In June, we announced MAI-Thinking-1, a general-purpose foundation model post-trained on top of MAI-Base-1 with strong reasoning performance. We’ve recently completed pre-training our new state-of-the-art model, MAI-Base-2, an MoE foundation model with 80 billion active and 2.8 trillion total parameters.
Microsoft’s AI Red Team Has Already Made the Case for Itself
Since 2018, a dedicated team within Microsoft has attacked machine learning systems to make them safer. But with the public release of new generative AI tools, the field is already evolving.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.
Defense at AI speed: Microsoft’s new multi-model agentic security system tops leading industry benchmark | Microsoft Security Blog
Today Microsoft is announcing a major step forward in AI-powered cyber defense: a new multi-model agentic scanning harness (codenamed MDASH).
Anshul Kundaje on Twitter / X
Brainstorming with frontier AI models in more open ended domains (like bio that do not have closed verifiable loops) that you have deep expertise in, really showcases the pretense of understanding that they display. 1/— Anshul Kundaje (@anshulkundaje) September 25, 2026
Artificial Intelligence Glossary | Stanford HAI
Artificial Intelligence (AI) is rapidly growing, with new terms and concepts being introduced at a breakneck speed. You may have seen terms such as AGI, machine learning, or foundation models in media headlines, emerging legislation, or in your everyday life.
Look-ahead Reasoning with a Learned Model in Imperfect Information Games
Test-time reasoning significantly enhances pre-trained AI agents' performance. However, it requires an explicit environment model, often unavailable or overly complex in real-world scenarios....
Microsoft for Startups | Microsoft
Build fast, scale smart, and sell more with the cutting-edge AI, global customer experience, and deep enterprise experience of Microsoft. Get started today.
LukeW | Common AI Product Issues
At this point, almost every software domain has launched or explored AI features. Despite the wide range of use cases, most of these implementations have been t...
Wafer - Ship the fastest inference in the world
Autonomous AI agents that profile, diagnose, and optimize GPU inference across your entire stack — from kernels to models to production pipelines.
ARC-AGI-3
ARC-AGI-3 is the first interactive reasoning benchmark for AI agents—play as humans and build agents that learn in novel environments.
Azure OpenAI in Foundry Models | Microsoft Azure
Access and fine-tune the latest AI reasoning and multimodal models, integrate AI agents, and deploy secure, enterprise-ready generative AI solutions.
Meet Foundry: An AI Startup that Builds, Evaluates, and Improves AI Agents
General 2 — Fairly Trained
These are the companies and models that we’ve certified so far. We encourage you to choose them when considering generative AI providers.
On the Value of Doing a PhD in the Age of AI
Many students who are just now embarking on a PhD in AI are understandably anxious. It feels like our field is zooming forward ever faster. Our clever methods are often surpassed before they are published. Our deep insights seem less critical to frontier lab progress. AI is automating aspects of the research process that used to require years of specialized training.
A significant portion of my work, for several months now, has been focused on AI. Specifically, understanding use of AI in healthcare and in public benefit determinations. So I've extensively used 'frontier models' from Anthropic and OpenAI as well as the Chinese models. I've used Grok a bit. 🧵
