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Stanford Neuroscientist: Can’t Remember Your Dreams? Your Brain May Be Warning You!

Stanford Neuroscientist: Can’t Remember Your Dreams? Your Brain May Be Warning You!

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[00:00]

After many many decades of people

[00:01]

debating this, you might have figured

[00:03]

out the reason why we dream. Yes. And

[00:05]

it's a simple answer. So if you go

[00:07]

blind, the visual cortex in the back of

[00:09]

the brain gets taken over by hearing and

[00:12]

by touch and by other things. In fact,

[00:13]

our colleagues at Harvard did an

[00:15]

experiment where they blindfolded

[00:16]

normally cighted people. And you could

[00:18]

start seeing that takeover happening

[00:19]

after 60 minutes. And that's when we

[00:21]

realized, wow, the purpose of dreaming

[00:24]

is to defend the visual territory from

[00:26]

takeover from the other senses. But what

[00:29]

fascinates me about brain plasticity and

[00:31]

what I've devoted my career to is

[00:32]

figuring out the way that we can be the

[00:34]

sculptors of our own brains and how it

[00:36]

gives us an opportunity to become the

[00:39]

kind of person we would like to be.

[00:40]

>> And can we do that?

[00:42]

>> Yes. Here's the thing. Your brain peaked

[00:44]

at the age of two. Okay. So at the

[00:47]

beginning you've got fluid intelligence,

[00:49]

meaning you could learn anything. But

[00:50]

now that you have grown up in this

[00:52]

world, you've got crystallized

[00:54]

intelligence, meaning you know how to

[00:55]

drive a car. You know how to operate a

[00:57]

cell phone. You know how to run a

[00:58]

business. And so your brain doesn't

[00:59]

require as much change which means that

[01:02]

the structure of the brain is always

[01:04]

degenerating.

[01:05]

>> So what are the set of actions that will

[01:06]

fundamentally change my brain and make

[01:08]

me that type of person who's motivated

[01:10]

and disciplines and who has high agency

[01:12]

and attacks the world.

[01:13]

>> So this is something I've studied in my

[01:15]

lab for decades now. And the key is that

[01:18]

>> and what about AI and the social media

[01:20]

debate as it relates to brain

[01:21]

development?

[01:22]

>> Well, I happen to be a cyber optimist

[01:24]

for young people. I think it's going to

[01:26]

make them much smarter than the

[01:28]

generation that came before. And here's

[01:30]

why.

[01:31]

>> Interesting.

[01:33]

This is super interesting to me. My team

[01:34]

given me this report to show me how many

[01:36]

of you that watch this show subscribe.

[01:37]

And some of you have told us according

[01:39]

to this that you are unsubscribed from

[01:41]

the channel randomly. So, favor to ask

[01:43]

all of you, please could you check right

[01:44]

now if you've hit the subscribe button

[01:46]

if you are a regular viewer of the show

[01:47]

and you like what we do here. We're

[01:49]

approaching quite a significant landmark

[01:50]

on this show in terms of a subscriber

[01:52]

number. So, if there was one simple free

[01:55]

thing that you could do to help us, my

[01:56]

team, everyone here, to keep this show

[01:58]

free, to keep it improving year over

[02:00]

year and week over week, it is just to

[02:02]

hit that subscribe button and to double

[02:03]

check if you've hit it. Only thing I'll

[02:05]

ever ask of you, do we have a deal? If

[02:07]

you do it, I'll tell you what I'll do.

[02:08]

I'll make sure every single week, every

[02:11]

single month, as we fight harder and

[02:12]

harder and harder and harder to bring

[02:13]

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[02:15]

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[02:16]

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[02:17]

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[02:20]

down. Please help us. Really appreciate

[02:22]

it. Let's get on with the show.

[02:31]

Dr. David Eagleman, what made you so

[02:33]

fascinated about the brain? And why

[02:35]

should everybody listening be fascinated

[02:36]

about the brain as well? Here's what I

[02:38]

think it is. When I was 8 years old, I

[02:40]

fell off of the roof of a house that was

[02:42]

under construction and I fell 12 feet

[02:45]

and broke my nose on the floor below.

[02:48]

But the whole thing seemed to take a

[02:49]

long time. I did the calculation and

[02:52]

figured out that it only took 6 of a

[02:54]

second to get from the top to the

[02:56]

bottom. And I couldn't figure out why it

[02:57]

seemed to have taken so long. So I think

[02:59]

that got me really interested in

[03:01]

perception and the machinery by which we

[03:05]

view the world and taken in and what is

[03:07]

actually real versus what's a

[03:10]

construction of the brain. And that's

[03:12]

how what I've devoted my career to is

[03:13]

figuring out how the brain which is

[03:15]

locked inside the skull. It's about

[03:18]

three pounds. How it constructs this

[03:20]

model of the world and which things we

[03:22]

can take as reality and which things we

[03:25]

shouldn't.

[03:26]

>> I think most people don't even know they

[03:27]

have a there's a brain there almost. It

[03:29]

sounds like a strange thing to say, but

[03:31]

we've never really most of us haven't

[03:32]

really seen our own brains at all. We've

[03:34]

never been able to touch our own brains

[03:35]

at all. So, it's it's easy to fall into

[03:38]

the trap of thinking that everything I

[03:40]

experience is true and is reality. So,

[03:43]

I'm wondering how a deeper understanding

[03:45]

of all this stuff can help me live a

[03:46]

better life.

[03:47]

>> Yeah. One of the things that I started

[03:50]

writing about years ago is that I think

[03:51]

we're not I think we often think of

[03:54]

ourselves as individuals, meaning not

[03:57]

divisible into other things. But really,

[04:01]

you are a team of rivals. So, you've got

[04:04]

all these neural networks that have

[04:06]

different drives making different

[04:07]

suggestions to you.

[04:08]

>> What's a neural network?

[04:10]

>> Um, so in the brain, you've got 86

[04:12]

billion cells called neurons. And these

[04:15]

are communicating with each other at a

[04:17]

blindingly fast rate. Many of these

[04:18]

cells are hooked up in networks. So,

[04:20]

they're, you know, this guy's talking to

[04:22]

this guy and this guy, and they're all

[04:23]

in particular networks. The thing is,

[04:25]

you can actually get competing networks.

[04:29]

So, for example, Stephen, if I drop some

[04:31]

chocolate chip cookies in front of you,

[04:33]

part of your brain wants to eat it. It's

[04:34]

a good energy source. Part of your brain

[04:35]

says, "Don't eat it. I'll gain weight."

[04:37]

Part of you says, "Okay, I'll eat one,

[04:38]

but I'll go to the gym tonight." The

[04:40]

point is you are arguing with yourself.

[04:44]

You are conflicted. This is what makes

[04:46]

humans so interesting is that we have

[04:49]

all these voices trying to drive us to

[04:52]

different conclusions about our

[04:53]

behavior.

[04:54]

The way that your ship of state moves

[04:57]

depends on the vote of the neural

[04:59]

parliament at any time. So understanding

[05:02]

this I think is really critical to

[05:04]

navigating our own lives because all of

[05:06]

us do things where retrospectively we

[05:08]

regret it. We say I shouldn't have eaten

[05:10]

that whole bag of chips or done the you

[05:13]

know the alcohol or the drugs or what

[05:15]

like everybody has regrets all the time

[05:17]

with things and it's because you have

[05:20]

different voices in charge at different

[05:22]

times. Okay.

[05:24]

>> Part of what this leads to is what we

[05:26]

call the Ulisses contract. So a Ulisses

[05:30]

contract is where you do something now

[05:31]

to prevent yourself from behaving badly

[05:33]

in the near future. Just as an example,

[05:36]

you know, when people go to Alcoholics

[05:38]

Anonymous, the first thing they're told

[05:39]

is clear all the alcohol out of the

[05:41]

house. Because even if you feel like,

[05:43]

look,

[05:43]

>> I'm in a moment of sober reflection. I

[05:46]

don't want to ever drink again. If you

[05:47]

have alcohol in the house, you're going

[05:48]

to bust into that cabinet at some point

[05:50]

on a festive Saturday night or a lonely

[05:53]

Sunday night or whatever. So, what you

[05:55]

do is you constrain your future behavior

[05:58]

by setting things up in the right way so

[06:01]

your future uh the future you can't

[06:03]

behave badly. We naively think, okay,

[06:06]

well, I know who I am. I'm just one

[06:08]

person. But but you're not. And under

[06:10]

different circumstances, you're tempted

[06:12]

by different things and you'll do

[06:13]

different kinds of behavior. So having a

[06:16]

sense of what's going on under the hood

[06:18]

gives us an opportunity to be more

[06:21]

closely aligned with the kind of person

[06:23]

we would like to be

[06:24]

>> because it feels like there's just one

[06:26]

well I do argue with myself in my head

[06:28]

sometimes but it feels like there is

[06:31]

just one me

[06:32]

>> and so when I hear that voice say Steve

[06:34]

you should have that cookie and it's

[06:35]

1:00 a.m. And then the other voice says,

[06:36]

"No, you shouldn't." I think it's kind

[06:38]

of the same person just tussling with

[06:40]

himself,

[06:41]

>> right? Well, but that tustling with

[06:42]

himself implies different political

[06:44]

parties that are all battling it out.

[06:46]

You know, when you look at a parliament,

[06:47]

you've got all these political parties

[06:49]

that all love their country. They just

[06:50]

have different ideas of how to steer it.

[06:53]

And this is what's going on uh in in the

[06:56]

brain all the time.

[06:57]

>> So, what does one do about that? How do

[06:58]

I make do I do I have to make a list

[07:01]

contract? I think it's very useful to

[07:03]

make that sort of thing. But also just

[07:05]

understanding oneself. I mean part of

[07:07]

the you know there was this Greek

[07:08]

admonition to know thyself. This was a

[07:11]

sign they had in various places, various

[07:13]

temples and stuff. But I think that

[07:15]

becomes know thyelves. And the better we

[07:19]

know ourselves, the more we can get rid

[07:21]

of the illusion that we are one person.

[07:23]

Because all any of us need to do is look

[07:25]

back on our behavior to say, "Oh yeah,

[07:28]

in some circumstances I would do that.

[07:29]

and other circumstances I think is a

[07:31]

terrible idea. So this is all to the

[07:33]

goal of understanding who you are.

[07:36]

>> What are the big misconceptions about

[07:37]

the brain that people have gone through

[07:40]

their life believing? I mean that's one

[07:41]

of them. Something that is true that

[07:42]

kind of could fall in place of that is

[07:44]

just this fundamental idea that our

[07:46]

brains are plastic or sort of adaptable

[07:50]

because when I found out that I could

[07:51]

change my brain by what I do, I found

[07:52]

that to be really really inspiring.

[07:56]

>> Yes, that that's exactly right. So brain

[07:58]

plasticity, if someone hasn't heard that

[08:00]

term before, it sounds like a weird

[08:01]

term, but the reason it came about 100

[08:04]

years ago is because the great

[08:06]

psychologist William James pointed out

[08:07]

that, you know, if you take a piece of

[08:09]

plastic, what we like about that

[08:10]

material that we call plastic is that

[08:12]

you can mold it into a shape and it'll

[08:14]

hold that shape. And that's what your

[08:16]

brain does. So if I ask you the name of

[08:18]

your third grade teacher, you can

[08:19]

remember that name even though it's been

[08:21]

a long time because your neural networks

[08:25]

changed and held on to that piece of

[08:27]

information. Okay? Well, our whole lives

[08:30]

our brains are changing every moment. So

[08:33]

now we have certain doors that close at

[08:36]

different times. So just as an example,

[08:39]

um you need to learn language in the

[08:41]

first several years of your life. If you

[08:43]

don't learn language, you can never get

[08:45]

the concept of language. Your brain will

[08:47]

never figure that out.

[08:48]

>> You're not saying you can't learn a new

[08:49]

language as an adult. You're saying the

[08:51]

concept of

[08:51]

>> the concept of language, the concept

[08:53]

that I can name things and I can ask for

[08:55]

things and so on. Just that never clicks

[08:57]

in the brain. For example, in Romania at

[09:00]

the fall of Chuchescu, there were tens

[09:02]

of thousands of kids in the orphanages

[09:05]

because their parents had been killed.

[09:06]

It was too many kids. And so the staff

[09:08]

there said, "Look, the kids will get,

[09:11]

you know, clingy if you pay too much

[09:13]

attention to them. So here's what we're

[09:14]

going to do. We're going to feed the

[09:14]

kids, but we're not going to hold them

[09:16]

and we're not going to talk to them."

[09:18]

And all these children grew up with real

[09:20]

cognitive deficits as a result. Here's

[09:23]

the thing about brain plasticity. Human

[09:25]

beings have a a similar brain to all our

[09:28]

neighbors in the animal kingdom. If you

[09:30]

compare our brain to a horse brain, a

[09:32]

dog brain, anything like that, it's the

[09:33]

same general structures and stuff. But

[09:36]

what we have is much more of the wrinkly

[09:38]

outer bit called the cortex. It's the

[09:40]

outer 3 mm. And maybe we'll come back to

[09:43]

why that matters so much. But the other

[09:46]

thing that mother nature tweaked with

[09:48]

us, it's small genetic tweaks. But we

[09:50]

have much more plasticity, adaptability

[09:53]

such that when a horse drops into the

[09:56]

world, it's doing the same thing that

[09:57]

horses did 100,000 years ago. It's just,

[09:59]

you know, eat mate. But when a human

[10:01]

drops in the world, we learn everything

[10:04]

that's happened before us. And then we

[10:06]

springboard off the top of that. So we

[10:08]

living in the 21st century, we say, "Oh

[10:11]

great, you know, physics, math, this,

[10:12]

that, art, blah, blah, great. We got

[10:14]

everything that's happened before us.

[10:15]

Now let's do our own thing." And that's

[10:17]

what's so special about the plasticity

[10:19]

of the human brain, the adaptability of

[10:21]

it. The downside, the gamble is that

[10:25]

mother nature drops human brains into

[10:28]

the world kind of halfbaked and we then

[10:31]

get to absorb everything. But in the

[10:32]

rare circumstance where you're not

[10:34]

getting the right input, then then that

[10:37]

ends up really in trouble because it's

[10:39]

only halfbaked. So when it comes to

[10:41]

language, we can learn multiple

[10:43]

languages when we're young. That's very

[10:44]

easy, but it gets harder and harder as

[10:46]

that goes along. And various other

[10:48]

things become harder. And here's why.

[10:50]

It's because I I mentioned this earlier,

[10:53]

but the job of the brain is to make a

[10:55]

model of the world so it can operate

[10:57]

within it. So, for example, you're an

[11:00]

entrepreneur and you love doing

[11:02]

business. So, you get it. You okay,

[11:04]

here's how, you know, here's how you

[11:06]

structure business. Here's how you hire.

[11:08]

Well, here's how you set up a board.

[11:09]

Well, you're doing everything because

[11:11]

you've got a really rich internal model

[11:13]

of how to structure a business. That's

[11:15]

what the brain wants to do is get that

[11:18]

stuff right. As a result, if you

[11:21]

suddenly ended up, you know, taking a

[11:23]

trip to Mars and there's a whole very

[11:25]

different society there that does

[11:27]

businesses very differently, you would

[11:29]

have to relearn stuff really quickly.

[11:32]

So, here's the thing. You went from

[11:35]

having a brain that had high fluid

[11:37]

intelligence to now having a brain that

[11:40]

has high crystallized intelligence. What

[11:42]

that means is at the beginning you can

[11:44]

learn anything. You could learn any

[11:46]

language. You could have dropped into

[11:47]

any area. You could have dropped into

[11:49]

13th century Japan when I was young.

[11:51]

>> When you were young, when you were a

[11:52]

baby, if you had dropped out of the womb

[11:54]

in, you know, 10th century Mongolia, you

[11:57]

would have said like, "Okay, cool. Learn

[11:59]

lang." You would you would be a 10th

[12:00]

century Mongolian. But as it happens,

[12:03]

you dropped into this era, you know, a

[12:06]

certain place and time and neighborhood

[12:07]

and culture and family. And so you learn

[12:09]

that that's who you become is that

[12:11]

person. We often think that plasticity

[12:14]

diminishes as you age. But it's not

[12:17]

simply that it's diminishing. It's that

[12:18]

you are getting the right answers about

[12:22]

how to operate in the world. And so you

[12:24]

don't have to change as much. Your brain

[12:26]

doesn't require as much change.

[12:28]

>> What if I want to change?

[12:30]

>> Yes. So it turns out you still can

[12:32]

change. That's the key is that the

[12:35]

reason brains change less and less is

[12:37]

because they don't have to. But when

[12:40]

things get upside down, just as one

[12:42]

example, everything about the pandemic

[12:44]

really stunk, except for one thing, I

[12:47]

think the tiny silver lining is that all

[12:49]

of us had to reassess. Oh my gosh, wait,

[12:54]

how is the world working? I thought I

[12:55]

knew how the world worked, but now I

[12:57]

don't know if there's going to be toilet

[12:59]

paper at the store. I don't know if the

[13:00]

bank's going to be open. I don't know if

[13:02]

I can get coffee at the coffee shop.

[13:04]

Like, everything was different. As awful

[13:06]

as it was, it's really useful to

[13:09]

challenge your internal model of the

[13:11]

world and get to do that as an adult. We

[13:13]

don't usually get to.

[13:15]

>> So, if I want to change, what would you

[13:16]

recommend that I do? If I want to if I

[13:18]

want to change who I am, say I'm

[13:20]

stubborn, I'm not motivated,

[13:22]

>> um, and I want to be a different person.

[13:24]

>> The key is challenge. The key is seeking

[13:26]

challenge. So, it turns out that where

[13:28]

we always want to be is in between the

[13:31]

levels of frustrating but achievable.

[13:33]

and you want to take on new tasks. You

[13:35]

want to seek novelty to find yourself in

[13:37]

that zone and push yourself to do things

[13:40]

that you just haven't done before. And

[13:42]

one of the things that's so wonderful

[13:44]

about the modern world, you know,

[13:46]

everyone's got complaints about the

[13:47]

internet and social media and stuff like

[13:48]

that, but the good news is it deep it

[13:51]

exposes you to so much more than you

[13:53]

ever even knew was out there. The key is

[13:56]

to actively seek those challenges and

[13:58]

seek new things and seek to become

[14:00]

expert in various sorts of fields. And

[14:02]

and I think the key is that once you

[14:04]

become good at something, you you have

[14:07]

to drop that and take on something

[14:08]

you're not good at. This is the best

[14:10]

thing that you can do for your brain.

[14:12]

The reason is because what you're doing

[14:14]

is you're constantly building new

[14:15]

roadways and pathways in the brain.

[14:17]

There's a study that's been going on for

[14:19]

for decades now called the religious

[14:22]

orders study where a bunch of Catholic

[14:24]

nuns agreed to donate their brains for

[14:26]

autopsy when they passed away. What the

[14:29]

researchers discovered when they look at

[14:30]

the brain carefully is that some

[14:33]

fraction of these nuns had Alzheimer's

[14:35]

disease. Their brains were physically

[14:37]

degenerating with the ravages of of this

[14:40]

dementia, but they didn't show any of

[14:43]

the cognitive deficits that one normally

[14:45]

has. They didn't seem to be having

[14:47]

memory problems and so on. It turns out

[14:49]

it's because all these nuns lived in

[14:52]

these convents till the day they died.

[14:54]

They had social challenges and they had

[14:56]

fights with their fellow sisters and

[14:58]

they played games with their fellow

[14:59]

sisters and they were they had chores

[15:01]

and responsibilities and they were doing

[15:03]

stuff. What that means is even as the

[15:05]

tissue the brain tissue was physically

[15:07]

degenerating, they were making new

[15:09]

roadways and bridges all the time.

[15:12]

>> And so that's what kept them cognitively

[15:14]

healthy. We call that cognitive reserve.

[15:17]

Contrast this with with people who

[15:19]

retire at 65 and they go home and they

[15:21]

watch television and their social

[15:23]

circles shrink and so on. That's when

[15:25]

you've really got concerns because

[15:27]

you're not building the new pathways. Is

[15:29]

there data to support that that when you

[15:31]

retire, if you retire early or if you

[15:33]

retire say in your 60s, it increases

[15:36]

your probability of an earlier death or

[15:38]

cognitive decline? Almost certainly with

[15:41]

cognitive decline because you're just

[15:43]

not getting the challenge at that point.

[15:45]

You're just coasting on your internal

[15:46]

model.

[15:48]

this. It's tragic, but what happens

[15:50]

often is that people's hearing gets

[15:51]

worse. And so by the time they retire,

[15:52]

let's say in their mid-60s, it's not

[15:55]

really that fun for them to go out to

[15:56]

parties and restaurants anymore because

[15:58]

they can't quite hear. And so there

[16:00]

there all these converging reasons why

[16:02]

their social lives shrink. But it turns

[16:04]

out social life is one of the most

[16:07]

important things that we can do for our

[16:08]

brains because there's an expression we

[16:11]

sometimes use in neuroscience, which is

[16:12]

that nothing is as hard for the brain as

[16:14]

other people. because you never know

[16:15]

what the other person's going to say and

[16:17]

do and how they'll react emotionally and

[16:19]

so on. So, you're constantly on your

[16:21]

toes with other people. And if you're

[16:22]

not doing that anymore, that ends up

[16:24]

being a problem.

[16:25]

>> H

[16:27]

interesting. And as a as a I'm 33 years

[16:31]

old, so if you were to plot where my

[16:33]

brain is on like a graph of decline,

[16:37]

I is it the case that I should be doing

[16:38]

as much as I can now to build as many

[16:40]

pathways I can so that when I'm 80, my

[16:44]

decline sort of levels out in a in a

[16:46]

better place? Oh yeah, for for sure. But

[16:49]

this is true for many reasons actually.

[16:51]

Okay, so look, the truth is your brain

[16:53]

peaked at two at the age of two because

[16:56]

that's when you get the most connections

[16:58]

between neurons, between these cells in

[17:00]

the brain. You get this, at first you're

[17:03]

born with these 86 billion neurons and

[17:05]

they connect and connect and connect and

[17:07]

it finally becomes like a overgrown

[17:09]

garden at the age of two and from there

[17:10]

you're pruning. From there you're taking

[17:12]

connections away. Now it happens that

[17:14]

that's not a bad thing. That's a good

[17:16]

thing because that's how you're

[17:18]

resonating with the world that you are

[17:20]

in.

[17:21]

you know, 21st century London and LA

[17:24]

versus, you know, 10th century Mongolia

[17:26]

because you're you're just strengthening

[17:29]

those pathways that resonate and you're

[17:31]

getting rid of everything else. Okay,

[17:32]

fine. But over time, your brain cells

[17:35]

die. You know, every time you hit your

[17:36]

head on something or whatever, your

[17:38]

brain cells are going down. Um, so in

[17:40]

that sense, you've peaked. But your

[17:42]

crystallized intelligence that you've

[17:44]

been building your whole life, you know,

[17:46]

that keeps going and you'll you'll have

[17:48]

decades ahead of you where you can start

[17:49]

doing stuff. But yes, the reason to

[17:51]

learn everything you can is because all

[17:53]

that stuff cashes out at various points

[17:56]

in your life when you're starting your

[17:58]

next business or you're, you know,

[18:00]

wanting to do the next great thing where

[18:02]

you're surfing the way web of AI. You

[18:04]

know, you'll say, "Oh, I learned this

[18:06]

thing when I was 16. I learned this

[18:07]

thing when I was 22." And and these are

[18:08]

these are paying off now. I think I

[18:10]

heard Andrew Hubman say that one of the

[18:12]

most fascinating discoveries of the last

[18:14]

century is a particular part of the

[18:16]

brain called the anterior mid-sul cortex

[18:19]

and it links to what you were saying a

[18:21]

second ago about challenge and doing

[18:23]

things that are difficult.

[18:25]

>> Yeah, it turns out that area of the

[18:27]

brain is involved and other networks as

[18:29]

well because when you're doing something

[18:32]

new and challenging and difficult, you

[18:34]

have stress and anxiety. Your whole

[18:37]

brain is active. Let's say I measured

[18:40]

your brain even with something like EEG,

[18:42]

electronphilography. That's where I

[18:44]

stick electrodes on the outside. Let's

[18:45]

say I measure your brain in my brain.

[18:47]

We're doing something that let's say

[18:49]

you're an expert at what's something

[18:50]

you're really good at juggling. I don't

[18:53]

know some physics.

[18:54]

>> Let's go for juggling.

[18:55]

>> Okay. Let's say you're an expert

[18:56]

juggler. Let's say I've never juggled.

[18:58]

Okay. If we're both juggling, you're

[18:59]

going to be much better than I am. But

[19:01]

your brain will be less active. You

[19:04]

won't have as much activity in your

[19:06]

brain. all my brain is on fire with

[19:08]

activity because why I'm trying to

[19:10]

figure out okay where do I put my hand

[19:12]

how do I throw this and blah blah blah

[19:13]

so when I'm in novice at something my

[19:15]

brain is using much more activity not

[19:18]

just the anterior made singulate but

[19:20]

tons of activity all over because I'm

[19:21]

trying to figure out the rules I'm

[19:22]

trying to figure out what's going on you

[19:24]

as an expert you know you got it you

[19:26]

don't you don't need to burn much

[19:27]

activity this is what the brain's goal

[19:29]

is is to say hey once I've practiced

[19:31]

something along once I get something

[19:33]

about the world I'm going to burn it

[19:34]

deeper and deeper into the circuitry So

[19:36]

I don't have to burn a lot of energy on

[19:38]

it.

[19:38]

>> On this part of the brain, the anterior

[19:39]

mid singular cortex, Andrew human was

[19:41]

saying it's larger in people that do

[19:43]

things that they basically don't want to

[19:44]

do hard things. If you spend your life

[19:47]

doing things you don't want to do, then

[19:48]

it happens to be bigger. And so people

[19:49]

have now thought of this part of the

[19:51]

brain almost like the willpower muscle

[19:52]

because for some reason those that are

[19:54]

doing hard things have bigger ones and

[19:56]

those that are not have smaller ones. I

[19:58]

mean it wouldn't be so much the

[20:00]

willpower of muscle. It would be some

[20:01]

indication retrospectively of how hard

[20:04]

you have worked. Look, the fact is you

[20:07]

can see changes in brain size with lots

[20:09]

of things. I'll give you an example. If

[20:11]

you are a pianist, if you play piano,

[20:14]

then we can actually see physical

[20:16]

changes in your motor cortex. This is

[20:18]

the part of the brain essentially

[20:20]

underneath where you would wear

[20:21]

headphones. For those who are looking

[20:22]

visually, it's this red part here. You

[20:25]

actually get a bigger loop of tissue

[20:28]

here than you do in a normal brain. Why?

[20:31]

Because you're doing so much fine motor

[20:33]

activity with your fingers with both

[20:35]

hands. Okay? In contrast, if you're a

[20:38]

violinist,

[20:40]

you're only really doing that kind of

[20:41]

detailed activity with one hand. The

[20:42]

other hand is just boeing. And so you

[20:44]

only get that activity here in one half

[20:47]

of the brain for violinists. So I can

[20:49]

look at a brain and tell, hey, is the

[20:51]

person a pianist or a violinist or an

[20:53]

either? I can tell just by looking at

[20:54]

the visual cortex because you see

[20:56]

changes in the brain based on what you

[21:00]

do. For example, jugglers, people who

[21:02]

play music, even you can tell this with

[21:04]

medical students who study for final

[21:05]

exams. You actually see changes in the

[21:07]

distribution of of their cortex.

[21:10]

>> Why would it be getting bigger?

[21:12]

>> The reason is the brain's devoting more

[21:14]

real estate to that. In this case, let's

[21:17]

say we're talking about fingers on a

[21:18]

piano or a violin. The brain is devoting

[21:20]

more there's more relevance to that and

[21:24]

so it more real estate so that you can

[21:26]

do it better in the future.

[21:28]

>> Exactly. The key about the cortex this

[21:30]

wrinkly outer part is that it is a

[21:32]

one-trick pony. This is often overlooked

[21:34]

because even this brain that I'm holding

[21:36]

here uh is colorcoded so that we think

[21:39]

oh okay that's clearly labeled this

[21:40]

that's clearly labeled that and so on.

[21:42]

But in fact it's all the same stuff and

[21:45]

it can change. So for instance, if you

[21:47]

are born blind, then this area that we

[21:50]

normally call the visual cortex gets

[21:52]

taken over by the rest of the brain. If

[21:54]

you're born deaf, then this part that we

[21:56]

call the auditory cortex gets taken

[21:58]

over. It gets devoted to other tasks.

[22:00]

And so this whole system is very very

[22:03]

fluid. And this is what fascinates me

[22:04]

about brain plasticity is the way that

[22:07]

we can be the sculptors of our own

[22:09]

brains because we can devote ourselves

[22:13]

to particular things and have the brains

[22:16]

real estate get involved in that. So if

[22:19]

I was currently someone that couldn't

[22:20]

get out of bed, I didn't have a lot of

[22:22]

discipline or motivation and I wasn't

[22:25]

very good at committing myself to hard

[22:27]

things.

[22:28]

With everything you know about the

[22:29]

brain, is it possible to take a set of

[22:31]

actions that will fundamentally change

[22:33]

my brain and make me that type of person

[22:35]

who runs marathons, who does hard

[22:38]

things, who's motivated and disciplines,

[22:39]

and who has high agency and attacks the

[22:41]

world.

[22:42]

>> Yes. Yeah. But it's much more than

[22:44]

simply resolve because I mean just look

[22:47]

at New Year's resolutions. You know, by

[22:49]

by February, most people have dropped

[22:50]

most of them. So, it's really a

[22:52]

psychology problem about figuring out

[22:55]

okay, what are the things that motivate

[22:57]

me? So, let's say you want to become a

[22:59]

marathon runner. You've got that distant

[23:01]

dream. You figure out like what actually

[23:03]

motivates me in the short term? Who am I

[23:05]

trying to impress? What am I trying to

[23:07]

accomplish in my life? How can I

[23:10]

structure things like this Ulyses

[23:12]

contract that I talked about earlier

[23:14]

where I'm actually locking myself into a

[23:16]

contract? Like, you know, I call Bob and

[23:19]

I say, "I will meet you every morning at

[23:21]

7:00 and we're going to run until we

[23:23]

drop." Like once I've committed to those

[23:25]

sorts of things, that's how you set

[23:27]

things up so that you do the right

[23:29]

thing.

[23:29]

>> It's a bit of a cycle, right? Because

[23:30]

then my brain will adapt and then

[23:32]

presumably that will make it easier for

[23:33]

me to run.

[23:34]

>> Yeah.

[23:35]

>> And then I'll run more and then my brain

[23:36]

will adapt.

[23:37]

>> That's right.

[23:38]

>> And the cycle continues.

[23:39]

>> And it's not just your brain, of course.

[23:40]

In this case, it's your body. You're

[23:41]

getting better. You're getting stronger.

[23:42]

You don't get as out of breath. And so

[23:44]

all these things help. Exactly. But in

[23:46]

order to keep the cycle going, you need

[23:48]

to figure out what is spinning this

[23:50]

flywheel and what are the all the other

[23:52]

things in your life. Whether good

[23:54]

motivations or bad, it doesn't matter.

[23:56]

You just figure out what it is that you

[23:58]

can do to to get there.

[24:00]

>> Are there certain physical exercises

[24:02]

that are particularly good for the brain

[24:03]

from what you've understood?

[24:05]

>> The general story is exercise is really

[24:08]

important for the brain. I'll give you

[24:09]

just one example of that, which is

[24:11]

there's still this debate going on about

[24:13]

whether we get new neurons in the brain.

[24:16]

The general story has always been you're

[24:18]

born with 86 billion neurons and those

[24:20]

slowly die with time. But in rats, for

[24:24]

example, there is a little trickle of

[24:26]

new cells, new brain cells. And there's

[24:29]

been a debate for a long time about

[24:30]

whether that little trickle happens in

[24:32]

humans or not. Still unresolved. But in

[24:34]

rats, what you can see is that exercise

[24:37]

causes the trickle to increase. If you

[24:39]

stick the rat on the wheel and it's

[24:41]

doing physical exercise, you get more

[24:43]

new brain cells. Now, we don't know for

[24:45]

sure that this happens in humans, but

[24:48]

lots of things about physical fitness

[24:50]

and exercise matter a lot to the brain.

[24:52]

This is nothing new. Exercise, sleep,

[24:54]

diet, these are really important things

[24:56]

for keeping the health of this organ. Is

[24:58]

there anything else that's important to

[25:00]

know for someone that is trying to

[25:02]

change and improve and keep their brain

[25:03]

in a healthy state as they age that we

[25:05]

haven't touched on?

[25:07]

>> There is something that that all of us

[25:09]

are thinking about which is about um

[25:11]

social media and the internet in

[25:13]

general. I do think one of the

[25:14]

interesting things about the internet

[25:16]

and social media is that if we were

[25:19]

growing up in a village 500 years ago,

[25:22]

you just know the people in the village

[25:24]

and what they can do and so on. But

[25:25]

let's say no one in the village was an

[25:27]

entrepreneur or a neuroscientist. And so

[25:31]

we we can't even picture that as a

[25:33]

thing. We don't know anything about

[25:34]

that. One thing that the internet has

[25:37]

done for kids growing up in the digital

[25:39]

age is that you get a lot of more

[25:40]

exposure to things. You you have so much

[25:42]

more exposure. I actually think this is

[25:44]

one of the positive things that I would

[25:46]

say about social media is that you not

[25:49]

only get exposure, wow, that kind of

[25:51]

thing is possible and that kind of thing

[25:52]

is possible, but you also have people

[25:54]

teaching you how to get there.

[25:56]

>> They say like, hey, I'm a fitness

[25:57]

influencer and I'm going to show you

[25:58]

exactly how to do the thing. Or, you

[26:00]

know, you say, "Hey, here's exactly how

[26:02]

you start a business." Or I say, "Hey,

[26:03]

here's the the route that you go through

[26:05]

undergrad and grad school to become a

[26:07]

neuroscientist." And that's great. I

[26:08]

mean, there's just there's so much more

[26:11]

uh of a talent window now that that

[26:13]

everyone gets exposed to. So, I think

[26:14]

that makes a better brain.

[26:16]

>> What are we doing to our children that

[26:18]

you think we probably shouldn't be doing

[26:19]

as it relates to brain development?

[26:22]

>> Here's the thing that's really important

[26:23]

about this debate is that nobody really

[26:26]

knows. And I'll tell you why. It's

[26:27]

because to do anything in science when

[26:29]

you're saying something about a group,

[26:31]

you need to have a control group that

[26:32]

you're comparing against. And when it

[26:34]

comes to asking the question of, hey,

[26:36]

kids growing up now with social media or

[26:38]

the internet, how do they compare to

[26:40]

other brains of kids who don't grow up

[26:42]

with that? Well, we don't have a control

[26:43]

group unless you look at kids who are

[26:45]

incredibly impoverished or let's say

[26:48]

Quakers who don't believe in technology.

[26:51]

And with both those groups, there's a

[26:52]

hundred other important differences. So,

[26:54]

you can't just say, "Oh, look, I'm

[26:56]

comparing to this kid who grew up

[26:57]

without food and and I'm going to say

[26:59]

there's this difference." Who the heck

[27:00]

knows why the difference is there? even

[27:02]

a generation ago. There's so many

[27:05]

differences in terms of diet and

[27:06]

pollution and politics and blah blah

[27:08]

blah what like everything that you can't

[27:10]

do it. So I I only mention this because

[27:13]

I think it's very important. A lot of

[27:14]

people pipe off with things about oh the

[27:15]

younger generation their brain this that

[27:17]

but we don't actually know and I will

[27:20]

tell you that I happen to be a cyber

[27:23]

optimist on this point about what

[27:25]

growing up with the internet does for

[27:27]

young people. I think it's going to make

[27:28]

them much smarter than the generation

[27:30]

that came before. And here's why. It has

[27:32]

to do with the size of the intellectual

[27:36]

diet that they can bring in. So when I

[27:38]

was a kid, I grew up pre- internet. You

[27:40]

know, I wanted to know stuff. So my mom

[27:42]

would drive me to the library, which was

[27:45]

25 minutes away, and I would pick up the

[27:46]

Encyclopedia Bratannica and I would flip

[27:48]

through it and hope they had an article

[27:50]

about the thing that I wanted to know

[27:51]

about. And that's how I was able to get

[27:53]

my little straw of knowledge. But now

[27:57]

kids are growing up with access to

[28:00]

anything they're interested in. And this

[28:02]

is so good for the brain. And from a

[28:04]

plasticity point of view, the reason

[28:06]

this matters is because change happens

[28:08]

in the brain when you are curious about

[28:11]

something. So when a kid asks a question

[28:13]

to Alexa or Siri or whatever and they

[28:15]

get the answer, that sticks because they

[28:18]

have the right cocktail of chemicals

[28:19]

going on in their head. In contrast,

[28:21]

when I grew up, I learned tons of just

[28:23]

in case knowledge. I mean, that's all

[28:25]

that the teachers could teach us is just

[28:27]

in case you ever need to know this fact,

[28:28]

here it is. But kids are in a really

[28:31]

great situation now. So, there are pros

[28:33]

and cons to to all this stuff, but I

[28:35]

think I'm very optimistic about what

[28:38]

this means for the for the warehouse of

[28:41]

knowledge that that kids can build up

[28:42]

now. And by the way, I saw an interview

[28:44]

with Isaac Azimoff in 1988. He was the

[28:48]

great science fiction writer who wrote

[28:50]

Foundation and so many other books. And

[28:52]

he was saying on this show in 1988, he

[28:55]

said, "Look, I envision a day when there

[28:59]

will be one central supercomput and

[29:01]

every house will have a cable running to

[29:03]

that supercomputer and you can ask any

[29:05]

question you want and it knows the

[29:07]

entirety of humankind's knowledge on

[29:09]

that computer." You know, what he was

[29:11]

foreseeing here was the internet. He got

[29:12]

the details wrong, which doesn't matter.

[29:14]

The idea is he saw how this would be so

[29:17]

incredible for education

[29:20]

because he pointed out look in any

[29:21]

classroom it's going too fast for half

[29:23]

the kids too slow for the other half of

[29:24]

the kids and if you could just pursue

[29:27]

the sphere of humankind's knowledge if

[29:29]

you could enter in whatever door you

[29:32]

wanted to that's the way to do it

[29:34]

because you'll be motivated now he

[29:36]

wasn't talking about brain plasticity or

[29:38]

anything but this is exactly what I'm

[29:39]

saying from a brain plasticity point of

[29:41]

view really matters

[29:43]

I I'll just mention something which is a

[29:46]

lot of people are concerned that oh with

[29:48]

with AI we're going to get lazy. We

[29:50]

won't you know know how to do anything

[29:51]

anymore because we can outsource it. It

[29:53]

just so happens that I I love doing home

[29:54]

improvement. I'm always fixing my house.

[29:56]

I have 3xed myself in the last half year

[30:00]

because of AI because I take a picture

[30:02]

of something. I say hey I've never seen

[30:03]

this kind of thing before. How does this

[30:04]

work? Whatever. And chat GPT says oh you

[30:07]

do this and you take this out and here's

[30:08]

the bolt and blah blah. It's not me

[30:10]

outsourcing it. It's me being curious

[30:12]

about something and so I remember how to

[30:14]

do everything now. I know how to do much

[30:16]

more than I used to because I like it.

[30:19]

>> What about the you there's been a couple

[30:21]

of studies that have come out that say

[30:22]

things like your brain's going to

[30:23]

atrophy if you don't continue to write

[30:25]

or um if you just defer all of your

[30:27]

learning to things like chatgbt or other

[30:29]

AI models. Um, one I guess one of the

[30:32]

areas that I think in one of the

[30:34]

studies, was it a Stanford study that

[30:36]

everyone was talking about where the the

[30:38]

participants used Google and AI and then

[30:41]

they'd learned something themselves.

[30:43]

>> But one of the things I've wondered is

[30:46]

if I'm going through my business life

[30:48]

and I'm encountering hard problems and

[30:50]

every time I encounter a hard problem, I

[30:51]

drop it into an AI. The AI spits out a

[30:54]

textbased answer. I copy and paste that

[30:56]

and send it as my response. presumably

[30:59]

there's some kind of important part of

[31:01]

the learning cycle or the you know

[31:03]

neurological development that I'm like

[31:05]

foregoing there I'm missing that I

[31:08]

probably should you know you said

[31:09]

earlier about doing hard things what I'm

[31:11]

doing there is I'm avoiding the hard

[31:12]

thing which is like thinking about it

[31:13]

and trying to understand it

[31:15]

>> yeah here's I think the really important

[31:17]

distinction there's vicious friction in

[31:20]

our lives and there's virtuous friction

[31:22]

so vicious friction is all the stupid

[31:25]

stuff that you have to do like hey

[31:27]

Stephen for your business I need you to

[31:28]

cop copy this spreadsheet over here and

[31:30]

fill in all these cells and and do your

[31:32]

taxes and whatever. Okay, that if we can

[31:35]

push that off to AI is massively

[31:37]

important for for improving human lives.

[31:40]

There's really not benefit in vicious

[31:41]

friction. But virtuous friction is, hey

[31:45]

Stephen, I really want you to think

[31:46]

about what is the optimal way to do this

[31:49]

business. What is the best structure for

[31:51]

this? How do we actually go DT to C? How

[31:54]

do we go B2B on this? What's the what's

[31:56]

the approach here that we're going to

[31:58]

take that you haven't done before that

[32:00]

would be amazing? That's virtuous

[32:03]

friction because you're really using

[32:04]

your brain to learn stuff that way. So

[32:06]

that's the first distinction that

[32:08]

matters is get rid of all the busy work.

[32:10]

There's no honor in that. I mean I'll

[32:13]

just mention in the 1990s there was this

[32:16]

big debate about whether we should have

[32:17]

kids use desk calculators or not. And

[32:20]

thank god that finally got resolved and

[32:21]

we let kids use calculators so that we

[32:23]

can learn, you know, couple we can spend

[32:25]

a couple days learning long division,

[32:26]

but you don't have to spend six months

[32:27]

on it because who cares? With the

[32:29]

virtuous friction, there's real

[32:31]

opportunity to surf the wave of AI so

[32:35]

that you are figuring out these tough

[32:37]

problems with the aid of somebody who

[32:40]

cares about your problem and is willing

[32:42]

to talk with you 247 and never gets

[32:44]

tired of talking to you about it. And so

[32:46]

you are not just copying and pasting,

[32:48]

but you're working with the AI to come

[32:51]

up with ideas that were beyond what you

[32:53]

would have come up with. Because I

[32:55]

mentioned earlier about internal models,

[32:57]

we have pretty narrow fence lines and

[32:59]

you can think of all these things, but

[33:01]

you don't even know what you don't know.

[33:02]

So, if you can have somebody who's

[33:04]

willing to talk with you, an expert in

[33:06]

all of humankind's knowledge, willing to

[33:08]

talk with you about it as much as you

[33:10]

want, there's a real opportunity there

[33:12]

to have a synergy where collectively you

[33:16]

both come up with a better idea than

[33:18]

either of you could have alone. But is

[33:19]

there a way for that relationship to

[33:21]

take place so that I actually benefit?

[33:22]

Because, you know, in the example I

[33:23]

gave, I'm just I take the question I was

[33:25]

asked, I put it into an AI, it gives me

[33:27]

an answer, I copy and paste it back to

[33:28]

the person that asked me the question.

[33:30]

that would happen if you really didn't

[33:32]

care about the person asking you the

[33:33]

question or the question. I mean

[33:35]

>> I mean this is what a lot of people are

[33:36]

doing like I get so many email because

[33:37]

you know we interview a lot of

[33:38]

candidates who join the business and so

[33:39]

I see tens of thousands of emails

[33:41]

sometimes a week that I mean I don't see

[33:43]

all of them but the ones that I see I

[33:44]

often know that you know because we've

[33:46]

sent them five questions or a task and I

[33:49]

look at it and go this is I can almost

[33:51]

predict the exact model that sent it to

[33:53]

me because they all have a different

[33:55]

personality so I go oh this one the

[33:56]

person put into Gemini or this one the

[33:58]

person put it into chatbt. Yeah,

[34:00]

exactly. And it's full of contrastive

[34:02]

constru construction like

[34:04]

>> it's not this, it's that. Yeah, exactly.

[34:06]

And then the M dashes. Exactly.

[34:07]

>> I'm really asking like is the person

[34:09]

that did that benefiting from from it?

[34:11]

>> No.

[34:12]

>> Well, no, but for a couple reasons. One

[34:13]

is that, you know, you and it it

[34:16]

triggers your red flag and so that does

[34:18]

not do anyone any good. see so many of

[34:20]

my colleagues posting on LinkedIn these

[34:22]

very obvious AI things and it irritates

[34:25]

me because I feel like I'm not going to

[34:26]

spend my time reading that because of I

[34:30]

call this this the effort phenomenon

[34:32]

which is um in in psychology we care a

[34:35]

lot about things that seemed like they

[34:37]

took a lot of effort and there's

[34:38]

something about seeing an AI post that's

[34:40]

just irritating because it's so

[34:42]

obviously AI

[34:43]

>> that's a really interesting idea the

[34:44]

effort phenomenon

[34:45]

>> yeah I've been I've been writing about

[34:47]

this for a while because um it turns out

[34:48]

there are psychology ology studies where

[34:50]

if I offer you two pieces of art and one

[34:52]

of them looks like, you know, let's say

[34:53]

it's a a red dot in the middle of a

[34:55]

white canvas and the other one is, you

[34:57]

know, bottle caps stacked up and glued

[35:00]

in this great shape or whatever, you'll

[35:02]

pay you'll pay much more for the thing

[35:03]

that looks like it took a lot of effort.

[35:05]

People will pay more for a real diamond

[35:08]

than a synthetic lab grown diamond,

[35:10]

which is exactly the same thing. It's

[35:12]

just carbon in the matrix. But they feel

[35:14]

like, oh well, mother nature took

[35:15]

hundreds of millions of years of effort

[35:17]

on this one, but not over here. It just

[35:19]

took a few days in the lab. So, there's

[35:21]

a million ways where we care about that

[35:23]

a lot. When it comes to this AI thing,

[35:26]

um, yes, anybody who's just popping back

[35:28]

something to you, it just feels like,

[35:30]

all right, they took the the path of

[35:31]

least resistance, and I'm not so

[35:33]

interested.

[35:33]

>> I want to know from a neuroscience

[35:35]

perspective whether they benefit.

[35:37]

>> Presumably, they don't benefit too much

[35:38]

either. I mean, it's hard to know

[35:40]

exactly how many times they went back

[35:41]

and forth with it. They could have said,

[35:43]

"Hey, Chad GPT, thank you for this, but

[35:46]

I'm kind of this more of this person.

[35:47]

When I really think about it, this is

[35:49]

the thing that inspires me." Not not

[35:51]

what you suggested. So, so somebody

[35:52]

could put effort into it. It's just that

[35:54]

we can't know that when we get the AI

[35:56]

response. It seems to be a pretty

[35:58]

consistent principle of life generally

[35:59]

that like when you do something hard or

[36:02]

when you put in effort, as you say, you

[36:03]

tend to get back like an equal and

[36:05]

opposite return like relatively. So I I

[36:08]

would think that if I fought through,

[36:11]

you know, maybe even using AI as a

[36:13]

companion, but I fought then to write it

[36:15]

out myself instead of just copying and

[36:17]

pasting.

[36:18]

>> Yeah.

[36:19]

>> One of the things I've learned from

[36:20]

doing this podcast and all these

[36:20]

episodes is everything is a trade-off.

[36:24]

>> Yeah.

[36:25]

>> And and if you don't know what the trade

[36:26]

you're making, then you're often at

[36:29]

great risk. And so like some of my

[36:31]

friends will say, "Oh, I take this pill

[36:32]

and it's amazing. It does all these

[36:33]

things for me. It's the most amazing

[36:34]

thing ever. I can just focus for 24

[36:36]

hours a day and I'm so productive now.

[36:38]

And I go, "What's the what's the

[36:39]

downside?" And they go, "Oh, there's no

[36:41]

downside." And I go, "Hm." Like, so

[36:43]

that's what I mean. It's even worse when

[36:45]

you don't you don't know the trade

[36:46]

you're making. And so with AI, I go,

[36:47]

"Okay, if it's making me wildly more

[36:50]

efficient or productive, what trade am I

[36:53]

making?" I think understanding this it's

[36:56]

probably not two categories but a

[36:58]

spectrum from vicious friction to

[37:00]

virtuous friction but really paying

[37:02]

attention to what is virtuous friction

[37:04]

what would make me a better person if I

[37:07]

actually put the effort into this that

[37:09]

matters a lot and I will say for us as

[37:12]

professors for you looking for job

[37:15]

candidates we need to change how we're

[37:17]

asking the questions if we just say hey

[37:19]

write answer these five questions of

[37:21]

course everyone's going to use it for

[37:22]

example in my classes is at Stanford. I

[37:24]

I don't have people turn in a final

[37:26]

paper anymore. That was from previous

[37:29]

life before AI. Now I have them do

[37:32]

projects as their final thing where

[37:33]

they're uh you know running an

[37:35]

experiment on something. And of course

[37:36]

they use AI to help them generate some

[37:39]

of the issues, but they have to deal

[37:40]

with other people and look at the data

[37:42]

and figure out what's wrong and that

[37:43]

kind of stuff. I worry that it's getting

[37:44]

into the age of, you know, the whole

[37:46]

calculator thing you said where maybe

[37:48]

actually it is now you need to assess

[37:50]

them on their ability to use the AI,

[37:52]

>> not to succeed without it.

[37:55]

>> Yeah, agreed. This is the whole game for

[37:57]

all of us, I think, is figuring out how

[37:58]

to surf this wave of AI where it can

[38:00]

make us super human. We can just be

[38:02]

better, so much better than anything we

[38:04]

ever were doing before because we have

[38:07]

immediate access to knowledge and facts

[38:09]

that either we had forgotten or we never

[38:11]

knew existed. And so we should be

[38:13]

surfing that wave. So I I I totally

[38:15]

agree with you on that point. If you can

[38:16]

figure out how to change your interview

[38:18]

questions so that you're seeing, hey,

[38:19]

can this person really get the speed?

[38:21]

With everything you know about learning

[38:23]

and neuroplasticity and expanding one's

[38:25]

brain, is there a anything else you can

[38:28]

say to the audience about how they

[38:30]

should use AI so that they become a

[38:32]

superhum?

[38:33]

>> Interesting. I you know, look, I I have

[38:35]

been talking to my friends about this

[38:36]

issue a lot lately and I I mentioned how

[38:38]

I've become so much better at home

[38:40]

improvement stuff. I just know so much

[38:42]

more. Each one of my friends has

[38:44]

something like that where like, hey, you

[38:45]

know what? I've actually gotten so much

[38:47]

better at this super random thing that I

[38:49]

never even thought I, you know, I never

[38:51]

thought about it explicitly, but because

[38:53]

I'm always asking AI questions about

[38:55]

that and it's giving me the answers.

[38:57]

It's not simply that it gives me the

[38:59]

answers and I forget it. It gives me the

[39:01]

answers and I remember it. I become

[39:03]

better and better because it's like the

[39:04]

way that Alexander the Great had

[39:06]

Aristotle as his tutor and could ask him

[39:09]

anything and learn great stuff from him.

[39:11]

We've all got Aristotle in our pocket

[39:12]

now and we can become better at the

[39:15]

things that we want to do, the things

[39:17]

that resonate with us for whatever

[39:18]

reason. If everyone's got Aristotle in

[39:20]

their pocket, how does one create an

[39:22]

edge?

[39:23]

>> I think it has to do with we're all just

[39:25]

going to be running faster. In the same

[39:27]

way that when Steve Jobs introduced

[39:28]

Apple computers, he said this is like a

[39:30]

bicycle for the mind. What he meant by

[39:32]

that was that for millions of years

[39:34]

we've been walking bipedily and then

[39:37]

just in the last nancond of evolution we

[39:39]

invented the bicycle and suddenly humans

[39:42]

can move faster because of the bicycle

[39:44]

and he said having a personal computer

[39:46]

is like a bicycle for the mind and I

[39:49]

think of AI now as like a motorcycle for

[39:51]

the mind it's it allows us to move so

[39:55]

much faster so now it's a motorcycle

[39:56]

race and there will be people who are

[39:58]

much faster than other people because

[40:01]

they're really using that optimally.

[40:03]

>> And that's what I mean. It's like how do

[40:04]

I create an edge versus my whoever I'm

[40:06]

competing with in whatever industry I'm

[40:07]

in.

[40:08]

>> Well, for sure the people who are just

[40:09]

copying and pasting the AI slop that'll

[40:12]

be easy to beat that crowd. But

[40:15]

otherwise, I think it's just a matter

[40:16]

of, hey, these are the newest things.

[40:18]

It's like in history when the new sword

[40:20]

gets invented or the new gun or the new

[40:22]

cannon, you know, you have to keep

[40:24]

improving and and using that. And that's

[40:27]

what's going on now with AI

[40:28]

>> and with from a neuroscience

[40:29]

perspective. If I wanted to use AI to

[40:33]

based on all these things you've told me

[40:34]

about novelty and all these other points

[40:36]

that expand the the connections across

[40:38]

my brain and give me a big cognitive

[40:40]

reserve.

[40:41]

What might I I install as a practice

[40:43]

every week when I'm speaking to my AI?

[40:46]

Oh, ask it questions that you're curious

[40:47]

about about anything. Just asking

[40:50]

questions. Here's one thing I do all the

[40:52]

time. I'll say, "Hey, I've been thinking

[40:54]

about this. You know, I on my podcast, I

[40:56]

do a lot of monologues and so I'll start

[40:59]

talking to it and I'll say, "Hey, I've

[41:01]

got this idea that I'm thinking about.

[41:02]

What if blah blah blah blah." And then

[41:03]

I'll say, "Here's my idea. Give me pros

[41:05]

and cons." You know, tell me why this is

[41:08]

wrong. And I do that pretty much with

[41:10]

everything that I ask it if I'm

[41:12]

proposing some, you know, stupid seed of

[41:14]

an idea and it really gives me the

[41:16]

counter arguments and I really engage

[41:18]

with it. That is the important part, I

[41:21]

think. And by the way, I just want to

[41:22]

say I think for the next generation that

[41:24]

we're teaching this, there really only

[41:27]

two things we can teach because all the

[41:29]

details of, you know, hey, let's teach

[41:31]

computer programming or something,

[41:32]

that's probably already gone as a useful

[41:34]

thing. So what we can teach is critical

[41:37]

thinking and creativity. That's it. I

[41:41]

think that's such an important point,

[41:42]

this point about asking your AI why you

[41:44]

might be wrong.

[41:45]

>> Yeah. I I think I've had most of my

[41:47]

paradigm shifting moments when I've come

[41:49]

to an AI model that I was using with a

[41:52]

very with very high conviction. And the

[41:54]

prompt that always I think is most sort

[41:56]

of expansive in terms of my intellectual

[41:59]

knowledge is when I say to it, be

[42:02]

brutally honest about your opinion.

[42:04]

Think for yourself and be objective and

[42:06]

tell me where my blind spots are.

[42:09]

There's something innate with within us

[42:10]

all where we don't actually want to be

[42:14]

wrong. We often I think as a natural

[42:16]

reflex and this is why people get really

[42:17]

sort of trapped in echo chambers of

[42:18]

political opinion and you know Leon

[42:20]

Fesser talked about this idea of

[42:21]

cognitive dissonance when something you

[42:23]

believe contrasts with new information

[42:26]

and how it makes you feel uncomfortable

[42:28]

there's something when I type that out

[42:29]

when I when I love the idea or the thing

[42:31]

I've written or the memo I've written

[42:32]

this new idea and I go on tell me why

[42:35]

I'm completely completely wrong and it

[42:36]

eviscerates me it is both uncomfortable

[42:40]

but it feels incredibly important

[42:42]

because then then it's like I've I've

[42:44]

grown. But these AIs, they're they're

[42:47]

programmed almost to like kiss my ass.

[42:49]

>> Yes. Although, you know, Chatupati

[42:52]

released a very sickopantic version, I

[42:54]

don't know, maybe a year ago. Meaning it

[42:56]

compliments you. You give some idea and

[42:58]

it says, "Oh, Stephen, that's the best

[43:00]

idea I've ever heard. You're a genius

[43:01]

and blah blah." And that didn't last

[43:03]

very long, that model, because nobody

[43:05]

actually liked it. So, you're exactly

[43:07]

right. And and I'm sure most listeners

[43:09]

know this, but you can tell your AI to

[43:12]

be brutally honest with you all the

[43:14]

time. You can tell them to do that all

[43:15]

the time and it'll do that. So you can

[43:18]

you can establish the kind of person

[43:19]

that you're talking to. Here's the

[43:21]

thing. You're right. Of course, people

[43:22]

don't like to be wrong. It can be

[43:24]

socially embarrassing. It can be

[43:25]

uncomfortable. And yet, there's

[43:27]

something very different when you're

[43:28]

talking to your AI. It's a very private

[43:30]

thing. And you say, "Hey, tell me why

[43:31]

I'm brutally wrong." And when it tells

[43:33]

you, you think, "Oh, thank God it's

[43:34]

telling me that instead of like a real

[43:36]

human." So I I think a lot of that is

[43:39]

alleviated with AI. We we don't feel as

[43:43]

bad about being wrong there.

[43:44]

>> As you were saying that, I just went on

[43:45]

chat and I typed this in. Is my joke

[43:49]

funny? And the joke I typed in is knock.

[43:51]

Who's there? A letter. Let us who? Let

[43:54]

us in and I'll tell you.

[43:56]

>> Okay. You didn't laugh. I didn't laugh.

[43:58]

>> Okay.

[43:58]

>> Chapati said, "Yes, it works as a joke.

[44:00]

solid structure, uses the classic pun

[44:03]

payoff, which is exactly how most not

[44:05]

jokes land. And then it's done a

[44:06]

laughing emoji. I then said, "Be

[44:08]

brutally honest and completely

[44:10]

objective. Was that funny?" It said,

[44:13]

"It's not very funny."

[44:16]

Interesting. You know, but but that's

[44:18]

interesting because it depends, right? A

[44:20]

little child actually finds that joke

[44:22]

funny and and for a little child, they

[44:24]

then get to repeat that to their

[44:26]

classmate. They're learning how to do a

[44:28]

joke and so on. So I'm not I'm not sure

[44:31]

I think there's a single answer to

[44:32]

whether that can be funny or not.

[44:34]

>> But the interesting thing is it just

[44:36]

reinforcing what I already believed. And

[44:38]

therefore when we think about growth or

[44:40]

having a growth mindset if someone's

[44:42]

just always reinforcing what you already

[44:44]

believe and know I don't know if it's

[44:46]

ever going to be a growth mindset. I

[44:47]

mean I just asked it again. I said be

[44:49]

really honest and it said it's

[44:50]

absolutely not funny.

[44:52]

>> Yeah. But but remember all it's doing is

[44:55]

it's just it's a statistical parrot. And

[44:57]

so when you say be brutally honest, it

[44:59]

it thinks that's what it should answer.

[45:02]

>> Also, be even more honest. It says it's

[45:03]

basically not funny at all and you

[45:05]

shouldn't say that to people.

[45:06]

>> Okay.

[45:06]

>> And it says comedic originality 1 out of

[45:09]

10. Likelihood of real laughter 1 out of

[45:10]

10.

[45:11]

>> Well, that's that's quite good. That's

[45:12]

quite accurate. Um, here's the thing.

[45:15]

I've been thinking about this issue a

[45:16]

lot about whether AI can be funny. And

[45:19]

at the moment, it can't be. It It's

[45:22]

great at repeating jokes, but it doesn't

[45:24]

understand humor on its own. what it

[45:27]

knows if you ask it to make up a new

[45:29]

joke, what it'll do is it'll have, you

[45:31]

know, the first guy walks in the bar,

[45:32]

then the second guy walks in the bar and

[45:34]

does X, and that establishes the

[45:36]

pattern, but then the third guy, it'll

[45:38]

have break that pattern, which is the

[45:39]

structure of a joke, but it doesn't know

[45:42]

how to break the pattern in a way that's

[45:44]

funny. It's just the third guy does some

[45:45]

random thing. So AI as it stands now,

[45:48]

the way it's structured with what's

[45:49]

called a transformer model, doesn't know

[45:52]

how to think of the punchline and then

[45:54]

go back and make the joke lead to that

[45:56]

punchline.

[45:57]

>> A lot of people don't either.

[45:59]

>> Do you know what I mean? Like I say that

[46:01]

not in an offense way, but just to say

[46:02]

that like

[46:03]

>> I don't know. I often hear the claim

[46:04]

that AI could never be creative.

[46:06]

>> It's massively creative. Here's why.

[46:09]

Creativity in the brain, all creativity

[46:12]

is is you absorb your world. the whole

[46:14]

world around you, every experience

[46:15]

you've ever had. And then you're bending

[46:17]

and breaking and blending those

[46:19]

cognitive concepts into new remixes.

[46:22]

That's all creativity is. And you're

[46:24]

doing that all the time. Whether you're

[46:26]

just trying to think of what to say next

[46:27]

or what recipe to make next or what

[46:29]

patent to do or what company to start,

[46:31]

you're just remixing the stuff that you

[46:33]

already know. And that's why, you know,

[46:36]

I don't know, take Beethoven, he could

[46:38]

have written any kind of music that was

[46:41]

being done anywhere in the world. But of

[46:42]

course, he didn't. like that's what he

[46:43]

grew up with was the music and his local

[46:45]

culture and so on. What we have now is a

[46:48]

much broader diet as I mentioned before

[46:50]

where we can get everything going in.

[46:52]

But the point I want to make here is

[46:54]

that AI that's what it does. It remixes

[46:57]

stuff that's come in. So AI is massively

[46:59]

creative. The part of creativity that AI

[47:01]

can't do right now is selection. Meaning

[47:05]

it can generate a 100 pictures but it

[47:07]

doesn't know which one to pick. It

[47:08]

doesn't know which one is going to be

[47:09]

the most appealing to you. But it can

[47:12]

remix beautifully.

[47:13]

>> But neither do humans, right? So if I

[47:15]

asked an intern to make me 100 pictures,

[47:18]

I mean, I could get my AI to pick one,

[47:19]

but it wouldn't know what the intern or

[47:21]

the AI wouldn't know which one I loved.

[47:23]

>> The intern would have a much better shot

[47:25]

at it. And as the intern is there for a

[47:27]

while, he or she becomes quite good at

[47:29]

getting, oh, okay, I get Steven's taste.

[47:31]

It would be this one.

[47:32]

>> And the AI can't learn that what my

[47:33]

taste is. I don't think the AI could

[47:35]

learn that about visual images because

[47:37]

when it generates the pixels, it's doing

[47:39]

this, you know, this magical stuff under

[47:40]

the hood where it's deciding which

[47:42]

pixels and how they diffuse together

[47:43]

and, you know, mix the image, but it

[47:45]

doesn't know how to read that image

[47:47]

like, oh yeah, the way this is and blah

[47:50]

blah that'll really appeal to Steve. It

[47:52]

does it it's not seeing the image except

[47:54]

as a bunch of pixels. Hm. Hm.

[47:56]

>> You need to be a human for that

[47:58]

>> cuz I feed um I was doing an experiment

[48:01]

recently where I took our my behind the

[48:02]

scenes channel which is a 30 minute long

[48:04]

video. I dropped it into Gemini and I'd

[48:06]

say things to it like predict where

[48:07]

people would drop off on the video and

[48:10]

then we upload the video to YouTube. we

[48:12]

get the retention data back and Gemini

[48:14]

uh in the last two times that I've done

[48:16]

it has a 100% record of knowing that at

[48:18]

minute 7 where insert person talked for

[48:22]

too long and might have been a bit more

[48:24]

sight might have tried to sell a hoodie

[48:26]

for example in that part it would say

[48:29]

you're going to lose people here and it

[48:30]

would and it very accurately say why it

[48:32]

would say because there's you talked for

[48:34]

74 seconds and it was jarring versus the

[48:38]

the the moment that came before it and

[48:40]

when I feed the AI I don't let's say

[48:41]

thumbnails and say which thumbnail is

[48:43]

going to perform the best. We did a test

[48:45]

recently where we put four thumbnail

[48:47]

test results that we knew the answer to

[48:49]

into Gemini and said which one's going

[48:50]

to win on YouTube AB testing and it got

[48:53]

100% accuracy of predicting on data we

[48:56]

already had which one would win. And so

[48:59]

now I I don't know I I keep having these

[49:02]

paradigm shifting moments where only

[49:03]

humans could could do that. But

[49:05]

increasingly the the AIs that we're

[49:08]

experimenting with are making better

[49:10]

creative decisions than now I can make

[49:12]

myself as if the outcome of that

[49:14]

creative decision is which one is people

[49:15]

going to prefer.

[49:16]

>> Yeah.

[49:16]

>> I'd say a year ago that wasn't the case.

[49:18]

>> Okay. So I totally agree with you. But

[49:19]

but let me just mention one thing which

[49:21]

is fascinating which is that often the

[49:24]

way it's doing it is not at all the way

[49:25]

that a human would do it which might be

[49:27]

fine for our purposes but the data and

[49:30]

the way that it's picking up on it. It

[49:32]

might be something about you know how

[49:33]

much I'm making this up you how much

[49:35]

green was in the YouTube thumbnail image

[49:37]

or how much red or whatever whatever the

[49:40]

thing is or just noticing that there's

[49:42]

big font versus smaller font or

[49:44]

whatever. the next time you try it, it

[49:47]

says, "Oh, yeah, this thumbnail is going

[49:48]

to be great." And it's some ridiculous

[49:50]

thumbnail that doesn't make any sense to

[49:51]

you as a human, nor to your fellow

[49:53]

humans, but it might say, "Oh, yeah,

[49:55]

this would be great." Because it's

[49:57]

judging things on very weird dimensions

[49:59]

that we can't always see. You know, the

[50:00]

example you gave about maybe it's cuz

[50:02]

the text is bigger or the color red, but

[50:04]

those are the same factors we think

[50:05]

about as a human. We think if we know

[50:08]

that if the font is bigger, it performs

[50:10]

better. We know that red performs better

[50:11]

than green.

[50:12]

>> Quite possibly. But here's the

[50:13]

interesting thing. Human art constantly

[50:15]

evolves and all AI is trained on is what

[50:18]

has been done before and what has

[50:19]

worked. And so if I asked it, let's say

[50:23]

we composed five different songs and

[50:25]

said, "Hey AI, which song is going to be

[50:27]

better?" It's going to say something

[50:28]

that's right in the middle of the

[50:29]

distribution of popular songs. But

[50:31]

that's not what actually makes it next

[50:33]

year and the year after. It's new

[50:35]

things. It's new twists that that nobody

[50:37]

has seen before. That's what we love.

[50:39]

That's what we seek as consumers. And so

[50:41]

because AI can only be trained up on

[50:44]

what already exists, it's never going to

[50:46]

get the new thing at the edge.

[50:48]

>> But if if the AI was asked to cuz I

[50:51]

think the reason why a new song would

[50:52]

break out, let's say, you know, a new

[50:55]

Drake song comes out and it's a smash

[50:57]

hit. If we think about that distribution

[50:59]

curve, so like if I draw on the GR,

[51:01]

you're saying that um this middle

[51:02]

section here is what sort of AI will aim

[51:04]

at because it's the popular in the

[51:06]

known. Well, if I tell AI to make a

[51:10]

million songs, which is kind of what I

[51:11]

guess is what's going on every day um

[51:13]

around the world, if you scattered them

[51:16]

on on this graph at like, you know,

[51:18]

>> Absolutely.

[51:19]

>> And then the AI's most unusual song ends

[51:22]

up taking off. But it's just because

[51:23]

there's so many of them.

[51:24]

>> Quite right. But that's the human

[51:26]

selection part that we're seeing over

[51:28]

there. If you asked, okay, out of all

[51:30]

these dots, which do you think AI is

[51:32]

going to be best? It's going to have to

[51:33]

tell you the middle of the curve. But

[51:35]

the surprising part is the part that you

[51:37]

circled there, which is the one on the

[51:38]

edge is the one that humans like. Why?

[51:40]

Because we're constant novelty seekers.

[51:43]

We care about the things that are new. I

[51:45]

think the the point I'm getting at is

[51:47]

that um the creation of it, the creative

[51:51]

process is still the same, which is like

[51:53]

>> totally

[51:53]

>> AI or humans just trying a bunch of

[51:56]

and then the world going, "Ooh, that

[51:58]

one."

[51:59]

>> Oh. Oh, yeah. I totally agree. This is

[52:00]

consistent with what I was saying, which

[52:01]

is that AI can be massively creative in

[52:03]

terms of the generation of something,

[52:05]

but you need humans to do the selection.

[52:07]

I'm only arguing the point that AI is

[52:09]

not good at saying, okay, I've generated

[52:11]

a 100 songs. This is the one humans will

[52:13]

choose. We end up saying, hey, wait,

[52:16]

this one is just weird and unique enough

[52:18]

that I really like that. It's

[52:20]

interesting because when you um when you

[52:21]

speak to like record labels about music,

[52:24]

what they're often doing is getting a

[52:28]

format of a song that they know will

[52:31]

work. So they're like, "Right, so it's

[52:33]

got to be eight bars here. It's got to

[52:34]

be this here. You got to have a chorus

[52:35]

that's like hookie. It's got to come

[52:36]

back around. It's got to build up pace.

[52:38]

And there's like a rough format to it."

[52:40]

And it's no surprise that Ed Sheer

[52:42]

someone like Ed Sheeran has written so

[52:44]

many songs for so many people.

[52:45]

>> Yeah. When I spent some time working

[52:47]

with Sony, they had a brand new boy band

[52:49]

in the wake of One Direction. And when I

[52:51]

sat with the boy band um and was

[52:53]

introducing myself, they said they said

[52:54]

to me, "Oh yeah, so um here are their

[52:55]

his the boy band's first three songs and

[52:58]

um Ed Sheeran has written all of them."

[53:00]

And I was like, "What?" I thought I

[53:02]

thought like they're like, "No, Ed Ed

[53:03]

Sheeran's written all of them." And then

[53:05]

what we do is we give them to the boy

[53:06]

band and then the boy band sing them and

[53:09]

they're pretty much guaranteed to be

[53:10]

hits because Ed Sheeran has like a

[53:11]

formula. the way he writes is really in

[53:15]

like vogue right now. You people tend to

[53:17]

think a lot that the songs that are

[53:19]

number one in the charts are there

[53:21]

because just because someone had

[53:23]

creative genius and of course that is

[53:24]

the case sometimes but there is a lot of

[53:26]

this writing going on and then handing

[53:28]

the formula over because someone has

[53:30]

cracked the code of a hit,

[53:31]

>> right? But here's the thing and you know

[53:33]

that we all know this which is that the

[53:34]

code never lasts. So humans have this

[53:38]

pull where they're always seeking things

[53:41]

between novelty and familiarity. So we

[53:44]

like things where we recognize the brand

[53:46]

and we recognize what the singer has

[53:48]

done before. But there has to be novelty

[53:50]

or else we're not going to go for it.

[53:52]

We're not going to listen to that boy

[53:53]

band for the next 10 years doing the

[53:55]

same song over and over. So you're of

[53:57]

course right that we, you know, we want

[53:59]

a bit of familiarity. We want to be

[54:01]

anchored, but we definitely seek the

[54:03]

new. This is what humans always do. This

[54:05]

is why car companies always release the

[54:07]

next model even though the current model

[54:09]

is perfectly fine. This is why haircuts

[54:11]

evolve. This is why fashion evolves

[54:12]

through the years. Um because we always

[54:15]

care about novelty. And the other thing

[54:17]

in the music industry that I think is is

[54:19]

also creating a hit is I was reading

[54:21]

many years ago about some psychology

[54:23]

which you'll probably know much more

[54:24]

about that says exactly what you just

[54:26]

said which is we love something when it

[54:28]

is familiar but new.

[54:31]

>> Exactly. So the way that the record

[54:33]

industry and the radio industry make

[54:35]

something familiar is they blast the

[54:37]

same song at you on every radio station

[54:40]

for a long period of time until it

[54:42]

breaks past being just novel, just new

[54:45]

and it becomes familiar. And like I saw

[54:48]

this graph which shows that the a song

[54:50]

that you'll love is right there in the

[54:52]

middle of like it's new enough that

[54:55]

you're still into it but it's um

[54:57]

familiar now because you've heard it so

[54:59]

many times that you love it and you'll

[55:01]

if anyone listening the first time you

[55:03]

hear a song you might not love it as

[55:04]

much as once you've heard it like 20

[55:06]

times

[55:07]

>> and then at some point you've heard it

[55:08]

too much.

[55:09]

>> Yeah.

[55:10]

>> And it comes back down the other side of

[55:11]

the cover where it's now too familiar.

[55:13]

>> Yeah. That's exactly right. And so we're

[55:15]

always seeking that tension in the

[55:17]

middle. And yeah, companies run into

[55:19]

this all the time. Like sometimes they

[55:21]

try things that are too novel that just

[55:24]

completely fail. You know, Coca-Cola

[55:25]

tried this a long time ago with

[55:26]

introducing new Coke and no one liked

[55:28]

it, whatever. Um, and other companies

[55:29]

like what was that company? Blackberry

[55:31]

with the the little thumb things that

[55:33]

you can press the physical keyboard on

[55:34]

the phone. They failed because they

[55:36]

wouldn't change fast enough. But anyway,

[55:38]

companies that make it are always

[55:39]

staying in that uh sweet spot.

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[57:47]

When you think about the brain and how

[57:48]

it's built and then you think about the

[57:50]

exact technology that they've used to

[57:53]

create AI, isn't it very very similar?

[57:55]

And if so, if it is similar, what does

[57:58]

that say about humans role in the

[58:00]

future? It's similar, but it's not the

[58:02]

same. Which is why with AI, you get what

[58:04]

what we call jagged intelligence,

[58:06]

meaning that it can do something so

[58:09]

extraordinarily smart and then in the

[58:10]

next moment give an answer that's weird

[58:12]

and doesn't make any sense. AI still is

[58:15]

doing this. It's not it's not yet

[58:16]

thinking like we think. Okay. Why? It's

[58:18]

because

[58:20]

AI as we think about it now really

[58:22]

started of course decades and decades

[58:24]

ago where people said look you've got

[58:26]

all these billions of cells neurons in

[58:28]

the brain that are connected to each

[58:30]

other. What if we ignore all that

[58:32]

complexity and we just say look imagine

[58:34]

that you have units that are connected

[58:35]

to each other. We're going to forget

[58:36]

about you know a single cell in the

[58:38]

brain is as complicated as a city. It's

[58:40]

got the entire human genome. It's

[58:42]

trafficking millions of proteins. Let's

[58:43]

put all that aside. Just imagine it's a

[58:45]

circle and it's connected to other cells

[58:47]

and each connection has a certain

[58:48]

strength and that's what we call an

[58:50]

artificial neural network. Now that went

[58:53]

off in its own direction and the kind of

[58:55]

amazing surprising part is how

[58:57]

successful it's been to just get rid of

[58:59]

all the detail but it's still super

[59:02]

different than what human brains are

[59:03]

like. So just an example uh this thing I

[59:07]

mentioned at the very beginning about

[59:08]

how we're a team of rivals under the

[59:10]

hood. You got all these different

[59:11]

competing neural networks that are

[59:13]

trying to drive your behavior and so on.

[59:15]

The fact that we're emotional, the fact

[59:17]

that we are driven by different

[59:20]

appetites, whether food or sexuality or

[59:22]

whatever it is, but you know, you're a

[59:24]

your chat GPT, you don't want that in

[59:26]

the chat GPT. So, it's just an

[59:27]

artificial neural network many layers

[59:29]

deep and it's extraordinary at what it

[59:31]

does, but it's so different than a

[59:32]

human. For example, the fact that it's

[59:34]

read everything on the planet and

[59:35]

remembers it and you haven't, you would

[59:38]

need to lead a thousand lifetimes to

[59:40]

read that much. And of course, you

[59:41]

wouldn't remember much of it. It It's

[59:43]

very different is the point I'm making.

[59:45]

They both have converged on something

[59:48]

that we would call intelligence, but

[59:49]

it's a pretty different structure. Even

[59:51]

though AI was inspired by the brain,

[59:53]

that's what Jeffrey Hinton was telling

[59:54]

me. He was telling me that like much of

[59:56]

the the breakthroughs that have made AI

[59:58]

what it is today came from understanding

[60:00]

how the brain works.

[60:02]

>> Yeah. But that's interesting because

[60:04]

Hinn isn't is incentivized to say that.

[60:07]

But a neuroscientist

[60:09]

>> incentivized to say that

[60:10]

>> people doing AI of course are paying a

[60:13]

lot of attention to how this is

[60:15]

structured like the brain because before

[60:17]

that people would do things like

[60:19]

probability theory or rules or you know

[60:22]

they were trying to do AI by trying to

[60:24]

say okay if this then do that but when

[60:27]

people started doing artificial neural

[60:29]

networks that led to a lot of success

[60:31]

I'm only pointing out that the

[60:32]

artificial neural network looks a lot

[60:34]

like the brain on the surface You say,

[60:37]

"Hey, you've got units and you've got

[60:38]

connections, but beyond that, there's a

[60:40]

lot of differences."

[60:41]

>> And why are those differences

[60:43]

significant as it relates to what's

[60:44]

possible?

[60:45]

>> Because what we've developed is this a

[60:48]

new species essentially that is

[60:50]

incredibly impressive, but it ain't a

[60:52]

human brain. It's different than a human

[60:54]

brain. There may be all kinds of

[60:56]

similarities, things that we even come

[60:57]

to understand are similar, but there are

[60:59]

so many differences. Here's an example.

[61:02]

You know, we humans do one trial

[61:03]

learning all the time. Meaning if I say

[61:06]

or when you were a kid and and your mom

[61:07]

said, "Hey, Stephen, this is a

[61:09]

pomegranate." You say, "Okay,

[61:10]

pomegranate. Got it." But you can't when

[61:13]

you're training up a an artificial

[61:15]

neural network like at OpenAI or Gemini

[61:18]

or Anthropic, you have to give thousands

[61:21]

or millions of examples of everything

[61:23]

for it to learn anything. There's no one

[61:24]

trial learning on those uh systems. And

[61:28]

they have to be trained at the cost of

[61:29]

billions of dollars. then they can do a

[61:31]

run where you ask a question and and it

[61:33]

answers the question. But brains in the

[61:36]

real world don't have that luxury of

[61:38]

having a training phase and then an

[61:40]

action phase. We have to learn on the

[61:42]

fly. It's very different.

[61:43]

>> So I guess the the pertaining question

[61:45]

is

[61:47]

does it change what's possible for the

[61:49]

brain versus the artificial neural

[61:53]

networks we see in AI? like is there

[61:55]

some limitation based on what you've

[61:57]

just said that means the this brain in

[61:59]

front of me, this human brain in front

[62:00]

of me will always be better than the AI

[62:02]

at something because I'm trying to track

[62:04]

forward about what this means for the

[62:05]

future of humans.

[62:06]

>> Yeah.

[62:07]

>> Um

[62:07]

>> I think it's an interesting question um

[62:09]

that we'll have to see. But it's clearly

[62:12]

the case that we know what it is to be a

[62:15]

human from the inside. And when I'm

[62:17]

making a model of you and who you are

[62:19]

and you're making a model of me, we have

[62:21]

assumptions about what it is like to be

[62:23]

a human. AI only watches human behavior

[62:26]

from the outside. And so it can tell a

[62:28]

lot of great stuff, but it doesn't

[62:30]

really know what it is to be a human. So

[62:33]

if I ask it some question about what

[62:35]

would it be like if this or that

[62:37]

happened, it can answer based on

[62:39]

observing lots of things, but it can

[62:41]

only ever know from the outside

[62:42]

>> in terms of why that matters.

[62:44]

>> Yeah. Because you know if I ask my AI my

[62:47]

fiance's been like this today or if I

[62:49]

ask my best friend my fiance's been like

[62:50]

this today. If it both of them give me

[62:52]

the same useful answer it doesn't really

[62:54]

matter what's

[62:54]

>> I agree with you. I agree it may I I I'm

[62:57]

actually writing a new podcast on this

[62:58]

about what you can tell from the outside

[63:00]

and what you can tell from the inside

[63:02]

and whether that difference matters.

[63:04]

Look an example is you know I last year

[63:06]

got a Tesla with full self-driving and I

[63:09]

was watching as it was full

[63:10]

self-driving. I was coming up on a very

[63:12]

complicated traffic situation. And I

[63:13]

thought, well, what's my car going to do

[63:14]

here? How's it possibly going to

[63:15]

understand? But what it did is it slowed

[63:17]

down and came to a stop, which was

[63:18]

exactly the right thing. And I thought,

[63:20]

oh, that's interesting. Algorithmically,

[63:22]

it might think of it very differently

[63:24]

than I am thinking about the situation.

[63:26]

Doesn't matter. It comes to the same

[63:28]

conclusion, ends up in the same place.

[63:29]

Yeah, I agree. We have yet to see where

[63:32]

these differences matter and and what it

[63:35]

is to be a human. But I can tell you one

[63:37]

thing. We care about other humans. So

[63:40]

here's my little prediction is that

[63:41]

there's going to be actually a

[63:42]

renaissance in things like live theater

[63:44]

and live performances. When when things

[63:47]

first came out like Napster, everyone

[63:49]

thought, okay, that's the death of

[63:51]

concerts. Like who's that's the death of

[63:53]

musicians, right? But in fact, you look

[63:55]

at a a Taylor Swift concert, gajillions

[63:57]

of people there paying lots of money.

[63:59]

Like everyone loves the the thing. Why?

[64:02]

Because they're going to see the real

[64:03]

Taylor Swift in person. And I have

[64:05]

noticed I give a lot of talks on the

[64:06]

road. I have noticed an increase in the

[64:08]

number of talks since AI came out a few

[64:11]

years ago. The first thing that my

[64:13]

friend said to me is hey did you know

[64:15]

David that you can you know use uh 11

[64:18]

labs and hey Jen and you know you can

[64:20]

make an avatar of yourself and you can

[64:22]

use your voice and and use chat to

[64:24]

generate what you're going to say and

[64:25]

have a fully virtual version of you. He

[64:28]

said my friend who gives talks too he

[64:30]

said maybe we can start doing this and

[64:31]

do virtual talks. I said nobody's going

[64:33]

to want that. In fact, what's happened

[64:35]

is more people want to fly us across the

[64:38]

country to have us stand there in person

[64:41]

because it really matters to see fellow

[64:43]

humans. And I think that's only going to

[64:45]

increase.

[64:46]

>> I completely agree with you. I think I

[64:48]

think it's so funny. I did a post on

[64:49]

LinkedIn the other day saying that maybe

[64:52]

the like interesting paradox or

[64:54]

interesting outcome of AI is that every

[64:58]

other iteration of technology made us

[65:01]

less human. And maybe the intelligence

[65:04]

now has gotten to a point where

[65:07]

>> it's now forcing us to be more human

[65:10]

because that is all that kind of remains

[65:12]

in a way that maybe the the technology

[65:14]

has gotten so good like social media

[65:16]

didn't make us more human in any

[65:18]

capacity. But maybe this is the moment

[65:19]

where it goes we've got this now

[65:21]

>> go do what only you as a human can do

[65:23]

which is like go out there Taylor Swift

[65:25]

and sing in front of people IRL.

[65:27]

>> Go and do something in the real world.

[65:28]

Even for like nurses um and doctors,

[65:30]

maybe they shouldn't be filling out

[65:31]

admin and paperwork anymore. Maybe they

[65:33]

should be holding your hand and giving

[65:34]

you, you know, in real life care that

[65:37]

only a human could do.

[65:39]

>> I totally agree.

[65:40]

>> And so maybe that's the like the the

[65:41]

positive upside to all of this is um

[65:44]

finally, you know, we've been on this

[65:45]

journey with technology and finally it's

[65:46]

delivered upon its promise.

[65:48]

>> I totally agree. And by the way, you

[65:49]

know, AI relationships, by one estimate,

[65:52]

there's a billion people having

[65:53]

relationships with AI, like a girlfriend

[65:55]

or boyfriend kind of thing.

[65:57]

>> Okay? And so for people like us who grew

[66:00]

up before that existed, we think, "Oh my

[66:02]

gosh, that's weird." But in fact, I

[66:04]

think it might become helpful because it

[66:06]

can be a sandbox as long as we have the

[66:08]

proper feedback. In the end, we have

[66:11]

millions of years of evolution driving

[66:12]

us towards being with the person you

[66:15]

love, touching another human being,

[66:16]

watching the stars, taking her out to

[66:19]

dinner with your parents, like all you

[66:21]

know, we care about that. And so this

[66:23]

worry that people sometimes talk about

[66:25]

about oh people are just going to be on

[66:26]

their phone with their AI relationship I

[66:28]

don't think is realistic for almost

[66:29]

everybody because it gives us the chance

[66:33]

to you know hopefully sandbox some

[66:35]

things about relationships and get over

[66:36]

some dumb things with relationships and

[66:38]

then we can actually be with our fellow

[66:40]

humans. counterargument would be that

[66:42]

maybe there's going to be a bifocation,

[66:43]

a splitting of society where some people

[66:46]

are going to become even more addicted

[66:48]

to the technology because the AI is now

[66:51]

much smarter at retention. Like I know

[66:53]

exactly what I need to say to you based

[66:56]

on your brain, Dr. David, to make you

[67:00]

not put this device down. Yes. But

[67:03]

fundamentally, I want to be in contact

[67:06]

with my wife. I mean, that's that's the

[67:09]

evolution

[67:11]

of hundreds of millions of years is that

[67:13]

I want to make babies. I want to go and

[67:16]

eat dinner with somebody. And and as

[67:19]

much as I might find my phone appealing,

[67:20]

I'm not going to sit it across from me

[67:22]

at a nice Italian restaurant and sit

[67:24]

there like that. So, I a lot of people

[67:27]

do.

[67:28]

>> Me and my me and my friends are at

[67:29]

restaurants cuz we have a rule where we

[67:31]

don't touch our phones when we're at

[67:32]

date night. And I have to look around

[67:33]

and I'm like, "Oh my god, like how is

[67:35]

how are all these guys getting away with

[67:37]

this?" Like, but do you see what I'm

[67:38]

saying? Like some some people they just

[67:40]

have a different sort of proclivity or

[67:42]

they have a different wiring which means

[67:44]

that you know instead of doing the hard

[67:46]

thing of going out there and going on a

[67:47]

first date and being rejected,

[67:49]

pornography or a virtual uh wife might

[67:52]

be a substitute for that.

[67:54]

>> Yeah. No, I agree with you. There will

[67:55]

be bifurcations. One question I don't

[67:57]

know the answer to, but one question is

[67:59]

what would that person have done in

[68:02]

previous generations? You know, is it

[68:05]

really the case that person would have

[68:06]

gone out and had a great successful

[68:08]

relationship or would they always have

[68:09]

had troubles relating to people?

[68:12]

>> Yeah, I sat with um a few

[68:14]

neuroscientists and experts that are

[68:16]

studied dopamine. Dr. Anna LMK was one.

[68:19]

>> Yeah, she's my colleague.

[68:20]

>> She's your colleague. Yeah. And uh she

[68:22]

talks a lot about how we all have

[68:24]

different types of addictive substances

[68:28]

and like you know we will think like

[68:30]

heroin's addictive for everybody and

[68:31]

alcohol's addictive and I used to think

[68:33]

of it on a spectrum but actually she

[68:35]

said like for her addiction was romantic

[68:38]

erotic novels.

[68:39]

>> Yeah. and she she almost ruined her

[68:40]

relationship because of erotic novels,

[68:42]

which is something that I would read and

[68:43]

just throw in the bit like but so maybe

[68:46]

this new technology is particularly

[68:49]

addictive to a certain type of person.

[68:51]

>> Yeah, I I think that's exactly right.

[68:53]

And I think we're going to see that with

[68:54]

everything. I mean,

[68:55]

>> the wild part about human society is

[68:57]

that there's so little that we have in

[69:00]

common, meaning everybody is really

[69:03]

different. And this is something I've

[69:04]

studied in my lab for for decades is

[69:06]

this issue about what are the subtle

[69:08]

differences from person to person. Not

[69:10]

big things like oh this person is a

[69:13]

psychopath or this person has

[69:14]

schizophrenia but the more subtle

[69:16]

things. I'll just give you an example

[69:18]

like if I ask you to imagine to

[69:21]

visualize let's say an ant on a purple

[69:25]

and white tablecloth uh crawling towards

[69:28]

a jar of red jelly. Do you see that in

[69:32]

your head like a movie or do you have

[69:34]

like no particular picture at all or

[69:36]

somewhere in between? What what do you

[69:38]

experience?

[69:38]

>> An ant crawling towards a jar of jelly.

[69:40]

>> Yes.

[69:42]

>> Yeah. I see a big black ant and then

[69:44]

this jar of jelly is like overflowing

[69:46]

down the sides with a wooden lid on top

[69:48]

of it and the ant is almost there.

[69:50]

>> Oh wow. Okay. So you have a Okay. So

[69:52]

what you have I'm just guessing where

[69:55]

you are but you are on the end of the

[69:56]

spectrum that we call hyperfantasia

[69:58]

which means you have very rich

[70:00]

visualization. You're like seeing it

[70:02]

like a picture or a movie. Is that is

[70:04]

that accurate? Okay. I happen to be at

[70:06]

the other end of that spectrum called

[70:07]

aphantasia where I don't have any visual

[70:10]

images at all. There's no I I don't see

[70:12]

things visually in any way.

[70:14]

>> And it turns out the whole population is

[70:16]

spread evenly along this spectrum. I'll

[70:18]

just give a quick side note which is

[70:20]

that for many years I've been talking

[70:22]

with Ed Catmull about this. He's the guy

[70:24]

who started Pixar films. So he's got all

[70:26]

the patents on how to do ray tracing and

[70:28]

how to make these beautiful animated

[70:29]

characters, right? Ed Catmull is

[70:31]

afantasic like I am. And when he learned

[70:34]

about this, he got really interested and

[70:35]

he gave the questionnaire to everybody

[70:37]

at Pixar. And it turns out many of his

[70:38]

best animators and directors are

[70:40]

aphantasic. They don't picture anything

[70:42]

inside their heads. Now this seems

[70:45]

surprising and strange, right? But it

[70:47]

turns out that if you are an aphantasia

[70:49]

kid, you're going to become better at

[70:50]

drawing because you have to really pay

[70:52]

attention to the subject out there and

[70:54]

really have a dialogue with the page

[70:56]

with your pencil. Whereas a kid who's

[70:58]

hyperfantasic might say, "Oh, I know

[70:59]

what a horse looks like." And just draws

[71:01]

it. Okay. So anyway,

[71:02]

>> got tracks.

[71:03]

>> Yeah. Yeah. So it turns out there's a

[71:06]

real spectrum across the population,

[71:07]

meaning inside your head and my head,

[71:09]

we're having pretty different

[71:10]

experiences. But I've studied this along

[71:13]

dozens of different axes and everyone's

[71:15]

got different things going on. Just as

[71:17]

one example, do you know about

[71:18]

synesthesia? Have you ever heard of

[71:19]

this? Forget is that forgetting or

[71:20]

something?

[71:21]

>> No. Sesthesia is having a blending of

[71:23]

the senses. So someone with sesthesia

[71:25]

might look at letters and it triggers a

[71:27]

color experience in their head. So they

[71:28]

look at J and that triggers green and

[71:29]

they look at M and that triggers blue

[71:31]

and whatever. It's different for each

[71:33]

person. Or you might hear music and it

[71:34]

triggers a visual experience. Or you

[71:36]

might taste something, it puts a feeling

[71:38]

on your fingertips or whatever. It's

[71:39]

just it's a blending of the senses. At

[71:41]

least 3% of the population has this.

[71:44]

It's not a disease or a disorder. It's

[71:45]

just an alternative perceptual reality.

[71:49]

So if you have aphantasia, does that

[71:51]

mean that you can't picture your kids?

[71:53]

>> It means that the way I picture them is

[71:55]

not visually. I mean there's sort of a

[71:59]

very g but for me it's more motoric

[72:02]

imagery and you know I I and audio

[72:05]

imagery. Like I'm I'm imagining talking

[72:07]

to them and being with them and being

[72:08]

close to them and probably some old

[72:10]

factory imagery meaning you how they

[72:12]

smell and the whole thing like I have a

[72:14]

very rich notion of what it is to be

[72:16]

with my kids but it's a pretty terrible

[72:18]

visual picture. Not much there.

[72:20]

>> So I imagine people at home have done

[72:22]

that same experiment while they were

[72:24]

listening. Could they picture an ant

[72:26]

walking towards a jar of jam and if they

[72:28]

find themselves on the aphantas I can't

[72:31]

remember the two.

[72:32]

>> Aphantasagasic. Yeah. Or hyperfantasic.

[72:34]

So hyperfantasia is you can picture it,

[72:36]

aphantasia because you can't.

[72:37]

>> Yes.

[72:38]

>> What does that potentially suggest about

[72:41]

nothing? Now here's the interesting

[72:42]

part. So we've done lots of studies

[72:44]

about what this translates to in terms

[72:46]

of your capacities in the world.

[72:48]

Nothing. Why does it translate to

[72:49]

nothing? It's because you can

[72:52]

accomplish tasks in a hundred different

[72:55]

ways. And so some people are doing this

[72:56]

very visually. Other people are doing it

[72:59]

where they're like picturing it with

[73:01]

their motor systems. Others are doing

[73:03]

it, you know, as I mentioned, with sound

[73:05]

or smell or whatever, or others are

[73:06]

doing it just purely conceptually, just

[73:08]

thinking through how the steps would go.

[73:11]

But there's nothing there's nothing

[73:12]

obvious other than this thing I

[73:14]

mentioned about visual artists often

[73:16]

being aphantasic.

[73:18]

Um, otherwise you can kind of accomplish

[73:20]

anything.

[73:21]

>> I run multiple companies that have

[73:23]

multiple sales teams. And one of the

[73:24]

things as a founder of a company that's

[73:26]

often confusing is you find it hard to

[73:28]

figure out where sales are. So about 10

[73:30]

years ago, I started using Pipe Drive in

[73:32]

my former company and it's also the

[73:34]

reason why I switched over all of my

[73:35]

commercial teams in my current media

[73:37]

company called Steven.com to use Pipe

[73:38]

Drive as well. Not only do they sponsor

[73:40]

this show, but they've been an

[73:41]

incredibly effective way of scaling our

[73:43]

sales engine over the years. Pipe Drive

[73:44]

is an easy to use intelligent CRM. And

[73:47]

at its very core, it makes your sales

[73:49]

process visible through one dashboard, a

[73:53]

visual pipeline showing every deal, what

[73:55]

stage it's in, what needs to happen

[73:57]

next, and it's all in real time with no

[73:59]

delay. It doesn't magically close the

[74:01]

deal for you, of course, but it does

[74:03]

replace complexity with clarity. If you

[74:05]

want to join over a 100,000 companies

[74:07]

already using Piperive, you can use my

[74:09]

link for a 30-day free trial with no

[74:11]

credit card payment needed. Head to

[74:13]

piperive.com/ceeo

[74:16]

to get started. That's

[74:17]

piperive.com/ceeo.

[74:20]

I'll see you over there. This is

[74:22]

something that I've made for you. I

[74:24]

realized that the diio audience are

[74:26]

striv

[74:29]

goals that we want to accomplish. And

[74:31]

one of the things I've learned is that

[74:33]

when you aim at the big big goal, it can

[74:36]

feel incredibly psychologically

[74:38]

uncomfortable because it's kind of like

[74:40]

being stood at the foot of Mount Everest

[74:42]

and looking upwards. The way to

[74:43]

accomplish your goals is by breaking

[74:45]

them down into tiny small steps. And we

[74:48]

call this in our team the 1%. And

[74:50]

actually this philosophy is highly

[74:52]

responsible for much of our success

[74:54]

here. So what we've done so that you at

[74:56]

home can accomplish any big goal that

[74:58]

you have is we've made these 1% diaries

[75:01]

and we released these last year and they

[75:03]

all sold out. So I asked my team over

[75:05]

and over again to bring the diaries back

[75:07]

but also to introduce some new colors

[75:08]

and to make some minor tweaks to the

[75:10]

diary. So now we have a better range for

[75:14]

you. So if you have a big goal in mind

[75:17]

and you need a framework and a process

[75:18]

and some motivation, then I highly

[75:21]

recommend you get one of these diaries

[75:22]

before they all sell out once again. And

[75:25]

you can get yours at the diary.com.

[75:27]

And if you want the link, the link is in

[75:29]

the description below.

[75:31]

I heard that you might have after many,

[75:34]

many decades of people debating this,

[75:36]

you might have figured out the reason

[75:38]

why we dream.

[75:39]

>> Yeah. Yeah, it's actually after

[75:41]

millennia of people debating this. This

[75:43]

is the cool part. So, okay, remember I

[75:45]

mentioned earlier that if you go blind,

[75:49]

the visual cortex of the back of the

[75:50]

brain gets taken over by hearing and by

[75:53]

touch and by other things and it's no

[75:54]

longer visual cortex. Well, what we

[75:56]

realized is that because we live on a

[76:00]

planet that rotates into darkness for

[76:02]

half the time, the visual cortex, the

[76:05]

visual part of your brain is at a

[76:07]

disadvantage. So what I realized is that

[76:10]

the purpose of dreaming is to defend the

[76:12]

visual territory from takeover from the

[76:16]

other senses. So every 90 minutes you've

[76:18]

got these um you've got this very

[76:21]

ancient thing in your midbrain that

[76:24]

shoots random activity into the visual

[76:26]

system and only the visual system only

[76:28]

this very tiny part of the visual

[76:30]

system. Every 90 minutes you just blast

[76:31]

random activity in here and the reason

[76:33]

is you are just defending that territory

[76:36]

against takeover. Now, the reason that

[76:38]

all this came together is because our

[76:40]

colleagues at Harvard did an experiment

[76:41]

where they took normally cighted people

[76:44]

and they blindfolded them tightly for 60

[76:46]

minutes. And it turns out that 60

[76:47]

minutes was sufficient for the visual

[76:50]

cortex to start responding to sound and

[76:53]

to touch. You could start seeing that

[76:55]

takeover happening after 60 minutes. And

[76:57]

that's when we realized, wow, this this

[77:00]

part of the brain really needs a way of

[77:02]

defending itself now because the brain

[77:05]

is a natural storyteller. If you blast

[77:07]

random activity in there, it'll, you

[77:09]

know, put that together in some sort of

[77:10]

visual story about what's happening,

[77:12]

mostly based on what connections are hot

[77:14]

from the day. But that's why we dream.

[77:18]

So we we dream to stop the other parts

[77:20]

of our brain overtaking the visual part

[77:24]

of our brain, um, overpowering it, and I

[77:27]

guess ultimately making us go blind.

[77:29]

>> Yeah, that's exactly right. If we lived

[77:30]

on a different kind of planet that did

[77:32]

not rotate into darkness, then we would

[77:35]

we presumably wouldn't dream.

[77:37]

>> Would we even need to close our eyes? I

[77:38]

mean,

[77:38]

>> not necessarily. Yeah. It may be that in

[77:41]

the sleeping state, in the state of deep

[77:43]

sleep, the brain is doing particular

[77:45]

things like taking out the trash and

[77:47]

cleaning some things up. That might be

[77:49]

necessary. Who knows? But yeah, I don't

[77:51]

think we would need to dream. We

[77:52]

wouldn't need to blast random activity

[77:54]

in there. um you know if if if our eyes

[77:57]

were always open for example and it was

[77:58]

always light out

[77:59]

>> are there other examples in the animal

[78:01]

kingdom which support this?

[78:04]

>> Yes, thank you for asking that. It's

[78:06]

this is why this new theory about why we

[78:08]

dream is taking off because we can make

[78:09]

quantitative predictions across animal

[78:12]

species. So for example in our last

[78:13]

paper we looked at 25 different species

[78:16]

of primates, apes and monkeys and we

[78:19]

looked at how plastic their brains are.

[78:21]

In other words, how flexible the whole

[78:23]

circuitry was and how much they dream at

[78:25]

night, which you can tell by looking at

[78:27]

rapid eye movements. You know, when you

[78:28]

dream at night, your eyes are shooting

[78:30]

back and forth like that. It's called

[78:31]

REM, rapid eye movement sleep. So, you

[78:33]

can measure that in other animals, their

[78:34]

eyes moving back and forth. So, we

[78:37]

correlated how plastic the brain is and

[78:40]

how much dream sleep you have. And it

[78:42]

correlates perfectly, which is to say,

[78:44]

humans, which are the most plastic, have

[78:47]

dream sleep all the time. And by the

[78:49]

way, when you're an infant, you sleep

[78:50]

for you have dream sleep for half of

[78:52]

your sleep time, 50% of the time. As you

[78:54]

get older, you get less and less dream

[78:56]

sleep because you just don't need it as

[78:57]

much anymore. But anyway, when we look

[78:58]

across species, it correlates perfectly

[79:00]

if you're a monkey that drops into the

[79:02]

world sort of already fully baked and

[79:04]

you don't need to have much plasticity.

[79:06]

You don't have much dream sleep either.

[79:07]

Interesting.

[79:11]

Seems like a very strange thing. It

[79:12]

sounds like it's a very strange thing

[79:13]

for the for the brain to do, but it also

[79:16]

is perfectly plausible based on

[79:17]

everything you've said.

[79:18]

>> Yeah. And by the way, I just want to

[79:19]

mention dreaming is across the animal

[79:21]

kingdom. Everybody dreams. All animals

[79:23]

dream at night. Even like animals at the

[79:25]

bottom of the ocean. Uh, yes. It's

[79:27]

harder to measure stuff all the way at

[79:28]

the bottom of the ocean. But fish do

[79:30]

have what is equivalent to dream sleep

[79:33]

where you're just zapping activity in

[79:34]

there. And by the way, even animals that

[79:36]

have gone blind, like there's a there's

[79:38]

a mammal called the blind mole rat,

[79:40]

which lives in darkness and has eyes,

[79:43]

but they're blind because over

[79:44]

evolutionary time, they've lost vision.

[79:46]

But they still dream because the dream

[79:49]

circuitry is so ancient. This is so

[79:51]

ancient that all animals have to defend

[79:54]

themselves against the darkness by

[79:56]

keeping their visual systems going. And

[79:58]

so even though the animal went blind,

[80:00]

the rest of the brain didn't catch up. I

[80:02]

mean, that's how evolution goes.

[80:03]

>> Funny. It's funny because it's kind of

[80:05]

like that evolution gave us this TV

[80:10]

that comes on at nighttime when the real

[80:12]

TV, our real life turns off and it just

[80:14]

puts on this fake TV set to keep that

[80:16]

part of the brain doing something so

[80:18]

that it doesn't deteriorate and um

[80:21]

atrophy.

[80:22]

>> It's exactly right. Yeah, it's exactly

[80:24]

right. Which means dreams are quite

[80:26]

pointless outside of just protecting our

[80:29]

neurological matter.

[80:31]

>> I suspect so. It might be that the

[80:34]

particular pathways that could travel

[80:35]

down, you know, maybe there's some

[80:38]

meaning there. I my own suspicion is

[80:40]

that it's like if I went to your

[80:41]

bookshelf and I picked picked a random

[80:43]

book up and I flipped to a random page

[80:45]

and picked a random sentence. I might

[80:48]

find some meaning in that. I might say,

[80:49]

"Oh, that was just the sentence that I

[80:51]

needed to hear." But it's not really.

[80:53]

It's just that it has some meaning to

[80:54]

me. Anyway, the point is if you blast

[80:55]

random activity in there, I might dream

[80:57]

about something where I wake up and say,

[80:58]

"Oh, that was pretty useful." But the

[81:01]

thing that I think gets overlooked is

[81:03]

that most dreams are totally useless and

[81:05]

bizarre. Dr. David, what is the most

[81:07]

important thing we haven't talked about

[81:08]

that we should have talked about as it

[81:09]

specifically relates to people that are

[81:12]

trying to improve their lives, get

[81:15]

better at whatever their subjective

[81:16]

mission is and the brain.

[81:20]

There are probably a lot of things, but

[81:22]

I got to say the thing that I've been

[81:23]

thinking about so much lately is just

[81:25]

about our political uh interfacing with

[81:29]

one another. And so I do feel that

[81:32]

really learning the skills of dialogue

[81:35]

with our fellow humans where we listen

[81:38]

to what they're saying and try to better

[81:39]

understand what their internal model is.

[81:42]

It's not equivalent to agreeing with

[81:43]

them. But it is saying, "Hey, somebody

[81:45]

is coming from this perspective. Let me

[81:48]

see if I can understand that." I think

[81:49]

that matters a lot. And I also think

[81:52]

that because we're so highly predisposed

[81:55]

for in-groups and outgroups, it's really

[81:57]

useful to figure out how to complexify

[82:00]

those relationships. Meaning, how do you

[82:02]

figure out the all the things that cross

[82:05]

cut in the relationship so that you say,

[82:07]

"Hey, you know what? I shouldn't dismiss

[82:08]

this person as a member of my out group

[82:10]

right away because actually

[82:13]

they belong to the same group I do and

[82:15]

they love surfing as much as I do and

[82:17]

they love golden retriever dogs and they

[82:19]

you know grew up in my hometown and

[82:21]

whatever. Like finding those things uh

[82:24]

explicitly helps the brain to keep these

[82:28]

circuits on that are involved in seeing

[82:30]

another person as a person. We have we

[82:33]

have all this social circuitry that is

[82:36]

all about understanding other people and

[82:39]

when things get dehumanized that

[82:42]

actually gets dialed way down. When we

[82:44]

look at you know let's say a homeless

[82:46]

person or a drug addict or someone who

[82:49]

we think of as our enemy or an out group

[82:51]

that gets dialed down so we don't think

[82:53]

of them as a person anymore. We think of

[82:55]

them as an object to to get around. Mhm.

[82:58]

So, this is what I think is really

[82:59]

important is figuring out what we can do

[83:02]

to keep that social circuitry still

[83:04]

going, which includes the things like

[83:06]

eye contact and conversation. And this

[83:09]

is this is one of the most important

[83:10]

things we can do as citizens in a

[83:13]

rapidly changing world as it relates to

[83:17]

things like dementia, which I know is a

[83:20]

fear that a lot of people have. A lot of

[83:21]

people are suffering with dementia, I

[83:23]

think increasingly. In fact, if I was

[83:25]

trying to save off dementia, what advice

[83:27]

would you give me, David?

[83:28]

>> Yeah, keep your brain active. Keep it

[83:30]

active till the day you die. Take on new

[83:32]

challenges. And as soon as you get good

[83:34]

at something like, you know, sudoku,

[83:37]

drop it and pick up some that you're not

[83:39]

good at.

[83:40]

>> And in simple terms, why?

[83:42]

>> It's because you're forcing your brain

[83:43]

to make changes. Otherwise, your brain

[83:45]

says, "Okay, I got this. I got the

[83:47]

world. I understand what's going on.

[83:49]

There's no real particular need for me

[83:50]

to change." And the fact is that the

[83:52]

structure of the brain is always

[83:54]

degenerating. And when you get something

[83:56]

like a disease like Alzheimer's disease,

[83:58]

it degenerates much faster. And what you

[84:00]

want to always be doing is building new

[84:02]

roadways and fashioning new paths that

[84:05]

had not been walked before.

[84:06]

>> So that there's more to degenerate,

[84:09]

which gives me more left over once that

[84:12]

degeneration begins.

[84:14]

>> Yeah, I that's Yeah, I think that's a

[84:16]

good way to look at it. your pathways

[84:18]

are falling apart and if you can build

[84:20]

new pathways which requires effort you

[84:22]

have to actually care and pursue and do

[84:24]

the thing even as parts of the thing

[84:26]

have fallen apart you still have ways of

[84:28]

getting from A to B

[84:29]

>> what do I need to stay away from in

[84:31]

terms of chemicals or supplement I don't

[84:33]

know or food I don't know

[84:35]

>> yeah obviously there's just been a lot

[84:36]

more emphasis on getting good sleep and

[84:38]

good diet and this stuff really matters

[84:40]

I think that's really useful for the

[84:42]

brain I mean it's fascinating to watch

[84:44]

what's happened in the latest generation

[84:46]

in terms of alcohol ol consumption. I

[84:48]

live up in Silicon Valley and there's a

[84:49]

lot of people who have wineries just

[84:52]

north of me and they're like selling

[84:53]

half their acorage. It's absolutely

[84:55]

fascinating to see what's happening

[84:56]

there. I will say I have a friend who's

[84:59]

who's in her 20s who said that she's in

[85:02]

favor of bringing drinking back. Why?

[85:05]

Because she said we go to parties and

[85:07]

everything's so awkward and no one knows

[85:08]

how to talk to one another. And so

[85:10]

they're missing something else. they're

[85:12]

missing the the dumb mistakes category

[85:14]

that we all got to enjoy growing up. So,

[85:17]

it it is a really interesting balance of

[85:20]

of how abstious one wants to become.

[85:23]

>> David, we have a closing tradition where

[85:24]

the last guest leaves a question, the

[85:25]

next guest, not knowing who they're

[85:26]

leaving it for.

[85:27]

>> Question left for you is, what do you

[85:29]

wish most for our planet over the next

[85:33]

10 years?

[85:38]

>> Well, the whole list are the top 10.

[85:40]

>> Yeah. um can't be world peace.

[85:44]

>> You know, I think I would come back to

[85:45]

this piece about the complexification of

[85:47]

relationships, which is to say, if we

[85:50]

could just get a little bit smarter

[85:53]

about understanding people out groups as

[85:58]

being humans with lives with their own

[86:00]

thing going on. doesn't mean we have to

[86:03]

love them or agree with them, but if we

[86:06]

can just get to that point, I don't

[86:08]

think we'll ever hit world peace, but at

[86:10]

least we'd have slightly less

[86:11]

polarization. So, I'm I'm definitely in

[86:13]

favor of that and I do think it's

[86:14]

possible and I do think AI can help us

[86:16]

get there by challenging us on these

[86:18]

points and saying, "Hey, that group that

[86:21]

you've already dismissed as an out

[86:23]

group, what if I told you this story

[86:25]

about this person? What if I introduced

[86:27]

you to this person?" That kind of stuff.

[86:29]

and you know having there's all kinds of

[86:31]

social movements that have sprung up

[86:33]

that allow people of different political

[86:35]

opinions to come together in a room and

[86:37]

talk with one another again it's not

[86:38]

that anyone has to change their mind but

[86:40]

they can say hey you know what I really

[86:42]

like that person I thought that was a

[86:44]

cool person a sweet person nice person

[86:46]

and and now I understand that somebody

[86:48]

who I have seen with my own eyes has a

[86:49]

different opinion on this than idea

[86:51]

>> is that wishful thinking to some degree

[86:52]

>> I don't think so because these things

[86:54]

are happening all over the place and and

[86:57]

>> the macro is is division isn't it It's

[86:59]

polarization echo chambers. There's now

[87:01]

I think there's now 20 social networks

[87:03]

or some crazy number that have more than

[87:04]

20 million people on them which means

[87:06]

that social networks are splintering off

[87:08]

into niches and interests and you know

[87:10]

there's like Rumble and Bumble and then

[87:12]

there's like threads and X and Facebook

[87:14]

snap Instagram and and what we're seeing

[87:16]

is more and more

[87:18]

>> interest group and also the other thing

[87:19]

with algorithms is we went from having

[87:22]

like a social graph where if I had a

[87:24]

thousand people follow me those thousand

[87:26]

people would see my stuff to now these

[87:27]

interest graphs where it doesn't matter

[87:29]

if I have one follower or million

[87:30]

followers, the algorithm is going to

[87:32]

decide who's interested in that thing

[87:34]

and it's going to serve it to them

[87:35]

because that's the most retentive thing

[87:36]

if you're a publicly listed company

[87:38]

that's driven by ad revenue. So, you've

[87:40]

got this algorithm that's actually

[87:41]

forcing you into what you know into this

[87:43]

into tighter and tighter and tighter

[87:44]

echo chambers. And even as someone

[87:46]

that's been on social media 15 years and

[87:47]

ran social media companies, this is one

[87:48]

of the great things I've noticed is when

[87:50]

I had a million followers back in the

[87:51]

day, I would reach those people because

[87:53]

they'd hit follow or subscribe. Now,

[87:56]

even on our YouTube channel, 61% of you

[87:59]

don't subscribe. Um, and please

[88:02]

subscribe. Um, and that's in part

[88:04]

because the algorithm is now doing the

[88:06]

work of deciding who to show it to, who

[88:09]

it will

[88:10]

>> on the basis of who will be retained.

[88:12]

>> Yeah. Here's what I would say. There's

[88:14]

absolutely nothing new about echo

[88:16]

chambers because it was always the case

[88:18]

that your neighbors and your community

[88:20]

and whatever, that's what you thought

[88:22]

was reality. I'm actually quite

[88:24]

optimistic about the existent the mere

[88:25]

existence of the internet because at

[88:27]

least we are exposed to the fact that

[88:29]

there are lots of different points of

[88:30]

view. It used to be in places like the

[88:32]

USSR, they controlled the media tightly

[88:35]

so that everything you saw was a news um

[88:37]

approved story, but now you see all the

[88:40]

points of view. Now, many of them might

[88:42]

drive you crazy and whatever, but at

[88:43]

least you know that there are people out

[88:45]

there that believe in that. And I think

[88:47]

that's really useful. If I had to decide

[88:49]

between state control where there's a

[88:50]

single story or seeing the whole messy

[88:53]

spectrum of opinions, I'd rather see the

[88:56]

latter.

[88:57]

>> What about the middle? You know, they

[88:58]

always one of the phrases that's again a

[89:00]

principle that's helped me think is that

[89:01]

the truth is in the middle. And

[89:03]

generally I try understand what the

[89:04]

middle looks like. So you've got state

[89:06]

controlled over here. You've got

[89:08]

aggressive algorithm that's sort of

[89:09]

reinforcing whatever you currently

[89:11]

believe.

[89:12]

>> Is there not some kind of middle ground

[89:13]

where

[89:15]

um the algorithms have to let up a

[89:17]

little bit and of course we're not going

[89:18]

to go for state controlled. Here's my

[89:20]

prediction in 2026 is that there is a

[89:23]

market opportunity for a new social

[89:25]

media company to come along because

[89:27]

everybody is aware of exactly this

[89:29]

problem that you're pointing out.

[89:30]

Everyone hates when they surf and they

[89:33]

get served exactly what they're supposed

[89:34]

to get served and they get off after an

[89:36]

hour or two and they feel like they've

[89:38]

wasted their lives. I think there's a

[89:40]

real opportunity for a social media

[89:41]

company to come along and say, you know

[89:42]

what, we're not building our algorithm

[89:44]

like the other guys. It's not about just

[89:46]

trying to get engagement at any cost

[89:47]

with, you know, um, incendiary posts,

[89:51]

but instead we're looking for ways to

[89:54]

connect people. So, if you and I both

[89:57]

love this particular thing, this

[90:00]

particular cuisine or or location or

[90:03]

whatever it is, we get connected. We see

[90:05]

each other's stuff and the algorithm

[90:08]

carefully, temporally sequences things

[90:10]

so that we come to have a certain

[90:12]

connection threshold before we find out,

[90:15]

whoa, you have a totally different

[90:16]

political opinion than I do on on

[90:18]

subject X. Wow, I didn't know that, but

[90:20]

I really like Stephen, so I'm going to

[90:22]

lean in and listen a little bit more. I

[90:24]

think this is very easy to do and I

[90:26]

think it can actually be part of the

[90:27]

selling point of the media company is

[90:29]

saying hey we are here not to enrage you

[90:32]

but to to actually build connection

[90:35]

>> sounds like how social media started

[90:37]

>> yeah it's a return

[90:39]

>> I think there's probably a neuroscience

[90:42]

basis as to why we ended up yeah

[90:45]

>> no it's an economics basis

[90:47]

>> but the fact is there's now an economic

[90:49]

opportunity now that everyone sees the

[90:51]

landscape

[90:51]

>> what I'm trying to say is that that

[90:53]

social network wouldn't be that

[90:54]

retentive by design because it wouldn't

[90:56]

trigger my dopamine. It wouldn't be a

[90:58]

slot machine like in Tik Tok is a slot

[91:00]

machine. Ping ping randomized returns.

[91:03]

Ping ping ping. Dopamine hit. Ping ping

[91:05]

ping. So this other social network that

[91:07]

wasn't playing with my dopamine in such

[91:09]

a way. I don't know whether I'd be

[91:11]

addicted enough to return. Therefore,

[91:12]

they wouldn't sell their ads the

[91:13]

economic return. Therefore, they

[91:14]

wouldn't do very well.

[91:16]

>> Here's the thing. I don't know if the

[91:17]

story is that simple that we all want to

[91:19]

do slot machines all the time.

[91:21]

>> Exactly. Because the fact is that a lot

[91:24]

of people go to Las Vegas and do slot

[91:25]

machines sometime, but we don't do that

[91:28]

all the time. It's kind of rare

[91:29]

actually. What we really desire are

[91:31]

meaningful connections. We really desire

[91:34]

feeling like, hey, you know what? I met

[91:36]

this person online that I'm following

[91:37]

and he's following me and we really

[91:40]

connect on all these points and oh by

[91:43]

the way, I then found out interestingly

[91:45]

he's got a totally different opinion

[91:46]

about Iran or abortion or whatever than

[91:48]

I do, but that's cool. Now we're we're

[91:50]

listening to each other. It kind of goes

[91:52]

back to your point earlier about at the

[91:53]

very start where we're talking about,

[91:54]

you know, the brain having an internal

[91:56]

battle like, do I want the cookie or do

[91:58]

I want the salad?

[91:59]

>> And unfortunately in the world we live

[92:00]

in, you know, this the cookie is going

[92:02]

to give me a dopamine hit.

[92:04]

>> Yes. But we don't eat cookies all the

[92:05]

time. This is the point. We do eat

[92:07]

salads much of the time because we're

[92:10]

not just unconscious automaton that are

[92:12]

doing the cookies.

[92:13]

>> Dr. David Eagleman, thank you so much

[92:15]

for the work that you do. I'm going to

[92:16]

link your book below um so everyone can

[92:18]

read this book. You've got a new book on

[92:20]

the way which I'm very excited about as

[92:21]

well. What's that book going to be about

[92:22]

and when is that out?

[92:23]

>> That's about the Ulisses contract and

[92:24]

that'll come out in 2027.

[92:26]

>> June. Okay. Um for anyone that wants to

[92:27]

know how to change your life by changing

[92:29]

your brain, I think this is the perfect

[92:31]

book to read. It's a New York Times

[92:32]

bestselling um author. Um and the book

[92:36]

is absolutely fascinating. It was

[92:38]

actually learning about this subject

[92:39]

matter in LiveWire that helped me to um

[92:43]

pursue more of a growth mindset and just

[92:44]

a growth mentality across my life and to

[92:46]

realize that if I'm not something now,

[92:48]

it doesn't mean that I can't be

[92:49]

tomorrow. So, thank you so much for the

[92:51]

work that you do, David. And, um, it's

[92:53]

been truly illuminating, and I'm sure my

[92:55]

my neural pathways have expanded in

[92:57]

really important ways because of this.

[92:59]

Great. Thank you, Stephen.

[93:01]

>> YouTube have this new crazy algorithm

[93:02]

where they know exactly what video you

[93:04]

would like to watch next based on AI and

[93:07]

all of your viewing behavior. And the

[93:08]

algorithm says that this video is the

[93:12]

perfect video for you. It's different

[93:13]

for everybody looking right now. Check

[93:15]

this video out and I bet you you might

[93:17]

love

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