Drawing on his previous work as the UK’s cybersecurity chief, Professor Ciaran Martin explores differentiated standards and public-private partnerships in cybersecurity, and Microsoft’s Tori Westerhoff examines the insights through an AI red-teaming lens. Show notes
Daphne Koller, Noubar Afeyan, and Dr. Eric Topol, leaders in AI-driven medicine, discuss how AI is changing biomedical research and discovery, from accelerating drug target identification and biotech R&D to helping pursue the “holy grail” of a virtual cell. Show notes
Professors Daniel Carpenter and Timo Minssen explore evolving pharma and medical device regulation, including the role of clinical trials, while Microsoft applied scientist Chad Atalla shares where AI governance stakeholders might find inspiration in the fields. Show notes
In this episode, Alta Charo, emerita professor of law and bioethics at the University of Wisconsin–Madison, joins Sullivan for a conversation on the evolving landscape of genome editing and its regulatory implications. Drawing on decades of experience in biotechnology policy, Charo emphasizes the importance of distinguishing between hazards and risks and describes the field's approach to regulating applications of technology rather than the technology itself. The discussion also explores opportu...
In the introductory episode of this new series, host Kathleen Sullivan and Senior Director Amanda Craig Deckard explore Microsoft’s efforts to draw on the experience of other domains to help advance the role of AI testing and evaluation as a governance tool.
Technologists Bill Gates and Sébastien Bubeck discuss the state of generative AI in medicine, how access to “medical intelligence” might help empower people across healthcare, and how AI’s accelerating improvements are likely to affect both delivery and discovery.
Ethan Mollick and Azeem Azhar, thought leaders at the forefront of AI’s influence on work, education, and society, discuss the impact of AI at the individual level and what that means for the healthcare workforce and the organizations and systems in medicine.
The emergence of foundation models has sparked interest in applications to single-cell biology, but when tested in zero-shot settings, they underperform compared to simpler methods. Alex Lu shares insights on why more research on AI models is needed in biological applications. Show notes
In this episode of Abstracts, Microsoft senior researchers Megan Stanley and Wessel Bruinsma join host Amber Tingle to discuss their groundbreaking work on environmental forecasting. Their new Nature publication, "A Foundation Model for the Earth System ," features Aurora, an AI model that redefines weather prediction and extends its capabilities to other environmental domains such as tropical cyclones and ocean wave forecasting. Read the paper: A Foundation Model for the Earth System...
In this discussion, Matthew Lungren, Jonathan Carlson, Smitha Saligrama, Will Guyman, and Cameron Runde explore how teams across Microsoft are working together to generate advanced AI capabilities and solutions for developers and clinicians around the globe.
Peter Lee and his coauthors, Carey Goldberg and Dr. Zak Kohane, reflect on how generative AI is unfolding in real-world healthcare, drawing on earlier guest conversations to examine what’s working, what’s not, and what questions still remain.
Silicon has long borne the burden of heat transfer in electronics, but in a post-Moore’s Law world, researchers like Hongxia Hao and Bing Lv are using AI to discover and design next-generation materials that exceed the limits of silicon’s thermal conductivity. Read the paper
New AI models aren’t just changing the world of research; they’re also poised to impact society. Xing Xie talks about Societal AI, a white paper that explores the changing landscape with an eye to future research and improved communication across disciplines. Read the paper
Healthcare experts Laura Adams, Vardit Ravitsky, and Dr. Roxana Daneshjou discuss responsible AI implementation in medicine, examining governance approaches, shifting patient-provider relationships, and the identification of bias to ensure equitable deployment.
Evangelist for patient empowerment Dave deBronkart and Manatt Heath’s Christina Farr discuss how generative AI is redefining healthcare by empowering patients, challenging traditional care models, and creating new opportunities for innovation and collaboration.
Microsoft’s Dr. Matthew Lungren and Epic’s Seth Hain discuss the challenges and opportunities of leveraging generative AI for enhanced patient care and improved clinical documentation and recordkeeping at scale—plus what’s next for the technology in the field.
Innovative AI research often depends on access to resources. Microsoft wants to help. Technical Advisor Evelyne Viegas and distinguished faculty from two Minority Serving Institutions discuss the benefits of Microsoft’s Accelerating Foundation Models Research program in their lives and research.
UC San Diego Health’s Dr. Christopher Longhurst and UC San Francisco Health’s Dr. Sara Murray explore how generative AI is changing patient care, clinical workflows, and decision-making and how they envision the technology impacting the future of healthcare.
Host Peter Lee, Microsoft Research president, discusses the motivation behind the new series and the GPT-4 encounter that helped him view the tech not only as a potential tool for improving healthcare but a chance to reexamine what it means to care for people.
Microsoft announced the creation of the first topoconductor and first QPU architecture with a topological core. Dr. Chetan Nayak, a technical fellow of Quantum Hardware at the company, discusses how the breakthroughs are redefining the field of quantum computing.
In this episode, guest host Chris Stetkiewicz talks with Microsoft Principal Researcher Akshay Nambi about his focus on developing AI-driven technology that addresses real-world challenges at scale. Drawing on firsthand experiences, Nambi combines his expertise in electronics and computer science to create systems that enhance road safety, agriculture, and energy infrastructure. He’s currently working on AI-powered tools to improve education, including a digital assistant that can help teachers ...
Struggles with programming languages helped research manager Shan Lu find her calling as a bug hunter. She discusses one bug that really haunted her, the thousands she’s identified since, and how she’s turning to LLMs to help make software more reliable.
How do you generate and test materials that don’t exist yet? Researchers Tian Xie and Ziheng Lu share the story behind MatterGen and MatterSim, AI tools poised to transform materials discovery and help drive advances in energy, manufacturing, and sustainability.
As the “biggest election year in history” comes to an end, researchers Madeleine Daepp and Robert Osazuwa Ness and Democracy Forward GM Ginny Badanes discuss AI’s impact on democracy, including Daepp and Ness’s research into the tech’s use in Taiwan and India.
Just after his NeurIPS 2024 keynote on the co-evolution of systems and AI, Microsoft CVP Lidong Zhou joins the podcast to discuss how rapidly advancing AI impacts the systems supporting it and the opportunities to use AI to enhance systems engineering itself. Learn more: Verus: A Practical Foundation for Systems Verification | Publication, November 2024 SuperBench: Improving Cloud AI Infrastructure Reliability with Proactive Validation | Publication, July 2024 BitNet: Scaling 1-bit Transformers ...
In this special edition of the podcast, Technical Fellow and Microsoft Research AI for Science Director Chris Bishop joins guest host Eliza Strickland in the Microsoft Booth at the 38th annual Conference on Neural Information Processing Systems (NeurIPS) in Vancouver, British Columbia, to talk about deep learning’s potential to improve the speed and scale at which scientific advancements can be made....
Researcher Jindong Wang and Associate Professor Steven Euijong Whang explore the NeurIPS 2024 work ERBench. ERBench leverages relational databases to create LLM benchmarks that can verify model rationale via keywords in addition to checking answer correctness. Read the paper Get datasets and codes...
Next-token prediction trains a language model on all tokens in a sequence. VP Weizhu Chen discusses his team’s 2024 NeurIPS paper on how distinguishing between useful and “noisy” tokens in pretraining can improve token efficiency and model performance. Read the paper Get the code
Pranjal Chitale discusses the 2024 NeurIPS work CVQA. Spanning 31 languages and the cultures of 30 countries, this VQA benchmark was created with native speakers and cultural experts to evaluate model performance across diverse linguistic and cultural contexts. Read the paper Get the dataset...
Can existing algorithms designed for simple reinforcement learning problems be used to solve more complex RL problems? Researcher Dylan Foster discusses the modular approach he and his coauthors explored in their 2024 NeurIPS paper on RL under latent dynamics. Read the paper
