The Importance of Causation in AI

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Ilya: I think one of the biggest concerns that I have personally is that there will end up being a race as we get closer. Are you optimistic that as AI's get more and more capable that the top groups really will work together instead of filling in competition with each other? He says he doesn't put too much back in evidential decision theory because it doesn't perform well in some of the weird philosophical thought experiments. Ilya: Causation is an element of a model, so when you're trying to make the world different than it would have been had we not existed, then you automatically are concerned about causality.

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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.

Staff