The Importance of Symbolic Reasoning in Computers

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Two common critiques you hear about the standard neural net paradigm is one that sort of symbolic thinking or reasoning is missing from today's systems. Another is that some kind of embodiment in the world and interaction with the world may be necessary to learn the way humans learn. I think it's definitely conceivable that the human brain is doing something that's quite a bit better than our artificial neural networks. And what it will mean primarily is that the amount of compute that will be required to reach to human-level intelligence will be larger than it seems now.

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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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