The Ethics of AI

3min Snip

00:00

Play full episode

Summary

Transcript

Episode notes

I'm sure a lot of the things that I say every day are very stereotypical. But we already are in a world where, you know, holograms are doing world tours. The fact that an algorithm can be biased is one that some people have difficulty wrapping their minds around. Data algorithms learn from data. So if data is biased, the algorithms are biased. They just pick it up from the data. Well, and the cheap tricks,you know, it sort of leans both ways.

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.

Support Mindscape on Patreon.

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.

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.