#46: Examining Regulation for ChatGPT: Dr. Pedro Domingos

AI Asia Pacific Institute Podcast

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The AI Asia Pacific Institute (AIAPI) is hosting a series of conversations with leading artificial intelligence (AI) experts to study ChatGPT and its risks, looking to arrive at tangible recommendations for regulators and policymakers. These experts include Dr. Toby Walsh, Dr. Stuart Russell, Dr. Pedro Domingos, and Dr. Luciano Floridi, as well as our internal advisory board and research affiliates. We have published a briefing note outlining some of the critical risks of generative AI and highlighting potential concerns.

The following is a conversation with Dr. Pedro Domingos.

Dr. Pedro Domingos is a professor emeritus of computer science and engineering at the University of Washington and the author of The Master Algorithm. He is a winner of the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the highest honors in data science and AI. He is a Fellow of the AAAS and AAAI, and has received an NSF CAREER Award, a Sloan Fellowship, a Fulbright Scholarship, an IBM Faculty Award, several best paper awards, and other distinctions. Dr. Domingos received an undergraduate degree (1988) and M.S. in Electrical Engineering and Computer Science (1992) from IST, in Lisbon, and an M.S. (1994) and Ph.D. (1997) in Information and Computer Science from the University of California at Irvine. He is the author or co-author of over 200 technical publications in machine learning, data mining, and other areas. He is a member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR. Dr. Domingos was program co-chair of KDD-2003 and SRL-2009, and served on the program committees of AAAI, ICML, IJCAI, KDD, NIPS, SIGMOD, UAI, WWW, and others. He has written for the Wall Street Journal, Spectator, Scientific American, Wired, and others. He helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks.

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