The Belief in Large Networks

2min Snip

00:00

Play full episode

Summary

Transcript

Episode notes

TPA3 is good for processing sequences of vectors. If you're processing 2,000 words, one way to think about that is you're really processing a sequence of vectors. This brings us to the third point you made about why it would have been hard to build something like TPA3 five years ago - just this belief in large networks. Why did people not believe that large networks would give them such a big advantage? And why was it that you and your colleagues open AI thought otherwise?

Read the full transcript here.

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