It's really great to be here.

And thank you so much for the invitation.

So yes, robot intelligence.

So I've titled the lecture "The Thinking Robot."

But of course, that immediately begs the question,

what on earth do we mean by thinking?

Well we could, of course, spend the whole

of the next hour debating what we mean by thinking.

But I should say that I'm particularly interested in

and will focus on embodied intelligence.

So in other words, the kind of intelligence that we have,

that animals including humans have, and that robots have.

So of course that slightly differentiates

what I'm talking about from AI.

But I regard robotics as a kind of subset of AI.

And of course one of the things that we discovered

in the last 60 odd years of artificial intelligence

is that the things that we thought were really difficult

actually are relatively easy.

Like playing chess, or go, for that matter.

Whereas the things that we originally thought

were really easy, like making a cup of tea, are really hard.

So it's kind of the opposite of what was expected.

So embodied intelligence in the real world

is really very difficult indeed.

And that's what I'm interested in.

So this is the outline of the talk.

I'm going to talk initially about intelligence

and offer some ideas, if you like,

for a way of thinking about intelligence

and breaking it down into categories

or types of intelligence.

And then I'm going to choose a particular one which

I've been really working on the last three or four years.

And it's what I call a generic architecture

for a functional imagination.

Or in short, robots with internal models.

So that's really what I want to focus on.

Because I really wanted to show you

some experimental work that we've

done the last couple of years in the lab.

I mean, I'm an electronics engineer.

I'm an experimentalist.

And so doing experiments is really important for me.

So the first thing that we ought to realize--

I'm sure we do realize-- is that intelligence is not one thing

that we all, animals, humans, and robots

have more or less of.

Absolutely not.

And you know, there are several ways of breaking intelligence

down into different kind of categories,

if you like, of intelligence, different types

of intelligence.

And here's one that I came up with in the last couple

of years.

It's certainly not the only way of thinking about intelligence.

But this really breaks intelligence into four,

if you like, types, four kinds of intelligence.

You could say kinds of minds, I guess.

The most fundamental is what we call

morphological intelligence.

And that's the intelligence that you get just

from having a physical body.

And there are some interesting questions

about how you design morphological intelligence.

You've probably all seen pictures of or movies of robots

that can walk, but in fact don't actually

have any computing, any computation whatsoever.

In other words, the behavior of walking

is an emergent property of the mechanics,

if you like, the springs and levers and so on in the robot.

So that's an example of morphological intelligence.

Individual intelligence is the kind of intelligence

that you get from learning individually.

Social intelligence, I think, is really

interesting and important.

And that's the one that I'm going

to focus on most in this talk.

Social intelligence is the intelligence

that you get from learning socially, from each other.

And of course, we are a social species.

And the other one which I've been

working on a lot in the last 20 odd years

is swarm intelligence.

So this is the kind of intelligence

that we see most particularly in social animals, insects.

The most interesting properties of swarm intelligence

tend to be emergent or self-organizing.

So in other words, the intelligence

is typically manifest as a collective behavior that

emerges from the, if you like, the micro interactions

between the individuals in that population.

So emergence and self-organization

are particularly interesting to me.

But I said this is absolutely not the only way

to think about intelligence.

And I'm going to show you another way

of thinking about intelligence which I particularly like.

And this is Dan Dennett's tower of generate and test.

So in Darwin's Dangerous Idea, and several other books,

I think, Dan Dennett suggests that a good way

of thinking about intelligence is to think about the fact

that all animals, including ourselves, need

to decide what actions to take.

So choosing the next action is really critically important.

I mean it's critically important for all of us, including

humans.

Even though the wrong action may not kill us,

as it were, for humans.

But for many animals, the wrong action

may well kill that animal.

And Dennett talks about what he calls

the tower of generate and test which I want to show you here.

It's a really cool breakdown, if you like, way

of thinking about intelligence.

So at the bottom of his tower are Darwinian creatures.

And the thing about Darwinian creatures

is that they have only one way of,

as it were, learning from, if you like,

generating and testing next possible actions.

And that is natural selection.

So Darwinian creatures in his schema cannot learn.

They can only try out an action.

If it kills them, well that's the end of that.

So by the laws of natural selection,

that particular action is unlikely to be

passed on to descendants.

Now, of course, all animals on the planet

are Darwinian creatures, including ourselves.

But a subset of what Dennett calls Skinnerian creatures.

So Skinnerian creatures are able to generate

a next possible candidate action, if you like,

a next possible action and try it out.

And here's the thing, if it doesn't kill them

but it's actually a bad action, then they'll learn from that.

Or even if it's a good action, a Skinnerian creature

will learn from trying out an action.

So really, Skinnerian creatures are a subset of Darwinians,

actually a small subset that are able to learn

by trial and error, individually learn by trial and error.

Now, the third layer, or story, if you

like, in Dennett's tower, he calls Popperian creatures,

after, obviously, the philosopher, Karl Popper.

And Popperian creatures have a big advantage

over Darwinians and Skinnerians in that they

have an internal model of themselves in the world.

And with an internal model, it means

that you can try out an action, a candidate

next possible action, if you like, by imagining it.

And it means that you don't have to actually have

to put yourself to the risk of trying it out

for real physically in the world,

and possibly it killing you, or at least harming you.

So Popperian creatures have this amazing invention,

which is internal modeling.

And of course, we are examples of Popperian creatures.

But there are plenty of other animals-- again,

it's not a huge proportion.

It's rather a small proportion, in fact, of all animals.

But certainly there are plenty of animals

that are capable in some form of modeling their world

and, as it were, imagining actions before trying them out.

And just to complete Dennett's tower,

he adds another layer that he calls Gregorian creatures.

Here's he's naming this layer after Richard Gregory,

the British psychologist.

And the thing that Gregorian creatures have

is that in addition to internal models,

they have mind tools like language and mathematics.

Especially language because it means that Gregorian creatures

can share their experiences.

In fact, a Gregorian creature could, for instance,

model in its brain, in its mind, the possible consequences

of doing a particular thing, and then actually pass

that knowledge to you.

So you don't even have to model it yourself.

So the Gregorian creatures really

have the kind of social intelligence

that we probably-- perhaps not uniquely,

but there are obviously only a handful

of species that are able to communicate, if you like,

traditions with each other.

So I think internal models are really, really interesting.

And as I say, I've been spending the last couple

of years thinking about robots with internal models.

And actually doing experiments with

robots with internal models.

So are robots with internal models self-aware?

Well probably not in the sense that-- the everyday

sense that we mean by self-aware, sentient.

But certainly internal models, I think,

can provide a minimal level of kind

of functional self-awareness.

And absolutely enough to allow us to ask what if questions.

So with internal models, we have potentially a really powerful

technique for robots.

Because it means that they can actually ask

themselves questions about what if I take this

or that next possible action.

So there's the action selection, if you like.

So really, I'm kind of following Dennett's model.

I'm really interested in building Popperian creatures.

Actually, I'm interested in building Gregorian creatures.

But that's another, if you like, another step in the story.

So really, here I'm focusing primarily

on Popperian creatures.

So robots with internal models.

And what I'm talking about in particular

is a robot with a simulation of itself

and it's currently perceived environment and of the actors

inside itself.

So it takes a bit of getting your head around.

The idea of a robot with a simulation of itself

inside itself.

But that's really what I'm talking about.

And the famous, the late John Holland, for instance,

rather perceptively wrote an internal model

that allows a system to look ahead

to the future consequences of actions

without committing itself to those actions.

I don't know whether John Holland

was aware of Dennett's tower.

Possibly not.

But really saying the same kind of thing as Dan Dennett.

Now before I come on to the work that I've been doing,

I want to show you some examples of-- a few examples,

there aren't many, in fact-- of robots with self-simulation.

The first one, as far as I'm aware,

was by Richard Vaughan and his team.

And he used a simulation inside a robot

to allow it to plan a safe route with incomplete knowledge.

So as far as I'm aware, this is the world's first example

of robots with self-simulation.

Perhaps an example that you might already be familiar with,

this is Josh Bongard and Hod Lipson's work.

Very notable, very interesting work.

Here, self-simulation, but for a different purpose.

So this is not self-simulation to choose,

as it were, gross actions in the world.

But instead, self-simulation to learn

how to control your own body.

So that the idea here is that if you have a complex body, then

a self-simulation is a really good way of figuring out

how to control yourself, including

how to repair yourself if parts of you

should break or fail or be damaged, for instance.

So that's a really interesting example

of what you can do with self-simulation.

And a similar idea, really, was tested