Research advances are driving real-world impact faster than ever. Recent developments in AI are reshaping the way people live, work, and think. In the latest episode of Microsoft Research Forum (opens in new tab), we explore how AI is transforming health care and the natural sciences, the intersection of AI and society, and the continuing evolution of foundational AI technologies.
Below is a brief recap of the event, including select quotes from the presentations. Full replays of each session and presentation will be available soon.
Keynote: The Revolution in Scientific Discovery
Chris Bishop, Technical Fellow and Director, Microsoft Research AI4Science
As in our debut event on January 30, this edition of Research Forum began with a keynote address by a leader from Microsoft Research. Chris Bishop shared some exciting real-world progress being made by his team toward modelling and predicting natural phenomena.
Chris Bishop: “In my view, the most important use case of AI will be to scientific discovery. And the reason I believe this is that it’s our understanding of the natural world obtained through scientific discovery, together with its application in the form of technology that has really transformed the human species.”
Panel discussion: Transforming the natural sciences with AI
Bonnie Kruft, Partner Deputy Director, Microsoft Research AI4Science (Host)
Rianne van den Berg, Principal Research Manager, Microsoft Research AI4Science
Tian Xie, Principal Research Manager, Microsoft Research AI4Science
Tristan Naumann, Principal Researcher, Microsoft Research Health Futures
Kristen Severson, Senior Researcher, Microsoft Research New England
Alex Lu, Senior Researcher, Microsoft Research New England
In a discussion hosted by Bonnie Kruft, Microsoft researchers presented their latest advancements in the fields of foundation models, drug discovery, material design, and machine learning. Panelists highlighted deep learning’s growing impact on the natural sciences.
Tristan Naumann: “Much of the data we have in healthcare is not nicely structured in a clean and easy to use way. And so, one of the things that’s really incredible about some of these recent advances in generative AI, specifically large language models (LLMs) and multimodal models, is really this opportunity to have a tool for universal structuring. And unlocking some of that data quickly and efficiently really opens up a lot of new opportunities.”
Tian Xie: “Similar (to) the field of health and in biology, machine learning is really beginning to interrupt some of the traditional pipelines that happened in materials discovery.”
Kristen Severson: “We have a lot of knowledge about diseases and how they manifest and we don’t want to leave that information on the table when we train a machine learning model. So, there’s not an interest in using solely black box approaches, but instead (in) using what’s already known.”
Alex Lu: “If you look at what particularly differentiates biology and I suspect by extension a lot of other scientific disciplines, the whole point is to try to discover something new. So, by definition, what that new thing is is not going to be captured in your original distribution of data.”
Rianne van den Berg: “One particular class of generative models that I’m very excited about and that’s becoming increasingly popular is that of diffusion models and score-based generative models. These models have been super successful already, for instance in high resolution image generation and video, and they’re also very naturally suited to target scientific discovery.”
Lightning talk: What’s new in AutoGen?
Chi Wang, Principal Researcher, Microsoft Research AI Frontiers
Chi Wang presented the latest updates on AutoGen – the multi-agent framework for next generation AI applications. The discussion covered milestones achieved, community feedback, exciting new features, and the research and related challenges on the road ahead. He also announced a recent milestone.
Chi Wang: “Our initial multiagent experiment on the challenging GAIA benchmark turned out to achieve the number one accuracy in the leaderboard in all three levels. That shows the power of AutoGen in solving complex tasks and big potential.”
Lightning talk: The metacognitive demands and opportunities of generative AI
Lev Tankelevitch, Senior Behavioral Science Researcher, Microsoft Research Cambridge (UK)
Lev Tankelevitch explored how metacognition—the psychological capacity to monitor and regulate one’s thoughts and behaviors—provides a valuable lens for understanding and addressing the usability challenges of generative AI systems. This includes prompting, assessing and relying on outputs, and workflow optimization, which require a high degree of metacognitive monitoring and control.
Lev Tankelevitch: «We believe that a metacognitive perspective can help us analyze, measure, and evaluate the usability challenges of generative AI, and it can help us design generative AI systems that can augment human agency and workflows.»
Lightning talk: Getting modular with language models: Building and reusing a library of experts for task generalization
Alessandro Sordoni, Principal Researcher, Microsoft Research Montreal
Alessandro Sordoni discussed recent research on building and re-using large collections of expert language models to improve zero-shot and few-shot generalization to unseen tasks.
Alessandro Sordoni: “Looking forward, I believe that an exciting direction would be to push this to fully decentralized training and continual improvement of language models in the sense that users can train their experts, then share them in the platform and the model gets better.”
Lightning talk: GigaPath: Real-World Pathology Foundation Model
Naoto Usuyama, Principal Researcher, Microsoft Research Health Futures
Naoto Usuyama presented GigaPath, a novel approach for training large vision transformers for gigapixel pathology images, utilizing a diverse, real-world cancer patient dataset, with the goal of laying a foundation for cancer pathology AI.
Naoto Usuyama: «This project (GigaPath) is not possible without many, many collaborators, and we are just scratching the surface. So, I’m very excited, and I really hope we can unlock the full potential of real-world patient data and advanced AI for cancer care and research.»
Lightning talk: Generative AI and plural governance: Mitigating challenges and surfacing opportunities
Madeleine Daepp (opens in new tab), Senior Researcher, Microsoft Research Redmond
Vanessa Gathecha (opens in new tab), Applied Researcher and Policy Analyst, Baraza Media Lab
This talk featured two expert speakers. Madeleine Daepp discussed the potential impacts and challenges of generative AI in a year with over 70 major global elections. Vanessa Gatheca, a 2024 Microsoft AI and Society fellow (opens in new tab), discussed her work on disinformation in Kenya and Sub-Saharan Africa.
Madeleine Daepp: «The disruption of our digital public sphere is an all-of-society problem that requires an all-of-society response. The AI and Society fellows program is helping to build much needed connections across places, across academic disciplines, and across societal sectors to help us understand the problem and work toward an impactful response.»
Artificial intelligence