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 Hofstadter 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.
What do you think of the term artificial intelligence
for our big and complicated and interesting field?
I'm not crazy about the term.
I think it has a few problems
because it means so many different things
to different people.
And intelligence is one 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 one 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 mixed, but probably is equally vague, I guess.
Is it the intelligence or the artificial
in terms of words that's most problematic, would you say?
Yeah, I think it's a little of both.
But it has some good size 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 sort of other aspects
of intelligence, it's sort of higher level.
So to you, cognition is this broad,
beautiful mess of things that encompasses the whole thing.
Memory, perception. Yeah, 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 one 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 Lacune 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 was strived 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.
You know, 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
that as opposed to simulating thinking
or carrying out intelligent processes
that we would call intelligent.
At a high level, if you look at the founding
of the field of 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.
I don't know how we will know for sure.
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.
That may not be the kinds of machines we have now.
And one 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 it's possible to create intelligence
without understanding our own mind?
You said 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.
I wouldn't have expected that.
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, you know, smarter than is with respect to what?
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, you know, 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, right?
I'm totally happy with that.
It doesn't bother me at all.
I guess the question is, you know,
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 Ph.D.
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.
I, you know, I don't think...
He doesn't like the fact that machines can recognize
spoken language really well.
Like 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 throughout many centuries that preceded it?
That's a really good question, and I have wondered about that.
Because I myself 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.
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.
So I don't know.
If you were to psychoanalyze yourself
in your own interest in AI,
what excites you about creating intelligence?
You said understanding our own cells?
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, et cetera.
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 intelligence
as just the property of any,
you can look at any level
and every level has some aspect of intelligence.
So we're just like one 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 want to,
I don't have a good enough definition of intelligence
to say that.
So let me do sort of multiple choice, I guess.
So you said ant colonies.
So our ant colonies intelligent 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 is 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.
I think that's kind of a mystery.
But I wouldn't say that, say that, you know,
the planets orbiting the sun is an intelligent system.
I mean, I would find that maybe not the right term
to describe that.
And this is, you know, there's all this debate in the field
of like what's the right way to define intelligence,
what's the right way to model intelligence?
Should we think about computation?
Should we think about dynamics?
And should we think about, you know, 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.
There's so much work to do.
So are we the most special kind of intelligence
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.
And because I'm part of, you know, human.
But to understanding the fundamentals of intelligence
what I'm getting at, is studying the human,
is sort of if everything we've talked about,
what you talked about in your book,
what 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 most self-aware.
It's the only system at least that I know of
that reflects on its own intelligence.
And you talk about the history of AI
and us in terms of creating artificial intelligence
being terrible at predicting the future with AI
with tech in general.
So why do you think we're so bad at predicting the future?
Are we hopelessly bad?
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, there's a write-up on it, everyone should read.
I think it's like a proposal that describes everything
that should be done in that project.
It's hilarious because you can explain it
but for 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 expected to get,
but I think that, and they're really,
Marvin Minsky is a super smart guy
and very sophisticated thinker,
but I think that no one 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.
It feels like it's no work at all.
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 super human level intelligence?
That is an unfair question.
A prediction that will most likely be wrong,
but it's just your notion because...
Okay, I'll say more than 100 years.
More than 100 years.
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 exponentially
with both compute and hardware and data.
That would be my guess.
I think that in the sort of going along
in the narrow AI that these current approaches
will get better,
I think there's some fundamental limits
to how far they're going to 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 feedforward networks and so on.
I don't think it's a sustainable approach
to understanding the world.
Yeah, I'm personally torn on it.
Everything you read about in the book,
instead 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.
It's really been my main focus of work.
I'm just surprised how far it gets.
And I'm also think we're really early on
in these efforts of these narrow AI.
I think there will be a lot of surprises
of how far it gets.
I think we'll be extremely impressed.
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,
it's 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 not undergrads, right?
But 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 we realize.
It's certainly possible.
One thing that surprised me when I was writing the book
is how far apart different people are in the field are
on their opinion of how far the field has come
and what has accomplished and what's going to 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?
Yeah, they're all over the place.
So, there's kind of the singularity transhumanism group.
I don't know exactly how to characterize that approach,
which is sort of exponential progress.
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.
They disagree with that.
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 there's quite a few of them,
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.
Just to linger on that point,
if I can take as an example, like Yann LeCun,
I don't know if you know about his work,
and so it hurts viewpoints on this.
I do.
He believes that there's a bunch of breakthroughs,
like fundamental, like Nobel Prizes, that are 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 one module that's helpful
in the bigger cognitive framework.
Right.
So I think what I understand, Yann LeCun,
is rightly saying supervised learning
is not sustainable.
We have to figure out how to do unsupervised learning,
that that's going to be the key.
And I think that's probably true.
I think unsupervised learning is going to be harder
than people think.
I mean, the way that we humans do it.
Then there's the opposing view,
the Gary Marcus kind of hybrid view
where deep learning is one part,
but we need to bring back kind of these symbolic approaches
and combine them.
Of course, no one knows how to do that very well.
Which is the more important part to emphasize
and how do they fit together?
What's the foundation?
What's the thing that's on top?
What's the cake?
What's the icing?
Right.
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's 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 like objects exist.
Causality exists.
These things that maybe we're born with, I don't know,
that machines don't have any of that.
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?
Who's the most impressive?
Is it the cognitive folks?
Is it Gary Marcus' camp?
The on-camp unsupervised and self-supervised.
There's the supervisors
and then there's the engineers who are actually building systems.
You have the Andre Karpathy at Tesla
building actual, 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.
These different views are not necessarily mutually exclusive
and I think people like Jan Lacoon
agrees with the developmental psychology,
causality, intuitive physics, et cetera.
But he still thinks that it's learning,
like end-to-end learning is the way to go.
We'll take this perhaps all the way.
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.
I've become more and more sort of an embodiment adherent
saying that, you know, without having a body,
it's going to be very hard to learn
what we need to learn about the world.
That's definitely something I'd love to talk about
in a little bit.
To step into the cognitive world,
then if you don't mind,
because you've done so many interesting things,
if you look to Copycat,
taking a couple of decades,
step back,
you'd Douglas Hofstadter
and others have created and developed Copycat
more than 30 years ago.
That's painful to hear.
What is it?
What is Copycat?
It's a program that makes analogies
in an idealized domain,
idealized world of letter strings.
So as you say, 30 years ago, wow.
So I started working on it when I started grad school in 1984.
Wow.
Dates me.
And it's based on Doug Hofstadter's ideas
about that analogy is really a core aspect of thinking.
I remember he has a really nice quote
in the book by himself
and Immanuel Sander called Surfaces and Essences.
I don't know if you've seen that book,
but it's about analogy.
He says, without concepts,
there can be no thought and without analogies,
there can be no concepts.
So the view is that analogy
is not just this kind of reasoning technique
where we go, you know,
shoe is to foot as glove is to what?
These kinds of things that we have on IQ tests or whatever.
But that it's much deeper,
it's much more pervasive in everything we do,
in our language, our thinking, our perception.
So he had a view that was a very active perception idea.
So the idea was that instead of having kind of
a passive network in which you have input
that's being processed through these feedforward layers
and then there's an output at the end,
that perception is really a dynamic process,
where our eyes are moving around
and they're getting information
and that information is feeding back
to what we look at next, influences what we look at next
and how we look at it.
So the copycat was trying to do that,
kind of simulate that kind of idea
where you have these agents,
it's kind of an agent-based system
and you have these agents that are picking things to look at
and deciding whether they were interesting or not
and whether they should be looked at more
and that would influence other agents.
How did they interact?
So they interacted through this global,
kind of what we call the workspace.
It's actually inspired by the old blackboard systems
where you would have agents that post information
on a blackboard, a common blackboard.
This is very old-fashioned AI.
Is that what we're talking about in physical space?
Is this a computer program?
It's a computer program.
So agents posting concepts on a blackboard?
Yeah, we called it a workspace
and the workspace is a data structure.
The agents are little pieces of code
that you could think of them as little detectors
or little filters that say,
I'm going to pick this place to look
and I'm going to look for a certain thing
and is this the thing I think is important?
Is it there?
So it's almost like a convolution in a way
except a little bit more general
and then highlighting it in the workspace.
Once it's in the workspace,
how do the things there highlight and relate to each other?
So there's different kinds of agents
that can build connections between different things.
So just to give you a concrete example,
what Copycat did was it made analogies
between strings of letters.
So here's an example.
ABC changes to ABD.
What does IJK change to?
And the program had some prior knowledge
about the alphabet.
It knew the sequence of the alphabet.
It had a concept of letter,
successor of letter.
It had concepts of sameness.
So it had some innate things programmed in.
But then it could do things like say,
discover that ABC is a group of letters in succession.
And then an agent can mark that.
So the idea that there could be a sequence of letters,
is that a new concept that's formed
or that's a concept that's innate?
That's a concept that's innate.
So can you form new concepts or are all concepts innate?
No.
So in this program,
all the concepts of the program were innate.
Because we weren't, I mean,
obviously that limits it quite a bit.
But what we were trying to do is say,
suppose you have some innate concepts.
How do you flexibly apply them to new situations?
And how do you make analogies?
Let's step back for a second.
So I really like that quote that you said,
without concepts there could be no thought
and without analogies there could be no concepts.
In a Santa Fe presentation,
you said that it should be one of the mantras of AI.
And that you also yourself said,
how to form and fluidly use concepts
is the most important open problem in AI.
Yes.
How to form and fluidly use concepts
is the most important open problem in AI.
So what is a concept and what is an analogy?
A concept is in some sense a fundamental unit of thought.
So say we have a concept of a dog.
Okay. And a concept is embedded in a whole space of concepts
so that there's certain concepts that are closer to it
or farther away from it.
Are these concepts, are they really like fundamental?
Like we mentioned innate, almost like exeomatic,
like very basic.
And then there's other stuff built on top of it.
Or does this include everything?
Are they complicated?
You can certainly form new concepts.
Right. I guess that's the question I'm asking.
Can you form new concepts that are complex combinations
of other concepts?
Yes, absolutely.
And that's kind of what we do in learning.
And then what's the role of analogies in that structure?
So analogy is when you recognize that one situation
is essentially the same as another situation
and essentially is kind of the keyword there
because it's not the same.
So if I say, last week I did a podcast interview
in actually like three days ago in Washington DC
and that situation was very similar to this situation
although it wasn't exactly the same.
It was a different person sitting across from me.
We had different kinds of microphones.
The questions were different.
The building was different.
There's all kinds of different things,
but really it was analogous.
Or I can say, so doing a podcast interview,
that's kind of a concept.
It's a new concept.
You know, I never had that concept before.
This year essentially.
I mean, and I can make an analogy with it
like being interviewed for a news article in a newspaper.
And I can say, well, you kind of play the same role
that the newspaper reporter played.
It's not exactly the same
because maybe they actually emailed me
some written questions rather than talking.
And the writing, the written questions play, you know,
our analogies to your spoken questions.
You know, there's just all kinds of similarities.
And this somehow probably connects to conversations
you have over Thanksgiving dinner,
just general conversations.
There's like a thread you can probably take
that just stretches out in all aspects of life
that connect to this podcast.
I mean, conversations between humans.
Sure.
And if I go and tell a friend of mine
about this podcast interview,
my friend might say, oh, the same thing happened to me.
You know, let's say, you know, you ask me some really hard question
and I have trouble answering it.
My friend could say, the same thing happened to me.
But it was like, it wasn't a podcast interview.
It wasn't, it was a completely different situation.
And yet my friend is seeing essentially the same thing.
You know, we say that very fluidly.
The same thing happened to me.
Essentially the same thing.
But we don't even say that, right?
We say the same thing.
It would imply it, yes.
Yeah.
And the view that kind of went into say, Copycat,
that whole thing is that act of saying the same thing
happened to me is making an analogy.
And in some sense, that's what underlies all of our concepts.
Why do you think analogy making that you're describing
is so fundamental to cognition?
Like it seems like it's the main element,
the connection of what we think of as cognition.
Yeah.
So it can be argued that all of this generalization we do
of concepts and recognizing concepts in different situations
is done by analogy.
That every time I'm recognizing that, say, you're a person,
that's by analogy because I have this concept of what person is
and I'm applying it to you.
And every time I recognize a new situation,
like one of the things I talked about in the book
was the concept of walking a dog,
that that's actually making an analogy because the details
are very different.
So reasoning could be reduced onto sensory analogy making.
So all the things we think of as like, yeah, like you said,
perception.
So what's perception is taking raw sensory input
and it's somehow integrating into our understanding
of the world, updating the understanding.
And all of that has just this giant mess of analogies
that are being made.
I think so, yeah.
If you just linger on it a little bit,
what do you think it takes to engineer a process like that
for us in our artificial systems?
We need to understand better, I think, how we do it,
how humans do it.
And it comes down to internal models, I think.
People talk a lot about mental models,
that concepts are mental models, that I can,
in my head, I can do a simulation of a situation
like walking a dog.
And that there's some work in psychology
that promotes this idea that all of concepts
are really mental simulations,
that whenever you encounter a concept
or situation in the world or you read about it or whatever,
you do some kind of mental simulation
that allows you to predict what's going to happen
to develop expectations of what's going to happen.
So that's the kind of structure I think we need,
is that kind of mental model that,
in our brains, somehow these mental models
are very much interconnected.
Again, so a lot of stuff we're talking about
are essentially open problems.
So if I ask a question,
I don't mean that you would know the answer,
only just hypothesizing,
but how big do you think is the network,
graph, data structure of concepts that's in our head?
If we're trying to build that ourselves,
we take it, that's one of the things
we take for granted, we think,
that's why we take common sense for granted,
we think common sense is trivial,
but how big of a thing of concepts is
that underlies what we think of as common sense, for example?
Yeah, I don't know,
and I don't even know what units to measure it in.
You say how big is it.
That's beautifully put, right?
But it's really hard to know.
We have, what, 100 billion neurons or something,
I don't know,
and they're connected via trillions of synapses
and there's all this chemical processing going on.
There's just a lot of capacity for stuff
and their information's encoded
in different ways in the brain,
it's encoded in chemical interactions,
it's encoded in electric, like, firing and firing rates,
and nobody really knows how it's encoded,
but it just seems like there's a huge amount of capacity.
So I think it's huge, it's just enormous,
and it's amazing how much stuff we know.
Yeah.
But we know, and not just know, like, facts,
but it's all integrated into this thing
that we can make analogies with.
Yes.
There's a dream of semantic web,
and there's a lot of dreams from expert systems
of building giant knowledge bases.
Do you see a hope for these kinds of approaches
of building, of converting Wikipedia
into something that could be used in analogy making?
Sure.
And I think people have made some progress along those lines.
I mean, people have been working on this for a long time,
but the problem is, and this, I think,
is the problem of common sense,
like, people have been trying to get these common sense networks.
Here at MIT, there's this concept net project, right?
But the problem is that, as I said,
most of the knowledge that we have is invisible to us.
It's not in Wikipedia.
It's very basic things about, you know,
intuitive physics, intuitive psychology,
intuitive metaphysics, all that stuff.
If you were to create a website that's described
intuitive physics, intuitive psychology,
would it be bigger or smaller than Wikipedia?
What do you think?
I guess described to whom?
I'm sorry, but...
No, it's really good.
Exactly right, yeah.
That's a hard question because, you know,
how do you represent that knowledge is the question, right?
I can certainly write down F equals MA
and Newton's Laws and a lot of physics can be deduced from that.
But that's probably not the best representation of that knowledge
for doing the kinds of reasoning we want a machine to do.
So, I don't know.
It's impossible to say now.
And people, you know, the projects,
like there's a famous psych project, right,
that Douglas Lanotte did that was trying...
I think it's still going.
I think it's still going.
And the idea was to try and encode all of common sense knowledge,
including all this invisible knowledge
in some kind of logical representation.
And it just never, I think, could do any of the things
that he was hoping it could do
because that's just the wrong approach.
Of course, that's what they always say, you know,
and then the history books will say,
well, the psych project finally found a breakthrough
in 2058 or something.
You know, so much progress has been made in just a few decades
that who knows what the next breakthroughs will be.
It could be.
It's certainly a compelling notion
what the psych project stands for.
I think Lanotte was one of the earliest people
to say common sense is what we need.
That's what we need.
All this, like, expert system stuff,
that is not going to get you to AI.
You need common sense.
And he basically gave up his whole academic career
to go pursue that.
And I totally admire that.
But I think that the approach itself will not
in 20, 40 or whatever.
What do you think is wrong with approach?
What kind of approach might be successful?
Well, I knew that.
Nobody knows the answer, right?
If I knew that, you know, one of my talks,
one of the people in the audience,
this is a public lecture,
one of the people in the audience said,
what AI companies are you investing in?
Like, well, I'm a college professor for one thing,
so I don't have a lot of extra funds to invest.
But also, like, no one knows what's going to work in AI, right?
That's the problem.
Let me ask another impossible question
in case you have a sense.
In terms of data structures that will store
this kind of information,
do you think they've been invented yet,
both in hardware and software?
Or is something else needs to be...
Are we totally...
I think something else has to be invented.
That's my guess.
Is the breakthroughs that's most promising,
would that be in hardware or in software?
Do you think we can get far with the current computers?
Or do we need to do something...
That's what you were saying.
I don't know if Turing computation is going to be sufficient.
Probably, I would guess it will.
I don't see any reason why we need anything else.
But so in that sense,
we have invented the hardware we need,
but we just need to make it faster and bigger.
And we need to figure out the right algorithms
and the right sort of architecture.
Turing, that's a very mathematical notion.
When we have to build intelligence,
it's not an engineering notion
where you throw all that stuff.
Well, I guess it is a question...
People have brought up this question,
and when you asked about,
is our current hardware...
Will our current hardware work?
Well, Turing computation says that
our current hardware is, in principle,
a Turing machine, right?
So all we have to do is make it faster and bigger.
But there have been people like Roger Penrose,
if you might remember,
that he said Turing machines cannot produce intelligence
because intelligence requires continuous valued numbers.
I mean, that was sort of my reading of his argument.
And quantum mechanics and whatever.
But I don't see any evidence for that,
that we need new computation paradigms.
But I don't think we're going to be able
to scale up our current approaches
to programming these computers.
What is your hope for approaches like Copycat
or other cognitive architectures?
I've talked to the creator of SOAR, for example.
I've used ActR myself.
I don't know if you're familiar with that.
Yeah, I am.
What do you think is...
What's your hope of approaches like that
in helping develop systems of greater and greater intelligence
in the coming decades?
Well, that's what I'm working on now,
is trying to take some of those ideas and extending it.
So I think there are some really promising approaches
that are going on now that have to do with
more active generative models.
So this is the idea of this simulation
in your head of a concept.
If you want to...
When you're perceiving a new situation,
you have some simulations in your head.
Those are generative models.
They're generating your expectations.
They're generating predictions.
So that's part of a perception.
You have them at the model that generates a prediction,
then you compare it with...
Yeah.
And then the difference...
And you also...
That generative model is telling you where to look
and what to look at and what to pay attention to.
And I think it affects your perception.
It's not that just you compare it with your perception.
It becomes your perception in a way.
It's kind of a mixture of the bottom-up
that bottom-up information coming from the world
and your top-down model being imposed on the world
is what becomes your perception.
So your hope is something like that
can improve perception systems
and that they can understand things better.
Yes.
Understand things.
Yes.
What's the...
What's the step?
What's the analogy-making step there?
Well, there...
The idea is that you have this pretty complicated
conceptual space.
You can talk about a semantic network
or something like that
with these different kinds of concept models
in your brain that are connected.
So let's take the example of walking a dog.
We were talking about that.
Okay.
Let's say I see someone out in the street walking a cat.
Some people walk their cats, I guess.
Yes.
It seems like a bad idea, but...
Yeah.
Good thing.
So there's connections between my model of a dog
and model of a cat.
And I can immediately see the analogy
that those are analogous situations.
But I can also see the differences
and that tells me what to expect.
So also, I have a new situation.
So another example with the walking the dog thing is
sometimes people...
I see people riding their bikes holding a leash
and the dog's running alongside.
Okay.
So I know that...
I recognize that as kind of a dog walking situation
even though the person's not walking, right?
And the dog's not walking.
Yeah.
Because I have these models that say,
okay, riding a bike is sort of similar to walking
or it's connected, it's a means of transportation.
But because they have their dog there,
I assume they're not going to work,
but they're going out for exercise.
And these analogies help me to figure out
kind of what's going on, what's likely.
But sort of these analogies are very human interpretable.
So that's that kind of space.
And then you look at something like
the current deep learning approaches,
kind of help you to take raw sensory information
and to sort of automatically build up hierarchies
of what you can even call them concepts.
They're just not human interpretable concepts.
What's your...
What's the link here?
Do you hope...
It's sort of the hybrid system question.
How do you think that two can start to meet each other?
What's the value of learning in this systems of forming
of analogy making?
The original goal of deep learning
in at least visual perception was that
you would get the system to learn to extract features
at these different levels of complexity.
So maybe edge detection and that would lead into
learning simple combinations of edges
and then more complex shapes and then whole objects
or faces.
And this was based on the ideas
of the neuroscientists, Hubel and Weasel,
who had seen laid out this kind of structure and brain.
And I think that's right to some extent.
Of course, people have found that the whole story
is a little more complex than that.
And the brain, of course, always is.
And there's a lot of feedback.
So I see that as absolutely a good brain-inspired approach
to some aspects of perception.
But one thing that it's lacking, for example,
is all of that feedback, which is extremely important.
The direct development that you mentioned.
The expectation, the conceptual level.
Going back and forth with the expectation
and the perception and just going back and forth.
So that is extremely important.
And one thing about deep neural networks
is that in a given situation,
they're trained, right?
They get these weights and everything.
But then now I give them a new image, let's say.
They treat every part of the image in the same way.
They apply the same filters at each layer
to all parts of the image.
There's no feedback to say like,
oh, this part of the image is irrelevant.
I shouldn't care about this part of the image.
Or this part of the image is the most important part.
And that's kind of what we humans are able to do
because we have these conceptual expectations.
So there's, by the way, a little bit of work in that.
There's certainly a lot more in what's called attention
in natural language processing knowledge.
It's an image that's exceptionally powerful.
And it's a very, just as you say, it's a really powerful idea.
But again, in sort of machine learning,
it all kind of operates in an automated way.
That's not human.
It's not dynamic.
I mean, in the sense that as a perception of a new example
is being processed, those attention's weights don't change.
Right.
So I mean, there's a kind of notion that there's not a memory.
So you're not aggregating the idea of this mental model.
Yes.
I mean, that seems to be a fundamental idea.
There's not a really powerful...
I mean, there's some stuff with memory,
but there's not a powerful way to represent the world
in some sort of way that's deeper than...
I mean, it's so difficult because neural networks do represent the world.
They do have a mental model.
Right.
But it just seems to be shallow.
It's hard to criticize them at the fundamental level.
To me, at least.
It's easy to criticize them.
Well, look, like exactly what you're saying,
mental models sort of almost put a psychology hat on say,
look, these networks are clearly not able to achieve what we humans do
with forming mental models, the analogy making so on.
But that doesn't mean that they fundamentally cannot do that.
It's very difficult to say that, at least to me.
Do you have a notion that the learning approach is really...
I mean, they're going to...
Not only are they limited today,
but they will forever be limited in being able to construct such mental models.
I think the idea of the dynamic perception is key here,
the idea that moving your eyes around and getting feedback.
And that's something that...
There's been some models like that.
There's certainly recurrent neural networks that operate over several time steps.
But the problem is that the actual, the recurrence is...
Basically, the feedback is, at the next time step,
is the entire hidden state of the network, which is...
And it turns out that that doesn't work very well.
The thing I'm saying is, mathematically speaking,
it has the information in that recurrence to capture everything.
It just doesn't seem to work.
Yeah, right.
It's the same Turing machine question, right?
Yeah, maybe theoretically, computers,
anything that's a universal Turing machine can be intelligent.
But practically, the architecture might be a very specific kind of architecture
to be able to create it.
I guess to ask almost the same question again is, how big of a role
do you think deep learning needs to play or needs to play in this, in perception?
I think that deep learning, as it currently exists,
that kind of thing will play some role.
But I think that there's a lot more going on in perception.
But who knows?
The definition of deep learning, I mean, it's pretty broad.
It's kind of an umbrella for a lot of different...
So what I mean is purely sort of neural networks.
Yeah, and a feed-forward neural networks.
Essentially.
Or there could be recurrence, but sometimes it feels like,
as I talk to Gary Marcus, it feels like the criticism of deep learning
is kind of like us birds criticizing airplanes for not flying well,
or that they're not really flying.
Do you think deep learning...
Do you think it could go all the way, like Yann LeCloon thinks?
Do you think that, yeah, the brute force learning approach can go all the way?
I don't think so, no.
I mean, I think it's an open question, but I tend to be on the innate-ness side
that there's some things that we've been evolved to be able to learn,
and that learning just can't happen without them.
So one example, here's an example I had in the book
that I think is useful to me, at least, in thinking about this.
So this has to do with the DeepMind Atari gameplay program,
and it learned to play these Atari video games
just by getting input from the pixels of the screen,
and it learned to play the game Breakout 1,000% better than Humans.
That was one of their results, and it was great.
And it learned this thing where it tunneled through the side of the bricks
in the Breakout game, and the ball could bounce off the ceiling
and then just wipe out bricks.
Okay, so there was a group who did an experiment
where they took the paddle that you move with the joystick
and moved it up two pixels or something like that.
And then they looked at a deep Q-learning system
that had been trained on Breakout and said,
could it now transfer its learning to this new version of the game?
Of course, a human could, and it couldn't.
Maybe that's not surprising, but I guess the point is
it hadn't learned the concept of a paddle.
It hadn't learned the concept of a ball or the concept of tunneling.
It was learning something... We, looking at it, anthropomorphized it
and said, oh, here's what it's doing and the way we describe it,
but it actually didn't learn those concepts.
And so because it didn't learn those concepts, it couldn't make this transfer.
Yeah, so that's a beautiful statement,
but at the same time, by moving the paddle,
we also anthropomorphize flaws to inject into the system
that will then flip how impressed we are by it.
What I mean by that is, to me, the Atari games were, to me, deeply impressive
that that was possible at all.
So I have to first pause on that, and people should look at that,
just like the Game of Go, which is fundamentally different to me
than what Deep Blue did.
Even though there's still tree search,
it's just everything DeepMind has done in terms of learning.
However limited it is, it's still deeply surprising to me.
Yeah, I'm not trying to say that what they did wasn't impressive.
I think it was incredibly impressive.
To me, it's interesting.
Is moving the board just another thing that needs to be learned?
So we've been able to, maybe,
been able to, through the current neural networks, learn very basic concepts
that are not enough to do this general reasoning,
and maybe with more data.
The interesting thing about the examples that you talk about,
and beautifully, is it's often flaws of the data.
Well, that's the question.
I think that is the key question,
whether it's a flaw of the data or not.
The reason I brought up this example was because you were asking,
do I think that learning from data could go all the way?
Yes.
And this was why I brought up the example, because I think,
and this is not at all to take away from the impressive work that they did.
But it's to say that when we look at what these systems learn,
do they learn the things that we humans consider to be the relevant concepts?
And in that example, it didn't.
Sure, if you train it on moving the paddle being in different places,
maybe it could deal with, maybe it would learn that concept.
I'm not totally sure.
But the question is scaling that up to more complicated worlds.
To what extent could a machine that only gets this very raw data learn
to divide up the world into relevant concepts?
And I don't know the answer, but I would bet that without some innate notion
that it can't do it.
Yeah, 10 years ago, I 100% agree with you as the most exquisite AI system,
but now I have a glimmer of hope.
Okay, I mean, that's fair enough.
And I think that's what deep learning did in the community is,
no, no, if I had to bet all my money,
100% deep learning will not take us all the way.
But there's still, I was so personally sort of surprised by the tar games,
by Go, by the power of self-play,
of just game playing, that I was like many other times just humbled
of how little I know about what's possible in this approach.
Yeah, I think fair enough, self-play is amazingly powerful.
And that goes way back to Arthur Samuel with his checker playing program,
which was brilliant and surprising that it did so well.
So just for fun, let me ask you on the topic of autonomous vehicles,
it's the area that I work at least these days most closely on.
And it's also area that I think is a good example that you use
is sort of an example of things we as humans don't always realize how hard it is to do.
It's like the constant trend in AI,
but the different problems that we think are easy when we first try them,
and then we realize how hard it is.
Okay, so why you've talked about autonomous driving being a difficult problem,
more difficult than we realize humans give a credit for.
Why is it so difficult? What are the most difficult parts in your view?
I think it's difficult because of the world is so open-ended
as to what kinds of things can happen.
So you have sort of what normally happens,
which is just you drive along and nothing surprising happens,
and autonomous vehicles can do the ones we have now,
evidently, can do really well on most normal situations
as long as the weather is reasonably good and everything.
But if some, we have this notion of edge case or things in the tail of the distribution
called the long tail problem,
which says that there's so many possible things that can happen
that was not in the training data of the machine
that it won't be able to handle it because it doesn't have common sense.
Right, it's the old, the paddle-moved problem.
Yeah, it's the paddle-moved problem, right.
And so my understanding, and you probably are more of an expert than I am on this,
is that current self-driving car vision systems have problems with obstacles,
meaning that they don't know which obstacles, which quote-unquote obstacles
they should stop for and which ones they shouldn't stop for.
And so a lot of times I read that they tend to slam on the brakes quite a bit,
and the most common accidents with self-driving cars are people rear-ending them,
because they were surprised, they weren't expecting the machine, the car to stop.
Yeah, so there's a lot of interesting questions there, whether,
because you mentioned kind of two things, so one is the problem of perception,
of understanding, of interpreting the objects that are detected correctly.
And the other one is more like the policy, the action that you take, how you respond to it.
So a lot of the cars breaking is a kind of notion of, to clarify,
there's a lot of different kind of things that are people calling autonomous vehicles,
but the L4 vehicles with a safety driver are the ones like Waymo and Cruz
and those companies, they tend to be very conservative and cautious.
So they tend to be very, very afraid of hurting anything or anyone
and getting in any kind of accidents.
So their policy is very kind of, that results in being exceptionally responsive
to anything that could possibly be an obstacle, right?
Right, which the human drivers around it, it behaves unpredictably.
That's not a very human thing to do, caution.
That's not the thing we're good at, especially in driving.
We're in a hurry, often angry, et cetera, especially in Boston.
And a lot of times, machine learning is not a huge part of that.
It's becoming more and more unclear to me how much, you know,
speaking to public information, because a lot of companies say they're doing deep learning
and machine learning just to attract good candidates.
The reality is, in many cases, it's still not a huge part of the perception.
There's LiDAR and there's other sensors that are much more liable for optical detection.
And then there's Tesla approach, which is vision only.
I think a few companies do that, but Tesla most sort of famously pushing that forward.
And that's because the LiDAR is too expensive, right?
Well, I mean, yes, but I would say if you were to free give to every Tesla vehicle,
Elon Musk fundamentally believes that LiDAR is a crutch, right?
Fantasy said that.
That if you want to solve the problem of machine learning, LiDAR is not,
should not be the primary sensor, is the belief.
The camera contains a lot more information.
So if you want to learn, you want that information.
But if you want to not to hit obstacles, you want LiDAR, right?
It's sort of this weird trade off because, yeah, so what Tesla vehicles have a lot of,
which is really the thing, the primary fallback sensor is LiDAR,
which is a very crude version of LiDAR.
So it's a good detector of obstacles, except when those things are standing, right?
The stopped vehicle.
Right.
That's why it had problems with crashing into stopfire trucks.
Stopfire trucks.
That's right.
So the hope there is that the vision sensor would somehow catch that and infer.
There's a lot of problems with perception.
They are doing actually some incredible stuff in the,
almost like an active learning space where it's constantly taking edge cases
and pulling back in.
There's this data pipeline.
Another aspect that is really important that people are studying now is called multi-task learning,
which is sort of breaking apart this problem, whatever the problem is,
in this case, driving into dozens or hundreds of little problems
that you can turn into learning problems.
So this giant pipeline, you know, it's kind of interesting.
I've been skeptical from the very beginning,
but become less and less skeptical over time how much of driving can be learned.
I still think it's much farther than the CEO of that particular company thinks it will be.
But it is constantly surprising that through good engineering and data collection
and active selection of data, how you can attack that long tail.
And it's an interesting open question that you're absolutely right.
There's a much longer tail in all these edge cases that we don't think about.
But it's a fascinating question that applies to natural language in all spaces.
How big is that long tail?
And I mean, not to linger on the point,
but what's your sense in driving in these practical problems of the human experience?
Can it be learned?
So the current, what are your thoughts of sort of Elon Musk's thought,
let's forget the thing that he says it'd be solved in a year,
but can it be solved in a reasonable timeline
or do fundamentally other methods need to be invented?
So I don't, I think that ultimately driving, so it's a trade-off in a way,
you know, being able to drive and deal with any situation that comes up
does require kind of full human intelligence.
And even in humans aren't intelligent enough to do it because humans,
I mean, most human accidents are because the human wasn't paying attention
or the humans drunk or whatever.
And not because they weren't intelligent enough.
And not because they weren't intelligent enough.
Right. Whereas the accidents with autonomous vehicles
is because they weren't intelligent enough.
They're always paying attention.
Yeah, they're always paying attention.
So it's a trade-off, you know, and I think that it's a very fair thing to say
that autonomous vehicles will be ultimately safer than humans
because humans are very unsafe.
It's kind of a low bar.
But just like you said, I think humans got a better rap, right?
Because we're really good at the common sense thing.
Yeah, we're great at the common sense thing.
We're bad at the paying attention thing.
Paying attention thing, right?
Especially when we're, you know, driving is kind of boring
and we have these phones to play with and everything.
But I think what's going to happen is that for many reasons,
not just AI reasons, but also like legal and other reasons
that the definition of self-driving is going to change
or autonomous is going to change.
It's not going to be just, I'm going to go to sleep in the back
and you just drive me anywhere.
It's going to be more certain areas are going to be instrumented
to have the sensors and the mapping and all the stuff you need
that the autonomous cars won't have to have full common sense.
And they'll do just fine in those areas
as long as pedestrians don't mess with them too much.
That's another question.
Oh, that's right.
But I don't think we will have fully autonomous self-driving
in the way that like most the average person thinks of it for a very long time.
And just to reiterate, this is the interesting open question
that I think I agree with you on is to solve fully autonomous driving.
You have to be able to engineer in common sense.
Yes.
I think it's an important thing to hear and think about.
I hope that's wrong, but I currently agree with you
that unfortunately you do have to have to be more specific
sort of these deep understandings of physics and of the way this world works.
And also human dynamics, like you mentioned, pedestrians and cyclists.
Actually, that's whatever that nonverbal communication is.
Some people call it.
There's that dynamic that is also part of this common sense.
Right.
We humans are pretty good at predicting what other humans are going to do.
And how our actions impact the behaviors.
So this is weird game theoretic dance that we're good at somehow.
And the funny thing is, because I've watched countless hours of pedestrian video
and talked to people, we humans are also really bad at articulating the knowledge we have.
Right.
Which has been a huge challenge.
Yes.
So you've mentioned embodied intelligence.
What do you think it takes to build a system of human level intelligence?
Does it need to have a body?
I'm not sure, but I'm coming around to that more and more.
And what does it mean to be, I don't mean to keep bringing up Yala Koon.
He looms very large.
Well, he certainly has a large personality, yes.
He thinks that the system needs to be grounded.
Meaning it needs to sort of be able to interact with reality,
but doesn't think it necessarily needs to have a body.
So what's the difference?
I guess I want to ask, when you mean body, do you mean you have to be able to play with the world?
Or do you also mean like there's a body that you have to preserve?
That's a good question.
I haven't really thought about that.
I think both, I would guess, because I think intelligence,
it's so hard to separate it from our desire for self-preservation,
our emotions, all that non-rational stuff that kind of gets in the way of logical thinking.
Because the way we're talking about human intelligence or human level intelligence, whatever that means,
a huge part of it is social.
We were evolved to be social and to deal with other people.
And that's just so ingrained in us that it's hard to separate intelligence from that.
I think AI, for the last 70 years or however long it's been around,
it has largely been separated.
There's this idea that it's kind of very Cartesian.
There's this thinking thing that we're trying to create,
but we don't care about all this other stuff.
And I think the other stuff is very fundamental.
So there's the idea that things like emotion get in the way of intelligence.
As opposed to being an integral part of it.
Integral part of it.
I'm Russian, so romanticize the notions of emotion and suffering
and all that kind of fear of mortality, those kinds of things.
So in AI, especially...
By the way, did you see that there was this recent thing going around the internet?
Some, I think he's a Russian or some Slavic, had written this thing
sort of anti the idea of superintelligence.
I forgot, maybe he's Polish.
Anyway, so he had all these arguments and one was the argument from Slavic pessimism.
My favorite.
Do you remember what the argument is?
It's like, nothing ever works.
Everything sucks.
So what do you think is the role?
That's such a fascinating idea that what we perceive as sort of the limits of human mind,
which is emotion and fear and all those kinds of things are integral to intelligence.
Could you elaborate on that?
But why is that important, do you think, for human level intelligence?
At least for the way the humans work.
It's a big part of how it affects how we perceive the world.
It affects how we make decisions about the world.
It affects how we interact with other people.
It affects our understanding of other people.
For me to understand what you're likely to do, I need to have kind of a theory of mind
and that's very much a theory of emotion and motivations and goals.
And to understand that, we have this whole system of mirror neurons.
We sort of understand your motivations through sort of simulating it myself.
So it's not something that I can prove that's necessary, but it seems very likely.
Okay, you've written the op-ed in the New York Times titled,
We Shouldn't Be Scared by Super Intelligent AI and it criticized a little bit Stuart Russell and Nick Bostrom.
Can you try to summarize that article's key ideas?
So it was spurred by earlier New York Times op-ed by Stuart Russell,
which was summarizing his book called Human Compatible.
And the article was saying, you know, if we have super intelligent AI,
we need to have its values aligned with our values and it has to learn about what we really want.
And he gave this example, what if we have a super intelligent AI
and we give it the problem of solving climate change
and it decides that the best way to lower the carbon in the atmosphere is to kill all the humans.
Okay, so to me, that just made no sense at all because a super intelligent AI,
first of all, trying to figure out what super intelligence means.
And it seems that something that super intelligent can't just be intelligent along this one dimension of,
okay, I'm going to figure out all the steps, the best optimal path to solving climate change
and not be intelligent enough to figure out that humans don't want to be killed,
that you could get to one without having the other.
And, you know, Bostrom in his book talks about the orthogonality hypothesis
where he says he thinks that a systems, I can't remember exactly what it is,
but like a systems goals and its values don't have to be aligned.
There's some orthogonality there, which didn't make any sense to me.
So you're saying in any system that's sufficiently not even super intelligent,
but as it approves greater and greater intelligence, there's a holistic nature that will sort of
attention that will naturally emerge that prevents it from sort of any one dimension running away.
Yeah, exactly.
So, you know, Bostrom had this example of the super intelligent AI that turns the world into paper clips
because its job is to make paper clips or something.
And that just as a thought experiment didn't make any sense to me.
Well, as a thought experiment, there's a thing that could possibly be realized.
Either.
So I think that, you know, what my op-ed was trying to do was say that intelligence is more complex
than these people are presenting it, that it's not like it's not so separable.
The rationality, the values, the emotions, all of that.
That it's the view that you could separate all these dimensions and build the machine that has one of these dimensions
and it's super intelligent in one dimension, but it doesn't have any of the other dimensions.
That's what I was trying to criticize that I don't believe that.
So can I read a few sentences from Yoshio Benjo, who is always super eloquent.
So he writes, I have the same impression as Melanie that our cognitive biases are linked with our ability to learn to solve many problems.
They may also be a limiting factor for AI.
However, this is a May, in quotes.
Things may also turn out differently and there's a lot of uncertainty about the capabilities of future machines.
But more importantly for me, the value alignment problem is a problem well before we reach some hypothetical superintelligence.
It is already posing a problem in the form of super powerful companies whose objective function may not be sufficiently aligned
with humanity's general well-being, creating all kinds of harmful side effects.
So he goes on to argue that the orthogonality and those kinds of things, the concerns of just aligning values with the capabilities of the system
is something that might come long before we reach anything like superintelligence.
So your criticism is kind of really nice to saying this idea of superintelligence systems seem to be dismissing fundamental parts of what intelligence would take.
And then Yoshio kind of says, yes, but if we look at systems that are much less intelligent, there might be these same kinds of problems that emerge.
Sure, but I guess the example that he gives there of these corporations, that's people, right?
Those are people's values.
I mean, we're talking about people, the corporations, their values are the values of the people who run those corporations.
But the idea is the algorithm, that's right.
So the fundamental element of what does the bad thing as a human being.
But the algorithm kind of controls the behavior of this mass of human beings.
Which algorithm?
For a company, that's the, for example, if it's an advertisement-driven company that recommends certain things and encourages engagement.
So it gets money by encouraging engagement.
And therefore, the company more and more, it's like the cycle that builds an algorithm that enforces more engagement and may perhaps more division in the culture and so on and so on.
I guess the question here is sort of, who has the agency?
So you might say, for instance, we don't want our algorithms to be racist.
And facial recognition, some people have criticized some facial recognition systems as being racist because they're not as good on darker skin and lighter skin.
But the agency there, the actual facial recognition algorithm isn't what has the agency.
It's not the racist thing, right?
It's the, I don't know, the combination of the training data, the cameras being used, whatever.
But my understanding of, and I say, I agree with Benjio there that he, you know, I think there are these value issues with our use of algorithms.
But my understanding of what Russell's argument was is more that the machine itself has the agency now.
It's the thing that's making the decisions and it's the thing that has what we would call values.
Yes.
So whether that's just a matter of degree, you know, it's hard to say, right?
But I would say that's sort of qualitatively different than a face recognition neural network.
And to broadly linger on that point, if you look at Elon Musk, or Stuart Russell, or Bostrom, people who are worried about existential risks of AI, however far into the future.
The argument goes is it eventually happens.
We don't know how far, but it eventually happens.
Do you share any of those concerns?
And what kind of concerns in general do you have about AI that approach anything like existential threat to humanity?
So I would say, yes, it's possible.
But I think there's a lot more closer in existential threats to humanity.
Because you said like 100 years for your time.
More than 100 years.
More than 100 years.
And so that means.
Maybe even more than 500 years.
I don't know.
So the existential threats are so far out that the future is, I mean, there'll be a million different technologies that we can't even predict now that will fundamentally change the nature of our behavior, reality, society, and so on before then.
I think so.
I think so.
And we have so many other pressing existential threats going on.
Nuclear weapons even.
Nuclear weapons, climate problems, poverty, possible pandemics.
You can go on and on.
And I think worrying about existential threat from AI is not the best priority for what we should be worried about.
That's kind of my view because we're so far away.
But I'm not necessarily criticizing Russell or Bostrom or whoever for worrying about that.
And I think some people should be worried about it.
It's certainly fine.
But I was more sort of getting at their view of what intelligence is.
So I was more focusing on like their view of super intelligence than just the fact of them worrying.
And the title of the article was written by the New York Times editors.
I wouldn't have called it that.
We shouldn't be scared by super intelligence.
No.
If you wrote it, it'd be like we should redefine what you mean by super intelligence.
I actually said something like super intelligence is not a sort of coherent idea.
But that's not like something New York Times would put in.
And the follow-up argument that Yoshio makes also not argument but a statement.
And I've heard him say it before and I think I agree.
He kind of has a very friendly way of phrasing it.
It's good for a lot of people to believe different things.
He's such a nice guy.
Yeah.
But it's also practically speaking like we shouldn't be like while your article stands like Stuart Russell does amazing work.
You do amazing work.
And even when you disagree about the definition of super intelligence or the usefulness of even the term,
it's still useful to have people that like use that term, right?
And then argue.
Sure.
I absolutely agree with Benjo there.
And I think it's great that New York Times will publish all this stuff.
That's right.
It's an exciting time to be here.
What do you think is a good test of intelligence?
Is natural language ultimately a test that you find the most compelling like the original or the higher levels of the Turing test?
Yeah.
I still think the original idea of the Turing test is a good test for intelligence.
I mean, I can't think of anything better.
You know, the Turing test, the way that it's been carried out so far has been very impoverished, if you will.
But I think a real Turing test that really goes into depth.
Like the one that I mentioned, I talked about in the book, I talked about Ray Kurzweil and Mitchell Kapoor have this bet, right?
That in 2029, I think is the date there, the machine will pass the Turing test and they have a very specific like how many hours expert judges and all of that.
And you know, Kurzweil says yes, Kapoor says no.
We can, we only have like nine more years to go to see.
But I, you know, if something, a machine could pass that, I would be willing to call it intelligent.
Of course, nobody will.
They will say that's just a language model, right?
If it does.
So you would be comfortable, so language, a long conversation that's, well, yeah, I mean, you're right, because I think probably to carry out that long conversation, you would literally need to have deep common sense understanding of the world.
I think so.
I think so.
And the conversation is enough to reveal that.
I think so.
So another super fun topic of complexity that you have worked on, written about.
Let me ask the basic question.
What is complexity?
So complexity is another one of those terms, like intelligence, perhaps overused.
But my book about complexity was about this wide area of complex systems, studying different systems in nature, in technology, in society, in which you have emergence, kind of like I was talking about with intelligence.
You know, we have the brain, which has billions of neurons.
And each neuron individually could be said to be not very complex compared to the system as a whole.
But the system, the interactions of those neurons and the dynamics creates these phenomena that we call intelligence or consciousness, you know, that are, we consider to be very complex.
So the field of complexity is trying to find general principles that underlie all these systems that have these kinds of emergent properties.
And the emergence occurs from like underlying the complex system is usually simple fundamental interactions.
Yes.
And the emergence happens when there's just a lot of these things interacting.
Yes.
Sort of what, and then most of science to date, can you talk about what is reductionism?
Well, reductionism is when you try and take a system and divide it up into its elements, whether those be cells or atoms or subatomic particles, whatever your field is.
And then try and understand those elements and then try and build up an understanding of the whole system by looking at sort of the sum of all the elements.
So what's your sense, whether we're talking about intelligence or these kinds of interesting complex systems, is it possible to understand them in a reductionist way?
Which is probably the approach of most of science today, right?
I don't think it's always possible to understand the things we want to understand the most.
So I don't think it's possible to look at single neurons and understand what we call intelligence, you know, to look at sort of summing up.
So sort of the summing up is the issue here that we're, you know, the one example is that the human genome, right?
So there was a lot of work on excitement about sequencing the human genome because the idea would be that we'd be able to find genes that underlies diseases.
But it turns out that, and it was a very reductionist idea.
We'd figure out what all the parts are, and then we would be able to figure out which parts cause which things.
But it turns out that the parts don't cause the things that we're interested in.
It's like the interactions, it's the networks of these parts.
And so that kind of reductionist approach didn't yield the explanation that we wanted.
What do you use the most beautiful complex system that you've encountered?
The most beautiful.
That you've been captivated by.
Is it sort of, I mean, for me, is the simplest to be cellular automata?
Oh, yeah.
So I was very captivated by cellular automata and worked on cellular automata for several years.
Do you find it amazing or is it surprising that such simple systems, such simple rules and cellular automata can create sort of seemingly unlimited complexity?
Yeah, that was very surprising to me.
How do you make sense of it?
How does that make you feel?
Is it just ultimately humbling or is there a hope to somehow leverage this into a deeper understanding and even able to engineer things like intelligence?
It's definitely humbling how humbling in that, also kind of awe-inspiring that it's that awe-inspiring part of mathematics that these incredibly simple rules can produce this very beautiful, complex, hard to understand behavior.
And it's mysterious and surprising still.
But exciting because it does give you kind of the hope that you might be able to engineer complexity just from simple rules from the beginning.
Can you briefly say what is the Santa Fe Institute, its history, its culture, its ideas, its future?
So I've never, as I mentioned to you, I've never been, but it's always been this, in my mind, this mystical place where brilliant people study the edge of chaos.
Yeah, exactly.
So the Santa Fe Institute was started in 1984 and it was created by a group of scientists, a lot of them from Los Alamos National Lab, which is about a 40-minute drive from the Santa Fe Institute.
They were mostly physicists and chemists, but they were frustrated in their field because they felt that their field wasn't approaching kind of big interdisciplinary questions like the kinds we've been talking about.
And they wanted to have a place where people from different disciplines could work on these big questions without sort of being siloed into physics, chemistry, biology, whatever.
So they started this institute, and this was people like George Cowan, who was a chemist in the Manhattan Project, and Nicholas Metropolis, who, a mathematician, physicist, Marie Gilman, physicist, and his own system, really big names here,
Ken Arrow, a Nobel Prize-winning economist, and they started having these workshops.
And this whole enterprise kind of grew into this research institute that itself has been kind of on the edge of chaos its whole life because it doesn't have a significant endowment.
And it's just been kind of living on whatever funding it can raise through donations and grants and however it can, you know, business associates and so on.
But it's a great place.
It's a really fun place to go think about ideas that you wouldn't normally encounter.
I saw Sean Carroll, so physicists.
Yeah, he's on the external faculty.
And you mentioned that there's, so there's some external faculty and there's people that are.
A very small group of resident faculty, maybe about 10 who are there on five-year terms that can sometimes get renewed.
And then they have some postdocs and then they have this much larger on the order of 100 external faculty or people like me who come and visit for various periods of time.
So what do you think is the future of the Santa Fe Institute?
Like what, and if people are interested, like what's there in terms of the public interaction or students or so on, that could be a possible interaction with the Santa Fe Institute or its ideas?
Yeah, so there's a few different things they do.
They have a complex system summer school for graduate students and postdocs and sometimes faculty attend too.
And that's a four-week very intensive residential program where you go and you listen to lectures and you do projects and people really like that.
I mean, it's a lot of fun.
They also have some specialty summer schools.
There's one on computational social science.
There's one on climate and sustainability, I think it's called.
There's a few.
And then they have short courses where just a few days on different topics.
They also have an online education platform that offers a lot of different courses and tutorials from SFI faculty,
including an introduction to complexity course that I taught.
Awesome.
And there's a bunch of talks too on online.
There's guest speakers and so on.
They host a lot of different things.
Yeah, they have sort of technical seminars and colloquia and they have a community lecture series like public lectures and they put everything on their YouTube channel so you can see it all.
Watch it.
Douglas Hofstadter, author of Ghetto Escherbach was your PhD advisor.
He mentioned a couple of times and collaborator.
Do you have any favorite lessons or memories from your time working with him that continues to this day?
Yes, but just even looking back throughout your time working with him.
So one of the things he taught me was that when you're looking at a complex problem to idealize it as much as possible to try and figure out what is the essence of this problem.
And this is how the copycat program came into being was by taking analogy making and saying how can we make this as idealized as possible but still retain really the important things we want to study.
And that's really kept, you know, been a core theme of my research, I think.
And I continue to try and do that and it's really very much kind of physics inspired.
Hofstadter was a PhD in physics that was his background.
Like first principles kind of thinking like you're reduced to the most fundamental aspect of the problem so that you can focus on solving that fundamental aspect.
Yeah, and in AI, you know, that was people used to work in these micro worlds, right?
Like the blocks world was very early important area in AI.
And then that got criticized because they said, oh, you know, you can't scale that to the real world.
And so people started working on much like more real world like problems.
But now there's been kind of a return even to the blocks world itself, you know, we've seen a lot of people who are trying to work on more of these very idealized problems or things like natural language and common sense.
So that's an interesting evolution of those ideas.
So that perhaps the blocks world represents the fundamental challenges of the problem of intelligence more than people realize.
It might, yeah.
Is there sort of when you look back at your body of work and your life you've worked in so many different fields, is there something that you're just really proud of in terms of ideas that you've gotten a chance to explore, create yourself?
So I am really proud of my work on the copycat project. I think it's really different from what almost everyone has done in AI.
I think there's a lot of ideas there to be explored.
And I guess one of the happiest days of my life, you know, aside from like the births of my children was the birth of copycat.
What it actually started to be able to make really interesting analogies.
And I remember that very clearly.
That was very exciting time.
Well, you kind of gave life.
Yes.
Artificial system.
That's right.
What in terms of what people can interact.
I saw there's like a, I think it's called meta copycat.
Meta cat.
Meta cat.
And there's a Python 3 implementation. If people actually want to play around with it and actually get into it and study it and maybe integrate into whether it's with deep learning or any of the kind of work they're doing.
What would you suggest they do to learn more about it and to take it forward in different kinds of directions?
Yeah.
So that there's a Douglas Hofstadter's book called fluid concepts and creative analogies talks in great detail about copycat.
I have a book called analogy making as perception, which is a version of my PhD thesis on it.
There's also code that's available that you can get it to run.
I have some links on my web page to where people can get the code for it.
And I think that that would really be the best way to get into it.
And play with it.
Well, Melanie was the honor talking to you.
I really enjoyed it.
Thank you so much for your time today.
Thanks.
It's been really great.
Thanks for listening to this conversation with Melanie Mitchell.
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And now, let me leave you with some words of wisdom from Douglas Hofstadter and Melanie Mitchell.
Without concepts, there can be no thought.
And without analogies, there can be no concepts.
And Melanie adds,
How to form and fluidly use concepts is the most important open problem in AI.
Thank you for listening and hope to see you next time.