Speaker 1: What's it like to have a much lower IQ than
Speaker 1: you currently have or to have a much higher IQ?
Speaker 1: Welcome to the inner Cosmos with me David Eagleman. I'm
Speaker 1: a neuroscientist and an author at Stanford and in these
Speaker 1: episodes we sail deeply into our three pound universe to
Speaker 1: understand why and how our lives look the way they do.
Speaker 1: Today's episode is about intelligence. What is it What would
Speaker 1: it be like to have the intelligence of a mosquito,
Speaker 1: or a horse or a squirrel. What would it be
Speaker 1: like to presumably understand only very basic things right around you,
Speaker 1: not doing sophisticated simulation of the future like we do
Speaker 1: as humans. And what can we say about the present
Speaker 1: and future of intelligence that is artificial? Okay, so let's
Speaker 1: start with this question of what is it like to
Speaker 1: have a different level of intelligence? I see people post
Speaker 1: this question sometimes online on forums like Quora. Someone will write,
Speaker 1: I have an IQ of sixty eight, what is it
Speaker 1: like to have a higher IQ? Now? First of all,
Speaker 1: I think this is an amazing question because it acknowledges
Speaker 1: that not everyone is having the same experience on the inside,
Speaker 1: and so someone is taking the time to ask, what
Speaker 1: would it be like to have what is called a
Speaker 1: higher intelligence level. What's the experience of that? Now, the
Speaker 1: interesting thing in life is that we can't run a
Speaker 1: control experiment on our own experience of the world, and
Speaker 1: so whatever IQ you have, you sort of have just
Speaker 1: that one experience of reality. But to get at this,
Speaker 1: let's start by thinking about what it would be like
Speaker 1: to have a much lower IQ than you do now.
Speaker 1: One way to get at this is to ask the
Speaker 1: question of what would it be like to be a squirrel.
Speaker 1: I'm choosing a squirrel just because I was watching one
Speaker 1: in my backyard yesterday, and I'm watching him run along
Speaker 1: the top of the fence and climb up the tree
Speaker 1: trunk and find a little scrap of food and look
Speaker 1: around nervously. And a squirrel's cerebrum has about one hundred
Speaker 1: million neurons, while ours has about one hundred billion neurons,
Speaker 1: so a thousand times more. Now, size isn't everything, which
Speaker 1: we'll return to a little bit. Presumably the issue is
Speaker 1: the algorithm that's running. But we can watch the behavior
Speaker 1: of lots and lots of squirrels over lots of time,
Speaker 1: and it certainly doesn't seem like they're having the kind
Speaker 1: of capacity for thought that we are. So I was
Speaker 1: watching this squirrel, and I thought, what would it be
Speaker 1: like to be able to jump around from branch to branch,
Speaker 1: but have no hope, presumably of ever discovering that force
Speaker 1: equals masstimes acceleration, or for that matter, or not even
Speaker 1: ever being able to discover that e equals mc squared,
Speaker 1: or just basic things like how do you build a chair,
Speaker 1: or how do you think about the competition between Amazon
Speaker 1: and Netflix, or just the idea that the telephone lines
Speaker 1: they're running on top of are carrying megabytes of information
Speaker 1: flowing as zeros and ones as one member of our
Speaker 1: species communicates to another, or that more broadly, we have
Speaker 1: airwaves and fiber optics carrying a flow of zetabytes information
Speaker 1: in a massive ocean of data around us. For the squirrel,
Speaker 1: none of this exists. For the squirrel, none of this
Speaker 1: is comprehensible. It's thinking about its acorn and where it
Speaker 1: hid its last one, and it's thinking about safety and
Speaker 1: maybe about mating. As far as we know, or as
Speaker 1: far as we can tell, which of course has its limitations.
Speaker 1: The squirrel is not ruminating on a play it saw
Speaker 1: last night. By the squirrel equivalent of Shakespeare and what
Speaker 1: it means about the aspirations of a monarch and the
Speaker 1: cruelty inherent in the competition for power. It's not thinking
Speaker 1: about how to get to the moon or how to
Speaker 1: build the next vaccine. Now, it's not to say that
Speaker 1: there aren't specialized kinds of intelligence. Every move that the
Speaker 1: squirrel makes along the tree branch is very impressive. There
Speaker 1: is no way that I can could hope to stick
Speaker 1: landing after landing like that from the branch of one
Speaker 1: tree to the next to the next. But the squirrel
Speaker 1: is capable of doing that. But even though it performs
Speaker 1: this incredible ballet in the face of gravity, it's presumably
Speaker 1: never going to get to the point where it can
Speaker 1: characterize gravity in equation form to understand how it should
Speaker 1: move if it were on a different planet, or to
Speaker 1: conceptualize gravity as curvature in the fabric of space time.
Speaker 1: Although humans are capable of getting that in high school,
Speaker 1: presumably squirrels of any age would find the concept well
Speaker 1: beyond what their brains could even hope to have a
Speaker 1: flicker of. And when you look around the animal kingdom
Speaker 1: around us, we find lots of creatures presumably occupying very
Speaker 1: different levels of intelligence. So a bat has let's call it,
Speaker 1: ten million neurons, and again it's not the size but
Speaker 1: the structure that matters.
Speaker 2: But presumably they are not writing the equivalent of bat
Speaker 2: books or building a little bat Internet where they can
Speaker 2: capture for eternity everything that every generation of bats before
Speaker 2: them has learned.
Speaker 1: A fish has only about one hundred thousand neurons, a
Speaker 1: house cricket has fifty thousand neurons. A common fruitfly has
Speaker 1: only about two five hundred neurons in the equivalent to
Speaker 1: its brain. So if you were a fruit fly, you
Speaker 1: simply don't have the notion of seeing the moon in
Speaker 1: the sky and thinking, okay, that's an orbiting sphere, and
Speaker 1: I'm going to derive a plan with my fellow flies
Speaker 1: to get there by building new technologies that we can
Speaker 1: fit our little bodies inside of so that we can
Speaker 1: survive in low oxygen. And what if you're a mosquito,
Speaker 1: with your little mosquito brain, all you know is the
Speaker 1: mad attraction to certain odors which indicate a warm blood
Speaker 1: and animal, and the pleasure of slipping your proboscis in
Speaker 1: and satisfying your thirst with the warm liquid. You presumably
Speaker 1: don't even have the concept of blood, that it has
Speaker 1: plasma and cells that are specialized for grabbing oxygen, and
Speaker 1: all kinds of useful machinery for defending the host animal,
Speaker 1: and so on and so on. So I've thought about
Speaker 1: these issues for years, and I think there's a way
Speaker 1: to understand what it is like to be so limited.
Speaker 1: The way that the mosquito might look at the human,
Speaker 1: if it could even have a concept of a human,
Speaker 1: how might look at the human and think, oh, my gosh,
Speaker 1: I can't believe they understand all that stuff, and they
Speaker 1: do it all at once. And the reason we can
Speaker 1: understand what it's like to be limited is because we
Speaker 1: are up against problems all the time that we're just
Speaker 1: smart enough to recognize, but not yet smart enough to solve.
Speaker 1: Take the origin of life, this is a massively difficult problem.
Speaker 1: I worked with brilliant scientists like Sidney Brenner and Francis
Speaker 1: Krik at the Salt Innstitute, who worked on theories of
Speaker 1: the origin of life. And these were among the smartest
Speaker 1: biologists of the twentieth century. And even they were like
Speaker 1: little fruit flies when trying to tackle that problem. They
Speaker 1: constantly were aware of the enormous gaps in whatever story
Speaker 1: they were hoping to put together, because we just don't
Speaker 1: have much of any lasting data from the past three
Speaker 1: point eight billion years, and we're talking about how trillions
Speaker 1: of atoms might come together in just the right way
Speaker 1: over time to form things that can self replicate. It's
Speaker 1: the kind of problem that when you really start to
Speaker 1: reach your arms down into it, you realize that even
Speaker 1: very smart human brains just aren't equipped for a problem
Speaker 1: of that size. Or just take something like thinking about
Speaker 1: the cosmos with one hundred billion galaxies in it and
Speaker 1: one hundred billion stars inside each of those galaxies, and
Speaker 1: uncountable numbers of planets rotating around those stars, and then
Speaker 1: trying to picture or answer whether there is life elsewhere
Speaker 1: in the galaxy and what it would look like. It's
Speaker 1: clear that our human brains aren't so good at grocking
Speaker 1: numbers like that. Even though we can estimate the numbers
Speaker 1: and we can use words to talk about them, we're
Speaker 1: not really capable of understanding them. And it's the same
Speaker 1: thing when we study neuroscience. Of course, we've got in
Speaker 1: the ballpark of eighty six billion neurons and each one
Speaker 1: of those is connected to so many of its neighbors,
Speaker 1: about ten thousand, that if you took a cubic millimeter
Speaker 1: of brain tissue, there are more connections in there than
Speaker 1: there are stars in the Milky Way galaxy. This thing
Speaker 1: that we're facing on a daily basis this thing the
Speaker 1: brain that tens of thousands of people on the planet's study,
Speaker 1: this three pound organ that we have completely cornered. It
Speaker 1: is so vastly complex that there is no way for
Speaker 1: a human brain to understand itself. And in neuroscience we
Speaker 1: have foundational problems that we can't answer, like why something
Speaker 1: feels good? Take an orgasm? Why does an orgasm feel good?
Speaker 1: We can, of course tell the evolutionary story, which is
Speaker 1: that it benefits the species to reproduce, so it is
Speaker 1: advantageous for it to feel good. But the question from
Speaker 1: a neurobiology point of view is how do you build
Speaker 1: a network that feels anything. Take a big, impressive artificial
Speaker 1: neural network like GPT four. It can do incredibly impressive
Speaker 1: work by taking some prompt and written language and generating
Speaker 1: words that would statistically go with that prompt and so on.
Speaker 1: It's mind blowing how well it appears to do. But
Speaker 1: GPT four presumably can't feel pain or pleasure. There's nothing
Speaker 1: about one of its sentences that it generates that it
Speaker 1: appreciates as hilarious or tear jerking. It doesn't have any
Speaker 1: capacity to feel concerned about its survival or demise. When
Speaker 1: the programmers turn the computer off. It's just running numbers
Speaker 1: down a long, complex algorithmic network, and that's it. So
Speaker 1: how do we ever come to feel something? This is
Speaker 1: perhaps the central unsolved question in neuroscience. It's usually summarized
Speaker 1: as consciousness, and specifically the hard problem of consciousness, which
Speaker 1: is to say, why does all this signaling moving through
Speaker 1: networks of cells feel like something? How could you ever
Speaker 1: program a computer to feel pain or detect some wavelength
Speaker 1: of electromagnetic radiation and as purple, or to detect some
Speaker 1: wavelength of electromagnetic radiation and experience it as purpleness, or
Speaker 1: to enjoy the beauty of a sunset. These are totally
Speaker 1: unsolved questions in neuroscience, and presumably there are whole classes
Speaker 1: of problems that we are not even smart enough to
Speaker 1: realize our questions that we could be asking. So, despite
Speaker 1: the incredible pride filling progress of our species. Our ignorance
Speaker 1: vastly outstrips our knowledge, and that affords us just a
Speaker 1: little bit of insight into the limitations of our brains,
Speaker 1: the glass walls of our fish bowl, and lets us
Speaker 1: even very roughly imagine what it would be like to
Speaker 1: have the intelligence of a squirrel. And these are the
Speaker 1: things that I was thinking about when I wrote a
Speaker 1: fictional short story called Descentive Species in my book Some
Speaker 1: and so I'm going to read it here, and then
Speaker 1: I'll come back to the question of intelligence. In the afterlife,
Speaker 1: you are treated to a generous opportunity. You can choose
Speaker 1: whatever you would like to be in the next life.
Speaker 1: Would you like to be a member of the opposite sex,
Speaker 1: born into royalty, a philosopher with bottomless profundity, a soldier
Speaker 1: facing triumphant battles. But perhaps you've just returned here from
Speaker 1: a hard life. Perhaps you were tortured by the enormity
Speaker 1: of the decisions and responsibilities that surrounded you. And now
Speaker 1: there's only one thing you yearn for, simplicity that's permissible.
Speaker 1: So for the next round, you choose to be a horse.
Speaker 1: You covet the bliss of that simple life. Afternoons of
Speaker 1: grazing and grassy fields, the handsome angles of your skeleton
Speaker 1: and the prominence of your muscles. The peace of the
Speaker 1: slow flicking tail, or the steam rifling through your nostrils.
Speaker 1: As you lope across snow blanketed planes, you announce your decision.
Speaker 1: Incantations are muttered, a wand is waved, and your body
Speaker 1: begins to metamorphose into a horse. Your muscles start to bulge,
Speaker 1: A mat of strong hair erupts to cover you like
Speaker 1: a comfortable blanket in winter. The thickening and lengthening of
Speaker 1: your neck immediately feels normal as it comes about. Your
Speaker 1: carotid arteries grow in diameter, your fingers blend hoofward, your
Speaker 1: knees stiffen, your hips strengthen. And meanwhile, as your skull
Speaker 1: lengthens into its new shape, your brain races in its changes.
Speaker 1: Your cortex retreats as your cerebellum grows. The homuncular smelts
Speaker 1: man to horse neurons, redirect synapses, unplug and replug on
Speaker 1: their way to equestrian patterns, and your dream of understanding
Speaker 1: what it is like to be a horse gallops toward
Speaker 1: you from the distance. Your concern about human affairs begins
Speaker 1: to slip away, Your cynicism about human behavior melts, and
Speaker 1: even your human way of thinking begins to drift away
Speaker 1: from you. Suddenly, for just a moment, you are aware
Speaker 1: of the problem you overlooked. The more you become a horse,
Speaker 1: the more you forget the original wish. You forget what
Speaker 1: it was like to be a human, wondering what it
Speaker 1: was like to be a horse. This moment of lucidity
Speaker 1: does not last long, but it serves as the punishment
Speaker 1: for your sins. A Promethean entrails hecking moment, crouching half horse,
Speaker 1: half man with the knowledge that you cannot appreciate the
Speaker 1: destination without knowing the starting point. You cannot revel in
Speaker 1: the simplicity unless you remember the alternatives. And that's not
Speaker 1: the worst of your revelation. You realize that the next
Speaker 1: time you return here with your thick horse brain, you
Speaker 1: won't have the capacity to ask to become a human again.
Speaker 1: You won't understand what a human is. Your choice to
Speaker 1: slide down the intelligent sladder is irreversible, and just before
Speaker 1: you lose your final human faculties, you painfully ponder what
Speaker 1: magnificent extraterrestrial creature enthralled with the idea of finding a
Speaker 1: simpler life, chose in the last round to be come
Speaker 1: a human. So in the story, I try to give
Speaker 1: a way to think about the possibility that something could
Speaker 1: be much smarter than us, that we are not at
Speaker 1: the top of the latter but maybe somewhere in the middle,
Speaker 1: and that to some other creatures in the universe, we
Speaker 1: would appear to be like the squirrels are to us.
Speaker 1: It certainly could be that in this vast cosmos there
Speaker 1: are intelligences that are so much higher than ours that
Speaker 1: we lack even a good imagination or vocabulary to paint
Speaker 1: these creatures in the same way that presumably the squirrels
Speaker 1: would be unable to give a reasonable description of us
Speaker 1: and what we're up to. Maybe these extraterrestrials can understand
Speaker 1: the entirety of cosmic evolution by the time they're in
Speaker 1: second grade, and they can keep in mind the trillions
Speaker 1: of animal species on this planet and all the other planets,
Speaker 1: and keep track of all the interactions and therefore understand
Speaker 1: the biological history and future of a planet at depth.
Speaker 1: While we mostly just use the word evolution to capture
Speaker 1: something that we can't comprehend at a deep level. Now,
Speaker 1: let me put on the table that I'm completely uncompelled
Speaker 1: by the claims that there are UFOs, or nowadays they're
Speaker 1: called UAPs. But I have a very smart friend named
Speaker 1: Kevin who told me the other day that he has
Speaker 1: no problem believing that that's true. Now, I'm not defending
Speaker 1: his position, but his stance was simply that if you
Speaker 1: imagine the aliens are much smarter than we are, then
Speaker 1: the particulars of what we're looking for, some Morse code
Speaker 1: signal or some take me to your leader's sign, that's
Speaker 1: actually the wrong thing for us to be looking for.
Speaker 1: Because if we imagine some civilization that is, say, totally
Speaker 1: differ from us and three million years ahead of us,
Speaker 1: and they are to us as we are to the squirrels,
Speaker 1: it's certainly not difficult to imagine the possibility that we
Speaker 1: are simply not smart enough to construct a good model
Speaker 1: of them and therefore even recognize them. Now, you might
Speaker 1: assume that if they're much smarter than us, then they
Speaker 1: could dumb themselves down to communicate with us the way
Speaker 1: that we sort of know how to talk with a
Speaker 1: child at a child's level. But our ability to model
Speaker 1: lesser intelligences is still pretty terrible. I mean, you still
Speaker 1: have no idea how to go out in your yard
Speaker 1: and communicate with squirrels. Just try having a meaningful conversation.
Speaker 1: Good luck. We're so much smarter than a squirrel, but
Speaker 1: we have no idea how to plug into their neural networks.
Speaker 1: Or just go to a zoo and try to have
Speaker 1: a conversation with a panda bear and commune unicate to him.
Speaker 1: Take me to your leader, or do that with a
Speaker 1: camel or a dolphin. You get the point, which is
Speaker 1: that just because you are smarter doesn't necessitate that you
Speaker 1: know how to talk to these other animals. And this
Speaker 1: is the situation that we could hypothetically be in with
Speaker 1: extraterrestrial civilizations. That we are here even though we don't
Speaker 1: recognize that they are there, because we don't even have
Speaker 1: the capacity to imagine them, and they have no meaningful
Speaker 1: way to communicate with us, because our needs and desires
Speaker 1: are so different from what they can even understand. Now,
Speaker 1: this lack of communication across species or across planets is
Speaker 1: really brought into relief when we consider that intelligence is
Speaker 1: not one thing, but there are many different behaviors that
Speaker 1: we might put under the umbrella of intelligence. To this point.
Speaker 1: In nineteen seventy four, the philosopher Thomas Nagel wrote an
Speaker 1: essay called what is it Like to Be a Bat?
Speaker 1: Because fundamentally, being a bat is a pretty different experience
Speaker 1: than being a different human. If you are a blind
Speaker 1: echo locating bat, you emit chirps in the dark, and
Speaker 1: you receive back echoes of your chirps, and you translate
Speaker 1: those air compression waves into a three dimensional picture of
Speaker 1: what is in front of you. You make a mental
Speaker 1: map of your surroundings this way. So Nagel asked this
Speaker 1: question of what it's like to be a bat in
Speaker 1: the context of consciousness, as in, given that we have
Speaker 1: such a different sensory world, is there any way that
Speaker 1: we could understand what it would be like to be
Speaker 1: in such a different way of detecting and sensing the world.
Speaker 1: But this same question could be applied to what we're
Speaker 1: thinking about here, which is intelligence. Intelligence in the context
Speaker 1: of a bat allows the bat to navigate around and
Speaker 1: find food, and talk with other and adapt when the
Speaker 1: conditions change. But it's hard to directly compare it to
Speaker 1: human intelligence because they have traits and adaptations that are
Speaker 1: very sophisticated in their own way. Like I said, with echolocation,
Speaker 1: they're creating this three D map in their space. They're
Speaker 1: using auditory information in real time, and they can have
Speaker 1: such precision that they can detect an object as thin
Speaker 1: as a hair and fly around, that they can figure
Speaker 1: out the size and shape and speed of objects like
Speaker 1: a little moth flying around, so they can zoom in
Speaker 1: on it and grab it. And they also have sophisticated
Speaker 1: social behavior, but presumably about different social things than what
Speaker 1: we care about. And we know that they do all
Speaker 1: kinds of problem solving, but it strikes me that it's
Speaker 1: really difficult to know what sorts of problems they solve,
Speaker 1: because some of the problems are so foreign to us
Speaker 1: that we don't even know how to think about them.
Speaker 1: So all this leads us back around to the main
Speaker 1: question for today, which is what is intelligence? How do
Speaker 1: we define it?
Speaker 2: Well?
Speaker 1: As it turns out, this has not been an easy
Speaker 1: question for scientists, and it has come with lots of debate.
Speaker 1: And this is one of those things where we all
Speaker 1: have an intuition about what we mean by the word.
Speaker 1: But the trick from a neuroscience perspective is how do
Speaker 1: you rigorously define it and therefore, how do you study it?
Speaker 1: When we talk about intelligence, let's say just human intelligence,
Speaker 1: what are we even talking about. We all have a
Speaker 1: sense of what an intelligent person is, but what is
Speaker 1: happening in their brain that is different from someone else
Speaker 1: who you might think is not so intelligent. How do
Speaker 1: giant networks of individual neurons, billions of them manipulate information
Speaker 1: that you've taken in before and simulate possible futures and
Speaker 1: evaluate those and throw out all the information that doesn't matter.
Speaker 1: And do people who are intelligent store knowledge in a
Speaker 1: different way, Maybe not categorically different, but just perhaps in
Speaker 1: a way that's more distilled or more easily retrievable. So
Speaker 1: these are the kind of questions we're facing now. The
Speaker 1: first thing to appreciate about intelligence in the brain is
Speaker 1: that size does not seem to matter. Andre the giant
Speaker 1: had a brain volume that might have been eight times
Speaker 1: the size of yours, but he was probably not eight
Speaker 1: times smarter than you. In fact, what is so remarkable
Speaker 1: is that brains that are enormous, like in elephants, and
Speaker 1: brains that are very tiny, like a little mouse brain
Speaker 1: can both tackle very complex problems like foraging for food
Speaker 1: and setting up a home and mating and defending itself
Speaker 1: against predators. The Spanish neuroscientist Santiago Romoni Cajol, like many
Speaker 1: neuroscientists before and after him, was really struck by this thought,
Speaker 1: and he had this beautiful comparison of large and small
Speaker 1: brains to large and small clocks like big ben and
Speaker 1: a wristwatch both tell the time with equal accuracy despite
Speaker 1: the size difference. So all this is to say that
Speaker 1: when we stare at brains, this secret of intelligence is
Speaker 1: not immediately obvious just from looking at the brain. Now,
Speaker 1: when we look across species, we can see what we
Speaker 1: might mean by intelligence. For example, good problem solving skills.
Speaker 1: Some primates are really good at using tools, like orangutans,
Speaker 1: while well other primates like bonobo's are really good at
Speaker 1: social intelligence. If you look at purposes, you find that
Speaker 1: they are better problem solvers and do much more than
Speaker 1: say other swimmers like catfish. And when we examine humans
Speaker 1: we see that somebody can be a genius in one
Speaker 1: domain but quite bad at another. Rinaldo is a genius
Speaker 1: at soccer, but he might not be so great at
Speaker 1: differential equations. I recently saw a video of a kid
Speaker 1: who can do a Rubik's cuban about three seconds, but
Speaker 1: he's autistic and therefore is not particularly good at anything
Speaker 1: involving social interaction. So how can we put a measure
Speaker 1: to what we are talking about here? About a century
Speaker 1: and a half ago people started working on the question
Speaker 1: of how you could quantify this. The British scientists Sir
Speaker 1: Francis Galton was one of the first that I know
Speaker 1: of who said we should be able to measure intelligence.
Speaker 1: So is that you could quantify it by measuring things
Speaker 1: like the strength of someone's eyesight and hearing, or the
Speaker 1: strength of their grip. So that approach didn't last long,
Speaker 1: but by nineteen oh five, two scientists, Alfred Binet and
Speaker 1: Theodore Simon built the Simon Benay test as a way
Speaker 1: of quantifying some number for intelligence. And then in nineteen
Speaker 1: sixteen here at Stanford University there was an educator named
Speaker 1: Lewis Turman who developed the test more and he renamed
Speaker 1: it the Stanford Bena test, which you might have heard
Speaker 1: of because it is still used now. These sorts of
Speaker 1: tests allow us to put a number on something, but
Speaker 1: we still know what exactly we're measuring. The psychologist Charles
Speaker 1: Spearman was intrigued by this question, and he made an observation,
Speaker 1: which is that if you do well on one task,
Speaker 1: something like verbal skills, you tend to also do well
Speaker 1: at other tasks, like spatial skills, and so these things correlated,
Speaker 1: and he speculates that there was some sort of general
Speaker 1: intelligence involved here, and so he used the letter G
Speaker 1: for this idea of a general intelligence factor, like a
Speaker 1: general skill set of the brain. And other researchers noticed
Speaker 1: this correlation also between very different sorts of tasks like
Speaker 1: memory and perception and language and solving new problems and
Speaker 1: pattern recognition and a whole bunch of others, and so
Speaker 1: it still wasn't clear what intelligence is, but it's clear
Speaker 1: that these things correlated, and so in nineteen twenty one
Speaker 1: researcher wrote that while it is difficult to define precisely
Speaker 1: what intelligence is, tests tested. So some people felt that
Speaker 1: there's one thing, this G, that underlies lots of different skills,
Speaker 1: and others felt that maybe these are completely separate things
Speaker 1: and intelligence is not one thing. So that's the debate
Speaker 1: that got rolling over a century ago and it remains
Speaker 1: an unsolved issue. The fact is that if intelligence were
Speaker 1: just one thing, you might expect to sometimes see a
Speaker 1: small bit of brain damage where someone loses skills across
Speaker 1: different types of intelligence, or with the introduction of brain
Speaker 1: imaging some decades ago, we might be able to see
Speaker 1: a single small network becoming active even with very different problems.
Speaker 1: But interestingly, this is still unresolved because some researchers ask
Speaker 1: participants to do very different kinds of tasks like verbal
Speaker 1: and perceptual and spatial things while their brain is getting scanned,
Speaker 1: and they find that all of these tasks lead to
Speaker 1: activity in an area called the lateral frontal cortex, and
Speaker 1: so it might be interpreted to support the unitary intelligence
Speaker 1: hypothesis because you're seeing one area becoming active even when
Speaker 1: people are doing different kinds of tasks. But on the
Speaker 1: other hand, we're always faced with the problem that our
Speaker 1: current brain reading technology only lights up areas where there's
Speaker 1: a lo a lot of activation, and it doesn't catch
Speaker 1: the areas that are more diffuse where the real detailed
Speaker 1: action might be happening. And there's also an issue that
Speaker 1: highly intelligent people find particular tasks less challenging and so
Speaker 1: they often show less activity in the frontal cortex, not more.
Speaker 1: And so it may be that even with our terrific technology,
Speaker 1: it's still a little bit too crude to tell us
Speaker 1: what intelligence is by simply going around and looking for
Speaker 1: a spot or a collection of spots in the brain.
Speaker 1: This is in the same way that you're not going
Speaker 1: to look at chat GPT and say, ah, what makes
Speaker 1: it intelligent? Are these few nodes here out of the
Speaker 1: billions of nodes. Instead, it's a function of the whole
Speaker 1: of the activity running through the enormous system. So it
Speaker 1: may turn out that intelligence is not going to be
Speaker 1: captured by a single brain area or even a system.
Speaker 1: For all we know, it might not even be about neurons,
Speaker 1: but about what's going on at the molecular level inside
Speaker 1: of neurons, which means we might just be looking for
Speaker 1: some correl it at the level of neurons. Now, all
Speaker 1: that speculative, but I just want to make clear that
Speaker 1: often in neuroscience we are like the drunk looking for
Speaker 1: the keys under the street light because the lighting is
Speaker 1: better there, even though we dropped our keys over there.
Speaker 1: Our technology has its limitations, and we often gravitate towards
Speaker 1: the street light and ask if we happen to be
Speaker 1: able to find the keys there, And sometimes that strategy
Speaker 1: works and sometimes it doesn't. Now, part of the challenge
Speaker 1: in asking what intelligence is is that the word probably
Speaker 1: tries to hold up too much weight by itself, because
Speaker 1: what we call intelligence is almost certainly made up of
Speaker 1: multiple facets. For example, some people break this down to
Speaker 1: analytic intelligence like you use in math problems, or creative
Speaker 1: intelligence like writing a caption for a cartoon, or practical
Speaker 1: intelligen just like how to operate well in the world.
Speaker 1: So one question is whether these different categories of intelligence
Speaker 1: truly represent different things with fence lines around them, or
Speaker 1: whether they're underpinned by the same mechanisms in the brain
Speaker 1: or overlapping mechanisms. But the problem is even trickier than that,
Speaker 1: because even within any of these categories, we still have
Speaker 1: to answer questions like how knowledge gets stored and retrieved,
Speaker 1: how it can get restructured, how it can get erased,
Speaker 1: and so on. So the question of what intelligence is
Speaker 1: has attracted scientists throughout the ages to propose all kinds
Speaker 1: of different answers, none of which may be mutually exclusive,
Speaker 1: but they're all different angles on answering what it is
Speaker 1: when somebody is intelligent. So let's look at some proposals.
Speaker 1: One proposal is that intelligence has to do with squelching distractors. Technically,
Speaker 1: this is called resolving cognitive conflict. So for example, let's
Speaker 1: say we're playing the Simon Says game, where I say,
Speaker 1: Simon says, look to your left, and then you do it.
Speaker 1: But let's say I say lift your arm, but I
Speaker 1: don't preface it with Simon says. Then what you're supposed
Speaker 1: to do is override your reflex to lift your arm.
Speaker 1: This is an example where you'd have cognitive conflict. So
Speaker 1: the way neuroscientists study this is, for example, by using
Speaker 1: something called a three back task. So imagine you're watching
Speaker 1: a series of faces getting presented on the screen. So
Speaker 1: first you see Tom Cruise, and then you see Beyonce,
Speaker 1: and then you see Taylor Swift, and then you see
Speaker 1: Anthony Hopkins and so on. Your job is simply to say,
Speaker 1: when you see a face that matched the face that
Speaker 1: you saw three faces ago, in other words, three back.
Speaker 1: If you then see Emma Thompson and then Taylor Swift again,
Speaker 1: you'd say, yes, Taylor's matched what I saw three faces ago.
Speaker 1: But if you see Zendaia and then Jennifer Lawrence and
Speaker 1: then Zendia again, that's a distractor because her face was
Speaker 1: only two a go. And so you're supposed to hold
Speaker 1: your tongue or specifically not press your button. So to
Speaker 1: perform this task requires not only a small window of
Speaker 1: working memory, but you have to squelch distractors. You have
Speaker 1: to squelch faces that matched what was two faces ago,
Speaker 1: or four faces ago or five faces. You can only
Speaker 1: hit the button when the face matches what was three ago. Now,
Speaker 1: you run this test on a whole bunch of people
Speaker 1: with different levels of G generalized intelligence score, and what
Speaker 1: you find is that people with a high G are
Speaker 1: better at the task, in large part because they don't
Speaker 1: respond to the distractors. When you do this in brain imaging,
Speaker 1: you find that particular areas come online, like the anterior
Speaker 1: singular texts and the lateral prefrontal cortex, and these areas
Speaker 1: seem to be necessary for overriding the cognitive conflict. So
Speaker 1: that's one idea for what intelligence is, but other studies
Speaker 1: suggest no, it's not about conflict resolution. Instead, intelligence is
Speaker 1: about how many things you can hold in working memory. So,
Speaker 1: for example, our visual memory can only hold let's say
Speaker 1: three or four objects in mind at any given time.
Speaker 1: So let's imagine that I show you some colored shapes
Speaker 1: like a green triangle and a red circle and a
Speaker 1: blue square, and then a moment later, I show you
Speaker 1: a similar image, and I ask you where any of
Speaker 1: these shapes are colors different? And you can probably do
Speaker 1: this for three or four objects. But as it turns out,
Speaker 1: some people are only able to retain the information from
Speaker 1: one or two objects, and other people can hold more,
Speaker 1: let's say five objects, And so some people have suggested
Speaker 1: that that is really related to intelligence, with the idea
Speaker 1: being that critical reasoning depends on how many things you
Speaker 1: can hold in your working memory. If you can hold
Speaker 1: more things in your head at any one time, you'll
Speaker 1: be better able to manipulate things for solving problems. So again,
Speaker 1: people have done brain imaging with EEG and fMRI and
Speaker 1: found a little area in the posterior paridal cortex that
Speaker 1: seems to give a memory bottleneck and correlates with what
Speaker 1: different people can hold in mind. Now it seems likely
Speaker 1: that working memory capacity won't be the final unlock to
Speaker 1: the question of intelligence, but it probably plays a role.
Speaker 1: So what other ideas are there? Well? As it turns out,
Speaker 1: people in the late nineteen nineties got excited about the
Speaker 1: idea of forming associations in the brain. And there's a
Speaker 1: particular type of receptor in the brain called an NMDA receptor.
Speaker 1: Don't worry about the details here, I'll link a paper
Speaker 1: on the website. But you can genetically engineer this receptor
Speaker 1: in a mouse and show that the mouse can link
Speaker 1: things more strongly, like this light predicts food, or this
Speaker 1: is the location where some reward is located. So a
Speaker 1: scientist named Joe Chen and his colleagues at Princeton engineered
Speaker 1: a strain of mouse to have more of this NMDA
Speaker 1: receptor subunit. And this hit the news at the end
Speaker 1: of the nineties because these mice called Doogie mice after
Speaker 1: the TV show Doogie Howser MD, which was about a
Speaker 1: really smart kid. These Doogie mice outperformed normal mice in
Speaker 1: recognizing things they had seen before, or swimming their way
Speaker 1: through a pool of milky water to remember where a
Speaker 1: hidden plat form was. Now, this news made a real
Speaker 1: splash when it came out because the idea was that wow,
Speaker 1: we've just invented intelligent mice. But we do have to
Speaker 1: ask whether we think the doogie mice are more intelligent
Speaker 1: just because they can do these laboratory tests better. After all,
Speaker 1: intelligence is more than simply nailing down associations, and the
Speaker 1: other thing to keep in mind is that all animals
Speaker 1: have to balance the things they know against exploring new possibilities.
Speaker 1: This is known as the balance between exploitation and exploration.
Speaker 1: The reason animals have to balance this is because the
Speaker 1: world changes and you never know exactly how and when
Speaker 1: it's going to change. So if you are an animal
Speaker 1: who's used to finding worms under the green rocks, you
Speaker 1: want to spend some of your time exploring under the
Speaker 1: blue rocks and the red rocks too, because you never
Speaker 1: know when things in the world are going to change.
Speaker 1: So the googie mice seemed to be more about exploiting
Speaker 1: knowledge that they learned and less about exploration. But that's
Speaker 1: not necessarily a good thing. It depends on what happens
Speaker 1: with the world. So just forming stronger associations is probably
Speaker 1: not going to be the full answer to what intelligence is. Now,
Speaker 1: there's another pathway we can sniff down when we're looking
Speaker 1: for the root of intelligence, and that is the Eureka moment.
Speaker 1: That is what happens when two concepts suddenly fit together.
Speaker 1: Like I remember the moment when I was a kid
Speaker 1: when I learned that fog is just the same thing
Speaker 1: as a cloud, but it's low to the ground, And
Speaker 1: it was a physical sensation for me to have these
Speaker 1: two concepts fit together. Or if you're a detective, you
Speaker 1: might have a bunch of clues on your desk and
Speaker 1: then suddenly, aha, it all coalesces into a narrative because
Speaker 1: all the facts fit. Now, what has just happened in
Speaker 1: your brain? And how does your brain know and alert
Speaker 1: you that a fit has been achieved. This is the
Speaker 1: restructuring of information. And I just want to make clear
Speaker 1: we are nothing like a computer that takes in files
Speaker 1: of facts. Instead, we're always structuring and restructuring information. Now.
Speaker 1: One of the places we can see that is when
Speaker 1: a monkey learns a task. What you'll notice is that
Speaker 1: you can't tell the monkey the rules of the task.
Speaker 1: They have to figure it out themselves by doing it
Speaker 1: over and over and getting reinforced with let's say, juice
Speaker 1: in their mouth or something like that. Over hundreds of trials,
Speaker 1: and monkeys can learn this way and they can get
Speaker 1: better through time. Their performance just rises like a shallowly
Speaker 1: sloped line. But if you give the same task to
Speaker 1: an undergraduate, something very different happens. They'll try a few
Speaker 1: things and then they'll suddenly get it, and they're performance
Speaker 1: jumps up. Suddenly they have an Aha moment, they have
Speaker 1: a Eureka. Now, this observation implies that humans are doing
Speaker 1: something that monkeys can't. Perhaps this has to do something
Speaker 1: with restructuring knowledge, or perhaps the human student gets to
Speaker 1: try out lots of hypotheses and evaluate them and then
Speaker 1: restructure things accordingly. But whatever the issue is, this certainly
Speaker 1: seems to play a role in what we think of
Speaker 1: as intelligence. And it also suggests that animal models of
Speaker 1: intelligence are going to be too limited for some of
Speaker 1: the forms of sophisticated reasoning that we care about. And
Speaker 1: I'll give you another thing that we might look for.
Speaker 1: What if intelligence is about the ability to make good
Speaker 1: predictions about the world. In previous episodes, I've talked about
Speaker 1: the internal model, and I've emphasized that the only reason
Speaker 1: the brain builds an internal model is so that we
Speaker 1: can make better predictions about the future. So emulation of
Speaker 1: possible futures is a giant part of what intelligent brains do.
Speaker 1: As the philosopher Carl Popper said, this is what allows
Speaker 1: our hypotheses to die in our stead. My friend and
Speaker 1: colleague Jeff Hawkins has emphasized this for a couple of decades,
Speaker 1: that we only have memory in order to make predictions.
Speaker 1: So the idea is that you write down things that
Speaker 1: happen to you that seem salient, and you use those
Speaker 1: building blocks to springboard into possible futures. As Jeff puts it,
Speaker 1: intelligence is the capacity of the brain to predict the
Speaker 1: future by analogy to the past, and we can find
Speaker 1: lots of evidence for that in examples of brain damage,
Speaker 1: where people lose the ability to store memory and as
Speaker 1: a result are unable to simulate the future. So this
Speaker 1: whole memory prediction framework almost certainly plays a role in intelligence.
Speaker 1: But there are a lot of unanswered questions here. For example,
Speaker 1: there are a huge number of possible future moves. How
Speaker 1: does the brain simulate them all? Perhaps an intelligence simulator
Speaker 1: saves time by developing tricks so that you don't have
Speaker 1: to simulate everything. So there are lots of proposals and
Speaker 1: possibilities for what intelligence is in the brain, and probably
Speaker 1: there are many other possibilities that we haven't even begun
Speaker 1: to explore or know how to explore. So I want
Speaker 1: to pose a question about intelligence, and this one is
Speaker 1: really important, and that is the question of why do
Speaker 1: we have lions in zoos? After all, a lion is
Speaker 1: so much more powerful than you are. A lion can
Speaker 1: easily kill a human. It has these razor sharp claws,
Speaker 1: and its body is all muscle and speed, and yet
Speaker 1: we put lions in zoos. How well, there's only one
Speaker 1: thing we have over lions, and that is intelligence, and
Speaker 1: intelligence enables control. We don't brute force the lion into
Speaker 1: the cage, we don't wrestle a man. Demand Instead, we
Speaker 1: do things like set up traps, or develop chemicals that
Speaker 1: happen to interact with their neurochemistry and put them to sleep,
Speaker 1: and then we package that into a syringe and use
Speaker 1: explosives to launch it really quickly down a metal barrel
Speaker 1: so it punctures their skin. All of these things are
Speaker 1: moves that the lion cannot possibly predict because it couldn't
Speaker 1: possibly conceive of them. And that's what makes so salient.
Speaker 1: Our contemporary discussions about AI, because often when someone is
Speaker 1: thinking about the question of whether AI could control humans,
Speaker 1: they think about physically manhandling us with robots. But that
Speaker 1: seems really unlikely because it's so hard to build physical robots.
Speaker 1: You're constantly tending to the toilet of the robot machinery,
Speaker 1: You're trying to keep all the pieces and parts together
Speaker 1: and not have a wire pop somewhere. But the important
Speaker 1: concept to get straight is that for AI to control humans,
Speaker 1: they don't need brute force. Why because intelligence enables control.
Speaker 1: Could we imagine a scenario in which the AI does
Speaker 1: something that we can't predict because we can't possibly conceive
Speaker 1: of it. Sure, And the interesting part is that there's
Speaker 1: a whole space of scenarios that we can conceive of
Speaker 1: and write science fiction novels about, But there's also the
Speaker 1: space of the unknowns Now, I'm not suggesting that modern
Speaker 1: AI is going to move in that direction, because at
Speaker 1: the moment it's just doing very sophisticated statistical games and
Speaker 1: it doesn't have any particular desire for power. But I
Speaker 1: think for sure things are going to get strange as
Speaker 1: we grow into a world with another intelligence, one which
Speaker 1: has read every single book and blog post ever written
Speaker 1: by humans, and knows every map that we've ever made,
Speaker 1: from streets to chemical signaling, and can create a video
Speaker 1: of any new idea, and can simulate new combinations of
Speaker 1: machines and fractions of a second. So this is the
Speaker 1: reason it's important to understand what intelligence is when we
Speaker 1: talk about artificial intelligence now. Earlier this year, I published
Speaker 1: a paper about how we might meaningfully assess intelligence in AI,
Speaker 1: and I discussed this in episode seven. In other words,
Speaker 1: how would we know if some artificial neural network like
Speaker 1: chat GPT we're actually intelligent versus just computing the probability
Speaker 1: of the next word based on a slurry of everything
Speaker 1: humans have ever written. Well, for sure, it is just
Speaker 1: computing the probability of the next word. But the surprise
Speaker 1: has been all the stuff that we didn't expect it
Speaker 1: to be able to do. With this straightforward statistical prediction model,
Speaker 1: it does more than it was programmed or expected to do.
Speaker 1: So that has left the whole field with a question
Speaker 1: of whether simply having enough data gives us something that
Speaker 1: is actually intelligent or whether it just seems intelligent. So
Speaker 1: in that previous episode, I proposed that the tests we
Speaker 1: currently have, like the Turing test, are outdated as a
Speaker 1: test for meaningful intelligence. Why because the Turing test can
Speaker 1: already be passed and it still doesn't tell us really
Speaker 1: what we need to know. And it's the same with
Speaker 1: other tests that have been proposed in the AST, like
Speaker 1: the Loveless test, which asks whether computers could ever be creative,
Speaker 1: and all it takes is a few seconds with mid
Speaker 1: journey or chat GPT to see that that landmark is
Speaker 1: also in the rear view mirror. So what I've proposed
Speaker 1: is not about moving the goalpost. It's about fundamentally asking
Speaker 1: what is the right test for a meaningful sort of intelligence.
Speaker 1: So what I suggested is that we will know if
Speaker 1: a system has some real intelligence once it starts doing
Speaker 1: meaningful scientific discovery and puts all the scientists out of business,
Speaker 1: because scientific discovery is something that requires a meaningful level
Speaker 1: of intelligence. And I'm not talking about the type of
Speaker 1: science that's just piecing together things in the literature, although
Speaker 1: that's of course very useful. I'm talking about the type
Speaker 1: of science where you think of something new that doesn't
Speaker 1: already exist, and you simulate that and you evaluate whether
Speaker 1: this crazy model you just came up with would give
Speaker 1: a good understanding of the facts on the ground. So,
Speaker 1: for example, when Alfred Wegner proposed that the continental plates
Speaker 1: were drifting, that gave a totally different explanation for all
Speaker 1: kinds of data, including the fact that South America and
Speaker 1: Africa seemed to plug into each other like puzzle pieces,
Speaker 1: And it gave an explanation for mountain ranges and so on.
Speaker 1: And he simulated what would be the case what we
Speaker 1: would expect to see if this were true, and he
Speaker 1: realized it made a good match to the data around him.
Speaker 1: Or when Einstein imagined what it would be like to
Speaker 1: ride on a beam of light and this is how
Speaker 1: he derived the theory of special relativity, or when Charles
Speaker 1: Darwin came up with a theory of evolution by natural
Speaker 1: selection by thinking about all the animals that weren't here.
Speaker 1: I suggest that these are the kind of things that
Speaker 1: humans can do that represent real intelligence, the kind of
Speaker 1: intelligence that has made our species more successful than any
Speaker 1: other on the planet. So is modern AI intelligent in
Speaker 1: this way? As of this recording, there's no simple answer
Speaker 1: to this. There are arguments on all sides that generative
Speaker 1: AI has actually reached some sort of intelligence or that
Speaker 1: it hasn't. But it's not easy at the moment to
Speaker 1: come to a clear conclusion on this. And although AI
Speaker 1: intelligence might not be quite the same thing as what
Speaker 1: we have, I suspect it's going to matter a lot
Speaker 1: for us to better understand what human intelligence is made of,
Speaker 1: so we can understand when AI grows up to be
Speaker 1: the same or better and why. And I suspect that
Speaker 1: the simple existence of AI is going to help us
Speaker 1: think through these problems, because we're going to try things
Speaker 1: and get over our naive assumptions about what intelligence might be.
Speaker 1: For example, from at least the nineteen fifty onward, the
Speaker 1: old way of trying to build artificial intelligence was to
Speaker 1: give a computer a giant list of facts. You explain
Speaker 1: that birds have wings and beaks and feathers, and they fly,
Speaker 1: and then maybe you have to teach it that there
Speaker 1: are some exceptions to the rule, like ostriches or penguins,
Speaker 1: and you keep giving it these rules and structure. And
Speaker 1: that approach never worked, and the field of artificial intelligence
Speaker 1: descended into its winter. So what we learned from that
Speaker 1: is that intelligence is probably not a series of propositions,
Speaker 1: but rather it's stored in a very different way, for example,
Speaker 1: a giant cascade of information in vast networks. And so
Speaker 1: studying intelligence that is artificial, that's what's going to sharpen
Speaker 1: our focus on intelligence that is evolved. So let's wrap up.
Speaker 1: As you know, if you've been a listener to this podcast,
Speaker 1: I'm obsessed with the way that we all see the
Speaker 1: world from different points of view, not least because we
Speaker 1: have subtly different genetic details in our brains from person
Speaker 1: to person, as well as different life experiences which have
Speaker 1: wired up the circuitry. And as a result, we also
Speaker 1: have different intelligences that allow us to see the world
Speaker 1: differently and sometimes with more or less clarity. And what
Speaker 1: we've done today is looked at the complexity of what
Speaker 1: seems like a simple question, what is intelligence? We know
Speaker 1: that there are differences between species and even within members
Speaker 1: of any species, but we don't always know how to
Speaker 1: capture that. And the fact that we can address the
Speaker 1: question but after one hundred years still not come to
Speaker 1: a clear answer probably indicates that the word intelligence simply
Speaker 1: holds up too many different things, different skills, whether that's
Speaker 1: the squelching of distractors or the number of things you
Speaker 1: can hold in memory at any given moment, or the
Speaker 1: formatting of information or making associations, or the ability to
Speaker 1: simulate possible futures. It seems to me that one of
Speaker 1: the most meaningful tests for the intelligence of our species
Speaker 1: will be this. Will we be able to define and
Speaker 1: understand intelligence before we create it and perhaps get taken
Speaker 1: over by it, That will be the true test of
Speaker 1: the intelligence of our species. Go to eagleman dot com
Speaker 1: slash podcast for more information and define further reading. Send
Speaker 1: me an email at podcast at eagleman dot com with
Speaker 1: questions or discussion, and I'll be making more episodes in
Speaker 1: which I address those. Until next time, I'm David Eagleman,
Speaker 1: and this is Inner Cosmos.