AI/ML News & Innovations Hub

North Star Data Center Policy Toolkit: State and Local Policy Interventions to Stop Rampant AI Data Center Expansion

This policy toolkit is primarily geared toward stopping, slowing, and restricting rampant data center development in the US at the local and state level. Our approach recognizes the extractive relationship between data centers and local communities: Hyperscale data centers deplete scarce natural resources, pollute local communities and increase the use of fossil fuels, raise energy […] The post North Star Data Center Policy Toolkit: State and Local Policy Interventions to Stop Rampant AI Data Center Expansion appeared first on AI Now Institute .

MLCommons Releases New MLPerf Inference v6.0 Benchmark Results

The most significant update to the benchmark suite to date, with new tests ensuring that it remains the most comprehensive measure of AI system performance The post MLCommons Releases New MLPerf Inference v6.0 Benchmark Results appeared first on MLCommons .

LWiAI Podcast #238 - GPT 5.4 mini, OpenAI Pivot, Mamba 3, Attention Residuals

OpenAI ships GPT-5.4 mini and nano, faster and more capable but up to 4x pricier, DLSS 5 looks like a real-time generative AI filter for video games | The Verge, and more!

Fine-tuning experiments on CoT controllability

Kei Nishimura-Gasparian is an Astra fellow and was the primary contributor to this work. Neev Parikh provided mentorship and feedback. Summary: We find that a small amount of fine-tuning on instruction following in the CoT generalizes to meaningful increases in CoT controllability on an out-of-distribution set of tasks (CoTControl eval suite). We fine-tune four reasoning models on small datasets (240 examples or ~100K-300K tokens of fine-tuning) of instruction-following reasoning data and OOD controllability rises from an average of 2.9% to 8.8% across four models. 1 We see the largest increases for instructions that request reasoning in a specified case, suppressing certain words, and adding provided sentences to the reasoning. While 8.8% remains low in absolute terms, this provides evidence that just a small amount of fine-tuning can increase controllability, suggesting that low CoT controllability may not be very robust to accidental optimization pressure. Limitations and caveats: It seems unlikely that frontier AI labs will do even a small amount of fine-tuning directly for controllability which makes our setup somewhat unrealistic. However, the fact that a slight improvement in these capabilities can be elicited with a small amount of fine-tuning suggests the capabilities are latent in the model rather than the fine-tuning teaching the model a new skill. We have not shown that this increase in controllability results in a decrease in monitorability, we will look at this…

Multimodal Embeddings and RAG: A Practical Guide

Multimodal embeddings allow AI systems to search and reason across text, images, audio, and video in their native formats. This blog covers the key intuitions behind how this all works and walks through three practical implementations using Weaviate and Gemini.

Claude Dispatch and the Power of Interfaces

We often lack the tools for the job, even if the AI is capable enough

Agent-driven development in Copilot Applied Science

I used coding agents to build agents that automated part of my job. Here's what I learned about working better with coding agents. The post Agent-driven development in Copilot Applied Science appeared first on The GitHub Blog .

Introducing MongoDB Agent Skills and Plugins for Coding Agents

Software engineering is evolving into agentic engineering. According to the Stack Overflow Developer Survey 2025, 84% of respondents use or plan to use AI tools in their development, up from 76% the previous year. At this rate, the tooling needs to keep pace. Last year, we introduced the MongoDB MCP Server to give agents the connectivity they need to interact with MongoDB, helping them generate context-aware code. But connectivity was only the start. Agents are generalists by design, and they don't inherently know the best practices and design patterns that real-world production systems demand. Today, we're addressing this by introducing official MongoDB Agent Skills: structured instructions, best practices, and resources that agents can discover and apply to generate more reliable code across the full development lifecycle, from schema design and performance optimization to implementing advanced capabilities like AI retrieval. To bring this directly into the tools you use, we're also launching plugins for Claude Code, Cursor, Gemini CLI, and VS Code, combining the MongoDB MCP Server and Agent Skills in a single, ready-to-use package. Turning coding agents into MongoDB experts Coding agents are great at producing working code, but they still make common mistakes in production systems, often defaulting to relational thinking that doesn't translate well to MongoDB, such as: Over-normalizing schemas, ignoring MongoDB's document-oriented strengths. Underusing compound indexes, c…

Enforcement of Chapter V under the EU AI Act

This page aims to provide an overview of the EU AI Act’s enforcement provisions relating to Chapter V, namely the provisions that impose obligations on providers of general-purpose AI (GPAI) models. It also aims to explore the role that other actors can play in the enforcement of the AI Act. Summary Coming up in this […]

Your Code is Your Schema: Weaviate Managed C# Client

Use semantic search and RAG in C# with the Weaviate Managed .NET client — attribute-driven schema, type-safe queries, and safe migrations, all in idiomatic .NET.

A Conversation with Carnegie Ethics Fellow Harsh Suri

This conversation features Harsh Suri, CEO and co-founder of The Geostrata, a youth-led independent policy and research think tank, based in India.

Predicting Rider Conversion in Sparse Data Environments with Bayesian Trees

At Lyft, understanding how riders go through our user experience is fundamental to operating a healthy marketplace. Specifically, it is important to have a robust model determining if a rider will actually request a ride after entering a destination and viewing a price and ETA. Accurately predicting this decision, that we call conversion , informs countless decisions across our platform. Whether it is to better balance supply and demand, improve user experiences, optimize recommendations and advertisement, understand long-term engagement, decide how to distribute coupons… rider conversion prediction is a central challenge for the Lyft business. However, predicting human behavior at scale is incredibly complex, and the exact same person might well open the app just to check current availability or actually to request a ride after viewing our prices. The contexts under which riders make their conversion decisions are extremely diverse and almost unique to each session. A user’s intent changes based on where they are and where they want to go, what time it is, their previous interactions with the platform, current supply-demand market conditions, to cite a few. When we try to model this using standard machine learning approaches, we run into a significant challenge: data sparsity . The Challenge of High Cardinality and Sparsity To accurately predict conversion, we need to slice our data very thinly across many categorical features. Imagine trying to predict the conversion proba…

Import AI 451: Political superintelligence; Google's society of minds, and a robot drummer

Are there any genies that can be put back in the bottle?

Prominent Scientists, Faith Leaders, Policymakers and Artists Call for a Prohibition on Superintelligence, as Poll Shows Americans Don’t Want It

Initial signatories include AI pioneers Yoshua Bengio and Geoffrey Hinton, leading media voices Steve Bannon and Glenn Beck, Obama's National Security Advisor Susan Rice, business trailblazers Steve Wozniak and Richard Branson, five Nobel Laureates, former Irish President Mary Robinson, actors Stephen Fry and Joseph Gordon-Levitt, and hundreds of others.

The Race to Production-Grade Diffusion LLMs with Stefano Ermon - #764

Today, we're joined by Stefano Ermon, associate professor at Stanford University and CEO of Inception Labs to discuss diffusion language models. We dig into how diffusion approaches—traditionally used for images—are being adapted for text and code generation, the technical challenges of applying continuous methods to discrete token spaces, and how diffusion models compare to traditional autoregressive LLMs. Stefano introduces Mercury 2, a commercial-scale diffusion LLM that can generate multiple tokens simultaneously and achieve inference speeds 5-10x faster than small frontier models, paving the way for latency-sensitive applications like voice interactions and fast agentic loops. We also cover the open research challenges in diffusion LLM training, serving infrastructure requirements, and post-training for diffusion-based systems. Finally, Stefano shares his perspective on whether diffusion models can rival or surpass autoregressive LLMs at scale, the advantages for highly controllable generation, and what the future of multimodal diffusion models might look like. The complete show notes for this episode can be found at https://twimlai.com/go/764.

LLM Architecture Gallery Diff Tool

Short note on the LLM Architecture Gallery diff tool for comparing two model architecture stacks side by side.

AI Opportunities in Africa: Governance, Infrastructure Gaps, and Global Competition Shape the Continent’s AI Future

Here is what happened in AI in Africa this week: 1. AI Surveillance Concerns Rise Across Africa A new […]

Red-Teaming Anthropic's Internal Agent Monitoring Systems

Update : Further details on this exercise are included in our Frontier Risk Report (February-March 2026), within the Anthropic section of Appendix B . In collaboration with Anthropic, a METR staff member (David Rein) recently spent three weeks red-teaming a subset of Anthropic’s internal agent monitoring and security systems, many of which are described in the Opus 4.6 Sabotage Risk Report (Appendix 8.4, especially 8.4.8). Anthropic provided substantial access to relevant internal systems and information, and made staff available to answer questions and provide feedback throughout the exercise. The exercise discovered several specific novel vulnerabilities, some of which have since been patched, and none of which severely undermine major claims in the Opus 4.6 Sabotage Risk Report. It also produced several artifacts, including agent trajectories containing covert attacks and a small attack strategy ideation test set. We expect both of these to be useful for ongoing improvements to Anthropic’s monitoring systems. The resulting 26 page report was shared with Anthropic, and a redacted version was shared with a subset of METR staff. We are exploring ways to incorporate more detailed takeaways from the exercise in future METR risk reports. This kind of adversarial testing by external researchers is valuable for discovering vulnerabilities, as well as for developing best practices for embedding third party evaluators inside frontier AI companies. We hope to do more exercises like…

Detecting supply chain attacks at scale with Deep Search

Poisoned LiteLLM packages on PyPI started stealing credentials. Using Deep Search and Code Search, we traced which public repos were protected by version pinning and which were left exposed. Here's how—and how you can do the same for any supply chain incident.

AI at the Edge is a different operating environment

What does “AI at the edge” really mean in 2026, and why does it matter now more than ever before? In this episode, we’re joined by Brandon Shibley, Edge AI Solutions Engineering Lead at Qualcomm’s Edge Impulse, to discuss the current state and future of Edge AI in 2026. We discuss Gen AI, Small Models, and Cascades of Models, along with real-world constraints like latency, power, and privacy. We also dive into the role of MLOps, evolving hardware, and how developers can start building practical edge AI systems today. Featuring: Brandon Shibley – LinkedIn Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Daniel Whitenack – Website , GitHub , X Links: Read our Ultimate Guide to Edge AI Download your copy of O'Reilly's AI at the Edge Check out the Edge Impulse blog Sign-up for an expert led trial of Edge Impulse Upcoming Events: Register for upcoming webinars here !

Argus launches natural gas price assessments in Brazilian market

Argus, a market intelligence and advisory firm in the energy and commodity markets, has launched Brazil’s first assessed daily price for the natural gas spot market. The move from the UK-headquartered company follows the opening of Brazil’s natural gas market to competition under a regulatory framework approved five years ago.

Introducing Lyria 3 Pro

Last month, we introduced Lyria 3, featuring custom music generation designed to spark creative expression. Now, we’re bringing our most advanced music generation model to more Google products, and introducing Lyria 3 Pro. This advanced version allows the creation of tracks up to 3 minutes long, with customization and creative control. Learn more: https://blog.google/innovation-and-ai/technology/ai/lyria-3-pro ___ Subscribe to our channel https://www.youtube.com/@googledeepmind Find us on X https://twitter.com/GoogleDeepMind Follow us on Instagram https://instagram.com/googledeepmind Add us on Linkedin https://www.linkedin.com/company/deepmind/

Beyond A/B Testing: Using Surrogacy and Region-Splits to Measure Long-Term Effects in Marketplaces

Image generated with Gemini 3 Pro (Google), 2026. Written by Amber Wang and Y oonji Kim at Lyft. Background Whenever you use the Lyft app, there is a complex balancing act happening behind the scenes. Various levers are used to keep the marketplace running smoothly; Base prices and coupons for riders affect demand, while driver pay and bonuses impact the level of available supply. Since every change to prices and payments impacts Lyft’s costs and revenue, they lead to key optimization problems, such as: How should we allocate budget between driver incentives and rider incentives? How do we invest resources to achieve x% rides growth, and how much does it cost in terms of short term profit? These are the questions the Foundational Models team at Lyft tries to answer in a systematic way. A key ingredient is understanding the effects of different types of investments — for instance, what will happen if we increase the total budget for driver incentives by x%? What will happen if we increase the rider price of all rides by y%? It’s worth noting that the long term effects of such decisions tend to dominate the short term effects: we may earn more short term profit from a ride if we charge riders more and pay drivers less, but lose riders and drivers in the long run. Estimating the long term effects of resource allocation decisions is challenging in a multi-sided marketplace such as Lyft. Because these decisions tend to be consequential, their effects go beyond first order effects…

Federal and State Representatives Visit Tübingen AI Center

BMFTR and MWK Discuss AI Research and Transfer Activities at Tübingen AI Center

The future of SCIP

We are excited to announce our transition to a community-driven open source project. While making this change, we reaffirm our deep commitment to remaining active members of the community.

Personality-driven AI agents: Operationalizing OCEAN traits for human-AI collaboration in the coding domain

As AI agents become collaborative partners in complex tasks, understanding how agent personality affects human-AI interaction becomes critical. While recent work explores personality customization in language models, little is known about how personality affects AI coding agents. We conducted the first exploratory study investigating: if OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) personality traits can be operationalized in AI coding agents, if users detect these personality differences, and how different personalities affect user trust and adoption. Participants completed refactoring tasks with three agent profiles. Results show that personality traits successfully translated into distinguishable behaviors reliably detected by users. While no universal 'best' personality emerged, individual preferences diverged substantially. Conscientiousness produced more consistent trust, while openness and extraversion polarized users. Some users experienced trust collapse from overconfidence and others found excessive caution inefficient. Our findings provide initial empirical evidence that OCEAN personality traits can be operationalized in AI coding agents, producing distinguishable behaviors, with implications for designing adaptive systems.

A new GPT-OSS benchmark and DeepSeek R1 updates for latency-optimized reasoning

MLPerf Inference v6.0 expands open-weight LLM coverage with a new GPT-OSS 120B benchmark and a latency-constrained interactive scenario for DeepSeek-R1 — the first MLPerf standard for speculative decoding. The post A new GPT-OSS benchmark and DeepSeek R1 updates for latency-optimized reasoning appeared first on MLCommons .

Major upgrades to Ray Serve: Online Inference with 88% lower latency and 11.1x higher throughput

#494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution

Jensen Huang is the co-founder and CEO of NVIDIA, the world’s most valuable company and the engine powering the AI computing revolution. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep494-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/jensen-huang-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: NVIDIA: https://nvidia.com NVIDIA on X: https://x.com/nvidia NVIDIA AI on X: https://x.com/NVIDIAAI NVIDIA on YouTube: https://youtube.com/@nvidia NVIDIA on Instagram:

Transcript for Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution | Lex Fridman Podcast #494

This is a transcript of Lex Fridman Podcast #494 with Jensen Huang. 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 – Introduction 0:33 – Extreme co-design and rack-scale engineering 3:18 – How Jensen runs

Speaking of Voxtral

Voxtral TTS: A frontier, open-weights text-to-speech model that’s fast, instantly adaptable, and produces lifelike speech for voice agents.

Import AI 450: China's electronic warfare model; traumatized LLMs; and a scaling law for cyberattacks

How will timeless minds value time?

Last Week in AI #339 - DLSS 5, OpenAI Superapp, MiniMax M2.7

DLSS 5 looks like a real-time generative AI filter for video games, OpenAI Reportedly Pivoting to a Focus on Business and Productivity Only, and more!

Statement: Head of US Policy on the White House AI legislative recommendations

The White House published it’s long-awaited AI legislative recommendations on Friday, and it still includes a call for Congress to […]

A Visual Guide to Attention Variants in Modern LLMs

From MHA and GQA to MLA, sparse attention, and hybrid architectures

Zero Introspection

The rejection of introspection by America's business leaders—combined with an unwillingness to defend the system that incubated their success—is a deeply troubling trend.

Impact of modelling assumptions on time horizon results

As METR’s time horizon task suite saturates, the results are becoming more sensitive to analysis choices. One example of this was the recent update to fix a modelling mistake with regularization, which decreased recent models’ 50% time horizon results by up to 20%, but had a smaller impact on earlier LLMs’ 50% time horizons. 1 In this post I’ll: Give a refresher on the current model used to calculate time horizon results and more detail about the regularization mistake METR recently fixed Go over what I see as the other main sources of uncertainty in time horizon results (outside of needing more tasks). Where possible, I’ll fit alternative models to show their impacts Wrap things up with general thoughts on how much weight people should put on the current estimates I hope this will help people better understand the modelling assumptions underlying the time horizon results, and how robust (or not) the results are. Summary There are many reasonable variations one could make to the TH modelling, and most of these end up having the effect of reducing recent 50% time horizon estimates (and often increase 80% time horizon estimates). The aspect I feel least certain about is noise in the task length estimates, which I hope to look into more in the future. I find that reasonable choices generally still leave us inside the CIs (which are very wide!). I think the most important source of uncertainty is the task distribution rather than analysis choices, as which tasks are included has…

MCP stories from the field

While direct API calls seem cheaper and easier, they lack the safety layer large organizations rely on. Tool connection protocols aren't dead; they remain vital for security, governance, and centralized control in big teams.

AI Expo Africa 9th Edition – Africa’s Largest Enterprise AI & Automation Event

AI Expo Africa 2026, Africa's Largest Enterprise AI & Automation Event will be running 9th Edition, JHB, South Africa 29-31 October

We spent 2 hours working in the future

Introduction METR aims to keep the public informed about the capabilities of and risks posed by AI — by some metrics the fastest-moving technology in history, and one that could speed up further as AI automates AI R&D. By late next year, the rate of model releases and the number of new evals required could be such that even keeping ourselves informed will be a challenge without effective AI assistance. We can’t afford to figure out AI-augmented workflows reactively, as they become necessary; we need to begin understanding them now. So we ran a 2-hour tabletop exercise: three METR researchers played themselves, with their current priorities , but pretending they had access to ~200-hour time horizon AIs – roughly what we expect 12–18 months from now. The goal was to learn what workflows emerge, what the bottlenecks are, and how much faster we’d actually be. The game Scenario The world METR has access to 200h time horizon AIs to automate our work; the rest of the world has access to real Feb 2026 technology (~12h TH AIs). We have versions of Codex/Claude Code + basic project management workflows that make sense for 200h TH AIs. We are otherwise living in Feb 2026, so we’re evaluating 2026 AIs, using the 2026 version of Inspect, communicating with people via email etc. AI capabilities AIs now have a ~200 human hour time horizon , but with a similar relative capabilities profile to early-2026 AIs. They’re staggeringly good at verifiable tasks and decent at messy tasks. AIs work t…

Securing Enterprise AI with Weaviate

A complete guide on how to secure Weaviate enterprise deployments with OIDC, RBAC, and multi-tenant isolation.

What the EU AI Act Means for Staffing Businesses

If your business uses AI to screen, rank, or match candidates, the EU now regulates those tools as high-risk systems. Here is what changed, what it means for your operating model, and what you should be doing about it.

Enhance Your In-IDE Data Browsing Experience With MongoDB

MongoDB is excited to announce the general availability of our enhanced data browsing experience in the MongoDB for Visual Studio (VS) Code extension. This new experience offers a unified workspace for developers to visually browse, query, and edit their data natively, streamlining workflows so they can manage their database right where they write their code. Evolving the developer workflow The modern developer’s workflow is incredibly fast-paced. With developers juggling an average of 14 different tools daily, the cognitive load of constantly jumping between applications can easily disrupt focus. When your application needs to evolve, working with your data shouldn’t force a break in your flow state. As the MongoDB for VS Code extension has grown to nearly 3 million downloads, we’ve seen firsthand how developers are pushing the boundaries of what an in-IDE (integrated development environment) database tool can do. While developers love accessing their data directly in the editor, we wanted to transform this experience to be even more visual, actionable, and seamless. Instead of switching to external terminals for quick tasks or taking the time to translate familiar MongoDB Shell commands into Extended JSON (EJSON), we are bringing a full-fledged, intuitive data management suite right to your VS Code sidebar. Exploring what’s new in the MongoDB for VS Code extension Here are the key improvements that transform the extension into a complete workflow solution: Paginated tree v…

Introducing Forge

Today, we’re introducing Forge, a system for enterprises to build frontier-grade AI models grounded in their proprietary knowledge.

Humility in the Age of Agentic Coding

What happens when an AI hater starts building with AI agents? In this episode, we talk with software engineer Steve Klabnik, known for his work on the Rust programming language, about his journey from criticizing AI to experimenting with it firsthand. We explore Steve’s programming language Rue, largely built with the help of AI tools like Claude, and discuss what this means for software engineering and the future of coding in an AI-driven world. Featuring: Steve Klabnik – LinkedIn Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Daniel Whitenack – Website , GitHub , X Links: The Rust Programming Language Rust Rue Daniel's RSA Meeting link for March 23, 2026 Daniel's RSA Meeting link for March 24-25, 2026 Upcoming Events: Register for upcoming webinars here !

Introducing Mistral Small 4

Mistral AI partners with NVIDIA to accelerate open frontier models

Elon Musk’s Grok appears to bypass Brazilian news paywalls, newspapers say

"In apparent violation of Brazilian law prohibiting the indiscriminate use and distribution of copyrighted journalistic content, Grok —the artificial intelligence (AI) chatbot developed by Elon Musk— has been ‘tearing down’ news outlets’ paywalls by delivering full newspaper articles that normally require a subscription to access. To test how this works in practice, O Globo newspaper […] The post Elon Musk’s Grok appears to bypass Brazilian news paywalls, newspapers say appeared first on LatAm Journalism Review by the Knight Center .

Elon Musk’s Grok appears to bypass Brazilian news paywalls, newspapers say

"In apparent violation of Brazilian law prohibiting the indiscriminate use and distribution of copyrighted journalistic content, Grok —the artificial intelligence (AI) chatbot developed by Elon Musk— has been ‘tearing down’ news outlets’ paywalls by delivering full newspaper articles that normally require a subscription to access. To test how this works in practice, O Globo newspaper […] The post Elon Musk’s Grok appears to bypass Brazilian news paywalls, newspapers say appeared first on LatAm Journalism Review by the Knight Center .

Leanstral: Open-Source foundation for trustworthy vibe-coding