And so the question is, if we take that data and then use these tools now that we have, these AI tools, to download that data into a large neural model, can we now understand how that brain is able to solve these tasks in a way that wasn't possible by just looking at the

activity patterns, which are areas of the brain that just glow and it's not really telling you

a lot about how they interact with each other, right? But we can do that now and we'll figure out

how they interact with each other different parts of the brain.

I'm Mary Long, and that's Terry Sinovsky.

He's the Francis Crick Chair at the Salk Institute for Biological Studies

and a distinguished professor at the University of San Diego.

His latest book is Chat Chagipit

and the Future of AI. If you're a regular listener of Motley Full Money, you've

probably heard us talk a fair bit about artificial intelligence. If you're new to the show,

I'd still wager that you've at least heard about ChatGPT. But what exactly are large

language models? How do they work? How do they

remember and reason? And if they're so good at human tasks, what actually makes them

different from us? My colleague Ricky Mulvey caught up with Sinovsky for a conversation

about how chatbots work, graduating from large language models to large

neural models, and the nature of consciousness.

One of the major themes of your book is how AI researchers are learning from brains and how

neuroscientists are learning from brains and how neuroscientists are

learning from large language models. One thing I think many listeners want to know, though,

is, is AI going to take my job? We have a lot of knowledge workers who listen to this podcast.

And I think it's a real worry when it can, you know, perform a lot of analysis, maybe a little better

than us humans can. As you've looked into these models, what's your advice to those folks worried

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