Rodney Brooks is a roboticist, former head of CSAIL at MIT, and co-founder of iRobot, Rethink Robotics, and Robust.AI. Please support this podcast by checking out our sponsors:
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EPISODE LINKS:
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OUTLINE:
0:00 - Introduction
1:31 - First robots
22:56 - Brains and computers
55:45 - Self-driving cars
1:15:55 - Believing in the impossible
1:26:45 - Predictions
1:37:47 - iRobot
2:05:09 - Sharing an office with AI experts
2:17:19 - Advice for young people
2:21:05 - Meaning of life

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The following is a conversation with Rodney Brooks, 1 of the greatest roboticists in history.

He led the computer science and artificial intelligence laboratory at MIT, then co-founded iRobot, which is 1 of the most successful robotics companies ever.

Then he co-founded Rethink Robotics that created some amazing collaborative robots like Baxter and Sawyer.

Finally, he co-founded Robust.ai, whose mission is to teach robots common sense, which is a lot harder than it sounds.

To support this podcast, please check out our sponsors in the description.

As a side note, let me say that Rodney is someone I've looked up to for many years in my now over 2 decade journey in robotics because 1, he's a legit great engineer of real world systems.

And 2, he's not afraid to state controversial opinions that challenge the way we see the AI world.

But of course, While I agree with him on some of his critical views of AI, I don't agree with some others.

And he's fully supportive of such disagreement.

Nobody ever built anything great by being fully agreeable.

There's always respect and love behind our interactions.

And when a conversation is recorded, like it was for this podcast, I think a little bit of disagreement is fun.

This is the Lex Friedman Podcast, and here is my conversation with Rodney Brooks.

What is the most amazing or beautiful robot that you've ever had the chance to work with?

I think it was Domo, which was made by 1 of my grad students, Aaron Edsinger.

It now sits in Daniela Russo's office, director of CSAIL.

He didn't give me a budget ahead of time.

He didn't tell me what he was going to do.

He and Jeff Weber, who is a mechanical engineer who Aaron insisted he bring with him when he became a grad student, built this beautiful, gorgeous robot, Domo, which is an upper torso, humanoid, 2 arms, with three-fingered hands, and face, eyeballs, not the eyeballs, but everything else, series elastic actuators.

You can interact with it, cable-driven, all the motors are inside, and it's just gorgeous.

Oh yeah, the eyeballs are actuated with cameras and you know so had a visual attention mechanism you know wow looking when people came in and looking in their face and talking with them.

It's just mechanically gorgeous as everything Aaron builds has always been mechanically gorgeous It's just exquisite in the detail We're talking about mechanically, like literally the amount of actuators, the actuators, the cables, he anodizes different parts, different colors, and it just looks like a work of art.

Do you find the face beautiful in robots?

When you make a robot, it's making a promise for how well it will be able to interact.

So I always encourage my students not to over-promise.

its essence, like the thing it presents, it should not over-promise.

Yeah, so the joke I make, which I think you'll get, is if your robot looks like Albert Einstein it should be as smart as Albert Einstein.

So the only thing in Domo's face is the eyeballs And because that's all it can do.

And so there is no, it's not like 1 of those Japanese robots that looks exactly like a person at all.

But see the thing is us humans and dogs too, don't just use eyes as attentional mechanisms.

They also use it to communicate, as part of the communication.

Like a dog can look at you, look at another thing and look back at you.

And that designates that we're going to be looking at that thing together.

Both Baxter and Sawyer at Rethink Robotics, they had a screen with, you know, graphic eyes.

So it wasn't actually where the cameras were pointing, but it the eyes would look in the direction it was about to move its arm.

So people in the factory nearby were not surprised by its motions because it gave that intent away.

Before we talk about Baxter, which I think is a beautiful robot, let's go back to the beginning.

When did you first fall in love with robotics?

We're talking about beauty and love to open the conversation.

I was born in the end of 1954, and I grew up in Adelaide, South Australia.

And I have these 2 books that are dated 1961.

So I'm guessing my mother found them in a store in 62 or 63.

How and why wonder book of electricity and a how and why wonder book of giant brains and robots.

And I learned how to build circuits, you know, when I was 8 or 9, simple circuits.

And I read, you know, learned the binary system and saw all these drawings mostly of robots.

And then I tried to build them for the rest of my childhood.

This was when the 2 books, I've still got them at home.

What does the robot mean in that context?

No, they were, some of the robots that they had were arms, you know, big arms to move nuclear material around.

But they had pictures of welding robots that look like humans under the sea welding stuff underwater.

But they were, you know, what people were thinking about for robots.

Were you thinking about arms with fingers?

No, actually, to be honest, I realized my limitation on building mechanical stuff.

So I just built the brains mostly out of different technologies as I got older.

I built a learning system which was chemical based and I had this ice cube tray, each well was a cell and by applying voltage to the 2 electrodes, it would build up a copper bridge.

So over time it would learn a simple network so I could teach it stuff.

And that was mostly things were driven by my budget and nails as electrodes and an ice cream, I mean, an ice cube tray was about my budget at that stage.

Later I managed to buy transistors and then I could build gates and flip-flops

So 1 of your first robots was an ice cube tray?

And it was very cerebral because it learned to add.

Well, just a decade or so before in 1950, Alan Turing wrote a paper that formulated the Turing test, and he opened that paper with the question, Can machines think?

Certainly machines can think because I believe you're a machine and I'm a machine and I believe we both think.

I think any other philosophical position is sort of a little ludicrous what does think mean if it's not something that we do.

So yes, machines can, but do we have a clue how to build such machines?

Are we capable of building such machines?

We think we're smart enough to do anything, but maybe we're not.

Maybe, you know, we're just not smart enough to build stuff like us.

of computer that Alan Turing was thinking about, do you think there is something fundamentally or significantly different between the computer between our ears, the biological computer that humans use, and the computer that he was thinking about from a sort of high level philosophical.

In fact, I'm halfway through a, I think it'll be about a 480 page book, the working title is Not Even Wrong.

And if I may, I'll tell you a bit about that book.

1 is the history of computation, what we call computation.

Goes all the way back to some manuscripts in Latin from 1614 and 1620 by Napier and Kepler through Babbage and Lovelace.

And then Turing's 1936 paper is what we think of as the invention of modern computation.

And that paper, by the way, did not set out to, you know, invent computation.

It set out to negatively answer 1 of Hilbert's 3 later set of problems.

He called it an effective way of getting answers.

And Hilbert really worked with rewriting rules, as did Church, who also at the same time, a month earlier than Turing, disproved Hilbert's 1 of these 3 hypotheses.

The other 2 had already been disproved by Gödel.

So Turing set out to disprove it because it's always easier to disprove these things than to prove that there is an answer.

And so he needed, and it really came from his professor while he was an undergrad at Cambridge, who turned it into, is there a mechanical process?

So he wanted to have a show a mechanical process that could calculate numbers, because that was a mechanical process that people use to generate tables.

They were called computers, the people at the time.

And they followed a set of rules where they had paper and they would write numbers down and based on the numbers, they'd keep writing other numbers.

And they would produce numbers for these tables, engineering tables, that the more iterations they did, the more significant digits came out.

And so Turing in that paper set out to define what sort of machine could do that mechanical machine, where it could produce an arbitrary number of digits in the same way a human computer did.

And he came up with a very simple set of constraints where there was an infinite supply of paper.

This is the tape of the Turing machine and each Turing machine had a set of and came with a set of instructions that as a person could do with pencil and paper, write down things on the tape and erase them and put new things there And he was able to show that that system was not able to do something that Hilbert had hypothesized.

But he had to show that this system was good enough to do whatever could be done, but couldn't do this other thing.

And there he said, and he says in the paper, I don't have any real arguments for this, but based on intuition.

And then if you look over the next, from 1936 up until really around 1975, you see people struggling with, is this really what computation is?

And so Marvin Minsky, very well known in AI, but also a fantastic mathematician in his book Finite and Infinite Machines from the mid-60s, which is a beautiful, beautiful mathematical book, says at the start of the book, well, what is computation?

Turing says it's this, and yeah, I sort of think it's that.

It doesn't really matter whether the stuff's made of wood or plastic.

It's just, you know, that relatively cheap stuff can do this stuff.

And Donald Knuth, in his first volume of his, you know, art of computer programming in around 1968, says, well, what's computation?

It's this stuff, like Turing says, that a person could do each step without too much trouble.

And so 1 of his examples of what would be too much trouble was a step which required knowing whether Fermat's last theorem was true or not, because it was not known at the time.

That's too much trouble for a person to do as a step.

And Hopcroft and Ullman sort of said a similar thing later that year.

Hopcroft and Ullman book, they're saying, well, you know, we don't really know what computation is, but intuition says this is sort of about right, And this is what it is.

It's a sort of agreed upon thing, which happens to be really easy to implement in silicon.

And then we had Moore's law, which took off, and it's been an incredibly powerful tool.

The version we have of computation, incredibly powerful.

So what we're talking about is there's an infinite tape with some simple rules of how to write on that tape, and that's what we're kind of thinking about.

Yeah, and it's modeled after humans, how humans do stuff.

And I think it's, Turing says in the 36th paper, 1 of the critical facts here is that a human has a limited amount of memory.

So that's what we're gonna put onto our mechanical computers.

So, So, you know, unlike mass, unlike mass or charge or, you know.

This is what we're going to call computation.

And then it had this really good implementation, which has completely changed our technological world.

Second part of the book, or argument of the book, I have this 2 by 2 matrix with science in the top row, engineering in the bottom row, left column is intelligence, right column is life.

So in the bottom row, the engineering, there's artificial intelligence and there's artificial life.

In the top row, there's neuroscience and abiogenesis.

How does non-living matter become living matter?

These 4 disciplines all came into their current form in the period 1945 to 1965.

There was neuroscience before, but it wasn't effective neuroscience.

It was at least ganglion and there's electrical charges, but no 1 knows what to do with it.

And furthermore, there are a lot of players who are common across them.

I've identified common players, except for artificial intelligence and abiogenesis.

I don't have, but any other pair, I can point to people who work them.

And a whole bunch of them, by the way, were at the Research Lab for Electronics at MIT, where Warren McCulloch held forth.

And in fact, McCulloch, Pitts, Letvin, and Maturana wrote the first paper on functional neuroscience called What the Frog's Eye Tells the Frog's Brain, where instead of it just being this bunch of nerves, they sort of showed what different anatomical components were doing and telling other anatomical components and generating behavior in the frog.

Would you put them as basically the fathers or 1 of the early pioneers of what are now called artificial neural networks?

Yeah, I mean, McCulloch and Pitts, Pitts was much younger than him, in 1943 had written a paper inspired by Bertrand Russell on a calculus for the ideas eminent in neural systems where they had tried to, without any real proof, they had tried to give a formalism for neurons basically in terms of logic, AND gates, OR gates, and NOT gates, with no real evidence that that was what was going on, but they talked about it and that was picked up by Minsky for his 1953 dissertation on which was a neural network we would call it today.

It was picked up by John von Neumann when he was designing the EDVAC computer in 1945.

He talked about its components being neurons based on references.

He's only got 3 references and 1 of them is the McCulloch-Pitts paper.

So all these people and then the AI people and the artificial life people, which was John von Neumann originally.

There's that overlap between all of them.

And 3 of these 4 disciplines turn to computation as their primary metaphor.

So I've got a couple of chapters in the book.

Because that's where our computers came from.

And then I've got another chapter, wait, people are computers, which is about computational neuroscience.

So there's this whole circle here, that computation is it.

And I have talked to people about, well, maybe it's not computation that goes on in the head.

Well, when Elon Musk's rocket goes up, Is it computing?

But we've got this idea, if you want to build an AI system, you write a computer program.

Yeah, in the sense, so the word computation very quickly starts doing a lot of work that it was not initially intended to do.

Is this like in the same, if you talk about the universe as essentially performing a computation?

Yeah, By the way, when you say computation in our conversation, do you tend to think of computation narrowly in the way Turing thought of computation?

But computation in the way Turing thinks about it and the way most people think about it actually fits very well with thinking like a hunter-gatherer.

There are places and there can be stuff in places and the stuff in places can change and it stays there until someone changes it and it's this metaphor of place and container which you know is a combination of our place cells in our hippocampus and our cortex.

But this is how we use metaphors for mostly to think about.

And when we get outside of our metaphor range, we have to invent tools which we can sort of switch on to use.

It can do stuff that our raw reasoning can't do, and we've got conventions of when you can use it or not.

But sometimes, you know, people try to, all the time, we always try to get physical metaphors for things, which is why quantum mechanics has been such a problem for a hundred years, because it's a particle.

And I say, no, it's some weird mathematical object that's different from those, but we want that metaphor.

Well, you know, I suspect that, you know, a hundred years or 200 years from now, neither quantum mechanics nor dark matter will be talked about in the same terms, in the same way that Flodgerson's theory eventually went away because it just wasn't an adequate explanatory metaphor.

That metaphor was the stuff, there is stuff in the burning, the burning is in the matter.

But as it turns out, the burning was outside the matter, it was the oxygen.

So our desire for metaphor and combined with our limited cognitive capabilities gets us into trouble.

Now, and people say, well, what is it then?

And I say, well, I wish I knew that if I'd spoke about that, but I, you know, it gives some ideas, but so, so there's 3 things.

Computation is sort of a particular thing we use.

So, you know, I used an example of a thing that's different from computation.

And there are some stationary points on the drum surface, because the waves are going up and down the stationary points.

Now, you could compute them to arbitrary precision, but the drum just knows them.

What was the very first computer program ever written by Ada Lovelace?

To compute Bernoulli numbers, and Bernoulli numbers are exactly what you need to find those stable points in the drum surface.

The arguments to divide were reversed in 1 place.

Computation is sort of a thing that's become dominant as a metaphor, but is it the right metaphor?

All 3 of these 4 fields adopted computation.

And a lot of it swirls around Warren McCulloch and all his students, and he funded a lot of people.

And our human metaphors, our limitations to human thinking will play into this.

So I have a little to say about computation.

So you're saying that there is a gap between the computer or the machine that performs computation and this machine that appears to have consciousness and intelligence.

And maybe it's not just the meat in your head, it's the rest of you too.

I mean, you actually have a neural system in your gut.

I tend to also believe, not believe, but we're now dancing around things we don't know, but I tend to believe other humans are important.

Like, so we're almost like, I just don't think we would ever have achieved the level of intelligence we have with other humans.

I'm not saying so confidently, but I have an intuition that some of the intelligence is in the interaction.

Yeah, and I think it seems to be very likely, again, this is speculation, but we, our species, and probably Neanderthals, to some extent, because you can find old bones where they seem to be counting on them by putting notches, that were Neanderthals had done.

We're able to put some of our stuff outside our body into the world, and then other people can share it.

And then we get these tools that become shared tools.

And so there's a whole coupling that would not occur in the single deep learning network, which was fed all of literature or something.

Yeah, the neural network can't step outside of itself.

But is there some, can we explore this dark room a little bit and try to get at something.

Where's the magic come from in the human brain that creates the mind?

What's your sense as scientists that try to understand it and try to build it, what are the directions if followed might be productive?

Is it creating large deep neural networks that do like self-supervised learning?

And just like we'll discover that when you make something large enough, some interesting things will emerge?

Is it through physics and chemistry and biology, like artificial life, Engel will sneak up in this 4 quadrant matrix that you mentioned?

Is there anything, your most, If you had to bet all your money, financial advice.

So every intelligence we know, animal intelligence, dog intelligence, octopus intelligence, which is a very different sort of architecture from us.

All the intelligences we know perceive the world in some way and then have action in the world, but they're able to perceive objects in a way which is actually pretty damn phenomenal and surprising.

We tend to think that the box over here between us, which is a sound box, I think, is a blue box.

But blueness is something that we construct with color constancy.

The blueness is not a direct function of the photons we're receiving.

It's actually context, which is why you can turn, you know, you've maybe seen the examples where someone turns a stop sign into a, some other sort of sign by just putting a couple of marks on them and the deep learning system gets it wrong.

And everyone says, but the stop sign's red.

You know, why is it, why is it thinking it's the other sort of sign?

Because redness is not intrinsic in just the photons.

It's actually a construction of an understanding of the whole world and the relationship between objects to get color constancy.

But our tendency in order that we get an archive paper really quickly is you just show a lot of data and give the labels and hope it figures it out.

But it's not figuring it out in the same way we do.

We have a very complex perceptual understanding of the world.

Dogs have a very different perceptual understanding based on smell.

They go smell a post, they can tell how many different dogs have visited it in the last 10 hours and how long ago.

There's all sorts of stuff that we just don't perceive about the world.

And just taking a single snapshot is not perceiving about the world.

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Rodney Brooks: Robotics | Lex Fridman Podcast #217