The Dangers of a Truly Super Intelligent AI

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One day we may build an AI that is really more intelligent than humans in or at least so intelligent that we can really think of as a general intelligence and not just a narrow intelligence. The possibility exists because there will be unexpected discoveries in ML. But there is also the possibility that indeed those AI systems will be very, very tricky to handle when they are super intelligent. This is a question that you think about a lot at OpenAI and how to deal with this and how we should do research about it in strategic implications.

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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