was used.

Dimensions of Data Quality: Toward Quality Data by Design, 1991 WangNonetheless, with the rise of Deep Learning hype, the idea that data quality no longer mattered lingered in the minds of the most tech savvy engineers.

Here’s why:Access is PowerIn domains with restricted or proprietary data, such as healthcare, lawyers, enterprise workflows or even user interaction data, ai agents can only be built by those with privileged…

Using plain retrieval for memory | Image by authorThis would be similar to how you build standard retrieval systems.

I think of long-term memory as two parts: pocket-sized facts and long-span memory of previous conversations.

Organizing long term memory | Image by authorFor the first part, pocket-sized facts, we can look at ChatGPT’s memory system as an example.

Simulating ChatGPT’s pocket-fact memory | Image by authorThen they classify the fact into a predefined bucket (such as profile, preferences, or projects) and either update an existing memory if it’s similar or create a new one if it’s not.

I hope this exercise helped you see how to implement memory in LLM systems if you’re new to it.

For each byte b i at position i, a set of preceding n-grams, g i,n = {b i-n+1 ,…, b i } are constructed for multiple values of n ∈ {3,…,8}.

Input Shape: (B, J, h e )) Output Shape: (B, J, h g )(Source: Author)Summary of the 3-step process to get the first patch embeddings:1.

Input Shape: (B, J, h g )) Output Shape: (B, J, d a ), where d a is the “attention dimension”.

The output is a sequence of predicted patch vectors, O j (shape: [B, J, h g ]), which encode the model’s high-level predictions.

2024, Figure 6)BLT showing competitive BPB (perplexity equivalent for byte models) and similar scaling laws to those of the tokenizer-based LLaMA models2.

uv init agentTest cd agentTest uv add openai-agentsA simple call to an agentA simple agent call is shown in the diagram below.

import streamlit as st import asyncio from agents import Agent, RunnerWe need the Streamlit package, of course, and asyncio because we will use its functionality to wait for the agent to complete before proceeding.

Next, we import the minimum from the agents package, Agent (to create an agent) and Runner (to run the agent).

import streamlit as st import asyncio from agents import Agent, Runner writer_agent = Agent( name="Writer agent", instructions=f"""Re-write the article so that it is suitable for kids aged around 8.

import streamlit as st import asyncio from agen…

Ideally, we want the reward model to generate positive values for good responses and negative values for bad responses.

It is necessary to pass both the initial prompt and the generated response as input to the reward model.

R₊ refers to the reward assigned to the better response while R₋ is a reward estimated for the worse response.

Training an original LLMThe trained reward model is then used to train the original LLM.

Then the input prompts, along with the output sequences, are fed to the reward model to estimate how good those responses are.

import dspy llm = dspy.LM('ollama_chat/llama3.2', api_base='http://localhost:11434', api_key='', temperature = 0.3) dspy.configure(lm=llm)We have our language model set up.

# { # "answer": "{answer} # note: the value you produce must be a single int value" # } # In adhering to this structure, your objective is: # Given the fields `question`, produce the fields `answer`.

# Respond with a JSON object in the following order of fields: `answer` # (must be formatted as a valid Python int).

# { # "reasoning": "{reasoning}", # "answer": "{answer} # note: the value you produce must be a single int value" # } # In adhering to this structure, your objective is: # Given the fields `question`, produce …

The source data set for each dashboard is the same, but stored in different formats.

According to its website, Taipy is …“… an open-source Python library for building production-ready front-end & back-end in no time.

This tabular representation of the chosen data effectively views the underlying CSV data file.

Make sure your source data file is in the correct location and referenced correctly in your code.

SummaryIn this article, I’ve attempted to provide a comprehensive guide to building an interactive sales performance dashboard with Taipy using a CSV file as its source data.

N8n is a source-available, low-code workflow automation platform that combines AI capabilities with business process automation.

Click on “Open instance” and then “Create Workflow”.

Image by the authorEvery workflow in n8n starts with a trigger, which determines when the workflow should run.

In this example, choose the Schedule, so the assistant will run every day at 8 a.m., right before you start your day.

However, since our workflow is simple, I’ll choose “Set Automatically” and leave the default description.

The thing that most resonated with me was when Jeffrey advised against replacing BLANKs with zeroes (or whatever explicit values) in Power BI calculations.

I’ve already written how you can handle BLANKs and replace them with zeroes, but in this article, I want to focus on the possible performance implications of this decision.

Setting the stageBefore we start, one important disclaimer: the recommendation not to replace BLANK with 0 is just that — a recommendation.

Why am I seeing all the products, when I filtered everything out, except product 364?

This is a simplistic overview of what happened:Image by authorBelieve it or not, there is an elegant solution to show blank values out-of-the-bo…

Despite the hardware having better compute performance, data movement can often become the bottleneck.

Roofline Modeling provides a visual, intuitive way to understand application performance, showing key characteristics like Operational Intensity, GPU capabilities, and attainable FLOP/s.

Roofline Modeling in ActionTo perform Roofline Modeling, we need to profile the application to understand the performance.

profiler.start() self.conv2(x) profiler.stop()LLMs and Roofline ModelingComing to the topic everyone has been waiting for – does Roofline Modeling help with LLM performance calculation?

ConclusionThe Roofline Model offers a powerful visual analysis of application performance optimizati…

I broke down scene understanding into several layers, detection, semantics, scene classification, and language generation.

First, good system design doesn’t come from stacking features, it comes from understanding the real problem deeply.

These modules are responsible for different aspects of scene understanding, from spatial layout and lighting conditions to semantic description and model coordination.

📖 Multimodal AI System Design SeriesThis article marks the beginning of a series that explores how I approached building a multimodal AI system, from early design concepts to major architectural shifts.

Through developing VisionScout, I’ve learned many valuable lessons about multimodal AI ar…

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,…

Mathematics and statistics, once the primary guides of machine learning research, now struggle to provide immediate insight into the latest breakthroughs.

This shift has prompted speculation about mathematics’ diminished role in machine learning research moving forward.

It is also the way that symmetries are usually leveraged when performing computations (for example, in machine learning).

One can reasonably argue that diagrammatic descriptions of well-known constructions, like products, are not useful for the machine learning researcher.

However, as we’ve demonstrated, while mathematics may not maintain the same role in machine learning research that it has held in the past, the success of…

Let's jump back to the 1970s, when Roger Schank introduced his "restaurant script" as a kind of dialogue system [1].

The minimum requirement we could have for a dialogue system is that it can stay on the task we gave them.

Concluding marksI have reviewed the making of current LLM dialogue systems, how and why it is insufficient.

The following are two research questions that I’m mostly excited about:(1) Better monitoring and control of dialogue systems with steering techniques.

CitationFor attribution of this in academic contexts or books, please cite this work as:Kenneth Li, "From prediction to purpose: a tutorial on LLM dialogue system", The Gradient, 2024.

Created Using GPT-4oToday we will Discuss:An overview of software engineering benchmarks.

A review of the SWE-Benchmark, the gold standard of software engineering AI evals.

💡 AI Concept of the Day: Software Engineering AI BenchmarksAs large language models (LLMs) find their way into software development workflows, the need for rigorous benchmarks to evaluate their coding capabilities has grown rapidly.

Today, software engineering benchmarks go far beyond simple code generation.

Built from real GitHub issues and corresponding pull requests, SWE-bench tasks models with generating code changes that resolve bugs and pass unit tests.

A debate about reasoning in AI models works like system1-system2.

A review of the MCP-Use framework to integrate with MCP serversYou can subscribe to The Sequence below:📝 Editorial: MiniMax-M1 is a Very Impressive ModelAlgorithmic innovation is always interesting when comes to LLMs.

Last week, we had a very interesting release of a highly innovative model that flew a bit under the radar.

MiniMax-M1 is a new 456B parameter model that redefines efficiency and scale for open-weight models.

In combination, this hybrid architecture allows the model to handle up to 1 million tokens of context natively.

Created Using GPT-4oHave you ever heard of V-JEPA?

This is one of the models that encompass Meta AI’s vision of AGI.

Meta AI's release of V-JEPA 2 (Visual Joint Embedding Predictive Architecture 2) marks a significant evolution in the domain of self-supervised learning and world modeling.

As a successor to the original V-JEPA framework introduced by Yann LeCun and collaborators, V-JEPA 2 extends the paradigm by enhancing architectural scale, pretraining methodology, and semantic abstraction capabilities.

Built upon the theoretical vision of autonomous systems that learn predictive models of the world without labeled supervision, V-JEPA 2 offers a glimpse into a future where embodied AI can …

Created Using GPT-4oMechanistic interpretability—the study of how neural networks internally represent and compute—seeks to illuminate the opaque transformations learned by modern models.

This phenomenon of polysemanticity complicates efforts to reverse-engineer networks and has led to a key theoretical insight: the superposition hypothesis.

The superposition hypothesis proposes that neural networks are not built around one-neuron-per-feature mappings, but rather represent features as directions in high-dimensional activation spaces.

Neural networks, constrained by finite width and encouraged by sparsity in data, adopt a compressed representation strategy in which meaning is woven through a…

While these systems can write and debug software, analyze repositories, and generate full applications, they require an environment where the resulting code can be executed safely, efficiently, and reproducibly.

Running untrusted, AI-generated code directly on host machines is a recipe for disaster.

To address this, a new wave of AI-focused sandbox environments has emerged.

These systems offer secure, fast, and scalable execution for agentic workflows, providing a foundational layer for the next generation of AI-driven development.

This essay explores the key platforms in this space, focusing on E2B, Daytona, Modal, and CodeSandbox, and unpacks their architecture, features, and benefits.

Created Using GPT-4oToday we will Discuss:An overview of AGI benchmarks.

Artificial General Intelligence (AGI) benchmarks are indispensable tools for evaluating the reasoning, adaptability, and problem-solving abilities of AI systems.

Unlike narrow AI benchmarks that focus on domain-specific tasks, AGI benchmarks measure the capacity for generalization across a wide array of challenges.

This essay explores key AGI benchmarks that are shaping the future of intelligent systems, emphasizing their significance and unique testing methodologies.

AGI benchmarks are designed to stress-test models' abilities to adapt, reason, and learn from minimal supervision.

You can subscribe to The Sequence below:📝 Editorial: The Gentle Singularity Is Already HereIn a recent and quietly radical blog post titled "The Gentle Singularity," OpenAI CEO Sam Altman dropped a thesis that reads more like a plot twist than a prediction: the singularity isn’t coming—it’s already arrived.

What makes this singularity "gentle" is its deceptive normalcy.

A gentle singularity doesn’t mean a safe one.

🔎 AI ResearchLab: FAIR at Meta + Mila / Polytechnique MontréalV-JEPA 2 is a large-scale self-supervised video model trained on over 1 million hours of internet video.

o3-ProOpenAI released o3-pro, a new version of its o3 model optimized for longer reasoning tasks.

Created Using GPT-4oI had different plans for this week’s research section but that Apple Research paper completely changed the schedule.

The Illusion of Thinking is causing quite a bit of controversy in the AI community by challenging some of the core assumptions about LLMs: are they able to reason?

Recent progress in LLMs has introduced a new class of systems known as Large Reasoning Models (LRMs).

These models explicitly generate intermediate thinking steps—such as Chain-of-Thought (CoT) reasoning and self-reflection—before providing an answer.

While they outperform standard LLMs on some benchmarks, this paper, "The Illusion of Thinking" challenges prevailing assumptions about their reas…

Created Using GPT-4oLLMs regularly dazzle us with words but can they operate in real world environments.

Today, we would like to deep dive into one of the most fascinating frontier of AI: spatial intelligence and world models.

Unlike LLMs that operate in purely symbolic spaces, spatially-grounded AI systems build internal models of their environments, enabling them to navigate, manipulate, and reason about the world around them.

This shift from language to space marks a foundational evolution in AI: one where models are not just intelligent about the world, but also in it.

This essay explores the emergence of spatially-grounded world models, with a focus on key architectural innovations, em…

Created Using GPT-4oDeerFlow (Deep Exploration and Efficient Research Flow) is an open-source multi-agent research automation framework developed by ByteDance and released under the MIT license in 2025.

Designed to address the increasing demand for scalable, auditable, and extensible research workflows, DeerFlow goes beyond the conventional single-agent LLM wrappers.

It implements a graph-based orchestration of specialized agents that automate research pipelines end-to-end.

Whether the task involves web search, data analysis, report generation, or podcast creation, DeerFlow delivers structured and multimodal outputs with minimal human intervention.

This essay explores DeerFlow's architectur…

Created Using GPT-4oToday we will Discuss:An overview of agentic benchmarks.

An intro to the amazing Web Arena benchmark for evaluating agents in web tasks.

💡 AI Concept of the Day: Getting Into Agentic BenchmarksAs AI evolves from static predictors to autonomous agents, there is a growing need for benchmarks that assess more than just input-output accuracy.

Traditional evaluations in language, vision, or code are inadequate for systems that plan, act, and adapt in dynamic environments.

Agentic AI benchmarks aim to fill this gap by evaluating models as decision-making entities capable of navigating complex tasks.

The first, Darwin Gödel Machine: Open-Ended Evolution of Self-Improving Agents, presents one of the most credible instantiations yet of recursive self-modifying agents.

The second, How Much Do Language Models Memorize?, introduces a principled and practically measurable framework for assessing the memorization capacity of modern LLMs.

Meanwhile, How Much Do Language Models Memorize?

Together, these two papers reveal opposite yet deeply intertwined aspects of AI model development.

How Much Do Language Models Memorize?

Created Using GPT-4oReinforcement learning has established itself as a key technique to enhance the capabilities of large language models (LLMs), particularly in complex reasoning tasks.

Established approaches such as Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning with Verifiable Rewards (RLVR) have delivered impressive results, aligning models with human preferences and improving factual correctness through testable reward structures.

In response to these limitations, the paper "Learning to Reason without External Rewards" proposes a radically different paradigm: Reinforcement Learning from Internal Feedback (RLIF).

This approach enables LLMs to learn from the…

Created Using GPT-4oInterpretability of advanced AI models has become a critical and thorny challenge as we reach the frontier of scale and capability.

We survey new techniques pushing the boundaries of interpretability, including mechanistic interpretability efforts and circuits-based analyses pioneered by organizations like Anthropic, along with automated approaches that enlist AI itself to explain AI.

We explore the provocative thesis that truly understanding frontier models may require a meta-model – an AI specifically designed to interpret other AI models.

Modern frontier models like GPT-4, Claude, and Gemini-1.5 operate with billions of parameters and exhibit emergent capabilities tha…

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

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

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

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

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

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

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

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

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

Continue reading on SyncedReview »

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

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

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

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

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

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

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

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

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

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

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

Можно и фразу.

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

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

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

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

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

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

В этой статье мы разберемся, чему научилась новая GPT o1, и как это повлияет на дальнейшую эволюцию ИИ.

Компания утверждает, что для них сброс счётчика линейки моделей к единичке знаменует собой переход к новой парадигме, и что эта нейросеть и вовсе демонстрирует новый уровень возможностей ИИ.

На форумах и в Твиттере была куча обсуждений, предвосхищений и хайпа, на фоне которых планка ожиданий некоторых людей взлетела до небес.

Издание Bloomberg рассказало, что в ходе внутренней демонстрации OpenAI показали концепцию из пяти уровней, помогающую отслеживать прогресс в создании ИИ.

Однако на уровне GPT-5 прирост в навыках может быть совсем другим (как в лучшую, так и в худшую сторону).

И в том, и в другом случаях объяснение действия не связано с реальным мотивом его сделать, и там, и там рождается поддельное (но правдоподобно звучащее) объяснение причин.

Просто сейчас это не воспринимается всерьёз, ведь LLM не распространены и не становятся ядром бизнес-процессов, включающих принятие решений.

Один и тот же текст запроса+ответа подаётся в модель, и производится оценка вероятности получить именно такой ответ при фиксированном запросе.

Это и желание продолжать существовать/жить, и нежелание умирать, и рассуждения об эмоциях и контроле.

Потому что абстракции, потому что обобщение, потому что это ровно то, за что мы ценим модели.

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

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

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

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

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

This has spoilers for MIT Mystery Hunt 2025.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Point processes and Markov ChainsBack to Markov chains.

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

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

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

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

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

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

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

The authoring experience is much more convenient for me there.

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

And check out our How Transformer LLMs Work course!

Superficially this looks fine, but of course the problem is hidden in how do we arrive with these equivalences?

Let's take a closer look: Kurzweil claims we should be seeing insect brain capability in $1000 computer in 2001.

Alright what would we expect from an insect brain?

It's 2024 at the time of writing this, and the best military technology that could benefit from an insect brain like capabilities is still on remote control.

That is of course if we actually get an insect brain this year.

Image restoration tasks like deblurring, denoising, and dehazing usually need distinct models for each degradation type, restricting their generalization in real-world scenarios with mixed or unknown degradations.

In this work, we propose \textbf{Defusion}, a novel all-in-one image restoration framework that utilizes visual instruction-guided degradation diffusion.

Unlike existing methods that rely on task-specific models or ambiguous text-based priors, Defusion constructs explicit \textbf{visual instructions} that align with the visual degradation patterns.

These instructions are grounded by applying degradations to standardized visual elements, capturing intrinsic degradation features whi…

To better understand the impact of microstructure on macroscopic response, multiscale modeling strategies are essential.

Numerical methods, such as the $\text{FE}^2$ approach, account for micro-macro interactions to predict the global response in a concurrent manner.

In this work, we employ neural operators to predict the microscale physics, resulting in a hybrid model that combines data-driven and physics-based approaches.

We apply this method to time-dependent solid mechanics problems involving viscoelastic material behavior, where the state is represented by internal variables only at the microscale.

The constitutive relations of the microscale are incorporated into the model architectur…

Effectively scaling up deep reinforcement learning models has proven notoriously difficult due to network pathologies during training, motivating various targeted interventions such as periodic reset and architectural advances such as layer normalization.

Instead of pursuing more complex modifications, we show that introducing static network sparsity alone can unlock further scaling potential beyond their dense counterparts with state-of-the-art architectures.

This is achieved through simple one-shot random pruning, where a predetermined percentage of network weights are randomly removed once before training.

Our analysis reveals that, in contrast to naively scaling up dense DRL networks, s…

We investigate a general approach for improving user prompts in text-to-image (T2I) diffusion models by finding prompts that maximize a reward function specified at test-time.

Although diverse reward models are used for evaluating image generation, existing automated prompt engineering methods typically target specific reward configurations.

Consequently, these specialized designs exhibit suboptimal performance when applied to new prompt engineering scenarios involving different reward models.

To address this limitation, we introduce RATTPO (Reward-Agnostic Test-Time Prompt Optimization), a flexible test-time optimization method applicable across various reward scenarios without modificatio…

While this process largely relies on expert knowledge, recent methods leverage reinforcement learning (RL) to enhance this filtering.

They achieve this by estimating proxy reward functions from available datasets and using regularization to generate more diverse candidates.

To remedy this issue, we take a robust RL approach and introduce a unified operator that seeks robustness to the uncertainty of the proxy reward function.

This general operator targets peakier sampling distributions while encompassing known soft RL operators.

Ultimately, our work offers a new, flexible perspective on discrete compositional generation tasks.

Multimodal large language models (MLLMs) have begun to demonstrate robust reasoning capabilities on general tasks, yet their application in the medical domain remains in its early stages.

However, existing approaches exhibit a deficiency in offering a comprehensive framework for searching and evaluating effective reasoning paths towards critical diagnosis.

The reasoning performance is determined by an MICS-Score, which assesses the quality of generated reasoning paths.

Eventually, we construct MMRP, a multi-task medical reasoning dataset with ranked difficulty, and Chiron-o1, a new medical MLLM devised via a curriculum learning strategy, with robust visual question-answering and generalizab…

This paper introduces TransDreamerV3, a reinforcement learning model that enhances the DreamerV3 architecture by integrating a transformer encoder.

The model is designed to improve memory and decision-making capabilities in complex environments.

We conducted experiments on Atari-Boxing, Atari-Freeway, Atari-Pong, and Crafter tasks, where TransDreamerV3 demonstrated improved performance over DreamerV3, particularly in the Atari-Freeway and Crafter tasks.

While issues in the Minecraft task and limited training across all tasks were noted, TransDreamerV3 displays advancement in world model-based reinforcement learning, leveraging transformer architectures.

PDFAbstract

Reconstructing the attractors of complex nonlinear dynamical systems from available measurements is key to analyse and predict their time evolution.

Here, we propose the use of Homogeneous Differentiators (HD) to effectively de-noise measurements and more faithfully reconstruct attractors of nonlinear systems.

Homogeneous Differentiators are supported by rigorous theoretical guarantees about their de-noising capabilities, and their results can be fruitfully combined with time-delay embedding, differential embedding and functional observability.

We apply our proposed HD-based methodology to simulated dynamical models of increasing complexity, from the Lorenz system to the Hindmarsh-Rose mode…

We present RAGentA, a multi-agent retrieval-augmented generation (RAG) framework for attributed question answering (QA).

With the goal of trustworthy answer generation, RAGentA focuses on optimizing answer correctness, defined by coverage and relevance to the question and faithfulness, which measures the extent to which answers are grounded in retrieved documents.

RAGentA uses a multi-agent architecture that iteratively filters retrieved documents, generates attributed answers with in-line citations, and verifies completeness through dynamic refinement.

Evaluated on a synthetic QA dataset derived from the FineWeb index, RAGentA outperforms standard RAG baselines, achieving gains of 1.09% in…

Rapid evolution of sensors and resource-efficient machine learning models have spurred the widespread adoption of wearable fitness tracking devices.

In contrast, Federated Learning (FL) enables a decentralized model training by communicating model updates rather than private wearable sensor data.

Additionally, to illustrate its usage, this paper presents a case study that implements a system based on the FedFitTech baseline, incorporating a client-side early stopping strategy and comparing the results.

For instance, this system allows wearable devices to optimize the trade-off between capturing common fitness activity patterns and preserving individuals' nuances, thereby enhancing both the …

Foundation models have revolutionized music information retrieval, but questions remain about their ability to generalize across diverse musical traditions.

This paper presents a comprehensive evaluation of five state-of-the-art audio foundation models across six musical corpora spanning Western popular, Greek, Turkish, and Indian classical traditions.

Notably, our approaches achieve state-of-the-art performance on five out of six evaluated datasets, demonstrating the effectiveness of foundation models for world music understanding.

We also find that our targeted fine-tuning approach does not consistently outperform probing across all settings, suggesting foundation models already encode su…

Benchmark datasets for network intrusion detection commonly rely on synthetically generated traffic, which fails to reflect the statistical variability and temporal drift encountered in operational environments.

This paper introduces MAWIFlow, a flow-based benchmark derived from the MAWILAB v1.1 dataset, designed to enable realistic and reproducible evaluation of anomaly detection methods.

Empirical results demonstrate that tree-based classifiers perform well on temporally static data but experience significant performance degradation over time.

These findings underscore the limitations of synthetic benchmarks and static models, and motivate the adoption of realistic datasets with explicit …

This study investigates how on-demand LLM-generated explanatory feedback influences learning in seven scenario-based tutor training lessons.

Analyzing over 2,600 lesson completions from 885 tutor learners, we compare posttest performance among learners across three groups: learners who received feedback generated by gpt-3.5-turbo, those who declined it, and those without access.

Learners with a higher predicted likelihood of engaging with LLM feedback scored significantly higher at posttest than those with lower propensity.

These moderate effects suggest that the effectiveness of LLM feedback depends on the learners' tendency to seek support.

Importantly, LLM feedback did not significantly …

Global localization is necessary for autonomous operations on the lunar surface where traditional Earth-based navigation infrastructure, such as GPS, is unavailable.

To help overcome odometry drift over long traverses, we propose LunarLoc, an approach to global localization that leverages instance segmentation for zero-shot extraction of boulder landmarks from onboard stereo imagery.

This method enables accurate and drift-free global localization in visually ambiguous settings.

LunarLoc achieves sub-cm level accuracy in multi-session global localization experiments, significantly outperforming the state of the art in lunar global localization.

To encourage the development of further methods…

While recent works such as GaLore, Fira, and APOLLO have proposed state-compressed variants to reduce memory consumption, a fundamental question remains: What is the minimal amount of optimizer state that is truly necessary to retain state-of-the-art performance in LLM pretraining?

Across multiple LLaMA models (60M-1B), SCALE matches or exceeds the performance of Adam while using only 35-45% of the total memory.

It also consistently outperforms memory-efficient optimizers such as GaLore, Fira, and APOLLO, making it a strong candidate for large-scale pretraining under memory constraints.

For the LLaMA 7B model, SCALE outperforms the state-of-the-art method APOLLO in terms of both perplexity …

Recent advancements in video diffusion models based on Diffusion Transformers (DiTs) have achieved remarkable success in generating temporally coherent videos.

Yet, a fundamental question persists: how do these models internally establish and represent temporal correspondences across frames?

Our analysis reveals that query-key similarities in specific, but not all, layers play a critical role in temporal matching, and that this matching becomes increasingly prominent during the denoising process.

We demonstrate practical applications of DiffTrack in zero-shot point tracking, where it achieves state-of-the-art performance compared to existing vision foundation and self-supervised video model…

There now exists many popular object detectors based on deep learning that can analyze images and extract locations and class labels for occurrences of objects.

For image time series (i.e., video or sequences of stills), tracking objects over time and preserving object identity can help to improve object detection performance, and is necessary for many downstream tasks, including classifying and predicting behaviors, and estimating total abundances.

Here we present yasmot, a lightweight and flexible object tracker that can process the output from popular object detectors and track objects over time from either monoscopic or stereoscopic camera configurations.

In addition, it includes functi…

Vision-language models (VLMs) excel at multimodal understanding, yet their text-only decoding forces them to verbalize visual reasoning, limiting performance on tasks that demand visual imagination.

To this end, we present a Machine Mental Imagery framework, dubbed as Mirage, which augments VLM decoding with latent visual tokens alongside ordinary text.

Begin by supervising the latent tokens through distillation from ground-truth image embeddings, we then switch to text-only supervision to make the latent trajectory align tightly with the task objective.

A subsequent reinforcement learning stage further enhances the multimodal reasoning capability.

Experiments on diverse benchmarks demonstr…

Medical image analysis is critical yet challenged by the need of jointly segmenting organs or tissues, and numerous instances for anatomical structures and tumor microenvironment analysis.

Existing studies typically formulated different segmentation tasks in isolation, which overlooks the fundamental interdependencies between these tasks, leading to suboptimal segmentation performance and insufficient medical image understanding.

To address this issue, we propose a Co-Seg++ framework for versatile medical segmentation.

Specifically, we introduce a novel co-segmentation paradigm, allowing semantic and instance segmentation tasks to mutually enhance each other.

Moreover, we devise a multi-tas…

LaTeX's precision and flexibility in typesetting have made it the gold standard for the preparation of scientific documentation.

Large Language Models (LLMs) present a promising opportunity for researchers to produce publication-ready material using LaTeX with natural language instructions, yet current benchmarks completely lack evaluation of this ability.

By introducing TeXpert, our benchmark dataset with natural language prompts for generating LaTeX code focused on components of scientific documents across multiple difficulty levels, we conduct an in-depth analysis of LLM performance in this regard and identify frequent error types.

Our evaluation across open and closed-source LLMs highli…

This paper presents the IT-IST submission to the IWSLT 2025 Shared Task on Instruction Following Speech Processing.

We submit results for the Short Track, i.e., speech recognition, translation, and spoken question answering.

Our model is a unified speech-to-text model that integrates a pre-trained continuous speech encoder and text decoder through a first phase of modality alignment and a second phase of instruction fine-tuning.

Crucially, we focus on using small-scale language model backbones (< 2B) and restrict to high-quality, CC-BY data along with synthetic data generation to supplement existing resources.

PDFAbstract

To tackle this challenge, we introduce MEXA, a training-free framework that performs modality- and task-aware aggregation of multiple expert models to enable effective multimodal reasoning across diverse and distinct domains.

MEXA then aggregates and reasons over these outputs using a Large Reasoning Model (LRM) to produce the final answer.

This modular design allows flexible and transparent multimodal reasoning across diverse domains without additional training overhead.

We extensively evaluate our approach on diverse multimodal benchmarks, including Video Reasoning, Audio Reasoning, 3D Understanding, and Medical QA.

MEXA consistently delivers performance improvements over strong multimoda…

Despite progress in controllable symbolic music generation, data scarcity remains a challenge for certain control modalities.

Composer-style music generation is a prime example, as only a few pieces per composer are available, limiting the modeling of both styles and fundamental music elements (e.g., melody, chord, rhythm).

First, we pre-train a REMI-based music generation model on a large corpus of pop, folk, and classical music.

Experimental results demonstrate that our method outperforms ablations and baselines, achieving more precise composer-style modeling and better musical aesthetics.

Additionally, we provide observations on how the model builds music concepts from the generality pre…

Sample efficiency is a crucial property of language models with practical implications for training efficiency.

This study analyzes multiple models of varying architectures and sizes, all trained on the same pre-training data.

By annotating relational facts with their frequencies in the training corpus, we examine how model performance varies with fact frequency.

Our findings show that most models perform similarly on high-frequency facts but differ notably on low-frequency facts.

This analysis provides new insights into the relationship between model architecture, size, and factual learning efficiency.

Driving scene identification, which assigns multiple non-exclusive class labels to a scene, provides the contextual awareness necessary for enhancing autonomous vehicles' ability to understand, reason about, and interact with the complex driving environment.

However, directly training a multi-label classification model for driving scene identification through multitask learning presents two main challenges: acquiring a balanced, comprehensively annotated multi-label dataset and balancing learning across different tasks.

This paper introduces a novel learning system that synergizes knowledge acquisition and accumulation (KAA) with consistency-based active learning (CAL) to address those chal…

Unified image understanding and generation has emerged as a promising paradigm in multimodal artificial intelligence.

Despite recent progress, the optimal architectural design for such unified models remains an open challenge.

In this work, we start by analyzing the modality alignment behaviors of task-specific expert models for understanding and generation, as well as current unified models.

Our analysis reveals a crucial observation: understanding tasks benefit from a progressively increasing modality alignment across network depth, which helps build up semantic information for better comprehension; In contrast, generation tasks follow a different trend: modality alignment increases in th…

Cloud contamination significantly impairs the usability of optical satellite imagery, affecting critical applications such as environmental monitoring, disaster response, and land-use analysis.

This research presents a Cloud-Attentive Reconstruction Framework that integrates SAR-optical feature fusion with deep learning-based image reconstruction to generate cloud-free optical imagery.

The proposed framework employs an attention-driven feature fusion mechanism to align complementary structural information from Synthetic Aperture Radar (SAR) with spectral characteristics from optical data.

Furthermore, a cloud-aware model update strategy introduces adaptive loss weighting to prioritize cloud…

Generating efficient and modality-specific representations for each utterance remains a significant challenge.

To address these challenges, we propose the Multi-modal Anchor Gated Transformer with Knowledge Distillation (MAGTKD) for the ERC task.

Specifically, prompt learning is employed to enhance textual modality representations, while knowledge distillation is utilized to strengthen representations of weaker modalities.

Furthermore, we introduce a multi-modal anchor gated transformer to effectively integrate utterance-level representations across modalities.

Extensive experiments on the IEMOCAP and MELD datasets demonstrate the effectiveness of knowledge distillation in enhancing modalit…

Recent advancements in large reasoning models (LRMs) like DeepSeek-R1 and OpenAI o1 series have achieved notable performance enhancements on complex reasoning tasks by scaling up the generation length by Chain-of-Thought (CoT).

However, an emerging issue is their inclination to produce excessively verbose reasoning processes, leading to the inefficiency problem.

In order to fill the blank, we propose a framework dubbed ConciseHint, which continuously encourages the reasoning model to speak concisely by injecting the textual hint (manually designed or trained on the concise data) during the token generation of the reasoning process.

Besides, ConciseHint is adaptive to the complexity of the q…

This paper presents two key contributions to the real-time classification of Instrumental Playing Techniques (IPTs) in the context of NIME and human-machine interactive systems: the EG-IPT dataset and the ipt∼ Max/MSP object.

The EG-IPT dataset, specifically designed for electric guitar, encompasses a broad range of IPTs captured across six distinct audio sources (five microphones and one direct input) and three pickup configurations.

The ipt∼ object is a new Max/MSP external enabling real-time classification of IPTs via pre-trained CNN models.

While in this paper it's demonstrated with the EG-IPT dataset, the ipt∼ object is adaptable to models trained on various instruments.

By integrating…

In March, we introduced Gemini Robotics, our most advanced VLA (vision language action) model, bringing Gemini 2.0’s multimodal reasoning and real-world understanding into the physical world.

Today, we’re introducing Gemini Robotics On-Device, our most powerful VLA model optimized to run locally on robotic devices.

Gemini Robotics On-Device shows strong general-purpose dexterity and task generalization, and it’s optimized to run efficiently on the robot itself.

Model capabilities and performanceGemini Robotics On-Device is a robotics foundation model for bi-arm robots, engineered to require minimal computational resources.

It builds on the task generalization and dexterity capabilities of G…

Explore the latest Gemini 2.5 model updates with enhanced performance and accuracy: Gemini 2.5 Pro now stable, Flash generally available, and the new Flash-Lite in preview.

We designed Gemini 2.5 to be a family of hybrid reasoning models that provide amazing performance, while also being at the Pareto Frontier of cost and speed.

Today, we’re taking the next step with our 2.5 Pro and Flash models by releasing them as stable and generally available.

And we’re bringing you 2.5 Flash-Lite in preview — our most cost-efficient and fastest 2.5 model yet.

Making 2.5 Flash and 2.5 Pro generally availableThanks to all of your feedback, today we’re releasing stable versions of 2.5 Flash and Pro, so you can build production applications with confidence.

Introducing Gemini 2.5 Flash-LiteWe’re also introducing a preview of the new Gemini 2.5 Flash-Lite, our most cost-effici…

Today, Eliza McNitt’s short film, “ANCESTRA,” premieres at the Tribeca Festival.

It’s the story of a mother, and what happens when her child is born with a hole in its heart.

Inspired by the dramatic events of McNitt's own birth, the film portrays a mother's love as a cosmic, life-saving force.

Together, we founded this partnership to put the world’s best generative AI into the hands of top filmmakers, to advance the frontiers of storytelling and technology.

“ANCESTRA” combined live-action scenes with sequences generated by Veo, our state-of-the-art video generation model.

Research How we're supporting better tropical cyclone prediction with AI ShareCopy link ×We’re launching Weather Lab, featuring our experimental cyclone predictions, and we’re partnering with the U.S. National Hurricane Center to support their forecasts and warnings this cyclone season.

Yet, improving the accuracy of cyclone predictions can help protect communities through more effective disaster preparedness and earlier evacuations.

Today, Google DeepMind and Google Research are launching Weather Lab, an interactive website for sharing our artificial intelligence (AI) weather models.

Weather Lab’s live and historical cyclone predictions Weather Lab shows live and historical cyclone predict…

We’re launching Weather Lab, featuring our experimental cyclone predictions, and we’re partnering with the U.S. National Hurricane Center to support their forecasts and warnings this cyclone season.

Gemini 2.5 has new capabilities in AI-powered audio dialog and generation.

Safety and responsibilityWe’ve proactively assessed potential risks throughout every stage of the development process for these native audio features, using what we’ve learned to inform our mitigation strategies.

Additionally, all audio outputs from our models are embedded with SynthID, our watermarking technology, to ensure transparency by making AI-generated audio identifiable.

Native audio capabilities for developersWe’re bringing native audio outputs to Gemini 2.5 models, giving developers new capabilities to build richer, more interactive applications via the Gemini API in Google AI Studio or Vertex AI.

To begin exploring, developers can try native audio dialog with Gemini 2.5 Flash pr…

Today, we’re announcing our newest generative media models, which mark significant breakthroughs.

These models create breathtaking images, videos and music, empowering artists to bring their creative vision to life.

Veo 3 and Imagen 4, our newest video and image generation models, push the frontier of media generation, with their groundbreaking new capabilities.

We're also expanding access to Lyria 2, giving musicians more tools to create music.

Using Google DeepMind’s most advanced models, Flow lets you weave cinematic films with more sophisticated control of characters, scenes and styles, to bring your story to life.

Today we’re announcing SynthID Detector, a verification portal to quickly and efficiently identify AI-generated content made with Google AI.

While originally focused on AI-generated imagery only, we’ve since expanded SynthID to cover AI-generated text, audio and video content, including content generated by our Gemini, Imagen, Lyria and Veo models across Google.

How SynthID Detector worksWhen you upload an image, audio track, video or piece of text created using Google's AI tools, the portal will scan the media for a SynthID watermark.

If a watermark is detected, the portal will highlight specific portions of the content most likely to be watermarked.

For audio, the portal pinpoints specifi…

Gemma 3n is a cutting-edge open model designed for fast, multimodal AI on devices, featuring optimized performance, unique flexibility with a 2-in-1 model, and expanded multimodal understanding with audio, empowering developers to build live, interactive applications and sophisticated audio-centric experiences.

Responsibility & Safety Advancing Gemini's security safeguards ShareCopy link ×We’re publishing a new white paper outlining how we’ve made Gemini 2.5 our most secure model family to date.

Indirect prompt injection presents a real cybersecurity challenge where AI models sometimes struggle to differentiate between genuine user instructions and manipulative commands embedded within the data they retrieve.

Pause video Play videoEvaluating baseline defense strategies Indirect prompt injection attacks are complex and require constant vigilance and multiple layers of defense.

Google DeepMind’s Security and Privacy Research team specialises in protecting our AI models from deliberate, malicious att…

We’ve applied these techniques to make breakthroughs in quantum computing, mathematics, life sciences and algorithmic discovery.

And we continue to double down on the breadth and depth of our fundamental research, working to invent the next big breakthroughs necessary for artificial general intelligence (AGI).

This is why we’re working to extend our best multimodal foundation model, Gemini 2.5 Pro, to become a “world model” that can make plans and imagine new experiences by understanding and simulating aspects of the world, just as the brain does.

Making Gemini a world model is a critical step in developing a new, more general and more useful kind of AI — a universal AI assistant.

This is a…

2.5 Pro performs better than everWe recently updated 2.5 Pro to help developers build richer, interactive web apps.

And, with its 1 million-token context window, 2.5 Pro has state-of-the-art long context and video understanding performance.

Since incorporating LearnLM, our family of models built with educational experts, 2.5 Pro is also now the leading model for learning.

In head-to-head comparisons evaluating its pedagogy and effectiveness, educators and experts preferred Gemini 2.5 Pro over other models across a diverse range of scenarios.

Read more in our updated Gemini 2.5 Pro model card and on the Gemini technology page.

An agent-to-agent protocol for deeper collaborationBox is championing an open AI ecosystem by embracing Google Cloud's Agent2Agent protocol, enabling all Box AI Agents to securely collaborate with diverse external agents from dozens of partners (a list that keeps growing).

By adopting the latest A2A specification, Box AI can ensure efficient and secure communication for complex, multi-system processes.

Google's Gemini 2.5 Pro: Provides the core text comprehension, reasoning, and generation; and in this enhanced protocol, Gemini models also aim to furnish deeper operational data (like token likelihoods) to its counterpart.

Furthering this commitment to an open and extensible ecosystem, Box A…

Despite these challenges, Hypros’ device represents a significant advancement in privacy-preserving patient monitoring, offering the potential to enhance hospital workflow efficiency and patient care without compromising individual privacy.

Patient monitoring with AI: Overcoming low-resolution data challengesWhile customized parametric algorithms can partially interpret sensor data, they have difficulty handling complex relationships and edge cases.

ML algorithms offer clear advantages, making AI a vital tool for a patient monitoring system.

In addition, manual data labeling can quickly become expensive as tight monitoring sends readings every few seconds, quickly producing large volumes of…

Challenges and mitigations for multi-class single-label video tasksUsing highly skewed data distributions may cause quality regression on the tuned model.

On the other hand, for several similar event types with dense time intervals, multi-class single-label recipes yield better model performance .

Prepare video tuning datasetThe Vertex Tuning API uses *.jsonl files for both training and validation datasets.

V. Set the hyperparameters for tuningAfter preparing your tuning dataset, you are ready to submit your first video tuning job!

With a dataset size of ~500 examples, starting with epochs = 5 is the default value for video tuning tasks.

New Gemini 2.5 Flash-Lite in public preview: Experience the cost-efficient Gemini 2.5 model yet with optimized performance for high-volume tasks.

Supervised Fine-Tuning (SFT) for Gemini 2.5 Flash: Customized AI for your businessAchieve unparalleled customization with the GA release of Supervised Fine-Tuning (SFT) for Gemini 2.5 Flash on Vertex AI.

We are also introducing preview pricing for Gemini 2.5 Flash-Lite, our most cost efficient Gemini 2.5 model yet.

For complete details on pricing for Gemini 2.5 Flash, Gemini 2.5 Pro, and the Gemini 2.5 Flash-Lite preview, please visit our pricing page.

Start moving to production today with Gemini 2.5 Flash and Gemini 2.5 Pro, now generally availab…

Today, we're incredibly proud to announce the graduation of the latest cohort from the Google for Startups Accelerator: AI First from Europe & Israel!

This milestone marks the culmination of an intensive three-months journey for these 14 innovative startups, who've dedicated themselves to growing their businesses and pushing the boundaries of artificial intelligence.

The hybrid program offered expert mentorship, robust technical support, and access to a powerful global network, empowering founders to scale their impact.

“With Google’s support, we brought our AI recruitment platform into its next generation — the most advanced in the world, with a business model built for $7M+ ARR within a y…

For example, consider the case where you are using accelerator chips to train a model that takes one month to complete.

Even with a somewhat smaller training workload, the cost savings with optimal checkpointing can be significant.

If you have a week-long training job spanning 1K VMs that cost $88/hour (a3-highgpu-8g), a 6.5% increase in Goodput on this training task could result in almost $1M in infrastructure savings.

More failures require more checkpointingProbabilistically, the mean time between failure (MTBF) of a training job decreases — failures happen more frequently — as the size of the cluster increases.

Therefore, it is important that foundation model producers take checkpoints m…

Know Your Customer (KYC) processes are foundational to any Financial Services Institution's (FSI) regulatory compliance practices and risk mitigation strategies.

Building robust AI agents is complex.

Google's Agent Development Kit (ADK) gives you essential tooling to build multi-agent workflows.

Plus, combining ADK with Search Grounding via Gemini can help give you higher fidelity and trustworthiness for tasks requiring external knowledge.

To that end, this post illustrates how Google Cloud's cutting-edge AI technologies - the Agent Development Kit (ADK), Vertex AI Gemini models, Search Grounding, and BigQuery - can be leveraged to build such a multi-agent KYC solution.

As AI moves from promising experiments to landing core business impact, the most critical question is no longer "What can it do?"

Ensuring the quality, reliability, and safety of your AI applications is a strategic imperative.

To guide you, evaluation must be your North Star—a constant process that validates your direction throughout the entire development lifecycle.

One year ago, we launched the Gen AI evaluation service, offering capabilities to evaluate various models including Google's foundation models, open models, proprietary foundation models, and customized models.

That's why today we're excited to dive into the new features of the Gen AI Evaluation Service, designed to help you sc…

Our goal is to provide a clear and actionable foundation for building secure and trustworthy AI agent systems that benefit society.

Agent actions and planning must be observable: Agent activities must be transparent and auditable through robust logging and clear action characterization.

Key risks, limitations, and challengesTraditional security paradigms, designed for static software or general AI, are insufficient for AI agents.

Response rendering: Safely rendering AI agent outputs into user-readable content is vital to prevent classic web vulnerabilities.

To learn more about how Google is approaching securing AI agents, please read our research paper.

Today, we’re excited to announce the preview of our new G4 VMs based on NVIDIA RTX PRO 6000 Blackwell Server edition — the first cloud provider to do so.

G4 VMs round out our 4th generation NVIDIA GPU portfolio and bring a new level of performance and flexibility to enterprises and creators.

G4 VMs also support the NVIDIA Dynamo inference framework to enable high-throughput, low-latency AI inference for generative models at scale.

For design and simulation workloads, G4 VMs support third-party engineering and graphics applications like Altair HyperWorks, Ansys Fluent, Autodesk AutoCAD, Blender, Dassault SolidWorks, and Unity.

Hyperdisk provides ultra-low latency and supports up to 500K IOPS…

Misstep 2: Underestimating collaboration design effortA critical error is under-resourcing the design of agent collaboration-particularly in defining roles, communication protocols , and conflict resolution strategies.

As MAS evolves, it's increasingly important to know what, when, and why a specialist agent should be engaged.

The best way to achieve this with MAS is by embedding responsible AI principles, including establishing clear rules, audit trails and transparency.

In one real-world scenario, AI handles complex offers but escalates edge cases, such as price-matching a competitor’s promotion, to a human for final judgment.

To ensure reliability, we conduct a variety of tests with our …

The pace of innovation in open-source AI is breathtaking, with models like Meta's Llama4 and DeepSeek AI's DeepSeek.

Today, we're excited to announce enhanced support and new, optimized recipes for the latest Llama4 and DeepSeek models, leveraging our cutting-edge AI Hypercomputer platform.

AI Hypercomputer helps build a strong AI infrastructure foundation using a set of purpose-built infrastructure components that are designed to work well together for AI workloads like training and inference.

In this blog, we’ll show you how to access Llama4 and DeepSeek models today on AI Hypercomputer.

To help simplify this process, we're releasing new recipes for serving Llama4 models on Google Cloud T…

First decision: No-code/low-code, or custom agents?

The first decision enterprises have to decide is: no-code/low-code options or build custom agents?

But if your use case requires orchestration of multiple agents and integration with custom tooling, you would have to build custom agents which leads to the next question.

That’s why in this blog, we center the discussion on building a custom agent using the open-sourced LangChain, LangGraph as an agentic framework, and Gemini 2.0 Flash as the LLM brain.

We have different agents (image analysis agent, audio analysis agent, and a video analysis agent) performing different tasks but all working together towards a common goal, object identificat…

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.

Today, we’re launching a limited-series podcast, AI Testing and Evaluation: Learnings from Science and Industry, to share insights from domains that have grappled with testing and measurement questions.

At the close of the podcast series, we’ll offer Microsoft’s deeper reflections on next steps toward more reliable and trustworthy approaches to AI evaluation.

While approaches to risk evaluation and testing vary significantly across the case studies, there was one consistent, top-level takeaway: evaluation frameworks always reflect trade-offs among different policy objectives, such as safety, efficiency, and innovation.

Experts across all eight fields noted that policymakers have had to weig…

We are excited to share our first big milestone in solving a grand challenge that has hampered the predictive power of computational chemistry, biochemistry, and materials science for decades.

For 60 years, people have designed practical approximations for the XC functional.

We can contrast the present state of computational chemistry with the state of aircraft engineering and design.

The result is Skala, an XC functional that generalizes to unseen molecules, reaching the accuracy needed to predict experiments.

At Flagship, we believe that openly shared, foundational advances in science – like this leap forward in DFT accuracy – can serve as powerful enablers of innovation.

To support this progress, we’ve identified three primary strategies to strengthen reasoning capabilities in both small and large language models: improve architectural design to boost performance in smaller models; incorporate mathematical reasoning techniques to increase reliability; and build stronger generalization capabilities to enable reasoning across a variety of fields.

Read more Opens in a new tabThe problem stems from how current language models operate.

rStar-Math is a method that uses Monte Carlo Tree Search (MCTS) to simulate deeper, more methodical reasoning in smaller models.

LIPS (LLM-based Inequality Prover with Symbolic Reasoning) is a system that combines LLMs’ pattern re…

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.

To address these vulnerabilities and improve memory safety, we’re rewriting SymCrypt (opens in new tab)—Microsoft’s open-source cryptographic library—in Rust.

For example, reasoning about C code often requires proving that two non-const pointers are live and non-overlapping, a property that can depend on external client code.

As a result, new tools have emerged specifically for verifying Rust code.

Some users compile our C code directly and may rely on specific toolchains or compiler features that complicate the adoption of Rust code.

Looking ahead, we plan to support direct use of the same cryptographic library in Rust without requiring C bindings.

To meet this need, we’re introducing BenchmarkQED, a new suite of tools that automates RAG benchmarking at scale, available on GitHub (opens in new tab).

AutoQ: Automated query synthesisThis limitation motivated the development of GraphRAG a system designed to answer global queries.

GraphRAG’s evaluation requirements subsequently led to the creation of AutoQ, a method for synthesizing these global queries for any dataset.

Synthesis process and example query for each of the four AutoQ query classes.

We hope these datasets, together with the BenchmarkQED tools (opens in new tab), help accelerate benchmark-driven development of RAG systems and AI question-answering.

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.

FrodoKEM is a key encapsulation mechanism (KEM) based on the Learning with Errors (LWE) problem, a cornerstone of lattice-based cryptography.

The Learning with Errors (LWE) problemThe LWE problem is a fundamental hard problem in lattice-based cryptography.

In other words, cryptanalysts and quantum researchers have not been able to devise an efficient quantum algorithm capable of solving the LWE problem and, hence, FrodoKEM.

ConclusionAfter years of research and analysis, the next generation of post-quantum cryptographic algorithms has arrived.

Further ReadingFor those interested in learning more about FrodoKEM, post-quantum cryptography, and lattice-based cryptography, the following resourc…

And single-cell foundation models claim to be capable of unraveling deeper insights than ever before.

Basically, we showed that single-cell foundation models perform worse in settings that are fundamental to biological discovery than much simpler machine learning and statistical methods that were used in the field before single-cell foundation models emerged and are the go-to standard for unpacking meaning from these complicated experiments.

And the way to understand this is because single-cell foundation models are trained in a way that tries to expose these models to millions of single-cells.

But let’s also talk about the impact for methodologists, people who are trying to improve these s…

This is such exciting work about environmental forecasting, so we’re happy to have the two of you join us today.

Mostly because AI weather forecasting models are computationally much more efficient and can even be more accurate.

What’s unfortunate though, about this big step forward, is that these developments are mostly limited to the setting of weather forecasting.

Weather forecasting is very important, obviously, but there are many other important environmental forecasting problems out there, such as air pollution forecasting or ocean wave forecasting.

STANLEY: Current approaches have really focused training very specifically on weather forecasting models.

LUNGREN: And now it really feels like this collaborative effort, you know, really can help start to extend that mission.

I think, you know, Will and Smitha, that we definitely feel the passion and the innovation.

Again, you know, in text, you refer to that earlier and certainly off the shelf, there’s really powerful applications.

LUNGREN: So, I think AI has always been thought of as a savior kind of technology.

And I guess for my part, I think really what we’re going to see is a massive unleash of creativity.

Magentic-UI seeks user approval before executing potentially irreversible actions, and the user can specify how often Magentic-UI needs approvals.

Magentic-UI seeks user approval before executing potentially irreversible actions, and the user can specify how often Magentic-UI needs approvals.

Figure 6: System architecture diagram of Magentic-UITo interact with Magentic-UI, users can enter a text message and attach images.

On the validation subset of GAIA (162 tasks), we show the results of Magentic-One operating in autonomous mode, Magentic-UI operating in autonomous mode (without the simulated user), Magentic-UI with simulated user (1) (smarter model), Magentic-UI with simulated user (2) (…

LEE: Yeah, yeah.

LEE: Yeah, yeah.

LEE: Yeah, yeah.

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

Yeah, yeah.

This difficulty rating is based on a detailed rubric, originally developed for human tasks and shown to work reliably when applied by AI models.

Top: For each AI model, (1) run the new system on the ADeLe benchmark, and (2) extract its ability profile.

Creating detailed AI ability profilesUsing the 0–5 rating for each ability, the team created comprehensive ability profiles of 15 LLMs.

This analysis revealed the following:When measured against human performance, AI systems show different strengths and weaknesses across the 18 ability scales.

This makes it possible to anticipate potential failures before deployment, adding the important step of reliability assessment for AI models.

A large language model (LLM) deployed to make treatment recommendations can be tripped up by nonclinical information in patient messages, like typos, extra white space, missing gender markers, or the use of uncertain, dramatic, and informal language, according to a study by MIT researchers.

These findings indicate that LLMs take nonclinical information into account for clinical decision-making in previously unknown ways.

It brings to light the need for more rigorous studies of LLMs before they are deployed for high-stakes applications like making treatment recommendations, the researchers say.

“These models are often trained and tested on medical exam questions but then used in tasks that a…

Launched in February of this year, the MIT Generative AI Impact Consortium (MGAIC), a presidential initiative led by MIT’s Office of Innovation and Strategy and administered by the MIT Stephen A. Schwarzman College of Computing, issued a call for proposals, inviting researchers from across MIT to submit ideas for innovative projects studying high-impact uses of generative AI models.

The call received 180 submissions from nearly 250 faculty members, spanning all of MIT’s five schools and the college.

The overwhelming response across the Institute exemplifies the growing interest in AI and follows in the wake of MIT’s Generative AI Week and call for impact papers.

Over 30 funding recipients p…

“My years of organizing learning and making communities — both in person and online — have shown me firsthand how powerful social interaction can be for motivation and curiosity,” Morris said.

Combining her observational skills with active community engagement, she works at the intersection of technology, education, and human connection to improve digital learning platforms.

This research builds on her experience increasing human connection in both physical and virtual learning environments.

“I’m developing a framework that combines AI-driven behavioral analysis with human expert assessment to study social learning dynamics,” she says.

“I aim to make two primary contributions: first, analys…

They found that certain design choices which control how the model processes input data can cause position bias.

“These models are black boxes, so as an LLM user, you probably don’t know that position bias can cause your model to be inconsistent.

They also found that using positional encodings to link words more strongly to nearby words can mitigate position bias.

The technique refocuses the model’s attention in the right place, but its effect can be diluted in models with more attention layers.

In the future, the researchers want to further explore the effects of positional encodings and study how position bias could be strategically exploited in certain applications.

Caren is a jazz musician who majored in computer science and engineering, and minored in music and theater arts.

They advise the college’s leadership on issues, offer constructive feedback, and serve as a sounding board for innovative new ideas.

The UAG has been an invaluable space for understanding the student experience more deeply.

“This kind of tribal knowledge doesn’t really permeate to all of MIT,” Schneider explains.

“We are MIT students.

On May 6, MIT AgeLab’s Advanced Vehicle Technology (AVT) Consortium, part of the MIT Center for Transportation and Logistics, celebrated 10 years of its global academic-industry collaboration.

Aviation’s model, built on highly trained personnel and strict predictability standards, contrasts sharply with the fragmented approach in the automotive industry.

Just as aviation doesn’t equate absence of failure with success, vehicle safety must be measured holistically and proactively.

In terms of the impact of AI on the automotive industry, Mauricio Muñoz, senior research engineer at AI Sweden, underscored that despite AI’s transformative potential, the automotive industry cannot rely on general …

The full-day symposium on May 1 was organized around four key themes: responsible health-care technology, artificial intelligence governance and ethics, technology in society and civic engagement, and digital inclusion and social justice.

The event also featured a poster session, where student researchers showcased projects they worked on throughout the year as SERC Scholars.

Bertsimas and his team work closely with the United Network for Organ Sharing (UNOS), a nonprofit that manages most of the national donation and transplant system through a contract with the federal government.

Tsai explained that with technology, it’s now possible for everyone to have a say — but doing so can be overw…

But most AI methods for classifying and processing wireless signals are power-hungry and can’t operate in real-time.

Now, MIT researchers have developed a novel AI hardware accelerator that is specifically designed for wireless signal processing.

Light-speed processingState-of-the-art digital AI accelerators for wireless signal processing convert the signal into an image and run it through a deep-learning model to classify it.

By developing an optical neural network architecture specifically for signal processing, which they call a multiplicative analog frequency transform optical neural network (MAFT-ONN), the researchers tackled that problem head-on.

Results in nanosecondsMAFT-ONN takes a…

The restoration is printed on a very thin polymer film, in the form of a mask that can be aligned and adhered to an original painting.

Kachkine says that a digital file of the mask can be stored and referred to by future conservators, to see exactly what changes were made to restore the original painting.

Still, there has been no way to translate digital restorations directly onto an original work, until now.

In recent years, digital restoration tools have opened a route to creating virtual representations of original, restored works.

For the painting that Kachkine used, the method was able to fill in thousands of losses in just a few hours.

Noting the transferable nature of their work for travel planning, the group sought to create a user-friendly framework that can act as an AI travel broker to help develop realistic, logical, and complete travel plans.

“Different complexities of travel planning are something everyone will have to deal with at some point.

“Our idea is not to ask LLMs to propose a travel plan.

It’s doing reasoning over a lot of different algorithms there to understand whether the planning problem is possible or not to solve.

That’s a pretty significant thing in travel planning.

That realization is what guided him, a decade ago, in the creation of MIT’s pioneering Institute for Data, Systems and Society (IDSS), aiming to foster a more deeply integrated and lasting set of collaborations than the usual temporary and ad hoc associations that occur for such work.

The book, “Data, Systems, and Society: Harnessing AI for Societal Good,” was published this March by Cambridge University Press.

“You get a complex interaction among these three components, and then there is data on all these pieces.

Data is sort of like a circle that sits in the middle of this triangle and connects all these pieces,” he says.

“If you’re tackling a societal problem, it’s very important to unde…

“AI aversion occurs when either of these conditions is not met, and AI appreciation occurs only when both conditions are satisfied.”The paper, “AI Aversion or Appreciation?

People will prefer AI only if they think the AI is more capable than humans and the task is nonpersonal.”He adds: “The key idea here is that high perceived capability alone does not guarantee AI appreciation.

Even if the AI is trained on a wealth of data, people feel AI can’t grasp their personal situations.

For instance, AI appreciation is more pronounced for tangible robots than for intangible algorithms.

In countries with lower unemployment, AI appreciation is more pronounced.

Rapidly adapting to these unknown disturbances inflight presents an enormous challenge for the drone’s flight control system.

The researchers train their control system to do both things simultaneously using a technique called meta-learning, which teaches the system how to adapt to different types of disturbances.

In the future, this adaptive control system could help autonomous drones more efficiently deliver heavy parcels despite strong winds or monitor fire-prone areas of a national park.

“Even if the wind disturbances are much stronger than we had seen during training, our technique shows that it can still handle them successfully,” Azizan adds.

The team is now performing hardware exper…

For the third year in a row, MIT's Envisioning the Future of Computing Prize asked students to describe, in 3,000 words or fewer, how advancements in computing could shape human society for the better or worse.

They celebrate the effects of the supplement, helping them manage vitamin deficiencies and chronic conditions like acid reflux and irritable bowel syndrome.

Named the winner of the $10,000 grand prize, Meyer says the essay and presentation preparation were extremely rewarding.

Caspar Hare, associate dean of SERC and professor of philosophy, launched the prize in 2023.

A final tally, which comprised 75 percent of their essay score and 25 percent of their presentation score, determined…

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.…

Virtual Personas for Language Models via an Anthology of BackstoriesWe introduce Anthology, a method for conditioning LLMs to representative, consistent, and diverse virtual personas by generating and utilizing naturalistic backstories with rich details of individual values and experience.

What does it mean for large language models (LLMs) to be trained on massive text corpora, collectively produced by millions and billions of distinctive human authors?

In this work, we introduce Anthology, an approach for steering LLMs to representative, consistent, and diverse virtual personas by providing richly detailed life narratives of individuals as conditioning context to models.

By grounding langu…

Linguistic Bias in ChatGPT: Language Models Reinforce Dialect DiscriminationSample language model responses to different varieties of English and native speaker reactions.

Over 1 billion people around the world speak varieties such as Indian English, Nigerian English, Irish English, and African-American English.

Then, we compared the language model responses to the “standard” varieties and the non-“standard” varieties.

Here, we included the original GPT-3.5 responses, plus responses from GPT-3.5 and GPT-4 where the models were told to imitate the style of the input.

That can reinforce barriers against speakers of non-“standard” varieties as AI models become increasingly used in …

Setting Up in the SageMaker AI ConsoleA new Generative AI category has been added under Task Type in the SageMaker AI console, allowing you to select these templates.

Whether you’re creating datasets for training or evaluating your models’ outputs, SageMaker AI provides the tools you need to succeed in building state-of-the-art generative AI solutions.To begin creating fine-tuning datasets with the new templates:Visit the Amazon SageMaker AI console.

She contributed to the successful launch of LLM evaluation tools on Amazon Bedrock and Amazon SageMaker Unified Studio.

Kavya Kotra is a Software Engineer on the Amazon SageMaker Ground Truth team, helping build scalable and reliable software a…

Unlike traditional software systems that follow predetermined paths, AI agents employ complex reasoning that often operates as a “black box.” Monitoring AI agents presents unique challenges for organizations seeking to maintain reliability, efficiency, and optimal performance in their AI implementations.

Today, we’re excited to announce a new integration between Arize AI and Amazon Bedrock Agents that addresses one of the most significant challenges in AI development: observability.

The integration between Arize AI and Amazon Bedrock Agents provides developers with comprehensive observability tools for tracing, evaluating, and monitoring AI agent applications.

os.environ["PHOENIX_COLLECTOR_…

This post describes how SkillShow used Amazon Transcribe and other Amazon Web Services (AWS) machine learning (ML) services to automate their video processing workflow, reducing editing time and costs while scaling their operations.

Solution overviewTo address these challenges, SkillShow partnered with AWS to develop an automated video processing pipeline.

By using this suite of AWS services, SkillShow was able to build a scalable, cost-effective, and highly automated video processing solution that addressed their key operational challenges.

For organizations looking to further enhance their video processing capabilities, Amazon Bedrock Data Automation offers additional possibilities.

Are y…

The hackathon event led to the creation of NewAssist—a real-time generative AI assistant designed to empower customer service agents with speech-to-text capabilities.

To achieve required accuracy, NewDay experimented with different chunking strategies and retrieval configurations while maintaining cost with Anthropic Claude 3 Haiku.

To learn more on how AWS can help you in your Generative AI Journey, visit : Transform your business with generative AI.

Sergio Zavota is an AI Architect at NewDay, specializing in MLOps and Generative AI.

Mayur Udernani leads AWS Generative AI & ML business with commercial enterprises in UK & Ireland.

Amazon SageMaker Canvas offers no-code solutions that simplify data wrangling, making time series forecasting accessible to all users regardless of their technical background.

In this post, we explore how SageMaker Canvas and SageMaker Data Wrangler provide no-code data preparation techniques that empower users of all backgrounds to prepare data and build time series forecasting models in a single interface with confidence.

Solution overviewUsing SageMaker Data Wrangler for data preparation allows for the modification of data for predictive analytics without programming knowledge.

WalkthroughThe following is a walkthrough of the solution for data preparation using Amazon SageMaker Canvas.

F…

In this post, we explore a solution that does exactly that—using Amazon Nova Pro, a multimodal large language model (LLM) from AWS, as the central orchestrator, along with powerful new Amazon Bedrock features like Amazon Bedrock Data Automation for processing multimodal data.

With a large context window (up to 300,000 tokens in Amazon Nova Lite and Amazon Nova Pro), it can manage long documents or conversation history when reasoning.

In our architecture, Amazon Bedrock Data Automation acts as a bridge between raw data and the agentic workflow.

Amazon Nova Lite manages moderately complex operations with balanced performance, and Amazon Nova Pro excels at sophisticated tasks requiring advance…

One particularly exciting development is the emergence of video generation capabilities, which offer unprecedented opportunities for companies across diverse industries.

In this post, we explore how to implement a robust AWS-based solution for video generation that uses the CogVideoX model and Amazon SageMaker AI.

Solution overviewOur architecture delivers a highly scalable and secure video generation solution using AWS managed services.

The AI processing pipeline uses SageMaker AI processing jobs to handle video generation tasks, decoupling intensive computation from the web interface for cost optimization and enhanced maintainability.

The model effectively translates text prompts into coh…

The EU AI ActThe European Union’s Artificial Intelligence Act (EU AI Act) establishes comprehensive regulations for the development, deployment, use, and provision of AI within the EU.

The EU AI Act may apply to activities both inside and outside the EU.

Therefore, even if your organization is not established in the EU, you may still be required to comply with the EU AI Act.

AWS is committed to making sure our AI services meet applicable regulatory requirements, including those of the EU AI Act.

AWS remains engaged with EU institutions and relevant authorities across EU member states on the enforcement of the EU AI Act.

The AWS DeepRacer Student Portal will no longer be available starting September 15, 2025.

This change comes as part of the broader transition of AWS DeepRacer from a service to an AWS Solution, representing an evolution in how we deliver AI & ML education.

Since its launch, the AWS DeepRacer Student Portal has helped thousands of learners begin their AI & ML journey through hands-on reinforcement learning experiences.

Starting July 14, 2025, the AWS DeepRacer Student Portal will enter a maintenance phase where new registrations will be disabled.

Going forward, AWS DeepRacer will be available as a solution in the AWS Solutions Library in the future, providing educational institutions and org…

To deploy a SageMaker HyperPod cluster, refer to the SageMaker HyperPod workshops (SLURM, Amazon EKS).

Amazon SageMaker StudioAmazon SageMaker Studio is a fully integrated development environment (IDE) designed to streamline the end-to-end ML lifecycle.

Data science journey on SageMaker HyperPod with SageMaker StudioAs a data scientist, after you set up the SageMaker HyperPod and SageMaker Studio integration, you can log in to the SageMaker Studio environment through your user profile.

In the SageMaker HyperPod cluster sections, you can explore cluster metrics thanks to the integration of SageMaker Studio with SageMaker HyperPod observability.

We recommend starting your journey by exploring…

Amazon Nova models and Amazon BedrockAmazon Nova models, unveiled at AWS re:Invent in December 2024, are built to deliver frontier intelligence at industry-leading price performance.

For more information on best practices for Amazon Nova prompting, refer to Prompting best practices for Amazon Nova understanding models.

ResultsOur evaluation of Amazon Nova models across meeting summarization and action item extraction tasks revealed clear performance-latency patterns.

The Amazon Nova family of understanding models (Nova Micro, Nova Lite, Nova Pro, and Nova Premier) offers a practical alternative to high-end models, significantly improving inference speed while reducing operational costs.

For…

In this post, we introduce a straightforward but powerful solution with accompanying code to text-to-SQL using a custom agent implementation along with Amazon Bedrock and Converse API.

A custom agent build using Converse APIConverse API is provided by Amazon Bedrock for you to be able to create conversational applications.

In this post, a custom agent called ConverseSQLAgent was created specifically for long-running agent executions and to follow a plan of execution.

The Agent loop: Agent planning, self-correction, and long-term learningThe agent contains several key features: planning and carry-over, execution and tool use, SQLAlchemy and self-correction, reflection and long-term learning …

In this post, we explore how generative AI can revolutionize threat modeling practices by automating vulnerability identification, generating comprehensive attack scenarios, and providing contextual mitigation strategies.

In a shift-left approach to security, threat modeling serves as a critical early intervention.

How generative AI can helpGenerative AI has revolutionized threat modeling by automating traditionally complex analytical tasks that required human judgment, reasoning, and expertise.

Generative AI brings powerful capabilities to threat modeling, combining natural language processing with visual analysis to simultaneously evaluate system architectures, diagrams, and documentation…

To speed up AI adoption across industries, HPE and NVIDIA today launched new AI factory offerings at HPE Discover in Las Vegas.

This now includes HPE OpsRamp Software, a validated observability solution for the NVIDIA Enterprise AI Factory, and HPE Morpheus Enterprise Software for orchestration.

This includes the next-generation HPE Private Cloud AI, co-engineered with NVIDIA and validated as part of the NVIDIA Enterprise AI Factory framework.

HPE Private Cloud AI includes the latest NVIDIA AI Blueprints, including the NVIDIA AI-Q Blueprint for AI agent creation and workflows.

To accelerate AI for financial services, HPE will co-test agentic AI workflows built on Accenture’s AI Refinery wit…

As you think through building AI agents, powering them with the largest model available may seem tempting, but in practice, the inference cost and latency often make it unsustainable, especially at enterprise scale.

In this session, we’ll explore the NVIDIA Data Flywheel Blueprint, an open reference architecture built on the NVIDIA NeMo microservices that enables you to distill knowledge from larger models into smaller, faster, and cost-efficient alternatives using real agent interaction data from production environments.

NVIDIA and its ecosystem of partners and customers are showcasing next-generation robots, automation and AI technologies designed to accelerate the continent’s leadership in smart manufacturing and logistics.

NVIDIA Technologies Boost Robotics DevelopmentCentral to advancing robotics development is Europe’s first industrial AI cloud, announced at NVIDIA GTC Paris at VivaTech earlier this month.

To help accelerate humanoid development, NVIDIA released NVIDIA Isaac GR00T N1.5 — an open foundation model for humanoid robot reasoning and skills.

Doosan Robotics, a company specializing in AI robotic solutions, will showcase its “sim to real” solution, using NVIDIA Isaac Sim and cuRobo.

The compan…

GeForce NOW is throwing open the vault doors to welcome the legendary Borderland series to the cloud.

Vault Hunters AssembleGear up for a world where loot is king and chaos is always just a trigger pull away.

The Borderlands series is known for its wild humor, outrageous characters and nonstop action — and now, its chaotic adventures can be streamed on GeForce NOW.

Members revisiting the classics or jumping in for the first time can start with Borderlands Game of the Year Enhanced, the original mayhem-fueled classic now polished and packed with downloadable content.

For more laughs and even wilder chaos, Borderlands 3 delivers the biggest loot explosion yet, with new worlds to explore.

By combining real-time telemetry with NVIDIA NIM inference microservices and AI-driven summarization, ITMonitron transforms fragmented monitoring into unified, actionable intelligence, cutting detection time and empowering faster decisions.

While these methods are powerful and well-suited for complex workflows, we believe both are over-engineered for the narrow, well-bounded task of outage validation.

They tend to hallucinate actions, especially when operating across ambiguous or weakly structured monitoring data.

It’s explicitly instructed to make decisions strictly based on available monitoring data and not to infer or assume beyond what’s verifiably present.

Advanced usability via Slack …

NVIDIA leverages data science and machine learning to optimize chip manufacturing and operations workflows—from wafer fabrication and circuit probing to packaged chip testing.

To address this, we leverage CUDA-X data science libraries, including cuDF and cuML, for rapid data transformations and scalable model experimentation.

To better reflect true performance, we rely on metrics like weighted accuracy and area under the precision-recall curve.

The marker on the ROC curve highlights a point with a true positive rate of 0.61 and a false positive rate of 0.13.

Evaluation metric: AUC for ROC curve vs precision-recall curveThe confusion matrix below—which corresponds to the marker on the plots …

NVIDIA’s Plug and Play: Project G-Assist Plug-In Hackathon — running virtually through Wednesday, July 16 — invites the community to explore AI and build custom G-Assist plug-ins for a chance to win prizes and be featured on NVIDIA social media channels.

First place will receive a GeForce RTX 5090 laptop, second place a GeForce RTX 5080 GPU and third a GeForce RTX 5070 GPU.

Save the date for NVIDIA’s How to Build a G-Assist Plug-In webinar on Wednesday, July 9, from 10-11 a.m. PT, to learn more about Project G-Assist capabilities, discover the fundamentals of building, testing and deploying Project G-Assist plug-ins, and participate in a live Q&A session.

NVIDIA’s technical blog walks throu…

The captured data is then used for advanced visualization and collaboration in the Hexagon Digital Reality (HxDR) platform powering Hexagon Reality Cloud Studio (RCS).

The captured data is then used for advanced visualization and collaboration in the Hexagon Digital Reality (HxDR) platform powering Hexagon Reality Cloud Studio (RCS).

“The age of general-purpose robotics has arrived, due to technological advances in simulation and physical AI,” said Deepu Talla, vice president of robotics and edge AI at NVIDIA.

“Hexagon’s new AEON humanoid embodies the integration of NVIDIA’s three-computer robotics platform and is making a significant leap forward in addressing industry-critical challenges.…

Those laws have, over the decades, required different agencies to create around 500 reports for the city to review.

The AI analyzed San Francisco’s laws, identifying every city-mandated report and highlighting ones that could be tweaked, combined with similar reports, or zeroed out altogether.

“Our model system reasons about a code’s provisions in the same way that we teach Statutory Interpretation 101 to law students,” Ho said.

STARA materially outperformed off-the-shelf LLMs that were asked to do the same searches, with its extraction accuracy 2.7x better than a base system, Ho said.

Ho and his team plan to publish a paper about STARA in the journal Proceedings of the International Confer…

Each certification exam has a list of recommended training and additional materials to review.

The number of questions on a certification exam varies by exam and is subject to change as we update it to align it with any changes in the technology and job role.

Most of our certification exams contain between 40 and 60 questions.

You can schedule certification exams up to 60 days in advance.

You may cancel an exam up to 24 hours in advance of a scheduled exam.

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Hear from NVIDIA Certification experts and exam champions who helped design the exams, along with certified professionals—both individuals and enterprise leaders—who will share how certification has helped advance careers and drive business value.

Wrap up the session with a live Q&A to have your questions answered in real time.

Live Q&A session in EMEA/APAC will include multilingual support in Japanese, Korean, Mandarin, and Simplified Chinese.

Access the recording and contact the NVIDIA China team to register here.

“By building Europe’s first industrial AI infrastructure, we’re enabling the region’s leading industrial companies to advance simulation-first, AI-driven manufacturing.”“Europe’s technological future needs a sprint, not a stroll,” said Timotheus Höttges, CEO of Deutsche Telekom AG.

Our economic success depends on quick decisions and collaborative innovations.”This AI infrastructure — Germany’s single largest AI deployment — is an important leap for the nation in establishing its own sovereign AI infrastructure and providing a launchpad to accelerate AI development and adoption across industries.

Driving Germany’s Industrial EcosystemDeutsche Telekom will operate the AI factory and provide A…

This post explores how recent advances in protein alignment accelerate protein science by using GPU-optimized alignment to enhance AI-driven drug discovery, structural prediction, and protein design at unprecedented speed.

Protein sequence alignment scales scientific insightSequence alignment might sound technical, but its importance is straightforward: scientists can compare protein sequences to find similarities.

Sequence alignment and lab-in-the-loop protein designAs an example of MMseqs2-GPU in a drug discovery workflow, the NVIDIA BioNeMo Blueprint for generative protein binder design illustrates the role of sequence alignment in AI-driven protein design.

The MSA-Search NIM (MMseqs2-GP…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Meta’s ad recommendation engine, powered by deep learning recommendation models (DLRMs), has been instrumental in delivering personalized ads to people.

Learning from sequences: developing new sequence learning architectures to replace traditional DLRM neural network architectures.

A paradigm shift with learning from sequences for recommendation systemsMeta’s new system for ads recommendations uses sequence learning at its core.

Scaling the new sequence learning paradigmFollowing the redesign to shift from sparse feature learning to event-based sequence learning, the next focus was scaling across two domains — scaling the sequence learning architecture and scaling event sequences to be long…

At Open Compute Project Summit (OCP) 2024, we’re sharing details about our next-generation network fabric for our AI training clusters.

We’ve expanded our network hardware portfolio and are contributing two new disaggregated network fabrics and a new NIC to OCP.

At Meta, we believe that open hardware drives innovation.

At Meta, we envision a future of AI hardware systems that are not only scalable, but also open and collaborative.

We encourage anyone who wants to help advance the future of networking hardware for AI to engage with OCP and Meta to help share the future of AI infrastructure.

At the Open Compute Project (OCP) Global Summit 2024, we’re showcasing our latest open AI hardware designs with the OCP community.

These innovations include a new AI platform, cutting-edge open rack designs, and advanced network fabrics and components.

The open future of AI infraMeta is committed to open source AI.

We must also prioritize open and standardized models so we can leverage collective expertise, make AI more accessible, and work towards minimizing biases in our systems.​Just as important, we also need open AI hardware systems.

By addressing AI’s infrastructure needs together, we can unlock the true promise of open AI for everyone.​

Why we need better population mapsAccurate estimates of population are taken for granted in many countries.

As the world’s natural resource and energy demands scale, accurate population estimates also offer significant opportunities to improve sustainability efforts.

In addition to total population counts, Meta’s population maps also include demographic breakdowns for groups such as the number of children under five, women of reproductive age, youth, and the elderly.

AI-powered population estimates have been scientifically evaluated to be among the most accurate in the world for mapping population distribution for a variety of geographies and use-cases.

Please visit the Data for Good websit…

Meta is revamping its new data center design to optimize for artificial intelligence and the same methodology will be applicable for future data center optimizations as well.

As Meta is revamping its new data center design to optimize for artificial intelligence, the same methodology will be applicable for future data center optimizations as well to improve operational efficiency.

A reinforcement learning approach to data center coolingReinforcement learning (RL) is good at modeling control systems as sequential state machines.

There are also various RL approaches reported such as, transforming cooling optimization via deep reinforcement learning and data center cooling using model-predicti…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Best practices for ethical LLM developmentNavigating the regulatory landscape r…

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

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

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

Performance: All adaptation methods for Closed LLMs ach…

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

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

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

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

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

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

This is the key idea behind Mixture of Expert LLMs.

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

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

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

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

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

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

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

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

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

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

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

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

Improving how t…

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

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

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

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

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

In our paper, Understanding LLMs Requires More Than Statistical Generalization, we argue that current machine learning theory cannot explain the interesting emergent properties of Large Language Models, such as reasoning or in-context learning.

Inductive biases affect which solution the neural network converges to, such as the model architecture or the optimization algorithm.

How do language complexity and model architecture affect generalization ability?

showed how different neural network architectures generalize better for different language types.

Presumably, we’ll need to find different complexity measures for different model architectures that consider their specific inductive biases.

TL;DR At a large-scale model training (in huge models), anomalies are not rare events but problematic patterns that drive failure.

The Neptune Scale experiment tracker supports fault tolerance and is designed to maintain progress despite hardware failures, making it adaptable for enterprise teams tackling LLM fine-tuning, compliance, and building domain-specific models.

Experiment tracking back then was straightforward—dealing mostly with single models or small-scale distributed systems.

One of the biggest lessons we’ve learned is that experiment tracking has evolved into experiment monitoring.

That’s why we’re focusing on building intelligent alerts and anomaly detection right into our exp…

Key-value (KV) caching is a clever trick to do that: At inference time, key and value matrices are calculated for each generated token.

Implementing K-V caching in large-scale production systems requires careful cache management, including choosing an appropriate strategy for cache invalidation and exploring opportunities for cache reuse.

Key-value (KV) caching is a clever trick to do just that – let’s see how it works and when to use it.

Transformer architecture overviewBefore we dive into KV caching, we will need to take a short detour to the attention mechanism used in transformers.

Understanding how it works is required to spot and appreciate how KV caching optimizes transformer inferen…

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

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

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

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

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

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

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

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

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

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

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

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

Sam Zimmerman, Kelley Rivoire, Thom…

#deepseek #llm #reinforcementlearning GRPO is one of the core advancements used in Deepseek-R1, but was introduced already last year in this paper that uses a combination of new RL techniques and iterative data collection to achieve remarkable performance on mathematics benchmarks with just a 7B model. Paper: https://arxiv.org/abs/2402.03300 Abstract:

Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature. In this paper, we introduce DeepSeekMath 7B, which continues pre-training DeepSeek-Coder-Base-v1.5 7B with 120B math-related tokens sourced from Common Crawl, together with natural language and code data. DeepSeekMath 7B has achie…

https://ykilcher.com/discord Links:

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#tokenization #llm #meta This paper does away with tokenization and creates an LLM architecture that operates on dynamically sized "patches" instead of tokens. By controlling the patch size, they gain a level of control over the tradeoff between model size and FLOPs and use that to achieve more favorable scaling behavior than classically tokenized LLMs. Paper: https://ai.meta.com/research/publications/byte-latent-transformer-patches-scale-better-than-tokens/

Code: https://github.com/facebookresearch/blt Abstract:

We introduce the Byte Latent Transformer (BLT), a new byte-level LLM architecture that, for the first time, matches tokenization-based LLM performance at scale with significant imp…

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

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

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

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

This paper (by Apple) questions the mathematical reasoning abilities of current LLMs and designs a synthetic template-based dataset distribution to investigate various aspects around LLM performance of high-school level math questions. Paper: https://arxiv.org/abs/2410.05229 Abstract:

Recent advancements in Large Language Models (LLMs) have sparked interest in their formal reasoning capabilities, particularly in mathematics. The GSM8K benchmark is widely used to assess the mathematical reasoning of models on grade-school-level questions. While the performance of LLMs on GSM8K has significantly improved in recent years, it remains unclear whether their mathematical reasoning capabilities hav…

This paper posits the interesting question: How much of the performance of Mamba, S4, and other state-space-like models is actually just attributable to some very core concepts - rather than their elaborate architectures. The authors construct minimal versions of GRUs and LSTMs and report competitive performance. Paper: https://arxiv.org/abs/2410.01201 Abstract:

The scalability limitations of Transformers regarding sequence length have renewed interest in recurrent sequence models that are parallelizable during training. As a result, many novel recurrent architectures, such as S4, Mamba, and Aaren, have been proposed that achieve comparable performance. In this work, we revisit traditional …

How can one best use extra FLOPS at test time? Paper: https://arxiv.org/abs/2408.03314 Abstract:

Enabling LLMs to improve their outputs by using more test-time computation is a critical step towards building generally self-improving agents that can operate on open-ended natural language. In this paper, we study the scaling of inference-time computation in LLMs, with a focus on answering the question: if an LLM is allowed to use a fixed but non-trivial amount of inference-time compute, how much can it improve its performance on a challenging prompt? Answering this question has implications not only on the achievable performance of LLMs, but also on the future of LLM pretraining and how one s…

Make a math explainer, get feedback, and receive prizes: https://some.3b1b.co

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

An equally valuable form of support is to simply share the videos. ------------------ These animations are largely made using a custom Python library, manim. See the FAQ comments here:

https://3b1b.co/faq#manim

https://github.com/3b1b/manim

https://github.com/ManimCommunity/manim/ All code for specific videos is visible here:

https://github.com/3b1b/videos/ The music is by Vincent Rubinetti.

https://www.vincentrubinetti.com

https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown

https://open.spotify.com/…

Addressing viewer questions from the last video.

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

An equally valuable form of support is to share the videos. ------------------ These animations are largely made using a custom Python library, manim. See the FAQ comments here:

https://3b1b.co/faq#manim

https://github.com/3b1b/manim

https://github.com/ManimCommunity/manim/ All code for specific videos is visible here:

https://github.com/3b1b/videos/ The music is by Vincent Rubinetti.

https://www.vincentrubinetti.com

https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown

https://open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u ------------------ 3blue1brown is a ch…

Qubits, state vectors, and Grover's algorithm for search.

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

An equally valuable form of support is to share the videos. The subtitles on this video were done using AI, and are likely imperfect, but they are open for community corrections at https://criblate.com/ Adam Brown's paper on the connection between Grover's Algorithm and block collisions:

https://arxiv.org/pdf/1912.02207 If you want to learn the relevant underlying quantum mechanics here, a very friendly resource is the course Mithuna at Looking Glass Universe is currently putting together. See, for instance, this explainer of a qubit:…

Full video: https://youtu.be/RQWpF2Gb-gU

From this video: https://youtu.be/hFMaT9oRbs4

From this video with Terry Tao: https://youtu.be/hFMaT9oRbs4

From this video with Terry Tao: https://youtu.be/hFMaT9oRbs4

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

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

Two colliding blocks compute pi, here we dig into the physics to explain why

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. The original paper by Gregory Galperin:

https://www.maths.tcd.ie/~lebed/Galperin.%20Playing%20pool%20with%20pi.pdf Adam Brown's paper on the analogy with Grover's Algorithm:

https://arxiv.org/pdf/1912.02207 Here's a lovely interactive built by GitHub user prajwalsouza after watching this video: https://prajwalsouza.github.io/Experiments/Colliding-Blocks.html Matt Parker's Pi Day video:

https://youtu.be/vlUTlbZT4ig NY Times blog post about this proble…

Full video: https://youtu.be/YdOXS_9_P4U

From this video on the cosmic distance ladder: https://youtu.be/YdOXS_9_P4U

Full video: https://youtu.be/YdOXS_9_P4U

Full video: https://youtu.be/YdOXS_9_P4U

How we know the distances to the planets, stars, and faraway galaxies.

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

FAQ with added details and corrections: https://terrytao.wordpress.com/2025/02/13/cosmic-distance-ladder-video-with-grant-sanderson-3blue1brown-commentary-and-corrections/ An equally valuable form of support is to simply share the videos. Terry and his collaborator Tanya have an Instagram about the cosmic distance ladder: https://www.instagram.com/cosmic_distance_ladder/ Artwork of Guillaume Le Gentil by Kurt Bruns

Artwork of Antonia Maury and Henrietta Leavitt by Talia Gershon: https://bit.ly/taliagershonart

Several of t…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide for using DeepSeek on Lambda:

https://docs.lambdalabs.com/education/large-language-models/deepseek-r1-ollama/?utm_source=two-minute-papers&utm_campaign=relevant-videos&utm_medium=video 📝 The paper is available here:

https://research.nvidia.com/labs/toronto-ai/UniRelight/ 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Apple Music Sing demonstration source: https://www.youtube.com/watch?v=Q6Qpsvwh6mQ 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 ge…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide for using DeepSeek on Lambda:

https://docs.lambdalabs.com/education/large-language-models/deepseek-r1-ollama/?utm_source=two-minute-papers&utm_campaign=relevant-videos&utm_medium=video 📝 Paper+code: https://github.com/lmgame-org/GamingAgent

Some results: https://huggingface.co/spaces/lmgame/lmgame_bench

Try it out: https://lmgame.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 supp…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide for using DeepSeek on Lambda:

https://docs.lambdalabs.com/education/large-language-models/deepseek-r1-ollama/?utm_source=two-minute-papers&utm_campaign=relevant-videos&utm_medium=video 📝 The papers are available here:

https://research.nvidia.com/labs/toronto-ai/stochastic-preconditioning/

https://zju3dv.github.io/freetimegs/ Play with it (interactive viewer): https://www.4dv.ai/viewer/salmon_10s?showdemo=4dv 📝 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/ar…

❤️ Check out the Fully Connected conference from Weights & Biases on June 17-18th in SF:

https://wandb.me/fc2025

Use the code FCSF2WP to get a ticket for free! 📝 The paper "Practical Inverse Rendering of Textured and Translucent Appearance" is available here:

https://weiphil.github.io/portfolio/practical_reconstruction 📝 Separable Subsurface Scattering:

https://users.cg.tuwien.ac.at/zsolnai/gfx/separable-subsurface-scattering-with-activision-blizzard/ Free rendering course!

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, Gordon Child, John Le, Jua…

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

https://research.nvidia.com/labs/toronto-ai/difix3d/

https://sites.google.com/view/cast4

https://syntec-research.github.io/UVGA/ 📝 My paper on simulations that look almost like reality is available for free here:

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

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedd…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide for using DeepSeek on Lambda:

https://docs.lambdalabs.com/education/large-language-models/deepseek-r1-ollama/?utm_source=two-minute-papers&utm_campaign=relevant-videos&utm_medium=video 📝 The paper "RenderFormer: Transformer-based Neural Rendering of Triangle Meshes with Global Illumination" is available here:

https://microsoft.github.io/renderformer/ 📝 Our neural rendering paper "Gaussian Material Synthesis" is available here:

https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Benji Rabhan, …

❤️ Try Macro for free and supercharge your learning: https://macro.com/papers 📝 The #NVIDIA papers are available here:

https://research.nvidia.com/labs/dir/cosmos-transfer1/

https://research.nvidia.com/labs/dir/cosmos-reason1/ 📝 My paper on simulations that look almost like reality is available for free here:

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

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Ric…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide for using DeepSeek on Lambda:

https://docs.lambdalabs.com/education/large-language-models/deepseek-r1-ollama/?utm_source=two-minute-papers&utm_campaign=relevant-videos&utm_medium=video 📝 The papers are available here:

https://research.nvidia.com/labs/toronto-ai/WeatherWeaver/

https://research.nvidia.com/labs/toronto-ai/DiffusionRenderer/ Source: https://www.youtube.com/watch?v=CVdtLieI5D0 📝 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/s41567-022-01…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide for using DeepSeek on Lambda:

https://docs.lambdalabs.com/education/large-language-models/deepseek-r1-ollama/?utm_source=two-minute-papers&utm_campaign=relevant-videos&utm_medium=video 📝 More on Veo3 available here:

https://deepmind.google/models/veo/ 📝 My paper on simulations that look almost like reality is available for free here:

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

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Benji Rabhan, B Shang, Christian Ahlin, …

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide for using DeepSeek on Lambda:

https://docs.lambdalabs.com/education/large-language-models/deepseek-r1-ollama/?utm_source=two-minute-papers&utm_campaign=relevant-videos&utm_medium=video 📝 AlphaEvolve: https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/

📝 My genetic algorithm for the Mona Lisa: https://users.cg.tuwien.ac.at/zsolnai/gfx/mona_lisa_parallel_genetic_algorithm/ 📝 My paper on simulations that look almost like reality is available for free here:

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

…

❤️ Check out DeepInfra and run DeepSeek or many other AI projects: https://deepinfra.com/papers 📝 The papers are available here:

https://anytop2025.github.io/Anytop-page/

https://zhongleilz.github.io/Sketch2Anim/ 📝 My paper on simulations that look almost like reality is available for free here:

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

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall,…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide for using DeepSeek on Lambda:

https://docs.lambdalabs.com/education/large-language-models/deepseek-r1-ollama/?utm_source=two-minute-papers&utm_campaign=relevant-videos&utm_medium=video 📝 The #NVIDIA paper "GENMO: A GENeralist Model for Human MOtion" is available here:

https://research.nvidia.com/labs/dair/genmo/ 📝 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 for SLAM:

https://www.youtube.com/watch?v=2GJuEIh4xGo

https://ww…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide for using DeepSeek on Lambda:

https://docs.lambdalabs.com/education/large-language-models/deepseek-r1-ollama/?utm_source=two-minute-papers&utm_campaign=relevant-videos&utm_medium=video OpenAI post: https://openai.com/index/expanding-on-sycophancy/ Paper on agreeableness: https://arxiv.org/abs/2212.09251 Source: https://x.com/georgejrjrjr/status/1917722125668081863/ 📝 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…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers Get Blender: https://www.blender.org/ Demo files: https://www.blender.org/download/demo-files/

Full donut tutorial: https://www.youtube.com/watch?v=4haAdmHqGOw&pp=ygUWYW5kcmV3IHByaWNlIGRvbnV0IDQuNA%3D%3D Our papers that uses Blender: https://users.cg.tuwien.ac.at/zsolnai/gfx/photorealistic-material-editing/

https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/ Subsurface scattering video source: https://www.youtube.com/shorts/YqxSzGAKiPM

Blue-noise dithered sampling: https://iliyan.com/publications/DitheredSampling Donate to Blender: https://fund.blender.org/ 📝 My paper on simulations th…

❤️ Check out DeepInfra and run DeepSeek or many other AI projects: https://deepinfra.com/papers 📝 The #nvidia paper "3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting" is available here:

https://research.nvidia.com/labs/toronto-ai/3DGUT/ 📝 Our Separable Subsurface Scattering paper: https://users.cg.tuwien.ac.at/zsolnai/gfx/separable-subsurface-scattering-with-activision-blizzard/

📝 SSS For Gaussian Splatting: https://sss.jdihlmann.com/ Sources:

https://x.com/jonstephens85/status/1911923445660893321?s=46

https://x.com/jonstephens85/status/1908730004973986175?s=46

https://www.youtube.com/watch?v=KPFPuXm2u7I

https://www.youtube.com/shorts/si1-zFZpeDE 📝 My paper on simu…

Mikita Shchutski | Lead Machine Learning Engineer, Quantori Training large medical models using electronic health records in order to create a highly informative medical embedding space

02:00-05:38 What do you think were the most outstanding underestimated news and achievements in AI field in 2020?

05:41-11:28 What do you think about trends in ML like transformers trying to replace LSTMs in NLP?

11:29-16:06 Are you working on any new types of models right now?

16:07-20:41 What is your opinion on the most underestimated ML subfield like Reinforcement Learning?

20:42-22:17 Your best recommendation for our community is to look into AI in the real physical world, right?

22:18-33:10 Do you think it is possible to achieve great results in creative AI, particularly in subjective beauty?

33:17-35:50 What prevents chat bots from reaching more intelligent levels?

36:03-39:39 What is…

DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

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

Андрей Нарцев, руководитель Applied ML в Яндекс Доставке, прочитал этот доклад на конференции AHA!25. Андрей разобрал ключевые аналитические, продуктовые и ML-вызовы, с которыми ребята столкнулись при внедрении динамического ценообразования для замедленных тарифов. А ещё поделился инсайтами и полезными практиками, которые помогли сделать ценообразование ещё более адаптивным. После просмотра этого доклада вы будете лучше понимать сложности внедрения ML-моделей в продукт, научитесь заранее предотвращать потенциальные проблемы и применять представленные подходы в своей работе. Больше классных материалов ищите в телеграм-канале Yandex for ML: https://t.me/yandexforml #ML #ценообразование #Яндек…

В этом году конференция Data Fest отмечала юбилей — 10 лет. За это время машинное обучение проникло во многие сферы нашей жизни, в том числе в рабочие процессы. Нам стало интересно: как развитие ML изменит нашу привычную реальность ещё через 10 лет? Мы спросили об этом у гостей конференции, и вот что из этого вышло! #DataFest #машинноеобучение #ML #искусственныйинтеллект #будущеетехнологий #нейросети #цифровоебудущее #технологии #Яндекс #AI

Это выступление на Data Fest в гостях у Яндекса в секции OptimalDL. Артур Соловьёв прочитал доклад на английском языке на тему: Fast and Approximate Responses. Узнать больше о мероприятиях для разработчиков можно тут: https://events.yandex.ru Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml #DataFest #OptimalDL #FastResponses #ApproximateComputing #MLOptimization #DeepLearning #AIperformance #инференс #оптимизация #нейросети #modelcompression #AI #машиннообучение #inferencespeed #lowlatencyAI #АртурСоловьёв #DLresearch #DataFest2025

Это доклад на Data Fest в гостях у Яндекса. В секции NLP Салават Гарифуллин рассказал, как создал первый ИИ-переводчик для славянской интерлингвы — искусственного межславянского языка. Узнать больше о мероприятиях для разработчиков можно тут: https://events.yandex.ru Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml #DataFest #NLP #ИИпереводчик #межславянскийязык #интерлингва #машинныйперевод #языки #AI #linguistics #нейросети #naturalanguageprocessing #перевод #lowresource #slaviclanguages #SalavatGarifullin #искусственныйинтеллект #languageAI #переводчик #DataFest2025

Это доклад на Data Fest в гостях у Яндекса. В секции Advanced LLMs Алёна Зайцева рассказала, как ребята делали умного помощника в Лавке на основе YaGPT. Узнать больше о мероприятиях для разработчиков можно тут: https://events.yandex.ru Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml #DataFest #AdvancedLLMs #ЯндексЛавка #YaGPT #LLM #умныйпомощник #AI #искусственныйинтеллект #генеративныйИИ #голосовойпомощник #NLP #персонализация #Lavka #AIassistant #frontendAI #backendAI #biglanguagemodels #AlenaZaytseva #DataFest2025

Это выступление на Data Fest в гостях у Яндекса в секции Advanced LLMs. Миле Митрович прочитал доклад на английском языке на тему: From Tokens to Thinking: How Reinforcement Learning Fuels Reasoning in LLMs. Узнать больше о мероприятиях для разработчиков можно тут: https://events.yandex.ru Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml #DataFest #AdvancedLLMs #MilaMitrovic #LLM #reinforcementlearning #reasoning #машинноемышление #RLHF #AI #большиеязыковыемодели #искусственныйинтеллект #нейросети #обучениесподкреплением #AIthinking #futureofAI #generativeAI #LLMarchitecture

Это доклад на Data Fest в гостях у Яндекса. В секции DL Frontiers Андрей Панфёров рассказал про обучение LLM в низкой точности вычислений. Узнать больше о мероприятиях для разработчиков можно тут: https://events.yandex.ru Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml #DataFest #DLFrontiers #LLM #LowPrecision #АндрейПанфёров #машинноеобучение #глубокообучение #искусственныйинтеллект #AI #нейросети #оптимизациямоделей #ML #большиемодели #обучениеLLM #AIтехнологии #MLинфраструктура #ресурсоэффективность

Это доклад на Data Fest в гостях у Яндекса. В секции Open Source Алексей Васильев рассказал о RePlay, библиотеке для создания рекомендательных систем. Узнать больше о мероприятиях для разработчиков можно тут: https://events.yandex.ru Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml #DataFest #OpenSource #RePlay #RecSys #MachineLearning #ML #рекомендации #алгоритмы #библиотека #персонализация #рекомендательныесистемы #opensource #recommendationengine #AI #АлексейВасильев #DataFest2025 #MLtools

Это доклад на Data Fest в гостях у Яндекса. В секции ML in Chemistry Ксения Никитина рассказала о хемоинформатике, научной области на стыке химии и информационных технологий. Узнать больше о мероприятиях для разработчиков можно тут: https://events.yandex.ru Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml #DataFest #MLinChemistry #хемоинформатика #машинноеобучение #искусственныйинтеллект #AI #наука #chemoinformatics #КсенияНикитина #химия #цифроваяхимия #AIвнауке #нейросетивхимии #интердисциплинарность #наукиожизни

Это выступление на Data Fest в гостях у Яндекса в секции ML in Manufacturing. Дмитрий Юновидов прочитал доклад на английском языке на тему: Science Like Industry. MLOps for the Effective R&D Development and Deployment of Computer Vision Models in Industrial Environments. Узнать больше о мероприятиях для разработчиков можно тут: https://events.yandex.ru Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml #DataFest #MLinManufacturing #MLOps #ComputerVision #AI #ML #ScienceLikeIndustry #промышленныйИИ #индустриальныйAI #машинноеобучение #искусственныйинтеллект #deployment #RND #ДмитрийЮновидов #MLOpsвпроизводстве #AIвпромышленности

Это доклад на Data Fest в гостях у Яндекса. В секции Practical ML Екатерина Глазкова рассказала о продуктовых применениях Yandex VLM. Узнать больше о мероприятиях для разработчиков можно тут: https://events.yandex.ru Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml #YandexVLM #VLM #мультимодели #AI #ML #DataFest #PracticalML #Яндекс #визуальноязыковыемодели #искусственныйинтеллект #мультимодальныйИИ #компьютерноезрение #ЕкатеринаГлазкова #технологииЯндекса #machinelearning

Это доклад на Data Fest в гостях у Яндекса. В секции Practical ML Дарья Виноградова рассказала о VLM в Алисе. Узнать больше о мероприятиях для разработчиков можно тут: https://events.yandex.ru Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml #DataFest #Яндекс #Алиса #VLM #VisionLanguageModel #машинноеобучение #нейросети #искусственныйинтеллект #LLM #голосовойпомощник #ИИ #мультимодальность #PracticalML

Это доклад на Data Fest в гостях у Яндекса. В секции Practical ML Анатолий Глушенко рассказал о персонализации общения Алисы с пользователем на основе LLM. Узнать больше о мероприятиях для разработчиков можно тут: https://events.yandex.ru Подписывайтесь на телеграм-канал Яндекса для ML-сообщества: https://t.me/yandexforml #DataFest #Яндекс #Алиса #LLM #персонализация #искусственныйинтеллект #машинноеобучение #голосовойассистент #пользовательскийопыт #AI #NLP #PracticalML #LargeLanguageModels

Спикеры: Анастасия Функнер, Ольга Павлова, Анна Ефимова, Ozon Банк

Тема доклада: MLOps, Data Science и Golang: Как Ozon Банк создает ML-платформу будущего

Мероприятие Wids-meetup-2025: https://ods.ai/events/wids-meetup-2025 Наши соц.сети:Telegram: https://t.me/datafestВконтакте: https://vk.com/datafestКанал с вакансиями в telegram: https://t.me/odsjobsКанал с апдейтами по курсам: https://t.me/odscoursesКак попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

Спикер: Алена Феногенова, AGI NLP TeamLead, Сбер

Мероприятие Wids-meetup-2025: https://ods.ai/events/wids-meetup-2025 Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

Спикер: Мария Бегичева, Senior DS, Сбер

Мероприятие Wids-meetup-2025: https://ods.ai/events/wids-meetup-2025 Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

Спикер: Полина Федотова, Главный инженер-разработчик, лид исследовательской команды, «Сбер Центр робототехники»

Мероприятие Wids-meetup-2025: https://ods.ai/events/wids-meetup-2025 Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

Спикеры: Нонна Шахова, Эмели Драль, Ирина Голощапова, Анастасия Никулина

Мероприятие Wids-meetup-2025: https://ods.ai/events/wids-meetup-2025

Как женщины меняют data science:

Старт в data science:

Работа и карьера:

Профессиональное развитие:

Будущее в data science: Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

Спикер: Анна Текучева, Data Scientist HML Wildberries

Мероприятие Wids-meetup-2025: https://ods.ai/events/wids-meetup-2025 Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

Канал с апдейтами по курсам: https://t.me/odscourses

Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

Спикер: Юлия Раковская, Руководитель центра исследований и разработки Сбера

Мероприятие Wids-meetup-2025: https://ods.ai/events/wids-meetup-2025 Наши соц.сети:

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Ежегодная серия соревнований по машинному обучению Data Fusion Contest стартовала. Страница с задачами: https://ods.ai/tracks/data-fusion-2025-competitions Во вторник 25 февраля (19:00 - 20:00 по мск) мы провели первый митап по Data Fusion Contest 2025 в ODS спейсе Spatial.Chat. Поговорили о задачах, ответим на ваши вопросы участников. В программе: Анатолий Глушенко Разбор задачи 1 “LabelCraft”

Алексей Натёкин Обзор задачи 2 “4Cast” и задачи 3 “Distribution” Команда Data Fusion Contest 2025 Q&A и обсуждение вопросов с участниками

Спикер: Анастасия Вепрева - разработчик моделей в области прогнозирования физико-химических свойств и биологических активностей малых молекул, сотрудница Центра ИИ в химии ИТМО

Мероприятие 21.02.2025: https://ods.ai/events/ai_chemistrymk1 Наши соц.сети:

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Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

Спикер: Антон Воронов, Газпром ИД, Заместитель директора департамента, Руководитель платформы Поиска Data Ёлка 2024 в гостях у VK: https://ods.ai/events/data-elka-24-vk-offline

Data Ёлка 2024: https://ods.ai/events/data-elka-2024

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Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

Спикер: Пётр Ермаков, ML Brand Director, Яндекс Data Ёлка 2024 в гостях у VK: https://ods.ai/events/data-elka-24-vk-offline

Data Ёлка 2024: https://ods.ai/events/data-elka-2024

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Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

Спикер: Валентин Малых, руководитель группы, MTS AI Data Ёлка 2024 в гостях у VK: https://ods.ai/events/data-elka-24-vk-offline

Data Ёлка 2024: https://ods.ai/events/data-elka-2024

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Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

Спикер: Николай Анохин, ведущий специалист по ML, AI VK Data Ёлка 2024 в гостях у VK: https://ods.ai/events/data-elka-24-vk-offline

Data Ёлка 2024: https://ods.ai/events/data-elka-2024

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Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

Спикер: Ирина Голощапова, CDO Raiffeisenbank Operations Data Ёлка 2024 в гостях у VK: https://ods.ai/events/data-elka-24-vk-offline

Data Ёлка 2024: https://ods.ai/events/data-elka-2024

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Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал с вакансиями в telegram: https://t.me/odsjobs

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Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

Спикер: Дмитрий Колодезев, директор, Промсофт Data Ёлка 2024 в гостях у VK: https://ods.ai/events/data-elka-24-vk-offline

Data Ёлка 2024: https://ods.ai/events/data-elka-2024

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Канал с вакансиями в telegram: https://t.me/odsjobs

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Как попасть в чат сообщества ODS Mattermost: https://ods.ai/tracks/mattermost

Patreon: https://www.patreon.com/primerlearning Ageless book: https://www.amazon.com/Ageless-Science-Getting-Older-Without/dp/0525566317/ Papers and other further reading:

Diversity of aging across the tree of life: https://pmc.ncbi.nlm.nih.gov/articles/PMC4157354/

Antagonistic pleiotropy and p53: https://pmc.ncbi.nlm.nih.gov/articles/PMC2771578/

An unsolved problem of biology (Medawar): https://ia903408.us.archive.org/31/items/medawar-1952-unsolved-problem/Medawar1952-Unsolved-Problem.pdf

Evolution of the mutation rate: https://pmc.ncbi.nlm.nih.gov/articles/PMC2910838/

Our World in Data Life Expectancy explainer: https://ourworldindata.org/life-expectancy-how-is-it-calculated-and-how-shoul…

Terence Tao is widely considered to be one of the greatest mathematicians in history.

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Sundar Pichai is CEO of Google and Alphabet.

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Sundar's X: https://x.com/sundarpichai

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James Holland is a historian specializing in World War II.

He hosts a podcast called WW2 Pod: We Have Ways of Making You Talk.

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

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

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

Janna Levin is a theoretical physicist and cosmologist specializing in black holes, cosmology of extra dimensions, topology of the universe, and gravitational waves.

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

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Jeffrey Wasserstrom is a historian of modern China.

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Robert Rodriguez is a legendary filmmaker and creator of Sin City, El Mariachi, Desperado, Spy Kids, Machete, From Dusk Till Dawn, Alita: Battle Angel, The Faculty, and his newest venture Brass Knuckle Films.

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Dave Smith is a comedian, libertarian, political commentator, and the host of Part of the Problem podcast.

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Douglas Murray is the author of On Democracies and Death Cults, The War on The West, and The Madness of Crowds.

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Ezra Klein is one of the most influential voices representing the left-wing of American politics.

He is a columnist for the NY Times and host of The Ezra Klein Show.

Derek Thompson is a writer at The Atlantic and host of the Plain English podcast.

Together they have written a new book titled Abundance that lays out a set of ideas for the future of the Democratic party.

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ThePrimeagen (aka Michael Paulson) is a programmer who has educated, entertained, and inspired millions of people to build software and have fun doing it.

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Narendra Modi is the Prime Minister of India.

On YouTube this episode is available in English, Hindi, Russian (and soon other languages).

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Dylan Patel is the founder of SemiAnalysis, a research & analysis company specializing in semiconductors, GPUs, CPUs, and AI hardware.

Nathan Lambert is a research scientist at the Allen Institute for AI (Ai2) and the author of a blog on AI called Interconnects.

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Marc Andreessen is an entrepreneur, investor, co-creator of Mosaic, co-founder of Netscape, and co-founder of the venture capital firm Andreessen Horowitz.

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LEE: Yeah, yeah.

It cannot—as, you know, Bill was saying—it cannot learn from your document.

And I don’t know if the two of you remember, but I ended up doing a lot of tests.

I don’t know if you know, but just recently, there was a paper that was published on a scientific discovery using o3- mini (opens in new tab).

Like, if you have a human trained for one task and you put them into another task, then you don’t … you often don’t know.

So I don’t think we should be surprised that business schools matter on this because we care about management.

That’s really going to change the way, like, middle school works, was my thinking at the time.

We’ve gone from AI being highly discriminative to AI that’s able to explore the world in particular ways.

The symptoms that they’re showing are quite different, and also their compliance is really, really different.

LEE: Yeah, really, really interesting.

And single-cell foundation models claim to be capable of unraveling deeper insights than ever before.

Basically, we showed that single-cell foundation models perform worse in settings that are fundamental to biological discovery than much simpler machine learning and statistical methods that were used in the field before single-cell foundation models emerged and are the go-to standard for unpacking meaning from these complicated experiments.

And the way to understand this is because single-cell foundation models are trained in a way that tries to expose these models to millions of single-cells.

But let’s also talk about the impact for methodologists, people who are trying to improve these s…

This is such exciting work about environmental forecasting, so we’re happy to have the two of you join us today.

Mostly because AI weather forecasting models are computationally much more efficient and can even be more accurate.

What’s unfortunate though, about this big step forward, is that these developments are mostly limited to the setting of weather forecasting.

Weather forecasting is very important, obviously, but there are many other important environmental forecasting problems out there, such as air pollution forecasting or ocean wave forecasting.

STANLEY: Current approaches have really focused training very specifically on weather forecasting models.

LUNGREN: And now it really feels like this collaborative effort, you know, really can help start to extend that mission.

I think, you know, Will and Smitha, that we definitely feel the passion and the innovation.

Again, you know, in text, you refer to that earlier and certainly off the shelf, there’s really powerful applications.

LUNGREN: So, I think AI has always been thought of as a savior kind of technology.

And I guess for my part, I think really what we’re going to see is a massive unleash of creativity.

LEE: Yeah, yeah.

LEE: Yeah, yeah.

LEE: Yeah, yeah.

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

Yeah, yeah.

Today I’m talking to two researchers, Hongxia Hao, a senior researcher at Microsoft Research AI for Science, and Bing Lv, an associate professor in physics at the University of Texas at Dallas.

Hongxia and Bing are co-authors of a paper called Probing the Limit of Heat Transfer in Inorganic Crystals with Deep Learning .

LV: So I think one of the biggest things as Hongxia said, right?

We have a lot of new materials, exotic materials, which some of them, Hongxia can elaborate a little bit more.

HAO: Yeah, yeah.

I’m here today with Xing Xie, a partner research manager at Microsoft Research and co-author of a white paper called Societal AI: Research Challenges and Opportunities .

HUIZINGA: So let’s start with a brief overview of the background for this white paper on Societal AI.

XIE: The idea for this white paper emerged in response to the shift we are witnessing in the AI landscape.

XIE: Rather than follow a traditional research methodology, we built this white paper around ten fundamental, foundational research questions.

HUIZINGA: Yeah, yeah, yeah.

Roxana is among the world’s thought leaders in AI, healthcare, and medicine, thanks in part to groundbreaking work on AI biases and trustworthiness.

When we think about AI, we think about it being very futuristic, but it’s trained on data from the past.

And so I think your struggles and frustrations on, you know, how to expand that nationwide, I think, are really, really informative.

And in a way, I think … I don’t know that I or my coauthors were satisfied with that.

You know, AI has all the time in the world [LAUGHS] to write you a text.

[LAUGHS]DEBRONKART: Ah, well, that’s … that’s even weirder.

I’m, as I said at the beginning, I’m glad to be alive and I’m really, really, really grateful to be given a chance to share my thoughts with your audience because I really like super smart nerds.

And it’s really to me like where I’m seeing kind of the first set of really kind of promising AI applications.

And so, to me, that’s really kind of where I see the most interesting opportunities for technology and for digital health.

Just really, really appreciate it.

We are sorry, the page you requested cannot be found.

The page you are looking for could not be found or is no longer available.

But there was that class I really, really enjoyed, which was mathematical logic.

Well, let’s get onto the topic of Accelerating Foundation Models Research and unpack the big idea behind that.

It might be confusing for some people, Accelerating Foundation Models Research.

And so when we started with Accelerating Foundation Models Research and from now on, I will say AFMR if that’s okay.

It’s about access to people, access to the resources and really co-designing so that we can really, really make more advances together.

Sara is vice president and chief health AI officer at UC San Francisco Health.

LONGHURST: So the pat response is AI won’t replace doctors, but AI will replace doctors who don’t use AI.

LEE: And I’m assuming a chief health AI officer is not a role that has been around for a long time.

LEE: Should I be impressed or concerned that the chief health AI officer at UC San Francisco Health is using ChatGPT off label?

We’ll delve into how patients are using generative AI for their own healthcare, the hype and reality of AI drug discovery, and more.

About two years ago, with Carey Goldberg and Zak Kohane, we wrote a book, The AI Revolution in Medicine.

If you’re a patient, in what ways could AI change your experience as you try to navigate a complex healthcare system?

A strange and bizarre thought, I admit, but a natural one, I think, for any human being that’s encountering this amazing AI technology for the first time.

And since then, of course, I’ve come to learn that many people have had similar experiences in their first encounters with AI.

And in fact, I’ve come to think of this as, somewhat tongue in cheek, the nine stages of AI grief.

In this episode, Kyle does an overview of the intersection of graph theory and computational complexity theory. In complexity theory, we are about the runtime of an algorithm based on its input size. For many graph problems, the interesting questions we want to ask take longer and longer to answer! This episode provides the fundamental vocabulary and signposts along the path of exploring the intersection of graph theory and computational complexity theory.

In this episode, listeners will learn about Actantial Networks—graph-based representations of narratives where nodes are actors (such as people, institutions, or abstract entities) and edges represent the actions or relationships between them. The one who will present these networks is our guest Armin Pournaki, a joint PhD candidate at the Max Planck Institute and Sciences, who specializes in computational social science, where he develops methods to extract and analyze political narratives using natural language processing and network science. Armin explains how these methods can expose conflicting narratives around the same events, as seen in debates on COVID-19, climate change, or the wa…

How to build artificial intelligence systems that understand cause and effect, moving beyond simple correlations? As we all know, correlation is not causation. "Spurious correlations" can show, for example, how rising ice cream sales might statistically link to more drownings, not because one causes the other, but due to an unobserved common cause like warm weather. Our guest, Utkarshani Jaimini, a researcher from the University of South Carolina's Artificial Intelligence Institute, tries to tackle this problem by using knowledge graphs that incorporate domain expertise. Knowledge graphs (structured representations of information) are combined with neural networks in the field of neurosymbo…

In this episode we talk with Manita Pote, a PhD student at Indiana University Bloomington, specializing in online trust and safety, with a focus on detecting coordinated manipulation campaigns on social media. Key insights include how coordinated reply attacks target influential figures like journalists and politicians, how machine learning models can detect these inauthentic campaigns using structural and behavioral features, and how deletion patterns reveal efforts to evade moderation or manipulate engagement metrics. Follow our guest X/Twitter Google Scholar Papers in focus Coordinated Reply Attacks in Influence Operations: Characterization and Detection ,2025 Manipulating Twitter throug…

Kyle discusses the history and proof for the small world hypothesis.

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

In this episode we talk with Bavo DC Campo, a data scientist and statistician, who shares his expertise on the intersection of actuarial science, fraud detection, and social network analytics. Together we will learn how to use graphs to fight against insurance fraud by uncovering hidden connections between fraudulent claims and bad actors. Key insights include how social network analytics can detect fraud rings by mapping relationships between policyholders, claims, and service providers, and how the BiRank algorithm, inspired by Google’s PageRank, helps rank suspicious claims based on network structure. Bavo will also present his iFraud simulator that can be used to model fraudulent networ…

In this episode we talk with Justin Wang Ngai Yeung, a PhD candidate at the Network Science Institute at Northeastern University in London, who explores how network science helps uncover criminal networks. Justin is also a member of the organizing committee of the satellite conference dealing with criminal networks at the network science conference in The Netherlands in June 2025. Listeners will learn how graph-based models assist law enforcement in analyzing missing data, identifying key figures in criminal organizations, and improving intervention strategies. Key insights include the challenges of incomplete and inaccurate data in criminal network analysis, how law enforcement agencies us…

In this episode today’s guest is Celine Wüst, a master’s student at ETH Zurich specializing in secure and reliable systems, shares her work on automated software testing for graph databases. Celine shows how fuzzing—the process of automatically generating complex queries—helps uncover hidden bugs in graph database management systems like Neo4j, FalconDB, and Apache AGE. Key insights include how state-aware query generation can detect critical issues like buffer overflows and crashes, the challenges of debugging complex database behaviors, and the importance of security-focused software testing. We'll also find out which Graph DB company offers swag for finding bugs in its software and get C…

In this episode, Gabriel Petrescu, an organizational network analyst, discusses how network science can provide deep insights into organizational structures using OrgXO, a tool that maps companies as networks rather than rigid hierarchies. Listeners will learn how analyzing workplace collaboration networks can reveal hidden influencers, organizational bottlenecks, and engagement levels, offering a data-driven approach to improving effectiveness and resilience. Key insights include how companies can identify overburdened employees, address silos between departments, and detect vulnerabilities where too few individuals hold critical knowledge. Real-life applications range from mergers and acq…

Is it better to have your work team fully connected or sparsely connected? In this episode we'll try to answer this question and more with our guest Hiroki Sayama, a SUNY Distinguished Professor and director of the Center for Complex Systems at Binghamton University. Hiroki delves into the applications of network science in organizational structures and innovation dynamics by showing his recent work of extracting network structures from organizational charts to enable insights into decision-making and performance, He'll also cover how network connectivity impacts team creativity and innovation. Key insights include how the structure of organizational networks—such as the depth of hierarchy …

A man goes into a bar… This is the beginning of a riddle that our guest, Yoed Kennet, an assistant professor at the Technion's Faculty of Data and Decision Sciences, uses to measure creativity in subjects. In our talk, Yoed speaks about how to combine cognitive science and network science to explore the complexities and decode the mysteries of the human mind. The listeners will learn how network science provides tools to map and analyze human memory, revealing how problem-solving and creativity emerge from changes in semantic memory structures. Key insights include the role of memory restructuring during moments of insight, the connection between semantic networks and creative thinking, and…

Data science skills, a data science bootcamp, and why Python and SQL still reign supreme: In this episode, Kirill Eremenko returns to the podcast to speak to Jon Krohn about SuperDataScience subscriber success stories, where to focus in a field that is evolving incredibly quickly, and why in-person working and networking might give you the edge over other candidates in landing a top AI role. Additional materials: ⁠⁠⁠⁠www.superdatascience.com/899⁠⁠⁠ This episode is brought to you by ⁠Adverity, the conversational analytics platform⁠ and by the ⁠Dell AI Factory with NVIDIA⁠. Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship informat…

In this Five-Minute Friday, Jon Krohn announces his new, free workshop on Agentic AI. On this four-hour comprehensive course, you’ll learn the key terminology for working with these flexible, multi-agent systems and then get to grips with developing and deploying this artificial “team of experts” for all your AI-driven projects. Additional materials: ⁠⁠⁠⁠⁠www.superdatascience.com/898⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Diane Hare talks to Jon Krohn about the power of storytelling for corporate buy-in of AI initiatives, how to actively implement AI to transform organizations, and how emerging professionals can upskill themselves. Hear how she discovered her background in storytelling at Ernst & Young and her work with Simon Sinek, which she finds to be integral to her process. Inspired by Sinek’s aphorism “start with why”, Diane notes that many companies neglect this crucial part of their mission because they never take the time to work on it. Additional materials: ⁠⁠⁠www.superdatascience.com/897⁠⁠ This episode is brought to you by Trainium2, the latest AI chip from AWS, by Adverity, the conversational ana…

The Economist reported that global Google searches for "AI unemployment" hit an all-time high earlier this year. But do we have to worry about AI taking our jobs? In this week’s Five-Minute Friday, Jon Krohn investigates whether the rise of AI has directly led to an increase in unemployment. Additional materials: ⁠⁠⁠⁠www.superdatascience.com/896 Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

How to get funded by a VC specializing in AI: Head of AIX Ventures Shaun Johnson talks to Jon Krohn about investment strategies, how to simplify AI adoption, why a little competition can be so beneficial to AI startups, and how Big Tech is circumventing anti-monopoly measures. Additional materials: ⁠⁠www.superdatascience.com/895⁠ This episode is brought to you by ⁠Adverity, the conversational analytics platform⁠ and by the ⁠Dell AI Factory with NVIDIA⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (10:36) What Shaun looks for when evaluating early-stage AI startups (19:11) Building…

In this episode of “In Case You Missed It”, Jon Krohn takes clips from interviews with guests in May 2025. From AI agent integration and RAG-based chatbots to education through virtual reality headsets and data harmonization, this episode explores how industry leaders are developing the tools and technologies that can improve operations, education, healthcare, and marketing. Highlight clips are with John Roese, Global Chief Technology Officer and Chief AI Officer at Dell Technologies (Episode 887), Senior Developer Relations Engineer at Posit, PBC Jeroen Janssens and Lead Data Scientist at Xomnia Thijs Nieuwdorp (Episode 885), Founder of CEEK Mary Spio (Episode 889), and Martin Brunthaler, …

Avery Smith is a passionate and motivational YouTuber and careers educator for data science. In this episode, Jon Krohn asks Avery about the tools and tricks he has learned from personal experience and from his students in how to get ahead in the tech industry. Avery shares the “learning ladder” he uses to help newcomers start on the right foot with great examples from former bootcamp students who have put his theories into practice. And, if you’re using LinkedIn to find jobs, Avery explains why this might be one of the reasons you’re not getting work. Additional materials: ⁠www.superdatascience.com/893 This episode is brought to you by Adverity, the conversational analytics platform and by…

Businesses have entered a “trough of disillusionment” for AI. In this Five-Minute Friday, Jon Krohn learns why Fortune 500 execs are so frustrated with the tools and how they can work their way up the “slope of enlightenment” towards effective AI. Hear why AI takeup hasn’t so far gone to plan in the corporate world and what that world needs from AI to encourage greater business engagement. Additional materials: ⁠⁠⁠www.superdatascience.com/892⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Martin Brunthaler talks to Jon Krohn about founding Adverity, a data analytics platform for marketing that simplifies integrating data from multiple sources and crunching them into actionable insights. Learn how Adverity became a data analytics powerhouse serving multiple industries, and why Martin thinks AI will strengthen rather than diminish the job market for data scientists, data analysts, and machine learning engineers. Additional materials: www.superdatascience.com/891 Today’s episode is brought to you by Trainium2, the latest AI chip from AWS and by the Dell AI Factory with NVIDIA Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for spo…

In this week’s Five-Minute Friday, Jon Krohn reveals highlights from Stanford University’s AI Index Report. Released a few weeks ago by the Institute for Human-Centered AI, this annual report details the incredible technical advances, policies, and investments in artificial intelligence. Hear which models achieve the best performance relative to their size, in what scenarios top AI systems can outperform humans (and when humans still outperform AI), and more in Jon’s five key takeaways. Additional materials: ⁠⁠www.superdatascience.com/890⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Founder of CEEK’s Mary Spio talks to Jon Krohn about how the platform contributes to the emerging community of digital creators with its blockchain-powered virtual experiences. Hear how Mary got her first investors for CEEK and how it is used across industries as diverse as education, entertainment, aviation, and healthcare. Additional materials: ⁠⁠⁠www.superdatascience.com/889⁠ This episode is brought to you by ⁠⁠Adverity, the conversational analytics platform⁠⁠ and by the ⁠⁠Dell AI Factory with NVIDIA⁠⁠. Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (03:42) What CEEK is and the m…

Mike Pell speaks to Jon Krohn about The Microsoft Garage, a program that drives the culture of innovation at the tech multinational, and how listeners can apply their principles to foster innovation in their workplace. In this Five-Minute Friday, you’ll hear more about Microsoft’s approaches to agentic AI, the future of human-AI collaboration in the workplace, and why experimentation and curiosity are critical skills for the future of work. Additional materials: ⁠www.superdatascience.com/888⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Jon Krohn speaks to John Roese about the promise of multi-agent teams for business, the benefits of agentic AI systems that can identify and complete tasks independently, and how these systems demand new authentication, authorization, security and knowledge-sharing standards. They also discuss how to use AI to refine project ideas down to a core business need, as well as the new and emerging careers in the tech industry and beyond, all thanks to AI. Additional materials: ⁠⁠www.superdatascience.com/887 This episode is brought to you by ⁠Adverity, the conversational analytics platform⁠ and by the ⁠Dell AI Factory with NVIDIA⁠. Interested in sponsoring a SuperDataScience Podcast episode? Email…

Our In Case You Missed It episode for April has clips on NVIDIA’s and Dell’s product and service offers including an overview of NVIDIA’s GPUs, AI Enterprise, and its microservices. You’ll also hear about AWS’ focus on bringing choice to customers and the incredible power of its Graviton CPU, how Zerve opens access to AI deployment, Merck KGaA, Darmstadt, Germany’s multi-chip integration, and why reliance on the cloud might soon become a practice of times past. Additional materials: ⁠www.superdatascience.com/886⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Jeroen Janssens and Thijs Nieuwdorp are data frame library Polars’ greatest advocates in this episode with Jon Krohn, where they discuss their book, Python Polars: The Definitive Guide, best practice for using Polars, why Pandas users are switching to Polars for data frame operations in Python, and how the library reduces memory usage and compute time up to 10x more than Pandas. Listen to the episode to be a part of an O’Reilly giveaway! Additional materials: ⁠www.superdatascience.com/885 This episode is brought to you by Trainium2, the latest AI chip from AWS, by Adverity, the conversational analytics platform and by the Dell AI Factory with NVIDIA. Interested in sponsoring a SuperDataScie…

In this episode of Data Science at Home, we explore the fascinating world of neuromorphic computing — a brain-inspired approach to computation that could reshape the future of AI and robotics.

The episode breaks down how neuromorphic systems differ from conventional AI architectures like transformers and LLMs, diving into spiking neural networks (SNNs), their benefits in energy efficiency and real-time processing, and their limitations in training and scalability.

Real-world applications are highlighted, including low-power drones, hearing aids, and event-based cameras.

Francesco closes with a vision of hybrid systems where neuromorphic chips and LLMs coexist, blending biological inspiratio…

This episode explores the invisible battlespace of cyber and electronic warfare, where AI takes center stage.

SponsorsBuilding multi-agent software is hard — agent-to-agent and agent-to-tool communication is still the wild west.

At the intersection of ethics and engineering, Amethix creates AI systems that don’t just function—they adapt, learn, and serve.

Discover more at amethix.comWarcoded is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers and defense innovators the tools to prototype, simulate, and deploy autonomous systems with confidence.

Swarming the Battlefield explores how artificial intelligence is revolutionizing combat through coordinated drone swarms.

This episode uncovers how these intelligent agents turn the chaos of the battlefield into a synchronized dance of machine warfare.

At the intersection of ethics and engineering, Amethix creates AI systems that don’t just function—they adapt, learn, and serve.

Discover more at amethix.comWarcoded is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers and defense innovators the tools to prototype, simulate, and deploy autonomous systems with confidence.

In this gripping follow-up, we dive into how AI is transforming kinetic operations—from identifying a threat to executing a strike.

At the intersection of ethics and engineering, Amethix creates AI systems that don’t just function—they adapt, learn, and serve.

Discover more at amethix.comWarcoded is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers and defense innovators the tools to prototype, simulate, and deploy autonomous systems with confidence.

Whether it’s in the sky, on the ground, or in orbit—if it’s intelligent and mobile, Intrepid helps you build it.

Welcome to DSH/WarcodedWe explore how AI is transforming ISR (Intelligence, Surveillance, Reconnaissance)—from satellite imagery to drone feeds.

At the intersection of ethics and engineering, Amethix creates AI systems that don’t just function—they adapt, learn, and serve.

Discover more at amethix.com.”Warcoded is brought to you by Intrepid AI.

From drones to satellites, Intrepid AI gives engineers and defense innovators the tools to prototype, simulate, and deploy autonomous systems with confidence.

Learn more at intrepid.ai.”#AI #defensetech #ISR #LLM #Warcoded #DataScienceAtHome #OSINT #SIGINT #dronewarfare

🎙️ In this episode of Data Science at Home, we sit down with Kenny Vaneetvelde, the mastermind behind Atomic Agents, a groundbreaking framework redefining AI development.

🔍 Discover how atomicity simplifies complex AI systems, why modularity matters more than ever, and how Atomic Agents is eliminating hidden assumptions and redundant complexity in AI workflows.

💡 From real-world applications to the tech stack behind the framework, Kenny takes us on a deep dive into this lightweight, powerful tool for creating consistent and brand-aligned AI.

📌 Timestamps:0:00 – Intro2:30 – Kenny’s journey in AI5:00 – What are Atomic Agents?

10:45 – Why atomicity matters in AI18:20 – The tech behind Atomic A…

This episode explores how to break down barriers by running massive AI models on “crappy machines”—affordable, low-spec devices.

🐦 Twitter: @DataScienceAtHome📘 LinkedIn: https://www.linkedin.com/in/fragadaleta/Instagram: https://www.instagram.com/datascienceathome/Facebook: https://www.facebook.com/datascienceAHLinkedIn: https://www.linkedin.com/company/data-science-at-home-podcastDiscord Channel: https://discord.gg/4UNKGf3NEW TO DATA SCIENCE AT HOME?

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

Whether you’re a data professional, tech enthusiast, or just curious about the field, our podcast delivers insights, interviews, and discussions.

Send us mail …

Enter WeightWatcher—the AI detective tool that peeks inside neural networks without needing their data.

🐦 Twitter: @DataScienceAtHome📘 LinkedIn: https://www.linkedin.com/in/fragadaleta/Instagram: https://www.instagram.com/datascienceathome/Facebook: https://www.facebook.com/datascienceAHLinkedIn: https://www.linkedin.com/company/data-science-at-home-podcastDiscord Channel: https://discord.gg/4UNKGf3NEW TO DATA SCIENCE AT HOME?

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

Whether you’re a data professional, tech enthusiast, or just curious about the field, our podcast delivers insights, interviews, and discussions.

Send us mail at:hello@datascienceathom…

From the viral article “Tech’s Dumbest Mistake: Why Firing Programmers for AI Will Destroy Everything” on my newsletter at https://defragzone.substack.com/p/techs-dumbest-mistake-why-firinghere are my thoughts about AI replacing programmers…✨ Connect with us!

🐦 Twitter: @DataScienceAtHome📘 LinkedIn: https://www.linkedin.com/in/fragadaleta/Instagram: https://www.instagram.com/datascienceathome/Facebook: https://www.facebook.com/datascienceAHLinkedIn: https://www.linkedin.com/company/data-science-at-home-podcastDiscord Channel: https://discord.gg/4UNKGf3NEW TO DATA SCIENCE AT HOME?

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

Whether you’re a data profes…

In this episode, we dive into the transformative world of AI, data analytics, and cloud infrastructure with Josh Miramant, CEO of Blue Orange Digital.

As a seasoned entrepreneur with over $25 million raised across ventures and two successful exits, Josh shares invaluable insights on scaling data-driven businesses, integrating machine learning frameworks, and navigating the rapidly evolving landscape of cloud data architecture.

From generative AI to large language models, Josh explores cutting-edge trends shaping financial services, real estate, and consumer goods.

Tune in for a masterclass in leveraging data for impact and innovation!

Linkshttps://blueorange.digital/https://blueorange.digit…

Here’s the updated text with links to the websites included:AI is revolutionizing the military with autonomous drones, surveillance tech, and decision-making systems.

In this episode of Data Science at Home, we expose the cutting-edge tech reshaping defense—and the chilling ethical questions that follow.

🐦 Twitter: @DataScienceAtHome📘 LinkedIn: Francesco Gad📷 Instagram: https://www.instagram.com/datascienceathome/📘 Facebook: https://www.facebook.com/datascienceAH💼 LinkedIn: https://www.linkedin.com/company/data-science-at-home-podcast💬 Discord Channel: https://discord.gg/4UNKGf3NEW TO DATA SCIENCE AT HOME?

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

S…

In this episode of Data Science at Home, we’re diving deep into the powerful strategies that top AI companies, like OpenAI, use to scale their systems to handle millions of requests every minute!

From stateless services and caching to the secrets of async processing, discover 8 essential strategies to make your AI and machine learning systems unstoppable.

Instagram: https://www.instagram.com/datascienceathome/Twitter: @datascienceathomeFacebook: 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 ma…

Together, they explore the growing importance of distinguishing human-written from AI-generated text, discussing real-world examples from social media to news.

How reliable are current detection tools like DetectGPT?

What are the ethical and technical challenges ahead as AI continues to advance?

And is the balance between innovation and regulation tipping in the right direction?

Tune in for insights on the future of AI text detection and the broader implications for media, academia, and policy.

Welcome to Data Science at Home, where we don’t just drink the AI Kool-Aid.

Today, we’re dissecting Sam Altman’s “AI manifesto”—a magical journey where, apparently, AI will fix everything from climate change to your grandma’s back pain.

In this episode, I’ll break down the bold (and often bizarre) claims in Altman’s grand speech for the Intelligence Age.

I’ll give you the real scoop on what’s realistic, what’s nonsense, and why some tech billionaires just can’t resist overselling.

Chapters00:00 – Intro00:18 – CEO of Baidu Statement on AI Bubble03:47 – News On Sam Altman Open AI06:43 – Online Manifesto “The Intelleigent Age”13:14 – Deep Learning16:26 – AI gets Better With Scale17:45 – Conclu…

In this episode of Data Science at Home, we dive into the hidden costs of AI’s rapid growth — specifically, its massive energy consumption.

With tools like ChatGPT reaching 200 million weekly active users, the environmental impact of AI is becoming impossible to ignore.

Each query, every training session, and every breakthrough come with a price in kilowatt-hours, raising questions about AI’s sustainability.

Join us, as we uncovers the staggering figures behind AI’s energy demands and explores practical solutions for the future.

From efficiency-focused algorithms and specialized hardware to decentralized learning, this episode examines how we can balance AI’s advancements with our planet’s …