David Eagleman

And this amount of time looking for lost items and this amount of time realizing you've forgotten someone's name and this amount of time falling and so on. Part of why I used the title Sum is because of the sum of events in your life like that. Part of it was because Cochito Ergo Sum. So it ended up just being the perfect title for me, even if it did lose a couple of readers there. Yeah.

Essentially, these artificial neural networks took off from a very simplified version of the brain, which is, hey, look, you've got units and they're connected. And what if we can change the strength between these connections? And in a very short time, that has now become this thing that has read everything ever written on the planet and can give extraordinary answers.

But it's not yet the brain or anything like it. It's just taking the very first idea about the brain and running with it. What a large language model does not have is an internal model of the world. It's just acting as a statistical parrot. It's saying, okay, given these words, what is the next word most likely to be given everything that I've ever read on the planet?

And so it's really good at that, but it has no model of the world, no physical model. And so things that a six-year-old can answer, it is stuck on. Now, this is not a criticism of it in the sense that it can do all kinds of amazing stuff and it's going to change the world, but it's not the brain yet. And there's still plenty of work to be done to get something that actually acts like the brain.

I suspect so, because there are 8.2 billion of us who have this functioning in our brains. And as far as we can tell, we're just made of physical stuff. We're just very sophisticated algorithms. And it's just a matter of cracking what that algorithm is.

The big textbook that we have in our field is called Principles of Neuroscience, and it's about 900 pages. And it's not actually principles. It's just a data dump of all this crazy stuff we know. And in 100 years, I expect it'll be like 90 pages.

We'll have things where we put big theoretical frameworks together and we say, ah, okay, look, all this other stuff, these are just expressions of this basic principle that we have now figured out. Do you pay much attention to behavioral economics? Yes, I do. What do you think of it? Oh, it's great. And that's probably the direction that a lot of fields will go is how do humans actually behave?

One of the big things that I find most interesting about behavioral economics comes back to this issue about the team of rivals. When people measure in the brain how we actually make decisions about whatever, There are totally separable networks going on. Some networks care about the valuation of something, the price point.

You have totally other networks that care about the anticipated emotional experience about something. You have other networks that care about the social context, like what do my friends think about this? You have mechanisms that care about short-term gratification. You have other mechanisms that are thinking about the long-term, what kind of person do I want to be?

All these things are battling it out under the hood. It's like the three stooges sticking each other in the eye and wrestling each other's arms and stuff. But what's fascinating is when you're standing in the grocery store aisle trying to decide which flavor of ice cream you're going to buy, you don't know about these raging battles happening under the hood.

You just stand there for a while and then you say, okay, I'll grab this one over here.

Oh, gosh, no. And the reason is because we've got all these billions of brains running around. What that tells us is it has to be pretty simple in principle. You got 19,000 genes. That's all you've got. Something about it has to be as simple as falling off a log for it to work out very well so often, billions of times.

I do now.