The Importance of Confidence in AI

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There are famous public intellectuals who warn against AI coming in the future and being super intelligent. They get a lot of pushback from people who spend their lives doing AI, but I don't find the pushback especially convincing because you can be so immersed in the day-to-day difficulties that you miss the possibility of something big going on. But if you take someone like Ray Kurzweil, who sort of promotes the idea of the singularity where he thinks that AI is going to become at the level of humans within the next 10 years or a billion times more intelligent than humans within 20 years or 30 years ... it's about to get very, very steep.

Artificial intelligence is better than humans at playing chess or go, but still has trouble holding a conversation or driving a car. A simple way to think about the discrepancy is through the lens of “common sense” — there are features of the world, from the fact that tables are solid to the prediction that a tree won’t walk across the street, that humans take for granted but that machines have difficulty learning. Melanie Mitchell is a computer scientist and complexity researcher who has written a new book about the prospects of modern AI. We talk about deep learning and other AI strategies, why they currently fall short at equipping computers with a functional “folk physics” understanding of the world, and how we might move forward.

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Melanie Mitchell received her Ph.D. in computer science from the University of Michigan. She is currently a professor of computer science at Portland State University and an external professor at the Santa Fe Institute. Her research focuses on genetic algorithms, cellular automata, and analogical reasoning. She is the author of An Introduction to Genetic Algorithms, Complexity: A Guided Tour, and most recently Artificial Intelligence: A Guide for Thinking Humans. She originated the Santa Fe Institute’s Complexity Explorer project, on online learning resource for complex systems.

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