AI/ML News & Innovations Hub

Why Every Brain Metaphor in History Has Been Wrong [SPECIAL EDITION]

What if everything we think we know about the brain is just a really good metaphor that we forgot was a metaphor? This episode takes you on a journey through the history of scientific simplification, from a young Karl Friston watching wood lice in his garden to the bold claims that your mind is literally software running on biological hardware. We bring together some of the most brilliant minds we've interviewed — Professor Mazviita Chirimuuta, Francois Chollet, Joscha Bach, Professor Luciano Floridi, Professor Noam Chomsky, Nobel laureate John Jumper, and more — to wrestle with a deceptively simple question: *When scientists simplify reality to study it, what gets captured and what gets lost?* *Key ideas explored:* *The Spherical Cow Problem* — Science requires simplification. We're limited creatures trying to understand systems far more complex than our working memory can hold. But when does a useful model become a dangerous illusion? *The Kaleidoscope Hypothesis* — Francois Chollet's beautiful idea that beneath all the apparent chaos of reality lies simple, repeating patterns — like bits of colored glass in a kaleidoscope creating infinite complexity. Is this profound truth or Platonic wishful thinking? *Is Software Really Spirit?* — Joscha Bach makes the provocative claim that software is literally spirit, not metaphorically. We push back hard on this, asking whether the "sameness" we see across different computers running the same program exists in nature or only in our…

OpenMined Joins Open Forum for AI to Advance Responsible Data Governance

We’re excited to announce that OpenMined has joined the Open Forum for AI (OFAI), an international initiative led by Carnegie Mellon University that’s bringing together academic institutions and nonprofit organizations to advance human-centered and ethical approaches to artificial intelligence. Launched at Carnegie Mellon University in 2024, OFAI was created to foster collaboration, transparency, and inclusion […] The post OpenMined Joins Open Forum for AI to Advance Responsible Data Governance appeared first on OpenMined .

When protections outlive their purpose: A lesson on managing defense systems at scale

User feedback led us to clean up outdated mitigations. See why observability and lifecycle management are critical for defense systems. The post When protections outlive their purpose: A lesson on managing defense systems at scale appeared first on The GitHub Blog .

Reframing Impact: AI Summit 2026

The 2026 AI Impact Summit in India is the latest iteration of an event that has become a bellwether for global discourse around the AI industry, especially the question of whether, and how, it can be governed. But it also demonstrates how important ideas can be invoked in ways that dilute their meaning or co-opt their force. In this series—produced by AI Now Institute, Aapti Institute, and The Maybe—we bring together leading advocates, builders, and thinkers from around the world who live and breathe substance, analysis, and meaningful action into these ideas. The post Reframing Impact: AI Summit 2026 appeared first on AI Now Institute .

OpenMined Featured in Communications of the ACM on the Future of Synthetic Data and AI Training

In a recent article published by the Communications of the ACM — the flagship publication of the Association for Computing Machinery — OpenMined’s Executive Director, Andrew Trask, was featured as a key voice in the growing conversation around synthetic data, AI training, and the critical importance of controlling how data shapes model behavior. The Growing […] The post OpenMined Featured in Communications of the ACM on the Future of Synthetic Data and AI Training appeared first on OpenMined .

What is GitOps? Extending devops to Kubernetes and beyond

Over the past decade, software development has been shaped by two closely related transformations. One is the rise of devops and continuous integration and continuous delivery (CI/CD), which brought development and operations teams together around automated, incremental software delivery. The other is the shift from monolithic applications to distributed, cloud-native systems built from microservices and containers, typically managed by orchestration platforms such as Kubernetes . While Kubernetes and similar platforms simplify many aspects of running distributed applications, operating these systems at scale is still complicated. Configuration sprawl, environment drift, and the need for rapid, reliable change all introduce operational challenges. GitOps emerged as a way to address those challenges by extending familiar devops and CI/CD techniques beyond application code and into infrastructure and system configuration. At the heart of GitOps is the concept of infrastructure as code (IaC). In a GitOps model, not only application code but also infrastructure definitions, deployment configurations, and operational settings are described in files stored in a version control system. Automated processes continuously compare the running system with those declarations and work to bring the live environment back into alignment when differences appear. In this approach, the version control repository serves as the system of record for how applications and their supporting infrastruct…

React tutorial: Get started with the React JavaScript library

Despite many worthy contenders , React remains the most popular front-end framework, and a key player in the JavaScript development landscape. React is the quintessential reactive engine , continually innovating alongside the rest of the industry. A flagship open source project at Facebook, React is now part of Meta Open Source. For developers new to JavaScript and web development, this tutorial will get you started with this vital technology. React is not only a front-end framework, but is a component in full-stack frameworks like Next.js . Newer additions like React server-side rendering (SSR) and React server components (RSC) further blur the line between server and client. Also see: Is the React compiler ready for primetime? Why React? React’s prominence makes it an obvious choice for developers just starting out with web development. It is often chosen for its ability to offer a smooth and encompassing developer experience (DX), which distinguishes it from frameworks like Vue, Angular, and Svelte . It could be said that React’s true “killer feature” is the perks that come with longstanding popularity: learning resources, community support, libraries, and developers are all plentiful in the React ecosystem. Installing React Real-world React requires running on the server with a build tool, which we will explore in the next section. But to get your feet wet, we can start out with an online playground. There are several high-quality playgrounds for React, including full-bl…

As artificial intelligence reshapes news, media double down on investigations and field reporting

“One newsroom response to the disruption of artificial intelligence in content generation is not technological but editorial. The Journalism and Technology Trends and Predictions 2026 report says that, in a world where generative systems can create and repackage information at scale, news outlets are redefining which content is worth producing. According to the report, prepared […] The post As artificial intelligence reshapes news, media double down on investigations and field reporting appeared first on LatAm Journalism Review by the Knight Center .

As artificial intelligence reshapes news, media double down on investigations and field reporting

“One newsroom response to the disruption of artificial intelligence in content generation is not technological but editorial. The Journalism and Technology Trends and Predictions 2026 report says that, in a world where generative systems can create and repackage information at scale, news outlets are redefining which content is worth producing. According to the report, prepared […] The post As artificial intelligence reshapes news, media double down on investigations and field reporting appeared first on LatAm Journalism Review by the Knight Center .

#489 – Paul Rosolie: Uncontacted Tribes in the Amazon Jungle

Paul Rosolie is a naturalist, explorer, author of a new book titled Junglekeeper, and is someone who has dedicated his life to protecting the Amazon rainforest. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep489-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/paul-rosolie-3-transcript CONTACT LEX: Feedback – give feedback to Lex: https://lexfridman.com/survey AMA – submit questions, videos or call-in: https://lexfridman.com/ama Hiring – join our team: https://lexfridman.com/hiring Other – other ways to get in touch: https://lexfridman.com/contact EPISODE LINKS: Junglekeeper (new book): https://amzn.to/4q7vpAp Paul’s Instagram: https://instagram.com/paulrosolie Junglekeepers Website: https://junglekeepers.org Paul’s Website: https://paulrosolie.com Mother

Transcript for Paul Rosolie: Uncontacted Tribes in the Amazon Jungle | Lex Fridman Podcast #489

This is a transcript of Lex Fridman Podcast #489 with Paul Rosolie. The timestamps in the transcript are clickable links that take you directly to that point in the main video. Please note that the transcript is human generated, and may have errors. Here are some useful links: Go back to this episode’s main page Watch the full YouTube version of the podcast Table of Contents Here are the loose “chapters” in the conversation. Click link to jump approximately to that part in the transcript: 0:00 – Episode highlight 1:08 – Introduction 3:59 – Uncontacted tribes in the Amazon Jungle

Vision RAG: Enabling Search on Any Documents

Information comes in many shapes and forms. While retrieval-augmented generation (RAG) primarily focuses on plain text, it overlooks vast amounts of data along the way. Most enterprise knowledge resides in complex documents, slides, graphics, and other multimodal sources. Yet, extracting useful information from these formats using optical character recognition (OCR) or other parsing techniques is often low-fidelity, brittle, and expensive. Vision RAG makes complex documents—including their figures and tables—searchable by using multimodal embeddings, eliminating the need for complex and costly text extraction. This guide explores how Voyage AI’s latest model powers this capability and provides a step-by-step implementation walkthrough. Vision RAG: Building upon text RAG Vision RAG is an evolution of traditional RAG built on the same two components: retrieval and generation. In traditional RAG, unstructured text data is indexed for semantic search. At query time, the system retrieves relevant documents or chunks and appends them to the user’s prompt so the large language model (LLM) can produce more grounded, context-aware answers. Figure 1. Text RAG with Voyage AI and MongoDB. Text RAG with Voyage AI and MongoDB Enterprise data, however, is rarely just clean plain text. Critical information often lives in PDFs, slides, diagrams, dashboards, and other visual formats. Today, this is typically handled by parsing tools and OCR services. Those approaches create several problems:…

Information-Driven Design of Imaging Systems

An encoder (optical system) maps objects to noiseless images, which noise corrupts into measurements. Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects. Many imaging systems produce measurements that humans never see or cannot interpret directly. Your smartphone processes raw sensor data through algorithms before producing the final photo. MRI scanners collect frequency-space measurements that require reconstruction before doctors can view them. Self-driving cars process camera and LiDAR data directly with neural networks. What matters in these systems is not how measurements look, but how much useful information they contain. AI can extract this information even when it is encoded in ways that humans cannot interpret. And yet we rarely evaluate information content directly. Traditional metrics like resolution and signal-to-noise ratio assess individual aspects of quality separately, making it difficult to compare systems that trade off between these factors. The common alternative, training neural networks to reconstruct or classify images, conflates the quality of the imaging hardware with the quality of the algorithm. We developed a framework that enables direct evaluation and optimization of imaging systems based on their information content. In our NeurIPS 2025 paper , we show that this information metric predicts system performance across four imaging domains, and that optimizing it prod…

2025 was the year of agents, what's coming in 2026?

In this start-of-year FC episode, Chris and Daniel break down what really mattered in AI in 2025, and what to expect in 2026. They explore the rise of AI agents, the practical reality of multimodal AI, and how reasoning models are reshaping workflows. The conversation dives into infrastructure and energy constraints, the continued value of predictive models, and why orchestration (not just better models) is becoming the defining skill for AI teams. The episode wraps with grounded 2026 predictions on where AI systems, tooling, and builders are headed next. Featuring: Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Daniel Whitenack – Website , GitHub , X Sponsor: Framer - The enterprise-grade website builder that lets your team ship faster. Get 30% off at framer.com/practicalai Upcoming Events: Register for upcoming webinars here !

Intelligent Robots in 2026: Are We There Yet? with Nikita Rudin - #760

Today, we're joined by Nikita Rudin, co-founder and CEO of Flexion Robotics to discuss the gap between current robotic capabilities and what’s required to deploy fully autonomous robots in the real world. Nikita explains how reinforcement learning and simulation have driven rapid progress in robot locomotion—and why locomotion is still far from “solved.” We dig into the sim2real gap, and how adding visual inputs introduces noise and significantly complicates sim-to-real transfer. We also explore the debate between end-to-end models and modular approaches, and why separating locomotion, planning, and semantics remains a pragmatic approach today. Nikita also introduces the concept of "real-to-sim", which uses real-world data to refine simulation parameters for higher fidelity training, discusses how reinforcement learning, imitation learning, and teleoperation data are combined to train robust policies for both quadruped and humanoid robots, and introduces Flexion's hierarchical approach that utilizes pre-trained Vision-Language Models (VLMs) for high-level task orchestration with Vision-Language-Action (VLA) models and low-level whole-body trackers. Finally, Nikita shares the behind-the-scenes in humanoid robot demos, his take on reinforcement learning in simulation versus the real world, the nuances of reward tuning, and offers practical advice for researchers and practitioners looking to get started in robotics today. The complete show notes for this episode can be found at…

Claude Code and What Comes Next

With the right tools, AI can accomplish impressive things

Normality test for residuals failed but QQ may be ok can I still do LMER?

Can I still use a LMER with the following Shapiro-Wilk normality test result on the residuals? The assumption of normality seems to show it's not normal, p=0.02 (142 observations) Though I think the QQ graph (below) may be not too bad? if so, how would I justify continuing with this? just say that the QQ graph shows it's close? If using LMER is not valid, what other tests could perhaps be used? All help is greatly appreciated! thanks! ---- Model m amount is the quantity eaten (trying to work out if there is an effect of side or diet) side is left/right (there about 10 feeder pairs, sides containing each diet were switched daily), diet is 'high nutrient' or 'low nutrient' (the model originally also had Day but this was not significant and was removed) pairID is the pair of feeders, there were about 10 pairs ____ R results > Shapiro-Wilk normality test > > data: residuals_m W = 0.978, p-value = 0.022 > print(levene_test_diet) Levene's Test for Homogeneity of Variance (center = median) Df F value Pr(>F) group 1 0.69 0.40725 140 > print(levene_test_side) Levene's Test for Homogeneity of Variance (center = median) Df F value Pr(>F) group 1 1.11 0.29 140 sorry if this has already been asked, just can't find (or perhaps understand) the other answers. I have seen an answer that says based on the QQ graph it is ok, I'm not sure how to know how close to the line the QQ graph needs to be for that to be valid. I'm happy to change the test if another test is better.

Lyft’s Feature Store: Architecture, Optimization, and Evolution

Written by Rohan Varshney , with support from Devon Mittow & Janice Lee . This article expands upon a presentation from the Feature Store Summit 2025, which can be viewed in full here . There is also another video available on the evolution of Lyft’s Feature Store from DE4AI 2024. Introduction and Core Purpose Lyft’s Feature Store stands as a core infrastructural pillar within its Data Platform organization, designed to optimize the management and deployment of Machine Learning (ML) features at massive scale. Its primary objective is to centralize feature engineering efforts, guaranteeing uniformity across diverse models and workflows that perform important data-driven decision making across the entire rideshare stack. By streamlining the entire lifecycle — from feature creation and storage to low-latency access and high-throughput processing — it facilitates effective offline and online model training and inference. This post will provide a refreshed look ( since 5 years ago ) at the architectural evolution, practical applications, performance tuning, and significant improvements in developer experience we’ve performed over the past few years to improve efficiency, scalability, performance, and user accessibility. Ultimately, we aim to illustrate how the Feature Store empowers Lyft engineers to develop highly effective service components and ML models, a capability that is becoming vital for emerging AI and Large Language Model (LLM) applications. Defining Our Audience and…

ESG obstacles immobilize 25% of global copper supply, says consultancy

As the international community accelerates its transition toward renewable energy and digital infrastructure, a significant paradox has emerged within the mining sector: More than a quarter of the total global output of copper remains inaccessible because of complications related to ESG, according to a study from GEM Mining Consulting.

Zero-Setup Federated Learning: Train Models Across Private Datasets Using Only Google Colab

Have you ever wanted to train a machine learning model on distributed private data without anyone sharing their raw data? In this tutorial, you’ll learn how to run a complete federated learning workflow directly from Google Colab—no local setup required. We’ll use the PIMA Indians Diabetes dataset split across two data owners to train a […] The post Zero-Setup Federated Learning: Train Models Across Private Datasets Using Only Google Colab appeared first on OpenMined .

AutoGrad Changed Everything (Not Transformers) [Dr. Jeff Beck]

Dr. Jeff Beck, mathematician turned computational neuroscientist, joins us for a fascinating deep dive into why the future of AI might look less like ChatGPT and more like your own brain. **SPONSOR MESSAGES START** — Prolific - Quality data. From real people. For faster breakthroughs. https://www.prolific.com/?utm_source=mlst — **END** *What if the key to building truly intelligent machines isn't bigger models, but smarter ones?* In this conversation, Jeff makes a compelling case that we've been building AI backwards. While the tech industry races to scale up transformers and language models, Jeff argues we're missing something fundamental: the brain doesn't work like a giant prediction engine. It works like a scientist, constantly testing hypotheses about a world made of *objects* that interact through *forces* — not pixels and tokens. *The Bayesian Brain* — Jeff explains how your brain is essentially running the scientific method on autopilot. When you combine what you see with what you hear, you're doing optimal Bayesian inference without even knowing it. This isn't just philosophy — it's backed by decades of behavioral experiments showing humans are surprisingly efficient at handling uncertainty. *AutoGrad Changed Everything* — Forget transformers for a moment. Jeff argues the real hero of the AI boom was automatic differentiation, which turned AI from a math problem into an engineering problem. But in the process, we lost sight of what actually makes intelligence work.…

What is cloud computing? From infrastructure to autonomous, agentic-driven ecosystems

Cloud computing continues to be the platform of choice for large applications and a driver of innovation in enterprise technology. Gartner forecasts public cloud spending alone to the public cloud services market alone will reach $1.42 trillion in current U.S. dollars, driven by AI workloads and enterprise modernization. Driving this growth are the rise of AI and machine learning on the cloud , adoption of edge computing , the maturation of serverless computing , the emergence of multicloud strategies , improved security and privacy, and more sustainable cloud practices. What is cloud computing? While often used broadly, the term cloud computing is defined as an abstraction of compute, storage, and network infrastructure assembled as a platform on which applications and systems are deployed quickly and scaled on the fly. Most cloud customers consume public cloud computing services over the internet, which are hosted in large, remote data centers maintained by cloud providers. The most common type of cloud computing, SaaS (software as service), delivers prebuilt applications to the browsers of customers who pay per seat or by usage, exemplified by such popular apps as Salesforce, Google Docs, or Microsoft Teams. 5 top trends in cloud computing Agentic cloud ecosystems: The shift from AI as a tool to AI as an autonomous operator within cloud environments. Sovereign and localized clouds: Meeting strict national data residency and digital sovereignty laws. Specialized AI hardwar…

The State Of LLMs 2025: Progress, Problems, and Predictions

A 2025 review of large language models, from DeepSeek R1 and RLVR to inference-time scaling, benchmarks, architectures, and predictions for 2026.

LLM Research Papers: The 2025 List (July to December)

In June, I shared a bonus article with my curated and bookmarked research paper lists to the paid subscribers who make this Substack possible.

Your Brain Doesn't Command Your Body. It Predicts It. [Max Bennett]

Tim sits down with Max Bennett to explore how our brains evolved over 600 million years—and what that means for understanding both human intelligence and AI. Max isn't a neuroscientist by training. He's a tech entrepreneur who got curious, started reading, and ended up weaving together three fields that rarely talk to each other: comparative psychology (what different animals can actually do), evolutionary neuroscience (how brains changed over time), and AI (what actually works in practice). *Your Brain Is a Guessing Machine* You don't actually "see" the world. Your brain builds a simulation of what it *thinks* is out there and just uses your eyes to check if it's right. That's why optical illusions work—your brain is filling in a triangle that isn't there, or can't decide if it's looking at a duck or a rabbit. *Rats Have Regrets* In a fascinating experiment called "Restaurant Row," rats make choices about waiting for food. When they skip a short wait for something they like and end up stuck with a long wait for something they don't—you can literally watch their brain imagine eating the food they passed up. They regret their choice and make different decisions next time. *Chimps Are Machiavellian* The most gripping story is about two chimps, Rock and Belle. Belle learns where food is hidden. Rock figures out he can just follow her and steal it. So Belle starts hiding the food when she finds it. Then Rock starts *pretending* not to watch her, then sprinting to grab the food o…

Atos exits South America following sale of regional business to Semantix

Global IT services company Atos has divested its South American business to Brazilian company Semantix, marking its exit from the regional market. The deal sees around 2,800 employees in Brazil, Argentina, Chile, Colombia, Uruguay and Peru transfer to Semantix, which now becomes one of the largest players in South America in the field of digital transformation and IT services.

Traditional X-Mas Stream

Letsgooo

Traditional Holiday Live Stream

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Why Scientists Can't Rebuild a Polaroid Camera [César Hidalgo]

César Hidalgo has spent years trying to answer a deceptively simple question: What is knowledge, and why is it so hard to move around? We all have this intuition that knowledge is just... information. Write it down in a book, upload it to GitHub, train an AI on it—done. But César argues that's completely wrong. Knowledge isn't a thing you can copy and paste. It's more like a living organism that needs the right environment, the right people, and constant exercise to survive. Guest: César Hidalgo, Director of the Center for Collective Learning The Big Ideas 1. Knowledge Follows Laws (Like Physics) Just as temperature and gravity follow predictable rules, so does knowledge. César outlines three laws: - Time: How knowledge grows (fast at first, then it plateaus) - Space: How knowledge spreads (it's way harder than you think) - Value: How we can measure a country's "knowledge potential" 2. You Can't Download Expertise The most memorable stories in this conversation prove that knowledge is embodied—it lives in people, teams, and organizations, not in manuals. 3. Why Big Companies Fail to Adapt César explains "architectural innovation"—the idea that small changes (like shipping books directly to customers) can require a completely different organizational structure. 4. The "Infinite Alphabet" of Economies Every skill, every industry, every capability is like a letter in an alphabet. César's research shows you can actually predict which countries will grow by counting their "letter…

TiDAR: Think in Diffusion, Talk in Autoregression (Paper Analysis)

Paper: https://arxiv.org/abs/2511.08923 Abstract: Diffusion language models hold the promise of fast parallel generation, while autoregressive (AR) models typically excel in quality due to their causal structure aligning naturally with language modeling. This raises a fundamental question: can we achieve a synergy with high throughput, higher GPU utilization, and AR level quality? Existing methods fail to effectively balance these two aspects, either prioritizing AR using a weaker model for sequential drafting (speculative decoding), leading to lower drafting efficiency, or using some form of left-to-right (AR-like) decoding logic for diffusion, which still suffers from quality degradation and forfeits its potential parallelizability. We introduce TiDAR, a sequence-level hybrid architecture that drafts tokens (Thinking) in Diffusion and samples final outputs (Talking) AutoRegressively - all within a single forward pass using specially designed structured attention masks. This design exploits the free GPU compute density, achieving a strong balance between drafting and verification capacity. Moreover, TiDAR is designed to be serving-friendly (low overhead) as a standalone model. We extensively evaluate TiDAR against AR models, speculative decoding, and diffusion variants across generative and likelihood tasks at 1.5B and 8B scales. Thanks to the parallel drafting and sampling as well as exact KV cache support, TiDAR outperforms speculative decoding in measured throughput and…

PhD Bodybuilder Predicts The Future of AI (97% Certain) [Dr. Mike Israetel]

This is a lively, no-holds-barred debate about whether AI can truly be intelligent, conscious, or understand anything at all — and what happens when (or if) machines become smarter than us. Dr. Mike Israetel is a sports scientist, entrepreneur, and co-founder of RP Strength (a fitness company). He describes himself as a "dilettante" in AI but brings a fascinating outsider's perspective. Jared Feather (IFBB Pro bodybuilder and exercise physiologist) The Big Questions: 1. When is superintelligence coming? 2. Does AI actually understand anything? 3. The Simulation Debate (The Spiciest Part) Tim says a simulation of fire doesn't get hot. They go back and forth on whether you could upload your mind to a computer — Mike says yes, Tim says absolutely not. 4. Will AI kill us all? (The Doomer Debate) Mike thinks the "AI will exterminate humanity" crowd has it backwards. His argument: any system smart enough to wage war is smart enough to realize cooperation is the winning strategy. Super-intelligent AI would want to *study* us, not destroy us. He uses the raccoon analogy to explain what agency really means. 5. What happens to human jobs and purpose? 6. Do we need suffering? In a surprisingly emotional moment, Tim asks if suffering gives life meaning. Mike's answer? "Fuck no. Desperately" Mikes channel: https://www.youtube.com/channel/UCfQgsKhHjSyRLOp9mnffqVg RESCRIPT INTERACTIVE PLAYER: https://app.rescript.info/public/share/GVMUXHCqctPkXH8WcYtufFG7FQcdJew_RL_MLgMKU1U --- TIMESTAMPS:…

There Is No Leaderboard for Safety — Andrew Gordon & Nora Petrova

People are using AI for mental health advice and life decisions, but there's no oversight and no safety ratings. We grade models on speed and smarts... but not on whether they're safe to use. Why isn't that just as important? Featuring Andrew Gordon and Nora Petrova from Prolific, discussing AI evaluation, benchmarks, and why human preference matters. 🎙️ Full episode: https://youtu.be/rqiC9a2z8Io #AIShorts #AISafety #MachineLearning

That’s a Wrap: MongoDB’s 2025 in Review & 2026 Predictions

It’s nearly the end of the year—again! That means it’s time for an end-of-year blog post that expresses disbelief at the passage of time. Which, as the saying goes, flies when you’re having fun. And definitely when you’re as busy as MongoDB was in 2025. It was a big year for the company—and more importantly, for the tens of thousands of customers and millions of developers who rely on MongoDB’s modern data platform for their most mission-critical workloads. At MongoDB, everything we do starts with our obsession with customers and their needs, and if there’s a theme to MongoDB’s 2025, it was (and will continue to be) enabling customer innovation and helping them succeed in the AI era. So here are a few highlights of how MongoDB acted on behalf of customers in 2025. From the acquisition of Voyage AI to customer success across industries, a lot happened in 2025. Let’s go!* *Read to the end for 2026 thoughts. 2025: The (MongoDB) year that was Voyage AI, modernization, and search In February, MongoDB announced the acquisition of Voyage AI, a pioneer in embedding and reranking models, to enhance the accuracy of AI applications. Integrating Voyage AI's advanced retrieval technology with MongoDB’s modern, AI-ready data platform addresses a critical challenge: LLM model hallucinations caused by a lack of context. By improving retrieval accuracy for specialized domains like finance and law, the integration enables businesses to deploy AI for mission-critical use cases. To learn more,…

The Shape of AI: Jaggedness, Bottlenecks and Salients

And why Nano Banana Pro is such a big deal

Cloud native explained: How to build scalable, resilient applications

What is cloud native? Cloud native defined The term “cloud-native computing” encompasses the modern approach to building and running software applications that exploit the flexibility, scalability, and resilience of cloud computing. The phrase is a catch-all that encompasses not just the specific architecture choices and environments used to build applications for the public cloud, but also the software engineering techniques and philosophies used by cloud developers. The Cloud Native Computing Foundation (CNCF) is an open source organization that hosts many important cloud-related projects and helps set the tone for the world of cloud development. The CNCF offers its own definition of cloud native: Cloud native practices empower organizations to develop, build, and deploy workloads in computing environments (public, private, hybrid cloud) to meet their organizational needs at scale in a programmatic and repeatable manner. It is characterized by loosely coupled systems that interoperate in a manner that is secure, resilient, manageable, sustainable, and observable. Cloud native technologies and architectures typically consist of some combination of containers, service meshes, multi-tenancy, microservices, immutable infrastructure, serverless, and declarative APIs — this list is not exhaustive. This definition is a good start, but as cloud infrastructure becomes ubiquitous, the cloud native world is beginning to spread behind the core of this definition. We’ll explore that ev…

Token-count-based Batching: Faster, Cheaper Embedding Inference for Queries

Embedding model inference often struggles with efficiency when serving large volumes of short requests—a common pattern in search, retrieval, and recommendation systems. At Voyage AI by MongoDB, we call these short requests queries, and other requests are called documents. Queries typically must be served with very low latency (typically 100–300 ms). Queries are typically short, and their token-length distribution is highly skewed. As a result, query inference tends to be memory-bound rather than compute-bound. Query traffic is pretty spiky, so autoscaling is too slow. In sum, serving many short requests sequentially is highly inefficient. In this blog post, we explore how batching can be used to serve queries more efficiently. We first discuss padding removal in modern inference engines, a key technique that enables effective batching. We then present practical strategies for forming batches and selecting an appropriate batch size. Finally, we walk through the implementation details and share the resulting performance improvements: a 50% reduction in GPU inference latency—despite using 3X fewer GPUs. Padding removal makes effective batching possible Given the patterns of query traffic, one straightforward idea is: can we batch them to improve inference efficiency? Padding removal, supported in inference engines like vLLM and SGLang, makes efficient batching possible. Most inference engines accept requests in the form (B, S), where B is the sequence number in the batch, and…

HELM Arabic

As part of our efforts to better understand the multilingual capabilities of large language models (LLMs), we present HELM Arabic, a leaderboard for transparent and reproducible evaluation of LLMs on Arabic language benchmarks. This leaderboard was produced in collaboration with Arabic.AI.

2026, the year we shine

Vukosi Marivate is a Professor of Computer Science and the ABSA UP Chair of Data Science at the University of Pretoria, South Africa🇿🇦. Vukosi leads the African Institute for Data Science and Artificial Intelligence (AfriDSAI). Additionally, he co-founded both the Deep Learning Indaba, co-founder of Lelapa AI, an African startup focused on AI for Africans […] The post 2026, the year we shine appeared first on Deep Learning Indaba .

Rethinking Pre-Training for Agentic AI with Aakanksha Chowdhery - #759

Today, we're joined by Aakanksha Chowdhery, member of technical staff at Reflection, to explore the fundamental shifts required to build true agentic AI. While the industry has largely focused on post-training techniques to improve reasoning, Aakanksha draws on her experience leading pre-training efforts for Google’s PaLM and early Gemini models to argue that pre-training itself must be rethought to move beyond static benchmarks. We explore the limitations of next-token prediction for multi-step workflows and examine how attention mechanisms, loss objectives, and training data must evolve to support long-form reasoning and planning. Aakanksha shares insights on the difference between context retrieval and actual reasoning, the importance of "trajectory" training data, and why scaling remains essential for discovering emergent agentic capabilities like error recovery and dynamic tool learning. The complete show notes for this episode can be found at https://twimlai.com/go/759.

Django tutorial: Get started with Django 6

Django is a one-size-fits-all Python web framework that was inspired by Ruby on Rails and uses many of the same metaphors to make web development fast and easy. Fully loaded and flexible, Django has become one of Python’s most widely used web frameworks. Now in version 6.0, Django includes virtually everything you need to build a web application of any size, and its popularity makes it easy to find examples and help for various scenarios. Plus, Django provides tools to allow your application to evolve and add features gracefully, and to migrate its data schema if there is one. Django also has a reputation for being complex, with many components and a good deal of “under the hood” configuration required. In truth, you can use Django to get a simple Python application up and running in relatively short order, then expand its functionality as needed. This article guides you through creating a basic application using Django 6.0. We’ll also touch on the most crucial features for web developers in the Django 6 release . What version of Python do I need? To install Django 6.0, you will need Python 3.12 or better. Ideally, you should use the most recent Python version that supports everything you want to do with your Django project, but in some cases, it may not be possible to update. If you’re stuck with an earlier version of Python, you may be able to use Django 5. Consult Django’s Python version table to find out which versions you can use. Installing Django Assuming you have Pyt…

NTT Data appoints Cristiano Rios as Director of Strategy & Operations

NTT Data has strengthened its leadership team in Brazil with Cristiano Rios, who will assume the position of Director of Strategy & Operations. Cristiano Rios has more than 20 years of experience in business consulting, specializing in strategy and operations in sectors including consumer & retail, healthcare, life sciences, and industrial.

From Python3.8 to Python3.10: Our Journey Through a Memory Leak

Image generated with ChatGPT (OpenAI), 2025. Intro When working with Python, memory management often feels like a solved problem. The garbage collector quietly does its job, and unlike C or C++, we rarely think about malloc or free. This doesn’t mean that there are no memory leaks in Python. Reference cycles, unreleased resources like connection pooling, global caches, etc can slowly inflate your process’s memory footprint. You might not notice it at first, until your worker starts OOM-ing, latency creeps up, or container restarts become mysteriously frequent. In this post, we’ll share the story of a real-world memory leak we encountered during a Python upgrade — how we discovered it, the tools and techniques we used to investigate, and the lessons we learned. What happened after upgrading to Python 3.10? Back in the summer of 2024, we had an initiative at Lyft to upgrade all of our Python services from v3.8 to 3.10 as v3.8 was scheduled to be EoL by the end of 2024. You can find more details on how our awesome Backend Foundations team at Lyft does Python upgrade across hundreds of repos at scale here . The upgrade involved two phases: the first phase was to upgrade all the dependencies to be Python 3.10 compatible, and the second phase was to upgrade the services to Python 3.10. The dependency upgrades went smoothly for all services and then the phase to upgrade all services to Python 3.10 rolled out. While all services were running Python 3.10 smoothly, there was one servi…

Titans: Learning to Memorize at Test Time (Paper Analysis)

Paper: https://arxiv.org/abs/2501.00663 Abstract: Over more than a decade there has been an extensive research effort on how to effectively utilize recurrent models and attention. While recurrent models aim to compress the data into a fixed-size memory (called hidden state), attention allows attending to the entire context window, capturing the direct dependencies of all tokens. This more accurate modeling of dependencies, however, comes with a quadratic cost, limiting the model to a fixed-length context. We present a new neural long-term memory module that learns to memorize historical context and helps attention to attend to the current context while utilizing long past information. We show that this neural memory has the advantage of fast parallelizable training while maintaining a fast inference. From a memory perspective, we argue that attention due to its limited context but accurate dependency modeling performs as a short-term memory, while neural memory due to its ability to memorize the data, acts as a long-term, more persistent, memory. Based on these two modules, we introduce a new family of architectures, called Titans, and present three variants to address how one can effectively incorporate memory into this architecture. Our experimental results on language modeling, common-sense reasoning, genomics, and time series tasks show that Titans are more effective than Transformers and recent modern linear recurrent models. They further can effectively scale to larger…

2025 Year in Review

An eventful year of progress in health and career, while making time for travel and reflection.

Why Vision Language Models Ignore What They See with Munawar Hayat - #758

In this episode, we’re joined by Munawar Hayat, researcher at Qualcomm AI Research, to discuss a series of papers presented at NeurIPS 2025 focusing on multimodal and generative AI. We dive into the persistent challenge of object hallucination in Vision-Language Models (VLMs), why models often discard visual information in favor of pre-trained language priors, and how his team used attention-guided alignment to enforce better visual grounding. We also explore a novel approach to generalized contrastive learning designed to solve complex, composed retrieval tasks—such as searching via combined text and image queries—without increasing inference costs. Finally, we cover the difficulties generative models face when rendering multiple human subjects, and the new "MultiHuman Testbench" his team created to measure and mitigate issues like identity leakage and attribute blending. Throughout the discussion, we examine how these innovations align with the need for efficient, on-device AI deployment. The complete show notes for this episode can be found at https://twimlai.com/go/758.

30% Faster Multimodal AI Training with Ray and Disaggregated Hybrid Parallelism

AI-powered assistants for scientific discovery

Andreas Geiger receives ERC Consolidator Grant

How to interpret DHARMa residual diagnostic showing bimodal pattern (“two humps”) in a negative binomial GLM?

I’m trying to fit a negative binomial GLM for a count response variable (stems per hectare). The data were significantly overdispersed. Thus I chose glm.nb() from MASS and then checked model fit using the DHARMa package. However, the DHARMa residual density plot shows a clear “two-hump”/bimodal pattern and significant quantile deviation, suggesting some form of nonlinearity or heteroskedasticity. The QQ plot did not show any substantial dispersion or outliers, and the KS test results were not significant. Can someone help me with approaches to deal with such an issue

From DeepSeek V3 to V3.2: Architecture, Sparse Attention, and RL Updates

Understanding How DeepSeek's Flagship Open-Weight Models Evolved

The complete guide to Node.js frameworks

Node.js is one of the most popular server-side platforms, especially for web applications. It gives you non-blocking JavaScript without a browser, plus an enormous ecosystem. That ecosystem is one of Node’s chief strengths, making it a go-to option for server development. This article is a quick tour of the most popular web frameworks for server development on Node.js . We’ll look at minimalist tools like Express.js, batteries-included frameworks like Nest.js, and full-stack frameworks like Next.js. You’ll get an overview of the frameworks and a taste of what it’s like to write a simple server application in each one. Minimalist web frameworks When it comes to Node web frameworks, minimalist doesn’t mean limited. Instead, these frameworks provide the essential features required to do the job for which they are intended. The frameworks in this list also tend to be highly extensible, so you can customize them as needed. With minimalist frameworks, pluggable extensibility is the name of the game. Express.js At over 47 million weekly downloads on npm, Express is one of the most-installed software packages of all time—and for good reason. Express gives you basic web endpoint routing and request-and-response handling inside an extensible framework that is easy to understand. Most other frameworks in this category have adopted the basic style of describing a route from Express. This framework is the obvious choice when you simply need to create some routes for HTTP, and you don’t m…