David Eagleman, Neuroscientist and Author, on the Brain as a Neural Parliament, AI Hallucinations, and Rethinking Democracy with AI

00:12:40 · David Eagleman, Dan TurchinPlay from here

[00:12:40] S0: Yeah. Great question. I think LLMs won't be the final thing, and already exactly as you said, lot of people are working on ways of mixing things. And I think the models currently by Anthropic and OpenAI and others are actually mixtures of things. A lot a lot of this proprietary and locked away exactly what they're doing, but it's not your grandmother's LLM. And I I do suspect that the transformer model, this particular architecture, has its limits, but boy, everybody is working on this now. I mean, every kid I teach at Stanford, that's all they're doing is, hey, what's the next thing that I can do here? How can I contribute? And so this is going to move very rapidly, but yes, I do think we'll need other pieces of architecture. I think when we look at the human brain, what we see is it ain't one thing. It's not like, Oh, here's the structure. Here's the architecture. Instead, it's this weird melange of all these different architectures smooshed together. This is how evolution always works. Evolution never says, Okay, I want to solve this problem, and then stops. Evolution just keeps going and often comes up with many, many solutions to the same problem. And this is what we see in the brain is that... What I described in my book Incognito is that the brain is a team of rivals. That's that's how I put it. I was basing this off, Doris Kearns Goodwin. Kearns Goodwin. Right. I [00:14:08] S2: almost Yeah. Did What's that? The reference to Abraham Lincoln? Exactly right. His cabinet [00:14:12] S0: was... Exact... Exactly right. He he I love that.

00:14:12 · David Eagleman, Dan TurchinPlay from here

[00:14:12] S0: was... Exact... Exactly right. He he I love that. [00:14:16] S2: Framework, by

00:14:16 · David EaglemanPlay from here

[00:14:16] S0: the way. Yeah. When he got elected president, he said, you know, look. I I wanna get all my enemies into my cabinet so I can have this this this diversity of viewpoints. So, anyway, I've suggested that that's really the way to think about the brain. If I put some, you know, chocolate bonbons in front of you, part of your brain wants to eat that, part of your brain wants to not eat it because it's all sugary, part of your brain wants... You know, says, I'll eat it, but I'll go to the gym tonight. You can argue with yourself and cuss at yourself and contract with yourself, and I think this is often overlooked, that we are made of lots of different drives. By the way, this is something I wrote about some years ago, but I just recently found a quotation from Friedrich Nietzsche, who casts this in his own language in the eighteen eighties. He said, essentially, that the brain is made up of lots of drives, and each drive philosophizes in its own spirit, by which he meant, you know, each one tries to stand on a soapbox and say, look, this is why you should eat the bonbons. Another one says, hey, look, this is a really good reason why you shouldn't eat the bonbons and so on. They're all philosophizing, trying to get what they want, and so the way I think this comes together is that the right way to think about the brain is like a neural parliament where you're made up of all these different political parties, all of which love their country but have different ways of steering the ship. And so they all vote, and at different times, by the way, the vote will tip in different directions, and that's the direction that the ship of state moves in, you being the ship of state. So coming back to your question, what we're gonna get with AI is if if it comes out like the human brain, we're probably gonna have lots of different kinds of architectures bolted together and some sort of voting, and and that's how it'll operate. And and by the way, that's, as I said, currently, how a lot of systems already are. For example, if you ask CHAT GPT a math question, it doesn't go through the LLM. It detects it's a math question and sends it off through the calculator. It saves a lot of, you know, resources that way.

00:16:33 · Dan TurchinPlay from here

Ad[00:16:33] S2: I'm proud to share details from today's sponsor. It is the AI Playbook, mastering the rare art of machine learning deployment, a wonderful book published by repeat guest on this podcast, doctor Eric Siegel, a prolific speaker, writer, professor, rapper, I dare say. It is a great read, very entertaining, very relatable. It presents the gold standard six step playbook for ushering predictive AI initiatives from concept to deployment. It is just coming out in paperback form. It's been a crowd pleaser since it launched a few years back, and you will so enjoy learning from the great doctor Eric Siegel. Go ahead and head out to bizml.com to get your copy of the AI playbook mastering the rare art of machine learning deployment from doctor Eric Siegel. Now back to today's amazing episode. We are gonna spend some... Not not Dave and Dan, but other people will spend somewhere around a trillion dollars on compute infrastructure, building up data centers and things in the next twelve months. And this conversation, and particularly, I'm so glad you referenced the brain running at 20 watts. It's just a reality check about, you I think you call it the three pound machine. Right? Yeah. The inner cosmos. Right? It's such a stark reminder about what's unique about the brain and why kinda synthetically generating them in data centers feels a little bit like a brute force approach, a little bit naive. What's your sense about the approach we're taking and kind of the... What I'll call brute force, you can choose to use a different term, but is that the best path to synthetically generating intelligence? I I I think I probably share the intuition that

00:18:28 · David Eagleman, Dan TurchinPlay from here

[00:18:28] S0: a lot of us have, which is that we're gonna build these data centers, and there will be scientific advances in the in the coming years that make them really obsolete. But that said, I'm sure there will be plenty of compute that we need for plenty of other things, so it'll get used somehow. But yes, maybe it'll turn out that we've got these giant data centers. I suspect that with many scientific trajectories you see this sort of thing. Just as an example, you know, I was in labs when the human genome project was really getting rolling, and people were spending a ton of money on these machines that pretty quickly became outdated, and now you can do sequencing with new tricks and techniques and just do it faster and better and cheaper. So this probably happens with everything. No exception here. By the way, we should mention that you studied with doctor Crick. That's right. That's right. He It [00:19:24] S2: was a little before your time, but you actually studied at Salk.

00:19:24 · Dan Turchin, David EaglemanPlay from here

[00:19:24] S2: was a little before your time, but you actually studied at Salk. [00:19:26] S0: Yeah. Exactly. I I did my postdoctoral fellowship at the Salk Institute. I studied with Francis Crick there, among other mentors, and it was... That was really a once in a lifetime experience for a young scientist because, I mean, yeah, for anyone who doesn't know, Francis Crick was the guy who co discovered the structure of DNA, but but then switched over by the by the nineteen nineties to try to figure out the brain. And it's a real opportunity for somebody like him. If you win the Nobel Prize when you're still young, you don't have any of the pressures of, oh, I gotta get this grant and run this lab and graduate these students and whatever. You just get to think big thoughts the rest of your life, and that's what he did. He was the most big thought thinking guy that I've ever had the pleasure to