How Deep Neural Networks Can Help You Train a System

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The transformers came out in late 2017, so maybe five years ago. The main usefulness of the transformer comes from what may appear to be a technicality at a sufficiently high level like the conversation that we are having. Once you have deep networks, the number of steps that's afforded to the parallel computer increases significantly. And because of it, it can do a lot more. Then the main innovation of the transformer is that it lets you process long sequences of vectors in a very computer-efficient way and most crucially, in a way that's easy to learn for the learning algorithm.

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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