The Future of AI Systems

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I think this is one area where humans and the human brain operates differently from our neural networks. So maybe we're confused about this idea that memorization is a bad thing just because in early AI systems, it happened to often show that you had not trained properly but it's not inherently a problem. Perhaps it is possible to do better than just memorizing everything. But my statement is different. My statement is that the gold standard formalism of generalization or at least a gold standard formalist, Bayesian inference is very consistent with you memorize everything yet you fully generalize. That's really interesting. We've talked about how these systems work. I want to now switch and talk about

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Can machines actually be intelligent? What sorts of tasks are narrower or broader than we usually believe? GPT-3 was trained to do a "single" task: predicting the next word in a body of text; so why does it seem to understand so many things? What's the connection between prediction and comprehension? What breakthroughs happened in the last few years that made GPT-3 possible? Will academia be able to stay on the cutting edge of AI research? And if not, then what will its new role be? How can an AI memorize actual training data but also generalize well? Are there any conceptual reasons why we couldn't make AIs increasingly powerful by just scaling up data and computing power indefinitely? What are the broad categories of dangers posed by AIs?

Ilya Sutskever is Co-founder and Chief Scientist of OpenAI, which aims to build artificial general intelligence that benefits all of humanity. He leads research at OpenAI and is one of the architects behind the GPT models. Prior to OpenAI, Ilya was co-inventor of AlexNet and Sequence to Sequence Learning. He earned his Ph.D. in Computer Science from the University of Toronto. Follow him on Twitter at @ilyasut.

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