The following is a conversation with Gary Marcus.

He's a professor emeritus at NYU, founder of Robust AI and Geometric Intelligence.

The latter is a machine learning company that was acquired by Uber in 2016.

He's the author of several books on natural and artificial intelligence, including his new book, Rebooting AI, Building Machines We Can Trust.

Gary has been a critical voice, highlighting the limits of deep learning and AI in general, and discussing the challenges before our AI community that must be solved in order to achieve artificial general intelligence.

As I'm having these conversations, I try to find paths toward insight, towards new ideas.

I'll often continuously try on several hats, several roles.

1, for example, is the role of a three-year-old who understands very little about anything and asks big what and why questions.

The other might be a role of a devil's advocate who presents counter ideas with a goal of arriving at greater understanding through debate.

Hopefully, both are useful, interesting, and even entertaining at times.

I ask for your patience as I learn to have better conversations.

This is the Artificial Intelligence Podcast.

If you enjoy it, subscribe on YouTube, give it 5 stars on iTunes, support it on Patreon, or simply connect with me on Twitter at Lex Friedman, spelled F-R-I-D-M-A-N.

And now, here's my conversation with Gary Marcus.

Do you think human civilization will 1 day have to face an AI-driven technological singularity that will, in a societal way, modify our place in the food chain of intelligent living beings on this planet?

I think our place in the food chain has already changed.

So there are lots of things people used to do by hand that they do with machine.

If you think of a singularity as like 1 single moment, which is I guess what it suggests.

I don't know if it'll be like that, but I think that there's a lot of gradual change and AI is getting better and better.

I mean, I'm here to tell you why I think it's not nearly as good as people think, but you know, the overall trend is clear.

Maybe, You know, maybe Ray Kurzweil thinks it's an exponential and I think it's linear.

In some cases, it's close to 0 right now, but it's all gonna happen.

I mean, we are gonna get to human level intelligence or whatever you want, what you will, artificial general intelligence at some point And that's certainly gonna change our place in the food chain, because a lot of the tedious things that we do now, we're gonna have machines do, and a lot of the dangerous things that we do now, we're gonna have machines do.

I think our whole lives are gonna change from people finding their meaning through their work, through people finding their meaning through creative expression.

So the singularity will be a very gradual, in fact, removing the meaning of the word singularity, it'll be a very gradual transformation in your view.

I think that it'll be somewhere in between, And I guess it depends what you mean by gradual and sudden.

I think it's important to realize that intelligence is a multidimensional variable.

So people sort of write this stuff as if IQ was 1 number, and the day that you hit 262 or whatever, you displaced the human beings.

And really, there's lots of facets to intelligence.

So there's verbal intelligence and there's motor intelligence and there's mathematical intelligence and so forth.

Machines in their mathematical intelligence far exceed most people already in their ability to play games.

They far exceed most people already in their ability to understand language.

They lag behind my five-year-old, far behind my five-year-old.

So There are some facets of intelligence the machines have grasped and some that they haven't.

We have a lot of work left to do to get them to say, understand natural language or to understand how to flexibly approach some kind of novel MacGyver problem solving kind of situation.

I don't know that all of these things will come at once.

I think there are certain vital prerequisites that we're missing now.

So for example, machines don't really have common sense now.

So they don't understand that bottles contain water and that people drink water to quench their thirst and that they don't want to dehydrate.

They don't know these basic facts about human beings and I think that that's a rate limiting step for many things.

It's a rate limiting step for reading, for example, because stories depend on things like, oh my God, that person's running out of water, that's why they did this thing.

Or if they only had water, they could put out the fire.

So you watch a movie and your knowledge about how things work matter.

And so a computer can't understand that movie if it doesn't have that background knowledge.

And so there are lots of places where if we had a good machine interpretable set of common sense, many things would accelerate relatively quickly, but I don't think even that is like a single point.

There's many different aspects of knowledge.

And we might, for example, find that we make a lot of progress on physical reasoning, getting machines to understand, for example, how keys fit into locks or that kind of stuff, or how this gadget here works and so forth and so on.

Machines might do that long before they do really good psychological reasoning.

Because it's easier to get kind of labeled data or to do direct experimentation on a microphone stand than it is to do direct experimentation on human beings to understand the levers that guide them.

That's a really interesting point actually.

Whether it's easier to gain common sense knowledge or psychological knowledge.

I would say the common sense knowledge includes both physical knowledge and psychological knowledge.

The argument I was making is physical knowledge might be more accessible because you could have a robot, for example, lift a bottle, try putting a bottle cap on it, see that it falls off if it does this, and see that it could turn it upside down and so the robot could do some experimentation.

We do some of our psychological reasoning by looking at our own minds.

So I can sort of guess how you might react to something based on how I think I would react to it.

And robots don't have that intuition and they also can't do experiments on people in the same way, or we'll probably shut them down.

So, you know, if we wanted to have robots figure out how I respond to pain by pinching me in different ways, like that's probably, you know, it's not gonna make it past the human subjects board, and, you know, companies are gonna get sued or whatever, so there's certain kinds of practical experience that are limited or off limits to robots.

What is more difficult to gain a grounding in?

Because to play devil's advocate, I would say that human behavior is easier expressed in data in digital form.

And so when you look at Facebook algorithms, they get to observe human behavior.

So you get to study and manipulate even a human behavior in a way that you perhaps cannot study or manipulate the physical world.

So it's true why you said pain is like physical pain,

Emotional pain might be much easier to experiment with, perhaps unethical, but nevertheless, some would argue it's already going on.

I think that you're right, for example, that Facebook does a lot of experimentation in psychological reasoning.

In fact, Zuckerberg talked about AI at a talk that he gave at NIPS.

I wasn't there, but the conference has been renamed NIRPS, but it used to be called NIPS when he gave the talk.

And he talked about Facebook basically having a gigantic theory of mind.

I think they have a really good idea of how to addict people to things.

They understand what draws people back to things.

I think they exploit it in ways that I'm not very comfortable with.

But even so, I think that there are only some slices of human experience that they can access through the kind of interface they have.

And of course, they're doing all kinds of VR stuff and maybe that'll change and they'll expand their data.

And I'm sure that that's part of their goal.

I think love, fear, insecurity, all of the things that, I would say some of the deepest things about human nature and the human mind could be explored through digital form.

It's that, You're actually the first person just now that brought up, I wonder what is more difficult?

Because I think folks who are the slow, and we'll talk a lot about deep learning, but the people who are thinking beyond deep learning are thinking about the physical world.

You starting to think about robotics in the home robotics.

How do we make robots manipulate objects which requires an understanding of the physical world and it requires common sense reasoning.

And that has felt to be like the next step for common sense reasoning, but you've now brought up the idea that there's also the emotional part.

And it's interesting whether that's hard or easy.

I think some parts of it are and some aren't.

So my company that I recently founded with Rod Brooks, you know, from MIT for many years and so forth, We're interested in both.

We're interested in physical reasoning and psychological reasoning, among many other things.

And there are pieces of each of these that are accessible.

So if you want a robot to figure out whether it can fit under a table, that's a relatively accessible piece of physical reasoning.

If you know the height of the table and you know the height of the robot, it's not that hard.

If you wanted to do physical reasoning about Jenga, it gets a little bit more complicated and you have to have higher resolution data in order to do it.

With psychological reasoning, it's not that hard to know, for example, that people have goals and they like to act on those goals, but it's really hard to know exactly what those goals are.

But ideas of frustration, I mean, you could argue it's extremely difficult to understand the sources of human frustration as they're playing Jenga with you or not, you could

There's some things that are gonna be obvious and some not, so like, I don't think anybody really can do this well yet, but I think it's not inconceivable to imagine machines in the not so distant future being able to understand that if people lose in a game that they don't like that.

Right, you know, that's not such a hard thing to program and it's pretty consistent across people.

Most people don't enjoy losing and so, you know, that makes it relatively easy to code.

On the other hand, if you wanted to capture everything about frustration, well, people can get frustrated for a lot of different reasons.

They might get sexually frustrated, they might get frustrated they can't get their promotion at work, all kinds of different things.

And the more you expand the scope, the harder it is for anything like the existing techniques to really do that.

So I'm talking to Garry Kasparov next week, and he seemed pretty frustrated with his game against T-Blue, so.

Yeah, well, I'm frustrated with my game against him last year, because I played him.

I was jet lagged and I hadn't played in 25 or 30 years, but the outcome is he completely destroyed me and it wasn't even close.

Have you ever been beaten in any board game by a machine?

I have, I actually played the predecessor to Deep Blue, Deep Thought I believe it was called, and that too crushed me.

And that was, and after that you realize it's over for us.

There's no point in my playing Deep Blue.

I mean, it's a waste of Deep Blue's computation.

I mean, I played Kasparov because we both gave lectures at the same event and he was playing 30 people.

I forgot to mention that Not only did he crush me, but he crushed 29 other people at the same time.

I mean, but the actual philosophical and emotional experience of being beaten by a machine, I imagine is, I mean, to you who thinks about these things, may be a profound experience.

Or no, it was a simple mathematical experience.

Yeah, I think a game like chess, particularly where you have perfect information, it's a two-player closed end, and there's more computation for the computer, it's no surprise the machine wins.

I mean, I'm not sad when a computer, I'm not sad when a computer calculates a cube root faster than me.

Like, I know I can't win that game, I'm not gonna try.

Well, with a system like AlphaGo or AlphaZero, do you see

in a system like that, even though it's simply playing a board game, but because there's a strong learning component?

thought you should mention that in the context of this conversation, because Kasparov and I are working on an article that's gonna be called AI is not magic.

And neither 1 of us thinks that it's magic.

And part of the point of this article is that AI is actually a grab bag of different techniques.

And some of them have, or they each have their own unique strengths and weaknesses.

So you read media accounts and it's like, ooh, AI, it must be magical or it can solve any problem.

Well, no, some problems are really accessible like chess and Go, and other problems like reading are completely outside the current technology.

And It's not like you can take the technology that drives AlphaGo and apply it to reading and get anywhere.

They built AlphaGo and they have, I wrote a piece recently, they lost, and you can argue about the word lost, but they spent $530 million more than they made last year.

So they're making huge investments, they have a large budget, and they have applied the same kinds of techniques to reading or to language.

It's just much less productive there, because it's a fundamentally different kind of problem.

Chess and Go and so forth are closed end problems.

The rules haven't changed in 2,500 years.

You can talk about the exponential as you look at the combinations of moves, but fundamentally, the Go board has

That's the only, those intersections are the only places that you can place your stone.

Whereas when you're reading, the next sentence could be anything.

You know, it's completely up to the writer what they're gonna do next.

You're clearly a brilliant mind who points out the emperor has no clothes, but so I'll play the role of a person who says,

you put clothes on the emperor, good luck

Romanticizes the notion of the emperor, period, suggesting that clothes don't even matter.

Okay, so that's really interesting that you're talking about language.

So there's the physical world of being able to move about the world, making an omelet and coffee and so on.

There's language where you first understand what's being written and then maybe even more complicated than that, having a natural dialogue.

And then there's the game of Go and Chess.

I would argue that language is much closer to Go than it is to the physical world.

When you say the possibility of the number of sentences that could come, it is huge, but it nevertheless is much more constrained.

It feels maybe I'm wrong than the possibilities that

There's something to what you say in some ways in which I disagree.

So 1 interesting thing about language is that it abstracts away.

This bottle, I don't know if it would be in the field of view, is on this table.

And I use the word on here, and I can use the word on here, maybe not here.

But that 1 word encompasses, you know, in analog space a sort of infinite number of possibilities.

So there is a way in which language filters down the variation of the world, and there's other ways.

So we have a grammar, and more or less, you have to follow the rules of that grammar.

You can break them a little bit, but by and large we follow the rules of grammar.

So there are ways in which language is a constrained system.

On the other hand, there are many arguments.

Let's say there's an infinite number of possible sentences, and you can establish that by just stacking them up.

You think that I think that there's water on the table.

Your mother thinks that you think that I think that the water's on the table.

Your brother thinks that maybe your mom is wrong to think that you think that I think, right?

So we can make sentences of infinite length or we can stack up adjectives.

This is a very silly example, a very, very silly example, a very, very, very, very, very, very silly example and so forth.

So there are good arguments that there's an infinite range of sentences, in any case, it's vast by any reasonable measure.

And for example, almost anything in the physical world we can talk about in the language world.

And interestingly, many of the sentences that we understand, we can only understand if we have a very rich model of the physical world.

So I don't ultimately want to adjudicate the debate that I think you just set up, but I find it interesting.

Maybe the physical world is even more complicated than language, I think that's fair, but-

language is- Language is really, really complicated.

Well, it's really, really hard for machines, for linguists, people trying to understand it.

It's not that hard for children, and that's part of what's driven my whole career.

I was a student of Steven Pinker's, and we were trying to figure out why kids could learn language when machines couldn't.

I think we're gonna get into language, we're gonna get into communication intelligence and neural networks and so on, but let me return to the high level, the futuristic for a brief moment.

So you've written in your book, in your new book, it would be arrogant to suppose that we could forecast where AI will be, or the impact it will have in a thousand years, or even 500 years.

What do AI systems with or without physical bodies look like 100 years from now?

If you would just, you can't predict, but if you were to philosophize and imagine, do.

Can I first justify the arrogance before you try to push me beyond it?

I mean, there are examples, like people figured out how electricity worked.

They had no idea that that was gonna lead to cell phones.

I mean, things can move awfully fast once new technologies are perfected.

Even when they made transistors, they weren't really thinking that cell phones would lead to social networking.

There are nevertheless predictions of the future, which are statistically unlikely to come to be, but nevertheless is the best.

Pick the least unlikely to be wrong thing, even though it's most very likely to be wrong.

I mean, here's some things that we can safely predict, I suppose.

We can predict that AI will be faster than it is now.

It will be better in the sense of being more general and applicable in more places.

I'm sort of modeling them in my head on Jeff Bezos' famous predictions.

He says, I can't predict the future, not in every way I'm paraphrasing.

But I can predict that people will never wanna pay more money for their stuff, they're never gonna want it to take longer to get there.

And you know, so like, you can't predict everything, but you can predict something.

Sure, of course it's going to be faster and better.

And what we can't really predict is the full scope of where AI will be in a certain period.

I mean, I think it's safe to say that, although I'm very skeptical about current AI, that it's possible to do much better.

You know, there's no in principle argument that says, AI is an insolvable problem, that there's magic inside our brains that will never be captured.

I mean, I've heard people make those kinds of arguments.

So AI is gonna come, and probably 500 years is plenty to get there.

And then once it's here, it really will change everything.

So when you say AI is gonna come, Are you talking about human level intelligence?

So I don't think that the ultimate AI, if there is such a thing, is going to look just like humans.

I think it's going to do some things that humans do better than current machines, like reason flexibly and understand language and so forth.

But it doesn't mean they have to be identical to humans.

So for example, humans have terrible memory and they suffer from what some people call motivated reasoning.

So they like arguments that seem to support them and they dismiss arguments that they don't like.

There's no reason that a machine should ever do that.

So you see that those limitations of memory as a bug, not a feature.

1 is I was on a panel with Danny Conneman, the Nobel Prize winner, last night, and we were talking about this stuff.

And I think what we converged on is that humans are a low bar to exceed.

They may be outside of our skill right now, but as AI programmers, but eventually AI will exceed it.

So we're not talking about human level AI, we're talking about general intelligence that can do all kinds of different things and do it without some of the flaws that human beings have.

The other thing I'll say is I wrote a whole book actually about the flaws of humans.

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Gary Marcus: Toward a Hybrid of Deep Learning and Symbolic AI | Lex Fridman Podcast #43