Speaker 1: Is AI an intelligent agent or is there a totally
Speaker 1: different way that we should be thinking about this. Perhaps
Speaker 1: it's more like a piece of cultural technology. What in
Speaker 1: the world is cultural technology? And how would rethinking this
Speaker 1: change the way we approach what to do next? And
Speaker 1: what does any of this have to do with the
Speaker 1: myth of the golom or Socrates, or the printing press
Speaker 1: or Martin Luther or the story of stone soup. Welcome
Speaker 1: to Intercosmos with me David Eagleman. I'm a neuroscientist and
Speaker 1: an author at Stanford and in these episodes we examine
Speaker 1: brains and the world around us to understand who we
Speaker 1: are and where we're going. So let's start with the
Speaker 1: appreciation that we are smack in the middle of the
Speaker 1: most dramatic technological shift in human history. Every few weeks,
Speaker 1: a new AI system is released that can answer questions
Speaker 1: of increasing complexity. As we've all seen, it can write
Speaker 1: beautiful prose. It can punch out incredibly good code for software.
Speaker 1: It composes music, It mimics voices. It produces images so
Speaker 1: realistic that we've long ago lost the ability to tell
Speaker 1: if a photo is real or not.
Speaker 2: And this increasingly applies.
Speaker 1: To video as well, So with every leap forward, the
Speaker 1: same questions become louder. Is this an intelligent agent? Is
Speaker 1: it conscious? Will it one day surpass us? And if so,
Speaker 1: what happens to us? Today's episode is about how to
Speaker 1: think about this from an angle that.
Speaker 2: Will probably surprise you.
Speaker 1: So as it stands now, in the public conversation about AI,
Speaker 1: we really have just one metaphor, which is that AI
Speaker 1: is an intelligent agent and of increasing intelligence. We all
Speaker 1: talk about these systems as digital minds that can reason
Speaker 1: and plan and act and perhaps even at some point desire.
Speaker 1: This narrative of an AI with its own mind has
Speaker 1: always been with us in science fiction, of course, but
Speaker 1: today we hear it constantly in policy conversations and in
Speaker 1: media headlines. And whether the tone is optimistic or anxious,
Speaker 1: the underlying premise is the same that these are minds
Speaker 1: in the making, that we are witnessing the birth of
Speaker 1: a new kind of intelligence. But what if that metaphor
Speaker 1: is misleading so much so that it's sending our conversations,
Speaker 1: our policy, our research priorities off course. So today's episode
Speaker 1: is about reframing what large AI models really are and
Speaker 1: what they aren't My guest today is Alison Gopnik. She's
Speaker 1: a professor of psychology at Berkeley, very well known in
Speaker 1: the areas of cognitive and language development. She studies infants
Speaker 1: and young children to understand how learning takes place. And
Speaker 1: she was just by the way, elected to the National
Speaker 1: Academy of Sciences. But I'm talking with her today about
Speaker 1: a new paper she co authored in the journal Science
Speaker 1: about AI with colleagues Henry Ferrell, Cosmishalsi, and James Evans.
Speaker 2: The paper argues that.
Speaker 1: We should stop thinking of large models as intelligent agents
Speaker 1: and instead see them as a new kind of cultural
Speaker 1: and social technology.
Speaker 2: Now what does that mean.
Speaker 1: Well, I'll give you a quick preview and then we'll
Speaker 1: jump into the interview. All throughout history, humans have built
Speaker 1: tools to organize information and transmit it. Think of spoken
Speaker 1: language and then writing, and then printing, libraries, television, the Internet.
Speaker 1: Each of these systems reshaped human culture, not because they
Speaker 1: were intelligent in themselves, but because they allowed information to
Speaker 1: be shared and transformed and coordinated in new ways. Just
Speaker 1: think of how the printing press amplified voices, or how
Speaker 1: something like markets distill the messy complexity of economies into
Speaker 1: a single price.
Speaker 2: Signal, how much does this thing cost?
Speaker 1: Or how bureaucracies take the chaos of signals and sort
Speaker 1: it into categories. These are not minds, but they are
Speaker 1: powerful technologies of culture, technologies that change how we all
Speaker 1: think and how we.
Speaker 2: Act and how we live.
Speaker 1: So the argument we'll hear today is that large AI
Speaker 1: models are best understood in this lineage. They don't think,
Speaker 1: but they process the vast collective output of human thought.
Speaker 1: They are trained on millions of texts and images and voices,
Speaker 1: everything from Shakespeare to Reddit threads to government paperwork, and
Speaker 1: they summarize and reorganize and remix that cultural data. And
Speaker 1: they also surface patterns in the data that maybe hadn't
Speaker 1: been seen before. And so when you're interacting with such
Speaker 1: a piece of technology, let's say, asking it to write
Speaker 1: you a poem or explain a concept, you're not talking
Speaker 1: to a mind. You're participating with a kind of cultural
Speaker 1: compression and recombination machine. So see what you think of
Speaker 1: the perspective that you hear today, because it can change
Speaker 1: our concerns and our eventual legislative approaches if we stop
Speaker 1: assuming that these are minds and instead treat them as
Speaker 1: cultural infrastructures like search engines or even democratic institutions, then
Speaker 1: we can start asking the questions.
Speaker 2: One quick thing before we jump into the interview.
Speaker 1: You've heard of large language models llms, and more recently
Speaker 1: large multimodal models that are trained on words and images
Speaker 1: and increasingly other data as well. So nowadays we just
Speaker 1: refer to these as large models. So here's my interview
Speaker 1: with Alison Gothnik. So, Alison, before we get started talking
Speaker 1: about AI, you have built a very wonderful career studying
Speaker 1: scientists who are unusually small and spend most of their
Speaker 1: time lying down, So tell us about that.
Speaker 3: So what I've always been most interested in is how
Speaker 3: is it that people can figure out the world around them?
Speaker 3: How is it that human beings with just a bunch
Speaker 3: of photons hitting our eyes and little disturbances of air
Speaker 3: at our ears, nevertheless, we know about a world of
Speaker 3: people and objects and ultimately quarks and distant planets. How
Speaker 3: could we ever do that? How could we ever learn
Speaker 3: so much from so little? And of course the people
Speaker 3: who are doing that more than anyone else are little children.
Speaker 3: So for the past forty years, what I've been doing
Speaker 3: is trying to figure out how is it that even
Speaker 3: little children can learn so much so quickly from such
Speaker 3: little information. And one of the questions is what kinds
Speaker 3: of computations, what's going on in their brains? What are
Speaker 3: their brains and minds doing that lets them solve these
Speaker 3: really deep problems so quickly and so effectively. And that's
Speaker 3: been the central idea in my career. And it's turned
Speaker 3: out that by looking at kids empirically, by actually studying
Speaker 3: them as scientists, we've discovered that they both know more
Speaker 3: and learn more than we ever would have thought before.
Speaker 3: They're the best learners.
Speaker 4: That we know of in the universe.
Speaker 2: Amazing.
Speaker 1: So we're in this quite remarkable time where for both
Speaker 1: of us we've been doing, you know, the same research
Speaker 1: that we've been doing for many decades, and one might
Speaker 1: have thought five years ago, okay, we'll probably be doing
Speaker 1: that in twenty twenty five, But suddenly the world has
Speaker 1: really changed around us because of A and so you
Speaker 1: and I both are spending a lot of our time
Speaker 1: writing about that and thinking about how to position AI,
Speaker 1: how to understand what it does and does not mean.
Speaker 1: So a lot of people, of course, are concerned about
Speaker 1: super intelligence and the alignment problem, and so on. But
Speaker 1: you and your colleagues have a quite different take that
Speaker 1: you just wrote up in the journal Science in March,
Speaker 1: and I thought it was a really lovely paper. So
Speaker 1: that's what I want to ask you about. So you
Speaker 1: are talking about the right way to look at large
Speaker 1: models is as a social and cultural technology.
Speaker 2: So let's unpack that, right.
Speaker 3: So, as I said, you know, my career has been
Speaker 3: about how could we learn as much as we do?
Speaker 3: And how do children learn as much as they do?
Speaker 3: And part of that has always been if we wanted
Speaker 3: to design a computer or design an artificial system that
Speaker 3: could learn the way children do, what would that system
Speaker 3: look like, what could we put in, what kinds of
Speaker 3: things would it have to do? So for twenty years
Speaker 3: I've been laborating with computer scientists about what would that
Speaker 3: kind of artificial system look like? But as you say,
Speaker 3: even though this has been a long project, in the
Speaker 3: last five years or so, these advances in AI have
Speaker 3: really made us think about that in a different way.
Speaker 3: And one of the interesting things is the big advances
Speaker 3: have been in machine learning. They've actually been in designing
Speaker 3: systems that don't just know things, but can learn things.
Speaker 3: And as I say, children are the best learners we
Speaker 3: know of in the universe. So there's been a really
Speaker 3: interesting development, which is a lot of the people in
Speaker 3: AI have been turning to developmental psychologists like me to say, look,
Speaker 3: could we get some clues from how children are learning
Speaker 3: to design systems that could learn that could learn in
Speaker 3: the same way. Now, the interesting thing is that what
Speaker 3: actually has happened in AI, specifically in the last five
Speaker 3: years or so are these large models, these large language
Speaker 3: models and more recently large language and vision models, and
Speaker 3: they are the things that have really revolutionized our everyday
Speaker 3: interactions with AI. It's important to say those are really
Speaker 3: really different from children, and in fact, they're different from humans.
Speaker 3: They're doing something that I think is really really different
Speaker 3: from human intelligence. And it's very natural for people to think, oh, okay,
Speaker 3: look I talked to chatchept and I get an answer back,
Speaker 3: it must have the same kind of intelligence that my
Speaker 3: friend does or my child does. And it turns out
Speaker 3: that that's not true. Those systems are really really different.
Speaker 1: So before we go on, let's unpack that a little
Speaker 1: bit in what ways are they different?
Speaker 3: So a very common kind of model for how a
Speaker 3: AI works is to think of it as if something
Speaker 3: like chatcheept is an agent, an intelligent agent in the world,
Speaker 3: like a person or even an animal that you know about.
Speaker 3: But that's actually, I think, an illusion. That's what we've argued.
Speaker 3: A better way to think about it is that as
Speaker 3: long as we've been human, we've learned from other people,
Speaker 3: and we've had great technological advances that have helped us
Speaker 3: to learn more effectively for more and more people. So
Speaker 3: if you think about language itself, or writing or print,
Speaker 3: those are all examples of technological changes that made us
Speaker 3: able to get more information from others. And what the
Speaker 3: large models do is not go out into the world
Speaker 3: and learn and think the way that babies do. What
Speaker 3: they do is summarize information that human beings have actually
Speaker 3: already discovered. So what they do is take all the
Speaker 3: information and knowledge that human beings have put out on
Speaker 3: the web essentially, and then summarize that in a way
Speaker 3: that lets other people access it more efficiently. So it's
Speaker 3: much more the technological development is much more like something
Speaker 3: like writing that lets you find out what other people
Speaker 3: are thinking than it is creating a system that could
Speaker 3: learn and think itself.
Speaker 2: Yeah.
Speaker 1: In a previous episode of Intercosmos. They did a calculation
Speaker 1: showing that the amount of information that one of these
Speaker 1: dllms consumes would take you one thousand lifetimes.
Speaker 2: For you to read.
Speaker 1: And so it's consumed more than you could ever imagine.
Speaker 1: And what it's doing fundamentally is when you ask it
Speaker 1: a question, it's giving you an echo of the human
Speaker 1: intelligence that's already in there. So I call this the
Speaker 1: echo intelligence solution, where we feel like, wow, that thing's
Speaker 1: really smart. But it's not smart. It's not smart in
Speaker 1: the same way that a human is. It's taking advantage
Speaker 1: of all the things that are already out there.
Speaker 3: So here's here's a way I like to I like
Speaker 3: to convey this. I think you know, storytelling, as you know,
Speaker 3: is really important. So here's two stories you could tell
Speaker 3: about how current ani work. So one story is sort
Speaker 3: of the story of the Gollum, right, the Rabbi of
Speaker 3: Progue and the Gollum. You create this artificial system and
Speaker 3: it's magical and you put special magic in it, and
Speaker 3: then it turns into something that's almost alive.
Speaker 1: And it's interesting for anyone who doesn't know about the
Speaker 1: story of the Gallum. That was a figure made of clay.
Speaker 1: He was brought to life and defended the community.
Speaker 3: But then, well, it turns out that these stories about
Speaker 3: what would happen if you had something that wasn't human,
Speaker 3: that was artificial that you brought to life are really
Speaker 3: ancient there, way before even the Industrial Revolution. And I
Speaker 3: can tell you right now it never ends well, the
Speaker 3: story always variably. The end of the story is that
Speaker 3: some terrible thing happens and the column goes mad and
Speaker 3: causes trouble and chaos, and.
Speaker 1: That inspired Frankenstein some hundreds of years later. They don't
Speaker 1: have the same character.
Speaker 3: So there's this very basic human fear about what would
Speaker 3: it be like if there was something that wasn't actually
Speaker 3: living that you treated as if it was living, as
Speaker 3: you treated as.
Speaker 4: If it was an agent.
Speaker 3: And I think that basic picture, that's the sci fi picture,
Speaker 3: that's the picture that a lot of people, including people
Speaker 3: in the AI world themselves, have about what's happened in Ai.
Speaker 3: Here's a really different story, also, a different ancient story.
Speaker 3: This is the story of stone Soup. So what's the
Speaker 3: story of stone soup. The story of stone Soup is
Speaker 3: there's visitors who come to a village and they say,
Speaker 3: we'd like some food and the villagers say, no, we
Speaker 3: don't have any extra food. And they say, it's okay,
Speaker 3: we're going to make stone soup. And they take out
Speaker 3: a big pot. They put a couple of stones in it,
Speaker 3: they put some water in, they start to boil it
Speaker 3: up and they say, this will be delicious. We're going
Speaker 3: to make stone soup just with these stones, and the
Speaker 3: villagers say really. They say, yeah, it would be even
Speaker 3: better if we had an onion and a carrot in it,
Speaker 3: but if we don't, we don't.
Speaker 4: And the villager says.
Speaker 3: I think I have an onion and a carrot somewhere,
Speaker 3: and they go and put it in and then they say,
Speaker 3: you know, when we made this for the rich people,
Speaker 3: we put barley and buttermilk in it, which makes it
Speaker 3: even better. But it's okay, it'll still be good stone soup.
Speaker 3: And another villager goes and gets the barley and buttermilk,
Speaker 3: and you can imagine.
Speaker 4: How this goes.
Speaker 3: And they say, the king said that we should put
Speaker 3: a chicken in it, which would make it really royal,
Speaker 3: but we don't have any chicken. So another villager goes
Speaker 3: and gets the chicken from the back, and by the
Speaker 3: time they're done of course, they have this really wonderful
Speaker 3: soup with all the contributions from all the villagers, and
Speaker 3: they go to eat it, and the villagers say, this
Speaker 3: is amazing. There's this wonderful soup and it was just
Speaker 3: made from stones. Okay, here's the modern version of this.
Speaker 3: There's a bunch of tech guys and they go to
Speaker 3: the village of computer users and they say, we're going
Speaker 3: to make artificial general intelligence just from gradient descent and
Speaker 3: transformers and a few algorithms. And the computer used to say,
Speaker 3: that sounds great. We're gonna have artificial general intelligence. And
Speaker 3: they say, yeah, but it would be better if we
Speaker 3: had more data. What we need for this, as you
Speaker 3: just said, David, is lots and lots of data.
Speaker 4: Could you guys put all of.
Speaker 3: Your texts and pictures on the internet for us and
Speaker 3: then let us use them to.
Speaker 4: Train our systems, And the computer user to say, oh,
Speaker 4: that sounds good.
Speaker 3: We'll just keep putting more of our pictures and our
Speaker 3: writings and our books on the internet, and I guess
Speaker 3: you can just use them all for free, and then
Speaker 3: the then the tech person, oh, this is really good.
Speaker 3: This is getting to be more intelligent. But you know,
Speaker 3: it still says really stupid things. A lot of the
Speaker 3: time it says weird things. So what we could do
Speaker 3: is reinforcement learning from human feedback, which is actually a
Speaker 3: really important part of these systems. What we'll do is
Speaker 3: we'll give them to humans and then people can say
Speaker 3: whether what they're saying is good or not, and then
Speaker 3: we'll use that for the training.
Speaker 4: The computer used to say, oh, okay, we're happy to
Speaker 4: do that.
Speaker 3: We'll actually go out and say whether this is good
Speaker 3: or not. There's a whole and this.
Speaker 4: Is literally true.
Speaker 3: There are whole villages in Kenya that we'll do this
Speaker 3: for very small amounts of money.
Speaker 4: And the tech pro said, oh.
Speaker 3: Look see it's even smarter, but it's still saying really
Speaker 3: stupid things sometimes. How about if you did prompt engineering.
Speaker 3: So think really hard about exactly how to ask it
Speaker 3: the right questions so that you can get the right answers,
Speaker 3: because otherwise it's going to say stupid things. And the
Speaker 3: users say, oh, okay, we'll do that. We'll sit down
Speaker 3: and we'll figure out how to do prompt engineering. At
Speaker 3: the end of this process, the tech bro say, seeing,
Speaker 3: we told you we made artificial general intelligence and it
Speaker 3: was just from a few algorithms and the computer you
Speaker 3: to say that's amazing, that's amazing, We're going to have
Speaker 3: artificial intelligence, and it's just you, brilliant tech guys who
Speaker 3: invented it. So, of course, the point of this is
Speaker 3: that it's a sort of debunking story, but it's also
Speaker 3: in both versions, a positive story, because the point is
Speaker 3: when you have a combination of lots and lots of contributions,
Speaker 3: of lots and lots of intelligent people, lots of humans
Speaker 3: who we know are intelligent, both in terms of the
Speaker 3: data they provide and in terms of things like reinforcement
Speaker 3: learning and from human feedback and prompt engineering, you've got
Speaker 3: something that that's bigger than any individual human could have.
Speaker 3: But it's not that what you've got is a gall
Speaker 3: it's not that what you've got is an agent that's
Speaker 3: gone out and been intelligent itself. It's really just a
Speaker 3: system for putting together the thoughts of other agents.
Speaker 1: So the lesson that surfaces here is that although we
Speaker 1: humans love to anthromorphize things and love to make inanimate
Speaker 1: objects into agents in our minds, it's probably not the
Speaker 1: right way to think about these language models. Or let
Speaker 1: me say, these large models, language, vision, multimodal. So what
Speaker 1: is the right way to think about it? So let's
Speaker 1: really unpack this issue about what a social or cultural
Speaker 1: technology is.
Speaker 3: Yeah, so, ever since we've been human, we've made progress
Speaker 3: by learning from other humans, and a number of people
Speaker 3: like Joseph Henrick, for example, and Brob Boyd have argued
Speaker 3: that that's kind of our human our great human gift,
Speaker 3: that's really our secret sauce is not so much that
Speaker 3: we can individually learn things that other creatures can't, although
Speaker 3: I think that's part of it, but that we can
Speaker 3: take advantage of all the things that other humans have
Speaker 3: done over many, many, many generations. I like to think
Speaker 3: of this in terms of the postmenopausal grandmother. I think,
Speaker 3: you know, one of the distinctive human things is that
Speaker 3: we have these postmenopausal grandmothers, and a lot of what
Speaker 3: they do is tell us about the things that they've
Speaker 3: learned in their long, wise lives. And by taking advantage
Speaker 3: of what granny says, you can make progress, even if
Speaker 3: what you're doing is now finding new things that you
Speaker 3: will tell your grandchildren. And that capacity is really the
Speaker 3: capacity that makes us special. And we've had special technologies
Speaker 3: ever since we evolved, that tuned up that capacity, that
Speaker 3: made it more powerful. So language itself, of course, which
Speaker 3: is one of our distinctive things about humans, lets us
Speaker 3: learn from others. But even more if you think about
Speaker 3: something like the invention of writing that enabled us to
Speaker 3: learn from you know, not just our own granny, but
Speaker 3: granny's who were far away in space and in time,
Speaker 3: and it's fascinating. Socrates famously has a whole section about
Speaker 3: why he thinks writing is a terrible idea because exactly
Speaker 3: because he thinks, people will read something in a book
Speaker 3: and they'll think it's actually a person. They'll think that
Speaker 3: it's a person who said this, and it's not. It's
Speaker 3: just something that's written in a book. You won't be
Speaker 3: able to have Socratic dialogues with something that's written in
Speaker 3: a book. It's not really a person, but because it's language,
Speaker 3: will treat it as if it's a person. So writing
Speaker 3: is a good example of something that even though the
Speaker 3: books aren't intelligent, the books in some sense don't know things.
Speaker 3: In another sense, we know things because of books a way.
Speaker 3: I think if this, sometimes they suppose someone asks you,
Speaker 3: who knows more me or the UC Berkeley library. Well,
Speaker 3: the library has much more knowledge in it. It's got
Speaker 3: vast amounts of knowledge that I could never actually have
Speaker 3: in my head. But it's not the sort of thing
Speaker 3: that knows. I'm the sort of person who knows and
Speaker 3: I know things because I can do things like consult
Speaker 3: the library. And then you have print, which is even
Speaker 3: more powerful and has even more powerful effects. You have
Speaker 3: video and film. You have pictures, which I think are
Speaker 3: a really important medium that we don't pay enough attention to.
Speaker 3: So when we talk about vision models, for example, they're
Speaker 3: not actually using vision. What they're using is all the
Speaker 3: pictures that we put on the Internet, and pictures are
Speaker 3: a really important source of communication to So to say
Speaker 3: that it's a cultural technology, to say it's one of
Speaker 3: these technologies that lets humans learn from other humans is
Speaker 3: not at all to dismiss it. Those cultural technologies are
Speaker 3: the things that have led, for better or for worse,
Speaker 3: to the world that we have now. But it's just
Speaker 3: a really different thing. It's what philosophers would call a
Speaker 3: category mistake to think that it's like an intelligent agent,
Speaker 3: which is not to say that at some point in
Speaker 3: the future AI might not develop intelligent agents, but that's
Speaker 3: not what the large models are doing, and that's a
Speaker 3: much much harder lift, something we're much further away from
Speaker 3: than this fantastic, interesting, powerful new cultural technology that we've invented.
Speaker 1: So when we're thinking about cultural technologies, those are all
Speaker 1: amazing examples that you gave about the invention of writing
Speaker 1: and the printing press and then the Internet and so on.
Speaker 1: In the paper that you wrote with your colleagues, you
Speaker 1: mentioned other things also, like markets and bureaucracies and representative democracies.
Speaker 1: Just give us a sense of how those are our
Speaker 1: cultural technologies as well.
Speaker 3: Yeah, so this paper, it was a wonderful collaboration between
Speaker 3: me and Henry Farrell, who's a really distinguished political scientist,
Speaker 3: James Evans, who's a fantastic sociologist, and Cosmo Shalizi, who's
Speaker 3: the statistician. So it was kind of like everybody in
Speaker 3: the social sciences. And what Henry and James have pointed
Speaker 3: out is that we have things like writing in print,
Speaker 3: but if you think about things like a market, this
Speaker 3: is an old observation in economics, what a market really
Speaker 3: does is to summarize information from lots and lots of
Speaker 3: individual people. Imagine that you're in a forager culture and
Speaker 3: you want to exchange you know, I have a two
Speaker 3: turnips and I want to exchange them for some beads. Right,
Speaker 3: I have to work that out for each individual group
Speaker 3: of people. And what a market does is let you
Speaker 3: do that literally on a planetary scale, lets you do
Speaker 3: it for billions of people. And the price is a
Speaker 3: kind of summary of here's all the desires and all
Speaker 3: the goals and all the preferences of all of these
Speaker 3: people just summarized in this one, you know number that
Speaker 3: you find when you look on an Amazon. Now, of
Speaker 3: course that's interesting because it didn't even rely on you know,
Speaker 3: markets start in the industrial age, but they don't rely
Speaker 3: on computations. They they're way before we have computers. They're
Speaker 3: way before we even have calculators. But the invention of
Speaker 3: markets was a kind of information processing invention that let
Speaker 3: us take individual desires and put them together. And democratic
Speaker 3: elections are like that too, where we have all of
Speaker 3: these people who have different preferences, and the democratic process
Speaker 3: lets us figure out a way of combining them all.
Speaker 3: And that's another thing that these large models can do,
Speaker 3: that can take lots of information from lots of different people,
Speaker 3: put it together in a single format. And again this
Speaker 3: is for good or for ill, and maybe we could
Speaker 3: talk about both of those sides in a bit.
Speaker 2: Great. Well, actually let's go there now.
Speaker 1: So all previous social and cultural technologies always come with
Speaker 1: good and bad.
Speaker 2: So what do you see.
Speaker 1: As far as large models go with our current moment
Speaker 1: of AI. What do you see as the potential good
Speaker 1: and bad?
Speaker 4: Yeah?
Speaker 3: So, one thing that I think is worth pointing out
Speaker 3: is that actually the big technological change was not the
Speaker 3: change in large models. It was the fact that around
Speaker 3: the year two thousand there was this remarkable thing happened
Speaker 3: that nobody really noticed or paid attention to, which is
Speaker 3: that all the previous media got turned into bits. So
Speaker 3: it's fascinating. Around two thousand, you get the first computerized movies,
Speaker 3: You get things like Toy Story and Pixar. You get PDFs.
Speaker 3: PDFs are taking print and turning them into bits. You
Speaker 3: get HDTV, so you get TV that's now digital, And suddenly,
Speaker 3: in a very short space of time, the only analog
Speaker 3: media are.
Speaker 4: In museums in kindergartens.
Speaker 3: You know, everything else is everything else is digital, which
Speaker 3: means that now not only do you have information, but
Speaker 3: it's infinitely reproducible, and it's instantaneously transmissible because it's in
Speaker 3: the format of bits. And once that happened, then it
Speaker 3: was just a matter of time before we found new
Speaker 3: ways of accessing and summarizing and organizing that information. And
Speaker 3: large models, I think are the are the result of that. Okay,
Speaker 3: so what do we know about these past changes in technologies?
Speaker 4: You go back to writing.
Speaker 3: As I mentioned, Socrates was very dubious about whether writing
Speaker 3: was going to be a good thing or not, because
Speaker 3: he pointed out that you don't have Socratic dialogues when
Speaker 3: you have books, when you have writing, and you don't
Speaker 3: memorize all of Homer when you have books, and he
Speaker 3: was right, we don't memorize all of Homer anymore. Well,
Speaker 3: we do tend to think that things that are written
Speaker 3: down are truer than they actually are.
Speaker 2: And by the way, in.
Speaker 1: The fifteenth century, once the printing press was invented, there
Speaker 1: were a lot of these same complaints that surfaced, where
Speaker 1: people said, look, you know, if you ask a kid
Speaker 1: a question, now they're just going to go to the
Speaker 1: shelf and pull it right off and there's the answer.
Speaker 2: They don't have to think about it.
Speaker 3: That's exactly right, and so there was misinformation. You know,
Speaker 3: when you can pass on information, one of the first
Speaker 3: things that happens is you can also pass on misinformation.
Speaker 3: An example that I really like to give is, so
Speaker 3: you were mentioning about the fifteenth century when printing starts.
Speaker 3: Something that I just found out recently that I think
Speaker 3: is really fascinating is, you know how we all have
Speaker 3: this mythology about Luther nailed the articles to the door,
Speaker 3: and that was, you know, the big defiance thing. Well,
Speaker 3: it turns out nailing things to the door was like
Speaker 3: having post it notes, Like everybody was always nailing things
Speaker 3: to the door. That was just like the way that
Speaker 3: you distributed it. What Luther did was print his ideas,
Speaker 3: and when they were printed, then they could be distributed
Speaker 3: to everybody, including like the common people. And that was
Speaker 3: the revolutionary thing. That was the disruptive That was the disruptive.
Speaker 1: Thing, analogous to let's say, off Hillary using radio in
Speaker 1: the thirties, reaching a much wider audience.
Speaker 3: But my favorite example is in the eighteenth century, the
Speaker 3: late eighteenth century, there were further technological changes in printing,
Speaker 3: which meant that it became extremely I mean Essentially, anybody
Speaker 3: could go and find a printing press and print a
Speaker 3: pamphlet and distributed.
Speaker 4: And there's a very good.
Speaker 3: Argument that this was responsible for the Enlightenment, that you know,
Speaker 3: something like it's not a coincidence that Ben Franklin was
Speaker 3: a printer. A lot of the source of the American
Speaker 3: Revolution was these pamphlets that were spreading new ideas from
Speaker 3: the Enlightenment, things like Tom Payne's common Sense, the idea
Speaker 3: that people could have a democratic state. Those were all
Speaker 3: things that were distributed through printing. So we all think
Speaker 3: that's great, like, that's fantastic, we could get things really quickly.
Speaker 3: But at the same time, the scholar Robert Darten pointed
Speaker 3: this out a long time ago. If you actually look
Speaker 3: and read all of the pamphlets that were produced in France,
Speaker 3: so they were happening in America, they were also happening
Speaker 3: in France.
Speaker 4: You will be amazed to hear this, David.
Speaker 3: But most of them were libel lies, misinformation, and a
Speaker 3: lot of soft core porn. That's the first thing when
Speaker 3: people have a new cultural technology, that's the first thing
Speaker 3: they use it for. And a lot of the French
Speaker 3: Revolution came because, for example, Marie Antoinette saying let them
Speaker 3: eat cake.
Speaker 4: That was a meme. That wasn't something that she said.
Speaker 3: That was something that came out of this underground of
Speaker 3: people just beating pamphlets. So you get the same benefits
Speaker 3: and drawbacks. You get information being distributed really quickly to
Speaker 3: many more people in a way that means that new
Speaker 3: ideas can spread. But it also means that misinformation and
Speaker 3: libel and other kinds of things you don't want can spread.
Speaker 3: And I think we see this happening with the current
Speaker 3: systems as well. So lms are representing the humans you know,
Speaker 3: who are putting You know that the soup just tastes
Speaker 3: like what all the people have put in the soup,
Speaker 3: And that means that if people are putting in things
Speaker 3: that are racist or sexist, or just wrong or misinformation
Speaker 3: or outrage, that's.
Speaker 4: What's going to show up from the llms.
Speaker 1: And of course that's no different than the Berkeley Library
Speaker 1: in the sense that if people are writing books and
Speaker 1: they understand something about planetary motion or dark energy or whatever,
Speaker 1: and they write books on it, that's that's all we
Speaker 1: have to draw from R two things.
Speaker 4: What is it that they can't do well? What they
Speaker 4: can't do is go out and find something new.
Speaker 3: And that's the great thing that human beings can do,
Speaker 3: and especially actually human children can do, is go out
Speaker 3: into the world say this is what everyone's told me
Speaker 3: is true, but you know what, I'm not sure it
Speaker 3: is true. Let me go and find out something new.
Speaker 3: That's what kids do, that's what teenagers do. That's what
Speaker 3: scientists do. Go out in the world, revise, change, think
Speaker 3: about things in new ways, find out something new about
Speaker 3: the world.
Speaker 4: And that's exactly the.
Speaker 3: Thing that ellms can't do. What elms can do is
Speaker 3: summarize what all the other humans have said. They can't
Speaker 3: go out and find something that's not just not just
Speaker 3: an extrapolation from what people have already done.
Speaker 1: So there's something really interesting about this, because what elms
Speaker 1: are great at doing, of course, is interpolating between different
Speaker 1: things that have been said, and sometimes those are lucune,
Speaker 1: those are holes that humans haven't explored in before for
Speaker 1: whatever reason. So what I and some of my colleagues
Speaker 1: has been doing for a while is, you know, pitching
Speaker 1: these really strange scientific questions at it and saying, you know,
Speaker 1: what's your hypothesis, and it comes up with something and
Speaker 1: we say, okay, you think you know. More broadly, just
Speaker 1: come up with something that could explain this, and you know,
Speaker 1: and you keep pushing it and it comes up with
Speaker 1: pretty creative things. And they're creative in the sense that
Speaker 1: they are remixes of what it's taken in before. And
Speaker 1: it can do something, you know, interpolating between points that
Speaker 1: are already known.
Speaker 2: Now I totally agree with you. Of course, what it
Speaker 2: can't do.
Speaker 1: Is think about something outside of the sphere of human knowledge,
Speaker 1: which is what let's say, you know Einstein does when
Speaker 1: he says, what if I'm writing on a photon and
Speaker 1: what would things look like?
Speaker 2: And so on.
Speaker 1: It comes up with a special theory of relativity that is,
Speaker 1: as you say, taking all the stuff that's coming forward
Speaker 1: and saying, hey, maybe that's not right and there's a.
Speaker 2: Completely different way to look at it.
Speaker 1: And of course with that, Record Buyers is the ability
Speaker 1: to not only come up with a new idea, but
Speaker 1: then simulate that idea to its conclusions and say, oh,
Speaker 1: you know, that explains things better than what we currently have.
Speaker 3: And a very important piece of that is it also
Speaker 3: involves going out and testing it, so you know, it
Speaker 3: wasn't just Einstein, it was people going out and actually,
Speaker 3: you know, measuring the eclipses. That meant that that theory
Speaker 3: was confirmed. So one of the things that even little
Speaker 3: kids do is test things, go out experiment. When kids
Speaker 3: do it, we call it getting into everything. But we've
Speaker 3: been recently doing a lot of work showing that even
Speaker 3: when little kids are getting into everything, what they're actually
Speaker 3: doing is trying to get data that they can use
Speaker 3: to change what they think, to revise what they think.
Speaker 3: And that's a very human kind of intelligence. The reason
Speaker 3: why the llms hallucinate is what people often call it.
Speaker 3: It's not that they're hallucinating, it's that they just don't
Speaker 3: They're not designed to know the difference between truth and falsehood.
Speaker 3: They're not their objective function, as they say. The thing
Speaker 3: they're trying to do is not to get the truth.
Speaker 3: It's not going out and doing an experiment. It's not
Speaker 3: changing their minds. It's trying to get the best summary
Speaker 3: of the things that they already have heard from they
Speaker 3: already have heard from other human beings.
Speaker 1: Now, just for clarification, everything that you and I are
Speaker 1: talking about right now is with current large models. But
Speaker 1: what a lot of people are talking about now is
Speaker 1: the third wave. The next wave of AI is probably
Speaker 1: going to be agents who are experimenting in the world,
Speaker 1: who are doing things in the world and gathering data
Speaker 1: that way, because clearly we've already done the common crawl,
Speaker 1: where these AI agents have crawled everything that has been
Speaker 1: written by humans and there's no more data to be had.
Speaker 2: So the next step.
Speaker 1: Is run experiments in the real world, try out hypotheses,
Speaker 1: and so that's going to change everything again.
Speaker 3: Yeah, I mean that's where the kids are like a
Speaker 3: wonderful because that's what kids are doing. And you know,
Speaker 3: you mentioned something that we think about something like Einstein
Speaker 3: as being this big change. But think about I think
Speaker 3: about this with my uh my grandchildren. For example, when
Speaker 3: I look at a phone, right or a computer, what
Speaker 3: I say to myself is okay, well you use.
Speaker 4: You use a keyboard.
Speaker 3: But then there's these other gimmicks about, like I could
Speaker 3: talk to it or I could touch it.
Speaker 4: It would work.
Speaker 3: My grandchildren, even my eighteen month old grandchildren, think, oh no,
Speaker 3: this is a system that you talk to and you
Speaker 3: touch and then things happen when you talk. You know,
Speaker 3: if you actually have this little physical object and you
Speaker 3: talk to it and touch it, you're going to get effects.
Speaker 3: And they think that the you know, the keyboard is like,
Speaker 3: what is this? This is this weird, strange, awkward thing
Speaker 3: that you know, peripheral device.
Speaker 4: We don't want to have anything to do with that.
Speaker 3: Eighteen month olds, right aren't reading, let alone using a keyboard,
Speaker 3: so they're even just you know, the difference between my
Speaker 3: vision of what a computer is and what my eighteen
Speaker 3: month old grandson's vision is is already a really radically
Speaker 3: different vision.
Speaker 2: That's right.
Speaker 1: So the long arc of moral progress, as well as
Speaker 1: the arc of human knowledge is all about is all
Speaker 1: about us passing on to the next generation. Hey, here's
Speaker 1: the things we've learned exactly, and then they pick it
Speaker 1: up and they springboard right off of that.
Speaker 3: So that's going to be you know, if you look
Speaker 3: at the difference between how much progress has been made
Speaker 3: with the LMS and something like robotics, where there's progress,
Speaker 3: but it's much slower and still very very far removed
Speaker 3: from what every baby's doing. You know, I do think
Speaker 3: and we've been thinking about whether you could design a
Speaker 3: system that, for instance, let me describe this one of
Speaker 3: the kinds of AI systems, not an LM. But a
Speaker 3: really different approach is what's called reinforcement learning. And reinforcement
Speaker 3: learning is when a system does something and then learns
Speaker 3: from the outcomes of what it does. But that's still
Speaker 3: very very labor and computation intensive. It's hard, it's hard
Speaker 3: to do efficiently, and it has a big problem, which
Speaker 3: is in reinforcement learning, what those systems are doing is
Speaker 3: just trying to, say, get a big higher score in game.
Speaker 3: So reinforcement learning is how they solved go and chess,
Speaker 3: So you can say, okay, you want to win the game.
Speaker 3: What do you have to do to win the game.
Speaker 3: But of course a lot of what children are doing
Speaker 3: is just trying.
Speaker 4: To figure out how the world works.
Speaker 3: It doesn't really matter whether you're winning or losing, or
Speaker 3: you're getting things or not. It's you just want to
Speaker 3: figure out how the world works. So one thing we've
Speaker 3: been doing is saying, what would happen if you had
Speaker 3: a reinforcement learning system and instead of trying to get
Speaker 3: a high score, it was trying to get more information,
Speaker 3: or it was trying to figure out how to be
Speaker 3: more effective or figure out how cause and effect worked.
Speaker 3: Would that be a better way of describing that certainly
Speaker 3: is much closer to what the kids are doing. But
Speaker 3: part of the problem is that in the systems that
Speaker 3: we have now you know, you were mentioning, well, you
Speaker 3: could go to a chat subutee and say, okay, give
Speaker 3: me five different ways of answering this question. But the
Speaker 3: way that they currently are generating that kind of variability
Speaker 3: and novelty is basically just by being random, just by
Speaker 3: what they sometimes call turning up the temperature, just doing
Speaker 3: things that are more different from one another. They're not
Speaker 3: able to say, Okay, this is a plausible answer, this
Speaker 3: could be the right answer, and this is just you know,
Speaker 3: completely irrelevant. That's part of the reason why they hallucinate
Speaker 3: is because they'll generate something and it seems like it
Speaker 3: could be potentially be an answer, and they're not evaluating
Speaker 3: does this make sense or not? And that's something that
Speaker 3: we know that kids are doing, and we don't have
Speaker 3: a very good account of.
Speaker 4: How they do it.
Speaker 3: So, you know, this is the old This shows how
Speaker 3: old I am. There used to be a show about
Speaker 3: you know, kids say the darkness things, and there's.
Speaker 4: Slightly more expletive latent things like that on the internet.
Speaker 3: Kids are always saying the creative strange things, but they're
Speaker 3: not random, like they're not. It's not that they're just
Speaker 3: randomly saying saying things that make no sense. They're really
Speaker 3: different from what a grown up would ever say, but
Speaker 3: they're not, but they kind of make sense. A lovely
Speaker 3: example recently is one of a post doc was taking
Speaker 3: her child out for her four year old out for
Speaker 3: a walk on the Berkeley campus and the campus has
Speaker 3: a companyli it's a bell tower that's, you know, a
Speaker 3: clock that's very high up on top of a tower.
Speaker 3: And a little boy looked at it and said, there's
Speaker 3: a clock up there.
Speaker 4: And then he thought to himself and he said, why
Speaker 4: did they put the clock up there? And he said
Speaker 4: it must be so the students and the children couldn't
Speaker 4: break it, which is of course a wonderful explanation, right,
Speaker 4: like you put it up really.
Speaker 3: High so it'll be out of reach of the students
Speaker 3: and they can't go and break the valuable clock. Not
Speaker 3: something that a grown up would think of, but something
Speaker 3: that's kind of plausible. And it's that kind of capacity
Speaker 3: to generate something that isn't random but makes sense that
Speaker 3: kids are really really good at doing. And we've done
Speaker 3: a bunch of experiments at show. In some cases they're
Speaker 3: better at doing that than grown ups are. But that's
Speaker 3: something that's still very much not part of what's available
Speaker 3: in AI. And there's an old observation in AI, sometimes
Speaker 3: called Mirovich's paradox haunts Morovit was originally the person who
Speaker 3: noticed it, which is that a lot of the things
Speaker 3: that we as humans think are really really hard and
Speaker 3: require a lot of intelligence, like playing chess, are actually
Speaker 3: not that hard for AI systems. Things that we think
Speaker 3: of is just taking for granted, like picking up a
Speaker 3: chess piece and putting it on the board, turn out
Speaker 3: to be a lot harder than playing chess. So the
Speaker 3: thing that the kids can do, which is take a
Speaker 3: bunch of you know, take a box full of mixed
Speaker 3: up chess pieces and pull out the right ones and
Speaker 3: put them in the right place, or have someone say,
Speaker 3: are right, now, we're going to play chess, but we're
Speaker 3: going to have a different role. You can move the
Speaker 3: ponds as many spaces as you want. Those kinds of
Speaker 3: abilities are exactly the ones that even the great chess
Speaker 3: playing programs are going to have a hard time of doing.
Speaker 1: That's right, and this is what I was mentioning before
Speaker 1: about this third wave. So the first wave of AI
Speaker 1: was really about reinforcement learning entirely. It was let's nail chess,
Speaker 1: let's nail go, and it's all about let's play millions
Speaker 1: of games new reinforcement. Then the second wave of AI
Speaker 1: was totally different. It was, Hey, let's just absorb everything
Speaker 1: that's out there. And so the third wave is really
Speaker 1: something like becoming closer to a child than interacting with
Speaker 1: the world and getting that feedback, right, And that's the future.
Speaker 1: I mean, we don't have that yet in twenty twenty five,
Speaker 1: but it'll be great to revi is that this part
Speaker 1: of the conversation in twenty twenty eight and see where
Speaker 1: things are and what they look like.
Speaker 3: Yeah, I mean it maybe that we're headed for another
Speaker 3: you know, the famous AI springs at AI Winters. My
Speaker 3: intuition is that we're getting to the limits of what
Speaker 3: the LLM cultural technology piece can do now that will
Speaker 3: change the world, There's no question about it. And in
Speaker 3: a way, you know the fact that kids can get
Speaker 3: on the internet and find out kids anyone can pick
Speaker 3: up something that's in their pocket and find out all
Speaker 3: of this information about the world that is going to
Speaker 3: change the world. But if we're thinking about intelligence, then
Speaker 3: I think we still have a long way to go
Speaker 3: to have something that has the kind of intelligence that
Speaker 3: every child has.
Speaker 1: Has in the AI being an intelligence being exactly right,
Speaker 1: exactly right.
Speaker 2: I'm curious what you think about this.
Speaker 1: I've been sort of campaigning on this point for a
Speaker 1: long time that I think the next generation is going
Speaker 1: to be smarter than we are simply because of their
Speaker 1: access to the world's knowledge. With the rectangle in their pocket,
Speaker 1: the moment they're curious about a question and have the
Speaker 1: right neurotransmitters present, they get the answer, and the exposure
Speaker 1: to everything that's known is so incredible as far as
Speaker 1: filling their warehouse so they can do remixes and come
Speaker 1: up with new ideas.
Speaker 2: What's your take hoting children or children growing up in
Speaker 2: the digital children.
Speaker 4: Growing up in the digital age.
Speaker 3: Yeah, so I think that's a really good That's obviously
Speaker 3: a really good, profound question. And I think again, if
Speaker 3: you think about those past examples, like what did writing
Speaker 3: and pictures and print and then the Internet itself, the
Speaker 3: ways that that really changed in some ways that really
Speaker 3: really beefed up human intelligence in really important significant ways.
Speaker 3: The amount of things that's just the number of things
Speaker 3: we know, right, the number of things we could know,
Speaker 3: the number of things we could access.
Speaker 4: All of those things.
Speaker 3: Those culture technologies have really changed, and I think there's
Speaker 3: every reason to believe that that will happen with the
Speaker 3: new technologies as well. Now. At the same time, there's
Speaker 3: always been these trade offs where other kinds of intelligence
Speaker 3: that we had before, like the kind of intelligence that
Speaker 3: you need to have to be a hunter gatherer, for example,
Speaker 3: being able to be out in the world, take in
Speaker 3: lots of information, go out and act, those kinds of
Speaker 3: intelligences might suffer and probably will suffer as a result
Speaker 3: of that. So, you know, being able to being able
Speaker 3: to build a house, right, which is a really useful
Speaker 3: thing to be able to do, is not something that
Speaker 3: you can do just based on YouTube videos, although maybe
Speaker 3: YouTube videos can maybe YouTube videos can help. My suspicion
Speaker 3: is that what tends to happen with these technological changes
Speaker 3: is they make all the difference in the world and
Speaker 3: nobody realizes it because by the time they've made all
Speaker 3: the difference, you just sort of take it for granted.
Speaker 3: So an example that I like to give is as
Speaker 3: I'm walking down the street. Now, I'm spending a lot
Speaker 3: of time decoding text, So as I walk down the street,
Speaker 3: I'm completely surrounded by these signs. You know, go call
Speaker 3: our lawyer for your accident in case you're in an accident,
Speaker 3: right or whatever, all the signs that are on the street.
Speaker 3: I don't think to myself, God, it's exhausting every minute.
Speaker 3: I never get to just walk down the street. I'm
Speaker 3: always putting all this energy into trying to take all
Speaker 3: this text and read it. And if you think about it,
Speaker 3: if you were a pre literate person, it probably would
Speaker 3: be exhausting if you had to sit down and figure
Speaker 3: out what is it that each one of these signs
Speaker 3: is saying. But of course we don't even I mean literally,
Speaker 3: we're not even conscious of it. It's just part of
Speaker 3: what goes on in the background. And we know from
Speaker 3: neuroscience that in fact, parts of our brain have been
Speaker 3: adapted to just do this kind of processing really quickly,
Speaker 3: so we don't even notice that we're doing it. And
Speaker 3: my suspicion is that that's what's going to happen with
Speaker 3: the next generation and the the internet and information. We'll
Speaker 3: be doing these things we won't even think about it.
Speaker 3: But there's a really important caveat to that, which is
Speaker 3: that if you go back to those examples of print
Speaker 3: and writing, why is it that we didn't just have
Speaker 3: all the evil misinformation and libel of the French Revolution indefinitely. Well,
Speaker 3: what's happened is every time we've had one of these
Speaker 3: new cultural technologies, we've also developed systems for keeping them
Speaker 3: under control. So newspapers, you know, again we just sort
Speaker 3: of take newspapers for granted, and newspapers are sort of disappearing,
Speaker 3: But newspapers were away of taking that printed information and
Speaker 3: curating it and having editors who said this is true,
Speaker 3: and this isn't true, and this is something we want
Speaker 3: to tell our readers, and this is something we don't
Speaker 3: want to tell our readers. And you had norms that
Speaker 3: developed things like journalism or journalism school or libel laws
Speaker 3: that said, no, here's a way that we can take
Speaker 3: this new culture and make control it in a way
Speaker 3: that will let the good parts come out and keep
Speaker 3: the bad parts under control. And every time there's a
Speaker 3: new cultural technology, we've had new laws, we've had new norms,
Speaker 3: we've had new institutions, we've had new kinds of people
Speaker 3: who were there to try to make these institutions work
Speaker 3: for and the same thing, by the way, it's true
Speaker 3: for markets. You know, think about the way that markets
Speaker 3: are great for coordinating people, but we all know all
Speaker 3: the terrible things that can happen with markets do so
Speaker 3: you need to have laws, you need to have institutions,
Speaker 3: you need to have ways to regulate them. And I
Speaker 3: think the same thing's going to be true with AI.
Speaker 3: If we're going to succeed, we're both going to have
Speaker 3: to have.
Speaker 4: Internal you know, internal norms.
Speaker 3: I think you see some of that happening with kids,
Speaker 3: where kids will say things like, oh no, I don't
Speaker 3: you know this is the wrong thing to pay attention to.
Speaker 3: If you go to that YouTube site, it's full of
Speaker 3: you know, it's full of nonsense. This one is actually
Speaker 3: this one is actually better. And also we're going to
Speaker 3: have to have all those boring things like legislation and
Speaker 3: code and regulatory agencies that's already sort of starting that
Speaker 3: will make sure that things are good rather than bad.
Speaker 3: Another example, I like to give a great technology, technological
Speaker 3: change that we don't think about very much. Think about
Speaker 3: it's nineteen hundred and people are saying.
Speaker 4: You know what we should do.
Speaker 3: We should take all our wooden houses and we should
Speaker 3: put electricity in them. So we should put wires that
Speaker 3: have electricity, which we know burns things, and we should
Speaker 3: put them in everybody's house, including everybody's wooden.
Speaker 4: House, and it'll be fine, right, it'll be great.
Speaker 3: Well. The only reason why we can do that, and
Speaker 3: we did have a lot of houses burned down to
Speaker 3: begin with, is because we have, as anyone who's you know,
Speaker 3: anyone who's a contractor or has said a reno, Nos,
Speaker 3: we have this thing called code, which is this book
Speaker 3: that's like this thick that has all the rules about
Speaker 3: here's what you have to do, here's how the wiring
Speaker 3: has to work, here's all the things that you have
Speaker 3: to do to make electricity work. And nobody thinks about
Speaker 3: that big book of code as being a fantastic human invention.
Speaker 3: We mostly think of it as, oh, God, that's why
Speaker 3: my contractor is charging me so much. But that invention,
Speaker 3: in a way, is just as important as the invention
Speaker 3: of electricity itself. The invention of electricity itself was a
Speaker 3: big scientific advance, but you couldn't use that unless you
Speaker 3: had this other kind of advance, which was the code,
Speaker 3: the laws, the regulations, the legislation. And I think that's
Speaker 3: something that a lot of people in AI are realizing.
Speaker 1: So when you're thinking about what will be the legislation
Speaker 1: and the norms that are coming down the pike, when
Speaker 1: you squint into the future, can you see what that's
Speaker 1: going to look like?
Speaker 3: I think that's a good question, and it will have
Speaker 3: to be somewhat different. You know, each one of the
Speaker 3: way that we deal with language, which is, you know,
Speaker 3: sort of having a moral principle that you shouldn't lie,
Speaker 3: for example, is different from what we have to do
Speaker 3: with writing, is different from what we do with print,
Speaker 3: is different from what we had to do with the Internet.
Speaker 4: One of the points that.
Speaker 3: We make in are in that paper that I think
Speaker 3: is really important is that there are real dangers in
Speaker 3: the fact that this technology has been monopolized by a
Speaker 3: few big agencies, for example, a few big companies.
Speaker 4: So one thing is.
Speaker 3: How are we going to make sure that that it's democratized,
Speaker 3: that in fact, people can use it outside of the
Speaker 3: control of just a few big companies.
Speaker 1: And because of just a quick interjection, I actually I
Speaker 1: have no worries about that, and I'll tell you why.
Speaker 2: It's because everything starts this way.
Speaker 1: And you know, we saw when deep Sea came out
Speaker 1: recently they had reportedly done it for six million bucks
Speaker 1: instead of billions. And it's only going to get cheaper
Speaker 1: and easier to do this.
Speaker 2: So I think it.
Speaker 1: Will quickly become democratized, just like every other example we've
Speaker 1: had of things like this leg printing. I mean, now
Speaker 1: we all have a printer on our desk at home, right,
Speaker 1: whereas a printing press was a big.
Speaker 2: Deal that only a few people had.
Speaker 4: Yeah, well, I think that's partly true.
Speaker 3: But on the other hand, the fact that these all
Speaker 3: depend on these pre trained systems that do take an
Speaker 3: enormous amount of money to get started, and even with
Speaker 3: deep Seak, it seems like part of what Deepseek was
Speaker 3: doing was taking advantage of the fact that this pre
Speaker 3: training had already been done by other systems.
Speaker 4: So I think that will happen.
Speaker 3: But I think the question about who will have control
Speaker 3: is a really important political and economic question. Here's another
Speaker 3: interesting point that my colleague Henry Ferrell made in that
Speaker 3: has made and we made in that science piece. If
Speaker 3: you think about cultural technologies they intrinsically always involve attention
Speaker 3: between the creators and the distributors. So the ideas cultural
Speaker 3: technology is I can get information from other people. That's
Speaker 3: essentially the idea, But that means someone has to make
Speaker 3: up that information, someone has to generate it, someone has
Speaker 3: to find out what's true, for example.
Speaker 4: And there's this kind of paradoxical synergy between the people.
Speaker 3: Who are out there creating things that are new and
Speaker 3: then the people who are distributing them. So there's no
Speaker 3: point in writing, as you and I both know as writers, right,
Speaker 3: there's no point. Well, or maybe there's some point, but
Speaker 3: there's not much point in writing a book unless you
Speaker 3: have a publisher who can actually get it out into
Speaker 3: the world.
Speaker 4: Right.
Speaker 3: But for the publisher, there's no point in being a
Speaker 3: publisher unless you can get people to write books for you,
Speaker 3: unless you can get new content, new ideas.
Speaker 4: But that also means that there's.
Speaker 3: This intrinsic tension, economic tension between who's going to pay right,
Speaker 3: So for the distributors, it's always going to be in
Speaker 3: their interest for the creators.
Speaker 4: To get paid as little as possible.
Speaker 3: And for the creators it's always going to be in
Speaker 3: their interests for the distributors to.
Speaker 4: Get paid as little as possible.
Speaker 3: So if you're thinking about that from an economic perspective,
Speaker 3: there's always going to be this tension between the people
Speaker 3: who are creating and the people who are distributing. And
Speaker 3: you can already see that in what's happened in journalism,
Speaker 3: for instance, or what's happened to the death of local newspapers.
Speaker 3: We had this kind of strange equilibrium where you know,
Speaker 3: the want ads and the weather reports paid for the
Speaker 3: investigative journalism and the arts criticism. And that's once that
Speaker 3: two thousand digital convergence happened, that wasn't going to be
Speaker 3: there anymore.
Speaker 4: And now it's.
Speaker 3: Really sort of up for grabs about who is how
Speaker 3: people are going to get compensated, and who is going
Speaker 3: to have the power of the distributors or the creators,
Speaker 3: And I think that's going to be a really important
Speaker 3: issue as we're going forward with this too.
Speaker 1: One idea that people have been floating recently is to
Speaker 1: have digital dividends come back to the creators, so that
Speaker 1: the people whose work has been used to train the
Speaker 1: models and maybe provided point one percent of the answer
Speaker 1: that that model just gave gets a few pennies back
Speaker 1: each time. That's idea how those economics models will evolve
Speaker 1: over the coming year.
Speaker 3: I mean, if you think about it, like what we
Speaker 3: did for print was we invented copyright law, so we
Speaker 3: have a whole lot of laws about Look, you can't,
Speaker 3: you know, I can't just take a David Eagleman book
Speaker 3: and copy it and say that it's mine. But of
Speaker 3: course that involves the actual text. So copyright depends on
Speaker 3: the fact that you're taking word for words something that
Speaker 3: someone else has written. How about if Google decides to
Speaker 3: use David Eagleman's book to train its large language model.
Speaker 3: It's not taking every single word, but the reason why
Speaker 3: it's giving you good answers about neuroscience is because it's
Speaker 3: been trained on those books. And we know as a
Speaker 3: matter of fact that those large models have been trained
Speaker 3: on my books and your books and all the other
Speaker 3: books that are out there.
Speaker 4: Now.
Speaker 3: They're not copying it down exactly, but the fact that
Speaker 3: you could go to chat ChiPT and it could tell
Speaker 3: you here's what Alison Govnik would say about about children's learning,
Speaker 3: comes because it's gone out there and used all that
Speaker 3: information in all of those books.
Speaker 4: So it's really tricky.
Speaker 2: Yeah.
Speaker 1: What I think is really tricky about this is the
Speaker 1: reason you and I are able to write books is
Speaker 1: because we've sat in thousands of lectures, we've heard ideas
Speaker 1: from people, and we've put things.
Speaker 2: Together in our own way and our own voice.
Speaker 1: And in a sense, the lllm's doing what humans do
Speaker 1: in terms of creativity.
Speaker 2: We're remixing all of our inputs.
Speaker 4: Yeah, but of course we wouldn't.
Speaker 3: Our books wouldn't be worth reading if they were just
Speaker 3: summaries of everything that had gone before. So part of
Speaker 3: what we want is to have exactly this.
Speaker 4: People in.
Speaker 3: A field called cultural evolution actually study how is it
Speaker 3: that information comes from one generation to another.
Speaker 4: And we've studied this in children. It's really fascinating.
Speaker 3: In The Gardener and the Carpenter, which is my book
Speaker 3: about parents and children, looking at how children learn from
Speaker 3: other people, there's lots of examples in there about how
Speaker 3: children can take information from their parents, for example, but
Speaker 3: they don't just swallow it hole they think about it
Speaker 3: in new ways, they remix it. In cultural evolution they
Speaker 3: talk about this as the balance between imitation and innovation.
Speaker 4: So you need both.
Speaker 3: You need to be able to imitate the other people
Speaker 3: around you to make progress, but then you also want
Speaker 3: to do something that's not just what the other people
Speaker 3: around you have done. I mean, there'd be no point
Speaker 3: in imitating if somebody hadn't innovated.
Speaker 4: At some point in the past.
Speaker 3: And it's a really hard scientific problem about how do
Speaker 3: you get that balance.
Speaker 4: And what you see with children is that.
Speaker 3: If you give children information, they will They're very likely,
Speaker 3: especially little children, will kind of imitate you literally. You know,
Speaker 3: you produce a gesture and the kids will imitate it.
Speaker 3: But then they'll also change it depending on whether they
Speaker 3: think it makes sense or not.
Speaker 1: Yeah, And as you know, in the animal kingdom, see
Speaker 1: that all animals have this trade off between exploration and exploitation.
Speaker 1: So they'll spend eighty percent of a time exploiting the
Speaker 1: things that they know, as in under that rock, there
Speaker 1: are these grubs that I eat, and then they'll spend
Speaker 1: twenty percent of their time exploring other places that they
Speaker 1: haven't seen before. Across the animal kingdom, we see this
Speaker 1: exploration exploitation trade off, and it strikes me that the
Speaker 1: imitation innovation trade off is essentially the cognitive equivalent analogy
Speaker 1: of that.
Speaker 4: No I think that's exactly right. And what I've argued.
Speaker 3: Is that if you think about childhood right, childhood is
Speaker 3: very evolutionarily paradoxical, like why would we have this long
Speaker 3: time when not only are we not producing anything, but
Speaker 3: we're extracting resources from the other people around us. And
Speaker 3: there's a really interesting biological generalization which is that the
Speaker 3: smarter the adultonimal, the longer childhood it has. And that's
Speaker 3: very very general. It's mammals, birds, marsupials, even insects see
Speaker 3: this relationship between the length of childhood and the intelligency
Speaker 3: in the anthropomorphic sense of the adult, how good the
Speaker 3: adult is learning and figuring out the world. And I've
Speaker 3: made the argument that this is really an example of
Speaker 3: this explore exploit trade off. So childhood is evolution's way
Speaker 3: of resolving the explore exploit trade off, because what it
Speaker 3: does is it gives you this period of childhood.
Speaker 4: Well you don't have.
Speaker 3: To worry about exploiting, you don't actually have to worry
Speaker 3: about taking care of yourself and going out in the
Speaker 3: world and getting resources. As I always say, babies and
Speaker 3: young children have exactly one utility function, as the economists say,
Speaker 3: which is be as cute as you possibly can be,
Speaker 3: and they're very very very good at maximizing that.
Speaker 4: And as long as you're as cute as you possibly
Speaker 4: can be, you don't have to worry about anything.
Speaker 3: Else, right, you don't have to worry about getting fed
Speaker 3: and taken care of.
Speaker 4: But what that means is that that.
Speaker 3: Frees you up to do the things that babies and
Speaker 3: young children do, which is play and explore and experiment
Speaker 3: and have weird, crazy, imaginative pretend and do all these
Speaker 3: kinds of explore functions. And then because the children are
Speaker 3: doing that, then the adults can take advantage of all
Speaker 3: the novelty and innovation that the children are producing. So
Speaker 3: and it's interesting that in the computer science literature they
Speaker 3: also talk a lot about this explore exploit trade off,
Speaker 3: and there's literally proofs that you can't. It's a trade off.
Speaker 3: You can't have both of them at the same time.
Speaker 3: And often the best solution is, and you know, I
Speaker 3: think people can feel this intuitively, is start out exploring
Speaker 3: when you're trying to solve.
Speaker 4: A new problem.
Speaker 3: Start out just brainstorming, having crazy ideas, not worrying too
Speaker 3: much about whether you're getting there or not. And then
Speaker 3: narrow in and say, Okay, now I have a solution.
Speaker 3: Now I want to figure out how do I find
Speaker 3: tune that solution. And I think childhood is nature's way
Speaker 3: of implementing that idea. Explore first, explore later, and again,
Speaker 3: if you're thinking about these artificial intelligence systems, it's a
Speaker 3: really deep problem about how could you get design a
Speaker 3: system that can trade off those two capacities in an
Speaker 3: intelligent way?
Speaker 1: And you know, I think that's that's the third wave
Speaker 1: that's coming. Yeah, that's the part that we don't have currently,
Speaker 1: But that's what's saying next.
Speaker 3: What's interesting is in humans, the way that we do
Speaker 3: it is because we have a particular what biologists call
Speaker 3: life history, We have a particular developmental history. We start
Speaker 3: out being children, we become adults. Then for humans, we
Speaker 3: become elders, we become post mental pausal grandmothers. Now we
Speaker 3: tend to think, and you hear a lot of this
Speaker 3: from AI guys, with guys being the relevant term that
Speaker 3: somehow the thirty five year old, the thirty five year
Speaker 3: old adult male is like the peak of the psychologist
Speaker 3: or the philosopher or the AI guy is the peak
Speaker 3: of human intelligence. And then there's this thing called intelligence
Speaker 3: that you can have a little of or a lot of,
Speaker 3: and the thirty five year olds or maybe even the
Speaker 3: twenty five year olds like they have the most of it,
Speaker 3: and then childhood is just building up to it, and
Speaker 3: then elderhood is just falling off from it. But that
Speaker 3: doesn't make much sense from an evolutionary perspective. In fact,
Speaker 3: what seems to be true is that we have these
Speaker 3: different functions, really different, radically different kinds of intelligence that
Speaker 3: trade off. They're not just different, they trade off against
Speaker 3: one another, like exploration exploitation, and our developmental trajectory from
Speaker 3: childhood to adulthood to elderhood is a way that we
Speaker 3: managed to deal with those kinds of trade offs. So
Speaker 3: we have this childhood that lets us explore and lets
Speaker 3: us get information from other people. We have this adulthood
Speaker 3: where we can go out and use that information to
Speaker 3: do all the things that Gronim's do, like find mates
Speaker 3: and resources and find our way in the hierarchy. And
Speaker 3: then we have this elderhood where now we're motivated to
Speaker 3: use our resources to help the next generation, to pass
Speaker 3: on the wisdom and information that we've got to the
Speaker 3: next generation.
Speaker 4: So away.
Speaker 3: I put this perhaps a little meanly sometimes, is basically,
Speaker 3: we're humans up to puberty and after menopause, and in
Speaker 3: between we're sort of basically glorified primates. In between we're
Speaker 3: doing all those things like you know, mating and predating
Speaker 3: and finding resources and all the fun stuff is what
Speaker 3: the kids and the grandmom's fit to do, which is
Speaker 3: play and explore and tell stories and pass on recipes
Speaker 3: and do Broadway show tunes, all the things that I
Speaker 3: do as a grandmother with my with my grandchildren. But
Speaker 3: there is something that's deeper than that, which is that
Speaker 3: typically AI has not kind of had this developmental perspective.
Speaker 3: And I think that's another thing that developmental science can
Speaker 3: contribute to AI, is think not just about that there's
Speaker 3: this thing called intelligence that we want more of or
Speaker 3: less of, but how do you in a society and
Speaker 3: in an individual across time trade off these really different
Speaker 3: kinds of intelligences.
Speaker 1: So to zoom this back out to the big picture,
Speaker 1: then the idea of not looking at AI as an
Speaker 1: intelligent agent, but looking at it as a cultural technology.
Speaker 1: One of the things that suggests, and you say this
Speaker 1: in the paper, is that it's not just engineers who
Speaker 1: should be studying AI, but it's social scientists who should
Speaker 1: be commenting on it.
Speaker 4: Yeah, so just yeah, it's sort of fascinating.
Speaker 3: So if we wanted to say, let's try and figure
Speaker 3: out what's going to be good, what's going to be bad,
Speaker 3: how do we regulate it, how do we make.
Speaker 4: It good or bad?
Speaker 3: The people we should be talking to are people who
Speaker 3: know something about how print and writing and pictures affected society,
Speaker 3: or how developing markets and democracies and bureaucracies restructured society.
Speaker 3: And they all had enormous effects, right, I mean in
Speaker 3: a way like it's easy and fun to bring another
Speaker 3: intelligent agent into the universe. It's a lot more fun
Speaker 3: than coding. Actually, we know how to do that and
Speaker 3: we're doing it all the time, and it makes a
Speaker 3: little difference, but not an enormous difference. Bringing a new
Speaker 3: cultural technology into the universe like print, that's a giant change.
Speaker 3: But we've done it before. It's not as if this
Speaker 3: is a singularity. It's not as if this isn't something
Speaker 3: that humans have done before. The people who know about
Speaker 3: it are psychologists and political scientists and sociologists and historians
Speaker 3: of technology, and.
Speaker 4: One thing that I think would be really.
Speaker 3: Really helpful would be to get this historical perspective into
Speaker 3: the way that we think about AI, rather than having
Speaker 3: the sort of Gallum Frankenstein's story perspective of this is
Speaker 3: something that's unlike anything that's ever happened in.
Speaker 4: Humankind before.
Speaker 3: It's something that is important and really has had significant effects,
Speaker 3: but it is something that's happened in human life before,
Speaker 3: and it's something that comes out of the way that
Speaker 3: human beings work, not something that comes out of some
Speaker 3: strange alien inhuman kind of development.
Speaker 1: That was my interview with Alison Gopnik, professor at Berkeley.
Speaker 1: There are two points I want to return to. I've
Speaker 1: previously argued this point in Inner Cosmos in episode seventy two,
Speaker 1: that AI is best thought of as a processor of
Speaker 1: the intelligence of billions of humans. And that's because I
Speaker 1: was noticing, even back then, the number of people who
Speaker 1: typed in a sophisticated question and they got back what
Speaker 1: appeared to be a sophisticated answer, and they concluded, Wow,
Speaker 1: this thing is truly intelligent, but they were simply confusing
Speaker 1: that with the intellectual endeavors of humans before them. Maybe
Speaker 1: dozens of people had written about that topic, or maybe
Speaker 1: hundreds or thousands, but they simply didn't know that, and
Speaker 1: so they heard the echo and they misinterpreted that as
Speaker 1: the proud voice of AI. So I named this the
Speaker 1: intelligence echo illusion. Second point is, right after my conversation
Speaker 1: with Alison, I found myself thinking a lot about the question,
Speaker 1: when there's a giant machine that collects up data and
Speaker 1: processes it, why are we so quick to anthropomorphize it,
Speaker 1: to see it as a being. Well, it turns out
Speaker 1: that brains are very quick to do that. For example,
Speaker 1: imagine you hear some sound in your home at night.
Speaker 1: You assume it's a living, intentional creature, even though you
Speaker 1: might discover after some investigation with a flashlight in a
Speaker 1: baseball bat that it was just the wind blowing and
Speaker 1: knocking something around. Presumably this is a survival mechanism to
Speaker 1: assume everything is alive and has intention. But I think
Speaker 1: there's another aspect to it also, which is that we
Speaker 1: seem unable to think about complex systems and so we
Speaker 1: have to assign a central character to it. Examples of this,
Speaker 1: which I'm going to talk about in an episode in
Speaker 1: a few weeks from now, but for now, I'll just
Speaker 1: use as an example what's known is the Great Man
Speaker 1: view of history. We look to some historical figure, this
Speaker 1: could be a woman as well, and we attribute a
Speaker 1: historical outcome to that person. But this is a very
Speaker 1: misleading narrative tendency, because anything that happens historically is a
Speaker 1: hugely complex event, shaped by thousands of people and often institutions,
Speaker 1: and sometimes random contingencies. We say that Hitler led Germany
Speaker 1: into World War Two, but that glosses over the collaborators
Speaker 1: and the predecessors and the cultural scaffolding that made all
Speaker 1: these things possible. My assertion is that this isn't just
Speaker 1: a storytelling shortcut. It reflects something deep about human cognition.
Speaker 1: We're just not able to hold that level of complexity
Speaker 1: in our heads, so we become story tellers. We anthropomorphize
Speaker 1: to one or a few central characters. In other words,
Speaker 1: it's really hard for our brains to grasp sprawling, distributed
Speaker 1: networks of influence, so we turn a vast social tapestry
Speaker 1: into a single silhouette. Okay, so now let's zoom back
Speaker 1: out to the main picture. What happens when we stop
Speaker 1: asking whether a large model is intelligent and instead ask
Speaker 1: what kind of cultural machine it is. Today's conversation with
Speaker 1: Alison Gopnik took us through a reframing that I think
Speaker 1: is clarifying and possibly really important. When we think of
Speaker 1: large models as minds, as proto agents, it's easy to
Speaker 1: get swept up in a speculative drama.
Speaker 2: We ask if.
Speaker 1: They'll surpass us, if they'll enslave us. But when we
Speaker 1: shift the lens, when we see these systems as cultural technologies,
Speaker 1: more akin to libraries or markets, then our questions become
Speaker 1: more grounded and actionable.
Speaker 2: Large models are changing.
Speaker 1: Everything about our lives, how we write, how we seek information,
Speaker 1: how we work. They are reorganizing the structure of knowledge
Speaker 1: in real time, and like every major information technology before them,
Speaker 1: whether that's writing or printing or broadcast media, these tools
Speaker 1: are going to shape culture not because they think, but
Speaker 1: simply because they change the flow of ideas. So maybe
Speaker 1: the first step towards wise stewardship of this new era
Speaker 1: is to improve our metaphors less Golum and Frankenstein, more
Speaker 1: printing press and library, less digital brain, more public infrastructure,
Speaker 1: less will it become sentient?
Speaker 2: And more? What cultural shifts does this give rise to?
Speaker 1: There's a lot at stake and how we frame these systems,
Speaker 1: because our analogies and our metaphors are never neutral. If
Speaker 1: we imagine a large model as a proto, human will
Speaker 1: fear it and will regulate it accordingly. But if we
Speaker 1: see it as a new kind of social technology, something
Speaker 1: like a market or a library or a sprawling editorial system,
Speaker 1: we can draw on the history of how societies have
Speaker 1: dealt with the cultural impact of information systems. In other words,
Speaker 1: how we think about new technology will shape the world
Speaker 1: we're about to live into. Go to eagleman dot com
Speaker 1: slash podcast for more information and find further reading. Check
Speaker 1: out my newsletter on substack and be a part of
Speaker 1: the online conversation there.
Speaker 2: Finally, you can watch.
Speaker 1: Videos of Inner Cosmos on YouTube, where you can leave
Speaker 1: comments Until next time. I'm David Eagleman and this is
Speaker 1: inner Cosmos. You can help you to have to