ACM ByteCast Association for Computing Machinery (ACM)

In this episode of ACM ByteCast, our special guest host Scott Hanselman (of The Hanselminutes Podcast) welcomes ACM Fellow Juan Gilbert, the Andrew Banks Family Preeminence Endowed Professor and Chair of the Computer & Information Science & Engineering Department at the University of Florida where he leads the Computing for Social Good Lab. The lab’s innovations include open-source voting technology to help make elections more secure, accessible, and usable; making voting technologies more transparent; increasing fairness and reducing bias in ML algorithms used in admissions and hiring decisions; and reducing conflicts during traffic stops. Gilbert’s many honors and recognitions include the Presidential Award for Excellence in Science, Mathematics, and Engineering Mentoring, the CRA A. Nico Habermann Award, and the National Medal of Technology and Innovation (NMTI).
Juan shares with Scott his surprise at being nominated for the NMTI, which he received in 2023 from President Joe Biden for pioneering a universal voting system that makes voting more reliable and accessible for everyone and for increasing diversity in the computer science workforce. He talks about his lab’s mission to change the world by solving real-world problems, and principles such as “barrier-free design” that he and his collaborators applied to his lab’s voting machine technology. They also discuss how his Application Quest (AQ) technology uses AI to help make fairer hiring decisions, and how his students’ Virtual Traffic Stops app helps protect both drivers and law enforcement safe during traffic stops. Juan also explains how he and his lab choose which projects they work on and teases the promise of brain-computer interaction technology.

In this episode of ACM ByteCast, Rashmi Mohan hosts ACM A.M. Turing Award laureate Yoshua Bengio, Professor at the University of Montreal, and Founder and Scientific Director of MILA (Montreal Institute for Learning Algorithms) at the Quebec AI Institute. Yoshua shared the 2018 Turing Award with Geoffrey Hinton and Yann LeCun for their work on deep learning. He is also a published author and the most cited scientist in Computer Science. Previously, he founded Element AI, a Montreal-based artificial intelligence incubator that turns AI research into real-world business applications, acquired by ServiceNow. He currently serves as technical and scientific advisor to Recursion Pharmaceuticals and scientific advisor for Valence Discovery. He is a Fellow of ACM, the Royal Society, the Royal Society of Canada, Officer of the Order of Canada, and recipient of the Killam Prize, Marie-Victorin Quebec Prize, and Princess of Asturias Award. Yoshua also serves on the United Nations Scientific Advisory Board for Independent Advice on Breakthroughs in Science and Technology and as a Canada CIFAR AI Chair. Yoshua traces his path in computing, from programming games in BASIC as an adolescent to getting interested in the synergy between the human brain and machines as a graduate student. He defines deep learning and talks about knowledge as the relationship between symbols, emphasizing that interdisciplinary collaborations with neuroscientists were key to innovations in DL. He notes his and his colleagues’ surprise in the speed of recent breakthroughs with transformer architecture and large language models and talks at length about about artificial general intelligence (AGI) and the major risks it will present, such as loss of control, misalignment, and nationals security threats. Yoshua stresses that mitigating these will require both scientific and political solutions, offers advice for researchers, and shares what he is most excited about with the future of AI.

In this episode, part of a special collaboration between ACM ByteCast and the American Medical Informatics Association (AMIA)’s For Your Informatics podcast, hosts Sabrina Hsueh and Karmen Williams welcome Francesca Rossi, IBM Fellow and AI Ethics Global Leader, and current President of the Association for the Advancement of Artificial Intelligence (AAAI). Rossi works at the Thomas J. Watson IBM Research Lab in New York. Her research interests focus on artificial intelligence, especially constraint reasoning, preferences, multi-agent systems, computational social choice, and collective decision making. She is also interested in ethical issues in the development and behavior of AI systems. She has published more than 200 scientific articles in journals and conference proceedings and is a fellow of both AAAI and EurAI. Rossi has been the president of the International Joint Conference on AI (IJCAI), an Executive Counselor of AAAI, the Editor-in-Chief of the Journal of AI Research, and serves n the Board of Directors of the Partnership on AI. She has also served as a program co-chair and steering committee member of the AAAI/ACM Conference on AI Ethics and Society (AIES).
Francesca shares how experiences with multidisciplinary work in computer science drew her to AI and ethics, and the challenges of synchronizing with people from a variety of different backgrounds at IBM. She also talks about her involvement in the development of AI ethics guidelines in Europe. She walks through some of her concerns around building ethical and responsible AI, such as bias, lack of availability, transparency of AI developers, data privacy, and the accuracy of generated content. Francesca emphasizes the importance of researchers working more closely with policymakers and the important role of conferences such as AIES (a collaboration between AAAI and ACM). She also offers suggestions for those interested in getting more engaged in AI ethics and recommendations for people interested in an AI career path, and advocates for common benchmarks that can help evaluate AI.

In this episode of ACM ByteCast, Bruke Kifle hosts Partha Talukdar, Senior Staff Research Scientist at Google Research India, where he leads a group focused on natural language processing (NLP), and an Associate Professor at the Indian Institute of Science (IISc) Bangalore. Partha was previously a postdoctoral fellow at Carnegie Mellon University’s Machine Learning Department and received his PhD in computer information science from the University of Pennsylvania. He is broadly interested in natural language processing, machine learning, and making language technologies more inclusive. Partha is a co-author of a book on graphs-based learning and the recipient of several awards, including the ACM India Early Career Researcher Award for combining deep scholarship of NLP, graphical knowledge representation, and machine learning to solve long-standing problems. He is also the founder of Kenome, an enterprise knowledge graph company with the mission to help enterprises make sense of big dark data.
Partha shares how exposure to language processing drew him to languages with limited resources and NLP. He and Bruke discuss the role of language in machine learning and whether current AI systems are merely memorizing and reproducing data or are actually capable of understanding. He also talks about his recent focus on inclusive and equitable language technology development through multilingual-multimodal Large Language Modeling, including Project Bindi. They discuss current limitations in machine learning in a world with more than 7,000 languages, as well as data scarcity and how knowledge graphs can mitigate this issue. Partha also shares his insights on balancing his time and priorities between industry and academia, recent breakthroughs that were impactful, and what he sees as key future achievements for language inclusion.

In this episode of ACM ByteCast, our special guest host Scott Hanselman (of The Hanselminutes Podcast) welcomes ACM Fellow Rosalind Picard, a scientist, inventor, engineer, and faculty member of MIT’s Media Lab, where she is also Founder and Director of the Affective Computing Research Group. She is the author of the book Affective Computing, and has founded several companies in the space of affective computing, including the startups Affectiva and Empatica, Inc. A named inventor on more than 100 patents, Rosalind is a member of the National Academy of Engineering and a Fellow of the National Academy of Inventors. Her contributions include wearable and non-contact sensors, algorithms, and systems for sensing, recognizing, and responding respectfully to human affective information. Her inventions have applications in autism, epilepsy, depression, PTSD, sleep, stress, dementia, autonomic nervous system disorders, human and machine learning, health behavior change, market research, customer service, and human-computer interaction, and are in use by thousands of research teams worldwide as well as in many products and services.In the episode, Rosalind talks about her work with the Affective Computing Research Group, and clarifies the meaning of “affective” in the context of her research. Scott and Rosalind discuss how her training as an electrical with a background in computer architecture and signal processing drew her to studying emotions and health indicators. They also talk about the importance of data accuracy, the implications of machine learning and language models to her field, and privacy and consent when it comes to reading into people’s emotional states.

In this episode of ACM ByteCast, Rashmi Mohan hosts 2021 ACM Fellow Edward Y. Chang, an Adjunct Professor in the Department of Computer Science at Stanford University. Prior to this role, he was a Director of Google Research and President of HTC Healthcare, among other roles. He is the Founder and CTO of Ally.ai, an organization making groundbreaking moves in the field using Generative AI technologies in various applications, most notably healthcare, sales planning, and corporate finance. He’s an accomplished author of multiple books and highly cited papers whose many awards and recognitions include the Google Innovation Award, IEEE Fellow, Tricorder XPRIZE, and the Presidential Award of Taiwan. Edward also also credited as the inventor of the digital video recorder (DVR), which replaced the traditional tape-based VCR in 1999 and introduced interactive features for streaming videos.
Edward, who was born in Taipei, discusses his career, from studying Operations Research at UC Berkeley to graduate work at Stanford University, where his classmates included the co-founders of Google and where his PhD dissertation focused on on a video streaming network that became DVR. Later, at Google, he worked on developing the data-centric approach to machine learning, and led development of parallel versions of commonly used ML algorithms that could handle large datasets, with the goal of improving the ML infrastructure accuracy to power Google’s multiple functions. He also shares his work at HTC in Taipei, which focused on healthcare projects, such as using VR technology to scan a patient’s brain; as well as his current interest, studying AI and consciousness. He talks about the challenges he’s currently facing in developing bleeding edge technologies at Ally.ai and addresses a fundamental question about the role of human in a future AI landscape.