The following is a conversation with Melanie Mitchell.

She's a professor of computer science at Portland State University and an external professor at Santa Fe Institute.

She has worked on and written about artificial intelligence from fascinating perspectives, including adaptive complex systems, genetic algorithms, and the copycat cognitive architecture, which places the process of analogy making at the core of human cognition.

From her doctoral work with her advisors, Douglas Hufstadter and John Holland, to today, she has contributed a lot of important ideas to the field of AI, including her recent book, simply called Artificial Intelligence, a guide for thinking humans.

This is the Artificial Intelligence podcast.

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And now, here's my conversation with Melanie Mitchell.

The name of your new book is Artificial Intelligence, subtitle, A Guide for Thinking Humans.

The name of this podcast is Artificial Intelligence.

So let me take a step back and ask the old Shakespeare question about roses.

And What do you think of the term artificial intelligence for our big and complicated and interesting field?

I think it has a few problems because it means so many different things to different people.

And intelligence is 1 of those words that isn't very clearly defined either.

There's so many different kinds of intelligence, degrees of intelligence, approaches to intelligence.

John McCarthy was the 1 who came up with the term artificial intelligence.

And from what I read, he called it that to differentiate it from cybernetics, which was another related movement at the time.

And he later regretted calling it artificial intelligence.

Herbert Simon was pushing for calling it complex information processing, which got nixed, but you know, probably is equally vague, I guess.

Is it the intelligence or the artificial in terms of words that's most problematic, would you say?

But it has some good sides because I personally was attracted to the field because I was interested in phenomenon of intelligence.

And if it was called complex information processing, maybe I'd be doing something wholly different now.

What do you think of, I've heard that term used, cognitive systems, for example, so using cognitive?

Yeah, I mean, cognitive has certain associations with it, and people like to separate things like cognition and perception, which I don't actually think are separate, but often people talk about cognition as being different from other aspects of intelligence.

So to you, cognition is this broad, beautiful mess of things that encompasses the whole thing.

LS I think it's hard to draw lines like that.

When I was coming out of grad school in 1990, which is when I graduated, that was during 1 of the AI winters.

And I was advised to not put AI, artificial intelligence on my CV, but instead call it intelligent systems.

So that was kind of a euphemism, I guess.

What about, to stick briefly on terms and words, the idea of artificial general intelligence, or like Jan LeCun prefers human level intelligence, sort of starting to talk about ideas that achieve higher and higher levels of intelligence and somehow artificial intelligence seems to be a term used more for the narrow, very specific applications of AI.

And sort of, what set of terms appeal to you to describe the thing that perhaps we strive to create?

People have been struggling with this for the whole history of the field and defining exactly what it is that we're talking about.

John Searle had this distinction between strong AI and weak AI.

And weak AI could be general AI, but his idea was strong AI was the view that a machine is actually thinking, as opposed to simulating thinking or carrying out processes that we would call intelligent.

At a high level, if you look at the founding of the field, McCarthy and Searle and so on, are we closer to having a better sense of that line between narrow, weak AI and strong AI?

Yes, I think we're closer to having a better idea of what that line is.

Early on, for example, a lot of people thought that playing chess would be, you couldn't play chess if you didn't have sort of general human-level intelligence.

And of course, once computers were able to play chess better than humans, that revised that view.

And people said, okay, well, maybe now we have to revise what we think of intelligence as.

And so that's kind of been a theme throughout the history of the field is that once a machine can do some task, we then have to look back and say, oh, well, that changes my understanding of what intelligence is, because I don't think that machine is intelligent.

At least, that's not what I want to call intelligence.

Do you think that line moves forever, or will we eventually really feel as a civilization like we crossed the line if it's possible?

It's hard to predict, but I don't see any reason why we couldn't in principle create something that we would consider intelligent.

Maybe our own view of what intelligence is will be refined more and more until we finally figure out what we mean when we talk about it.

But I think eventually we will create machines in a sense that have intelligence.

They may not be the kinds of machines we have now.

And 1 of the things that that's going to produce is making us sort of understand our own machine-like qualities that we, in a sense, are mechanical in the sense that Cells are kind of mechanical.

They have algorithms, they process information by, and somehow out of this mass of cells, we get this emergent property that we call intelligence.

But underlying it is really just cellular processing and lots and lots and lots of it.

Do you think we'll be able to, do you think it's possible to create intelligence without understanding our own mind?

You said sort of in that process we'll understand more and more, but do you think it's possible to sort of create without really fully understanding from a mechanistic perspective, sort of from a functional perspective how our mysterious mind works?

If I had to bet on it, I would say no, we do have to understand our own minds, at least to some significant extent.

But I think that's a really big open question.

I've been very surprised at how far kind of brute force approaches based on say big data and huge networks can take us.

And they have nothing to do with the way our minds work.

So that's been surprising to me, so it could be wrong.

To explore the psychological and the philosophical, do you think we're okay as a species with something that's more intelligent than us?

Do you think perhaps the reason we're pushing that line further and further is we're afraid of acknowledging that there's something stronger, better, smarter than us humans?

Well, I'm not sure we can define intelligence that way because smarter than is with respect to what, you know, computers are already smarter than us in some areas.

They can multiply much better than we can.

They can figure out driving routes to take much faster and better than we can.

They have a lot more information to draw on.

They know about traffic conditions and all that stuff.

So for any given particular task, sometimes computers are much better than we are, and we're totally happy with that.

I guess the question is, which things about our intelligence would we feel very sad or upset that machines had been able to recreate?

So in the book, I talk about my former PhD advisor, Douglas Hofstadter, who encountered a music generation program.

And that was really the line for him, that if a machine could create beautiful music, that would be terrifying for him.

Because that is something he feels is really at the core of what it is to be human, creating beautiful music, art, literature.

He doesn't like the fact that machines can recognize spoken language really well.

He personally doesn't like using speech recognition, but I don't think it bothers him to his core because it's like, okay, that's not at the core of humanity.

But it may be different for every person, what really they feel would usurp their humanity.

And I think maybe it's a generational thing also.

Maybe our children or our children's children will be adapted.

They'll adapt to these new devices that can do all these tasks and say, yes, this thing is smarter than me in all these areas, but that's great, because it helps me.

Looking at the broad history of our species, why do you think so many humans have dreamed of creating artificial life and artificial intelligence throughout the history of our civilization?

So not just this century or the 20th century, but really many, throughout many centuries that preceded it?

That's a really good question, and I have wondered about that.

I myself, you know, was driven by curiosity about my own thought processes and thought it would be fantastic to be able to get a computer to mimic some of my thought processes and I'm not sure why we're so driven.

I think We want to Understand ourselves better and we also want machines to do things for us.

But I don't know, there's something more to it because it's so deep in the kind of mythology or the ethos of our species.

And I don't think other species have this drive.

If you were to sort of psychoanalyze yourself in your own interest in AI, are you, What excites you about creating intelligence?

Yeah, I think that's what drives me particularly.

I'm really interested in human intelligence.

But I'm also interested in the phenomenon of intelligence more generally.

And I don't think humans are the only thing with intelligence, or even animals.

But I think intelligence is a concept that encompasses a lot of complex systems.

And if you think of things like insect colonies or cellular processes or the immune system or all kinds of different biological or even societal processes have as an emergent property some aspects of what we would call intelligence.

You know, they have memory, they process information, they have goals, they accomplish their goals, etc.

And to me, the question of what is this thing we're talking about here was really fascinating to me, and exploring it using computers seemed to be a good way to approach the question.

So do you think kind of of intelligence, do you think of our universe as a kind of hierarchy of complex systems And then intelligence is just the property of any, you can look at any level and every level has some aspect of intelligence.

So we're just like 1 little speck in that giant hierarchy of complex systems.

I don't know if I would say any system like that has intelligence, but I guess what I wanna, I don't have a good enough definition of intelligence to say that.

So let me do sort of a multiple choice, I guess.

Are the bacteria in our body intelligent?

And then going to the physics world, molecules and the behavior at the quantum level of electrons and so on.

Are those kinds of systems, do they possess intelligence?

Like where's the line that feels compelling to you?

I don't know, I mean, I think intelligence is a continuum And I think that the ability to in some sense have intention, have a goal, have some kind of self-awareness is part of it.

So I'm not sure if, you know, it's hard to know where to draw that line.

But I wouldn't say that the planets orbiting the Sun is an intelligent system.

I mean, I would find that maybe not the right term to describe that.

And there's all this debate in the field of what's the right way to define intelligence?

What's the right way to model intelligence?

And should we think about free energy and all of that stuff?

And I think that it's a fantastic time to be in the field because there's so many questions and so much we don't understand.

So are we the most special kind of intelligence in this kind of, you said there's a bunch of different elements and characteristics of intelligence systems and colonies.

Is human intelligence the thing in our brain?

Is that the most interesting kind of intelligence in this continuum?

Well, it's interesting to us because it is us.

I mean, interesting to me, yes, because I'm part of the human.

But to understanding the fundamentals of intelligence, what I'm getting at, is studying the human, if everything we've talked about, what you talk about in your book, just the AI field, this notion, yes it's hard to define, but it's usually talking about something that's very akin to human intelligence.

Yeah, to me it is the most interesting because it's the most complex, I think.

It's the only system, at least that I know of, that

And you talk about the history of AI, and us, in terms of creating artificial intelligence, being terrible at predicting the future with AI or with tech in general.

So why do you think we're so bad at predicting the future?

So no matter what, whether it's this decade or the next few decades, every time we make a prediction, there's just no way of doing it well, or as the field matures, we'll be better and better at it?

I believe as the field matures, we will be better.

And I think the reason that we've had so much trouble is that we have so little understanding of our own intelligence.

So there's the famous story about Marvin Minsky assigning computer vision as a summer project to his undergrad students.

And I believe that's actually a true story.

Yeah, no, there's a write-up on it that everyone should read.

It's like a, I think it's like a proposal that describes everything that should be done in that project.

It's hilarious because it, I mean, you can explain it, but from my recollection, it describes basically all the fundamental problems of computer vision, many of which still haven't been solved.

Yeah, and I don't know how far they really expect it to get.

But I think that, and they're really, you know, Marvin Minsky is a super smart guy and very sophisticated thinker.

But I think that no 1 really understands or understood, still doesn't understand how complicated, how complex the things that we do are because they're so invisible to us.

To us, vision, being able to look out at the world and describe what we see, that's just immediate.

So it didn't seem like it would be that hard.

But there's so much going on unconsciously, sort of invisible to us, that I think we overestimate how easy it will be to get computers to do it.

And sort of for me to ask an unfair question, you've done research, you've thought about many different branches of AI through this book, widespread looking at where AI has been, where it is today.

If you were to make a prediction, how many years from now would we as a society create something that you would say achieved human level intelligence or superhuman level intelligence?

A prediction that will most likely be wrong.

And I quoted somebody in my book who said that human-level intelligence is 100 Nobel prizes away, which I like because it's a nice way to sort of, it's a nice unit for prediction.

And it's like that many fantastic discoveries have to be made.

And of course, there's no Nobel Prize in AI, not yet at least.

If we look at that 100 years, your sense is really the journey to intelligence has to go through something more complicated that's akin to our own cognitive systems, understanding them, being able to create them in the artificial systems, as opposed to sort of taking the machine learning approaches of today and really scaling them and scaling them and scaling them exponentially with both compute and hardware and data.

I think that in the sort of going along in the narrow AI that these current approaches will get better.

You know, I think there's some fundamental limits to how far they're gonna get.

I might be wrong, but that's what I think.

And there's some fundamental weaknesses that they have that I talk about in the book that just comes from this approach of supervised learning, requiring sort of feed forward networks and so on.

I don't think it's a sustainable approach to understanding the world.

Sort of, I've, everything you read about in the book and sort of what we're talking about now, I agree with you, but I'm more and more, depending on the day, first of all, I'm deeply surprised by the success of machine learning and deep learning in general.

From the very beginning, when I was, it's really been my main focus of work.

And I'm also, I think we're really early on in these efforts of these narrow AI.

So I think there'll be a lot of surprise of how far it gets.

Like my sense is everything I've seen so far, and we'll talk about autonomous driving and so on, I think we can get really far.

But I also have a sense that we will discover, just like you said, is that even though we'll get really far in order to create something like our own intelligence is actually much farther than we realize.

I think these methods are a lot more powerful than people give them credit for actually.

So of course there's the media hype, but I think there's a lot of researchers in the community, especially like not undergrads, right?

But like people who've been in AI, they're skeptical about how far deep learning can get.

And I'm more and more thinking that it can actually get farther than they'll realize.

1 thing that surprised me when I was writing the book is how far apart different people in the field are

their opinion of how far the field has come and what it's accomplished and what's gonna happen next.

What's your sense of the different, who are the different people, groups, mindsets, thoughts in the community about where AI is today.

So there's kind of the singularity transhumanism group.

I don't know exactly how to characterize that approach.

We're almost at the hugely accelerating part of the exponential.

And in the next 30 years, we're going to see super intelligent AI and all that, and we'll be able to upload our brains and that.

So there's that kind of extreme view that I think most people who work in AI don't have.

But there are people who maybe aren't singularity people, but they do think that the current approach of deep learning is going to scale and is going to kind of go all the way basically and take us to true AI or human-level AI or whatever you want to call it.

And a lot of them, like a lot of the people I've met who work at big tech companies in AI groups kind of have this view that we're really not that far, you know.

Just to linger on that point, if I can take as an example, Jan Lekun, I don't know if you know about his work and so, or his viewpoints on this.

He believes that there's a bunch of breakthroughs, like fundamental, like Nobel Prizes, that's needed still.

But I think he thinks those breakthroughs will be built on top of deep learning.

And then there's some people who think we need to kind of put deep learning to the side a little bit as just 1 module that's helpful in the bigger cognitive framework.

So I think, from what I understand, Jan LeCun is rightly saying, supervised learning is not sustainable.

We have to figure out how to do unsupervised learning, that that's gonna be the key.

I think unsupervised learning is going to be harder than people think.

Then there's the opposing view, you know, there's the Gary Marcus kind of hybrid view where deep learning is 1 part, but we need to bring back kind of these symbolic approaches and combine them.

Of course, no 1 knows how to do that very well.

Which is the more important part to emphasize.

Then there's people pushing different things.

There's the causality people who say, deep learning as it's formulated today completely lacks any notion of causality and that dooms it.

And therefore we have to somehow give it some kind of notion of causality.

There's a lot of push from the more cognitive science crowd saying, we have to look at developmental learning, we have to look at how babies learn, we have to look at intuitive physics, all these things we know about physics.

And as somebody kind of quipped, we also have to teach machines intuitive metaphysics, which means objects exist.

You know, they look at a group of pixels and maybe they get 10 million examples, but they can't necessarily learn that there are objects in the world.

So there's just a lot of pieces of the puzzle that people are promoting, and with different opinions of how important they are and how close we are to being able to put them all together to create general intelligence.

Looking at this broad field, what do you take away from it?

Is it the cognitive folks, the Gary Marcus camp, the Yon camp.

It's unsupervised and there's self-supervised, there's the supervisors, and then there's the engineers who are actually building systems.

You have sort of the Andrej Karpathy at Tesla building actual, you know, it's not philosophy, it's real systems that operate in the real world.

What do you take away from all this beautiful variety?

I don't know if, you know, these different views are not necessarily mutually exclusive.

And I think people like Jan LeCun agrees with the developmental psychology, causality, intuitive physics, etc.

But he still thinks that it's learning, like end-to-end learning is the way to go.

Yeah, and that we don't need, there's no sort of innate stuff that has to get built in.

This is, you know, it's because, it's a hard problem.

I personally, you know, I'm very sympathetic to the cognitive science side because that's kind of where I came in to the field.

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Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI | Lex Fridman Podcast #61