ACM ByteCast

Eric Allman - Episode 85

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.

Peter Stone - Episode 84

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.

Monica Bertagnolli - Episode 83

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.

Ray Eitel-Porter - Episode 82

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.

Nicole Forsgren - Episode 81

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. 

Andrew Barto and Richard Sutton - Episode 80

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.

Dawn Song - Episode 79

In this episode of ACM ByteCast, our special guest host Scott Hanselman (of The Hanselminutes Podcast) welcomes ACM Fellow Dawn Song, Professor in Computer Science at UC Berkeley, Co-Director of Berkeley Center for Responsible Decentralized Intelligence (RDI), and Founder of Oasis Labs. Her research interest lies in AI safety and security, Agentic AI, deep learning, security and privacy, and decentralization technology. Dawn is the recipient of numerous awards including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, ACM SIGSAC Outstanding Innovation Award, and more than 10 Test-of-Time awards and Best Paper awards from top conferences in Computer Security and Deep Learning. She has been recognized as Most Influential Scholar (AMiner Award) for being the most cited scholar in computer security. Dawn is an IEEE Fellow and an Elected Member of American Academy of Arts and Sciences. She is also a serial entrepreneur and has been named on the Female Founder 100 List by Inc. and Wired25 List of Innovators.

Dawn shares her academic journey in cybersecurity, which used to be a much smaller field and how the MacArthur Fellowship (aka the “Genius Grant”) and other prestigious recognitions enabled her to pursue impactful multidisciplinary research. Dawn and Scott cover a myriad of topics around Agentic AI, including current and future security vulnerabilities from AI-powered malicious attacks, Dawn’s popular MOOC at RDI, and the associated AgentX-AgentBeats global competition (with more than $1 million in prizes and resources) focused on standardized, reproducible agent evaluation benchmarks to advance the field as a public good.

AgentX-AgentBeats Agentic AI Competition
Berkeley RDI Agentic AI MOOC

Russ Cox - Episode 78

In this episode of ACM ByteCast, Bruke Kifle hosts Russ Cox, Distinguished Engineer at Google. Previously, he was the Go language technical lead at Google, where he led the development of Go for more than a decade, with a particular focus on improving the security and reliability of using software dependencies. With Jeff Dean, he created Google Code Search, which let developers grep the world's public source code. He also worked for many years on the Plan 9 operating system from Bell Labs and holds degrees from Harvard and MIT. Russ is a member of the ACM Queue Editorial Board.

In the interview, Russ details his journey from the Commodore 64 to Bell Labs, where he met Rob Pike (a co-designer of Go) and contributed to Plan 9 working alongside other legendary figures. Russ shares lessons learned while working on Google Code Search (a highly complex C++ program) and how that informed his later approach to the development and evolution of Go. They delve into the role of Go in the AI era and the future of computing. Russ also discusses the open-source community and collaboration around Go, touches on mentorship and leadership, and offers advice for aspiring builders.

Anusha Nerella - Episode 77

In this episode of ACM ByteCast, Rashmi Mohan hosts Anusha Nerella, a Senior Software Engineer at State Street. She has more than 13 years of experience working on building scalable systems using AI/ML in the domain of high-frequency trading systems and is passionate about driving adoption of automation in the FinTech industry. Anusha is a member of the ACM Practitioner Board, the Forbes Technology Council, and is an IEEE Senior Member and Chair of IEEE Women in Engineering Philadelphia chapter. She has served as a judge in hackathons and devotes significant time mentoring students and professionals on the use of AI technologies, building enterprise-grade software, and all things FinTech.

Anusha traces her journey from growing up with limited access to technology to teaching herself programming to working at global firms including Barclays and Citibank and leading enterprise-scale AI initiatives. Anusha and Rashmi discuss the challenges of applying AI to a field where money and personal data are at stake, and workflows that prioritize trust, security, and compliance. They touch on the importance of clear data lineage, model interpretability, and auditability. The discussion also covers observability, tooling, and the use of LLMs in finance. Along the way, Anusha shares her personal philosophy when it comes to building systems where speed and reliability can be competing priorities.

Ilias Diakonikolas - Episode 76

In this episode of ACM ByteCast, Bruke Kifle hosts 2024 ACM Grace Murray Hopper Award recipient Ilias Diakonikolas, Professor at the University of Wisconsin, Madison, where he researches the algorithmic foundations of machine learning and statistics. Ilias received the prestigious award for developing the first efficient algorithms for high-dimensional statistical tasks that are also robust, meaning they perform well even when the data significantly deviates from ideal modelling assumptions. His other honors and recognitions include a Sloan Fellowship, the NSF CAREER Award, the best paper award at NeurIPS 2019, and the IBM Research Pat Goldberg Best Paper Award. He authored a textbook titled Algorithmic High-Dimensional Robust Statistics.

In the interview, Ilias describes his early love of math as a student in Greece, which led him on a research journey in theoretical statistics and algorithms at Columbia University and, later, at UC Berkeley. He defines “robust statistics” and how it aids in detecting “data poisoning.” Ilias and Bruke explore statistical v. computational efficiency, the practical applications of this research in machine learning and trustworthy AI, and future directions in algorithmic design. Ilias also offers valuable advice to future researchers.