Name: The limits of rationalism: From Socrates to neural networks | Dan Shipper: Full Interview
Uploaded: 2025-11-21T18:04:08.000Z
Duration: 57 min 47 s
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I get to talk to people all the time about how they use AI in their work and in their lives, and

Basic quantifiers

There's many different ways of knowing things and many different ways of understanding things.

What both of those ways of seeing the world are trying to do is reduce the world into a set of really clean universal laws that apply in any situation.

And what language models see instead is a dense web of causal relationships between different parts of the world that all come together in unique, very context specific ways to produce what comes next.

And what's really interesting about neural networks is the way that they think or the way that they operate is a lot like human intuition.

Human intuition is also trained by thousands and thousands and thousands of hours of direct experience.

The reason I love that is because I hope that it makes more visible to us the value and importance of intuitive thought.

I'm the co founder and CEO of EVERY and I'm the host of the AI and I podcast.

Chapter one, the Limits of Rationalism from Socrates to Neural Networks I think rationalism is one of the most important ideas in the last 2000 years.

Rationalism is really the idea that if we can be explicit about what we know, if we can really reduce what we know down into a set of theories, a set of rules for how the world works, that is true knowledge about the world and that is distinct from everything else that kind of messes with our heads, messes with how we do, how we, how we operate in society.

And you may not have heard that word, or maybe you have, but

it is built into the way that you see the world.

For example, the way computers work, or the way vaccines work, or the way that we predict the weather, or the way that we try to make decisions when we're thinking about.

I don't want to be too emotional about this.

I want to get really precise about my thinking on this issue.

Even the way that we do therapy, a lot of therapy is about rationalizing or rationalizing through what you think and what you feel.

All that stuff comes from an extensive lineage of ideas that started in ancient Greece, really blossomed during the Enlightenment, and

now is like the bedrock of our culture and the way that we think about the world.

I think the father of rationalism is Socrates, the philosopher.

Socrates is one of the first people to really examine the question of what we know and how, what is true and what's not true to be able to describe what we know.

And how we know it to make that clear and explicit so that only people that knew the truth, that knew how the world really works, were the ones that were steering the state.

That really became the birth of philosophy is this idea that if you inquire deeply into what is usually kind of like the inexplicit intuitions that we have about the world, you can identify a set of rules or a theory about what the world is like and what's true and what's not that you can lay out explicitly and that you can use to decide the difference between true and false.

I think that you can trace the birth of rationalism to this dialogue Protagoras.

And in the dialogue, it's a debate between Socrates on the one hand and Protagoras.

And Protagoras is what we call a sophist.

And it's where the term sophistry, which means like kind of, you know, someone who says really compelling things but is actually full of shit.

What Protagoras and Socrates are debating is can excellence be taught?

And excellence, the word is often translated in English as virtue, but

I think a more appropriate translation is excellence.

And in ancient Greece, like that kind of excellence was, was really prized.

It's sort of like, it's sort of like a general ability to.

Be good at important things in life and in society.

And they approach it from very different angles.

Protagoras believes that everyone has the capacity, every human has the capacity to be excellent.

And he tells this big myth about how we as humans gained the capacity to be excellent.

And Socrates is saying, no, no, no, I don't want any of that.

I want you to say explicitly what it is and what it's not and what are the components of it.

And that's a really big moment, at least the way that Plato writes it.

Socrates kind of like takes apart Protagoras and it's pretty clear by the end that Protagoras doesn't know, doesn't have any way to define in a non contradictory way what excellence is, what it means to be good.

And the implication is that then he doesn't know it.

And that sort of set Western society on this path of trying to find really clear definitions and theories for the things that we talk about and to identify knowledge, the ability to know something, or whether or not you know something with whether or not you can really clearly define it.

And that idea became incredibly important in the scientific enlightenment thinkers on the philosophy side, like Descartes and on the science side, like Newton and Galileo, took this idea and used it as a new method to understand and explain the world.

So what it became is can we use mathematics to explain and predict different things in the world?

And from Socrates, the Galileo to Newton, they continually reinforced this idea that in order to truly know something, you have to be able to describe it explicitly, you have to have a theory about it, you have to be able to describe it mathematically.

Ideally, the world around us is shaped by this framework.

So everything from smartphones to computers to cars to rockets to cameras to electricity, every appliance in your house, vaccines, everything in our world is shaped with this idea or this way of seeing the world.

And you can find this too in the rest of culture.

Anytime you see a book or a movie or a blog post or whatever, talking about the five laws of power or the five laws of negotiation, all that stuff is ways that physics and rationalism in general has sort of seeped into the everyday, everyday way that we think about the world.

And to be clear, it's been super successful.

But in areas of the world like psychology or economics or neuroscience, it has been really hard to make progress in the same way that physics has made progress.

I think if you look, for example, at the social sciences, a lot of the way that the social sciences are structured is inspired by physics.

What we're trying to do is take very complex higher level phenomena, so like maybe psychology or economics or any other branch of social science, and

we're trying to reduce it down to a set of definitions and a theory and a set of rules for how things in that domain work.

And what's really interesting is if you look at those fields.

So like psychology, for example, it's in the middle of a gigantic replication crisis.

Even though we spent 100 years doing psychology research, the body of knowledge that we've been able to build there, in terms of its universal applicability, our ability to find universal laws in the same way that Newton found universal laws seems pretty suspect.

And we feel like we can't stop doing it because we have no better alternative.

Another really interesting, an important part of the world that this way of looking at things didn't work for in many ways is AI.

So this is usually the part of an explanation where I try to define it.

And what's really interesting is there's no universal, agreed upon definition for this.

We've struggled to come up with a universal definition for what it is to know something or a universal definition for what anxiety is.

For example, in psychology is another really good example.

There are a lot of ways to kind of gesture at what AI is, but

really it's like obviously, Maybe not obviously, AI stands for artificial intelligence.

And the AI's project is to build a computer that can think and learn in the same way that humans learn.

And because of the way that computers work, for a very long time, that was a really hard problem.

AI started as a field in the 50s at Dartmouth.

And you can actually look at the original paper, they were very optimistic.

They were like, maybe a summer's worth of work and we'll have nailed this.

And the way that they defined it is to be able to reduce down human intelligence into a system of symbols that they could combine together based on explicit rules that would mimic human intelligence.

And so there's a really clear through line from Socrates original project to the Enlightenment to the original approach that AI theorists took called symbolic AI.

The idea that you could embody thinking in essentially like logic, logical symbols, and

transformations between logical symbols, which is, it's very similar to just basic philosophy.

And there are actually a lot of early successes.

For example, the two founding fathers of AI, Herbert Simon and Alan Newell, built this machine that they called the general problem solver.

And what's really interesting is it wasn't even built as a computer because computers were extremely expensive back then.

They originally codified the general problem solver on paper and then executed it themselves by hand.

Actually, I think one of them had their family do it with them to try to, to try to simulate how a computer would work to solve complex problems.

And the General problem Solver, they tried to reduce down complex real world situations into simple logical logic problems that look a little bit like games.

And then they tried to see if they could build a computer that would solve some of those games.

And they were actually quite successful at first.

What they found was it worked really well for simple problems.

But as problems got more and more complex, the search space of possible solutions got really, really, really, really big.

And so by representing the problem in that way, the systems that they built started to fail as soon as they moved away from toy problems to more complex ones.

I think a really interesting and simple example of this is thinking about how you might decide whether an email in your inbox is spam or whether it's important.

And you might say something like, if it mentions that I won the lottery, it's spam, right?

And so that sort of like if then rule is a lot like the kinds of rules that early symbolic AI theorists were trying to come up with to help you solve any problem is to codify.

Like, if XYZ is true, then here are the implications.

What happens is, if you look at that really closely, there are always lots of little exceptions.

So an example might be if it says emergency, maybe you want to put that at the top of your inbox.

But very quickly you'll have spammers, obviously, being like, just put emergency in the subject line and they'll shoot to the top.

So then you have to kind of like, create another rule, which is, it's emergency, but

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The limits of rationalism: From Socrates to neural networks | Dan Shipper: Full Interview

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