The History of Neural Networks

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Minsky and Pappert doubted that there could be a learning algorithm, but they were very quickly shown to be wrong. Not until the later part of the 70s did people come up with an algorithm called back propagation. And is that basically the kind of learning algorithms we still use? Absolutely. We use people use back propagate based on calculus. Yeah. It's and it's very elegant. And it seems to work. Okay. So for the deep neural network, or just neural networks in general, the trick was to teach the whole network had a learning sense. That's what learning seems to consist of in the brain.

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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