What Parameter Golf taught us about AI-assisted research

Learn how OpenAI built a secure sandbox for Codex on Windows, enabling safe, efficient coding agents with controlled file access and network restrictions.

CFTC grants no-action relief on event contract reporting, easing swap data duties for DCMs, DCOs and participants amid growing legal fights.

Primary care AI startup Navina has closed a $15 million Series A funding round, bringing its total raised to $22 million since its commercial launch. The round was led by Vertex Ventures Israel, with participation from Schusterman Family Investments and Grove Ventures. The company’s platform uses machine learning to pull data from electronic health records […]

At first glance, Microsoft Foundry looks like a big grab bag of every AI-adjacent service that Microsoft has offered in the last decade, plus some new ones. In Microsoft’s own words, “Foundry consolidates several previous Azure AI services and tools into a unified platform” and “unifies agents, models, and tools under a single management grouping.” Microsoft Foundry helps application developers to build and deploy agents, which may use models and tools. It also helps machine learning (ML) engineers and data scientists to fine-tune models, run evaluations, and manage model deployments. Finally, it helps IT administrators and platform engineers to govern AI resources, enforce policies, and manage access across teams. It isn’t quite a floor wax and a dessert topping, but it does try to serve three distinct audiences. Key capabilities of Microsoft Foundry for building agents include multi-agent orchestration, workflows, a tool catalog, memory, knowledge integration, and publishing. Key cap

This post contains a list of the AI-related seminars that are scheduled to take place between 5 May and 30 June 2026. All events detailed here are free and open for anyone to attend virtually. 5 May 2026 Perspectives after the MUSAiC Project Speaker: Bob L. T. Sturm (KTH Royal Institute of Technology) Organised by: […]

In 2021, I was developing software for an aerospace manufacturer and met with our machine learning team to discuss innovative approaches for tracking FOD (free-orbiting debris), a major security and operational concern in the industry. What struck me wasn’t the algorithms or tracking equipment, but the terabytes of data (up to petabytes) that were being produced. Old-school problems of limited hardware resources and inefficient data compression were bottlenecking cutting-edge visual learning models and traditional tracking solutions alike. The team was smart and could fine-tune quickly, but the real challenge was making sure our infrastructure could scale with them. In aerospace, performance hinges on how fast systems can absorb and interpret massive telemetry streams, and storage is often the silent limiter. When you’re generating terabytes to petabytes of data in a single test cycle, even a brief stall in the storage layer becomes a bottleneck. A few milliseconds of delay between wha

May 1, 2026 — Today’s advances in robotics are often driven by breakthroughs in artificial intelligence, machine learning, and perception. But in complex and constrained environments, the limiting factor is […] The post DARPA Issues RFI on Embedding Intelligence into Robotic Materials appeared first on AIwire.

Or why what appears powerful can be methodologically fragile The post Why Powerful Machine Learning Is Deceptively Easy appeared first on Towards Data Science.