ACM ByteCast

In this episode of ACM ByteCast, Rashmi Mohan hosts 2025 ACM Fellow Cynthia Rudin, the Gilbert, Louis, and Edward Lehrman Distinguished Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, Mathematics, and Biostatistics and Bioinformatics at Duke University, where she leads the Interpretable Machine Learning Lab. Her lab, which seeks to design predictive ML models that people can understand, focuses on areas including healthcare, criminal justice, and energy reliability. Among her honors, she has received the Squirrel Award for Artificial Intelligence from the Association for the Advancement of Artificial Intelligence (AAAI), as well as the IJCAI John McCarthy Award. Rudin was recently named an ACM Fellow for contributions to and leadership in interpretable machine learning and societal applications.

In the interview, Cynthia clarifies the crucial distinction between "interpretable" and “explainable" AI and makes the argument that true interpretability is foundational to trustworthy, ethical AI. She shares her extensive field experience collaborating with Con Edison engineers on power grid maintenance, neurologists on medical diagnostics, and the Cambridge Police Department on crime series detection, countering the widespread industry myth that AI performance must be sacrificed for transparency. She describes an innovative paradigm her lab developed to solve the "interaction bottleneck" between data scientists and domain experts, leveraging "Rashomon sets" to generate millions of equally accurate models simultaneously, using human-computer interaction (HCI) tools to create visual, encyclopedia-like interfaces.

In this episode of ACM ByteCast, our special guest host Scott Hanselman (of The Hanselminutes Podcast) welcomes ACM Fellow Eric Allman, a foundational figure of the early Internet as the developer of Sendmail and its precursor Delivermail (for the original ARPANET) in the late 1970s at UC Berkeley. Sendmail is the mail transfer agent that powered a large portion of global email infrastructure through the formative years of the network and helped shape how messages move across the web. Allman is also an ACM Distinguished Engineer and was inducted into the Internet Hall of Fame in 2014.

The conversation explores the origins of Internet email, the messy realities of building software that must operate at planetary scale, and what lessons today’s engineers can learn from the systems and design decisions that quietly underpin modern computing. Eric shares his work at UC Berkeley spanning a variety of domains, from user interfaces to neural networks. He and Scott touch on current AI capabilities, including their personal experiments in assistive coding with current models such as Claude, and discuss into the programming languages Python, C#, TypeScript, and JavaScript. Eric also shares candid thoughts on letting go of computing after retirement.

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.