Speaker 1: Is it possible that we're thinking about intelligence in the
Speaker 1: wrong way? Instead of being something inside individual brains, is
Speaker 1: intelligence instead something that emerges from lots of brains that
Speaker 1: are constantly working to align with one another. And if
Speaker 1: we take on that lens, what does this mean about
Speaker 1: the way that we can build AI agents or the
Speaker 1: way that they can make us better? What is the
Speaker 1: difference between information and information with a purpose? Today we're
Speaker 1: going to speak with Daniel Persick, a cognitive scientist who
Speaker 1: leads the Human Computer Interaction team at Amazon's AGI Lab.
Speaker 1: So get ready for a great brain stretch. Welcome to
Speaker 1: Intercosmos with me David Eagleman. I'm a neuroscientist and an
Speaker 1: author at Stanford and in these episodes, as we sail
Speaker 1: deeply into our three pound universe to understand how we
Speaker 1: see the world and soon how AI might come to
Speaker 1: understand the world with us. Let's think about the word intelligence.
Speaker 1: You might justifiably assume that neuroscientists have an agreed upon
Speaker 1: definition for this, but we actually don't. However one thinks
Speaker 1: about intelligence, I think it's a fair assumption that most
Speaker 1: of us, when we think about it, assume that intelligence
Speaker 1: is something that happens inside a single head, in other words,
Speaker 1: a brain processing information. This statement seems so obvious that
Speaker 1: it hardly invites inspection, but if you step back and
Speaker 1: look at how intelligence actually unfolds in a human life,
Speaker 1: a different picture can start to emerge. Our thinking is
Speaker 1: shaped by other people from the very beginning. We learn
Speaker 1: by watching, by imitating, by trying to communicate, and eventually
Speaker 1: by negotiating meaning with the people around us. Even our
Speaker 1: most private thoughts are built from tools that are fundamentally social,
Speaker 1: things like language and symbols and shared concepts and cultural norms.
Speaker 1: So this may sound strange, but this is what we're
Speaker 1: going to talk about today, and the idea will become
Speaker 1: very clear. Most of humanity's greatest achievements didn't come from
Speaker 1: lone geniuses working in isolation, but from really dense networks
Speaker 1: of minds interacting over time. When we look at things
Speaker 1: like science or art, or morality or technology, it almost
Speaker 1: never makes sense to interpret these as products of individual intelligence,
Speaker 1: but instead they are collective processes that allow ideas to
Speaker 1: collide and to form into something and to continuously evolve.
Speaker 1: So intelligence in this sense may be less like a
Speaker 1: thing we possess and more like something that emerges between us. Now,
Speaker 1: this broader perspective becomes especially important as we find ourselves
Speaker 1: flinging headlong into the era of artificial intelligence. With every
Speaker 1: passing week, we're getting AI acting more like a participant.
Speaker 1: We're getting systems that communicate but also agents that act
Speaker 1: on our behalf to do things in the world. And
Speaker 1: soon these agents will collaborate with each other at their
Speaker 1: time scales and spatial scales. So if intelligence is social
Speaker 1: by nature, then building the future world of AI might
Speaker 1: end up requiring more than just dumping billions into scaling
Speaker 1: up the training data for these systems. It may instead
Speaker 1: require understanding how minds relate to one another in the
Speaker 1: first place. And that's where today's conversation begins. Today I'm
Speaker 1: joined by Danielle Persk. She's a cognitive scientist who leads
Speaker 1: the Human Computer Interaction team at Amazon's AGI Lab. Danielle
Speaker 1: uses insights from the evolution and development of human intelligence
Speaker 1: to inform how we can not only make AI smarter,
Speaker 1: but build AI that also makes us smarter. Here's my
Speaker 1: conversation with Danielle Persk.
Speaker 2: Intelligence in humans is really social, and that is the
Speaker 2: thing that differentiates our intelligence from other species. Even other
Speaker 2: species that are closely related to us have similar brain
Speaker 2: structures and function similar genetics. And what we are really
Speaker 2: optimizing for is representing other minds. So not only are
Speaker 2: infants human infants inferring the existence of other minds, but
Speaker 2: once this thing exists, we are optimized for aligning our minds. Evolutionarily,
Speaker 2: we had to cooperate to survive. Infants need to be
Speaker 2: able to have their caretaker's attention on them to survive,
Speaker 2: and in terms of being able to learn about the world,
Speaker 2: once infants have a model of other minds, then they
Speaker 2: can manipulate it. They can direct their caretaker's attention point
Speaker 2: what's that, and magically they'll have a label for.
Speaker 3: This thing that they're looking at in their environment.
Speaker 1: So they're doing prompt engineering.
Speaker 3: Great technology. Yeah, okay, so we know that.
Speaker 2: You know, throughout the course of human evolution, we became
Speaker 2: increasingly dependent upon cooperating to to stay alive and adapt
Speaker 2: to new environment. So it makes sense that there'd be
Speaker 2: this extreme pressure on being able to predict each other's
Speaker 2: behaviors to understand our minds, and then with infants, developmentally,
Speaker 2: we have also the benefit of being able to learn
Speaker 2: much more efficiently even language itself, from representing other minds.
Speaker 1: Okay, so it turns out that we can do a
Speaker 1: much better job of predicting if we can imagine what
Speaker 1: it's like to be inside other people's heads. Right, So,
Speaker 1: if I want to know what some non player character
Speaker 1: is going to do in a video game whatever, they
Speaker 1: have certain behaviors. But if I want to know, let's
Speaker 1: say what you're going to do next, or say next,
Speaker 1: if I have a model of your mind and what
Speaker 1: you know and you don't know and all that stuff,
Speaker 1: I can make a better prediction.
Speaker 2: And so you've said that there's information and then information
Speaker 2: with a purpose, and that information with a purpose really matters.
Speaker 3: So you've used the example of like the.
Speaker 2: Land rover on Mars not being able to fix itself,
Speaker 2: and like a wolf that gets its like trap.
Speaker 1: Right, the Curiosity Rover went up to Mars. We had
Speaker 1: spent like a billion something dollars on it. It did
Speaker 1: a great job on Mars, but eventually it got its
Speaker 1: right front wheel stuck in the Martian soil and it
Speaker 1: died couldn't get out. But if you can trast that
Speaker 1: with a wolf who gets its leg cond of trap.
Speaker 1: It'll chew its leg off and then figure out how
Speaker 1: to walk on three legs, which is extraordinary because a
Speaker 1: wolf's brain didn't evolve for three legs. But it can
Speaker 1: figure it out because it's live wired. It has brain
Speaker 1: plasticity and figure out, Okay, how do I adjust everything.
Speaker 3: So that I can survival?
Speaker 1: Depends upon it exactly. That's the key. It has relevance
Speaker 1: to the animal.
Speaker 2: Right, So all animals have a drive to survive, a
Speaker 2: drive to reproduce, But humans also have a drive to
Speaker 2: align our minds because it helps us cooperate, it helps
Speaker 2: us survive, and it helps us to learn extremely efficiently.
Speaker 2: So we don't just model other minds. That would just
Speaker 2: be the information part. We are optimized for aligning our minds.
Speaker 2: So it's information with a purpose.
Speaker 1: Okay, so aligning our minds this is the key thing
Speaker 1: and at the center of your interests. And so then
Speaker 1: you went into looking into AGI. So first of all,
Speaker 1: tell us what artificial general intelligence is to you.
Speaker 2: Well, I think most of the labs that are trying
Speaker 2: to build something like AGI, they all have their own definitions.
Speaker 2: None of them are really very good. But the one
Speaker 2: thing that unifies all of them is that they are
Speaker 2: all benchmarked to human intelligence. And this goes all the
Speaker 2: way back to the origin of the field of AI.
Speaker 2: So in nineteen fifty six, a group of these engineers
Speaker 2: and mathematicians got together. They were going to solve intelligence
Speaker 2: and build thinking machines, and the idea is that these
Speaker 2: thinking machines would think like us.
Speaker 3: It obviously took a.
Speaker 2: Very long time to realize, Oh, that's a lot harder
Speaker 2: than we thought that it was. But now we are
Speaker 2: back to aiming for something like that original goal of
Speaker 2: building thinking machines that think like us.
Speaker 3: We call it AGI.
Speaker 2: Again, have slightly different operationalizations. But I think that we're
Speaker 2: all running towards the wrong thing. And that's because I
Speaker 2: don't think that intelligence can exist in a machine. It
Speaker 2: doesn't exist in individual humans. It's something that emerges from
Speaker 2: our interactions because we have this drive to align our representations,
Speaker 2: and of course we all have very different representations.
Speaker 3: Right When I used to teach.
Speaker 2: Cognitive science, I would teach about this condition called a fantasia,
Speaker 2: and once every couple of classes a student would come
Speaker 2: up to.
Speaker 1: Me quick In fantations where you can't imagine, you don't
Speaker 1: have any visual representation on the Yes.
Speaker 2: Yes, a student would come up to me and say, wait,
Speaker 2: you mean there are people who can actually imagine things.
Speaker 3: Their mind's eye is not just a metaphor. It's a
Speaker 3: thing that.
Speaker 2: People experience, and they wouldn't know because they don't suffer
Speaker 2: from other types of death. It's just one of the
Speaker 2: many ways in which human cognition and experience can very
Speaker 2: And when I imagine in apple, it's different than when
Speaker 2: you imagine an apple. We all have different associations. So
Speaker 2: when we come together and we have to use words
Speaker 2: to try to align our minds, there's necessarily going to
Speaker 2: be friction, especially when we're trying to talk about abstract things,
Speaker 2: especially when we're talking about things at the bleeding edge
Speaker 2: of our knowledge, like science.
Speaker 3: How do you align.
Speaker 2: Your representations when there's not even a word for something.
Speaker 2: So intelligence emerges as a function of trying to align
Speaker 2: our minds and oftentimes creating new concepts to achieve that.
Speaker 1: Okay, so when you're talking about aligning minds, it's because
Speaker 1: I've got my whole internal world. You've got your whole
Speaker 1: internal world that is built by each of our our
Speaker 1: trajectories through space time. We've had different experiences all these things.
Speaker 1: So we come together and we've got completely different worlds
Speaker 1: running on the inside. And that's what conversation is about.
Speaker 1: We're trying to align things that way.
Speaker 2: And there are neuroscientists who measure when people are either
Speaker 2: communicating in real time or if they're listening to a story,
Speaker 2: if they're watching something on a screen, you can measure
Speaker 2: the degree of neuralsynchrony, how close they are to be
Speaker 2: on the same wavelength, and that predicts all sorts of
Speaker 2: things like how much they like each other, how much
Speaker 2: they understood the story, and how much they liked the story,
Speaker 2: how similarly they remember things.
Speaker 1: Okay, so this is what humans do. We get together
Speaker 1: in conversation all the time and we try to achieve
Speaker 1: that synchrony in terms of oh, okay, wait, you have
Speaker 1: a different view than I do on this, here's how
Speaker 1: we can make progress. This is the Socratic dialectic, right.
Speaker 1: This is what Socrates love to do, is have these
Speaker 1: conversations where the truth emerges, something bigger than either person
Speaker 1: knew when they started the conversation.
Speaker 2: And on that point too, I think a lot of
Speaker 2: us think that we know things, but actually when we're
Speaker 2: forced to describe something we realized we don't.
Speaker 1: Yeah. Actually, in my next book, I'm talking about this
Speaker 1: as a Potempkin village. Yeah, so you know. The Potemkin village,
Speaker 1: for anyone doesn't remember, is when it was Catherine the
Speaker 1: Great of Russia was heading down the river with a
Speaker 1: bunch of dignitaries that she was trying to impress. She
Speaker 1: hired this skuy Potempkin. Actually he was her lover as
Speaker 1: well as a military general, but she got him to
Speaker 1: go down the river a long way and build what
Speaker 1: looked like a facade of a village so that when
Speaker 1: the ship went by, all the dignitaries would be impressed
Speaker 1: that there was this village. And he got all these
Speaker 1: peasants like walk around happily and stuff, But there were
Speaker 1: no buildings. It was just the front face of the building.
Speaker 1: And then when the ship passed, he deconstructed this and
Speaker 1: went ahead and built another village so that they passed
Speaker 1: another great village, so it looked like things were really
Speaker 1: happening there. Anyway, Cognition is often like this, where we think, oh, yeah,
Speaker 1: I got it. Here's an example that I often use
Speaker 1: is for anybody listening, take out a piece of paper,
Speaker 1: and draw a bicycle, draw a bike.
Speaker 3: I've tried this, yeah hard, Yeah.
Speaker 1: Exactly, it turns out, I mean something as simple as
Speaker 1: a bike what you see every day. Yeah, you start realizing, wait,
Speaker 1: actually I don't know exactly where this goes and what's
Speaker 1: the thing and so anyway, Yes, this is an example
Speaker 1: of where we think we have deep knowledge and sometimes
Speaker 1: it's just the facade of something that we know.
Speaker 2: Yeah, and you can apply it on all different levels.
Speaker 2: So you're describing, like the visual imagery might not be
Speaker 2: very stable, but a lot of concepts are not stable either,
Speaker 2: and we invent ways of making them more stable. Words
Speaker 2: are a classic example of that. Once you have a
Speaker 2: word for something, you can more easily trigger it, you
Speaker 2: can more easily remember it, you can use it and
Speaker 2: manipulate it and apply it to different things. But there's
Speaker 2: a whole class of things that we're constantly inventing to
Speaker 2: better align our minds. They're called cognitive technologies. So writing
Speaker 2: would be one of the original ones. But symbols like math, logic, Yeah.
Speaker 1: So unpack that. What's an example of this?
Speaker 3: So literally, any word that you learn. Let's go back
Speaker 3: to apples.
Speaker 2: So children see apples, they don't have a word associated
Speaker 2: with it, so the likelihood that it's going to spontaneously
Speaker 2: sort of emerge in their mind is very low. Maybe
Speaker 2: if they've seen a couple then there will be some
Speaker 2: sort of increased likelihood or lowered threshold. But once they
Speaker 2: have a word for that thing, that they reliably associate
Speaker 2: it with it, then anybody who says the word anytime
Speaker 2: they hear it, now their brain will elicit that activity
Speaker 2: and it becomes a more stable representation. That's an obvious
Speaker 2: example where where there's actually a physical thing that the
Speaker 2: word can refer to.
Speaker 3: But what about.
Speaker 2: Concepts like love and justice that you can't see you're
Speaker 2: saying By assigning a word to it, then we make
Speaker 2: that stable, and then it become it can become associated
Speaker 2: with a whole web of other concepts, and that web
Speaker 2: becomes increasingly stable when when we can.
Speaker 3: Make the associations.
Speaker 2: More robust, more reliable, and then further when we can
Speaker 2: invent things like science where we can really validate causal
Speaker 2: relationships between things, and that makes our representations even more stable.
Speaker 1: I see, so human brains interact with one another and
Speaker 1: work on how do we make these representations stable? How
Speaker 1: do we get knowledge coming out like a Socratic dialectic,
Speaker 1: but with with everybody all involved and so on. And
Speaker 1: so your idea when you moved into this field of AGI,
Speaker 1: howeveryone wants to define it? What was your idea?
Speaker 2: Well, so I left academia, which I absolutely loved, but
Speaker 2: I felt an urgency to validate this theory.
Speaker 3: I don't I mean, it's just a theory at this point.
Speaker 2: How do we know whether we are optimized to align
Speaker 2: our minds?
Speaker 3: There's so much evidence to suggest that we do.
Speaker 2: But as Richard Feyman said, you don't really know if
Speaker 2: you understand something until you can build it.
Speaker 3: And so I thought, well, maybe I could build this thing.
Speaker 3: And the moment is just right because AI is taking
Speaker 3: off again.
Speaker 2: It's waking up from one of the winters, and I
Speaker 2: was watching the scaling have really impressive results with a
Speaker 2: deep learning, which felt really good because as somebody who
Speaker 2: had a background in neuroscience, just like, oh yeah, inspiration
Speaker 2: from brains is actually proving to be really effective. So
Speaker 2: I moved into tech and started collaborating with the engineers
Speaker 2: who were trying to build ever more capable intelligence. I'm
Speaker 2: now in one of these frontier AGI labs and the
Speaker 2: thing that we are going to be doing, which I
Speaker 2: think is really differentiated from other approaches, is try to
Speaker 2: build the communicative drive. Can we build agents that are
Speaker 2: optimized for understanding each other's perspectives? And from that, can
Speaker 2: we get emergent behaviors, emergent capabilities that we wouldn't get
Speaker 2: from a single model on its own.
Speaker 1: So I just want to slow that down. So communicative drive,
Speaker 1: that's the first time we've heard the term, So tell
Speaker 1: us what that means.
Speaker 2: So communicative drive is the phrase that I use to
Speaker 2: describe this compulsion that we have to align our minds
Speaker 2: to establish representational alignment. You can imagine how the communicative
Speaker 2: drive would interact with other dispositions that humans have. And importantly,
Speaker 2: you have to think at the population level. So again
Speaker 2: we have variation for every trait, and some of us
Speaker 2: are more open, some of us are more closed to experience.
Speaker 3: But you can imagine. Okay, so in the case of
Speaker 3: somebody who's really closed, Let's say that they are.
Speaker 2: In some communicative exchange and they detect a mismatch. So
Speaker 2: somebody is clearly not understanding what they are saying.
Speaker 3: They have two choices. They can update.
Speaker 2: Their perspectives to the other person's, or they can try
Speaker 2: to get the other person's perspective to look more like theirs.
Speaker 2: What would it take to get another person to come
Speaker 2: to your perspective.
Speaker 3: You'd have to create an artifact. You'd have to create.
Speaker 2: A word or a piece of art or a theory
Speaker 2: to get them to really understand and take on your
Speaker 2: perspective and.
Speaker 3: Close that gap.
Speaker 2: But if you're a very open person, if you're creative,
Speaker 2: that might be your default. But if you're a little
Speaker 2: bit more reserved, maybe you just take on the other
Speaker 2: person's perspective.
Speaker 1: Is it that way or the other way? Sorry? If
Speaker 1: I'm very open, I feel like I would take the
Speaker 1: other person's perspective, so you.
Speaker 2: Can actually imagine both situations. Yes, So I score very
Speaker 2: high on openness, and when I'm in communicative exchanges, I
Speaker 2: often feel like, oh, wow, yeah, that's I've never thought
Speaker 2: of it that way, or maybe I have, and I
Speaker 2: want to add all these things and like it's a
Speaker 2: very cooperative thing. I'm more thinking about the dynamics of
Speaker 2: people who want to maintain tradition and status quo versus
Speaker 2: people who want to challenge that. So oftentimes that maps
Speaker 2: onto the dimension of openness. So if you see that
Speaker 2: everybody around you seems to hold a different perspective than
Speaker 2: you do, you're more likely to conform to their perspectives.
Speaker 2: If you're somebody who might be a little bit more conservative,
Speaker 2: not wanting to ruffle feathers that kind of thing.
Speaker 1: Well, I'm just trying to stand why I use the
Speaker 1: word conservative there, because conservative meaning are like iinin exactly.
Speaker 1: Oh but you're saying, maintain the group traditions. Yeah, okay,
Speaker 1: got it.
Speaker 3: As opposed to being iconoclastic and innovative.
Speaker 1: I see, I see how you're using it. Okay, great.
Speaker 1: So this is the idea is that people are always talking,
Speaker 1: and depending on your personality type, what you're trying to
Speaker 1: do is either align yourself with them or them with
Speaker 1: you or whatever, or meet in the middle. But this,
Speaker 1: this you feel, is the key to what human societies
Speaker 1: bring as opposed to looking at individual brains. You know,
Speaker 1: the history of neuroscience is all about looking at individual brains. Oh,
Speaker 1: this is how the visual system works, as how decision
Speaker 1: making works, how hearing works, whatever. But there's this new
Speaker 1: feel that's been growing for the last twenty or thirty years,
Speaker 1: which is called social neuroscience, which is all about, gosh,
Speaker 1: we've got a lot of circuitry in our brains that
Speaker 1: care about other brains. So this is the heart of
Speaker 1: your interest. Is what happens when people are talking and aligning?
Speaker 1: And why are we so driven to communicate instead of
Speaker 1: let's imagine that you and I set down on a
Speaker 1: bus next to each other, we'd probably chat as opposed
Speaker 1: to just sit there and deal with our own brains. Okay,
Speaker 1: so how does this map onto what you're interested in
Speaker 1: doing in AI?
Speaker 2: Yes, so I am concerned about building something that resembles
Speaker 2: our own intelligence, or something that resembles us because we
Speaker 2: have all sorts of flaws and biases.
Speaker 3: The variability, I.
Speaker 2: Think is very useful, and we wouldn't be intelligent in
Speaker 2: the way that we are without the variability. And you
Speaker 2: might call some of that variability the bias, the unique
Speaker 2: biases that we have.
Speaker 3: But I think if we try to reproduce.
Speaker 2: All of that, we're going to get a mirror of ourselves,
Speaker 2: and that's not always the most effective way to augment
Speaker 2: our intelligence. And I should back up and say, why
Speaker 2: are we doing any of this? Why do we want
Speaker 2: to build intelligence that looks like us. I think the
Speaker 2: assumption that a lot of these the people, the engineers,
Speaker 2: and these labs have is that, oh, of course it's
Speaker 2: going to be extremely useful for us. It's going to
Speaker 2: unlock this unprecedented era of human flourishing. But the assumption
Speaker 2: that it's going to be really useful for us, I
Speaker 2: think is taken for granted, and if you really think
Speaker 2: about it, well, how because a lot of the examples
Speaker 2: that we have from recent technology and algorithms is that
Speaker 2: they actually take away our agency. We lose hours to scrolling,
Speaker 2: we get stuck in echo chambers, we have autocomplete takeaway
Speaker 2: our thinking, and we're starting to see.
Speaker 3: The same kinds of things with chatbots.
Speaker 2: We're also seeing that people are using these technologies and
Speaker 2: very much augmenting their their own intelligence. I feel sometimes
Speaker 2: like I'm having entirely new thoughts at an unprecedented pace
Speaker 2: when I'm going back and forth, just like when you
Speaker 2: were having amazing conversations with other people. We use each
Speaker 2: other's minds as tools, but you can just do that
Speaker 2: at a more rapid pace. So it's not a foregone
Speaker 2: inclusion that giving the AI more capabilities, making it smarter
Speaker 2: and giving it more agency is going to be good
Speaker 2: for us. I think we have to turn that on
Speaker 2: its head and say, what would it take to make
Speaker 2: AI that makes us smarter and gives us more agency?
Speaker 2: And that would be, by definition, something that is good
Speaker 2: for us. So how do we do that? I don't
Speaker 2: think that we want to have agents that have their
Speaker 2: own drives to survive and manipulate us and have all
Speaker 2: of the status seeking U situations that we have. But
Speaker 2: if they were motivated to align their representations with ours,
Speaker 2: that could actually be really useful for unlocking our potential
Speaker 2: and for helping us learn. And as we're giving them
Speaker 2: these capabilities to do that, they have to figure out.
Speaker 2: One of the ways that we are able to generalize
Speaker 2: and continually learn is that we are constantly negotiating meaning
Speaker 2: and coming up and with the friction of the interactions
Speaker 2: with each other, we are able to do continual learning
Speaker 2: because we're not optimizing for one thing, one niche, one environment,
Speaker 2: one particular problem. We are optimizing for aligning our minds
Speaker 2: with many minds, and all of them. These targets are
Speaker 2: all moving targets, so it's kind of an escape velocity
Speaker 2: from really focusing on one thing and our ability to
Speaker 2: do that not only allows us to continually learn, but
Speaker 2: it gives us superpowers.
Speaker 1: So what does this look like for you?
Speaker 2: Though?
Speaker 1: If you had a world five years in the future
Speaker 1: that you were able to sort of define where this
Speaker 1: is going, what's ok Does it mean that there are
Speaker 1: lots of AI agents and they are talking with humans
Speaker 1: and they're trying to align their thinking with humans and
Speaker 1: the humans a with the AI or its look like
Speaker 1: there's one AI give us a sense of this world?
Speaker 2: Okay, So there's at least two important things here. One
Speaker 2: is that right now agents are not reliable, so they're
Speaker 2: not useful. And I think the idea there is that
Speaker 2: they are fundamentally different than llms. They are embodied in
Speaker 2: some kind of environment, even if it's the digital environment,
Speaker 2: but we can't yet get them to do long horizon
Speaker 2: you know, actions in a reliable way, and so they're
Speaker 2: not yet useful.
Speaker 1: Right now, you're talking about AI agents.
Speaker 2: Most people have interacted with chatbots and that's what they
Speaker 2: think AI is, or that's what they think generative AI is.
Speaker 2: Maybe they know of you know, the image generators too,
Speaker 2: but a lot of us are interacting with chatbots. Those
Speaker 2: are llms that are predicting the next text token. Large
Speaker 2: language models, yes, large language models, but they don't actually
Speaker 2: do things. Agents, in contrast, can actually take actions and
Speaker 2: do things on our behalf and in order.
Speaker 3: So are lab is.
Speaker 2: Working on building computer use agents. So if I want
Speaker 2: an agent to book me a flight or order me
Speaker 2: a dinner, I can say that and then it can
Speaker 2: go off and use whatever websites or software tools to
Speaker 2: do those things.
Speaker 3: That's the hope.
Speaker 2: You've seen a couple of these agents, computer use agents
Speaker 2: come out, and it's really exciting to see them start
Speaker 2: to do things, but they're not reliable. And because they're
Speaker 2: not reliable, they might do the thing that you ask
Speaker 2: them to do one out of ten times, and again
Speaker 2: that's exciting, but that's not very useful, right.
Speaker 1: You mean it's because they make mistakes. It's not the
Speaker 1: way we would say an employee is not reliable because
Speaker 1: he's out back smoking a cigarette. Is that they're trying
Speaker 1: to do stuff is just a clicking on the wrong
Speaker 1: thing and getting it.
Speaker 2: Wrong, that's right y, Yes, So working on making these
Speaker 2: agents reliable is necessary for making them useful. But we
Speaker 2: can imagine that unlocking a whole new set of capabilities
Speaker 2: and ways that they would augment humans because rather than
Speaker 2: just having a conversation, and conversations can be very useful.
Speaker 2: All of the things that you do in your daily life,
Speaker 2: all the things that you're using a computer for, the
Speaker 2: vast majority of them are probably not worthy of your time.
Speaker 2: You're doing things on the computer to actually achieve other
Speaker 2: things in the real world. So what if you could
Speaker 2: have agents reliably execute a lot of the things that
Speaker 2: you're doing.
Speaker 3: And in.
Speaker 2: Knowledge work, you know, we're using a ton of different
Speaker 2: tools I call them arbitrary skills, processing invoices or something
Speaker 2: that everybody does using doing their taxes.
Speaker 3: Like, do you really have to becommon expert.
Speaker 2: At using these tools or is that maybe not the
Speaker 2: best use of our human potential, our cognitive potential. If
Speaker 2: we could have agents that knew how to use all
Speaker 2: of the tools that we did, that would save us
Speaker 2: a ton of time, and it would have cascading implications
Speaker 2: for how people collaborate with other people in the real world.
Speaker 2: Human collaboration would be different because we'd be freed up
Speaker 2: to focus on more creative things, more strategic decisions. Having
Speaker 2: the sorts of debates that we have to advance whatever
Speaker 2: shared goals that we have. So this is the sort
Speaker 2: of first part. We have to just get the agents
Speaker 2: to reliably click or scroll when we need them to.
Speaker 2: But if you play that forward, what does reliability actually
Speaker 2: mean when we have higher level goals.
Speaker 3: It's not just.
Speaker 2: Knowing where to click or knowing when to scroll. It's
Speaker 2: actually understanding the goal. And that goal might require breaking
Speaker 2: the breaking it down into subtasks, and then going and
Speaker 2: doing all of those things, and there might be many
Speaker 2: ways of doing it, and there's not necessarily a right
Speaker 2: or a wrong way of doing it. So at the
Speaker 2: end of the day, reliability ends up becoming about understanding
Speaker 2: our minds.
Speaker 1: So the idea is if I could have an AI
Speaker 1: agent that understands my mind, that has a model of me,
Speaker 1: including what I know and don't know, and what my
Speaker 1: goals are long term and short term, then it could
Speaker 1: do a better job at what needs to be done.
Speaker 1: Because when it comes to a choice point is is oh,
Speaker 1: I know what Eagleman wants. He likes this kind of thing,
Speaker 1: and that might be something that emerges not just from
Speaker 1: patterns of looking at my behavior, but actually understanding internally,
Speaker 1: having some theory of my mind.
Speaker 3: I think that it.
Speaker 2: Would need that, yes, and I think that we would
Speaker 2: need to be able to interact with it in the
Speaker 2: way that we interact with other teammates, where we're negotiating,
Speaker 2: meaning in real time, where we're going back to earlier
Speaker 2: in our conversation. Sometimes we don't realize that we're not
Speaker 2: clearly thinking about something, and so having that reflected back
Speaker 2: and being able to go through this exchange a dialectic.
Speaker 2: It can refine our thinking, sharpen our thinking.
Speaker 1: So this is a thing I've been wondering about for
Speaker 1: a while, which is if you're looking at something from
Speaker 1: the outside, you can actually get a lot of data
Speaker 1: about it. And by outside I mean as opposed to
Speaker 1: from the inside. If I have a theory of your mind,
Speaker 1: the question is if I, just if I could observe
Speaker 1: all of your behavior without knowing anything about what's in
Speaker 1: your mind, could I nonetheless do just as good a job.
Speaker 2: Well, I think that this is what our models of
Speaker 2: other mind essentially are. It's making sense of behavior. Yeah,
Speaker 2: it's just that the behavior, again is multiply redundant, and
Speaker 2: there are many different cues that we can attend to,
Speaker 2: and we're prioritizing some cues over others, and then it
Speaker 2: is more efficient for us to represent that there's a
Speaker 2: mind behind the eyes.
Speaker 1: Yeah, very good. Do you see a world where we
Speaker 1: would have lots of AI agents that are also speeding
Speaker 1: with one another in terms of communicative drive of saying, hey,
Speaker 1: this is what I've learned, and I know and blah
Speaker 1: blah blah, and they develop a better, something bigger as
Speaker 1: a result of the communication. They have a better understanding
Speaker 1: of the world because they're talking with one another, because
Speaker 1: just like humans, each of them is going to have
Speaker 1: some trajectory through space time.
Speaker 2: Okay, this gets into the second thing that I think
Speaker 2: is really important about how agents can be useful. So
Speaker 2: the first thing is they can do the digital drudgery
Speaker 2: for us. They can save time. I call this They
Speaker 2: can become our collective subconscious because we won't have to
Speaker 2: spend our conscious time attending to things. We can relegate
Speaker 2: so the agents can be doing in parallel all of
Speaker 2: this stuff, so we don't have to become expert in
Speaker 2: these arbitrary skills. But they are also as they are
Speaker 2: doing that for a lot of people, they're learning a
Speaker 2: bunch of different skills they're learning how to navigate different
Speaker 2: websites and use different software tools. And so if I
Speaker 2: need to for my job learn something really quickly, they
Speaker 2: can redistribute the skills that they've learned from my teammates,
Speaker 2: and they can give me the context that I need,
Speaker 2: not to become expert in that tool, but to be
Speaker 2: able to establish representational alignment with my teammate who uses
Speaker 2: that tool. So they can help coordinate a team's behavior
Speaker 2: by understanding all of the things that we do, all
Speaker 2: the goals that we have, all the tools that we
Speaker 2: use to achieve our goals.
Speaker 1: Now, normally that would happen. You go up to Susie
Speaker 1: and you say, hey, you know, I need to talk
Speaker 1: to you, and Susie says, no, I use a different
Speaker 1: word than you're using.
Speaker 3: It for ten minutes to even establish common ground.
Speaker 1: Got it. But you're saying that AI could help with
Speaker 1: that interaction like the third person in the room and say, hey,
Speaker 1: you know what, Danielle, this is what you need to
Speaker 1: know about and Susie, this is what you need to know.
Speaker 1: And I noticed you guys are using this same word,
Speaker 1: but you mean different things by it's that kind of thing.
Speaker 2: Yes, that's so yes to your point, I do think
Speaker 2: that these agents that are working in parallel and understanding
Speaker 2: our context will probably detect a ton of inefficiencies and
Speaker 2: how we're doing things, and they will come up with
Speaker 2: better ways of doing I would hope that they would.
Speaker 3: Do that great.
Speaker 2: One of the things that we learned is we were
Speaker 2: trying to train our agent how to use Gmail, is
Speaker 2: that wow, most people are actually really bad at using Gmail.
Speaker 1: In what way?
Speaker 3: So we don't know how to do.
Speaker 2: The search queries effectively. We mostly just stumble through. And
Speaker 2: it's the power users who sometimes build hold businesses around
Speaker 2: you know, the Google Suite and Gmail. They they know
Speaker 2: exactly how it was designed, all of the affordances that
Speaker 2: it has, all of the new features, how that can
Speaker 2: make things actually more efficient.
Speaker 3: They get it.
Speaker 2: But most people don't have the time to keep up
Speaker 2: with all of the new things that you can do,
Speaker 2: and when they sit down to look at their email,
Speaker 2: they just want to you know, send that email off,
Speaker 2: or just want to find that thing, and so they're
Speaker 2: not deeply engaged with learning all of the things you
Speaker 2: can do.
Speaker 1: So if you had this AI agent sitting on the
Speaker 1: shoulder in that sense, they would let's Sayea, they would
Speaker 1: teach you. Hey, look, here's the thing you need to
Speaker 1: know today, Daniel, is that the idea is that it
Speaker 1: would help you to be a better Gmail user in
Speaker 1: this particular example.
Speaker 2: Well, so, I actually think the longer term goal is
Speaker 2: that humans are spending far.
Speaker 3: Less time looking at screens.
Speaker 1: Excellent.
Speaker 2: Yes, there are exceptions which include when the actual tool
Speaker 2: scaffolds your thinking. So I use the example of Adobe
Speaker 2: Creative Suite. If you want to edit a photo or
Speaker 2: create a podcast or a video, if you didn't have
Speaker 2: all of the UIs and all of the dropdowns, yes,
Speaker 2: then you probably wouldn't even know where to start. You
Speaker 2: wouldn't even know what was possible.
Speaker 3: So some of the tools.
Speaker 2: Are actually really helpful in scaffolding your understanding of what's possible,
Speaker 2: whereas other tools just distract so much from the actual goal.
Speaker 2: We have to learn how to use the tools to
Speaker 2: do the actual research that we that we care about.
Speaker 2: So having agents take over the things that we don't
Speaker 2: care about is great, and then we can focus on
Speaker 2: the interactions that really do matter, the visualizations that really
Speaker 2: do matter.
Speaker 1: So let me just understand that. So you're saying, let's
Speaker 1: say future software are ten years from now. I open it,
Speaker 1: there's just a few simple things on the menu, but
Speaker 1: there's lots of hidden power which my AI agent can
Speaker 1: help me expose and uncover through time.
Speaker 2: Yes, and I'm imagining things where Okay, the agent understands
Speaker 2: my goal, has my context and can generate on the
Speaker 2: fly only the UI, the button or the search field
Speaker 2: that I need in that moment. Every time I open
Speaker 2: my computer, I feel anxious. I swipe to another tab
Speaker 2: and it's like it's.
Speaker 3: Swiping my memory. What was I doing again? And that
Speaker 3: is that is constant.
Speaker 2: You know, you've got most people have a lot of
Speaker 2: tabs open, They've got a lot of tools open, and
Speaker 2: it's just our cognition is not meant for all of that.
Speaker 2: There's a lot of cognitive loads. So if we could
Speaker 2: simplify that, that would augment our cognition. The other way
Speaker 2: that agents could really augment our potential is by helping
Speaker 2: us learn. So this kind of flows from it has
Speaker 2: a model of my mind, my teammate's mind, It can
Speaker 2: help us communicate at the right level of abstraction, save
Speaker 2: us time. But also if it has a model of
Speaker 2: my mind and it has my context, it knows the
Speaker 2: things that I know and don't know, the skills that
Speaker 2: I have, and the things that I care about. Then
Speaker 2: say I want to learn something totally new. Maybe it's
Speaker 2: not just a software tool. Maybe it's something like quantum mechanics,
Speaker 2: And that's really difficult to understand. You need analogies, But
Speaker 2: what are the right analogies that are going to work
Speaker 2: for me? Well, if it has a model of my mind,
Speaker 2: then it can, in a personalized way help me come
Speaker 2: to the understanding that I need to get the big picture,
Speaker 2: and it can sort of follow that in a way
Speaker 2: create a curriculum for me that gives me the information
Speaker 2: that's not too challenging, not too easy, that's right in
Speaker 2: that sweet.
Speaker 1: Spot between frustrating and achievable. Yes, yeah, that's really interesting.
Speaker 1: That'll have clear implications in education as well, keeping people
Speaker 1: right at their right spot there. Okay, so let me
Speaker 1: return to a question, because I just want to make
Speaker 1: sure I understood coming back to this idea of communicative
Speaker 1: drive and agents like you and I learning from one another.
Speaker 1: Will AI agents learn from one another, not just between
Speaker 1: agent and human, but agent to agent.
Speaker 2: Yes, And now you're really bringing it all together. So
Speaker 2: I think that agents interacting with other agents will be
Speaker 2: able to learn all sorts of patterns that maybe we
Speaker 2: haven't yet learned, and detect all sorts of inefficiencies and
Speaker 2: be more efficient in some ways.
Speaker 3: But if they also have a.
Speaker 2: Not only the ability to model our minds, but a
Speaker 2: motivation to then that's not going to be restricted knowledge
Speaker 2: to them. They're not going to go off and speciate
Speaker 2: and have all of this you know, intell that we
Speaker 2: don't have. They're going to try to communicate their insights
Speaker 2: to us. So chess players are now so much better
Speaker 2: after we've built AI that's really good at chess. So
Speaker 2: we can co evolve with this new species of intelligence.
Speaker 2: And if it's motivated to bring us along to establish
Speaker 2: representational alignment, then I think we will continue to get smarter.
Speaker 1: Do you see a situation where representational alignment just isn't possible?
Speaker 1: For example, let's say I came to you and said, hey, Danielle,
Speaker 1: I really want to teach you about these really important
Speaker 1: pieces of Mongolian history. And let's say you just don't
Speaker 1: care about Mongolian history, and I'm trying to tell you
Speaker 1: the state in this emperor or whatever. It's not going
Speaker 1: to go very far.
Speaker 2: That is a really good question. I think you nailed
Speaker 2: it when you said just don't care. In the same
Speaker 2: way that it's really hard to teach a child something
Speaker 2: that they don't care about, it's not relevant to them.
Speaker 2: I think it will be hard to establish representational alignment
Speaker 2: with anybody if they don't care. But in principle, I
Speaker 2: think it's possible, given that there is ant It might
Speaker 2: just be a matter of finding the right analogies. And
Speaker 2: again this goes to your work. There's so much plasticity
Speaker 2: in the brain.
Speaker 3: I think.
Speaker 2: Correct me if I'm wrong, But in principle, there's no
Speaker 2: limit to what we could learn to not only understand,
Speaker 2: but even have a phenomenological experience of if there is
Speaker 2: structure to that information.
Speaker 1: Yes, but all of brain plasticity is driven by relevance.
Speaker 1: In other words, do I care about it? I can't
Speaker 1: think right. So if my AI agent comes to me
Speaker 1: and says, look, I just realized this great thing about
Speaker 1: how you could redesign this computer chip in this way,
Speaker 1: and maybe it starts telling me all this detailed stuff
Speaker 1: and I just don't care. It's not achieving representational alignments.
Speaker 2: That's true, And maybe in some cases, Okay, some people
Speaker 2: don't care, other people do care.
Speaker 3: Go to the people who do care.
Speaker 2: But also I guess this also begs the question why
Speaker 2: do some people care about things? Because it's relevant? So
Speaker 2: make it relevant. Tell the person as an agent, lead
Speaker 2: the person to the insights that they need to have
Speaker 2: to about something. One of the things that is associated
Speaker 2: with becoming expert in something is that you really like
Speaker 2: the things that you become expert in because it is
Speaker 2: satisfying to understand, and the more bits and pieces of
Speaker 2: information that you can integrate into a holistic web of knowledge,
Speaker 2: the better it feels.
Speaker 3: So I think that this is my vision for a
Speaker 3: very happy future, is that.
Speaker 2: We're all learning all the time, and the more we
Speaker 2: learn and the more sort of accurate our shared world
Speaker 2: models map onto reality, the more satisfying it will be
Speaker 2: to continue to learn.
Speaker 1: I totally agree, and I love thinking about this from
Speaker 1: the point of view of education, because if you think
Speaker 1: of this sphere of humankind's knowledge, we know more than
Speaker 1: any of us could possibly learn in a lifetime. Yeah,
Speaker 1: So the key is to find out what are the
Speaker 1: doors on the outside of this sphere that you love
Speaker 1: or that I love? Given our totally different backgrounds and whatever.
Speaker 1: Certain things really fascinate me and other things I wish
Speaker 1: but they don't. Okay. So if I can enter this
Speaker 1: door and you enter this other door and we end
Speaker 1: up learning the same kind of stuff, it's really great. Yeah.
Speaker 1: And Isaac Asimov actually really cared about this topic way
Speaker 1: back in the day, and he did an interview on LERR,
Speaker 1: which was this PBS talk show thing way back in
Speaker 1: the eighties, and he envisioned this was before the Internet,
Speaker 1: and he said, I envisioned a day where there's a
Speaker 1: huge supercomputer that has all of human kinds of knowledge,
Speaker 1: and everyone has a cable running from this computer to
Speaker 1: their home and you can ask the computer any question
Speaker 1: you want. But his point was you could take your
Speaker 1: own inroad into this sphere of knowledge. Okay. So your
Speaker 1: point is if these AI agents have some theory about
Speaker 1: our minds, as in your mind, in my mind, everybody
Speaker 1: is an individual, and then can cast things in a
Speaker 1: certain way like look, here's a way that you might
Speaker 1: care about it, then we're all going to learn faster
Speaker 1: and better.
Speaker 3: Yeah.
Speaker 2: Yeah, And I also think that this is not just
Speaker 2: you know, academic stuff, intellectual stuff.
Speaker 3: Anything can be interesting.
Speaker 2: You know, people in their knitting communities can become really
Speaker 2: passionate about innovating new ways and in sharing that.
Speaker 3: Within a community.
Speaker 2: And I think that this also gets to one of
Speaker 2: the sort of essences of what we care about as humans.
Speaker 2: It's this idea of optimal distinctiveness. So we simultaneously need
Speaker 2: communities for belonging.
Speaker 3: This is an evolutionarily ancient thing.
Speaker 2: It's not just humans, but we need to feel like
Speaker 2: we belong within a community.
Speaker 3: We've got our in group, we've got our tribe.
Speaker 2: Not always bad, and maybe it's just something that we
Speaker 2: will always need as being human, but we also need
Speaker 2: to feel like we're unique and that we have something
Speaker 2: to contribute to the community. We are optimally distinct in
Speaker 2: our contributions. So I imagine a future where agents with
Speaker 2: models of our minds really allow us to be diverse.
Speaker 2: They're not flattening the experiences or the capabilities, but they're
Speaker 2: encouraging diversity and variability. But they're also building mechanisms for
Speaker 2: us to align our minds. They're building bridges throughout the
Speaker 2: various experiences and capabilities so great.
Speaker 1: So this leads me to this question that I've been
Speaker 1: wondering about which is. You know, in political debates that
Speaker 1: we see lots nowadays, there isn't a representational alignment. If
Speaker 1: you know someone's on the left and someone's on the right,
Speaker 1: they end up saying we're not going to have Thanksgiving
Speaker 1: dinner together instead of saying, oh, let me understand. How
Speaker 1: can I understand? So where does representational alignment break down?
Speaker 1: And might AI help us there someday?
Speaker 2: I think representational alignment breaks down when two minds have
Speaker 2: such different starting points, such different sets of maybe analogies, experiences,
Speaker 2: and also motivations, things.
Speaker 3: That they care about, that it's just really hard.
Speaker 2: And let's assume that they can get past the initial
Speaker 2: emotional friction of just knowing that they come from different groups.
Speaker 3: That's not trivial.
Speaker 2: Sometimes just knowing that somebody is from a different group
Speaker 2: prevents any sort of establishing of common ground. But assuming
Speaker 2: that you can get over that, then you just might
Speaker 2: not have enough overlap in your representations. I suspect that
Speaker 2: that's very rare. Just by nature of being a human
Speaker 2: embodied in the way that we are having experiences in
Speaker 2: the world and the way that we do, there's gonna
Speaker 2: be enough overlap as an entry point into being able
Speaker 2: to establish some kind of alignment.
Speaker 1: So sorry, you're saying it's rare that people would have
Speaker 1: such different pints view that can't get there, because it
Speaker 1: is the case that people don't get there or won't
Speaker 1: get there. But you're saying if people tried harder, let's say,
Speaker 1: with political arguments. Yes.
Speaker 2: I also think that as we interact with this hypothetical
Speaker 2: agent of the future that has models of our minds,
Speaker 2: it will be modeling for us behavior that helps us
Speaker 2: establish alignment. And so in the same way that we
Speaker 2: learn from others who model good behavior and then we
Speaker 2: start to reflect that subconsciously, I think it could nudge
Speaker 2: us into more pro social interaction.
Speaker 1: I totally agree with this. I did a podcast episode
Speaker 1: on this about this AI research group in Europe that
Speaker 1: released some chat bots onto a Reddit channel that does debate,
Speaker 1: and they didn't tell anybody that these were aibots, so
Speaker 1: they got in big trouble. Everyone was mad about it.
Speaker 1: But what happened was these AI bots would come in
Speaker 1: and take someone who had a particular point of view,
Speaker 1: and they would take the other point of view and
Speaker 1: they would discuss, and on this particular channel, you get
Speaker 1: points if you successfully convince somebody of your point of view.
Speaker 1: And so it turns out these bots did six times
Speaker 1: better than humans do on average in terms of changing
Speaker 1: the other person's mind. So everyone freaked out about this
Speaker 1: and said, oh my god, these AI debate bots can
Speaker 1: manipulate us. And but it turns out the really amazing
Speaker 1: part is they weren't doing anything manipulative. They weren't lying,
Speaker 1: they weren't doing anything. They were just better debaters in
Speaker 1: the sense that they were empathic, they were calm, they
Speaker 1: presented their arguments well, And I thought, God, we can
Speaker 1: really learn from that if we teach our children to
Speaker 1: be better discussings.
Speaker 3: Totally.
Speaker 1: Yeah, So I love that. I love that point. But
Speaker 1: what you're saying is, so you think people with differentints
Speaker 1: of view can do representational alignment. But that's a separate issue,
Speaker 1: which is that how do we do things culturally and
Speaker 1: teaching them to be better at conversation.
Speaker 2: Yeah, and even having an awareness that other people are different.
Speaker 3: It's not personal.
Speaker 2: If you disagree or if you have different rituals, different
Speaker 2: ways of doing things, that's fine. So I think even
Speaker 2: just exposure to variability diversity can get us part of
Speaker 2: the way there.
Speaker 1: Oh, that's fascinating. Cool. The other thing I was going
Speaker 1: to ask you about is you mentioned earlier just tangentially
Speaker 1: said something about archaeology.
Speaker 2: Ah, yes, okay, So there over the past two decades,
Speaker 2: there's been an update in our understanding of the evolution
Speaker 2: of the types of sophisticated reasoning and thinking symbolic thinking
Speaker 2: capabilities that humans have.
Speaker 3: We used to think.
Speaker 2: That they emerged suddenly in what we call the cognitive
Speaker 2: revolution that happened thirty to forty thousand years ago, and
Speaker 2: that's because.
Speaker 1: The so humans were like other primates and then suddenly,
Speaker 1: thirty four thousand years ago something happened.
Speaker 2: Okay, yeah, and archaeologists were looking at evidence in Europe
Speaker 2: and the caves and you know, the art and things
Speaker 2: like that, and it just it seemed like there was
Speaker 2: a discontinuity. But then they started exploring throughout Africa and
Speaker 2: using more nuanced methods, and a lot more of them
Speaker 2: started doing this, and they started finding evidence from as
Speaker 2: early as three hundred thousand years ago that we were
Speaker 2: cognitively modern. But what seems to be the important thing
Speaker 2: was population density and contact with other groups. So the
Speaker 2: idea is that our sophisticated cognitive capabilities are latent until
Speaker 2: we come into contact with each other, until we poke
Speaker 2: each other's brains. And you see that this evidence ebbs
Speaker 2: and flows, It appears and disappears as a function of
Speaker 2: these group donaities.
Speaker 1: Yeah, oh fascinating. Okay, So when humans come into contact
Speaker 1: with other humans, but not just their own tribe, presumably
Speaker 1: other tribes, bigger and bigger civilizations, slightly.
Speaker 3: Different ways of making their tools and their weapons and
Speaker 3: their jewelry.
Speaker 1: Yes, oh excellent, Oh that's beautiful. So, by the way,
Speaker 1: is it thought that there was some discontinuity where that
Speaker 1: became possible. In other words, if you stick a bunch
Speaker 1: of capuchin monkeys together.
Speaker 3: They will never they'll never get there, right.
Speaker 1: Right, So there's so there's something different.
Speaker 2: And I mean, I think it's this stability of our
Speaker 2: the models of our minds, and I think it's this
Speaker 2: communicative drive. But you need a critical density of people
Speaker 2: and you also need the variability.
Speaker 1: Okay, so let's just summarize it. So it's having theory
Speaker 1: of mind in other words, knowing Okay, Danielle has her
Speaker 1: own thoughts, her own representations in there, and then you
Speaker 1: add that to the density of people.
Speaker 3: And the variability of different groups coming together ability.
Speaker 1: That's excellent.
Speaker 3: Yeah.
Speaker 2: So the takeaway from this archaeological evidence is that becoming
Speaker 2: cognitively modern was this slow, gradual process over the course
Speaker 2: of the last couple hundred thousand years.
Speaker 1: But it was predicated on pop density yeah, and people
Speaker 1: coming together.
Speaker 2: Yeah.
Speaker 1: Oh excellent. Oh I love that. That's so interesting, And it.
Speaker 2: Goes along with this idea that we're not just biologically evolving,
Speaker 2: we're culturally evolving. And cultural evolution does a lot of
Speaker 2: the work in explaining human behavior.
Speaker 1: Yeah, and biological evolution, of course, is super slow, but
Speaker 1: cultural evolution is so rapid.
Speaker 3: Yes.
Speaker 2: And actually I think that agents that have models of
Speaker 2: our minds can help reconcile some of the tensions that
Speaker 2: we're seeing because cultural evolution is outpacing biological evolution. So
Speaker 2: what happens when you've succeeded in society, you've done well
Speaker 2: in education, you go to the workplace and you're supposed to,
Speaker 2: you know, contribute your intelligence. You end up staring at
Speaker 2: a screen for most of the day and not interacting
Speaker 2: with other people. The infrastructure actually doesn't really support unlocking
Speaker 2: our potential because of all of the sort of arbitrary
Speaker 2: things that have happened culturally. We've built these incredible devices
Speaker 2: and we've co evolved with them, and now they're extensions
Speaker 2: of our intelligence. But they're also we're also conforming to
Speaker 2: them rather than the other way around.
Speaker 1: True, But they are social, as in social media and
Speaker 1: so on. I mean, when I'm staring at my screen,
Speaker 1: I'm interacting with thousands of people in various ways, whether
Speaker 1: I'm looking at extra Instagram or I'm doing emails. In
Speaker 1: a sense, it's more social than humans ever could have been.
Speaker 2: What this is the problem when the algorithms are not
Speaker 2: aligned with our well being, with our potential. So I
Speaker 2: see there are so many mistakes that we've made over
Speaker 2: the past ten fifteen years that we can learn from
Speaker 2: and hopefully not repeat with more capable AI.
Speaker 1: But out of curiosity, if I'm on X and I
Speaker 1: see that there are different points of view about this
Speaker 1: political thing that I happen to care about, then I'm
Speaker 1: getting exposed to lots of points of view.
Speaker 2: Right.
Speaker 3: Well, I'm not saying it's all bad. Certainly, Yes, I think.
Speaker 2: That having online communities is absolutely a step in the
Speaker 2: right direction for connecting us. It's fantastic but the way
Speaker 2: that they are optimized and the attention economy not serving
Speaker 2: us but serving advertisers is a problem.
Speaker 1: But what would you change about let's say something like X.
Speaker 1: What would you change about social media to make it
Speaker 1: so it's more optimized?
Speaker 3: Well, I think optimization is the problem.
Speaker 1: So sorry, I met more optimized for a communicative drive.
Speaker 2: I wouldn't think of it that way because I think
Speaker 2: that you have the agents, but then you also have
Speaker 2: how they are dynamically interacting with each other. And X
Speaker 2: is or any social media platform, is one narrow way
Speaker 2: of facilitating interactions. In the case of X, it's very
Speaker 2: short form blurbs, and it appeals to the fact of
Speaker 2: human nature that we are more interested in things that
Speaker 2: have shock value and things that are negative or disgusting,
Speaker 2: and how do you work against that. It's not about
Speaker 2: informational exchange, although some people lean more in terms of
Speaker 2: caring about that, and so you do see some of that.
Speaker 1: Yeah, so that's interesting. So we are in the sense
Speaker 1: of population density for three hundred thousand year old tribes.
Speaker 1: It is the case that you see on X all
Speaker 1: kinds of points of view that you didn't know existed.
Speaker 1: And well, I don't know if you do.
Speaker 2: Because you're in your echo chamber, you typically tend to
Speaker 2: not be exposed to a lot of variability.
Speaker 1: Well, you know what's interesting, what you're exposed to is
Speaker 1: the most extreme views of the other side because people
Speaker 1: in your echo chamber say, look at what this idiot
Speaker 1: is on the other side of the aisle said.
Speaker 3: And the polarize it.
Speaker 1: Yeah exactly, Yeah, okay, So wrapping up for today, the
Speaker 1: key thing is that intelligence is not just about what's
Speaker 1: happening in an individual brain, but it's social.
Speaker 2: Yes, this is true about all intelligences. All intelligences emerge
Speaker 2: as a function of their environments and interacting with their
Speaker 2: environments the problems within the environments that the organisms have
Speaker 2: to solve. The most challenging problems in humans environments are
Speaker 2: understanding other humans because we have to figure out how
Speaker 2: to cooperate, We have to figure out.
Speaker 3: How to align our minds.
Speaker 2: So our intelligence emerges from our interactions with each other,
Speaker 2: and we continually ratchet up our intelligence by co evolving
Speaker 2: with each other.
Speaker 1: That was my conversation with Danielle Persik. One of the
Speaker 1: main threads was that maybe we shouldn't be thinking of
Speaker 1: intelligence as something that's packaged up inside a single head.
Speaker 1: Another way to look at it is as something that
Speaker 1: emerges through interaction, through the friction, through the shared effort
Speaker 1: of understanding another mind. Human intelligence has always been shaped
Speaker 1: by this social dimension. You can see this from the
Speaker 1: way that infants learn about the world to the way
Speaker 1: that societies build knowledge over generations. What today's conversation invites
Speaker 1: us to reconsider is the idea that learning and understanding,
Speaker 1: and fundamentally alignment are really the central features of intelligence.
Speaker 1: Our ability to model other minds, to recognize that other
Speaker 1: people see the world differently, that they know different things,
Speaker 1: that they care about different things. This is what allows
Speaker 1: cooperation and culture and cumulative progress. Through this lens, intelligence
Speaker 1: is about negotiating meaning. Now, if we take on that lens,
Speaker 1: the future of AI looks very different from what most
Speaker 1: people are thinking about now, because this shifts the conversation
Speaker 1: away from only asking how capable AI systems are going
Speaker 1: to be. Now we're pushed to ask how they participate
Speaker 1: in our cognitive ecosystems. In other words, in Danielle's view,
Speaker 1: how can we develop agents who help us think better
Speaker 1: by reducing friction and clarifying misunderstandings between people and supporting
Speaker 1: learning at the right level of abstraction, instead of merely
Speaker 1: replacing us, which is the doomsayers version of the future.
Speaker 1: Could AI agents serve as mediators and translators and collaborators.
Speaker 1: And there's another issue I found fascinating. Alignment is something
Speaker 1: humans have always learned through interactions. So perhaps instead of
Speaker 1: just viewing AI alignment as a technical problem to be solved,
Speaker 1: we could see it as a behavior to be modeled.
Speaker 1: Systems that reflect our values back to us might teach
Speaker 1: us how to communicate more effectively with one another, even
Speaker 1: in moments of disagreement. If you're a regular listener to
Speaker 1: this podcast, you know that I'm obsessed with issues about
Speaker 1: the brain and polarization, and so this possibility that AI
Speaker 1: might actually be able to mediate between us and help
Speaker 1: us get our curiosity back that feels especially consequential. So
Speaker 1: as AI agents become more embedded in our daily life,
Speaker 1: the choices that we make now about their design are
Speaker 1: going to shape how we relate to them and to
Speaker 1: our fellow humans. The question is whether they will help
Speaker 1: intelligence continue to sprout in new directions. In other words,
Speaker 1: maybe will be even more intelligent as a species thanks
Speaker 1: to our machines. Go to eagleman dot com slash podcast
Speaker 1: for more information and to find further reading. Join the
Speaker 1: weekly discussions on my substack, and check out and subscribe
Speaker 1: to Inner Cosmos on YouTube for videos of each episode
Speaker 1: and to leave comments. Until next time, I'm David Eagleman,
Speaker 1: and this is Inner Cosmos.