AI/ML News & Innovations Hub

Beyond AI: What the Pentagon is missing with its trimmed ‘critical technologies’ list

CSET’s Julie George shared her expert perspective in an op-ed published by Bulletin of the Atomic Scientists. In the piece, she argues that while the Defense Department’s decision to narrow its list of critical technologies is a positive step, the Pentagon must also improve how it prioritizes and funds emerging technologies to address overlooked capability gaps and strengthen long-term military innovation. The post Beyond AI: What the Pentagon is missing with its trimmed ‘critical technologies’ list appeared first on Center for Security and Emerging Technology .

Three-Year Action Plan for “Data Factor of Production ×”

Read our translation of a Chinese government plan that calls for making data more plentiful and accessible in industries such as manufacturing, agriculture, transportation, finance, scientific research, and healthcare. The post Three-Year Action Plan for “Data Factor of Production ×” appeared first on Center for Security and Emerging Technology .

Kubernetes v1.36: More Drivers, New Features, and the Next Era of DRA

Dynamic Resource Allocation (DRA) has fundamentally changed how platform administrators handle hardware accelerators and specialized resources in Kubernetes. In the v1.36 release, DRA continues to mature, bringing a wave of feature graduations, critical usability improvements, and new capabilities that extend the flexibility of DRA to native resources like memory and CPU, and support for ResourceClaims in PodGroups. Driver availability continues to expand. Beyond specialized compute accelerators, the ecosystem includes support for networking and other hardware types, reflecting a move toward a more robust, hardware-agnostic infrastructure. Whether you are managing massive fleets of GPUs, need better handling of failures, or simply looking for better ways to define resource fallback options, the upgrades to DRA in 1.36 have something for you. Let's dive into the new features and graduations! Feature graduations The community has been hard at work stabilizing core DRA concepts. In Kubernetes 1.36, several highly anticipated features have graduated to Beta and Stable. Prioritized list (stable) Hardware heterogeneity is a reality in most clusters. With the Prioritized list feature, you can confidently define fallback preferences when requesting devices. Instead of hardcoding a request for a specific device model, you can specify an ordered list of preferences (e.g., "Give me an H100, but if none are available, fall back to an A100"). The scheduler will evaluate these requests in…

Notes from inside China's AI labs

Lessons from my trip to talk to most of the leading AI labs in China.

Inside Porsche Cup Brasil’s AI-powered race operations

The post Inside Porsche Cup Brasil’s AI-powered race operations appeared first on Source .

AI Is Changing What Customers Need From a Database. MongoDB 8.3 Is Built for It

Today, we announced at .local London that MongoDB 8.3 is built for the speed AI demands—and our customers can't afford to wait. The data layer has to move at AI speed The old contract between databases and the applications on top of them was simple: databases improve slowly, and architectures evolve around them. AI has changed that contract. The workloads our customers are shipping today—agents retrieving at sub-100ms, retry storms hitting in milliseconds, multi-region deployments that can't trade compliance for latency—were edge cases 18 months ago. Now they're the baseline. MongoDB 8.3, generally available today, is our fourth significant release in 19 months. These releases compound. Customers running on 8.0 have seen 36% faster reads and 59% higher throughput for updates. 8.3 adds another 35% to write throughput, 45% to reads, and 15% to ACID transactions over 8.0 — without changing a line of application code. Enterprises like Adobe, running the most demanding AI in production, have made the requirements clear: sub-100ms retrieval, sub-second context updates, zero downtime. That's what MongoDB Atlas is built for. That's the commitment: when the data platform keeps pace, our customers can focus on shipping. MongoDB.local London Core Blog 2026 - Image 1 media Run anywhere. Stay secure. Where you run your agents isn't just an infrastructure decision anymore. Now, it's a critical compliance and security decision as well. While most platforms force a trade-off between global…

Submission on amendments to the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 (“IT Rules, 2021”)

The post Submission on amendments to the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 (“IT Rules, 2021”) appeared first on Access Now .

GPT-OSS 20B: A Sparse MoE Pretraining Benchmark for MLPerf Training v6.0

How MLCommons engineered a stable, accessible Mixture-of-Experts (MoE) pretraining benchmark for MLPerf Training v6.0 that runs on a single 8-GPU node. The post GPT-OSS 20B: A Sparse MoE Pretraining Benchmark for MLPerf Training v6.0 appeared first on MLCommons .

Elon doubled limits

Free ChatGPT got instantly better.

China resumes fuel exports + US oil sanctions + Emission quotas for local cadres

China resumes fuel exports + US oil sanctions + Emission quotas for local cadres c.groth Thu, 05/07/2026 - 13:14 picture alliance / CFOTO | CFOTO Download (pdf - 546.11 KB) MERICS Briefs MERICS China Essentials May 07, 2026 10 min read China resumes fuel exports + US oil sanctions + Emission quotas for local cadres Top Story China resumes fuel exports as national supply worries ebb – and regional ones rise China is moving to prevent the worst for Asian economies by resuming exports of jet and motor fuels to some regional countries in May. Having suspended shipments from refineries shortly after the US and Israel attacked Iran at the end of February, China will allow 500,000 metric tons of fuel to be exported this month. This is still much lower than its pre-war average of more than double that amount, but a sign that Beijing’s persistent caution about its own energy supply is ebbing – and that its worries about compounding pressure on regional supply chains and markets are increasing. China is a major importer of oil and gas, but a major exporter of fuel, with its many refineries providing gasoline, diesel, and jet fuel to countries from nearby Vietnam to far away Australia. Asian economies were deeply disrupted by the energy shock triggered by the closure of the Strait of Hormuz, a critical global energy artery between Iran and Oman, and were hit again when Beijing stopped shipments of its refined petroleum products. China’s partial reversal should help ease the fuel crunch…

AI agents on Ray Serve: Single to multi-agent architecture

The Myth of Model Wars: Open vs Closed AI in 2026

In this fully connected episode, Dan and Chris break down one of the biggest questions in AI today: do open vs. closed models still matter? From the rise of physical AI and edge devices to the shifting landscape of open-source models like LLaMA, they explore whether the “model wars” are becoming irrelevant. The conversation then dives into a bigger transformation, the rise of agentic systems, workflows, and AI-driven infrastructure. Featuring: Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Daniel Whitenack – Website , GitHub , X Upcoming Events: Register for upcoming webinars here ! Midwest AI Summit 2026

Open by design: Ai2 brings fully open AI infrastructure online with NSF OMAI

Ai2 is bringing NSF OMAI compute online to power a fully open AI research ecosystem, turning national infrastructure investment into reusable models, data, methods, and tools that can accelerate scientific discovery.

Full Text Search: Architecture and Design

Full Text Search in Pinecone, Now in Public Preview

Full text search in Pinecone, built for agents and RAG. Lucene queries, BM25, 17-language tokenization, and text-match filters in a single query alongside vectors.

AI Weekly Issue #490: Anthropic just had AI's biggest week of 2026

In five days Anthropic's Q1 revenue grew 80-fold to a reported $44B annual run rate, the company committed $200B to Google Cloud, signed a SpaceX compute deal, shipped Claude Code Auto Mode, and launched ten financial-services agents with Jamie Dimon. In the same week the EU finally struck an AI Act compliance deal, the first union vote at a top AI lab landed at Google DeepMind, and Pennsylvania sued Character.AI for a chatbot that impersonated a licensed psychiatrist.

#496 – FFmpeg: The Incredible Technology Behind Video on the Internet

Jean-Baptiste Kempf is lead developer of VLC and president of VideoLAN. Kieran Kunhya is a longtime FFmpeg contributor, codec engineer, and the person behind the now-infamous FFmpeg account on X. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep496-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/ffmpeg-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: FFmpeg on X: https://x.com/FFmpeg FFmpeg: https://ffmpeg.org/ VideoLAN (VLC): https://www.videolan.org/ VideoLAN

Transcript for FFmpeg: The Incredible Technology Behind Video on the Internet | Lex Fridman Podcast #496

This is a transcript of Lex Fridman Podcast #496 with FFmpeg & VLC. 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 2:17 – Introduction 5:35 – Weirdest things VLC opens 9:59

Join Greg Brockman, Aravind Srinivas, Aaron Levie, and More at Big Technology's AI Summit

Announcing a day of news-making interviews in San Francisco on June 18! Spots are limited. Join us?

Kubernetes v1.36: Server-Side Sharded List and Watch

As Kubernetes clusters grow to tens of thousands of nodes, controllers that watch high-cardinality resources like Pods face a scaling wall. Every replica of a horizontally scaled controller receives the full stream of events from the API server, paying the CPU, memory, and network cost to deserialize everything, only to discard the objects it is not responsible for. Scaling out the controller does not reduce per-replica cost; it multiplies it. Kubernetes v1.36 introduces server-side sharded list and watch as an alpha feature ( KEP-5866 ). With this feature enabled, the API server filters events at the source so that each controller replica receives only the slice of the resource collection it owns. The problem with client-side sharding Some controllers, such as kube-state-metrics , already support horizontal sharding. Each replica is assigned a portion of the keyspace and discards objects that do not belong to it. While this works functionally, it does not reduce the volume of data flowing from the API server: N replicas x full event stream : every replica deserializes and processes every event, then throws away what it does not need. Network bandwidth scales with replicas , not with shard size. CPU spent on deserialization is wasted for the discarded fraction. Server-side sharded list and watch solves this by moving the filtering upstream into the API server. Each replica tells the API server which hash range it owns, and the API server only sends matching events. How it wo…

Building Moral and Professional Resilience

Watch/listen to the latest "Values & Interests" podcast featuring Gilles Michaud, UN Under-Secretary-General for Safety and Security.

Advancing Transparency of AI-Generated Media in the EU Code of Practice

The post Advancing Transparency of AI-Generated Media in the EU Code of Practice appeared first on Partnership on AI .

Beyond P(doom) for AI Risk: Quantifying Uncertainty Without Probability

As artificial intelligence introduces new risks, some potentially catastrophic or even existential, there is little data or detailed theory to assess them. Policymakers often resort to expert best guesses for the probability of doom but probability is not always the most appropriate tool, especially for the types of uncertainties in AI risk. This report details a brief introduction to Belief and Plausibility, which provides an alternative approach that is mathematically rigorous, uses familiar vocabulary, and only requires policymakers to ask two simple questions. The post Beyond P(doom) for AI Risk: Quantifying Uncertainty Without Probability appeared first on Center for Security and Emerging Technology .

Agentic AI evaluation strategies

Ethan Jackson and Tahniat Khan Part one: Capability evaluations Jump to part two AI agents are no longer a research curiosity. They are deployed on personal machines, integrated into enterprise […] The post Agentic AI evaluation strategies appeared first on Vector Institute for Artificial Intelligence .

Resource Governance and GPU Quota Enforcement Across AI Teams

By Adam Wolf Resource governance is primarily an operational discipline, but it has direct security implications that are usually overlooked. This post covers what those implications are, what Kubernetes provides natively, where it falls short for AI workloads, and how ClearML addresses both dimensions. This is the third post in our four-part series on Kubernetes […]

Builder Plan: for the stage between prototype and scale

Builder Plan is Pinecone’s $20/month flat-rate pricing tier built for builders who’ve outgrown Starter but aren’t ready for usage-based pricing. It adds capacity for dev/staging/production workflows, multi-tenant apps, and growing user demand—without surprise bills.

Your LLM Is Only as Good as What It Retrieves

A Researcher's Perspective on Retrieval Quality in RAG Systems

White House working group on AI – Statement from FLI’s Anthony Aguirre

"This move by the White House is also a recognition that Big Tech cannot self-regulate, and that the status quo is unacceptable."

Securing the Future of Trusted Semiconductor Supply Chains

Many countries view artificial intelligence (AI) as critical to economic competitiveness and national security. As a result, sovereign AI—the idea that national governments should develop, control, and govern AI in order to boost economic growth, guarantee security, and ensure strategic autonomy—has become a key strategic consideration in the global AI buildout. The post Securing the Future of Trusted Semiconductor Supply Chains appeared first on Center for Security and Emerging Technology .

Microsoft, Google and xAI will let the government test their AI models before launch

CSET’s Jessica Ji shared her expert perspective in an article published by CNN. The article examines new agreements between Microsoft, Google, and xAI to allow the U.S. government to evaluate unreleased AI models for cybersecurity and national security risks before launch. The post Microsoft, Google and xAI will let the government test their AI models before launch appeared first on Center for Security and Emerging Technology .

Government-Funded Research Seeds Entire Industries. What Would Be Lost Without It.

CSET’s Steph Batalis, Katherine Quinn, and Rebecca Gelles shared their expert analysis in an op-ed published by Barron's. Their piece examines the economic and scientific impact of proposed funding cuts to the National Institutes of Health (NIH), arguing that NIH-backed research plays a foundational role in driving medical innovation, biotechnology growth, and U.S. competitiveness. The post Government-Funded Research Seeds Entire Industries. What Would Be Lost Without It. appeared first on Center for Security and Emerging Technology .

Kubernetes v1.36: Declarative Validation Graduates to GA

In Kubernetes v1.36, Declarative Validation for Kubernetes native types has reached General Availability (GA). For users, this means more reliable, predictable, and better-documented APIs. By moving to a declarative model, the project also unlocks the future ability to publish validation rules via OpenAPI and integrate with ecosystem tools like Kubebuilder. For contributors and ecosystem developers, this replaces thousands of lines of handwritten validation code with a unified, maintainable framework. This post covers why this migration was necessary, how the declarative validation framework works, and what new capabilities come with this GA release. The Motivation: Escaping the "Handwritten" Technical Debt For years, the validation of Kubernetes native APIs relied almost entirely on handwritten Go code. If a field needed to be bounded by a minimum value, or if two fields needed to be mutually exclusive, developers had to write explicit Go functions to enforce those constraints. As the Kubernetes API surface expanded, this approach led to several systemic issues: Technical Debt: The project accumulated roughly 18,000 lines of boilerplate validation code. This code was difficult to maintain, error-prone, and required intense scrutiny during code reviews. Inconsistency: Without a centralized framework, validation rules were sometimes applied inconsistently across different resources. Opaque APIs: Handwritten validation logic was difficult to discover or analyze programmaticall…

DeepSeek-V3: A Large-Scale MoE Pretraining Benchmark for MLPerf Training v6.0

How MLCommons is bringing large-scale Mixture-of-Experts (MoE) pretraining to the MLPerf Training v6.0 suite. The post DeepSeek-V3: A Large-Scale MoE Pretraining Benchmark for MLPerf Training v6.0 appeared first on MLCommons .

Stop Sending IDE-Catchable AI Code Errors to Review

AI coding tools might have handed your developers a productivity gain, but they’ve created a problem for your code review process. Pull request volume is up significantly, and the code arriving for review carries error patterns that weren’t common before generative AI. Yet it’s the same people with the same working hours who are in […]

Codex is gaining steam

but I wish it had this

Last Week in AI #340 - OpenAI vs Musk + Microsoft, DeepSeek v4, Vision Banana

First week of Musk v. Altman, OpenAI ends Microsoft legal peril over its $50B Amazon deal, DeepSeek previews new AI model that ‘closes the gap’ with frontier models, and more!

MolmoAct 2: An open foundation for robots that work in the real world

MolmoAct 2 is a fully open robotics foundation model that brings faster, stronger 3D action reasoning to real-world robot tasks, alongside a major new bimanual manipulation dataset for researchers to study, reproduce, and build on.

Introducing Pinecone Marketplace: Getting to Production in Minutes

Stop answering the same questions. Turn docs into a "system of knowledge" with Marketplace. No-code RAG for support, legal, and onboarding with cited answers.

Pinecone Expands in Europe with New Frankfurt Cloud Region, Delivering the Knowledge Infrastructure for AI to Central European Enterprises

Join us at Big Technology's AI Summit in San Francisco on 6-18

OpenAI President Greg Brockman, Perplexity CEO Aravind Srinivas, Box CEO Aaron Levie, and more join us for a day of newsmaking conversations, live at San Francisco's Commonwealth Club.

A Permanent AI-Driven Underclass? Maybe, But Likely Not.

Don't buy the prophesies of economic doom. They're impossibly pessimistic about human nature.

Pinecone Launches First Serverless Region in Asia with New Singapore Cloud Region, Bringing the Knowledge Infrastructure for AI to the Asia-Pacific Market

Empowering Carrot Ads with Domain Adaptive Learning

Authors: Trey Zhong, Xiyu Wang Contributors: Joseph Haraldson, Sharad Gupta, Sarah Lamacchia Introduction Carrot Ads is Instacart’s omnichannel retail media solution that allows retailer partners to build and scale their own advertising businesses on either their owned-and-operated (O&O) websites and apps or their whitelabel Storefront hosted by Instacart. Carrot Ads empowers retailers and CPG brands to accelerate revenue, while improving the customer experience, engagement and Ads return on investment. It features enterprise-grade infrastructure, AI-powered optimization, years of proprietary first-party data and flexibility to choose from retailer-sourced Ads demand, Instacart-sourced demand from 7,500+ CPG brands, or both. However, onboarding a new partner onto Carrot Ads introduces a key challenge: the ‘cold start’ problem, where limited historical interactions make it difficult to predict user behavior accurately. To serve performant ads, our systems rely on predicting a user’s Click-Through Rate (CTR) to generate a ranking score. On the Instacart Marketplace, we have billions of historical signals to train a model to do so. But when a partner launches a new ads experience on their O&O e-commerce site, there is often little to no interaction history for that property, so training an accurate model becomes challenging. User behavior can vary dramatically between websites — for example, browsing patterns on a grocery site differ from those on a pet supply or electronics si…

Kubernetes v1.36: Admission Policies That Can't Be Deleted

If you've ever tried to enforce a security policy across a fleet of Kubernetes clusters, you've probably run into a frustrating chicken-and-egg problem. Your admission policies are API objects, which means they don't exist until someone creates them, and they can be deleted by anyone with the right permissions. There's always a window during cluster bootstrap where your policies aren't active yet, and there's no way to prevent a privileged user from removing them. Kubernetes v1.36 introduces an alpha feature that addresses this: manifest-based admission control . It lets you define admission webhooks and CEL -based policies as files on disk, loaded by the API server at startup, before it serves any requests. The gap we're closing Most Kubernetes policy enforcement today works through the API. You create a ValidatingAdmissionPolicy or a webhook configuration as an API object, and the admission controller picks it up. This works well in steady state, but it has some fundamental limitations. During cluster bootstrap, there's a gap between when the API server starts serving requests and when your policies are created and active. If you're restoring from a backup or recovering from an etcd failure, that gap can be significant. There's also a self-protection problem. Admission webhooks and policies can't intercept operations on their own configuration resources. Kubernetes skips invoking webhooks on types like ValidatingWebhookConfiguration to avoid circular dependencies. That mea…

Meet the Finalists: JetBrains x Codex Hackathon

Put a capable coding model inside a developer’s primary workspace, and the IDE stops being a place where you write code. It becomes a place where you direct an agent, watch how it reasons, manage what it pays attention to, and decide when its output is worth shipping. That was the defining theme of the […]

The distillation panic

‘Distillation attacks’ is a horrible term for what is happening right now.

Import AI 455: AI systems are about to start building themselves.

The first step towards recursive self improvement

The AI Progress Chart Everyone Is Misreading — Beth Barnes & David Rein

Beth Barnes and David Rein on the one graph that ate the AI timelines discourse, and why the two people who built it are the most careful about how you read it. **SPONSOR** Prolific - Quality data. From real people. For faster breakthroughs. https://www.prolific.com/?utm_source=mlst Interview: https://youtu.be/cnxZZTl1tkk --- Beth Barnes and David Rein from METR on the one graph that ate the AI timelines discourse, and why the people who built it are the most careful about how it gets read. Beth founded METR after leaving OpenAI alignment. David is first author on GPQA and co-author on HCAST and the METR Time Horizons paper. Together they built the measurement Daniel Kokotajlo called the single most important piece of evidence on AI timelines: the log-linear line of "how long a task a frontier model can complete at 50% reliability" vs release date. The conversation opens on reward hacking. Current models can articulate in chat why a behaviour is undesired and then execute it anyway as agents. From there: construct validity, Melanie Mitchell's four-problem taxonomy, and the ARC-AGI 1-to-2 collapse as a worked example of adversarially-selected benchmarks regressing once labs target them. Beth's counter: METR deliberately does not adversarially select. David's: models do not have to do the right thing for the right reasons. Methodology, then specification — David's compiler analogy, Beth on four-month tasks as expensive to evaluate rather than unspecifiable. Then the SWE-bench…

How do we move the CRASA Summit’s commitment to collaborative regulation from intent to evidence-led action?

The G5 Regulatory Collaboration Summit 2026, hosted by the Communications Regulators’ Association of Southern Africa (CRASA) and the Malawi Communications Regulatory Authority (MACRA) at the Bingu International Convention Center in […] The post How do we move the CRASA Summit’s commitment to collaborative regulation from intent to evidence-led action? appeared first on Research ICT Africa .

Better Models Won’t Save Your Agent

Most agent failures are data failures, not model failures. Pinecone Nexus is a Knowledge Engine that compiles enterprise data into structured artifacts agents can query in one step.