WEBVTT - Ep34  "What is intelligence?"

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<v Speaker 1>What's it like to have a much lower IQ than

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<v Speaker 1>you currently have or to have a much higher IQ?

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<v Speaker 1>Welcome to the inner Cosmos with me David Eagleman. I'm

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<v Speaker 1>a neuroscientist and an author at Stanford and in these

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<v Speaker 1>episodes we sail deeply into our three pound universe to

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<v Speaker 1>understand why and how our lives look the way they do.

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<v Speaker 1>Today's episode is about intelligence. What is it What would

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<v Speaker 1>it be like to have the intelligence of a mosquito,

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<v Speaker 1>or a horse or a squirrel. What would it be

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<v Speaker 1>like to presumably understand only very basic things right around you,

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<v Speaker 1>not doing sophisticated simulation of the future like we do

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<v Speaker 1>as humans. And what can we say about the present

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<v Speaker 1>and future of intelligence that is artificial? Okay, so let's

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<v Speaker 1>start with this question of what is it like to

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<v Speaker 1>have a different level of intelligence? I see people post

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<v Speaker 1>this question sometimes online on forums like Quora. Someone will write,

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<v Speaker 1>I have an IQ of sixty eight, what is it

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<v Speaker 1>like to have a higher IQ? Now? First of all,

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<v Speaker 1>I think this is an amazing question because it acknowledges

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<v Speaker 1>that not everyone is having the same experience on the inside,

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<v Speaker 1>and so someone is taking the time to ask, what

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<v Speaker 1>would it be like to have what is called a

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<v Speaker 1>higher intelligence level. What's the experience of that? Now, the

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<v Speaker 1>interesting thing in life is that we can't run a

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<v Speaker 1>control experiment on our own experience of the world, and

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<v Speaker 1>so whatever IQ you have, you sort of have just

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<v Speaker 1>that one experience of reality. But to get at this,

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<v Speaker 1>let's start by thinking about what it would be like

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<v Speaker 1>to have a much lower IQ than you do now.

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<v Speaker 1>One way to get at this is to ask the

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<v Speaker 1>question of what would it be like to be a squirrel.

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<v Speaker 1>I'm choosing a squirrel just because I was watching one

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<v Speaker 1>in my backyard yesterday, and I'm watching him run along

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<v Speaker 1>the top of the fence and climb up the tree

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<v Speaker 1>trunk and find a little scrap of food and look

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<v Speaker 1>around nervously. And a squirrel's cerebrum has about one hundred

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<v Speaker 1>million neurons, while ours has about one hundred billion neurons,

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<v Speaker 1>so a thousand times more. Now, size isn't everything, which

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<v Speaker 1>we'll return to a little bit. Presumably the issue is

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<v Speaker 1>the algorithm that's running. But we can watch the behavior

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<v Speaker 1>of lots and lots of squirrels over lots of time,

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<v Speaker 1>and it certainly doesn't seem like they're having the kind

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<v Speaker 1>of capacity for thought that we are. So I was

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<v Speaker 1>watching this squirrel, and I thought, what would it be

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<v Speaker 1>like to be able to jump around from branch to branch,

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<v Speaker 1>but have no hope, presumably of ever discovering that force

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<v Speaker 1>equals masstimes acceleration, or for that matter, or not even

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<v Speaker 1>ever being able to discover that e equals mc squared,

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<v Speaker 1>or just basic things like how do you build a chair,

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<v Speaker 1>or how do you think about the competition between Amazon

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<v Speaker 1>and Netflix, or just the idea that the telephone lines

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<v Speaker 1>they're running on top of are carrying megabytes of information

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<v Speaker 1>flowing as zeros and ones as one member of our

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<v Speaker 1>species communicates to another, or that more broadly, we have

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<v Speaker 1>airwaves and fiber optics carrying a flow of zetabytes information

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<v Speaker 1>in a massive ocean of data around us. For the squirrel,

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<v Speaker 1>none of this exists. For the squirrel, none of this

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<v Speaker 1>is comprehensible. It's thinking about its acorn and where it

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<v Speaker 1>hid its last one, and it's thinking about safety and

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<v Speaker 1>maybe about mating. As far as we know, or as

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<v Speaker 1>far as we can tell, which of course has its limitations.

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<v Speaker 1>The squirrel is not ruminating on a play it saw

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<v Speaker 1>last night. By the squirrel equivalent of Shakespeare and what

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<v Speaker 1>it means about the aspirations of a monarch and the

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<v Speaker 1>cruelty inherent in the competition for power. It's not thinking

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<v Speaker 1>about how to get to the moon or how to

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<v Speaker 1>build the next vaccine. Now, it's not to say that

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<v Speaker 1>there aren't specialized kinds of intelligence. Every move that the

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<v Speaker 1>squirrel makes along the tree branch is very impressive. There

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<v Speaker 1>is no way that I can could hope to stick

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<v Speaker 1>landing after landing like that from the branch of one

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<v Speaker 1>tree to the next to the next. But the squirrel

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<v Speaker 1>is capable of doing that. But even though it performs

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<v Speaker 1>this incredible ballet in the face of gravity, it's presumably

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<v Speaker 1>never going to get to the point where it can

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<v Speaker 1>characterize gravity in equation form to understand how it should

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<v Speaker 1>move if it were on a different planet, or to

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<v Speaker 1>conceptualize gravity as curvature in the fabric of space time.

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<v Speaker 1>Although humans are capable of getting that in high school,

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<v Speaker 1>presumably squirrels of any age would find the concept well

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<v Speaker 1>beyond what their brains could even hope to have a

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<v Speaker 1>flicker of. And when you look around the animal kingdom

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<v Speaker 1>around us, we find lots of creatures presumably occupying very

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<v Speaker 1>different levels of intelligence. So a bat has let's call it,

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<v Speaker 1>ten million neurons, and again it's not the size but

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<v Speaker 1>the structure that matters.

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<v Speaker 2>But presumably they are not writing the equivalent of bat

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<v Speaker 2>books or building a little bat Internet where they can

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<v Speaker 2>capture for eternity everything that every generation of bats before

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<v Speaker 2>them has learned.

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<v Speaker 1>A fish has only about one hundred thousand neurons, a

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<v Speaker 1>house cricket has fifty thousand neurons. A common fruitfly has

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<v Speaker 1>only about two five hundred neurons in the equivalent to

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<v Speaker 1>its brain. So if you were a fruit fly, you

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<v Speaker 1>simply don't have the notion of seeing the moon in

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<v Speaker 1>the sky and thinking, okay, that's an orbiting sphere, and

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<v Speaker 1>I'm going to derive a plan with my fellow flies

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<v Speaker 1>to get there by building new technologies that we can

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<v Speaker 1>fit our little bodies inside of so that we can

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<v Speaker 1>survive in low oxygen. And what if you're a mosquito,

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<v Speaker 1>with your little mosquito brain, all you know is the

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<v Speaker 1>mad attraction to certain odors which indicate a warm blood

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<v Speaker 1>and animal, and the pleasure of slipping your proboscis in

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<v Speaker 1>and satisfying your thirst with the warm liquid. You presumably

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<v Speaker 1>don't even have the concept of blood, that it has

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<v Speaker 1>plasma and cells that are specialized for grabbing oxygen, and

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<v Speaker 1>all kinds of useful machinery for defending the host animal,

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<v Speaker 1>and so on and so on. So I've thought about

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<v Speaker 1>these issues for years, and I think there's a way

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<v Speaker 1>to understand what it is like to be so limited.

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<v Speaker 1>The way that the mosquito might look at the human,

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<v Speaker 1>if it could even have a concept of a human,

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<v Speaker 1>how might look at the human and think, oh, my gosh,

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<v Speaker 1>I can't believe they understand all that stuff, and they

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<v Speaker 1>do it all at once. And the reason we can

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<v Speaker 1>understand what it's like to be limited is because we

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<v Speaker 1>are up against problems all the time that we're just

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<v Speaker 1>smart enough to recognize, but not yet smart enough to solve.

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<v Speaker 1>Take the origin of life, this is a massively difficult problem.

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<v Speaker 1>I worked with brilliant scientists like Sidney Brenner and Francis

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<v Speaker 1>Krik at the Salt Innstitute, who worked on theories of

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<v Speaker 1>the origin of life. And these were among the smartest

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<v Speaker 1>biologists of the twentieth century. And even they were like

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<v Speaker 1>little fruit flies when trying to tackle that problem. They

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<v Speaker 1>constantly were aware of the enormous gaps in whatever story

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<v Speaker 1>they were hoping to put together, because we just don't

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<v Speaker 1>have much of any lasting data from the past three

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<v Speaker 1>point eight billion years, and we're talking about how trillions

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<v Speaker 1>of atoms might come together in just the right way

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<v Speaker 1>over time to form things that can self replicate. It's

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<v Speaker 1>the kind of problem that when you really start to

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<v Speaker 1>reach your arms down into it, you realize that even

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<v Speaker 1>very smart human brains just aren't equipped for a problem

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<v Speaker 1>of that size. Or just take something like thinking about

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<v Speaker 1>the cosmos with one hundred billion galaxies in it and

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<v Speaker 1>one hundred billion stars inside each of those galaxies, and

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<v Speaker 1>uncountable numbers of planets rotating around those stars, and then

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<v Speaker 1>trying to picture or answer whether there is life elsewhere

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<v Speaker 1>in the galaxy and what it would look like. It's

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<v Speaker 1>clear that our human brains aren't so good at grocking

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<v Speaker 1>numbers like that. Even though we can estimate the numbers

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<v Speaker 1>and we can use words to talk about them, we're

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<v Speaker 1>not really capable of understanding them. And it's the same

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<v Speaker 1>thing when we study neuroscience. Of course, we've got in

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<v Speaker 1>the ballpark of eighty six billion neurons and each one

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<v Speaker 1>of those is connected to so many of its neighbors,

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<v Speaker 1>about ten thousand, that if you took a cubic millimeter

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<v Speaker 1>of brain tissue, there are more connections in there than

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<v Speaker 1>there are stars in the Milky Way galaxy. This thing

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<v Speaker 1>that we're facing on a daily basis this thing the

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<v Speaker 1>brain that tens of thousands of people on the planet's study,

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<v Speaker 1>this three pound organ that we have completely cornered. It

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<v Speaker 1>is so vastly complex that there is no way for

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<v Speaker 1>a human brain to understand itself. And in neuroscience we

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<v Speaker 1>have foundational problems that we can't answer, like why something

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<v Speaker 1>feels good? Take an orgasm? Why does an orgasm feel good?

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<v Speaker 1>We can, of course tell the evolutionary story, which is

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<v Speaker 1>that it benefits the species to reproduce, so it is

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<v Speaker 1>advantageous for it to feel good. But the question from

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<v Speaker 1>a neurobiology point of view is how do you build

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<v Speaker 1>a network that feels anything. Take a big, impressive artificial

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<v Speaker 1>neural network like GPT four. It can do incredibly impressive

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<v Speaker 1>work by taking some prompt and written language and generating

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<v Speaker 1>words that would statistically go with that prompt and so on.

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<v Speaker 1>It's mind blowing how well it appears to do. But

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<v Speaker 1>GPT four presumably can't feel pain or pleasure. There's nothing

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<v Speaker 1>about one of its sentences that it generates that it

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<v Speaker 1>appreciates as hilarious or tear jerking. It doesn't have any

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<v Speaker 1>capacity to feel concerned about its survival or demise. When

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<v Speaker 1>the programmers turn the computer off. It's just running numbers

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<v Speaker 1>down a long, complex algorithmic network, and that's it. So

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<v Speaker 1>how do we ever come to feel something? This is

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<v Speaker 1>perhaps the central unsolved question in neuroscience. It's usually summarized

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<v Speaker 1>as consciousness, and specifically the hard problem of consciousness, which

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<v Speaker 1>is to say, why does all this signaling moving through

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<v Speaker 1>networks of cells feel like something? How could you ever

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<v Speaker 1>program a computer to feel pain or detect some wavelength

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<v Speaker 1>of electromagnetic radiation and as purple, or to detect some

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<v Speaker 1>wavelength of electromagnetic radiation and experience it as purpleness, or

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<v Speaker 1>to enjoy the beauty of a sunset. These are totally

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<v Speaker 1>unsolved questions in neuroscience, and presumably there are whole classes

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<v Speaker 1>of problems that we are not even smart enough to

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<v Speaker 1>realize our questions that we could be asking. So, despite

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<v Speaker 1>the incredible pride filling progress of our species. Our ignorance

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<v Speaker 1>vastly outstrips our knowledge, and that affords us just a

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<v Speaker 1>little bit of insight into the limitations of our brains,

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<v Speaker 1>the glass walls of our fish bowl, and lets us

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<v Speaker 1>even very roughly imagine what it would be like to

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<v Speaker 1>have the intelligence of a squirrel. And these are the

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<v Speaker 1>things that I was thinking about when I wrote a

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<v Speaker 1>fictional short story called Descentive Species in my book Some

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<v Speaker 1>and so I'm going to read it here, and then

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<v Speaker 1>I'll come back to the question of intelligence. In the afterlife,

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<v Speaker 1>you are treated to a generous opportunity. You can choose

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<v Speaker 1>whatever you would like to be in the next life.

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<v Speaker 1>Would you like to be a member of the opposite sex,

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<v Speaker 1>born into royalty, a philosopher with bottomless profundity, a soldier

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<v Speaker 1>facing triumphant battles. But perhaps you've just returned here from

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<v Speaker 1>a hard life. Perhaps you were tortured by the enormity

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<v Speaker 1>of the decisions and responsibilities that surrounded you. And now

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<v Speaker 1>there's only one thing you yearn for, simplicity that's permissible.

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<v Speaker 1>So for the next round, you choose to be a horse.

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<v Speaker 1>You covet the bliss of that simple life. Afternoons of

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<v Speaker 1>grazing and grassy fields, the handsome angles of your skeleton

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<v Speaker 1>and the prominence of your muscles. The peace of the

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<v Speaker 1>slow flicking tail, or the steam rifling through your nostrils.

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<v Speaker 1>As you lope across snow blanketed planes, you announce your decision.

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<v Speaker 1>Incantations are muttered, a wand is waved, and your body

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<v Speaker 1>begins to metamorphose into a horse. Your muscles start to bulge,

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<v Speaker 1>A mat of strong hair erupts to cover you like

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<v Speaker 1>a comfortable blanket in winter. The thickening and lengthening of

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<v Speaker 1>your neck immediately feels normal as it comes about. Your

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<v Speaker 1>carotid arteries grow in diameter, your fingers blend hoofward, your

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<v Speaker 1>knees stiffen, your hips strengthen. And meanwhile, as your skull

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<v Speaker 1>lengthens into its new shape, your brain races in its changes.

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<v Speaker 1>Your cortex retreats as your cerebellum grows. The homuncular smelts

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<v Speaker 1>man to horse neurons, redirect synapses, unplug and replug on

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<v Speaker 1>their way to equestrian patterns, and your dream of understanding

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<v Speaker 1>what it is like to be a horse gallops toward

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<v Speaker 1>you from the distance. Your concern about human affairs begins

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<v Speaker 1>to slip away, Your cynicism about human behavior melts, and

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<v Speaker 1>even your human way of thinking begins to drift away

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<v Speaker 1>from you. Suddenly, for just a moment, you are aware

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<v Speaker 1>of the problem you overlooked. The more you become a horse,

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<v Speaker 1>the more you forget the original wish. You forget what

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<v Speaker 1>it was like to be a human, wondering what it

0:15:47.440 --> 0:15:51.520
<v Speaker 1>was like to be a horse. This moment of lucidity

0:15:52.040 --> 0:15:56.200
<v Speaker 1>does not last long, but it serves as the punishment

0:15:56.320 --> 0:16:02.840
<v Speaker 1>for your sins. A Promethean entrails hecking moment, crouching half horse,

0:16:03.320 --> 0:16:07.520
<v Speaker 1>half man with the knowledge that you cannot appreciate the

0:16:07.600 --> 0:16:12.840
<v Speaker 1>destination without knowing the starting point. You cannot revel in

0:16:12.920 --> 0:16:18.400
<v Speaker 1>the simplicity unless you remember the alternatives. And that's not

0:16:18.480 --> 0:16:21.960
<v Speaker 1>the worst of your revelation. You realize that the next

0:16:22.000 --> 0:16:26.160
<v Speaker 1>time you return here with your thick horse brain, you

0:16:26.200 --> 0:16:31.240
<v Speaker 1>won't have the capacity to ask to become a human again.

0:16:31.920 --> 0:16:37.440
<v Speaker 1>You won't understand what a human is. Your choice to

0:16:37.680 --> 0:16:43.080
<v Speaker 1>slide down the intelligent sladder is irreversible, and just before

0:16:43.160 --> 0:16:49.080
<v Speaker 1>you lose your final human faculties, you painfully ponder what

0:16:49.760 --> 0:16:55.600
<v Speaker 1>magnificent extraterrestrial creature enthralled with the idea of finding a

0:16:55.720 --> 0:17:00.320
<v Speaker 1>simpler life, chose in the last round to be come

0:17:01.040 --> 0:17:06.880
<v Speaker 1>a human. So in the story, I try to give

0:17:06.920 --> 0:17:10.920
<v Speaker 1>a way to think about the possibility that something could

0:17:11.000 --> 0:17:13.720
<v Speaker 1>be much smarter than us, that we are not at

0:17:13.760 --> 0:17:16.480
<v Speaker 1>the top of the latter but maybe somewhere in the middle,

0:17:16.880 --> 0:17:20.239
<v Speaker 1>and that to some other creatures in the universe, we

0:17:20.320 --> 0:17:23.359
<v Speaker 1>would appear to be like the squirrels are to us.

0:17:24.080 --> 0:17:27.640
<v Speaker 1>It certainly could be that in this vast cosmos there

0:17:27.680 --> 0:17:31.560
<v Speaker 1>are intelligences that are so much higher than ours that

0:17:31.600 --> 0:17:36.160
<v Speaker 1>we lack even a good imagination or vocabulary to paint

0:17:36.200 --> 0:17:40.280
<v Speaker 1>these creatures in the same way that presumably the squirrels

0:17:40.520 --> 0:17:44.120
<v Speaker 1>would be unable to give a reasonable description of us

0:17:44.160 --> 0:17:48.720
<v Speaker 1>and what we're up to. Maybe these extraterrestrials can understand

0:17:48.760 --> 0:17:51.679
<v Speaker 1>the entirety of cosmic evolution by the time they're in

0:17:51.720 --> 0:17:55.280
<v Speaker 1>second grade, and they can keep in mind the trillions

0:17:55.320 --> 0:17:59.600
<v Speaker 1>of animal species on this planet and all the other planets,

0:17:59.840 --> 0:18:03.880
<v Speaker 1>and keep track of all the interactions and therefore understand

0:18:04.040 --> 0:18:08.800
<v Speaker 1>the biological history and future of a planet at depth.

0:18:09.640 --> 0:18:13.440
<v Speaker 1>While we mostly just use the word evolution to capture

0:18:13.560 --> 0:18:18.760
<v Speaker 1>something that we can't comprehend at a deep level. Now,

0:18:18.880 --> 0:18:21.879
<v Speaker 1>let me put on the table that I'm completely uncompelled

0:18:21.960 --> 0:18:25.480
<v Speaker 1>by the claims that there are UFOs, or nowadays they're

0:18:25.520 --> 0:18:29.480
<v Speaker 1>called UAPs. But I have a very smart friend named

0:18:29.560 --> 0:18:32.040
<v Speaker 1>Kevin who told me the other day that he has

0:18:32.119 --> 0:18:35.400
<v Speaker 1>no problem believing that that's true. Now, I'm not defending

0:18:35.400 --> 0:18:38.720
<v Speaker 1>his position, but his stance was simply that if you

0:18:38.880 --> 0:18:43.760
<v Speaker 1>imagine the aliens are much smarter than we are, then

0:18:43.800 --> 0:18:48.040
<v Speaker 1>the particulars of what we're looking for, some Morse code

0:18:48.080 --> 0:18:51.840
<v Speaker 1>signal or some take me to your leader's sign, that's

0:18:51.880 --> 0:18:54.240
<v Speaker 1>actually the wrong thing for us to be looking for.

0:18:54.760 --> 0:18:59.720
<v Speaker 1>Because if we imagine some civilization that is, say, totally

0:18:59.760 --> 0:19:03.360
<v Speaker 1>differ from us and three million years ahead of us,

0:19:03.960 --> 0:19:07.399
<v Speaker 1>and they are to us as we are to the squirrels,

0:19:08.200 --> 0:19:12.399
<v Speaker 1>it's certainly not difficult to imagine the possibility that we

0:19:12.520 --> 0:19:16.800
<v Speaker 1>are simply not smart enough to construct a good model

0:19:16.800 --> 0:19:21.760
<v Speaker 1>of them and therefore even recognize them. Now, you might

0:19:21.800 --> 0:19:24.720
<v Speaker 1>assume that if they're much smarter than us, then they

0:19:24.720 --> 0:19:28.440
<v Speaker 1>could dumb themselves down to communicate with us the way

0:19:28.480 --> 0:19:30.639
<v Speaker 1>that we sort of know how to talk with a

0:19:30.760 --> 0:19:35.480
<v Speaker 1>child at a child's level. But our ability to model

0:19:35.720 --> 0:19:39.600
<v Speaker 1>lesser intelligences is still pretty terrible. I mean, you still

0:19:39.600 --> 0:19:41.800
<v Speaker 1>have no idea how to go out in your yard

0:19:41.960 --> 0:19:46.560
<v Speaker 1>and communicate with squirrels. Just try having a meaningful conversation.

0:19:46.920 --> 0:19:50.359
<v Speaker 1>Good luck. We're so much smarter than a squirrel, but

0:19:50.400 --> 0:19:54.119
<v Speaker 1>we have no idea how to plug into their neural networks.

0:19:54.720 --> 0:19:56.560
<v Speaker 1>Or just go to a zoo and try to have

0:19:56.600 --> 0:20:01.080
<v Speaker 1>a conversation with a panda bear and commune unicate to him.

0:20:01.520 --> 0:20:03.840
<v Speaker 1>Take me to your leader, or do that with a

0:20:03.880 --> 0:20:06.000
<v Speaker 1>camel or a dolphin. You get the point, which is

0:20:06.000 --> 0:20:10.520
<v Speaker 1>that just because you are smarter doesn't necessitate that you

0:20:10.640 --> 0:20:14.080
<v Speaker 1>know how to talk to these other animals. And this

0:20:14.160 --> 0:20:18.000
<v Speaker 1>is the situation that we could hypothetically be in with

0:20:18.160 --> 0:20:22.920
<v Speaker 1>extraterrestrial civilizations. That we are here even though we don't

0:20:23.000 --> 0:20:26.640
<v Speaker 1>recognize that they are there, because we don't even have

0:20:26.720 --> 0:20:31.320
<v Speaker 1>the capacity to imagine them, and they have no meaningful

0:20:31.359 --> 0:20:34.840
<v Speaker 1>way to communicate with us, because our needs and desires

0:20:34.880 --> 0:20:38.960
<v Speaker 1>are so different from what they can even understand. Now,

0:20:39.080 --> 0:20:43.760
<v Speaker 1>this lack of communication across species or across planets is

0:20:43.800 --> 0:20:48.040
<v Speaker 1>really brought into relief when we consider that intelligence is

0:20:48.080 --> 0:20:51.680
<v Speaker 1>not one thing, but there are many different behaviors that

0:20:51.720 --> 0:20:56.800
<v Speaker 1>we might put under the umbrella of intelligence. To this point.

0:20:56.880 --> 0:21:00.800
<v Speaker 1>In nineteen seventy four, the philosopher Thomas Nagel wrote an

0:21:00.920 --> 0:21:05.000
<v Speaker 1>essay called what is it Like to Be a Bat?

0:21:05.800 --> 0:21:09.399
<v Speaker 1>Because fundamentally, being a bat is a pretty different experience

0:21:09.440 --> 0:21:12.200
<v Speaker 1>than being a different human. If you are a blind

0:21:12.320 --> 0:21:15.919
<v Speaker 1>echo locating bat, you emit chirps in the dark, and

0:21:15.960 --> 0:21:20.000
<v Speaker 1>you receive back echoes of your chirps, and you translate

0:21:20.200 --> 0:21:24.119
<v Speaker 1>those air compression waves into a three dimensional picture of

0:21:24.160 --> 0:21:26.680
<v Speaker 1>what is in front of you. You make a mental

0:21:27.119 --> 0:21:30.760
<v Speaker 1>map of your surroundings this way. So Nagel asked this

0:21:30.880 --> 0:21:33.280
<v Speaker 1>question of what it's like to be a bat in

0:21:33.359 --> 0:21:37.080
<v Speaker 1>the context of consciousness, as in, given that we have

0:21:37.200 --> 0:21:40.879
<v Speaker 1>such a different sensory world, is there any way that

0:21:40.920 --> 0:21:44.159
<v Speaker 1>we could understand what it would be like to be

0:21:44.240 --> 0:21:47.600
<v Speaker 1>in such a different way of detecting and sensing the world.

0:21:48.000 --> 0:21:50.240
<v Speaker 1>But this same question could be applied to what we're

0:21:50.280 --> 0:21:54.640
<v Speaker 1>thinking about here, which is intelligence. Intelligence in the context

0:21:54.680 --> 0:21:58.080
<v Speaker 1>of a bat allows the bat to navigate around and

0:21:58.200 --> 0:22:01.639
<v Speaker 1>find food, and talk with other and adapt when the

0:22:01.680 --> 0:22:05.800
<v Speaker 1>conditions change. But it's hard to directly compare it to

0:22:05.880 --> 0:22:10.760
<v Speaker 1>human intelligence because they have traits and adaptations that are

0:22:10.880 --> 0:22:15.120
<v Speaker 1>very sophisticated in their own way. Like I said, with echolocation,

0:22:15.200 --> 0:22:18.200
<v Speaker 1>they're creating this three D map in their space. They're

0:22:18.280 --> 0:22:22.640
<v Speaker 1>using auditory information in real time, and they can have

0:22:22.760 --> 0:22:26.280
<v Speaker 1>such precision that they can detect an object as thin

0:22:26.359 --> 0:22:30.359
<v Speaker 1>as a hair and fly around, that they can figure

0:22:30.359 --> 0:22:33.560
<v Speaker 1>out the size and shape and speed of objects like

0:22:33.600 --> 0:22:36.439
<v Speaker 1>a little moth flying around, so they can zoom in

0:22:36.520 --> 0:22:39.720
<v Speaker 1>on it and grab it. And they also have sophisticated

0:22:39.880 --> 0:22:44.840
<v Speaker 1>social behavior, but presumably about different social things than what

0:22:44.880 --> 0:22:47.400
<v Speaker 1>we care about. And we know that they do all

0:22:47.520 --> 0:22:50.120
<v Speaker 1>kinds of problem solving, but it strikes me that it's

0:22:50.160 --> 0:22:54.480
<v Speaker 1>really difficult to know what sorts of problems they solve,

0:22:55.000 --> 0:22:57.880
<v Speaker 1>because some of the problems are so foreign to us

0:22:58.280 --> 0:23:01.439
<v Speaker 1>that we don't even know how to think about them.

0:23:01.960 --> 0:23:04.560
<v Speaker 1>So all this leads us back around to the main

0:23:04.680 --> 0:23:08.679
<v Speaker 1>question for today, which is what is intelligence? How do

0:23:08.720 --> 0:23:10.080
<v Speaker 1>we define it?

0:23:10.880 --> 0:23:11.080
<v Speaker 2>Well?

0:23:11.080 --> 0:23:13.359
<v Speaker 1>As it turns out, this has not been an easy

0:23:13.440 --> 0:23:16.520
<v Speaker 1>question for scientists, and it has come with lots of debate.

0:23:17.119 --> 0:23:18.960
<v Speaker 1>And this is one of those things where we all

0:23:18.960 --> 0:23:22.080
<v Speaker 1>have an intuition about what we mean by the word.

0:23:22.800 --> 0:23:25.840
<v Speaker 1>But the trick from a neuroscience perspective is how do

0:23:25.880 --> 0:23:28.919
<v Speaker 1>you rigorously define it and therefore, how do you study it?

0:23:29.880 --> 0:23:32.919
<v Speaker 1>When we talk about intelligence, let's say just human intelligence,

0:23:32.960 --> 0:23:35.439
<v Speaker 1>what are we even talking about. We all have a

0:23:35.920 --> 0:23:39.439
<v Speaker 1>sense of what an intelligent person is, but what is

0:23:39.520 --> 0:23:43.160
<v Speaker 1>happening in their brain that is different from someone else

0:23:43.160 --> 0:23:45.760
<v Speaker 1>who you might think is not so intelligent. How do

0:23:46.000 --> 0:23:52.639
<v Speaker 1>giant networks of individual neurons, billions of them manipulate information

0:23:52.720 --> 0:23:56.199
<v Speaker 1>that you've taken in before and simulate possible futures and

0:23:56.280 --> 0:24:00.359
<v Speaker 1>evaluate those and throw out all the information that doesn't matter.

0:24:01.000 --> 0:24:04.720
<v Speaker 1>And do people who are intelligent store knowledge in a

0:24:04.760 --> 0:24:08.679
<v Speaker 1>different way, Maybe not categorically different, but just perhaps in

0:24:08.680 --> 0:24:12.080
<v Speaker 1>a way that's more distilled or more easily retrievable. So

0:24:12.119 --> 0:24:14.800
<v Speaker 1>these are the kind of questions we're facing now. The

0:24:14.840 --> 0:24:18.439
<v Speaker 1>first thing to appreciate about intelligence in the brain is

0:24:18.440 --> 0:24:23.360
<v Speaker 1>that size does not seem to matter. Andre the giant

0:24:23.680 --> 0:24:26.880
<v Speaker 1>had a brain volume that might have been eight times

0:24:26.920 --> 0:24:30.520
<v Speaker 1>the size of yours, but he was probably not eight

0:24:30.600 --> 0:24:34.760
<v Speaker 1>times smarter than you. In fact, what is so remarkable

0:24:35.080 --> 0:24:39.520
<v Speaker 1>is that brains that are enormous, like in elephants, and

0:24:39.720 --> 0:24:43.080
<v Speaker 1>brains that are very tiny, like a little mouse brain

0:24:43.960 --> 0:24:48.320
<v Speaker 1>can both tackle very complex problems like foraging for food

0:24:48.359 --> 0:24:52.040
<v Speaker 1>and setting up a home and mating and defending itself

0:24:52.080 --> 0:24:58.400
<v Speaker 1>against predators. The Spanish neuroscientist Santiago Romoni Cajol, like many

0:24:58.440 --> 0:25:02.000
<v Speaker 1>neuroscientists before and after him, was really struck by this thought,

0:25:02.359 --> 0:25:06.200
<v Speaker 1>and he had this beautiful comparison of large and small

0:25:06.240 --> 0:25:11.480
<v Speaker 1>brains to large and small clocks like big ben and

0:25:11.840 --> 0:25:17.359
<v Speaker 1>a wristwatch both tell the time with equal accuracy despite

0:25:17.440 --> 0:25:20.320
<v Speaker 1>the size difference. So all this is to say that

0:25:20.400 --> 0:25:24.159
<v Speaker 1>when we stare at brains, this secret of intelligence is

0:25:24.200 --> 0:25:46.720
<v Speaker 1>not immediately obvious just from looking at the brain. Now,

0:25:46.920 --> 0:25:50.000
<v Speaker 1>when we look across species, we can see what we

0:25:50.119 --> 0:25:54.600
<v Speaker 1>might mean by intelligence. For example, good problem solving skills.

0:25:55.359 --> 0:25:59.320
<v Speaker 1>Some primates are really good at using tools, like orangutans,

0:25:59.680 --> 0:26:02.760
<v Speaker 1>while well other primates like bonobo's are really good at

0:26:02.880 --> 0:26:07.280
<v Speaker 1>social intelligence. If you look at purposes, you find that

0:26:07.320 --> 0:26:11.160
<v Speaker 1>they are better problem solvers and do much more than

0:26:11.200 --> 0:26:15.480
<v Speaker 1>say other swimmers like catfish. And when we examine humans

0:26:15.840 --> 0:26:19.160
<v Speaker 1>we see that somebody can be a genius in one

0:26:19.280 --> 0:26:23.280
<v Speaker 1>domain but quite bad at another. Rinaldo is a genius

0:26:23.280 --> 0:26:25.600
<v Speaker 1>at soccer, but he might not be so great at

0:26:25.640 --> 0:26:29.080
<v Speaker 1>differential equations. I recently saw a video of a kid

0:26:29.119 --> 0:26:32.119
<v Speaker 1>who can do a Rubik's cuban about three seconds, but

0:26:32.200 --> 0:26:36.280
<v Speaker 1>he's autistic and therefore is not particularly good at anything

0:26:36.359 --> 0:26:40.800
<v Speaker 1>involving social interaction. So how can we put a measure

0:26:40.880 --> 0:26:44.439
<v Speaker 1>to what we are talking about here? About a century

0:26:44.440 --> 0:26:46.960
<v Speaker 1>and a half ago people started working on the question

0:26:47.480 --> 0:26:52.160
<v Speaker 1>of how you could quantify this. The British scientists Sir

0:26:52.240 --> 0:26:54.560
<v Speaker 1>Francis Galton was one of the first that I know

0:26:54.600 --> 0:26:58.880
<v Speaker 1>of who said we should be able to measure intelligence.

0:26:59.400 --> 0:27:02.840
<v Speaker 1>So is that you could quantify it by measuring things

0:27:02.920 --> 0:27:06.719
<v Speaker 1>like the strength of someone's eyesight and hearing, or the

0:27:06.760 --> 0:27:10.240
<v Speaker 1>strength of their grip. So that approach didn't last long,

0:27:10.720 --> 0:27:14.480
<v Speaker 1>but by nineteen oh five, two scientists, Alfred Binet and

0:27:14.520 --> 0:27:19.080
<v Speaker 1>Theodore Simon built the Simon Benay test as a way

0:27:19.119 --> 0:27:24.080
<v Speaker 1>of quantifying some number for intelligence. And then in nineteen

0:27:24.160 --> 0:27:27.640
<v Speaker 1>sixteen here at Stanford University there was an educator named

0:27:27.680 --> 0:27:30.880
<v Speaker 1>Lewis Turman who developed the test more and he renamed

0:27:30.880 --> 0:27:33.720
<v Speaker 1>it the Stanford Bena test, which you might have heard

0:27:33.720 --> 0:27:36.480
<v Speaker 1>of because it is still used now. These sorts of

0:27:36.520 --> 0:27:39.080
<v Speaker 1>tests allow us to put a number on something, but

0:27:39.119 --> 0:27:43.439
<v Speaker 1>we still know what exactly we're measuring. The psychologist Charles

0:27:43.480 --> 0:27:46.840
<v Speaker 1>Spearman was intrigued by this question, and he made an observation,

0:27:47.480 --> 0:27:50.800
<v Speaker 1>which is that if you do well on one task,

0:27:50.960 --> 0:27:54.359
<v Speaker 1>something like verbal skills, you tend to also do well

0:27:54.440 --> 0:27:58.840
<v Speaker 1>at other tasks, like spatial skills, and so these things correlated,

0:27:59.240 --> 0:28:02.480
<v Speaker 1>and he speculates that there was some sort of general

0:28:02.720 --> 0:28:06.040
<v Speaker 1>intelligence involved here, and so he used the letter G

0:28:07.240 --> 0:28:10.679
<v Speaker 1>for this idea of a general intelligence factor, like a

0:28:10.760 --> 0:28:14.960
<v Speaker 1>general skill set of the brain. And other researchers noticed

0:28:14.960 --> 0:28:19.520
<v Speaker 1>this correlation also between very different sorts of tasks like

0:28:19.760 --> 0:28:24.640
<v Speaker 1>memory and perception and language and solving new problems and

0:28:24.720 --> 0:28:27.800
<v Speaker 1>pattern recognition and a whole bunch of others, and so

0:28:27.840 --> 0:28:31.040
<v Speaker 1>it still wasn't clear what intelligence is, but it's clear

0:28:31.080 --> 0:28:34.200
<v Speaker 1>that these things correlated, and so in nineteen twenty one

0:28:34.560 --> 0:28:38.320
<v Speaker 1>researcher wrote that while it is difficult to define precisely

0:28:38.360 --> 0:28:44.280
<v Speaker 1>what intelligence is, tests tested. So some people felt that

0:28:44.400 --> 0:28:48.120
<v Speaker 1>there's one thing, this G, that underlies lots of different skills,

0:28:48.160 --> 0:28:50.680
<v Speaker 1>and others felt that maybe these are completely separate things

0:28:50.760 --> 0:28:54.360
<v Speaker 1>and intelligence is not one thing. So that's the debate

0:28:54.400 --> 0:28:57.200
<v Speaker 1>that got rolling over a century ago and it remains

0:28:57.240 --> 0:29:01.120
<v Speaker 1>an unsolved issue. The fact is that if intelligence were

0:29:01.200 --> 0:29:03.959
<v Speaker 1>just one thing, you might expect to sometimes see a

0:29:04.040 --> 0:29:08.160
<v Speaker 1>small bit of brain damage where someone loses skills across

0:29:08.160 --> 0:29:11.880
<v Speaker 1>different types of intelligence, or with the introduction of brain

0:29:11.960 --> 0:29:14.440
<v Speaker 1>imaging some decades ago, we might be able to see

0:29:14.840 --> 0:29:19.320
<v Speaker 1>a single small network becoming active even with very different problems.

0:29:20.080 --> 0:29:25.400
<v Speaker 1>But interestingly, this is still unresolved because some researchers ask

0:29:25.480 --> 0:29:29.280
<v Speaker 1>participants to do very different kinds of tasks like verbal

0:29:29.280 --> 0:29:33.200
<v Speaker 1>and perceptual and spatial things while their brain is getting scanned,

0:29:33.560 --> 0:29:36.440
<v Speaker 1>and they find that all of these tasks lead to

0:29:36.480 --> 0:29:40.600
<v Speaker 1>activity in an area called the lateral frontal cortex, and

0:29:40.680 --> 0:29:44.920
<v Speaker 1>so it might be interpreted to support the unitary intelligence

0:29:45.000 --> 0:29:49.239
<v Speaker 1>hypothesis because you're seeing one area becoming active even when

0:29:49.240 --> 0:29:52.080
<v Speaker 1>people are doing different kinds of tasks. But on the

0:29:52.120 --> 0:29:55.080
<v Speaker 1>other hand, we're always faced with the problem that our

0:29:55.200 --> 0:29:59.680
<v Speaker 1>current brain reading technology only lights up areas where there's

0:29:59.800 --> 0:30:02.280
<v Speaker 1>a lo a lot of activation, and it doesn't catch

0:30:02.320 --> 0:30:05.600
<v Speaker 1>the areas that are more diffuse where the real detailed

0:30:05.600 --> 0:30:08.600
<v Speaker 1>action might be happening. And there's also an issue that

0:30:08.960 --> 0:30:14.240
<v Speaker 1>highly intelligent people find particular tasks less challenging and so

0:30:14.280 --> 0:30:17.360
<v Speaker 1>they often show less activity in the frontal cortex, not more.

0:30:17.760 --> 0:30:21.560
<v Speaker 1>And so it may be that even with our terrific technology,

0:30:22.160 --> 0:30:24.320
<v Speaker 1>it's still a little bit too crude to tell us

0:30:24.400 --> 0:30:27.920
<v Speaker 1>what intelligence is by simply going around and looking for

0:30:27.960 --> 0:30:30.320
<v Speaker 1>a spot or a collection of spots in the brain.

0:30:31.200 --> 0:30:32.640
<v Speaker 1>This is in the same way that you're not going

0:30:32.720 --> 0:30:36.280
<v Speaker 1>to look at chat GPT and say, ah, what makes

0:30:36.280 --> 0:30:39.840
<v Speaker 1>it intelligent? Are these few nodes here out of the

0:30:39.880 --> 0:30:43.440
<v Speaker 1>billions of nodes. Instead, it's a function of the whole

0:30:43.560 --> 0:30:47.640
<v Speaker 1>of the activity running through the enormous system. So it

0:30:47.680 --> 0:30:49.920
<v Speaker 1>may turn out that intelligence is not going to be

0:30:50.000 --> 0:30:54.360
<v Speaker 1>captured by a single brain area or even a system.

0:30:54.880 --> 0:30:57.280
<v Speaker 1>For all we know, it might not even be about neurons,

0:30:57.360 --> 0:31:00.480
<v Speaker 1>but about what's going on at the molecular level inside

0:31:00.520 --> 0:31:02.840
<v Speaker 1>of neurons, which means we might just be looking for

0:31:02.920 --> 0:31:06.480
<v Speaker 1>some correl it at the level of neurons. Now, all

0:31:06.520 --> 0:31:09.000
<v Speaker 1>that speculative, but I just want to make clear that

0:31:09.160 --> 0:31:13.080
<v Speaker 1>often in neuroscience we are like the drunk looking for

0:31:13.160 --> 0:31:16.080
<v Speaker 1>the keys under the street light because the lighting is

0:31:16.160 --> 0:31:18.560
<v Speaker 1>better there, even though we dropped our keys over there.

0:31:19.240 --> 0:31:23.480
<v Speaker 1>Our technology has its limitations, and we often gravitate towards

0:31:23.560 --> 0:31:25.920
<v Speaker 1>the street light and ask if we happen to be

0:31:25.960 --> 0:31:28.640
<v Speaker 1>able to find the keys there, And sometimes that strategy

0:31:28.640 --> 0:31:32.760
<v Speaker 1>works and sometimes it doesn't. Now, part of the challenge

0:31:32.800 --> 0:31:37.400
<v Speaker 1>in asking what intelligence is is that the word probably

0:31:37.480 --> 0:31:41.080
<v Speaker 1>tries to hold up too much weight by itself, because

0:31:41.320 --> 0:31:44.800
<v Speaker 1>what we call intelligence is almost certainly made up of

0:31:45.040 --> 0:31:49.480
<v Speaker 1>multiple facets. For example, some people break this down to

0:31:50.200 --> 0:31:54.920
<v Speaker 1>analytic intelligence like you use in math problems, or creative

0:31:54.960 --> 0:31:59.560
<v Speaker 1>intelligence like writing a caption for a cartoon, or practical

0:31:59.600 --> 0:32:01.920
<v Speaker 1>intelligen just like how to operate well in the world.

0:32:02.600 --> 0:32:06.200
<v Speaker 1>So one question is whether these different categories of intelligence

0:32:06.280 --> 0:32:10.240
<v Speaker 1>truly represent different things with fence lines around them, or

0:32:10.280 --> 0:32:13.360
<v Speaker 1>whether they're underpinned by the same mechanisms in the brain

0:32:13.880 --> 0:32:18.040
<v Speaker 1>or overlapping mechanisms. But the problem is even trickier than that,

0:32:18.200 --> 0:32:21.640
<v Speaker 1>because even within any of these categories, we still have

0:32:21.720 --> 0:32:25.920
<v Speaker 1>to answer questions like how knowledge gets stored and retrieved,

0:32:26.280 --> 0:32:29.240
<v Speaker 1>how it can get restructured, how it can get erased,

0:32:29.280 --> 0:32:32.520
<v Speaker 1>and so on. So the question of what intelligence is

0:32:33.280 --> 0:32:36.920
<v Speaker 1>has attracted scientists throughout the ages to propose all kinds

0:32:36.920 --> 0:32:40.440
<v Speaker 1>of different answers, none of which may be mutually exclusive,

0:32:40.480 --> 0:32:44.720
<v Speaker 1>but they're all different angles on answering what it is

0:32:44.800 --> 0:32:49.200
<v Speaker 1>when somebody is intelligent. So let's look at some proposals.

0:32:49.560 --> 0:32:55.600
<v Speaker 1>One proposal is that intelligence has to do with squelching distractors. Technically,

0:32:55.600 --> 0:32:59.040
<v Speaker 1>this is called resolving cognitive conflict. So for example, let's

0:32:59.040 --> 0:33:03.280
<v Speaker 1>say we're playing the Simon Says game, where I say,

0:33:03.600 --> 0:33:05.959
<v Speaker 1>Simon says, look to your left, and then you do it.

0:33:06.400 --> 0:33:09.880
<v Speaker 1>But let's say I say lift your arm, but I

0:33:09.880 --> 0:33:13.000
<v Speaker 1>don't preface it with Simon says. Then what you're supposed

0:33:13.040 --> 0:33:17.480
<v Speaker 1>to do is override your reflex to lift your arm.

0:33:18.120 --> 0:33:22.120
<v Speaker 1>This is an example where you'd have cognitive conflict. So

0:33:22.160 --> 0:33:25.880
<v Speaker 1>the way neuroscientists study this is, for example, by using

0:33:25.960 --> 0:33:31.040
<v Speaker 1>something called a three back task. So imagine you're watching

0:33:31.080 --> 0:33:34.280
<v Speaker 1>a series of faces getting presented on the screen. So

0:33:34.320 --> 0:33:37.880
<v Speaker 1>first you see Tom Cruise, and then you see Beyonce,

0:33:38.240 --> 0:33:41.360
<v Speaker 1>and then you see Taylor Swift, and then you see

0:33:41.560 --> 0:33:45.200
<v Speaker 1>Anthony Hopkins and so on. Your job is simply to say,

0:33:45.640 --> 0:33:48.920
<v Speaker 1>when you see a face that matched the face that

0:33:48.960 --> 0:33:52.520
<v Speaker 1>you saw three faces ago, in other words, three back.

0:33:53.160 --> 0:33:57.760
<v Speaker 1>If you then see Emma Thompson and then Taylor Swift again,

0:33:57.840 --> 0:34:02.760
<v Speaker 1>you'd say, yes, Taylor's matched what I saw three faces ago.

0:34:03.640 --> 0:34:07.600
<v Speaker 1>But if you see Zendaia and then Jennifer Lawrence and

0:34:07.640 --> 0:34:11.880
<v Speaker 1>then Zendia again, that's a distractor because her face was

0:34:11.920 --> 0:34:14.040
<v Speaker 1>only two a go. And so you're supposed to hold

0:34:14.080 --> 0:34:17.840
<v Speaker 1>your tongue or specifically not press your button. So to

0:34:17.920 --> 0:34:21.560
<v Speaker 1>perform this task requires not only a small window of

0:34:21.600 --> 0:34:26.040
<v Speaker 1>working memory, but you have to squelch distractors. You have

0:34:26.080 --> 0:34:29.319
<v Speaker 1>to squelch faces that matched what was two faces ago,

0:34:29.440 --> 0:34:32.520
<v Speaker 1>or four faces ago or five faces. You can only

0:34:32.600 --> 0:34:37.160
<v Speaker 1>hit the button when the face matches what was three ago. Now,

0:34:37.320 --> 0:34:39.000
<v Speaker 1>you run this test on a whole bunch of people

0:34:39.120 --> 0:34:43.759
<v Speaker 1>with different levels of G generalized intelligence score, and what

0:34:43.800 --> 0:34:48.040
<v Speaker 1>you find is that people with a high G are

0:34:48.280 --> 0:34:51.000
<v Speaker 1>better at the task, in large part because they don't

0:34:51.040 --> 0:34:54.960
<v Speaker 1>respond to the distractors. When you do this in brain imaging,

0:34:55.000 --> 0:34:59.400
<v Speaker 1>you find that particular areas come online, like the anterior

0:34:59.440 --> 0:35:03.440
<v Speaker 1>singular texts and the lateral prefrontal cortex, and these areas

0:35:03.440 --> 0:35:08.040
<v Speaker 1>seem to be necessary for overriding the cognitive conflict. So

0:35:08.160 --> 0:35:11.960
<v Speaker 1>that's one idea for what intelligence is, but other studies

0:35:12.000 --> 0:35:16.600
<v Speaker 1>suggest no, it's not about conflict resolution. Instead, intelligence is

0:35:16.640 --> 0:35:20.840
<v Speaker 1>about how many things you can hold in working memory. So,

0:35:20.960 --> 0:35:24.759
<v Speaker 1>for example, our visual memory can only hold let's say

0:35:25.040 --> 0:35:27.600
<v Speaker 1>three or four objects in mind at any given time.

0:35:27.920 --> 0:35:31.280
<v Speaker 1>So let's imagine that I show you some colored shapes

0:35:31.360 --> 0:35:35.040
<v Speaker 1>like a green triangle and a red circle and a

0:35:35.120 --> 0:35:38.799
<v Speaker 1>blue square, and then a moment later, I show you

0:35:38.840 --> 0:35:42.279
<v Speaker 1>a similar image, and I ask you where any of

0:35:42.320 --> 0:35:46.000
<v Speaker 1>these shapes are colors different? And you can probably do

0:35:46.120 --> 0:35:49.600
<v Speaker 1>this for three or four objects. But as it turns out,

0:35:49.640 --> 0:35:52.759
<v Speaker 1>some people are only able to retain the information from

0:35:52.920 --> 0:35:55.839
<v Speaker 1>one or two objects, and other people can hold more,

0:35:55.920 --> 0:35:59.080
<v Speaker 1>let's say five objects, And so some people have suggested

0:35:59.120 --> 0:36:02.320
<v Speaker 1>that that is really related to intelligence, with the idea

0:36:02.440 --> 0:36:05.920
<v Speaker 1>being that critical reasoning depends on how many things you

0:36:05.920 --> 0:36:09.120
<v Speaker 1>can hold in your working memory. If you can hold

0:36:09.160 --> 0:36:12.040
<v Speaker 1>more things in your head at any one time, you'll

0:36:12.040 --> 0:36:16.400
<v Speaker 1>be better able to manipulate things for solving problems. So again,

0:36:16.440 --> 0:36:20.239
<v Speaker 1>people have done brain imaging with EEG and fMRI and

0:36:20.360 --> 0:36:23.600
<v Speaker 1>found a little area in the posterior paridal cortex that

0:36:23.760 --> 0:36:28.440
<v Speaker 1>seems to give a memory bottleneck and correlates with what

0:36:28.640 --> 0:36:48.520
<v Speaker 1>different people can hold in mind. Now it seems likely

0:36:48.920 --> 0:36:53.000
<v Speaker 1>that working memory capacity won't be the final unlock to

0:36:53.080 --> 0:36:56.760
<v Speaker 1>the question of intelligence, but it probably plays a role.

0:36:57.600 --> 0:37:00.680
<v Speaker 1>So what other ideas are there? Well? As it turns out,

0:37:00.719 --> 0:37:03.480
<v Speaker 1>people in the late nineteen nineties got excited about the

0:37:03.520 --> 0:37:08.040
<v Speaker 1>idea of forming associations in the brain. And there's a

0:37:08.080 --> 0:37:13.320
<v Speaker 1>particular type of receptor in the brain called an NMDA receptor.

0:37:13.840 --> 0:37:16.200
<v Speaker 1>Don't worry about the details here, I'll link a paper

0:37:16.239 --> 0:37:19.960
<v Speaker 1>on the website. But you can genetically engineer this receptor

0:37:20.000 --> 0:37:23.040
<v Speaker 1>in a mouse and show that the mouse can link

0:37:23.120 --> 0:37:27.480
<v Speaker 1>things more strongly, like this light predicts food, or this

0:37:27.680 --> 0:37:31.440
<v Speaker 1>is the location where some reward is located. So a

0:37:31.520 --> 0:37:35.320
<v Speaker 1>scientist named Joe Chen and his colleagues at Princeton engineered

0:37:35.360 --> 0:37:38.320
<v Speaker 1>a strain of mouse to have more of this NMDA

0:37:38.600 --> 0:37:41.239
<v Speaker 1>receptor subunit. And this hit the news at the end

0:37:41.239 --> 0:37:45.359
<v Speaker 1>of the nineties because these mice called Doogie mice after

0:37:45.400 --> 0:37:48.239
<v Speaker 1>the TV show Doogie Howser MD, which was about a

0:37:48.239 --> 0:37:52.839
<v Speaker 1>really smart kid. These Doogie mice outperformed normal mice in

0:37:52.920 --> 0:37:56.759
<v Speaker 1>recognizing things they had seen before, or swimming their way

0:37:56.760 --> 0:37:59.200
<v Speaker 1>through a pool of milky water to remember where a

0:37:59.440 --> 0:38:03.239
<v Speaker 1>hidden plat form was. Now, this news made a real

0:38:03.280 --> 0:38:05.919
<v Speaker 1>splash when it came out because the idea was that wow,

0:38:05.960 --> 0:38:09.880
<v Speaker 1>we've just invented intelligent mice. But we do have to

0:38:09.920 --> 0:38:13.759
<v Speaker 1>ask whether we think the doogie mice are more intelligent

0:38:14.520 --> 0:38:18.640
<v Speaker 1>just because they can do these laboratory tests better. After all,

0:38:18.719 --> 0:38:24.000
<v Speaker 1>intelligence is more than simply nailing down associations, and the

0:38:24.040 --> 0:38:26.680
<v Speaker 1>other thing to keep in mind is that all animals

0:38:26.800 --> 0:38:32.000
<v Speaker 1>have to balance the things they know against exploring new possibilities.

0:38:32.040 --> 0:38:36.439
<v Speaker 1>This is known as the balance between exploitation and exploration.

0:38:37.080 --> 0:38:39.560
<v Speaker 1>The reason animals have to balance this is because the

0:38:39.600 --> 0:38:42.960
<v Speaker 1>world changes and you never know exactly how and when

0:38:43.000 --> 0:38:46.160
<v Speaker 1>it's going to change. So if you are an animal

0:38:46.200 --> 0:38:49.920
<v Speaker 1>who's used to finding worms under the green rocks, you

0:38:49.960 --> 0:38:53.120
<v Speaker 1>want to spend some of your time exploring under the

0:38:53.120 --> 0:38:55.319
<v Speaker 1>blue rocks and the red rocks too, because you never

0:38:55.480 --> 0:38:58.120
<v Speaker 1>know when things in the world are going to change.

0:38:58.840 --> 0:39:03.840
<v Speaker 1>So the googie mice seemed to be more about exploiting

0:39:03.920 --> 0:39:08.319
<v Speaker 1>knowledge that they learned and less about exploration. But that's

0:39:08.320 --> 0:39:11.000
<v Speaker 1>not necessarily a good thing. It depends on what happens

0:39:11.040 --> 0:39:16.760
<v Speaker 1>with the world. So just forming stronger associations is probably

0:39:16.800 --> 0:39:21.319
<v Speaker 1>not going to be the full answer to what intelligence is. Now,

0:39:21.360 --> 0:39:23.960
<v Speaker 1>there's another pathway we can sniff down when we're looking

0:39:23.960 --> 0:39:28.600
<v Speaker 1>for the root of intelligence, and that is the Eureka moment.

0:39:28.800 --> 0:39:33.800
<v Speaker 1>That is what happens when two concepts suddenly fit together.

0:39:34.600 --> 0:39:36.920
<v Speaker 1>Like I remember the moment when I was a kid

0:39:37.680 --> 0:39:40.719
<v Speaker 1>when I learned that fog is just the same thing

0:39:40.760 --> 0:39:42.960
<v Speaker 1>as a cloud, but it's low to the ground, And

0:39:43.000 --> 0:39:46.080
<v Speaker 1>it was a physical sensation for me to have these

0:39:46.120 --> 0:39:51.239
<v Speaker 1>two concepts fit together. Or if you're a detective, you

0:39:51.320 --> 0:39:53.799
<v Speaker 1>might have a bunch of clues on your desk and

0:39:53.840 --> 0:39:58.080
<v Speaker 1>then suddenly, aha, it all coalesces into a narrative because

0:39:58.080 --> 0:40:01.279
<v Speaker 1>all the facts fit. Now, what has just happened in

0:40:01.360 --> 0:40:04.480
<v Speaker 1>your brain? And how does your brain know and alert

0:40:04.520 --> 0:40:08.120
<v Speaker 1>you that a fit has been achieved. This is the

0:40:08.600 --> 0:40:12.520
<v Speaker 1>restructuring of information. And I just want to make clear

0:40:12.600 --> 0:40:16.359
<v Speaker 1>we are nothing like a computer that takes in files

0:40:16.440 --> 0:40:23.040
<v Speaker 1>of facts. Instead, we're always structuring and restructuring information. Now.

0:40:23.040 --> 0:40:24.839
<v Speaker 1>One of the places we can see that is when

0:40:25.440 --> 0:40:29.239
<v Speaker 1>a monkey learns a task. What you'll notice is that

0:40:29.360 --> 0:40:31.880
<v Speaker 1>you can't tell the monkey the rules of the task.

0:40:31.920 --> 0:40:34.000
<v Speaker 1>They have to figure it out themselves by doing it

0:40:34.080 --> 0:40:37.680
<v Speaker 1>over and over and getting reinforced with let's say, juice

0:40:37.680 --> 0:40:40.800
<v Speaker 1>in their mouth or something like that. Over hundreds of trials,

0:40:41.280 --> 0:40:43.160
<v Speaker 1>and monkeys can learn this way and they can get

0:40:43.200 --> 0:40:48.480
<v Speaker 1>better through time. Their performance just rises like a shallowly

0:40:48.640 --> 0:40:51.799
<v Speaker 1>sloped line. But if you give the same task to

0:40:51.880 --> 0:40:56.719
<v Speaker 1>an undergraduate, something very different happens. They'll try a few

0:40:56.800 --> 0:41:00.600
<v Speaker 1>things and then they'll suddenly get it, and they're performance

0:41:00.800 --> 0:41:04.279
<v Speaker 1>jumps up. Suddenly they have an Aha moment, they have

0:41:04.320 --> 0:41:08.840
<v Speaker 1>a Eureka. Now, this observation implies that humans are doing

0:41:08.920 --> 0:41:12.880
<v Speaker 1>something that monkeys can't. Perhaps this has to do something

0:41:12.920 --> 0:41:17.000
<v Speaker 1>with restructuring knowledge, or perhaps the human student gets to

0:41:17.120 --> 0:41:20.440
<v Speaker 1>try out lots of hypotheses and evaluate them and then

0:41:20.560 --> 0:41:25.000
<v Speaker 1>restructure things accordingly. But whatever the issue is, this certainly

0:41:25.040 --> 0:41:27.360
<v Speaker 1>seems to play a role in what we think of

0:41:27.440 --> 0:41:32.279
<v Speaker 1>as intelligence. And it also suggests that animal models of

0:41:32.320 --> 0:41:36.120
<v Speaker 1>intelligence are going to be too limited for some of

0:41:36.160 --> 0:41:41.160
<v Speaker 1>the forms of sophisticated reasoning that we care about. And

0:41:41.200 --> 0:41:43.160
<v Speaker 1>I'll give you another thing that we might look for.

0:41:43.480 --> 0:41:47.120
<v Speaker 1>What if intelligence is about the ability to make good

0:41:47.320 --> 0:41:51.720
<v Speaker 1>predictions about the world. In previous episodes, I've talked about

0:41:51.719 --> 0:41:56.920
<v Speaker 1>the internal model, and I've emphasized that the only reason

0:41:57.080 --> 0:42:00.840
<v Speaker 1>the brain builds an internal model is so that we

0:42:00.880 --> 0:42:05.480
<v Speaker 1>can make better predictions about the future. So emulation of

0:42:05.520 --> 0:42:09.440
<v Speaker 1>possible futures is a giant part of what intelligent brains do.

0:42:10.280 --> 0:42:14.200
<v Speaker 1>As the philosopher Carl Popper said, this is what allows

0:42:14.360 --> 0:42:19.719
<v Speaker 1>our hypotheses to die in our stead. My friend and

0:42:19.760 --> 0:42:23.000
<v Speaker 1>colleague Jeff Hawkins has emphasized this for a couple of decades,

0:42:23.320 --> 0:42:27.200
<v Speaker 1>that we only have memory in order to make predictions.

0:42:27.480 --> 0:42:29.960
<v Speaker 1>So the idea is that you write down things that

0:42:30.080 --> 0:42:33.200
<v Speaker 1>happen to you that seem salient, and you use those

0:42:33.360 --> 0:42:38.719
<v Speaker 1>building blocks to springboard into possible futures. As Jeff puts it,

0:42:39.239 --> 0:42:43.880
<v Speaker 1>intelligence is the capacity of the brain to predict the

0:42:44.000 --> 0:42:48.560
<v Speaker 1>future by analogy to the past, and we can find

0:42:48.600 --> 0:42:52.760
<v Speaker 1>lots of evidence for that in examples of brain damage,

0:42:52.760 --> 0:42:57.000
<v Speaker 1>where people lose the ability to store memory and as

0:42:57.040 --> 0:43:00.759
<v Speaker 1>a result are unable to simulate the future. So this

0:43:00.800 --> 0:43:04.759
<v Speaker 1>whole memory prediction framework almost certainly plays a role in intelligence.

0:43:05.000 --> 0:43:07.520
<v Speaker 1>But there are a lot of unanswered questions here. For example,

0:43:08.200 --> 0:43:12.600
<v Speaker 1>there are a huge number of possible future moves. How

0:43:12.640 --> 0:43:16.600
<v Speaker 1>does the brain simulate them all? Perhaps an intelligence simulator

0:43:17.000 --> 0:43:20.200
<v Speaker 1>saves time by developing tricks so that you don't have

0:43:20.320 --> 0:43:25.480
<v Speaker 1>to simulate everything. So there are lots of proposals and

0:43:25.560 --> 0:43:30.400
<v Speaker 1>possibilities for what intelligence is in the brain, and probably

0:43:30.440 --> 0:43:33.120
<v Speaker 1>there are many other possibilities that we haven't even begun

0:43:33.200 --> 0:43:36.879
<v Speaker 1>to explore or know how to explore. So I want

0:43:36.920 --> 0:43:40.200
<v Speaker 1>to pose a question about intelligence, and this one is

0:43:40.239 --> 0:43:44.239
<v Speaker 1>really important, and that is the question of why do

0:43:44.400 --> 0:43:50.160
<v Speaker 1>we have lions in zoos? After all, a lion is

0:43:50.320 --> 0:43:54.480
<v Speaker 1>so much more powerful than you are. A lion can

0:43:54.680 --> 0:43:58.400
<v Speaker 1>easily kill a human. It has these razor sharp claws,

0:43:58.480 --> 0:44:03.400
<v Speaker 1>and its body is all muscle and speed, and yet

0:44:04.200 --> 0:44:09.120
<v Speaker 1>we put lions in zoos. How well, there's only one

0:44:09.160 --> 0:44:13.600
<v Speaker 1>thing we have over lions, and that is intelligence, and

0:44:13.840 --> 0:44:21.160
<v Speaker 1>intelligence enables control. We don't brute force the lion into

0:44:21.160 --> 0:44:25.080
<v Speaker 1>the cage, we don't wrestle a man. Demand Instead, we

0:44:25.160 --> 0:44:29.839
<v Speaker 1>do things like set up traps, or develop chemicals that

0:44:29.920 --> 0:44:34.000
<v Speaker 1>happen to interact with their neurochemistry and put them to sleep,

0:44:34.080 --> 0:44:37.560
<v Speaker 1>and then we package that into a syringe and use

0:44:37.680 --> 0:44:40.960
<v Speaker 1>explosives to launch it really quickly down a metal barrel

0:44:41.000 --> 0:44:44.399
<v Speaker 1>so it punctures their skin. All of these things are

0:44:44.520 --> 0:44:49.160
<v Speaker 1>moves that the lion cannot possibly predict because it couldn't

0:44:49.200 --> 0:44:54.880
<v Speaker 1>possibly conceive of them. And that's what makes so salient.

0:44:55.080 --> 0:45:00.840
<v Speaker 1>Our contemporary discussions about AI, because often when someone is

0:45:00.880 --> 0:45:04.400
<v Speaker 1>thinking about the question of whether AI could control humans,

0:45:05.080 --> 0:45:09.120
<v Speaker 1>they think about physically manhandling us with robots. But that

0:45:09.160 --> 0:45:12.760
<v Speaker 1>seems really unlikely because it's so hard to build physical robots.

0:45:12.760 --> 0:45:16.880
<v Speaker 1>You're constantly tending to the toilet of the robot machinery,

0:45:16.880 --> 0:45:19.360
<v Speaker 1>You're trying to keep all the pieces and parts together

0:45:19.440 --> 0:45:22.719
<v Speaker 1>and not have a wire pop somewhere. But the important

0:45:22.760 --> 0:45:26.840
<v Speaker 1>concept to get straight is that for AI to control humans,

0:45:26.880 --> 0:45:33.840
<v Speaker 1>they don't need brute force. Why because intelligence enables control.

0:45:34.760 --> 0:45:38.959
<v Speaker 1>Could we imagine a scenario in which the AI does

0:45:39.040 --> 0:45:43.680
<v Speaker 1>something that we can't predict because we can't possibly conceive

0:45:43.719 --> 0:45:47.000
<v Speaker 1>of it. Sure, And the interesting part is that there's

0:45:47.040 --> 0:45:50.440
<v Speaker 1>a whole space of scenarios that we can conceive of

0:45:50.520 --> 0:45:53.600
<v Speaker 1>and write science fiction novels about, But there's also the

0:45:53.680 --> 0:45:59.320
<v Speaker 1>space of the unknowns Now, I'm not suggesting that modern

0:45:59.360 --> 0:46:01.960
<v Speaker 1>AI is going to move in that direction, because at

0:46:02.000 --> 0:46:05.960
<v Speaker 1>the moment it's just doing very sophisticated statistical games and

0:46:06.040 --> 0:46:10.040
<v Speaker 1>it doesn't have any particular desire for power. But I

0:46:10.080 --> 0:46:13.200
<v Speaker 1>think for sure things are going to get strange as

0:46:13.280 --> 0:46:18.240
<v Speaker 1>we grow into a world with another intelligence, one which

0:46:18.440 --> 0:46:21.000
<v Speaker 1>has read every single book and blog post ever written

0:46:21.080 --> 0:46:24.000
<v Speaker 1>by humans, and knows every map that we've ever made,

0:46:24.280 --> 0:46:28.400
<v Speaker 1>from streets to chemical signaling, and can create a video

0:46:28.560 --> 0:46:32.200
<v Speaker 1>of any new idea, and can simulate new combinations of

0:46:32.320 --> 0:46:35.880
<v Speaker 1>machines and fractions of a second. So this is the

0:46:35.920 --> 0:46:40.799
<v Speaker 1>reason it's important to understand what intelligence is when we

0:46:40.880 --> 0:46:45.520
<v Speaker 1>talk about artificial intelligence now. Earlier this year, I published

0:46:45.560 --> 0:46:51.080
<v Speaker 1>a paper about how we might meaningfully assess intelligence in AI,

0:46:51.600 --> 0:46:55.000
<v Speaker 1>and I discussed this in episode seven. In other words,

0:46:55.280 --> 0:46:58.320
<v Speaker 1>how would we know if some artificial neural network like

0:46:58.440 --> 0:47:04.440
<v Speaker 1>chat GPT we're actually intelligent versus just computing the probability

0:47:04.480 --> 0:47:07.280
<v Speaker 1>of the next word based on a slurry of everything

0:47:07.360 --> 0:47:10.440
<v Speaker 1>humans have ever written. Well, for sure, it is just

0:47:10.520 --> 0:47:13.839
<v Speaker 1>computing the probability of the next word. But the surprise

0:47:13.960 --> 0:47:16.680
<v Speaker 1>has been all the stuff that we didn't expect it

0:47:16.719 --> 0:47:21.360
<v Speaker 1>to be able to do. With this straightforward statistical prediction model,

0:47:21.760 --> 0:47:25.839
<v Speaker 1>it does more than it was programmed or expected to do.

0:47:26.960 --> 0:47:29.600
<v Speaker 1>So that has left the whole field with a question

0:47:29.680 --> 0:47:33.799
<v Speaker 1>of whether simply having enough data gives us something that

0:47:33.960 --> 0:47:40.000
<v Speaker 1>is actually intelligent or whether it just seems intelligent. So

0:47:40.160 --> 0:47:43.200
<v Speaker 1>in that previous episode, I proposed that the tests we

0:47:43.280 --> 0:47:48.440
<v Speaker 1>currently have, like the Turing test, are outdated as a

0:47:48.520 --> 0:47:52.680
<v Speaker 1>test for meaningful intelligence. Why because the Turing test can

0:47:52.719 --> 0:47:55.640
<v Speaker 1>already be passed and it still doesn't tell us really

0:47:55.680 --> 0:47:57.920
<v Speaker 1>what we need to know. And it's the same with

0:47:58.000 --> 0:48:00.680
<v Speaker 1>other tests that have been proposed in the AST, like

0:48:01.120 --> 0:48:05.280
<v Speaker 1>the Loveless test, which asks whether computers could ever be creative,

0:48:05.719 --> 0:48:08.359
<v Speaker 1>and all it takes is a few seconds with mid

0:48:08.440 --> 0:48:12.360
<v Speaker 1>journey or chat GPT to see that that landmark is

0:48:12.440 --> 0:48:15.759
<v Speaker 1>also in the rear view mirror. So what I've proposed

0:48:15.840 --> 0:48:20.000
<v Speaker 1>is not about moving the goalpost. It's about fundamentally asking

0:48:20.080 --> 0:48:24.680
<v Speaker 1>what is the right test for a meaningful sort of intelligence.

0:48:25.520 --> 0:48:28.000
<v Speaker 1>So what I suggested is that we will know if

0:48:28.040 --> 0:48:32.200
<v Speaker 1>a system has some real intelligence once it starts doing

0:48:32.760 --> 0:48:37.360
<v Speaker 1>meaningful scientific discovery and puts all the scientists out of business,

0:48:37.920 --> 0:48:42.840
<v Speaker 1>because scientific discovery is something that requires a meaningful level

0:48:42.840 --> 0:48:45.440
<v Speaker 1>of intelligence. And I'm not talking about the type of

0:48:45.480 --> 0:48:49.240
<v Speaker 1>science that's just piecing together things in the literature, although

0:48:49.280 --> 0:48:52.160
<v Speaker 1>that's of course very useful. I'm talking about the type

0:48:52.200 --> 0:48:56.000
<v Speaker 1>of science where you think of something new that doesn't

0:48:56.040 --> 0:49:00.680
<v Speaker 1>already exist, and you simulate that and you evaluate whether

0:49:00.840 --> 0:49:04.799
<v Speaker 1>this crazy model you just came up with would give

0:49:04.840 --> 0:49:08.520
<v Speaker 1>a good understanding of the facts on the ground. So,

0:49:08.600 --> 0:49:12.160
<v Speaker 1>for example, when Alfred Wegner proposed that the continental plates

0:49:12.200 --> 0:49:17.359
<v Speaker 1>were drifting, that gave a totally different explanation for all

0:49:17.480 --> 0:49:20.600
<v Speaker 1>kinds of data, including the fact that South America and

0:49:20.719 --> 0:49:23.520
<v Speaker 1>Africa seemed to plug into each other like puzzle pieces,

0:49:23.840 --> 0:49:26.480
<v Speaker 1>And it gave an explanation for mountain ranges and so on.

0:49:27.000 --> 0:49:30.120
<v Speaker 1>And he simulated what would be the case what we

0:49:30.120 --> 0:49:33.359
<v Speaker 1>would expect to see if this were true, and he

0:49:33.400 --> 0:49:37.680
<v Speaker 1>realized it made a good match to the data around him.

0:49:37.960 --> 0:49:40.880
<v Speaker 1>Or when Einstein imagined what it would be like to

0:49:41.120 --> 0:49:43.919
<v Speaker 1>ride on a beam of light and this is how

0:49:43.920 --> 0:49:48.120
<v Speaker 1>he derived the theory of special relativity, or when Charles

0:49:48.200 --> 0:49:51.000
<v Speaker 1>Darwin came up with a theory of evolution by natural

0:49:51.040 --> 0:49:55.200
<v Speaker 1>selection by thinking about all the animals that weren't here.

0:49:56.000 --> 0:49:58.719
<v Speaker 1>I suggest that these are the kind of things that

0:49:58.880 --> 0:50:02.840
<v Speaker 1>humans can do that represent real intelligence, the kind of

0:50:02.840 --> 0:50:07.080
<v Speaker 1>intelligence that has made our species more successful than any

0:50:07.120 --> 0:50:11.000
<v Speaker 1>other on the planet. So is modern AI intelligent in

0:50:11.040 --> 0:50:14.840
<v Speaker 1>this way? As of this recording, there's no simple answer

0:50:14.840 --> 0:50:18.320
<v Speaker 1>to this. There are arguments on all sides that generative

0:50:18.320 --> 0:50:20.719
<v Speaker 1>AI has actually reached some sort of intelligence or that

0:50:20.760 --> 0:50:23.279
<v Speaker 1>it hasn't. But it's not easy at the moment to

0:50:23.320 --> 0:50:27.440
<v Speaker 1>come to a clear conclusion on this. And although AI

0:50:27.640 --> 0:50:30.040
<v Speaker 1>intelligence might not be quite the same thing as what

0:50:30.120 --> 0:50:32.520
<v Speaker 1>we have, I suspect it's going to matter a lot

0:50:32.560 --> 0:50:37.520
<v Speaker 1>for us to better understand what human intelligence is made of,

0:50:38.160 --> 0:50:41.920
<v Speaker 1>so we can understand when AI grows up to be

0:50:42.040 --> 0:50:45.680
<v Speaker 1>the same or better and why. And I suspect that

0:50:45.800 --> 0:50:49.120
<v Speaker 1>the simple existence of AI is going to help us

0:50:49.480 --> 0:50:52.640
<v Speaker 1>think through these problems, because we're going to try things

0:50:52.680 --> 0:50:56.719
<v Speaker 1>and get over our naive assumptions about what intelligence might be.

0:50:57.080 --> 0:51:01.080
<v Speaker 1>For example, from at least the nineteen fifty onward, the

0:51:01.239 --> 0:51:05.279
<v Speaker 1>old way of trying to build artificial intelligence was to

0:51:05.320 --> 0:51:09.760
<v Speaker 1>give a computer a giant list of facts. You explain

0:51:09.840 --> 0:51:12.560
<v Speaker 1>that birds have wings and beaks and feathers, and they fly,

0:51:13.000 --> 0:51:14.640
<v Speaker 1>and then maybe you have to teach it that there

0:51:14.680 --> 0:51:18.080
<v Speaker 1>are some exceptions to the rule, like ostriches or penguins,

0:51:18.320 --> 0:51:21.399
<v Speaker 1>and you keep giving it these rules and structure. And

0:51:21.440 --> 0:51:26.120
<v Speaker 1>that approach never worked, and the field of artificial intelligence

0:51:26.239 --> 0:51:29.960
<v Speaker 1>descended into its winter. So what we learned from that

0:51:30.880 --> 0:51:34.880
<v Speaker 1>is that intelligence is probably not a series of propositions,

0:51:34.920 --> 0:51:38.880
<v Speaker 1>but rather it's stored in a very different way, for example,

0:51:38.920 --> 0:51:44.360
<v Speaker 1>a giant cascade of information in vast networks. And so

0:51:45.239 --> 0:51:49.720
<v Speaker 1>studying intelligence that is artificial, that's what's going to sharpen

0:51:49.760 --> 0:51:55.480
<v Speaker 1>our focus on intelligence that is evolved. So let's wrap up.

0:51:55.920 --> 0:51:58.400
<v Speaker 1>As you know, if you've been a listener to this podcast,

0:51:58.520 --> 0:52:01.560
<v Speaker 1>I'm obsessed with the way that we all see the

0:52:01.600 --> 0:52:05.120
<v Speaker 1>world from different points of view, not least because we

0:52:05.160 --> 0:52:08.080
<v Speaker 1>have subtly different genetic details in our brains from person

0:52:08.120 --> 0:52:11.560
<v Speaker 1>to person, as well as different life experiences which have

0:52:11.640 --> 0:52:14.880
<v Speaker 1>wired up the circuitry. And as a result, we also

0:52:14.960 --> 0:52:19.280
<v Speaker 1>have different intelligences that allow us to see the world

0:52:19.719 --> 0:52:23.839
<v Speaker 1>differently and sometimes with more or less clarity. And what

0:52:23.880 --> 0:52:27.200
<v Speaker 1>we've done today is looked at the complexity of what

0:52:27.320 --> 0:52:31.200
<v Speaker 1>seems like a simple question, what is intelligence? We know

0:52:31.320 --> 0:52:34.680
<v Speaker 1>that there are differences between species and even within members

0:52:34.680 --> 0:52:37.400
<v Speaker 1>of any species, but we don't always know how to

0:52:37.480 --> 0:52:41.239
<v Speaker 1>capture that. And the fact that we can address the

0:52:41.360 --> 0:52:43.880
<v Speaker 1>question but after one hundred years still not come to

0:52:43.920 --> 0:52:49.160
<v Speaker 1>a clear answer probably indicates that the word intelligence simply

0:52:49.520 --> 0:52:53.600
<v Speaker 1>holds up too many different things, different skills, whether that's

0:52:53.640 --> 0:52:56.920
<v Speaker 1>the squelching of distractors or the number of things you

0:52:56.960 --> 0:52:59.520
<v Speaker 1>can hold in memory at any given moment, or the

0:53:00.239 --> 0:53:05.400
<v Speaker 1>formatting of information or making associations, or the ability to

0:53:05.440 --> 0:53:09.200
<v Speaker 1>simulate possible futures. It seems to me that one of

0:53:09.239 --> 0:53:13.279
<v Speaker 1>the most meaningful tests for the intelligence of our species

0:53:13.600 --> 0:53:18.400
<v Speaker 1>will be this. Will we be able to define and

0:53:18.680 --> 0:53:25.080
<v Speaker 1>understand intelligence before we create it and perhaps get taken

0:53:25.120 --> 0:53:28.640
<v Speaker 1>over by it, That will be the true test of

0:53:28.719 --> 0:53:36.080
<v Speaker 1>the intelligence of our species. Go to eagleman dot com

0:53:36.120 --> 0:53:41.200
<v Speaker 1>slash podcast for more information and define further reading. Send

0:53:41.239 --> 0:53:45.000
<v Speaker 1>me an email at podcast at eagleman dot com with

0:53:45.160 --> 0:53:48.279
<v Speaker 1>questions or discussion, and I'll be making more episodes in

0:53:48.320 --> 0:53:53.560
<v Speaker 1>which I address those. Until next time, I'm David Eagleman,

0:53:53.840 --> 0:54:03.680
<v Speaker 1>and this is Inner Cosmos.