Forthcoming machine learning and AI seminars: May 2026 edition

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

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.

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we meet PhD students and early-career researchers, find out how machine learning is used for particle physics discoveries, cast an eye over the latest AI Index […]

The best machine learning model is not one model The post Ensembles of Ensembles of Ensembles: A Guide to Stacking appeared first on Towards Data Science.

Getting stalled enterprise AI rollouts in the EMEA region moving again will require CIOs to aggressively audit their systems. Over the past 18 months, AI deployments across Europe advanced far beyond initial testing. Companies poured capital into large language models and machine learning, expecting heavy operational upgrades. IDC research reveals that boards are slowing down, […] The post IDC: How EMEA CIOs can jumpstart AI rollouts appeared first on AI News.

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology’s impact on society. Organoid computing with Dr Ewelina Kurtys This month Ben chats with Dr Ewelina Kurtys on the uses of organoids and energy […]