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
Google Chrome may be taking up more of your storage than expected thanks to a large on-device AI model file that, in some cases, is being automatically downloaded to the browser's system folders. Users who have noticed unexplained drops in their available desktop device storage are now discovering that Chrome is installing a 4GB weights.bin file inside their browser directory when certain AI features are enabled.
The weights.bin file in question is connected to Google's Gemini Nano AI model, which powers Chrome AI tools like scam detection, writing assistance, autofill, and suggestion features. As the Gemini Nano model is designed to run lo …
Read the full story at The Verge.
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: […]
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.
Let’s be honest about what’s happening in the market: Public cloud has become the easy button for AI. It offers immediate access to compute, storage, managed services, foundation model ecosystems, automation tools, and global reach. For enterprises that want to launch quickly, it is hard to argue against it. You do not need to spend years standing up infrastructure, hiring specialized operations teams, or engineering your own scalable environment before you can test your first use case.
This is exactly why adoption continues even as confidence in cloud resilience becomes more complicated. This article about the expanding cloud market makes the point clearly. Enterprises are not pulling back from hyperscale clouds despite numerous outages. They continue to move forward because the benefits of agility, scalability, and rapid deployment are too valuable to ignore. The cloud remains deeply embedded in business operations, and for many organizations, stepping away would undo years, often de
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.
