The Dangers of Concentrating Power in AI

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I think there's a lot of truth to the underlying idea that AI is a concentrating technology simply because of the need of a very large cluster. The biggest AI systems will always be the most capable and most powerful. I think we will see this kind of gradual increase where as the capabilities increase, people will find new ways to use them in order to grow the economy,. or in harmful ways like you mentioned hacking or some other ways manipulation.

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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