The Benefits of Neural Networks

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A lot of machine learning systems, not just neural networks are easy to fool. Big data somehow causes networks to focus on certain features that make it fairly easy to fool them. And I read recently that image recognition algorithms are much more sensitive to textures than human beings are rather than edges. So if you showed a picture of a cat with the skin texture of an elephant, it will certainly say it's an elephant. Right? But we humans look at it and say, Oh, it's good at recognizing faces. But you don't actually know for sure that that's what it's recognizing.

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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