ACM ByteCast

In this episode of ACM ByteCast, Rashmi Mohan hosts 2024 ACM/AAAI Allen Newell Award recipient Peter Stone, Professor at the University of Texas at Austin and Chief Scientist at Sony AI. He received the award for significant contributions to the theory and practice of AI, especially in reinforcement learning (RL), multiagent systems, transfer learning, and intelligent robotics. As a leading figure in AI research, Stone has fundamentally advanced how autonomous agents learn, plan, and collaborate. His groundbreaking work on RL algorithms has enabled robots to acquire skills through experience. He is an ACM, AAAI, AAAS, and IEEE Fellow, an Alfred P. Sloan Research Fellow, and a Fulbright Scholar. At UT Austin, he is the founder and director of the Learning Agents Research Group (LARG) within the Artificial Intelligence Laboratory, as well as Founding Director of Texas Robotics. In the past, he also worked at AT&T Labs - Research and co-founded Cogitai, Inc. (acquired by Sony).

Peter explores the intersection of professional research and personal passion, detailing how his lifelong love for soccer fueled his involvement in RoboCup, where he aims to develop humanoid robots capable of competing at a World Cup level by 2050. The conversation highlights his leadership as the Chief Scientist of Sony AI, focusing on landmark projects like GT Sophy, an AI that mastered the complexities of Gran Turismo, and the development of FHIBE, an ethically sourced dataset designed to mitigate bias in machine learning. Throughout the interview, Stone emphasizes the importance of ad hoc teamwork—the ability of autonomous agents to collaborate on the fly with unfamiliar partners. He also shares his passion for undergraduate research and advocacy for AI education at all levels.

In this episode, part of a special collaboration between ACM ByteCast and the American Medical Informatics Association (AMIA)’s For Your Informatics podcast, Sabrina Hsueh and Li Zhou host Monica Bertagnolli, a surgical oncologist, physician-scientist, and President Elect of the National Academy of Medicine—the first woman to hold that position in NAM’s history. She previously served as the 17th Director of the National Institutes of Health and the 16th Director of the National Cancer Institute (NCI), as well as President of the American Society of Clinical Oncology. In the past, she was the Richard E. Wilson Professor of Surgery in surgical oncology at Harvard Medical School, a surgeon at Brigham and Women’s Hospital, and a member of the Gastrointestinal Cancer Treatment and Sarcoma Centers at Dana-Farber Cancer Institute.

In the interview, Dr. Bertagnolli shares her unique journey from Princeton engineering to cancer surgery and national leadership. She emphasizes collaboration, system thinking, and bringing an engineering mindset of “pilot, test, scale, and continuously improve” to AI in healthcare. She highlights her role in founding mCODE, an initiative to improve patient care through oncological data interoperability, and how NAM's six core commitments and ten guiding principles for responsible AI address issues of bias and equity. Dr. Bertagnolli also offers insights on the growing erosion of trust in science and medicine—and how to restore it.

In this episode, part of a special collaboration between ACM ByteCast and the American Medical Informatics Association (AMIA)’s For Your Informatics podcast, Sabrina Hsueh and Li Zhou host AI safety and ethics expert Ray Eitel-Porter, Luminary and Senior Advisor for AI at Accenture and an Intellectual Forum Senior Research Associate at Jesus College, the University of Cambridge. Previously, he served as Accenture's Global Responsible AI Lead. Ray is the author of Governing the Machine and sits on several boards and councils advising on data analytics and strategy.

In the interview, Ray shares how he was inspired to research responsible AI by data privacy concerns and how biased datasets harm models. He describes his objective as helping people understand the potential risks of emerging technologies in order to confidently use them. He discusses case studies from his book where companies successfully implement responsible AI practices in the workplace, and shares how his framework will be useful even as technologies continue to emerge and change. Finally, Ray offers some advice for younger professionals in AI and medicine.

In this episode of ACM ByteCast, Rashmi Mohan hosts software development productivity expert Nicole Forsgren, Senior Director of Developer Intelligence at Google. Forsgren co-founded DevOps Research and Assessment (DORA), a Google Cloud team that utilizes opinion polling to improve software delivery and operations performance. Forsgren also serves on the ACM Queue Editorial Board. Previously, she led productivity efforts at Microsoft and GitHub, and was a tenure track professor at Utah State University and Pepperdine University. Forsgren co-authored the award-winning book Accelerate: The Science of Lean Software and DevOps and the recently published Frictionless: 7 Steps to Remove Barriers, Unlock Value, and Outpace Your Competition in the AI Era.

In this interview, Forsgren shares her journey from psychology and family science to computer science and how she became interested in evidence-based arguments for software delivery methods. She discusses her role at Google utilizing emerging and agentic workflows to improve internal systems for developers. She reflects on her academic background, as the idea for DORA emerged from her PhD program, and her time at IBM. Forsgren also shares the relevance of the DORA metrics in a rapidly changing industry, and how she's adjusting her framework to adapt to new AI tools.

In this episode of ACM ByteCast, Rashmi Mohan hosts 2024 ACM A.M. Turing Award laureates Andrew Barto and Richard Sutton. They received the Turing Award for developing the conceptual and algorithmic foundations of reinforcement learning, a computational framework that underpins modern AI systems such as AlphaGo and ChatGPT. Barto is Professor Emeritus in the Department of Information and Computer Sciences at the University of Massachusetts, Amherst. His honors include the UMass Neurosciences Lifetime Achievement Award, the IJCAI Award for Research Excellence, and the IEEE Neural Network Society Pioneer Award. He is a Fellow of IEEE and AAAS. Sutton is a Professor in Computing Science at the University of Alberta, a Research Scientist at Keen Technologies (an artificial general intelligence company) and Chief Scientific Advisor of the Alberta Machine Intelligence Institute (Amii). In the past he was a Distinguished Research Scientist at Deep Mind and served as a Principal Technical Staff Member in the AI Department at the AT&T Shannon Laboratory. His honors include the IJCAI Research Excellence Award, a Lifetime Achievement Award from the Canadian Artificial Intelligence Association, and an Outstanding Achievement in Research Award from the University of Massachusetts at Amherst. Sutton is a Fellow of the Royal Society of London, AAAI, and the Royal Society of Canada.

In the interview, Andrew and Richard reflect on their long collaboration together and the personal and intellectual paths that led both researchers into CS and reinforcement learning (RL), a field that was once largely neglected. They touch on interdisciplinary explorations across psychology (animal learning), control theory, operations research, cybernetics, and how these inspired their computational models. They also explain some of their key contributions to RL, such as temporal difference (TD) learning and how their ideas were validated biologically with observations of dopamine neurons. Barto and Sutton trace their early research to later systems such as TD-Gammon, Q-learning, and AlphaGo and consider the broader relationship between humans and reinforcement learning-based AI, and how theoretical explorations have evolved into impactful applications in games, robotics, and beyond.