In this episode, Pedro Domingos - AI - 2040 - Lauren Hawker Zafer is joined by Pedro Domingos.
This unique conversation explores AI's impact on politics, particularly in voter targeting and campaign strategies, and the concept of AI as a tool for enhancing collective intelligence.
Domingos, with over 200 technical publications and numerous accolades, shares insights on the future of AI, its challenges, and opportunities.
Who is Pedro Domingos?
Pedro Domingos is a renowned AI researcher, tech industry insider, and Professor Emeritus of Computer Science and Engineering at the University of Washington. He is the author of the best-selling book The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Basic Books, 2015), which has been translated into over twelve languages and sold over 300,000 copies.
He won the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the highest honors in data science and AI. Domingos is Fellow of the AAAS and AAAI and received an NSF CAREER Award, a Sloan Fellowship, a Fulbright Scholarship, an IBM Faculty Award, several best paper awards, and other distinctions.
Pedro 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.
Pedro is the author/co-author of over 200 technical publications in machine learning, data science, and other areas. He’s 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.
He was the program co-chair of KDD-2003 and SRL-2009, and I've served on the program committees of AAAI, ICML, IJCAI, KDD, NIPS, SIGMOD, UAI, WWW, and others.
His work has been featured in the Wall Street Journal, Spectator, Scientific American, Wired, and elsewhere. Lastly, Domingos helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks. He lives in Seattle.
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