The Importance of General Purpose Tools in Deep Learning

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I'm curious if you have a perspective on that. It seems like many more tasks are narrow than people realize. One of the things that most impressed me about GP3 is that while you trained it to do just one thing, it was able to do many things as a consequence. I think what's happening here is that our intuitions about intelligence are not exactly perfect. But with the situation we are today with deep learning, we do have quite general purpose tools. If you want to get really good results on some tasks, if you can collect a large amount of data, you will in fact get very good results on this task.

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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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