ACM ByteCast

In this episode of ACM ByteCast, host Juan Miguel de Joya welcomes 2025 ACM Luiz André Barroso Award recipient Ricardo Baeza-Yates, the Search Chief Scientist at You.com, holding part-time professor appointments at KTH Royal Institute of Technology (Sweden), Universitat Pompeu Fabra (Spain), and Universidad de Chile. The award recognizes his pioneering contributions to algorithms and information retrieval as well as his leadership in fostering a vibrant transnational research community across Latin America. Baeza-Yates is widely regarded as one of the world’s foremost researchers in information retrieval, celebrated especially for pioneering innovative data structures that have shaped the field. His work has produced influential algorithms for string searching and fuzzy matching, including the well-known Shift-Or algorithm. As a practitioner, Baeza-Yates served as VP of Research for Yahoo Labs, secured 14 patents, and co-founded several startups in Chile and Spain, including Theodora AI, devoted to mitigating technological bias. Among his honors, he received the CLEI Distinction for Contributions to Computing in Latin America in 2009, the Spanish “Ángela Ruiz Robles” Award for research excellence and entrepreneurship in applied computing in 2018, the 2024 Chilean National Prize for Applied Sciences and Technology, and the first Merit Award from the Chilean Computing Science Society in 2025. Ricardo is a member of Academia Europaea, and a Fellow of ACM and IEEE. He is the co-author of Modern Information Retrieval, which became the field's most cited textbook.

Ricardo shares his unconventional path into computing, from influential teachers to his discovery of the mathematical and logical beauty of algorithms. He discusses his current research, which focuses on evaluating AI by examining failures, harm, and risk rather than simply measuring success, since errors can have profound consequences in fields like medicine and law, where users may not have the expertise to recognize errors. He advocates for combining reliable search with AI to provide AI agents with accurate, trustworthy information. The conversation also explores the limitations and social consequences of increasingly replacing traditional search with AI-generated responses, including the risk of "cognitive offloading." Ricardo also shares his views on how AI is shifting computer science, offers advice for future AI developers, highlighting ACM’s principles for responsible computing, and advocates for more inclusion of Latin American perspectives in computing and AI.

In this episode of ACM ByteCast, our special guest host Scott Hanselman (of The Hanselminutes Podcast) welcomes ACM Queue Editorial Board member Kelly Shortridge, Chief Product Officer at Fastly, where she previously served as VP of Security Products. Shortridge is the author of Security Chaos Engineering: Sustaining Resilience in Software and Systems (O'Reilly). An accomplished product executive, software innovator, and internationally recognized technical expert on resilience in complex systems, she is known for the application of behavioral economics, resilience, and DevOps principles to cybersecurity, and modernizing security programs.

In the Kelly explains that security chaos engineering is really about resilience engineering—building systems that can recover quickly from inevitable failures. She makes an argument that organizations should prioritize adaptability, redundancy, and recovery over prevention, and encourages greater collaboration between security and platform engineering teams. The wide-ranging conversation covers “metrics theater,” the cost-resilience tradeoff, why software has unique advantages for simulation that we're not leveraging, and where LLMs fit (and don't fit) in security workflows.

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.