Tom Griffiths

This is the one thing which is true.

And I think when we come to thinking about information processing systems, it's really clear that we're going to have to have these nice sort of mutually reinforcing perspectives that give us explanations at these different levels of analysis.

And what it really means to have a complete answer is understanding those different levels and then how the explanations we have at those different levels relate to one another.

I think the other thing that's important is that there's not necessarily a one-to-one mapping between those levels.

You can have Bayesian inference be your computational level explanation for something, and you can have five different ways of creating approximations to Bayesian inference that all show up in human minds and brains in different places.

then those things are going to be realized in brains and neurons in different ways as well.

You can come up with different ways of instantiating those different algorithms.

I think that's also important when we try and look for silver bullet explanations where you're like, this is the one way that the brain does Bayesian inference or whatever it is.

My expectation is that it's unlikely it will find those just because there are so many ways of approximating those ideal solutions and different ones work in different circumstances.

That's kind of what keeps statisticians and computer scientists busy coming up with new kinds of algorithms.

And so when we think about something like the laws of thought,

The level at which I think we can be most successful is at that computational level.

I think that's where we can really say, these are the principles that really govern intelligence, whether it's in humans or machines or in aliens that we haven't met yet.

We should expect them to have something like Bayesian inference be a good explanation for how it is they solve inductive problems, something like logic giving us a good story about how they might solve deductive problems.

It's going to become more complicated as we start to go down into those other levels of analysis where that's where a lot of the complexity arises and where we might not expect that we're going to find simple stories.

In fact, it might be a multiplicity of stories, each of which has some simple components.

That's part of, I think, what makes it interesting to be a cognitive scientist.

All right.

Thank you.