00:00:00
After many many decades of people
00:00:01
debating this, you might have figured
00:00:03
out the reason why we dream. Yes. And
00:00:05
it's a simple answer. So if you go
00:00:07
blind, the visual cortex in the back of
00:00:09
the brain gets taken over by hearing and
00:00:12
by touch and by other things. In fact,
00:00:13
our colleagues at Harvard did an
00:00:15
experiment where they blindfolded
00:00:16
normally cighted people. And you could
00:00:18
start seeing that takeover happening
00:00:19
after 60 minutes. And that's when we
00:00:21
realized, wow, the purpose of dreaming
00:00:24
is to defend the visual territory from
00:00:26
takeover from the other senses. But what
00:00:29
fascinates me about brain plasticity and
00:00:31
what I've devoted my career to is
00:00:32
figuring out the way that we can be the
00:00:34
sculptors of our own brains and how it
00:00:36
gives us an opportunity to become the
00:00:39
kind of person we would like to be.
00:00:40
>> And can we do that?
00:00:42
>> Yes. Here's the thing. Your brain peaked
00:00:44
at the age of two. Okay. So at the
00:00:47
beginning you've got fluid intelligence,
00:00:49
meaning you could learn anything. But
00:00:50
now that you have grown up in this
00:00:52
world, you've got crystallized
00:00:54
intelligence, meaning you know how to
00:00:55
drive a car. You know how to operate a
00:00:57
cell phone. You know how to run a
00:00:58
business. And so your brain doesn't
00:00:59
require as much change which means that
00:01:02
the structure of the brain is always
00:01:04
degenerating.
00:01:05
>> So what are the set of actions that will
00:01:06
fundamentally change my brain and make
00:01:08
me that type of person who's motivated
00:01:10
and disciplines and who has high agency
00:01:12
and attacks the world.
00:01:13
>> So this is something I've studied in my
00:01:15
lab for decades now. And the key is that
00:01:18
>> and what about AI and the social media
00:01:20
debate as it relates to brain
00:01:21
development?
00:01:22
>> Well, I happen to be a cyber optimist
00:01:24
for young people. I think it's going to
00:01:26
make them much smarter than the
00:01:28
generation that came before. And here's
00:01:30
why.
00:01:31
>> Interesting.
00:01:33
This is super interesting to me. My team
00:01:34
given me this report to show me how many
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of you that watch this show subscribe.
00:01:37
And some of you have told us according
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to this that you are unsubscribed from
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the channel randomly. So, favor to ask
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all of you, please could you check right
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now if you've hit the subscribe button
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if you are a regular viewer of the show
00:01:47
and you like what we do here. We're
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approaching quite a significant landmark
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on this show in terms of a subscriber
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number. So, if there was one simple free
00:01:55
thing that you could do to help us, my
00:01:56
team, everyone here, to keep this show
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free, to keep it improving year over
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year and week over week, it is just to
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hit that subscribe button and to double
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check if you've hit it. Only thing I'll
00:02:05
ever ask of you, do we have a deal? If
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you do it, I'll tell you what I'll do.
00:02:08
I'll make sure every single week, every
00:02:11
single month, as we fight harder and
00:02:12
harder and harder and harder to bring
00:02:13
you the guests and conversations that
00:02:15
you want to hear, I've stayed true to
00:02:16
that promise since the very beginning of
00:02:17
the D of Sio, and I will not let you
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down. Please help us. Really appreciate
00:02:22
it. Let's get on with the show.
00:02:31
Dr. David Eagleman, what made you so
00:02:33
fascinated about the brain? And why
00:02:35
should everybody listening be fascinated
00:02:36
about the brain as well? Here's what I
00:02:38
think it is. When I was 8 years old, I
00:02:40
fell off of the roof of a house that was
00:02:42
under construction and I fell 12 feet
00:02:45
and broke my nose on the floor below.
00:02:48
But the whole thing seemed to take a
00:02:49
long time. I did the calculation and
00:02:52
figured out that it only took 6 of a
00:02:54
second to get from the top to the
00:02:56
bottom. And I couldn't figure out why it
00:02:57
seemed to have taken so long. So I think
00:02:59
that got me really interested in
00:03:01
perception and the machinery by which we
00:03:05
view the world and taken in and what is
00:03:07
actually real versus what's a
00:03:10
construction of the brain. And that's
00:03:12
how what I've devoted my career to is
00:03:13
figuring out how the brain which is
00:03:15
locked inside the skull. It's about
00:03:18
three pounds. How it constructs this
00:03:20
model of the world and which things we
00:03:22
can take as reality and which things we
00:03:25
shouldn't.
00:03:26
>> I think most people don't even know they
00:03:27
have a there's a brain there almost. It
00:03:29
sounds like a strange thing to say, but
00:03:31
we've never really most of us haven't
00:03:32
really seen our own brains at all. We've
00:03:34
never been able to touch our own brains
00:03:35
at all. So, it's it's easy to fall into
00:03:38
the trap of thinking that everything I
00:03:40
experience is true and is reality. So,
00:03:43
I'm wondering how a deeper understanding
00:03:45
of all this stuff can help me live a
00:03:46
better life.
00:03:47
>> Yeah. One of the things that I started
00:03:50
writing about years ago is that I think
00:03:51
we're not I think we often think of
00:03:54
ourselves as individuals, meaning not
00:03:57
divisible into other things. But really,
00:04:01
you are a team of rivals. So, you've got
00:04:04
all these neural networks that have
00:04:06
different drives making different
00:04:07
suggestions to you.
00:04:08
>> What's a neural network?
00:04:10
>> Um, so in the brain, you've got 86
00:04:12
billion cells called neurons. And these
00:04:15
are communicating with each other at a
00:04:17
blindingly fast rate. Many of these
00:04:18
cells are hooked up in networks. So,
00:04:20
they're, you know, this guy's talking to
00:04:22
this guy and this guy, and they're all
00:04:23
in particular networks. The thing is,
00:04:25
you can actually get competing networks.
00:04:29
So, for example, Stephen, if I drop some
00:04:31
chocolate chip cookies in front of you,
00:04:33
part of your brain wants to eat it. It's
00:04:34
a good energy source. Part of your brain
00:04:35
says, "Don't eat it. I'll gain weight."
00:04:37
Part of you says, "Okay, I'll eat one,
00:04:38
but I'll go to the gym tonight." The
00:04:40
point is you are arguing with yourself.
00:04:44
You are conflicted. This is what makes
00:04:46
humans so interesting is that we have
00:04:49
all these voices trying to drive us to
00:04:52
different conclusions about our
00:04:53
behavior.
00:04:54
The way that your ship of state moves
00:04:57
depends on the vote of the neural
00:04:59
parliament at any time. So understanding
00:05:02
this I think is really critical to
00:05:04
navigating our own lives because all of
00:05:06
us do things where retrospectively we
00:05:08
regret it. We say I shouldn't have eaten
00:05:10
that whole bag of chips or done the you
00:05:13
know the alcohol or the drugs or what
00:05:15
like everybody has regrets all the time
00:05:17
with things and it's because you have
00:05:20
different voices in charge at different
00:05:22
times. Okay.
00:05:24
>> Part of what this leads to is what we
00:05:26
call the Ulisses contract. So a Ulisses
00:05:30
contract is where you do something now
00:05:31
to prevent yourself from behaving badly
00:05:33
in the near future. Just as an example,
00:05:36
you know, when people go to Alcoholics
00:05:38
Anonymous, the first thing they're told
00:05:39
is clear all the alcohol out of the
00:05:41
house. Because even if you feel like,
00:05:43
look,
00:05:43
>> I'm in a moment of sober reflection. I
00:05:46
don't want to ever drink again. If you
00:05:47
have alcohol in the house, you're going
00:05:48
to bust into that cabinet at some point
00:05:50
on a festive Saturday night or a lonely
00:05:53
Sunday night or whatever. So, what you
00:05:55
do is you constrain your future behavior
00:05:58
by setting things up in the right way so
00:06:01
your future uh the future you can't
00:06:03
behave badly. We naively think, okay,
00:06:06
well, I know who I am. I'm just one
00:06:08
person. But but you're not. And under
00:06:10
different circumstances, you're tempted
00:06:12
by different things and you'll do
00:06:13
different kinds of behavior. So having a
00:06:16
sense of what's going on under the hood
00:06:18
gives us an opportunity to be more
00:06:21
closely aligned with the kind of person
00:06:23
we would like to be
00:06:24
>> because it feels like there's just one
00:06:26
well I do argue with myself in my head
00:06:28
sometimes but it feels like there is
00:06:31
just one me
00:06:32
>> and so when I hear that voice say Steve
00:06:34
you should have that cookie and it's
00:06:35
1:00 a.m. And then the other voice says,
00:06:36
"No, you shouldn't." I think it's kind
00:06:38
of the same person just tussling with
00:06:40
himself,
00:06:41
>> right? Well, but that tustling with
00:06:42
himself implies different political
00:06:44
parties that are all battling it out.
00:06:46
You know, when you look at a parliament,
00:06:47
you've got all these political parties
00:06:49
that all love their country. They just
00:06:50
have different ideas of how to steer it.
00:06:53
And this is what's going on uh in in the
00:06:56
brain all the time.
00:06:57
>> So, what does one do about that? How do
00:06:58
I make do I do I have to make a list
00:07:01
contract? I think it's very useful to
00:07:03
make that sort of thing. But also just
00:07:05
understanding oneself. I mean part of
00:07:07
the you know there was this Greek
00:07:08
admonition to know thyself. This was a
00:07:11
sign they had in various places, various
00:07:13
temples and stuff. But I think that
00:07:15
becomes know thyelves. And the better we
00:07:19
know ourselves, the more we can get rid
00:07:21
of the illusion that we are one person.
00:07:23
Because all any of us need to do is look
00:07:25
back on our behavior to say, "Oh yeah,
00:07:28
in some circumstances I would do that.
00:07:29
and other circumstances I think is a
00:07:31
terrible idea. So this is all to the
00:07:33
goal of understanding who you are.
00:07:36
>> What are the big misconceptions about
00:07:37
the brain that people have gone through
00:07:40
their life believing? I mean that's one
00:07:41
of them. Something that is true that
00:07:42
kind of could fall in place of that is
00:07:44
just this fundamental idea that our
00:07:46
brains are plastic or sort of adaptable
00:07:50
because when I found out that I could
00:07:51
change my brain by what I do, I found
00:07:52
that to be really really inspiring.
00:07:56
>> Yes, that that's exactly right. So brain
00:07:58
plasticity, if someone hasn't heard that
00:08:00
term before, it sounds like a weird
00:08:01
term, but the reason it came about 100
00:08:04
years ago is because the great
00:08:06
psychologist William James pointed out
00:08:07
that, you know, if you take a piece of
00:08:09
plastic, what we like about that
00:08:10
material that we call plastic is that
00:08:12
you can mold it into a shape and it'll
00:08:14
hold that shape. And that's what your
00:08:16
brain does. So if I ask you the name of
00:08:18
your third grade teacher, you can
00:08:19
remember that name even though it's been
00:08:21
a long time because your neural networks
00:08:25
changed and held on to that piece of
00:08:27
information. Okay? Well, our whole lives
00:08:30
our brains are changing every moment. So
00:08:33
now we have certain doors that close at
00:08:36
different times. So just as an example,
00:08:39
um you need to learn language in the
00:08:41
first several years of your life. If you
00:08:43
don't learn language, you can never get
00:08:45
the concept of language. Your brain will
00:08:47
never figure that out.
00:08:48
>> You're not saying you can't learn a new
00:08:49
language as an adult. You're saying the
00:08:51
concept of
00:08:51
>> the concept of language, the concept
00:08:53
that I can name things and I can ask for
00:08:55
things and so on. Just that never clicks
00:08:57
in the brain. For example, in Romania at
00:09:00
the fall of Chuchescu, there were tens
00:09:02
of thousands of kids in the orphanages
00:09:05
because their parents had been killed.
00:09:06
It was too many kids. And so the staff
00:09:08
there said, "Look, the kids will get,
00:09:11
you know, clingy if you pay too much
00:09:13
attention to them. So here's what we're
00:09:14
going to do. We're going to feed the
00:09:14
kids, but we're not going to hold them
00:09:16
and we're not going to talk to them."
00:09:18
And all these children grew up with real
00:09:20
cognitive deficits as a result. Here's
00:09:23
the thing about brain plasticity. Human
00:09:25
beings have a a similar brain to all our
00:09:28
neighbors in the animal kingdom. If you
00:09:30
compare our brain to a horse brain, a
00:09:32
dog brain, anything like that, it's the
00:09:33
same general structures and stuff. But
00:09:36
what we have is much more of the wrinkly
00:09:38
outer bit called the cortex. It's the
00:09:40
outer 3 mm. And maybe we'll come back to
00:09:43
why that matters so much. But the other
00:09:46
thing that mother nature tweaked with
00:09:48
us, it's small genetic tweaks. But we
00:09:50
have much more plasticity, adaptability
00:09:53
such that when a horse drops into the
00:09:56
world, it's doing the same thing that
00:09:57
horses did 100,000 years ago. It's just,
00:09:59
you know, eat mate. But when a human
00:10:01
drops in the world, we learn everything
00:10:04
that's happened before us. And then we
00:10:06
springboard off the top of that. So we
00:10:08
living in the 21st century, we say, "Oh
00:10:11
great, you know, physics, math, this,
00:10:12
that, art, blah, blah, great. We got
00:10:14
everything that's happened before us.
00:10:15
Now let's do our own thing." And that's
00:10:17
what's so special about the plasticity
00:10:19
of the human brain, the adaptability of
00:10:21
it. The downside, the gamble is that
00:10:25
mother nature drops human brains into
00:10:28
the world kind of halfbaked and we then
00:10:31
get to absorb everything. But in the
00:10:32
rare circumstance where you're not
00:10:34
getting the right input, then then that
00:10:37
ends up really in trouble because it's
00:10:39
only halfbaked. So when it comes to
00:10:41
language, we can learn multiple
00:10:43
languages when we're young. That's very
00:10:44
easy, but it gets harder and harder as
00:10:46
that goes along. And various other
00:10:48
things become harder. And here's why.
00:10:50
It's because I I mentioned this earlier,
00:10:53
but the job of the brain is to make a
00:10:55
model of the world so it can operate
00:10:57
within it. So, for example, you're an
00:11:00
entrepreneur and you love doing
00:11:02
business. So, you get it. You okay,
00:11:04
here's how, you know, here's how you
00:11:06
structure business. Here's how you hire.
00:11:08
Well, here's how you set up a board.
00:11:09
Well, you're doing everything because
00:11:11
you've got a really rich internal model
00:11:13
of how to structure a business. That's
00:11:15
what the brain wants to do is get that
00:11:18
stuff right. As a result, if you
00:11:21
suddenly ended up, you know, taking a
00:11:23
trip to Mars and there's a whole very
00:11:25
different society there that does
00:11:27
businesses very differently, you would
00:11:29
have to relearn stuff really quickly.
00:11:32
So, here's the thing. You went from
00:11:35
having a brain that had high fluid
00:11:37
intelligence to now having a brain that
00:11:40
has high crystallized intelligence. What
00:11:42
that means is at the beginning you can
00:11:44
learn anything. You could learn any
00:11:46
language. You could have dropped into
00:11:47
any area. You could have dropped into
00:11:49
13th century Japan when I was young.
00:11:51
>> When you were young, when you were a
00:11:52
baby, if you had dropped out of the womb
00:11:54
in, you know, 10th century Mongolia, you
00:11:57
would have said like, "Okay, cool. Learn
00:11:59
lang." You would you would be a 10th
00:12:00
century Mongolian. But as it happens,
00:12:03
you dropped into this era, you know, a
00:12:06
certain place and time and neighborhood
00:12:07
and culture and family. And so you learn
00:12:09
that that's who you become is that
00:12:11
person. We often think that plasticity
00:12:14
diminishes as you age. But it's not
00:12:17
simply that it's diminishing. It's that
00:12:18
you are getting the right answers about
00:12:22
how to operate in the world. And so you
00:12:24
don't have to change as much. Your brain
00:12:26
doesn't require as much change.
00:12:28
>> What if I want to change?
00:12:30
>> Yes. So it turns out you still can
00:12:32
change. That's the key is that the
00:12:35
reason brains change less and less is
00:12:37
because they don't have to. But when
00:12:40
things get upside down, just as one
00:12:42
example, everything about the pandemic
00:12:44
really stunk, except for one thing, I
00:12:47
think the tiny silver lining is that all
00:12:49
of us had to reassess. Oh my gosh, wait,
00:12:54
how is the world working? I thought I
00:12:55
knew how the world worked, but now I
00:12:57
don't know if there's going to be toilet
00:12:59
paper at the store. I don't know if the
00:13:00
bank's going to be open. I don't know if
00:13:02
I can get coffee at the coffee shop.
00:13:04
Like, everything was different. As awful
00:13:06
as it was, it's really useful to
00:13:09
challenge your internal model of the
00:13:11
world and get to do that as an adult. We
00:13:13
don't usually get to.
00:13:15
>> So, if I want to change, what would you
00:13:16
recommend that I do? If I want to if I
00:13:18
want to change who I am, say I'm
00:13:20
stubborn, I'm not motivated,
00:13:22
>> um, and I want to be a different person.
00:13:24
>> The key is challenge. The key is seeking
00:13:26
challenge. So, it turns out that where
00:13:28
we always want to be is in between the
00:13:31
levels of frustrating but achievable.
00:13:33
and you want to take on new tasks. You
00:13:35
want to seek novelty to find yourself in
00:13:37
that zone and push yourself to do things
00:13:40
that you just haven't done before. And
00:13:42
one of the things that's so wonderful
00:13:44
about the modern world, you know,
00:13:46
everyone's got complaints about the
00:13:47
internet and social media and stuff like
00:13:48
that, but the good news is it deep it
00:13:51
exposes you to so much more than you
00:13:53
ever even knew was out there. The key is
00:13:56
to actively seek those challenges and
00:13:58
seek new things and seek to become
00:14:00
expert in various sorts of fields. And
00:14:02
and I think the key is that once you
00:14:04
become good at something, you you have
00:14:07
to drop that and take on something
00:14:08
you're not good at. This is the best
00:14:10
thing that you can do for your brain.
00:14:12
The reason is because what you're doing
00:14:14
is you're constantly building new
00:14:15
roadways and pathways in the brain.
00:14:17
There's a study that's been going on for
00:14:19
for decades now called the religious
00:14:22
orders study where a bunch of Catholic
00:14:24
nuns agreed to donate their brains for
00:14:26
autopsy when they passed away. What the
00:14:29
researchers discovered when they look at
00:14:30
the brain carefully is that some
00:14:33
fraction of these nuns had Alzheimer's
00:14:35
disease. Their brains were physically
00:14:37
degenerating with the ravages of of this
00:14:40
dementia, but they didn't show any of
00:14:43
the cognitive deficits that one normally
00:14:45
has. They didn't seem to be having
00:14:47
memory problems and so on. It turns out
00:14:49
it's because all these nuns lived in
00:14:52
these convents till the day they died.
00:14:54
They had social challenges and they had
00:14:56
fights with their fellow sisters and
00:14:58
they played games with their fellow
00:14:59
sisters and they were they had chores
00:15:01
and responsibilities and they were doing
00:15:03
stuff. What that means is even as the
00:15:05
tissue the brain tissue was physically
00:15:07
degenerating, they were making new
00:15:09
roadways and bridges all the time.
00:15:12
>> And so that's what kept them cognitively
00:15:14
healthy. We call that cognitive reserve.
00:15:17
Contrast this with with people who
00:15:19
retire at 65 and they go home and they
00:15:21
watch television and their social
00:15:23
circles shrink and so on. That's when
00:15:25
you've really got concerns because
00:15:27
you're not building the new pathways. Is
00:15:29
there data to support that that when you
00:15:31
retire, if you retire early or if you
00:15:33
retire say in your 60s, it increases
00:15:36
your probability of an earlier death or
00:15:38
cognitive decline? Almost certainly with
00:15:41
cognitive decline because you're just
00:15:43
not getting the challenge at that point.
00:15:45
You're just coasting on your internal
00:15:46
model.
00:15:48
this. It's tragic, but what happens
00:15:50
often is that people's hearing gets
00:15:51
worse. And so by the time they retire,
00:15:52
let's say in their mid-60s, it's not
00:15:55
really that fun for them to go out to
00:15:56
parties and restaurants anymore because
00:15:58
they can't quite hear. And so there
00:16:00
there all these converging reasons why
00:16:02
their social lives shrink. But it turns
00:16:04
out social life is one of the most
00:16:07
important things that we can do for our
00:16:08
brains because there's an expression we
00:16:11
sometimes use in neuroscience, which is
00:16:12
that nothing is as hard for the brain as
00:16:14
other people. because you never know
00:16:15
what the other person's going to say and
00:16:17
do and how they'll react emotionally and
00:16:19
so on. So, you're constantly on your
00:16:21
toes with other people. And if you're
00:16:22
not doing that anymore, that ends up
00:16:24
being a problem.
00:16:25
>> H
00:16:27
interesting. And as a as a I'm 33 years
00:16:31
old, so if you were to plot where my
00:16:33
brain is on like a graph of decline,
00:16:37
I is it the case that I should be doing
00:16:38
as much as I can now to build as many
00:16:40
pathways I can so that when I'm 80, my
00:16:44
decline sort of levels out in a in a
00:16:46
better place? Oh yeah, for for sure. But
00:16:49
this is true for many reasons actually.
00:16:51
Okay, so look, the truth is your brain
00:16:53
peaked at two at the age of two because
00:16:56
that's when you get the most connections
00:16:58
between neurons, between these cells in
00:17:00
the brain. You get this, at first you're
00:17:03
born with these 86 billion neurons and
00:17:05
they connect and connect and connect and
00:17:07
it finally becomes like a overgrown
00:17:09
garden at the age of two and from there
00:17:10
you're pruning. From there you're taking
00:17:12
connections away. Now it happens that
00:17:14
that's not a bad thing. That's a good
00:17:16
thing because that's how you're
00:17:18
resonating with the world that you are
00:17:20
in.
00:17:21
you know, 21st century London and LA
00:17:24
versus, you know, 10th century Mongolia
00:17:26
because you're you're just strengthening
00:17:29
those pathways that resonate and you're
00:17:31
getting rid of everything else. Okay,
00:17:32
fine. But over time, your brain cells
00:17:35
die. You know, every time you hit your
00:17:36
head on something or whatever, your
00:17:38
brain cells are going down. Um, so in
00:17:40
that sense, you've peaked. But your
00:17:42
crystallized intelligence that you've
00:17:44
been building your whole life, you know,
00:17:46
that keeps going and you'll you'll have
00:17:48
decades ahead of you where you can start
00:17:49
doing stuff. But yes, the reason to
00:17:51
learn everything you can is because all
00:17:53
that stuff cashes out at various points
00:17:56
in your life when you're starting your
00:17:58
next business or you're, you know,
00:18:00
wanting to do the next great thing where
00:18:02
you're surfing the way web of AI. You
00:18:04
know, you'll say, "Oh, I learned this
00:18:06
thing when I was 16. I learned this
00:18:07
thing when I was 22." And and these are
00:18:08
these are paying off now. I think I
00:18:10
heard Andrew Hubman say that one of the
00:18:12
most fascinating discoveries of the last
00:18:14
century is a particular part of the
00:18:16
brain called the anterior mid-sul cortex
00:18:19
and it links to what you were saying a
00:18:21
second ago about challenge and doing
00:18:23
things that are difficult.
00:18:25
>> Yeah, it turns out that area of the
00:18:27
brain is involved and other networks as
00:18:29
well because when you're doing something
00:18:32
new and challenging and difficult, you
00:18:34
have stress and anxiety. Your whole
00:18:37
brain is active. Let's say I measured
00:18:40
your brain even with something like EEG,
00:18:42
electronphilography. That's where I
00:18:44
stick electrodes on the outside. Let's
00:18:45
say I measure your brain in my brain.
00:18:47
We're doing something that let's say
00:18:49
you're an expert at what's something
00:18:50
you're really good at juggling. I don't
00:18:53
know some physics.
00:18:54
>> Let's go for juggling.
00:18:55
>> Okay. Let's say you're an expert
00:18:56
juggler. Let's say I've never juggled.
00:18:58
Okay. If we're both juggling, you're
00:18:59
going to be much better than I am. But
00:19:01
your brain will be less active. You
00:19:04
won't have as much activity in your
00:19:06
brain. all my brain is on fire with
00:19:08
activity because why I'm trying to
00:19:10
figure out okay where do I put my hand
00:19:12
how do I throw this and blah blah blah
00:19:13
so when I'm in novice at something my
00:19:15
brain is using much more activity not
00:19:18
just the anterior made singulate but
00:19:20
tons of activity all over because I'm
00:19:21
trying to figure out the rules I'm
00:19:22
trying to figure out what's going on you
00:19:24
as an expert you know you got it you
00:19:26
don't you don't need to burn much
00:19:27
activity this is what the brain's goal
00:19:29
is is to say hey once I've practiced
00:19:31
something along once I get something
00:19:33
about the world I'm going to burn it
00:19:34
deeper and deeper into the circuitry So
00:19:36
I don't have to burn a lot of energy on
00:19:38
it.
00:19:38
>> On this part of the brain, the anterior
00:19:39
mid singular cortex, Andrew human was
00:19:41
saying it's larger in people that do
00:19:43
things that they basically don't want to
00:19:44
do hard things. If you spend your life
00:19:47
doing things you don't want to do, then
00:19:48
it happens to be bigger. And so people
00:19:49
have now thought of this part of the
00:19:51
brain almost like the willpower muscle
00:19:52
because for some reason those that are
00:19:54
doing hard things have bigger ones and
00:19:56
those that are not have smaller ones. I
00:19:58
mean it wouldn't be so much the
00:20:00
willpower of muscle. It would be some
00:20:01
indication retrospectively of how hard
00:20:04
you have worked. Look, the fact is you
00:20:07
can see changes in brain size with lots
00:20:09
of things. I'll give you an example. If
00:20:11
you are a pianist, if you play piano,
00:20:14
then we can actually see physical
00:20:16
changes in your motor cortex. This is
00:20:18
the part of the brain essentially
00:20:20
underneath where you would wear
00:20:21
headphones. For those who are looking
00:20:22
visually, it's this red part here. You
00:20:25
actually get a bigger loop of tissue
00:20:28
here than you do in a normal brain. Why?
00:20:31
Because you're doing so much fine motor
00:20:33
activity with your fingers with both
00:20:35
hands. Okay? In contrast, if you're a
00:20:38
violinist,
00:20:40
you're only really doing that kind of
00:20:41
detailed activity with one hand. The
00:20:42
other hand is just boeing. And so you
00:20:44
only get that activity here in one half
00:20:47
of the brain for violinists. So I can
00:20:49
look at a brain and tell, hey, is the
00:20:51
person a pianist or a violinist or an
00:20:53
either? I can tell just by looking at
00:20:54
the visual cortex because you see
00:20:56
changes in the brain based on what you
00:21:00
do. For example, jugglers, people who
00:21:02
play music, even you can tell this with
00:21:04
medical students who study for final
00:21:05
exams. You actually see changes in the
00:21:07
distribution of of their cortex.
00:21:10
>> Why would it be getting bigger?
00:21:12
>> The reason is the brain's devoting more
00:21:14
real estate to that. In this case, let's
00:21:17
say we're talking about fingers on a
00:21:18
piano or a violin. The brain is devoting
00:21:20
more there's more relevance to that and
00:21:24
so it more real estate so that you can
00:21:26
do it better in the future.
00:21:28
>> Exactly. The key about the cortex this
00:21:30
wrinkly outer part is that it is a
00:21:32
one-trick pony. This is often overlooked
00:21:34
because even this brain that I'm holding
00:21:36
here uh is colorcoded so that we think
00:21:39
oh okay that's clearly labeled this
00:21:40
that's clearly labeled that and so on.
00:21:42
But in fact it's all the same stuff and
00:21:45
it can change. So for instance, if you
00:21:47
are born blind, then this area that we
00:21:50
normally call the visual cortex gets
00:21:52
taken over by the rest of the brain. If
00:21:54
you're born deaf, then this part that we
00:21:56
call the auditory cortex gets taken
00:21:58
over. It gets devoted to other tasks.
00:22:00
And so this whole system is very very
00:22:03
fluid. And this is what fascinates me
00:22:04
about brain plasticity is the way that
00:22:07
we can be the sculptors of our own
00:22:09
brains because we can devote ourselves
00:22:13
to particular things and have the brains
00:22:16
real estate get involved in that. So if
00:22:19
I was currently someone that couldn't
00:22:20
get out of bed, I didn't have a lot of
00:22:22
discipline or motivation and I wasn't
00:22:25
very good at committing myself to hard
00:22:27
things.
00:22:28
With everything you know about the
00:22:29
brain, is it possible to take a set of
00:22:31
actions that will fundamentally change
00:22:33
my brain and make me that type of person
00:22:35
who runs marathons, who does hard
00:22:38
things, who's motivated and disciplines,
00:22:39
and who has high agency and attacks the
00:22:41
world.
00:22:42
>> Yes. Yeah. But it's much more than
00:22:44
simply resolve because I mean just look
00:22:47
at New Year's resolutions. You know, by
00:22:49
by February, most people have dropped
00:22:50
most of them. So, it's really a
00:22:52
psychology problem about figuring out
00:22:55
okay, what are the things that motivate
00:22:57
me? So, let's say you want to become a
00:22:59
marathon runner. You've got that distant
00:23:01
dream. You figure out like what actually
00:23:03
motivates me in the short term? Who am I
00:23:05
trying to impress? What am I trying to
00:23:07
accomplish in my life? How can I
00:23:10
structure things like this Ulyses
00:23:12
contract that I talked about earlier
00:23:14
where I'm actually locking myself into a
00:23:16
contract? Like, you know, I call Bob and
00:23:19
I say, "I will meet you every morning at
00:23:21
7:00 and we're going to run until we
00:23:23
drop." Like once I've committed to those
00:23:25
sorts of things, that's how you set
00:23:27
things up so that you do the right
00:23:29
thing.
00:23:29
>> It's a bit of a cycle, right? Because
00:23:30
then my brain will adapt and then
00:23:32
presumably that will make it easier for
00:23:33
me to run.
00:23:34
>> Yeah.
00:23:35
>> And then I'll run more and then my brain
00:23:36
will adapt.
00:23:37
>> That's right.
00:23:38
>> And the cycle continues.
00:23:39
>> And it's not just your brain, of course.
00:23:40
In this case, it's your body. You're
00:23:41
getting better. You're getting stronger.
00:23:42
You don't get as out of breath. And so
00:23:44
all these things help. Exactly. But in
00:23:46
order to keep the cycle going, you need
00:23:48
to figure out what is spinning this
00:23:50
flywheel and what are the all the other
00:23:52
things in your life. Whether good
00:23:54
motivations or bad, it doesn't matter.
00:23:56
You just figure out what it is that you
00:23:58
can do to to get there.
00:24:00
>> Are there certain physical exercises
00:24:02
that are particularly good for the brain
00:24:03
from what you've understood?
00:24:05
>> The general story is exercise is really
00:24:08
important for the brain. I'll give you
00:24:09
just one example of that, which is
00:24:11
there's still this debate going on about
00:24:13
whether we get new neurons in the brain.
00:24:16
The general story has always been you're
00:24:18
born with 86 billion neurons and those
00:24:20
slowly die with time. But in rats, for
00:24:24
example, there is a little trickle of
00:24:26
new cells, new brain cells. And there's
00:24:29
been a debate for a long time about
00:24:30
whether that little trickle happens in
00:24:32
humans or not. Still unresolved. But in
00:24:34
rats, what you can see is that exercise
00:24:37
causes the trickle to increase. If you
00:24:39
stick the rat on the wheel and it's
00:24:41
doing physical exercise, you get more
00:24:43
new brain cells. Now, we don't know for
00:24:45
sure that this happens in humans, but
00:24:48
lots of things about physical fitness
00:24:50
and exercise matter a lot to the brain.
00:24:52
This is nothing new. Exercise, sleep,
00:24:54
diet, these are really important things
00:24:56
for keeping the health of this organ. Is
00:24:58
there anything else that's important to
00:25:00
know for someone that is trying to
00:25:02
change and improve and keep their brain
00:25:03
in a healthy state as they age that we
00:25:05
haven't touched on?
00:25:07
>> There is something that that all of us
00:25:09
are thinking about which is about um
00:25:11
social media and the internet in
00:25:13
general. I do think one of the
00:25:14
interesting things about the internet
00:25:16
and social media is that if we were
00:25:19
growing up in a village 500 years ago,
00:25:22
you just know the people in the village
00:25:24
and what they can do and so on. But
00:25:25
let's say no one in the village was an
00:25:27
entrepreneur or a neuroscientist. And so
00:25:31
we we can't even picture that as a
00:25:33
thing. We don't know anything about
00:25:34
that. One thing that the internet has
00:25:37
done for kids growing up in the digital
00:25:39
age is that you get a lot of more
00:25:40
exposure to things. You you have so much
00:25:42
more exposure. I actually think this is
00:25:44
one of the positive things that I would
00:25:46
say about social media is that you not
00:25:49
only get exposure, wow, that kind of
00:25:51
thing is possible and that kind of thing
00:25:52
is possible, but you also have people
00:25:54
teaching you how to get there.
00:25:56
>> They say like, hey, I'm a fitness
00:25:57
influencer and I'm going to show you
00:25:58
exactly how to do the thing. Or, you
00:26:00
know, you say, "Hey, here's exactly how
00:26:02
you start a business." Or I say, "Hey,
00:26:03
here's the the route that you go through
00:26:05
undergrad and grad school to become a
00:26:07
neuroscientist." And that's great. I
00:26:08
mean, there's just there's so much more
00:26:11
uh of a talent window now that that
00:26:13
everyone gets exposed to. So, I think
00:26:14
that makes a better brain.
00:26:16
>> What are we doing to our children that
00:26:18
you think we probably shouldn't be doing
00:26:19
as it relates to brain development?
00:26:22
>> Here's the thing that's really important
00:26:23
about this debate is that nobody really
00:26:26
knows. And I'll tell you why. It's
00:26:27
because to do anything in science when
00:26:29
you're saying something about a group,
00:26:31
you need to have a control group that
00:26:32
you're comparing against. And when it
00:26:34
comes to asking the question of, hey,
00:26:36
kids growing up now with social media or
00:26:38
the internet, how do they compare to
00:26:40
other brains of kids who don't grow up
00:26:42
with that? Well, we don't have a control
00:26:43
group unless you look at kids who are
00:26:45
incredibly impoverished or let's say
00:26:48
Quakers who don't believe in technology.
00:26:51
And with both those groups, there's a
00:26:52
hundred other important differences. So,
00:26:54
you can't just say, "Oh, look, I'm
00:26:56
comparing to this kid who grew up
00:26:57
without food and and I'm going to say
00:26:59
there's this difference." Who the heck
00:27:00
knows why the difference is there? even
00:27:02
a generation ago. There's so many
00:27:05
differences in terms of diet and
00:27:06
pollution and politics and blah blah
00:27:08
blah what like everything that you can't
00:27:10
do it. So I I only mention this because
00:27:13
I think it's very important. A lot of
00:27:14
people pipe off with things about oh the
00:27:15
younger generation their brain this that
00:27:17
but we don't actually know and I will
00:27:20
tell you that I happen to be a cyber
00:27:23
optimist on this point about what
00:27:25
growing up with the internet does for
00:27:27
young people. I think it's going to make
00:27:28
them much smarter than the generation
00:27:30
that came before. And here's why. It has
00:27:32
to do with the size of the intellectual
00:27:36
diet that they can bring in. So when I
00:27:38
was a kid, I grew up pre- internet. You
00:27:40
know, I wanted to know stuff. So my mom
00:27:42
would drive me to the library, which was
00:27:45
25 minutes away, and I would pick up the
00:27:46
Encyclopedia Bratannica and I would flip
00:27:48
through it and hope they had an article
00:27:50
about the thing that I wanted to know
00:27:51
about. And that's how I was able to get
00:27:53
my little straw of knowledge. But now
00:27:57
kids are growing up with access to
00:28:00
anything they're interested in. And this
00:28:02
is so good for the brain. And from a
00:28:04
plasticity point of view, the reason
00:28:06
this matters is because change happens
00:28:08
in the brain when you are curious about
00:28:11
something. So when a kid asks a question
00:28:13
to Alexa or Siri or whatever and they
00:28:15
get the answer, that sticks because they
00:28:18
have the right cocktail of chemicals
00:28:19
going on in their head. In contrast,
00:28:21
when I grew up, I learned tons of just
00:28:23
in case knowledge. I mean, that's all
00:28:25
that the teachers could teach us is just
00:28:27
in case you ever need to know this fact,
00:28:28
here it is. But kids are in a really
00:28:31
great situation now. So, there are pros
00:28:33
and cons to to all this stuff, but I
00:28:35
think I'm very optimistic about what
00:28:38
this means for the for the warehouse of
00:28:41
knowledge that that kids can build up
00:28:42
now. And by the way, I saw an interview
00:28:44
with Isaac Azimoff in 1988. He was the
00:28:48
great science fiction writer who wrote
00:28:50
Foundation and so many other books. And
00:28:52
he was saying on this show in 1988, he
00:28:55
said, "Look, I envision a day when there
00:28:59
will be one central supercomput and
00:29:01
every house will have a cable running to
00:29:03
that supercomputer and you can ask any
00:29:05
question you want and it knows the
00:29:07
entirety of humankind's knowledge on
00:29:09
that computer." You know, what he was
00:29:11
foreseeing here was the internet. He got
00:29:12
the details wrong, which doesn't matter.
00:29:14
The idea is he saw how this would be so
00:29:17
incredible for education
00:29:20
because he pointed out look in any
00:29:21
classroom it's going too fast for half
00:29:23
the kids too slow for the other half of
00:29:24
the kids and if you could just pursue
00:29:27
the sphere of humankind's knowledge if
00:29:29
you could enter in whatever door you
00:29:32
wanted to that's the way to do it
00:29:34
because you'll be motivated now he
00:29:36
wasn't talking about brain plasticity or
00:29:38
anything but this is exactly what I'm
00:29:39
saying from a brain plasticity point of
00:29:41
view really matters
00:29:43
I I'll just mention something which is a
00:29:46
lot of people are concerned that oh with
00:29:48
with AI we're going to get lazy. We
00:29:50
won't you know know how to do anything
00:29:51
anymore because we can outsource it. It
00:29:53
just so happens that I I love doing home
00:29:54
improvement. I'm always fixing my house.
00:29:56
I have 3xed myself in the last half year
00:30:00
because of AI because I take a picture
00:30:02
of something. I say hey I've never seen
00:30:03
this kind of thing before. How does this
00:30:04
work? Whatever. And chat GPT says oh you
00:30:07
do this and you take this out and here's
00:30:08
the bolt and blah blah. It's not me
00:30:10
outsourcing it. It's me being curious
00:30:12
about something and so I remember how to
00:30:14
do everything now. I know how to do much
00:30:16
more than I used to because I like it.
00:30:19
>> What about the you there's been a couple
00:30:21
of studies that have come out that say
00:30:22
things like your brain's going to
00:30:23
atrophy if you don't continue to write
00:30:25
or um if you just defer all of your
00:30:27
learning to things like chatgbt or other
00:30:29
AI models. Um, one I guess one of the
00:30:32
areas that I think in one of the
00:30:34
studies, was it a Stanford study that
00:30:36
everyone was talking about where the the
00:30:38
participants used Google and AI and then
00:30:41
they'd learned something themselves.
00:30:43
>> But one of the things I've wondered is
00:30:46
if I'm going through my business life
00:30:48
and I'm encountering hard problems and
00:30:50
every time I encounter a hard problem, I
00:30:51
drop it into an AI. The AI spits out a
00:30:54
textbased answer. I copy and paste that
00:30:56
and send it as my response. presumably
00:30:59
there's some kind of important part of
00:31:01
the learning cycle or the you know
00:31:03
neurological development that I'm like
00:31:05
foregoing there I'm missing that I
00:31:08
probably should you know you said
00:31:09
earlier about doing hard things what I'm
00:31:11
doing there is I'm avoiding the hard
00:31:12
thing which is like thinking about it
00:31:13
and trying to understand it
00:31:15
>> yeah here's I think the really important
00:31:17
distinction there's vicious friction in
00:31:20
our lives and there's virtuous friction
00:31:22
so vicious friction is all the stupid
00:31:25
stuff that you have to do like hey
00:31:27
Stephen for your business I need you to
00:31:28
cop copy this spreadsheet over here and
00:31:30
fill in all these cells and and do your
00:31:32
taxes and whatever. Okay, that if we can
00:31:35
push that off to AI is massively
00:31:37
important for for improving human lives.
00:31:40
There's really not benefit in vicious
00:31:41
friction. But virtuous friction is, hey
00:31:45
Stephen, I really want you to think
00:31:46
about what is the optimal way to do this
00:31:49
business. What is the best structure for
00:31:51
this? How do we actually go DT to C? How
00:31:54
do we go B2B on this? What's the what's
00:31:56
the approach here that we're going to
00:31:58
take that you haven't done before that
00:32:00
would be amazing? That's virtuous
00:32:03
friction because you're really using
00:32:04
your brain to learn stuff that way. So
00:32:06
that's the first distinction that
00:32:08
matters is get rid of all the busy work.
00:32:10
There's no honor in that. I mean I'll
00:32:13
just mention in the 1990s there was this
00:32:16
big debate about whether we should have
00:32:17
kids use desk calculators or not. And
00:32:20
thank god that finally got resolved and
00:32:21
we let kids use calculators so that we
00:32:23
can learn, you know, couple we can spend
00:32:25
a couple days learning long division,
00:32:26
but you don't have to spend six months
00:32:27
on it because who cares? With the
00:32:29
virtuous friction, there's real
00:32:31
opportunity to surf the wave of AI so
00:32:35
that you are figuring out these tough
00:32:37
problems with the aid of somebody who
00:32:40
cares about your problem and is willing
00:32:42
to talk with you 247 and never gets
00:32:44
tired of talking to you about it. And so
00:32:46
you are not just copying and pasting,
00:32:48
but you're working with the AI to come
00:32:51
up with ideas that were beyond what you
00:32:53
would have come up with. Because I
00:32:55
mentioned earlier about internal models,
00:32:57
we have pretty narrow fence lines and
00:32:59
you can think of all these things, but
00:33:01
you don't even know what you don't know.
00:33:02
So, if you can have somebody who's
00:33:04
willing to talk with you, an expert in
00:33:06
all of humankind's knowledge, willing to
00:33:08
talk with you about it as much as you
00:33:10
want, there's a real opportunity there
00:33:12
to have a synergy where collectively you
00:33:16
both come up with a better idea than
00:33:18
either of you could have alone. But is
00:33:19
there a way for that relationship to
00:33:21
take place so that I actually benefit?
00:33:22
Because, you know, in the example I
00:33:23
gave, I'm just I take the question I was
00:33:25
asked, I put it into an AI, it gives me
00:33:27
an answer, I copy and paste it back to
00:33:28
the person that asked me the question.
00:33:30
that would happen if you really didn't
00:33:32
care about the person asking you the
00:33:33
question or the question. I mean
00:33:35
>> I mean this is what a lot of people are
00:33:36
doing like I get so many email because
00:33:37
you know we interview a lot of
00:33:38
candidates who join the business and so
00:33:39
I see tens of thousands of emails
00:33:41
sometimes a week that I mean I don't see
00:33:43
all of them but the ones that I see I
00:33:44
often know that you know because we've
00:33:46
sent them five questions or a task and I
00:33:49
look at it and go this is I can almost
00:33:51
predict the exact model that sent it to
00:33:53
me because they all have a different
00:33:55
personality so I go oh this one the
00:33:56
person put into Gemini or this one the
00:33:58
person put it into chatbt. Yeah,
00:34:00
exactly. And it's full of contrastive
00:34:02
constru construction like
00:34:04
>> it's not this, it's that. Yeah, exactly.
00:34:06
And then the M dashes. Exactly.
00:34:07
>> I'm really asking like is the person
00:34:09
that did that benefiting from from it?
00:34:11
>> No.
00:34:12
>> Well, no, but for a couple reasons. One
00:34:13
is that, you know, you and it it
00:34:16
triggers your red flag and so that does
00:34:18
not do anyone any good. see so many of
00:34:20
my colleagues posting on LinkedIn these
00:34:22
very obvious AI things and it irritates
00:34:25
me because I feel like I'm not going to
00:34:26
spend my time reading that because of I
00:34:30
call this this the effort phenomenon
00:34:32
which is um in in psychology we care a
00:34:35
lot about things that seemed like they
00:34:37
took a lot of effort and there's
00:34:38
something about seeing an AI post that's
00:34:40
just irritating because it's so
00:34:42
obviously AI
00:34:43
>> that's a really interesting idea the
00:34:44
effort phenomenon
00:34:45
>> yeah I've been I've been writing about
00:34:47
this for a while because um it turns out
00:34:48
there are psychology ology studies where
00:34:50
if I offer you two pieces of art and one
00:34:52
of them looks like, you know, let's say
00:34:53
it's a a red dot in the middle of a
00:34:55
white canvas and the other one is, you
00:34:57
know, bottle caps stacked up and glued
00:35:00
in this great shape or whatever, you'll
00:35:02
pay you'll pay much more for the thing
00:35:03
that looks like it took a lot of effort.
00:35:05
People will pay more for a real diamond
00:35:08
than a synthetic lab grown diamond,
00:35:10
which is exactly the same thing. It's
00:35:12
just carbon in the matrix. But they feel
00:35:14
like, oh well, mother nature took
00:35:15
hundreds of millions of years of effort
00:35:17
on this one, but not over here. It just
00:35:19
took a few days in the lab. So, there's
00:35:21
a million ways where we care about that
00:35:23
a lot. When it comes to this AI thing,
00:35:26
um, yes, anybody who's just popping back
00:35:28
something to you, it just feels like,
00:35:30
all right, they took the the path of
00:35:31
least resistance, and I'm not so
00:35:33
interested.
00:35:33
>> I want to know from a neuroscience
00:35:35
perspective whether they benefit.
00:35:37
>> Presumably, they don't benefit too much
00:35:38
either. I mean, it's hard to know
00:35:40
exactly how many times they went back
00:35:41
and forth with it. They could have said,
00:35:43
"Hey, Chad GPT, thank you for this, but
00:35:46
I'm kind of this more of this person.
00:35:47
When I really think about it, this is
00:35:49
the thing that inspires me." Not not
00:35:51
what you suggested. So, so somebody
00:35:52
could put effort into it. It's just that
00:35:54
we can't know that when we get the AI
00:35:56
response. It seems to be a pretty
00:35:58
consistent principle of life generally
00:35:59
that like when you do something hard or
00:36:02
when you put in effort, as you say, you
00:36:03
tend to get back like an equal and
00:36:05
opposite return like relatively. So I I
00:36:08
would think that if I fought through,
00:36:11
you know, maybe even using AI as a
00:36:13
companion, but I fought then to write it
00:36:15
out myself instead of just copying and
00:36:17
pasting.
00:36:18
>> Yeah.
00:36:19
>> One of the things I've learned from
00:36:20
doing this podcast and all these
00:36:20
episodes is everything is a trade-off.
00:36:24
>> Yeah.
00:36:25
>> And and if you don't know what the trade
00:36:26
you're making, then you're often at
00:36:29
great risk. And so like some of my
00:36:31
friends will say, "Oh, I take this pill
00:36:32
and it's amazing. It does all these
00:36:33
things for me. It's the most amazing
00:36:34
thing ever. I can just focus for 24
00:36:36
hours a day and I'm so productive now.
00:36:38
And I go, "What's the what's the
00:36:39
downside?" And they go, "Oh, there's no
00:36:41
downside." And I go, "Hm." Like, so
00:36:43
that's what I mean. It's even worse when
00:36:45
you don't you don't know the trade
00:36:46
you're making. And so with AI, I go,
00:36:47
"Okay, if it's making me wildly more
00:36:50
efficient or productive, what trade am I
00:36:53
making?" I think understanding this it's
00:36:56
probably not two categories but a
00:36:58
spectrum from vicious friction to
00:37:00
virtuous friction but really paying
00:37:02
attention to what is virtuous friction
00:37:04
what would make me a better person if I
00:37:07
actually put the effort into this that
00:37:09
matters a lot and I will say for us as
00:37:12
professors for you looking for job
00:37:15
candidates we need to change how we're
00:37:17
asking the questions if we just say hey
00:37:19
write answer these five questions of
00:37:21
course everyone's going to use it for
00:37:22
example in my classes is at Stanford. I
00:37:24
I don't have people turn in a final
00:37:26
paper anymore. That was from previous
00:37:29
life before AI. Now I have them do
00:37:32
projects as their final thing where
00:37:33
they're uh you know running an
00:37:35
experiment on something. And of course
00:37:36
they use AI to help them generate some
00:37:39
of the issues, but they have to deal
00:37:40
with other people and look at the data
00:37:42
and figure out what's wrong and that
00:37:43
kind of stuff. I worry that it's getting
00:37:44
into the age of, you know, the whole
00:37:46
calculator thing you said where maybe
00:37:48
actually it is now you need to assess
00:37:50
them on their ability to use the AI,
00:37:52
>> not to succeed without it.
00:37:55
>> Yeah, agreed. This is the whole game for
00:37:57
all of us, I think, is figuring out how
00:37:58
to surf this wave of AI where it can
00:38:00
make us super human. We can just be
00:38:02
better, so much better than anything we
00:38:04
ever were doing before because we have
00:38:07
immediate access to knowledge and facts
00:38:09
that either we had forgotten or we never
00:38:11
knew existed. And so we should be
00:38:13
surfing that wave. So I I I totally
00:38:15
agree with you on that point. If you can
00:38:16
figure out how to change your interview
00:38:18
questions so that you're seeing, hey,
00:38:19
can this person really get the speed?
00:38:21
With everything you know about learning
00:38:23
and neuroplasticity and expanding one's
00:38:25
brain, is there a anything else you can
00:38:28
say to the audience about how they
00:38:30
should use AI so that they become a
00:38:32
superhum?
00:38:33
>> Interesting. I you know, look, I I have
00:38:35
been talking to my friends about this
00:38:36
issue a lot lately and I I mentioned how
00:38:38
I've become so much better at home
00:38:40
improvement stuff. I just know so much
00:38:42
more. Each one of my friends has
00:38:44
something like that where like, hey, you
00:38:45
know what? I've actually gotten so much
00:38:47
better at this super random thing that I
00:38:49
never even thought I, you know, I never
00:38:51
thought about it explicitly, but because
00:38:53
I'm always asking AI questions about
00:38:55
that and it's giving me the answers.
00:38:57
It's not simply that it gives me the
00:38:59
answers and I forget it. It gives me the
00:39:01
answers and I remember it. I become
00:39:03
better and better because it's like the
00:39:04
way that Alexander the Great had
00:39:06
Aristotle as his tutor and could ask him
00:39:09
anything and learn great stuff from him.
00:39:11
We've all got Aristotle in our pocket
00:39:12
now and we can become better at the
00:39:15
things that we want to do, the things
00:39:17
that resonate with us for whatever
00:39:18
reason. If everyone's got Aristotle in
00:39:20
their pocket, how does one create an
00:39:22
edge?
00:39:23
>> I think it has to do with we're all just
00:39:25
going to be running faster. In the same
00:39:27
way that when Steve Jobs introduced
00:39:28
Apple computers, he said this is like a
00:39:30
bicycle for the mind. What he meant by
00:39:32
that was that for millions of years
00:39:34
we've been walking bipedily and then
00:39:37
just in the last nancond of evolution we
00:39:39
invented the bicycle and suddenly humans
00:39:42
can move faster because of the bicycle
00:39:44
and he said having a personal computer
00:39:46
is like a bicycle for the mind and I
00:39:49
think of AI now as like a motorcycle for
00:39:51
the mind it's it allows us to move so
00:39:55
much faster so now it's a motorcycle
00:39:56
race and there will be people who are
00:39:58
much faster than other people because
00:40:01
they're really using that optimally.
00:40:03
>> And that's what I mean. It's like how do
00:40:04
I create an edge versus my whoever I'm
00:40:06
competing with in whatever industry I'm
00:40:07
in.
00:40:08
>> Well, for sure the people who are just
00:40:09
copying and pasting the AI slop that'll
00:40:12
be easy to beat that crowd. But
00:40:15
otherwise, I think it's just a matter
00:40:16
of, hey, these are the newest things.
00:40:18
It's like in history when the new sword
00:40:20
gets invented or the new gun or the new
00:40:22
cannon, you know, you have to keep
00:40:24
improving and and using that. And that's
00:40:27
what's going on now with AI
00:40:28
>> and with from a neuroscience
00:40:29
perspective. If I wanted to use AI to
00:40:33
based on all these things you've told me
00:40:34
about novelty and all these other points
00:40:36
that expand the the connections across
00:40:38
my brain and give me a big cognitive
00:40:40
reserve.
00:40:41
What might I I install as a practice
00:40:43
every week when I'm speaking to my AI?
00:40:46
Oh, ask it questions that you're curious
00:40:47
about about anything. Just asking
00:40:50
questions. Here's one thing I do all the
00:40:52
time. I'll say, "Hey, I've been thinking
00:40:54
about this. You know, I on my podcast, I
00:40:56
do a lot of monologues and so I'll start
00:40:59
talking to it and I'll say, "Hey, I've
00:41:01
got this idea that I'm thinking about.
00:41:02
What if blah blah blah blah." And then
00:41:03
I'll say, "Here's my idea. Give me pros
00:41:05
and cons." You know, tell me why this is
00:41:08
wrong. And I do that pretty much with
00:41:10
everything that I ask it if I'm
00:41:12
proposing some, you know, stupid seed of
00:41:14
an idea and it really gives me the
00:41:16
counter arguments and I really engage
00:41:18
with it. That is the important part, I
00:41:21
think. And by the way, I just want to
00:41:22
say I think for the next generation that
00:41:24
we're teaching this, there really only
00:41:27
two things we can teach because all the
00:41:29
details of, you know, hey, let's teach
00:41:31
computer programming or something,
00:41:32
that's probably already gone as a useful
00:41:34
thing. So what we can teach is critical
00:41:37
thinking and creativity. That's it. I
00:41:41
think that's such an important point,
00:41:42
this point about asking your AI why you
00:41:44
might be wrong.
00:41:45
>> Yeah. I I think I've had most of my
00:41:47
paradigm shifting moments when I've come
00:41:49
to an AI model that I was using with a
00:41:52
very with very high conviction. And the
00:41:54
prompt that always I think is most sort
00:41:56
of expansive in terms of my intellectual
00:41:59
knowledge is when I say to it, be
00:42:02
brutally honest about your opinion.
00:42:04
Think for yourself and be objective and
00:42:06
tell me where my blind spots are.
00:42:09
There's something innate with within us
00:42:10
all where we don't actually want to be
00:42:14
wrong. We often I think as a natural
00:42:16
reflex and this is why people get really
00:42:17
sort of trapped in echo chambers of
00:42:18
political opinion and you know Leon
00:42:20
Fesser talked about this idea of
00:42:21
cognitive dissonance when something you
00:42:23
believe contrasts with new information
00:42:26
and how it makes you feel uncomfortable
00:42:28
there's something when I type that out
00:42:29
when I when I love the idea or the thing
00:42:31
I've written or the memo I've written
00:42:32
this new idea and I go on tell me why
00:42:35
I'm completely completely wrong and it
00:42:36
eviscerates me it is both uncomfortable
00:42:40
but it feels incredibly important
00:42:42
because then then it's like I've I've
00:42:44
grown. But these AIs, they're they're
00:42:47
programmed almost to like kiss my ass.
00:42:49
>> Yes. Although, you know, Chatupati
00:42:52
released a very sickopantic version, I
00:42:54
don't know, maybe a year ago. Meaning it
00:42:56
compliments you. You give some idea and
00:42:58
it says, "Oh, Stephen, that's the best
00:43:00
idea I've ever heard. You're a genius
00:43:01
and blah blah." And that didn't last
00:43:03
very long, that model, because nobody
00:43:05
actually liked it. So, you're exactly
00:43:07
right. And and I'm sure most listeners
00:43:09
know this, but you can tell your AI to
00:43:12
be brutally honest with you all the
00:43:14
time. You can tell them to do that all
00:43:15
the time and it'll do that. So you can
00:43:18
you can establish the kind of person
00:43:19
that you're talking to. Here's the
00:43:21
thing. You're right. Of course, people
00:43:22
don't like to be wrong. It can be
00:43:24
socially embarrassing. It can be
00:43:25
uncomfortable. And yet, there's
00:43:27
something very different when you're
00:43:28
talking to your AI. It's a very private
00:43:30
thing. And you say, "Hey, tell me why
00:43:31
I'm brutally wrong." And when it tells
00:43:33
you, you think, "Oh, thank God it's
00:43:34
telling me that instead of like a real
00:43:36
human." So I I think a lot of that is
00:43:39
alleviated with AI. We we don't feel as
00:43:43
bad about being wrong there.
00:43:44
>> As you were saying that, I just went on
00:43:45
chat and I typed this in. Is my joke
00:43:49
funny? And the joke I typed in is knock.
00:43:51
Who's there? A letter. Let us who? Let
00:43:54
us in and I'll tell you.
00:43:56
>> Okay. You didn't laugh. I didn't laugh.
00:43:58
>> Okay.
00:43:58
>> Chapati said, "Yes, it works as a joke.
00:44:00
solid structure, uses the classic pun
00:44:03
payoff, which is exactly how most not
00:44:05
jokes land. And then it's done a
00:44:06
laughing emoji. I then said, "Be
00:44:08
brutally honest and completely
00:44:10
objective. Was that funny?" It said,
00:44:13
"It's not very funny."
00:44:16
Interesting. You know, but but that's
00:44:18
interesting because it depends, right? A
00:44:20
little child actually finds that joke
00:44:22
funny and and for a little child, they
00:44:24
then get to repeat that to their
00:44:26
classmate. They're learning how to do a
00:44:28
joke and so on. So I'm not I'm not sure
00:44:31
I think there's a single answer to
00:44:32
whether that can be funny or not.
00:44:34
>> But the interesting thing is it just
00:44:36
reinforcing what I already believed. And
00:44:38
therefore when we think about growth or
00:44:40
having a growth mindset if someone's
00:44:42
just always reinforcing what you already
00:44:44
believe and know I don't know if it's
00:44:46
ever going to be a growth mindset. I
00:44:47
mean I just asked it again. I said be
00:44:49
really honest and it said it's
00:44:50
absolutely not funny.
00:44:52
>> Yeah. But but remember all it's doing is
00:44:55
it's just it's a statistical parrot. And
00:44:57
so when you say be brutally honest, it
00:44:59
it thinks that's what it should answer.
00:45:02
>> Also, be even more honest. It says it's
00:45:03
basically not funny at all and you
00:45:05
shouldn't say that to people.
00:45:06
>> Okay.
00:45:06
>> And it says comedic originality 1 out of
00:45:09
10. Likelihood of real laughter 1 out of
00:45:10
10.
00:45:11
>> Well, that's that's quite good. That's
00:45:12
quite accurate. Um, here's the thing.
00:45:15
I've been thinking about this issue a
00:45:16
lot about whether AI can be funny. And
00:45:19
at the moment, it can't be. It It's
00:45:22
great at repeating jokes, but it doesn't
00:45:24
understand humor on its own. what it
00:45:27
knows if you ask it to make up a new
00:45:29
joke, what it'll do is it'll have, you
00:45:31
know, the first guy walks in the bar,
00:45:32
then the second guy walks in the bar and
00:45:34
does X, and that establishes the
00:45:36
pattern, but then the third guy, it'll
00:45:38
have break that pattern, which is the
00:45:39
structure of a joke, but it doesn't know
00:45:42
how to break the pattern in a way that's
00:45:44
funny. It's just the third guy does some
00:45:45
random thing. So AI as it stands now,
00:45:48
the way it's structured with what's
00:45:49
called a transformer model, doesn't know
00:45:52
how to think of the punchline and then
00:45:54
go back and make the joke lead to that
00:45:56
punchline.
00:45:57
>> A lot of people don't either.
00:45:59
>> Do you know what I mean? Like I say that
00:46:01
not in an offense way, but just to say
00:46:02
that like
00:46:03
>> I don't know. I often hear the claim
00:46:04
that AI could never be creative.
00:46:06
>> It's massively creative. Here's why.
00:46:09
Creativity in the brain, all creativity
00:46:12
is is you absorb your world. the whole
00:46:14
world around you, every experience
00:46:15
you've ever had. And then you're bending
00:46:17
and breaking and blending those
00:46:19
cognitive concepts into new remixes.
00:46:22
That's all creativity is. And you're
00:46:24
doing that all the time. Whether you're
00:46:26
just trying to think of what to say next
00:46:27
or what recipe to make next or what
00:46:29
patent to do or what company to start,
00:46:31
you're just remixing the stuff that you
00:46:33
already know. And that's why, you know,
00:46:36
I don't know, take Beethoven, he could
00:46:38
have written any kind of music that was
00:46:41
being done anywhere in the world. But of
00:46:42
course, he didn't. like that's what he
00:46:43
grew up with was the music and his local
00:46:45
culture and so on. What we have now is a
00:46:48
much broader diet as I mentioned before
00:46:50
where we can get everything going in.
00:46:52
But the point I want to make here is
00:46:54
that AI that's what it does. It remixes
00:46:57
stuff that's come in. So AI is massively
00:46:59
creative. The part of creativity that AI
00:47:01
can't do right now is selection. Meaning
00:47:05
it can generate a 100 pictures but it
00:47:07
doesn't know which one to pick. It
00:47:08
doesn't know which one is going to be
00:47:09
the most appealing to you. But it can
00:47:12
remix beautifully.
00:47:13
>> But neither do humans, right? So if I
00:47:15
asked an intern to make me 100 pictures,
00:47:18
I mean, I could get my AI to pick one,
00:47:19
but it wouldn't know what the intern or
00:47:21
the AI wouldn't know which one I loved.
00:47:23
>> The intern would have a much better shot
00:47:25
at it. And as the intern is there for a
00:47:27
while, he or she becomes quite good at
00:47:29
getting, oh, okay, I get Steven's taste.
00:47:31
It would be this one.
00:47:32
>> And the AI can't learn that what my
00:47:33
taste is. I don't think the AI could
00:47:35
learn that about visual images because
00:47:37
when it generates the pixels, it's doing
00:47:39
this, you know, this magical stuff under
00:47:40
the hood where it's deciding which
00:47:42
pixels and how they diffuse together
00:47:43
and, you know, mix the image, but it
00:47:45
doesn't know how to read that image
00:47:47
like, oh yeah, the way this is and blah
00:47:50
blah that'll really appeal to Steve. It
00:47:52
does it it's not seeing the image except
00:47:54
as a bunch of pixels. Hm. Hm.
00:47:56
>> You need to be a human for that
00:47:58
>> cuz I feed um I was doing an experiment
00:48:01
recently where I took our my behind the
00:48:02
scenes channel which is a 30 minute long
00:48:04
video. I dropped it into Gemini and I'd
00:48:06
say things to it like predict where
00:48:07
people would drop off on the video and
00:48:10
then we upload the video to YouTube. we
00:48:12
get the retention data back and Gemini
00:48:14
uh in the last two times that I've done
00:48:16
it has a 100% record of knowing that at
00:48:18
minute 7 where insert person talked for
00:48:22
too long and might have been a bit more
00:48:24
sight might have tried to sell a hoodie
00:48:26
for example in that part it would say
00:48:29
you're going to lose people here and it
00:48:30
would and it very accurately say why it
00:48:32
would say because there's you talked for
00:48:34
74 seconds and it was jarring versus the
00:48:38
the the moment that came before it and
00:48:40
when I feed the AI I don't let's say
00:48:41
thumbnails and say which thumbnail is
00:48:43
going to perform the best. We did a test
00:48:45
recently where we put four thumbnail
00:48:47
test results that we knew the answer to
00:48:49
into Gemini and said which one's going
00:48:50
to win on YouTube AB testing and it got
00:48:53
100% accuracy of predicting on data we
00:48:56
already had which one would win. And so
00:48:59
now I I don't know I I keep having these
00:49:02
paradigm shifting moments where only
00:49:03
humans could could do that. But
00:49:05
increasingly the the AIs that we're
00:49:08
experimenting with are making better
00:49:10
creative decisions than now I can make
00:49:12
myself as if the outcome of that
00:49:14
creative decision is which one is people
00:49:15
going to prefer.
00:49:16
>> Yeah.
00:49:16
>> I'd say a year ago that wasn't the case.
00:49:18
>> Okay. So I totally agree with you. But
00:49:19
but let me just mention one thing which
00:49:21
is fascinating which is that often the
00:49:24
way it's doing it is not at all the way
00:49:25
that a human would do it which might be
00:49:27
fine for our purposes but the data and
00:49:30
the way that it's picking up on it. It
00:49:32
might be something about you know how
00:49:33
much I'm making this up you how much
00:49:35
green was in the YouTube thumbnail image
00:49:37
or how much red or whatever whatever the
00:49:40
thing is or just noticing that there's
00:49:42
big font versus smaller font or
00:49:44
whatever. the next time you try it, it
00:49:47
says, "Oh, yeah, this thumbnail is going
00:49:48
to be great." And it's some ridiculous
00:49:50
thumbnail that doesn't make any sense to
00:49:51
you as a human, nor to your fellow
00:49:53
humans, but it might say, "Oh, yeah,
00:49:55
this would be great." Because it's
00:49:57
judging things on very weird dimensions
00:49:59
that we can't always see. You know, the
00:50:00
example you gave about maybe it's cuz
00:50:02
the text is bigger or the color red, but
00:50:04
those are the same factors we think
00:50:05
about as a human. We think if we know
00:50:08
that if the font is bigger, it performs
00:50:10
better. We know that red performs better
00:50:11
than green.
00:50:12
>> Quite possibly. But here's the
00:50:13
interesting thing. Human art constantly
00:50:15
evolves and all AI is trained on is what
00:50:18
has been done before and what has
00:50:19
worked. And so if I asked it, let's say
00:50:23
we composed five different songs and
00:50:25
said, "Hey AI, which song is going to be
00:50:27
better?" It's going to say something
00:50:28
that's right in the middle of the
00:50:29
distribution of popular songs. But
00:50:31
that's not what actually makes it next
00:50:33
year and the year after. It's new
00:50:35
things. It's new twists that that nobody
00:50:37
has seen before. That's what we love.
00:50:39
That's what we seek as consumers. And so
00:50:41
because AI can only be trained up on
00:50:44
what already exists, it's never going to
00:50:46
get the new thing at the edge.
00:50:48
>> But if if the AI was asked to cuz I
00:50:51
think the reason why a new song would
00:50:52
break out, let's say, you know, a new
00:50:55
Drake song comes out and it's a smash
00:50:57
hit. If we think about that distribution
00:50:59
curve, so like if I draw on the GR,
00:51:01
you're saying that um this middle
00:51:02
section here is what sort of AI will aim
00:51:04
at because it's the popular in the
00:51:06
known. Well, if I tell AI to make a
00:51:10
million songs, which is kind of what I
00:51:11
guess is what's going on every day um
00:51:13
around the world, if you scattered them
00:51:16
on on this graph at like, you know,
00:51:18
>> Absolutely.
00:51:19
>> And then the AI's most unusual song ends
00:51:22
up taking off. But it's just because
00:51:23
there's so many of them.
00:51:24
>> Quite right. But that's the human
00:51:26
selection part that we're seeing over
00:51:28
there. If you asked, okay, out of all
00:51:30
these dots, which do you think AI is
00:51:32
going to be best? It's going to have to
00:51:33
tell you the middle of the curve. But
00:51:35
the surprising part is the part that you
00:51:37
circled there, which is the one on the
00:51:38
edge is the one that humans like. Why?
00:51:40
Because we're constant novelty seekers.
00:51:43
We care about the things that are new. I
00:51:45
think the the point I'm getting at is
00:51:47
that um the creation of it, the creative
00:51:51
process is still the same, which is like
00:51:53
>> totally
00:51:53
>> AI or humans just trying a bunch of
00:51:56
and then the world going, "Ooh, that
00:51:58
one."
00:51:59
>> Oh. Oh, yeah. I totally agree. This is
00:52:00
consistent with what I was saying, which
00:52:01
is that AI can be massively creative in
00:52:03
terms of the generation of something,
00:52:05
but you need humans to do the selection.
00:52:07
I'm only arguing the point that AI is
00:52:09
not good at saying, okay, I've generated
00:52:11
a 100 songs. This is the one humans will
00:52:13
choose. We end up saying, hey, wait,
00:52:16
this one is just weird and unique enough
00:52:18
that I really like that. It's
00:52:20
interesting because when you um when you
00:52:21
speak to like record labels about music,
00:52:24
what they're often doing is getting a
00:52:28
format of a song that they know will
00:52:31
work. So they're like, "Right, so it's
00:52:33
got to be eight bars here. It's got to
00:52:34
be this here. You got to have a chorus
00:52:35
that's like hookie. It's got to come
00:52:36
back around. It's got to build up pace.
00:52:38
And there's like a rough format to it."
00:52:40
And it's no surprise that Ed Sheer
00:52:42
someone like Ed Sheeran has written so
00:52:44
many songs for so many people.
00:52:45
>> Yeah. When I spent some time working
00:52:47
with Sony, they had a brand new boy band
00:52:49
in the wake of One Direction. And when I
00:52:51
sat with the boy band um and was
00:52:53
introducing myself, they said they said
00:52:54
to me, "Oh yeah, so um here are their
00:52:55
his the boy band's first three songs and
00:52:58
um Ed Sheeran has written all of them."
00:53:00
And I was like, "What?" I thought I
00:53:02
thought like they're like, "No, Ed Ed
00:53:03
Sheeran's written all of them." And then
00:53:05
what we do is we give them to the boy
00:53:06
band and then the boy band sing them and
00:53:09
they're pretty much guaranteed to be
00:53:10
hits because Ed Sheeran has like a
00:53:11
formula. the way he writes is really in
00:53:15
like vogue right now. You people tend to
00:53:17
think a lot that the songs that are
00:53:19
number one in the charts are there
00:53:21
because just because someone had
00:53:23
creative genius and of course that is
00:53:24
the case sometimes but there is a lot of
00:53:26
this writing going on and then handing
00:53:28
the formula over because someone has
00:53:30
cracked the code of a hit,
00:53:31
>> right? But here's the thing and you know
00:53:33
that we all know this which is that the
00:53:34
code never lasts. So humans have this
00:53:38
pull where they're always seeking things
00:53:41
between novelty and familiarity. So we
00:53:44
like things where we recognize the brand
00:53:46
and we recognize what the singer has
00:53:48
done before. But there has to be novelty
00:53:50
or else we're not going to go for it.
00:53:52
We're not going to listen to that boy
00:53:53
band for the next 10 years doing the
00:53:55
same song over and over. So you're of
00:53:57
course right that we, you know, we want
00:53:59
a bit of familiarity. We want to be
00:54:01
anchored, but we definitely seek the
00:54:03
new. This is what humans always do. This
00:54:05
is why car companies always release the
00:54:07
next model even though the current model
00:54:09
is perfectly fine. This is why haircuts
00:54:11
evolve. This is why fashion evolves
00:54:12
through the years. Um because we always
00:54:15
care about novelty. And the other thing
00:54:17
in the music industry that I think is is
00:54:19
also creating a hit is I was reading
00:54:21
many years ago about some psychology
00:54:23
which you'll probably know much more
00:54:24
about that says exactly what you just
00:54:26
said which is we love something when it
00:54:28
is familiar but new.
00:54:31
>> Exactly. So the way that the record
00:54:33
industry and the radio industry make
00:54:35
something familiar is they blast the
00:54:37
same song at you on every radio station
00:54:40
for a long period of time until it
00:54:42
breaks past being just novel, just new
00:54:45
and it becomes familiar. And like I saw
00:54:48
this graph which shows that the a song
00:54:50
that you'll love is right there in the
00:54:52
middle of like it's new enough that
00:54:55
you're still into it but it's um
00:54:57
familiar now because you've heard it so
00:54:59
many times that you love it and you'll
00:55:01
if anyone listening the first time you
00:55:03
hear a song you might not love it as
00:55:04
much as once you've heard it like 20
00:55:06
times
00:55:07
>> and then at some point you've heard it
00:55:08
too much.
00:55:09
>> Yeah.
00:55:10
>> And it comes back down the other side of
00:55:11
the cover where it's now too familiar.
00:55:13
>> Yeah. That's exactly right. And so we're
00:55:15
always seeking that tension in the
00:55:17
middle. And yeah, companies run into
00:55:19
this all the time. Like sometimes they
00:55:21
try things that are too novel that just
00:55:24
completely fail. You know, Coca-Cola
00:55:25
tried this a long time ago with
00:55:26
introducing new Coke and no one liked
00:55:28
it, whatever. Um, and other companies
00:55:29
like what was that company? Blackberry
00:55:31
with the the little thumb things that
00:55:33
you can press the physical keyboard on
00:55:34
the phone. They failed because they
00:55:36
wouldn't change fast enough. But anyway,
00:55:38
companies that make it are always
00:55:39
staying in that uh sweet spot.
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00:57:47
When you think about the brain and how
00:57:48
it's built and then you think about the
00:57:50
exact technology that they've used to
00:57:53
create AI, isn't it very very similar?
00:57:55
And if so, if it is similar, what does
00:57:58
that say about humans role in the
00:58:00
future? It's similar, but it's not the
00:58:02
same. Which is why with AI, you get what
00:58:04
what we call jagged intelligence,
00:58:06
meaning that it can do something so
00:58:09
extraordinarily smart and then in the
00:58:10
next moment give an answer that's weird
00:58:12
and doesn't make any sense. AI still is
00:58:15
doing this. It's not it's not yet
00:58:16
thinking like we think. Okay. Why? It's
00:58:18
because
00:58:20
AI as we think about it now really
00:58:22
started of course decades and decades
00:58:24
ago where people said look you've got
00:58:26
all these billions of cells neurons in
00:58:28
the brain that are connected to each
00:58:30
other. What if we ignore all that
00:58:32
complexity and we just say look imagine
00:58:34
that you have units that are connected
00:58:35
to each other. We're going to forget
00:58:36
about you know a single cell in the
00:58:38
brain is as complicated as a city. It's
00:58:40
got the entire human genome. It's
00:58:42
trafficking millions of proteins. Let's
00:58:43
put all that aside. Just imagine it's a
00:58:45
circle and it's connected to other cells
00:58:47
and each connection has a certain
00:58:48
strength and that's what we call an
00:58:50
artificial neural network. Now that went
00:58:53
off in its own direction and the kind of
00:58:55
amazing surprising part is how
00:58:57
successful it's been to just get rid of
00:58:59
all the detail but it's still super
00:59:02
different than what human brains are
00:59:03
like. So just an example uh this thing I
00:59:07
mentioned at the very beginning about
00:59:08
how we're a team of rivals under the
00:59:10
hood. You got all these different
00:59:11
competing neural networks that are
00:59:13
trying to drive your behavior and so on.
00:59:15
The fact that we're emotional, the fact
00:59:17
that we are driven by different
00:59:20
appetites, whether food or sexuality or
00:59:22
whatever it is, but you know, you're a
00:59:24
your chat GPT, you don't want that in
00:59:26
the chat GPT. So, it's just an
00:59:27
artificial neural network many layers
00:59:29
deep and it's extraordinary at what it
00:59:31
does, but it's so different than a
00:59:32
human. For example, the fact that it's
00:59:34
read everything on the planet and
00:59:35
remembers it and you haven't, you would
00:59:38
need to lead a thousand lifetimes to
00:59:40
read that much. And of course, you
00:59:41
wouldn't remember much of it. It It's
00:59:43
very different is the point I'm making.
00:59:45
They both have converged on something
00:59:48
that we would call intelligence, but
00:59:49
it's a pretty different structure. Even
00:59:51
though AI was inspired by the brain,
00:59:53
that's what Jeffrey Hinton was telling
00:59:54
me. He was telling me that like much of
00:59:56
the the breakthroughs that have made AI
00:59:58
what it is today came from understanding
01:00:00
how the brain works.
01:00:02
>> Yeah. But that's interesting because
01:00:04
Hinn isn't is incentivized to say that.
01:00:07
But a neuroscientist
01:00:09
>> incentivized to say that
01:00:10
>> people doing AI of course are paying a
01:00:13
lot of attention to how this is
01:00:15
structured like the brain because before
01:00:17
that people would do things like
01:00:19
probability theory or rules or you know
01:00:22
they were trying to do AI by trying to
01:00:24
say okay if this then do that but when
01:00:27
people started doing artificial neural
01:00:29
networks that led to a lot of success
01:00:31
I'm only pointing out that the
01:00:32
artificial neural network looks a lot
01:00:34
like the brain on the surface You say,
01:00:37
"Hey, you've got units and you've got
01:00:38
connections, but beyond that, there's a
01:00:40
lot of differences."
01:00:41
>> And why are those differences
01:00:43
significant as it relates to what's
01:00:44
possible?
01:00:45
>> Because what we've developed is this a
01:00:48
new species essentially that is
01:00:50
incredibly impressive, but it ain't a
01:00:52
human brain. It's different than a human
01:00:54
brain. There may be all kinds of
01:00:56
similarities, things that we even come
01:00:57
to understand are similar, but there are
01:00:59
so many differences. Here's an example.
01:01:02
You know, we humans do one trial
01:01:03
learning all the time. Meaning if I say
01:01:06
or when you were a kid and and your mom
01:01:07
said, "Hey, Stephen, this is a
01:01:09
pomegranate." You say, "Okay,
01:01:10
pomegranate. Got it." But you can't when
01:01:13
you're training up a an artificial
01:01:15
neural network like at OpenAI or Gemini
01:01:18
or Anthropic, you have to give thousands
01:01:21
or millions of examples of everything
01:01:23
for it to learn anything. There's no one
01:01:24
trial learning on those uh systems. And
01:01:28
they have to be trained at the cost of
01:01:29
billions of dollars. then they can do a
01:01:31
run where you ask a question and and it
01:01:33
answers the question. But brains in the
01:01:36
real world don't have that luxury of
01:01:38
having a training phase and then an
01:01:40
action phase. We have to learn on the
01:01:42
fly. It's very different.
01:01:43
>> So I guess the the pertaining question
01:01:45
is
01:01:47
does it change what's possible for the
01:01:49
brain versus the artificial neural
01:01:53
networks we see in AI? like is there
01:01:55
some limitation based on what you've
01:01:57
just said that means the this brain in
01:01:59
front of me, this human brain in front
01:02:00
of me will always be better than the AI
01:02:02
at something because I'm trying to track
01:02:04
forward about what this means for the
01:02:05
future of humans.
01:02:06
>> Yeah.
01:02:07
>> Um
01:02:07
>> I think it's an interesting question um
01:02:09
that we'll have to see. But it's clearly
01:02:12
the case that we know what it is to be a
01:02:15
human from the inside. And when I'm
01:02:17
making a model of you and who you are
01:02:19
and you're making a model of me, we have
01:02:21
assumptions about what it is like to be
01:02:23
a human. AI only watches human behavior
01:02:26
from the outside. And so it can tell a
01:02:28
lot of great stuff, but it doesn't
01:02:30
really know what it is to be a human. So
01:02:33
if I ask it some question about what
01:02:35
would it be like if this or that
01:02:37
happened, it can answer based on
01:02:39
observing lots of things, but it can
01:02:41
only ever know from the outside
01:02:42
>> in terms of why that matters.
01:02:44
>> Yeah. Because you know if I ask my AI my
01:02:47
fiance's been like this today or if I
01:02:49
ask my best friend my fiance's been like
01:02:50
this today. If it both of them give me
01:02:52
the same useful answer it doesn't really
01:02:54
matter what's
01:02:54
>> I agree with you. I agree it may I I I'm
01:02:57
actually writing a new podcast on this
01:02:58
about what you can tell from the outside
01:03:00
and what you can tell from the inside
01:03:02
and whether that difference matters.
01:03:04
Look an example is you know I last year
01:03:06
got a Tesla with full self-driving and I
01:03:09
was watching as it was full
01:03:10
self-driving. I was coming up on a very
01:03:12
complicated traffic situation. And I
01:03:13
thought, well, what's my car going to do
01:03:14
here? How's it possibly going to
01:03:15
understand? But what it did is it slowed
01:03:17
down and came to a stop, which was
01:03:18
exactly the right thing. And I thought,
01:03:20
oh, that's interesting. Algorithmically,
01:03:22
it might think of it very differently
01:03:24
than I am thinking about the situation.
01:03:26
Doesn't matter. It comes to the same
01:03:28
conclusion, ends up in the same place.
01:03:29
Yeah, I agree. We have yet to see where
01:03:32
these differences matter and and what it
01:03:35
is to be a human. But I can tell you one
01:03:37
thing. We care about other humans. So
01:03:40
here's my little prediction is that
01:03:41
there's going to be actually a
01:03:42
renaissance in things like live theater
01:03:44
and live performances. When when things
01:03:47
first came out like Napster, everyone
01:03:49
thought, okay, that's the death of
01:03:51
concerts. Like who's that's the death of
01:03:53
musicians, right? But in fact, you look
01:03:55
at a a Taylor Swift concert, gajillions
01:03:57
of people there paying lots of money.
01:03:59
Like everyone loves the the thing. Why?
01:04:02
Because they're going to see the real
01:04:03
Taylor Swift in person. And I have
01:04:05
noticed I give a lot of talks on the
01:04:06
road. I have noticed an increase in the
01:04:08
number of talks since AI came out a few
01:04:11
years ago. The first thing that my
01:04:13
friend said to me is hey did you know
01:04:15
David that you can you know use uh 11
01:04:18
labs and hey Jen and you know you can
01:04:20
make an avatar of yourself and you can
01:04:22
use your voice and and use chat to
01:04:24
generate what you're going to say and
01:04:25
have a fully virtual version of you. He
01:04:28
said my friend who gives talks too he
01:04:30
said maybe we can start doing this and
01:04:31
do virtual talks. I said nobody's going
01:04:33
to want that. In fact, what's happened
01:04:35
is more people want to fly us across the
01:04:38
country to have us stand there in person
01:04:41
because it really matters to see fellow
01:04:43
humans. And I think that's only going to
01:04:45
increase.
01:04:46
>> I completely agree with you. I think I
01:04:48
think it's so funny. I did a post on
01:04:49
LinkedIn the other day saying that maybe
01:04:52
the like interesting paradox or
01:04:54
interesting outcome of AI is that every
01:04:58
other iteration of technology made us
01:05:01
less human. And maybe the intelligence
01:05:04
now has gotten to a point where
01:05:07
>> it's now forcing us to be more human
01:05:10
because that is all that kind of remains
01:05:12
in a way that maybe the the technology
01:05:14
has gotten so good like social media
01:05:16
didn't make us more human in any
01:05:18
capacity. But maybe this is the moment
01:05:19
where it goes we've got this now
01:05:21
>> go do what only you as a human can do
01:05:23
which is like go out there Taylor Swift
01:05:25
and sing in front of people IRL.
01:05:27
>> Go and do something in the real world.
01:05:28
Even for like nurses um and doctors,
01:05:30
maybe they shouldn't be filling out
01:05:31
admin and paperwork anymore. Maybe they
01:05:33
should be holding your hand and giving
01:05:34
you, you know, in real life care that
01:05:37
only a human could do.
01:05:39
>> I totally agree.
01:05:40
>> And so maybe that's the like the the
01:05:41
positive upside to all of this is um
01:05:44
finally, you know, we've been on this
01:05:45
journey with technology and finally it's
01:05:46
delivered upon its promise.
01:05:48
>> I totally agree. And by the way, you
01:05:49
know, AI relationships, by one estimate,
01:05:52
there's a billion people having
01:05:53
relationships with AI, like a girlfriend
01:05:55
or boyfriend kind of thing.
01:05:57
>> Okay? And so for people like us who grew
01:06:00
up before that existed, we think, "Oh my
01:06:02
gosh, that's weird." But in fact, I
01:06:04
think it might become helpful because it
01:06:06
can be a sandbox as long as we have the
01:06:08
proper feedback. In the end, we have
01:06:11
millions of years of evolution driving
01:06:12
us towards being with the person you
01:06:15
love, touching another human being,
01:06:16
watching the stars, taking her out to
01:06:19
dinner with your parents, like all you
01:06:21
know, we care about that. And so this
01:06:23
worry that people sometimes talk about
01:06:25
about oh people are just going to be on
01:06:26
their phone with their AI relationship I
01:06:28
don't think is realistic for almost
01:06:29
everybody because it gives us the chance
01:06:33
to you know hopefully sandbox some
01:06:35
things about relationships and get over
01:06:36
some dumb things with relationships and
01:06:38
then we can actually be with our fellow
01:06:40
humans. counterargument would be that
01:06:42
maybe there's going to be a bifocation,
01:06:43
a splitting of society where some people
01:06:46
are going to become even more addicted
01:06:48
to the technology because the AI is now
01:06:51
much smarter at retention. Like I know
01:06:53
exactly what I need to say to you based
01:06:56
on your brain, Dr. David, to make you
01:07:00
not put this device down. Yes. But
01:07:03
fundamentally, I want to be in contact
01:07:06
with my wife. I mean, that's that's the
01:07:09
evolution
01:07:11
of hundreds of millions of years is that
01:07:13
I want to make babies. I want to go and
01:07:16
eat dinner with somebody. And and as
01:07:19
much as I might find my phone appealing,
01:07:20
I'm not going to sit it across from me
01:07:22
at a nice Italian restaurant and sit
01:07:24
there like that. So, I a lot of people
01:07:27
do.
01:07:28
>> Me and my me and my friends are at
01:07:29
restaurants cuz we have a rule where we
01:07:31
don't touch our phones when we're at
01:07:32
date night. And I have to look around
01:07:33
and I'm like, "Oh my god, like how is
01:07:35
how are all these guys getting away with
01:07:37
this?" Like, but do you see what I'm
01:07:38
saying? Like some some people they just
01:07:40
have a different sort of proclivity or
01:07:42
they have a different wiring which means
01:07:44
that you know instead of doing the hard
01:07:46
thing of going out there and going on a
01:07:47
first date and being rejected,
01:07:49
pornography or a virtual uh wife might
01:07:52
be a substitute for that.
01:07:54
>> Yeah. No, I agree with you. There will
01:07:55
be bifurcations. One question I don't
01:07:57
know the answer to, but one question is
01:07:59
what would that person have done in
01:08:02
previous generations? You know, is it
01:08:05
really the case that person would have
01:08:06
gone out and had a great successful
01:08:08
relationship or would they always have
01:08:09
had troubles relating to people?
01:08:12
>> Yeah, I sat with um a few
01:08:14
neuroscientists and experts that are
01:08:16
studied dopamine. Dr. Anna LMK was one.
01:08:19
>> Yeah, she's my colleague.
01:08:20
>> She's your colleague. Yeah. And uh she
01:08:22
talks a lot about how we all have
01:08:24
different types of addictive substances
01:08:28
and like you know we will think like
01:08:30
heroin's addictive for everybody and
01:08:31
alcohol's addictive and I used to think
01:08:33
of it on a spectrum but actually she
01:08:35
said like for her addiction was romantic
01:08:38
erotic novels.
01:08:39
>> Yeah. and she she almost ruined her
01:08:40
relationship because of erotic novels,
01:08:42
which is something that I would read and
01:08:43
just throw in the bit like but so maybe
01:08:46
this new technology is particularly
01:08:49
addictive to a certain type of person.
01:08:51
>> Yeah, I I think that's exactly right.
01:08:53
And I think we're going to see that with
01:08:54
everything. I mean,
01:08:55
>> the wild part about human society is
01:08:57
that there's so little that we have in
01:09:00
common, meaning everybody is really
01:09:03
different. And this is something I've
01:09:04
studied in my lab for for decades is
01:09:06
this issue about what are the subtle
01:09:08
differences from person to person. Not
01:09:10
big things like oh this person is a
01:09:13
psychopath or this person has
01:09:14
schizophrenia but the more subtle
01:09:16
things. I'll just give you an example
01:09:18
like if I ask you to imagine to
01:09:21
visualize let's say an ant on a purple
01:09:25
and white tablecloth uh crawling towards
01:09:28
a jar of red jelly. Do you see that in
01:09:32
your head like a movie or do you have
01:09:34
like no particular picture at all or
01:09:36
somewhere in between? What what do you
01:09:38
experience?
01:09:38
>> An ant crawling towards a jar of jelly.
01:09:40
>> Yes.
01:09:42
>> Yeah. I see a big black ant and then
01:09:44
this jar of jelly is like overflowing
01:09:46
down the sides with a wooden lid on top
01:09:48
of it and the ant is almost there.
01:09:50
>> Oh wow. Okay. So you have a Okay. So
01:09:52
what you have I'm just guessing where
01:09:55
you are but you are on the end of the
01:09:56
spectrum that we call hyperfantasia
01:09:58
which means you have very rich
01:10:00
visualization. You're like seeing it
01:10:02
like a picture or a movie. Is that is
01:10:04
that accurate? Okay. I happen to be at
01:10:06
the other end of that spectrum called
01:10:07
aphantasia where I don't have any visual
01:10:10
images at all. There's no I I don't see
01:10:12
things visually in any way.
01:10:14
>> And it turns out the whole population is
01:10:16
spread evenly along this spectrum. I'll
01:10:18
just give a quick side note which is
01:10:20
that for many years I've been talking
01:10:22
with Ed Catmull about this. He's the guy
01:10:24
who started Pixar films. So he's got all
01:10:26
the patents on how to do ray tracing and
01:10:28
how to make these beautiful animated
01:10:29
characters, right? Ed Catmull is
01:10:31
afantasic like I am. And when he learned
01:10:34
about this, he got really interested and
01:10:35
he gave the questionnaire to everybody
01:10:37
at Pixar. And it turns out many of his
01:10:38
best animators and directors are
01:10:40
aphantasic. They don't picture anything
01:10:42
inside their heads. Now this seems
01:10:45
surprising and strange, right? But it
01:10:47
turns out that if you are an aphantasia
01:10:49
kid, you're going to become better at
01:10:50
drawing because you have to really pay
01:10:52
attention to the subject out there and
01:10:54
really have a dialogue with the page
01:10:56
with your pencil. Whereas a kid who's
01:10:58
hyperfantasic might say, "Oh, I know
01:10:59
what a horse looks like." And just draws
01:11:01
it. Okay. So anyway,
01:11:02
>> got tracks.
01:11:03
>> Yeah. Yeah. So it turns out there's a
01:11:06
real spectrum across the population,
01:11:07
meaning inside your head and my head,
01:11:09
we're having pretty different
01:11:10
experiences. But I've studied this along
01:11:13
dozens of different axes and everyone's
01:11:15
got different things going on. Just as
01:11:17
one example, do you know about
01:11:18
synesthesia? Have you ever heard of
01:11:19
this? Forget is that forgetting or
01:11:20
something?
01:11:21
>> No. Sesthesia is having a blending of
01:11:23
the senses. So someone with sesthesia
01:11:25
might look at letters and it triggers a
01:11:27
color experience in their head. So they
01:11:28
look at J and that triggers green and
01:11:29
they look at M and that triggers blue
01:11:31
and whatever. It's different for each
01:11:33
person. Or you might hear music and it
01:11:34
triggers a visual experience. Or you
01:11:36
might taste something, it puts a feeling
01:11:38
on your fingertips or whatever. It's
01:11:39
just it's a blending of the senses. At
01:11:41
least 3% of the population has this.
01:11:44
It's not a disease or a disorder. It's
01:11:45
just an alternative perceptual reality.
01:11:49
So if you have aphantasia, does that
01:11:51
mean that you can't picture your kids?
01:11:53
>> It means that the way I picture them is
01:11:55
not visually. I mean there's sort of a
01:11:59
very g but for me it's more motoric
01:12:02
imagery and you know I I and audio
01:12:05
imagery. Like I'm I'm imagining talking
01:12:07
to them and being with them and being
01:12:08
close to them and probably some old
01:12:10
factory imagery meaning you how they
01:12:12
smell and the whole thing like I have a
01:12:14
very rich notion of what it is to be
01:12:16
with my kids but it's a pretty terrible
01:12:18
visual picture. Not much there.
01:12:20
>> So I imagine people at home have done
01:12:22
that same experiment while they were
01:12:24
listening. Could they picture an ant
01:12:26
walking towards a jar of jam and if they
01:12:28
find themselves on the aphantas I can't
01:12:31
remember the two.
01:12:32
>> Aphantasagasic. Yeah. Or hyperfantasic.
01:12:34
So hyperfantasia is you can picture it,
01:12:36
aphantasia because you can't.
01:12:37
>> Yes.
01:12:38
>> What does that potentially suggest about
01:12:41
nothing? Now here's the interesting
01:12:42
part. So we've done lots of studies
01:12:44
about what this translates to in terms
01:12:46
of your capacities in the world.
01:12:48
Nothing. Why does it translate to
01:12:49
nothing? It's because you can
01:12:52
accomplish tasks in a hundred different
01:12:55
ways. And so some people are doing this
01:12:56
very visually. Other people are doing it
01:12:59
where they're like picturing it with
01:13:01
their motor systems. Others are doing
01:13:03
it, you know, as I mentioned, with sound
01:13:05
or smell or whatever, or others are
01:13:06
doing it just purely conceptually, just
01:13:08
thinking through how the steps would go.
01:13:11
But there's nothing there's nothing
01:13:12
obvious other than this thing I
01:13:14
mentioned about visual artists often
01:13:16
being aphantasic.
01:13:18
Um, otherwise you can kind of accomplish
01:13:20
anything.
01:13:21
>> I run multiple companies that have
01:13:23
multiple sales teams. And one of the
01:13:24
things as a founder of a company that's
01:13:26
often confusing is you find it hard to
01:13:28
figure out where sales are. So about 10
01:13:30
years ago, I started using Pipe Drive in
01:13:32
my former company and it's also the
01:13:34
reason why I switched over all of my
01:13:35
commercial teams in my current media
01:13:37
company called Steven.com to use Pipe
01:13:38
Drive as well. Not only do they sponsor
01:13:40
this show, but they've been an
01:13:41
incredibly effective way of scaling our
01:13:43
sales engine over the years. Pipe Drive
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is an easy to use intelligent CRM. And
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at its very core, it makes your sales
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delay. It doesn't magically close the
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01:14:17
piperive.com/ceeo.
01:14:20
I'll see you over there. This is
01:14:22
something that I've made for you. I
01:14:24
realized that the diio audience are
01:14:26
striv
01:14:29
goals that we want to accomplish. And
01:14:31
one of the things I've learned is that
01:14:33
when you aim at the big big goal, it can
01:14:36
feel incredibly psychologically
01:14:38
uncomfortable because it's kind of like
01:14:40
being stood at the foot of Mount Everest
01:14:42
and looking upwards. The way to
01:14:43
accomplish your goals is by breaking
01:14:45
them down into tiny small steps. And we
01:14:48
call this in our team the 1%. And
01:14:50
actually this philosophy is highly
01:14:52
responsible for much of our success
01:14:54
here. So what we've done so that you at
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you have is we've made these 1% diaries
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and to make some minor tweaks to the
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the description below.
01:15:31
I heard that you might have after many,
01:15:34
many decades of people debating this,
01:15:36
you might have figured out the reason
01:15:38
why we dream.
01:15:39
>> Yeah. Yeah, it's actually after
01:15:41
millennia of people debating this. This
01:15:43
is the cool part. So, okay, remember I
01:15:45
mentioned earlier that if you go blind,
01:15:49
the visual cortex of the back of the
01:15:50
brain gets taken over by hearing and by
01:15:53
touch and by other things and it's no
01:15:54
longer visual cortex. Well, what we
01:15:56
realized is that because we live on a
01:16:00
planet that rotates into darkness for
01:16:02
half the time, the visual cortex, the
01:16:05
visual part of your brain is at a
01:16:07
disadvantage. So what I realized is that
01:16:10
the purpose of dreaming is to defend the
01:16:12
visual territory from takeover from the
01:16:16
other senses. So every 90 minutes you've
01:16:18
got these um you've got this very
01:16:21
ancient thing in your midbrain that
01:16:24
shoots random activity into the visual
01:16:26
system and only the visual system only
01:16:28
this very tiny part of the visual
01:16:30
system. Every 90 minutes you just blast
01:16:31
random activity in here and the reason
01:16:33
is you are just defending that territory
01:16:36
against takeover. Now, the reason that
01:16:38
all this came together is because our
01:16:40
colleagues at Harvard did an experiment
01:16:41
where they took normally cighted people
01:16:44
and they blindfolded them tightly for 60
01:16:46
minutes. And it turns out that 60
01:16:47
minutes was sufficient for the visual
01:16:50
cortex to start responding to sound and
01:16:53
to touch. You could start seeing that
01:16:55
takeover happening after 60 minutes. And
01:16:57
that's when we realized, wow, this this
01:17:00
part of the brain really needs a way of
01:17:02
defending itself now because the brain
01:17:05
is a natural storyteller. If you blast
01:17:07
random activity in there, it'll, you
01:17:09
know, put that together in some sort of
01:17:10
visual story about what's happening,
01:17:12
mostly based on what connections are hot
01:17:14
from the day. But that's why we dream.
01:17:18
So we we dream to stop the other parts
01:17:20
of our brain overtaking the visual part
01:17:24
of our brain, um, overpowering it, and I
01:17:27
guess ultimately making us go blind.
01:17:29
>> Yeah, that's exactly right. If we lived
01:17:30
on a different kind of planet that did
01:17:32
not rotate into darkness, then we would
01:17:35
we presumably wouldn't dream.
01:17:37
>> Would we even need to close our eyes? I
01:17:38
mean,
01:17:38
>> not necessarily. Yeah. It may be that in
01:17:41
the sleeping state, in the state of deep
01:17:43
sleep, the brain is doing particular
01:17:45
things like taking out the trash and
01:17:47
cleaning some things up. That might be
01:17:49
necessary. Who knows? But yeah, I don't
01:17:51
think we would need to dream. We
01:17:52
wouldn't need to blast random activity
01:17:54
in there. um you know if if if our eyes
01:17:57
were always open for example and it was
01:17:58
always light out
01:17:59
>> are there other examples in the animal
01:18:01
kingdom which support this?
01:18:04
>> Yes, thank you for asking that. It's
01:18:06
this is why this new theory about why we
01:18:08
dream is taking off because we can make
01:18:09
quantitative predictions across animal
01:18:12
species. So for example in our last
01:18:13
paper we looked at 25 different species
01:18:16
of primates, apes and monkeys and we
01:18:19
looked at how plastic their brains are.
01:18:21
In other words, how flexible the whole
01:18:23
circuitry was and how much they dream at
01:18:25
night, which you can tell by looking at
01:18:27
rapid eye movements. You know, when you
01:18:28
dream at night, your eyes are shooting
01:18:30
back and forth like that. It's called
01:18:31
REM, rapid eye movement sleep. So, you
01:18:33
can measure that in other animals, their
01:18:34
eyes moving back and forth. So, we
01:18:37
correlated how plastic the brain is and
01:18:40
how much dream sleep you have. And it
01:18:42
correlates perfectly, which is to say,
01:18:44
humans, which are the most plastic, have
01:18:47
dream sleep all the time. And by the
01:18:49
way, when you're an infant, you sleep
01:18:50
for you have dream sleep for half of
01:18:52
your sleep time, 50% of the time. As you
01:18:54
get older, you get less and less dream
01:18:56
sleep because you just don't need it as
01:18:57
much anymore. But anyway, when we look
01:18:58
across species, it correlates perfectly
01:19:00
if you're a monkey that drops into the
01:19:02
world sort of already fully baked and
01:19:04
you don't need to have much plasticity.
01:19:06
You don't have much dream sleep either.
01:19:07
Interesting.
01:19:11
Seems like a very strange thing. It
01:19:12
sounds like it's a very strange thing
01:19:13
for the for the brain to do, but it also
01:19:16
is perfectly plausible based on
01:19:17
everything you've said.
01:19:18
>> Yeah. And by the way, I just want to
01:19:19
mention dreaming is across the animal
01:19:21
kingdom. Everybody dreams. All animals
01:19:23
dream at night. Even like animals at the
01:19:25
bottom of the ocean. Uh, yes. It's
01:19:27
harder to measure stuff all the way at
01:19:28
the bottom of the ocean. But fish do
01:19:30
have what is equivalent to dream sleep
01:19:33
where you're just zapping activity in
01:19:34
there. And by the way, even animals that
01:19:36
have gone blind, like there's a there's
01:19:38
a mammal called the blind mole rat,
01:19:40
which lives in darkness and has eyes,
01:19:43
but they're blind because over
01:19:44
evolutionary time, they've lost vision.
01:19:46
But they still dream because the dream
01:19:49
circuitry is so ancient. This is so
01:19:51
ancient that all animals have to defend
01:19:54
themselves against the darkness by
01:19:56
keeping their visual systems going. And
01:19:58
so even though the animal went blind,
01:20:00
the rest of the brain didn't catch up. I
01:20:02
mean, that's how evolution goes.
01:20:03
>> Funny. It's funny because it's kind of
01:20:05
like that evolution gave us this TV
01:20:10
that comes on at nighttime when the real
01:20:12
TV, our real life turns off and it just
01:20:14
puts on this fake TV set to keep that
01:20:16
part of the brain doing something so
01:20:18
that it doesn't deteriorate and um
01:20:21
atrophy.
01:20:22
>> It's exactly right. Yeah, it's exactly
01:20:24
right. Which means dreams are quite
01:20:26
pointless outside of just protecting our
01:20:29
neurological matter.
01:20:31
>> I suspect so. It might be that the
01:20:34
particular pathways that could travel
01:20:35
down, you know, maybe there's some
01:20:38
meaning there. I my own suspicion is
01:20:40
that it's like if I went to your
01:20:41
bookshelf and I picked picked a random
01:20:43
book up and I flipped to a random page
01:20:45
and picked a random sentence. I might
01:20:48
find some meaning in that. I might say,
01:20:49
"Oh, that was just the sentence that I
01:20:51
needed to hear." But it's not really.
01:20:53
It's just that it has some meaning to
01:20:54
me. Anyway, the point is if you blast
01:20:55
random activity in there, I might dream
01:20:57
about something where I wake up and say,
01:20:58
"Oh, that was pretty useful." But the
01:21:01
thing that I think gets overlooked is
01:21:03
that most dreams are totally useless and
01:21:05
bizarre. Dr. David, what is the most
01:21:07
important thing we haven't talked about
01:21:08
that we should have talked about as it
01:21:09
specifically relates to people that are
01:21:12
trying to improve their lives, get
01:21:15
better at whatever their subjective
01:21:16
mission is and the brain.
01:21:20
There are probably a lot of things, but
01:21:22
I got to say the thing that I've been
01:21:23
thinking about so much lately is just
01:21:25
about our political uh interfacing with
01:21:29
one another. And so I do feel that
01:21:32
really learning the skills of dialogue
01:21:35
with our fellow humans where we listen
01:21:38
to what they're saying and try to better
01:21:39
understand what their internal model is.
01:21:42
It's not equivalent to agreeing with
01:21:43
them. But it is saying, "Hey, somebody
01:21:45
is coming from this perspective. Let me
01:21:48
see if I can understand that." I think
01:21:49
that matters a lot. And I also think
01:21:52
that because we're so highly predisposed
01:21:55
for in-groups and outgroups, it's really
01:21:57
useful to figure out how to complexify
01:22:00
those relationships. Meaning, how do you
01:22:02
figure out the all the things that cross
01:22:05
cut in the relationship so that you say,
01:22:07
"Hey, you know what? I shouldn't dismiss
01:22:08
this person as a member of my out group
01:22:10
right away because actually
01:22:13
they belong to the same group I do and
01:22:15
they love surfing as much as I do and
01:22:17
they love golden retriever dogs and they
01:22:19
you know grew up in my hometown and
01:22:21
whatever. Like finding those things uh
01:22:24
explicitly helps the brain to keep these
01:22:28
circuits on that are involved in seeing
01:22:30
another person as a person. We have we
01:22:33
have all this social circuitry that is
01:22:36
all about understanding other people and
01:22:39
when things get dehumanized that
01:22:42
actually gets dialed way down. When we
01:22:44
look at you know let's say a homeless
01:22:46
person or a drug addict or someone who
01:22:49
we think of as our enemy or an out group
01:22:51
that gets dialed down so we don't think
01:22:53
of them as a person anymore. We think of
01:22:55
them as an object to to get around. Mhm.
01:22:58
So, this is what I think is really
01:22:59
important is figuring out what we can do
01:23:02
to keep that social circuitry still
01:23:04
going, which includes the things like
01:23:06
eye contact and conversation. And this
01:23:09
is this is one of the most important
01:23:10
things we can do as citizens in a
01:23:13
rapidly changing world as it relates to
01:23:17
things like dementia, which I know is a
01:23:20
fear that a lot of people have. A lot of
01:23:21
people are suffering with dementia, I
01:23:23
think increasingly. In fact, if I was
01:23:25
trying to save off dementia, what advice
01:23:27
would you give me, David?
01:23:28
>> Yeah, keep your brain active. Keep it
01:23:30
active till the day you die. Take on new
01:23:32
challenges. And as soon as you get good
01:23:34
at something like, you know, sudoku,
01:23:37
drop it and pick up some that you're not
01:23:39
good at.
01:23:40
>> And in simple terms, why?
01:23:42
>> It's because you're forcing your brain
01:23:43
to make changes. Otherwise, your brain
01:23:45
says, "Okay, I got this. I got the
01:23:47
world. I understand what's going on.
01:23:49
There's no real particular need for me
01:23:50
to change." And the fact is that the
01:23:52
structure of the brain is always
01:23:54
degenerating. And when you get something
01:23:56
like a disease like Alzheimer's disease,
01:23:58
it degenerates much faster. And what you
01:24:00
want to always be doing is building new
01:24:02
roadways and fashioning new paths that
01:24:05
had not been walked before.
01:24:06
>> So that there's more to degenerate,
01:24:09
which gives me more left over once that
01:24:12
degeneration begins.
01:24:14
>> Yeah, I that's Yeah, I think that's a
01:24:16
good way to look at it. your pathways
01:24:18
are falling apart and if you can build
01:24:20
new pathways which requires effort you
01:24:22
have to actually care and pursue and do
01:24:24
the thing even as parts of the thing
01:24:26
have fallen apart you still have ways of
01:24:28
getting from A to B
01:24:29
>> what do I need to stay away from in
01:24:31
terms of chemicals or supplement I don't
01:24:33
know or food I don't know
01:24:35
>> yeah obviously there's just been a lot
01:24:36
more emphasis on getting good sleep and
01:24:38
good diet and this stuff really matters
01:24:40
I think that's really useful for the
01:24:42
brain I mean it's fascinating to watch
01:24:44
what's happened in the latest generation
01:24:46
in terms of alcohol ol consumption. I
01:24:48
live up in Silicon Valley and there's a
01:24:49
lot of people who have wineries just
01:24:52
north of me and they're like selling
01:24:53
half their acorage. It's absolutely
01:24:55
fascinating to see what's happening
01:24:56
there. I will say I have a friend who's
01:24:59
who's in her 20s who said that she's in
01:25:02
favor of bringing drinking back. Why?
01:25:05
Because she said we go to parties and
01:25:07
everything's so awkward and no one knows
01:25:08
how to talk to one another. And so
01:25:10
they're missing something else. they're
01:25:12
missing the the dumb mistakes category
01:25:14
that we all got to enjoy growing up. So,
01:25:17
it it is a really interesting balance of
01:25:20
of how abstious one wants to become.
01:25:23
>> David, we have a closing tradition where
01:25:24
the last guest leaves a question, the
01:25:25
next guest, not knowing who they're
01:25:26
leaving it for.
01:25:27
>> Question left for you is, what do you
01:25:29
wish most for our planet over the next
01:25:33
10 years?
01:25:38
>> Well, the whole list are the top 10.
01:25:40
>> Yeah. um can't be world peace.
01:25:44
>> You know, I think I would come back to
01:25:45
this piece about the complexification of
01:25:47
relationships, which is to say, if we
01:25:50
could just get a little bit smarter
01:25:53
about understanding people out groups as
01:25:58
being humans with lives with their own
01:26:00
thing going on. doesn't mean we have to
01:26:03
love them or agree with them, but if we
01:26:06
can just get to that point, I don't
01:26:08
think we'll ever hit world peace, but at
01:26:10
least we'd have slightly less
01:26:11
polarization. So, I'm I'm definitely in
01:26:13
favor of that and I do think it's
01:26:14
possible and I do think AI can help us
01:26:16
get there by challenging us on these
01:26:18
points and saying, "Hey, that group that
01:26:21
you've already dismissed as an out
01:26:23
group, what if I told you this story
01:26:25
about this person? What if I introduced
01:26:27
you to this person?" That kind of stuff.
01:26:29
and you know having there's all kinds of
01:26:31
social movements that have sprung up
01:26:33
that allow people of different political
01:26:35
opinions to come together in a room and
01:26:37
talk with one another again it's not
01:26:38
that anyone has to change their mind but
01:26:40
they can say hey you know what I really
01:26:42
like that person I thought that was a
01:26:44
cool person a sweet person nice person
01:26:46
and and now I understand that somebody
01:26:48
who I have seen with my own eyes has a
01:26:49
different opinion on this than idea
01:26:51
>> is that wishful thinking to some degree
01:26:52
>> I don't think so because these things
01:26:54
are happening all over the place and and
01:26:57
>> the macro is is division isn't it It's
01:26:59
polarization echo chambers. There's now
01:27:01
I think there's now 20 social networks
01:27:03
or some crazy number that have more than
01:27:04
20 million people on them which means
01:27:06
that social networks are splintering off
01:27:08
into niches and interests and you know
01:27:10
there's like Rumble and Bumble and then
01:27:12
there's like threads and X and Facebook
01:27:14
snap Instagram and and what we're seeing
01:27:16
is more and more
01:27:18
>> interest group and also the other thing
01:27:19
with algorithms is we went from having
01:27:22
like a social graph where if I had a
01:27:24
thousand people follow me those thousand
01:27:26
people would see my stuff to now these
01:27:27
interest graphs where it doesn't matter
01:27:29
if I have one follower or million
01:27:30
followers, the algorithm is going to
01:27:32
decide who's interested in that thing
01:27:34
and it's going to serve it to them
01:27:35
because that's the most retentive thing
01:27:36
if you're a publicly listed company
01:27:38
that's driven by ad revenue. So, you've
01:27:40
got this algorithm that's actually
01:27:41
forcing you into what you know into this
01:27:43
into tighter and tighter and tighter
01:27:44
echo chambers. And even as someone
01:27:46
that's been on social media 15 years and
01:27:47
ran social media companies, this is one
01:27:48
of the great things I've noticed is when
01:27:50
I had a million followers back in the
01:27:51
day, I would reach those people because
01:27:53
they'd hit follow or subscribe. Now,
01:27:56
even on our YouTube channel, 61% of you
01:27:59
don't subscribe. Um, and please
01:28:02
subscribe. Um, and that's in part
01:28:04
because the algorithm is now doing the
01:28:06
work of deciding who to show it to, who
01:28:09
it will
01:28:10
>> on the basis of who will be retained.
01:28:12
>> Yeah. Here's what I would say. There's
01:28:14
absolutely nothing new about echo
01:28:16
chambers because it was always the case
01:28:18
that your neighbors and your community
01:28:20
and whatever, that's what you thought
01:28:22
was reality. I'm actually quite
01:28:24
optimistic about the existent the mere
01:28:25
existence of the internet because at
01:28:27
least we are exposed to the fact that
01:28:29
there are lots of different points of
01:28:30
view. It used to be in places like the
01:28:32
USSR, they controlled the media tightly
01:28:35
so that everything you saw was a news um
01:28:37
approved story, but now you see all the
01:28:40
points of view. Now, many of them might
01:28:42
drive you crazy and whatever, but at
01:28:43
least you know that there are people out
01:28:45
there that believe in that. And I think
01:28:47
that's really useful. If I had to decide
01:28:49
between state control where there's a
01:28:50
single story or seeing the whole messy
01:28:53
spectrum of opinions, I'd rather see the
01:28:56
latter.
01:28:57
>> What about the middle? You know, they
01:28:58
always one of the phrases that's again a
01:29:00
principle that's helped me think is that
01:29:01
the truth is in the middle. And
01:29:03
generally I try understand what the
01:29:04
middle looks like. So you've got state
01:29:06
controlled over here. You've got
01:29:08
aggressive algorithm that's sort of
01:29:09
reinforcing whatever you currently
01:29:11
believe.
01:29:12
>> Is there not some kind of middle ground
01:29:13
where
01:29:15
um the algorithms have to let up a
01:29:17
little bit and of course we're not going
01:29:18
to go for state controlled. Here's my
01:29:20
prediction in 2026 is that there is a
01:29:23
market opportunity for a new social
01:29:25
media company to come along because
01:29:27
everybody is aware of exactly this
01:29:29
problem that you're pointing out.
01:29:30
Everyone hates when they surf and they
01:29:33
get served exactly what they're supposed
01:29:34
to get served and they get off after an
01:29:36
hour or two and they feel like they've
01:29:38
wasted their lives. I think there's a
01:29:40
real opportunity for a social media
01:29:41
company to come along and say, you know
01:29:42
what, we're not building our algorithm
01:29:44
like the other guys. It's not about just
01:29:46
trying to get engagement at any cost
01:29:47
with, you know, um, incendiary posts,
01:29:51
but instead we're looking for ways to
01:29:54
connect people. So, if you and I both
01:29:57
love this particular thing, this
01:30:00
particular cuisine or or location or
01:30:03
whatever it is, we get connected. We see
01:30:05
each other's stuff and the algorithm
01:30:08
carefully, temporally sequences things
01:30:10
so that we come to have a certain
01:30:12
connection threshold before we find out,
01:30:15
whoa, you have a totally different
01:30:16
political opinion than I do on on
01:30:18
subject X. Wow, I didn't know that, but
01:30:20
I really like Stephen, so I'm going to
01:30:22
lean in and listen a little bit more. I
01:30:24
think this is very easy to do and I
01:30:26
think it can actually be part of the
01:30:27
selling point of the media company is
01:30:29
saying hey we are here not to enrage you
01:30:32
but to to actually build connection
01:30:35
>> sounds like how social media started
01:30:37
>> yeah it's a return
01:30:39
>> I think there's probably a neuroscience
01:30:42
basis as to why we ended up yeah
01:30:45
>> no it's an economics basis
01:30:47
>> but the fact is there's now an economic
01:30:49
opportunity now that everyone sees the
01:30:51
landscape
01:30:51
>> what I'm trying to say is that that
01:30:53
social network wouldn't be that
01:30:54
retentive by design because it wouldn't
01:30:56
trigger my dopamine. It wouldn't be a
01:30:58
slot machine like in Tik Tok is a slot
01:31:00
machine. Ping ping randomized returns.
01:31:03
Ping ping ping. Dopamine hit. Ping ping
01:31:05
ping. So this other social network that
01:31:07
wasn't playing with my dopamine in such
01:31:09
a way. I don't know whether I'd be
01:31:11
addicted enough to return. Therefore,
01:31:12
they wouldn't sell their ads the
01:31:13
economic return. Therefore, they
01:31:14
wouldn't do very well.
01:31:16
>> Here's the thing. I don't know if the
01:31:17
story is that simple that we all want to
01:31:19
do slot machines all the time.
01:31:21
>> Exactly. Because the fact is that a lot
01:31:24
of people go to Las Vegas and do slot
01:31:25
machines sometime, but we don't do that
01:31:28
all the time. It's kind of rare
01:31:29
actually. What we really desire are
01:31:31
meaningful connections. We really desire
01:31:34
feeling like, hey, you know what? I met
01:31:36
this person online that I'm following
01:31:37
and he's following me and we really
01:31:40
connect on all these points and oh by
01:31:43
the way, I then found out interestingly
01:31:45
he's got a totally different opinion
01:31:46
about Iran or abortion or whatever than
01:31:48
I do, but that's cool. Now we're we're
01:31:50
listening to each other. It kind of goes
01:31:52
back to your point earlier about at the
01:31:53
very start where we're talking about,
01:31:54
you know, the brain having an internal
01:31:56
battle like, do I want the cookie or do
01:31:58
I want the salad?
01:31:59
>> And unfortunately in the world we live
01:32:00
in, you know, this the cookie is going
01:32:02
to give me a dopamine hit.
01:32:04
>> Yes. But we don't eat cookies all the
01:32:05
time. This is the point. We do eat
01:32:07
salads much of the time because we're
01:32:10
not just unconscious automaton that are
01:32:12
doing the cookies.
01:32:13
>> Dr. David Eagleman, thank you so much
01:32:15
for the work that you do. I'm going to
01:32:16
link your book below um so everyone can
01:32:18
read this book. You've got a new book on
01:32:20
the way which I'm very excited about as
01:32:21
well. What's that book going to be about
01:32:22
and when is that out?
01:32:23
>> That's about the Ulisses contract and
01:32:24
that'll come out in 2027.
01:32:26
>> June. Okay. Um for anyone that wants to
01:32:27
know how to change your life by changing
01:32:29
your brain, I think this is the perfect
01:32:31
book to read. It's a New York Times
01:32:32
bestselling um author. Um and the book
01:32:36
is absolutely fascinating. It was
01:32:38
actually learning about this subject
01:32:39
matter in LiveWire that helped me to um
01:32:43
pursue more of a growth mindset and just
01:32:44
a growth mentality across my life and to
01:32:46
realize that if I'm not something now,
01:32:48
it doesn't mean that I can't be
01:32:49
tomorrow. So, thank you so much for the
01:32:51
work that you do, David. And, um, it's
01:32:53
been truly illuminating, and I'm sure my
01:32:55
my neural pathways have expanded in
01:32:57
really important ways because of this.
01:32:59
Great. Thank you, Stephen.
01:33:01
>> YouTube have this new crazy algorithm
01:33:02
where they know exactly what video you
01:33:04
would like to watch next based on AI and
01:33:07
all of your viewing behavior. And the
01:33:08
algorithm says that this video is the
01:33:12
perfect video for you. It's different
01:33:13
for everybody looking right now. Check
01:33:15
this video out and I bet you you might
01:33:17
love
