Tom Griffiths

There are fundamental questions that we want to answer about how human minds work.

And the answers to those questions are important, not just for understanding humans, but also for building intelligent machines.

Yeah, I mean, I think there is.

So one interesting fact is that the people who set out with starting that enterprise of mathematical physics

had in mind doing the same thing for understanding how thought works.

The very earliest philosophers and scientists in the inception of science itself were really thinking about mathematics as a tool for understanding both the external world and the internal world.

We see that in Descartes and Leibniz and many of these people talking about that idea.

I think it's possible for us to end up with something that looks like a set of laws as long as we think carefully about what it is that we're trying to characterize.

We want to understand how intelligence works, and there are many different ways you could think about that.

You could think about it abstractly in terms of what are the general principles that would govern intelligence.

And concretely in terms of how is it that things like brains work in order to produce intelligence.

And at those different levels, I think there are things that we can point to that are generalizations that maybe have the right character to be things that we could call laws.

Yeah, and it's worth pointing out, he was also, in many ways, our first cognitive scientist in terms of trying to come up with the laws that characterized.

In his case, it's not clear whether he was focused on argument or thought, but doing some of the first work in logic that really provided the foundations for

modern approaches to mathematical logic.

Yeah.

I think that gets us back to this idea of what levels we're trying to understand thought at.

In cognitive science, following the work of David Marr, we talk about there being different levels of analysis that we can apply to information processing systems.

The most abstract of those is what Marr called the computational level, which is trying to understand

what it is that a system is doing in terms of what problem it's solving, and then what the ideal solution to that problem looks like.