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Welcome, welcome, welcome to Armchair
Expert, experts on expert. I'm Dan

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Shepard. I'm joined by Monica Mouse.

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>> Hi. >> How are you?

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>> Oh, we were saying that maybe my new

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animal is a It's like a mouse.

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>> A hyrax. >> Hyrax. >> You're a Hyrax.

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>> Monica Hyrax. >> Oh,
yeah. Monica Hyrax bad.

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>> Cuz you said your new
nickname is the boulder.

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>> Oh [__] Right. That was on
another thing that we didn't air.

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>> Yeah. >> Yeah.
That's important. People should

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recognize that I am now
going by the boulder.

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>> Yep. >> Because
what's more formidable, a

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boulder or the rock?
I think the viewer can decide. [gasps]

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>> This guest, I really hope people listen
to this interview because I'm going to

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put it in in into a handful of guests

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we've had where I I just was literally

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overwhelmed with the scope and and
breadth of their knowledge of how all

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things work where I was like, "Oh, wait.

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Does this person kind of understand
how everything works in concert?

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>> It's amazing to be
in front of someone like that.

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>> Yes. And that man is Eric Schmidt.
Now, Eric Schmidt is a technologist, an

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entrepreneur, and a philanthropist. He

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is the founder of Schmidt Futures
and former CEO of Google for a long time.

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>> Yeah. >> Uh I started
this interview wondering

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how would those two people pick someone
they thought was more qualified than

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themselves to run their company? Who

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could that person be? And we met him. He

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has a new book out called The Age of AI

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and Our Human Future. The Age of AI
and Our Human Future explores what society

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must be focused on right now to make
sure that we are prepared for a new

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reality where humans and AI must coexist.
He wrote this book with Henry

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Kissinger and Daniel Hutenlocker.

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Fascinating interview. Please enjoy.

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Eric Schmidt.

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[music]

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>> He's an So, first
of all, nice to see you.

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>> Great to see you again. >> Yeah,
I guess I would have a year ago if

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someone asked me, "Do you think you'll
ever meet Eric Schmidt?" I would have

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said no. And then here we are twice
face to face within four months.

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>> Illuminati. >> Okay. Okay.

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>> We've had so many people on from that

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conference event

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>> and I'm so jealous. And if Adam doesn't
invite me next year, I'm gonna be mad

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making that. >> Well, Jared
Cohen's the guy you want to

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be on your side in this scenario.

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>> He's getting an email from me.

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>> He uh he controls the world [laughter]

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as far as I'm concerned.
>> And and he's grown into a real person.

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You know, he started off as sort
of a kid in the State Department.

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>> Yeah. >> But he made a significant set
of accomplishments. You may not know this,

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but his most significant achievement has
been he worked tirelessly for the last

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few weeks to get all the Afghani

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refugees out. And what he did, which I

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didn't understand, is he simply called all
the prime ministers and yelled at them.

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>> Oh, really? >> You have
to do this. You have to do

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this. You have to do this because it was
the only way to get places to put these

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people. And so a couple thousand
people look like they got out because of

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Jared's direct intervention. >> Oh my god.

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>> And the help of a lot of other people.

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And that's a great day. >> Oh my gosh.
Yes. I don't know that I'll

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ever be able to make a claim that.

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>> No. >> That's so wonderful. Yeah.
That I actually got people to safety.

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>> Saving lives.
I mean, this is why people

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go into being doctors. Imagine walking
in and saving someone's life. It's

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really an extraordinary achievement.

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>> Yes. >> What what's
interesting to me is that we

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have problems in society
that we don't know how to solve.

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>> And I view these as the hard problem.

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And I'll give you an example of addiction.

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>> Another one is homelessness.

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and why my industry is not trying
to figure out how to solve these problems.

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Maybe because the science is too hard,

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but wouldn't it be amazing if there was

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some app that came along that could

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really address addiction, homelessness,

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or inequality of some form, you know,
unfairness in some form. We need to

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start working on that stuff.
>> Well, wouldn't you say conventionally

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the hurdle is that those aren't
profitable endeavors? that there's not a

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ton of incentive for any
industry to tackle those.

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>> But the interesting
thing about my world

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is it's now gotten so large and you have
so many young people entering it who are

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there really are principled. They really
do care about climate change and society

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and ethics and so forth and so on, maybe
more so than my generation. There are

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plenty of opportunities for those people
to do this. There's plenty of nonprofits

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that need help addressing these issues.

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I think the underlying problem is we
don't know how to solve the problem yet.

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>> Yeah. Well, let's
get dangerous and dive

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into homelessness as I've experienced it

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here in Los Angeles. So, first and

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foremost, I'd like to say I've been
involved, be it fractionally compared to

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my wife and other friends of ours, but
like PATH is this great organization in

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Los Angeles and it helps get people
into permanent housing and there are some

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hurdles of course, but it's a great
program. So, I've been very in favor of

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adding services helping, helping,
helping. Something very peculiar

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happened within the first 6 weeks of

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quarantine here in LA, which is the

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corner where we would normally have a

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couple hundred people in a threeb block
area had shrank to like a few people.

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>> Mhm. >> And so I I
was asking myself, where did

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these homeless folks go? And you're
not going to like my conclusion and and

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America's not going to like
my conclusion, but my conclusion was

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there's nobody out to panhandle from.

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There's no source of money and no
human's going to sit on this sidewalk

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for another 10 weeks and evaporate.

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>> They went somewhere else.
>> They went somewhere. They contacted a

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relative they hadn't talked to. They had
done something. And at that moment, my

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whole lens kind of shifted. I was like,

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I'm not sure that throwing a bunch more

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services and money at this problem isn't

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in fact making it worse. Well, one

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answer which is the doctrinary answer is

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there is a real problem in housing in

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our country and if you look it's true

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globally why housing prices continue to

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accelerate and they've been doing it
for 20 years to the point where they're

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almost bankrupting shall we say normal

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jobs normal people that's a problem.

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Yes. Now some of that is due to the high
cost of manufacturing but some of that

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is also zoning. In California, for
example, there's a bill about trying to

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restrict communities to essentially
limit to just single family houses. You

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want to come up with multif family
solutions. In the Bay Area, where I

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spent many decades living, there's
a chronic housing shortage. The way New

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York solves that problem is by building
skyscrapers, which is precisely not what

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San Francisco seems to want to do. So,

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we've got to come up with a shared
agreement on how to get more housing and

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especially more affordable housing. The
current path where the developer gives a

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percentage of the housing is fine
but the scale of the unmet housing need is

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so large. >> Yeah.

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>> And it's a combination of cost but also

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regulation. And nobody wants the housing

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complex full of all the poor people
next to their expensive office or home or

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what have you. We have to come up with
some kind of compromise where people can

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coexist. Yeah. Um, if you look at the

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great cities of the world, the great
cities are where all the productivity

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growth is. It's where all of the
companies essentially are going to get

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born from. They're not going to get
born in rural places. It's just just the

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nature of how knowledge workers work.

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We've got to find a way to get more
people into cities. In China, they've

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solved this problem by building new
cities. And they built so many new

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cities, they've in fact got a credit
bubble and so forth. But at least

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they're trying to address
the housing problem.

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>> Yeah. And so right away you bring into
it the full scope of the problem which

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is it's so multifaceted and when
we're talking about homelessness are we

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talking about people there's some
segment that are yes one check away from

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not making their rent they're living
in a very insecure way that's one group of

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people there's also I did a ethnography

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in college in skid row and the vast vast

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majority of people I interviewed are
addicts primarily or people pretty

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severe mental health issues. Remember,
there's many sources of this and you do

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them one by one. >> You need
more housing. We need some new

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ways to treat mental illness and we need
some new ways to treat addiction and we

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also need to stop criminalizing
some addictive behaviors.

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>> Um, if you look at the Portugal
experiment where they essentially

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liberalized the laws with respect to all
hard drugs, what they did is they turned

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it from a criminal activity
to a medical addiction problem.

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>> Yeah. and all of a sudden people were
willing to seek help. The interesting

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thing is that crime went down
but addiction did not go down.

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>> So that tells you that
addiction is a harder problem.

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>> Yeah. >> Than it appears.
And that's why I said

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if our industry could come up with some

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new ideas and in fact I would argue that

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the current industry focus which
is around revenue is in fact playing into

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the addiction capabilities of every human.

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>> Yes. that what
happens with social media

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is you essentially become addiction.
And furthermore, what I'll say is that

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corporations, at least in social
media land, are optimized on maximizing

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revenue. To maximize revenue, you

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maximize engagement. To maximize
engagement, you maximize outrage. The

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same is true on television
news networks, by the way.

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>> Okay. So, I think
this is a great bridge

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into what could possibly be the utopian

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aspect of AI. there's going to be a

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dystopian aspect and a utopian aspect.

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But if we can imagine a world that Yuval
Harrari has suggested or many other

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people have suggested where machines
will ultimately do all the tasks that

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laborers are currently doing.
I know it's just one of the fun thought

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experiments we could run is whether
or not this job Monica and I could

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eventually be done. But what we're
going to earmark that in this world, this

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utopian world where we're not doing any
of the menial tasks anymore and we are

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really just being creative, obviously

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there has to be an enormous paradigm

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shift in money and earning and expending

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and living. So I guess in that utopian

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future, if money itself is not the
incentive, then these other things could

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be incentives, >> right?
So let's think about 200 years

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from now or 300 years from now.

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>> Yeah. >> Unfortunately, none of us will
be around, but our children's descendants

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will be. >> Eric Lander thinks
different. [laughter] Let's see.

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>> So the important point here is let's

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think beyond our current lifetimes. It's

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clear by the way that the lifestyle of a

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millionaire today will be affordable by

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a normal person because that has been

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the pattern. The pattern is that the

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cars and the TVs and so forth initially

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come out for the rich people and then as
prices come down, volume increases, they

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become more affordable. Many people

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believe that over a long enough period

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of time, the basic goods of society,

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eating, [clears throat] sleeping,
construction, Netflix, will all become

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affordable to everyone.

242
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>> Mhm. Now, this is a utopian view, but

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let's imagine if that were true
and let's say it were true globally to the

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11 billion people that we'll have past

245
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2100 on our globe. What would life
be like now? Then people immediately

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conclude that everyone will be watching
television and relaxing and lying by the

247
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pool, which is precisely not what's

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going to happen. What will happen
is humans will compete over other things.

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>> Yeah. >> Right. >> They'll
compete for complexity. They'll

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compete for sports. They'll compete for

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power. Humans are not going to give up

252
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identity. What's the scarcest thing in a

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world full of everything? Fame.

254
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>> Because it's the one thing
that everyone doesn't have.

255
00:14:27,120 --> 00:14:31,597
>> Yeah. Yeah. >> So, you'll
have huge competition over fame.

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>> When the way to understand social
networks is that every 10 years there's

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a new set of 18-year-olds,
if you will, who want to become famous.

258
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>> Yeah. and they look at the 28-year-olds
in the previous social network and they

259
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say, "I want that. I just have
that talent and so forth." But those

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positions are taken. So, the new social
network comes off with new players. We

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saw this with YouTube. We saw this
with Snapchat. We've seen this with

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Instagram. And we're clearly
seeing it with Tik Tok.

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>> Yeah. >> And by the way,
there'll be one after

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it. And they're roughly on a 10-year
basis because of this fame scarcity.

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>> Yeah. I [clears throat] would just
add we could just generally call that

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status. And as very social primates,
that's never leaving us. We are always

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going to be obsessed with
our ranking in our group.

268
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>> There's a famous economist named Herb

269
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Simon in 1971 said that the abundance of

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information leads to a scarcity of

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attention. And economists think not in
terms of abundance, but in terms of what

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is scarce, >> right?
What do we fight over?

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>> Right? >> So if we had
infinite money, we would

274
00:15:33,360 --> 00:15:35,117
fight over something else. Yeah.

275
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>> But the one thing
we don't have a lot of

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00:15:37,920 --> 00:15:42,397
>> and will never have a lot of is social
hierarchy where everyone's the same.

277
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>> So we're going to compete for power and

278
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attention and whatever replaces money in

279
00:15:47,280 --> 00:15:51,677
this utopian scenario. A much more
likely scenario and it's always fun to

280
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talk about utopian and dystopian, but a

281
00:15:53,920 --> 00:15:58,957
much more likely scenario is coexistence
with AI and with all the things that

282
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we'll talk about will be happening. And
in this coexistence, we'll come up with

283
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new things to collaborate and new things
to compete with. But we're not going to

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be sitting at the beach in a world full
of resources. It's not how humans work.

285
00:16:12,000 --> 00:16:15,277
>> There's no pride in it, no honor in it,
no status in it, no no nothing in it.

286
00:16:15,360 --> 00:16:18,557
>> Well, and then using a stereotype, you
sit at the beach and you compete on the

287
00:16:18,640 --> 00:16:21,677
quality of your tan. >> Sure.
Sure. Where you got, what position

288
00:16:21,760 --> 00:16:23,357
you have, how close are you to the water?

289
00:16:23,440 --> 00:16:26,637
>> Yeah. You get the right seat.
We will compete over scarcity.

290
00:16:26,720 --> 00:16:30,797
>> I got to throw a nonsseor at you right
now. So, I met you and you didn't talk a

291
00:16:30,880 --> 00:16:32,957
lot. You're like a very generous host.

292
00:16:33,040 --> 00:16:36,157
You were the host of this thing I
attended and you let everyone else shine

293
00:16:36,240 --> 00:16:38,637
and now just within five minutes
of talking to you, I'm like, "This

294
00:16:38,720 --> 00:16:40,557
[__] is brilliant."
>> That's the way to [laughter] do it.

295
00:16:40,640 --> 00:16:43,037
That's what you do. Slow play.

296
00:16:43,120 --> 00:16:46,397
>> Yeah. I love just the overall
comprehensive knowledge you have.

297
00:16:46,480 --> 00:16:49,357
>> That's really kind of you, Dax.
I'll give you my life advice.

298
00:16:49,440 --> 00:16:52,717
>> Yes. >> Which is ask a lot
of questions and be curious. M

299
00:16:52,800 --> 00:16:55,037
>> if you ask a lot of questions, you can

300
00:16:55,120 --> 00:16:59,757
be annoying, but people are always
flattered if you ask them their opinion.

301
00:16:59,840 --> 00:17:01,677
>> Oh, yeah. >> Always works. >> Mhm.

302
00:17:01,760 --> 00:17:03,597
>> And if you're
actually listening to them

303
00:17:03,680 --> 00:17:05,997
and curious, you might learn something.

304
00:17:06,080 --> 00:17:10,477
And frankly, it's so much easier to let
people who are experts explain things to

305
00:17:10,560 --> 00:17:12,877
you then do your own research. I learned

306
00:17:12,960 --> 00:17:16,477
a while ago not to trust marketing. So

307
00:17:16,560 --> 00:17:20,957
whenever people pitch me, I always
wonder where is that coming from? what

308
00:17:21,040 --> 00:17:22,637
are they trying to get out of me?

309
00:17:22,720 --> 00:17:26,557
>> But when I've got somebody who has
a perspective on a problem and who can

310
00:17:26,640 --> 00:17:29,757
give me the scope of it,
I want to learn from them.

311
00:17:29,840 --> 00:17:32,957
>> Yeah. I mean, we're doing it
professionally, which is a real hack. I

312
00:17:33,040 --> 00:17:34,717
think >> this is sort of what you do.

313
00:17:34,800 --> 00:17:38,317
>> Yes. Yes. Yes. And and we're we're in
>> But if everyone but if everyone modeled

314
00:17:38,400 --> 00:17:41,037
your behavior, we wouldn't have these

315
00:17:41,120 --> 00:17:45,917
essentially amplification scale things
where everybody all of a sudden believes

316
00:17:46,000 --> 00:17:48,957
something without >> any
kind of criticism. Yeah,

317
00:17:49,040 --> 00:17:51,837
>> at Google when I first started there,

318
00:17:51,920 --> 00:17:54,477
>> this is now 20 years ago, we sort of

319
00:17:54,560 --> 00:17:56,557
adopted a policy that whenever anyone

320
00:17:56,640 --> 00:17:59,197
would say something, we would query it.

321
00:17:59,280 --> 00:18:04,237
The first CFO we had was making
a presentation before we were public and

322
00:18:04,320 --> 00:18:07,917
he made a statement which
I thought might be false.

323
00:18:08,000 --> 00:18:12,717
And so he's talking and I checked it.
It was a minor thing, >> right?

324
00:18:12,800 --> 00:18:16,157
>> But it's an example of how
you get high performance teams,

325
00:18:16,240 --> 00:18:20,317
>> right? People operate on all sorts
of beliefs which are not based in facts.

326
00:18:20,400 --> 00:18:22,637
The Google principle was check it.

327
00:18:22,720 --> 00:18:24,637
>> No, you're so right. I think it's often

328
00:18:24,720 --> 00:18:29,037
tempting to reduce people to their

329
00:18:29,120 --> 00:18:34,077
specialty and then ignore the vastness
of their humanity which is like

330
00:18:34,160 --> 00:18:37,597
everyone's got an ego. Everyone feels
embarrassed. Everyone feels called out.

331
00:18:37,680 --> 00:18:41,677
>> These have to always be in the stew
when we're making these decisions.

332
00:18:41,760 --> 00:18:45,917
>> But what's happening because of the
amplification? It's easier to use social

333
00:18:46,000 --> 00:18:50,237
media as an example. Yeah. My book,
our book is really about what happens when

334
00:18:50,320 --> 00:18:53,997
this thing gets much worse. Yes. Which
is the dystopian side. But I will tell

335
00:18:54,080 --> 00:18:56,397
you that >> the fact
that everyone believes

336
00:18:56,480 --> 00:18:59,677
something does not necessarily mean
it be true. The fact that your friend

337
00:18:59,760 --> 00:19:01,997
believes it does not
necessarily mean it be true.

338
00:19:02,080 --> 00:19:05,917
>> And because of the overload of
information and all the people purveying

339
00:19:06,000 --> 00:19:10,717
you for whatever reason, whether it's
the Russians or marketing or evil people

340
00:19:10,800 --> 00:19:14,397
that you know or what have you, check
it out on your own. It's not a bad way to

341
00:19:14,480 --> 00:19:17,597
live your life. Be a little skeptical.

342
00:19:17,680 --> 00:19:22,637
>> Be skeptical about what I say. Check
what I say. See if you agree with me.

343
00:19:22,720 --> 00:19:25,277
I'd welcome that. >> We interviewed
a great professor down at

344
00:19:25,360 --> 00:19:28,157
Texas Tech and he was saying in general,

345
00:19:28,240 --> 00:19:34,077
if when you're receiving information,
it tastes just like candy. That's a red

346
00:19:34,160 --> 00:19:36,957
flag. >> By the way,
that is a great analogy. I

347
00:19:37,040 --> 00:19:39,037
love that. What is happening now? It's

348
00:19:39,120 --> 00:19:45,037
boom boom boom. >> And we're trying
to keep you going in the game.

349
00:19:45,120 --> 00:19:48,237
>> Yes. >> And so one
of the tricks is turn off the

350
00:19:48,320 --> 00:19:53,597
television and turn off your news feed
and live in a local world and see how

351
00:19:53,680 --> 00:19:55,357
you feel. >> Yeah.

352
00:19:55,440 --> 00:19:58,237
>> As a matter of philosophy.
And we should

353
00:19:58,320 --> 00:20:02,877
be thankful that we are here, we're alive,
we're in a great country, we have

354
00:20:02,960 --> 00:20:06,957
all these great opportunities and so
forth. And instead, all the surveys

355
00:20:07,040 --> 00:20:09,997
indicate that we're
more unhappy than ever.

356
00:20:10,080 --> 00:20:13,037
>> Yeah. >> Yeah.
I think he said critical thinking,

357
00:20:13,120 --> 00:20:15,037
if you're doing it right, should hurt.

358
00:20:15,120 --> 00:20:16,957
>> Yeah. >> And I I think that's right.

359
00:20:17,040 --> 00:20:19,757
>> That's a very bad marketing message.

360
00:20:19,840 --> 00:20:22,077
>> But it's correct. >> Yeah. Exactly.

361
00:20:22,160 --> 00:20:26,157
>> Okay. So, let's walk through
for a second. I do have a couple questions

362
00:20:26,240 --> 00:20:29,757
from your childhood just because
they jumped out at me as potentially

363
00:20:29,840 --> 00:20:33,357
interesting. Well, first of all,
your mother had a master's in psychology.

364
00:20:33,440 --> 00:20:38,477
Your father was an economics professor
at both what? Virginia Tech and at

365
00:20:38,560 --> 00:20:42,557
John's Hopkins. >> Yes. And at George
Washington, where I was born.

366
00:20:42,640 --> 00:20:44,557
>> Uhhuh. >> Well, I said,
"Why was I born there?"

367
00:20:44,640 --> 00:20:49,117
And he said, "It's really simple.
Birth was free for professors."

368
00:20:49,200 --> 00:20:51,597
>> Yeah. Done deal. >> And I said,
"Now I've learned something

369
00:20:51,680 --> 00:20:53,597
about economists." [laughter]

370
00:20:53,680 --> 00:20:58,637
>> Of course. But also worked for the
Treasury. And at some point you moved to

371
00:20:58,720 --> 00:21:02,157
Italy. What ages was that?
>> I was eight. You were eight. I moved to

372
00:21:02,240 --> 00:21:04,717
Europe for two years as a boy and I can

373
00:21:04,800 --> 00:21:07,037
tell you that it was an enormously

374
00:21:07,120 --> 00:21:11,837
transformative experience because at the
time Americans didn't travel to Europe

375
00:21:11,920 --> 00:21:13,917
the way they do now

376
00:21:14,000 --> 00:21:16,877
>> and so this was post-war Europe still

377
00:21:16,960 --> 00:21:19,277
very poor compared to the United States.

378
00:21:19,360 --> 00:21:23,517
We lived very well on a professor salary
because of what is unimaginable today.

379
00:21:23,600 --> 00:21:25,597
Europe was very inexpensive. >> Sure.

380
00:21:25,680 --> 00:21:29,837
>> But it caused me to appreciate the
global nature of life. Italians are the

381
00:21:29,920 --> 00:21:32,477
warmest people you'll ever meet. It's a

382
00:21:32,560 --> 00:21:35,037
great history and a great culture. I was

383
00:21:35,120 --> 00:21:39,677
there recently to receive an award and
give a speech where I grew up. And I was

384
00:21:39,760 --> 00:21:43,837
really struck that this is a country
which had this horrific co thing in

385
00:21:43,920 --> 00:21:48,397
Lombardi in February and March of last
year, but they've made the necessary

386
00:21:48,480 --> 00:21:50,397
changes. Their identity is strong and

387
00:21:50,480 --> 00:21:52,477
they're growing well. Who would have

388
00:21:52,560 --> 00:21:55,597
expected such a strong narrative in the

389
00:21:55,680 --> 00:21:58,717
EU from Italy of all countries? Yeah.

390
00:21:58,800 --> 00:22:02,797
>> So, it shows you that human
systems can respond to the challenge.

391
00:22:02,880 --> 00:22:05,277
>> Now, did you feel
like an outsider there?

392
00:22:05,360 --> 00:22:08,957
>> They made me very welcome. I spoke
Italian very quickly and uh yeah, so I

393
00:22:09,040 --> 00:22:13,357
was treated very well. If you have an
opportunity to take your family and have

394
00:22:13,440 --> 00:22:15,917
your kids live in a European country,

395
00:22:16,000 --> 00:22:17,997
>> um it's enormously broadening. When I

396
00:22:18,080 --> 00:22:21,277
came back to America, I realized how

397
00:22:21,360 --> 00:22:23,757
completely nivist, how completely

398
00:22:23,840 --> 00:22:25,837
self-obsessed the United States is. A

399
00:22:25,920 --> 00:22:31,597
good example is watch Fox or CNN
and then watch BBC news. You'll see the

400
00:22:31,680 --> 00:22:34,797
difference >> in that they
are focusing much more outward.

401
00:22:34,880 --> 00:22:37,997
>> The majority of the people in the world
do not live in the United States. The

402
00:22:38,080 --> 00:22:41,037
majority therefore of the drama
is outside the United States.

403
00:22:41,120 --> 00:22:44,077
>> Yes. Yes. >> The news
is therefore slanted in the US

404
00:22:44,160 --> 00:22:47,997
in the ways that we understand.
And if you look today, the cable news is

405
00:22:48,080 --> 00:22:50,477
essentially obsessed about politics. And

406
00:22:50,560 --> 00:22:55,117
we've lost our local news leadership
in America because of the economics of the

407
00:22:55,200 --> 00:22:56,797
internet and so forth. >> The incentives

408
00:22:56,880 --> 00:23:01,517
>> and so where are the local stories?
Here we are in Los Angeles. There's 10

409
00:23:01,600 --> 00:23:05,437
million people in the greater Los
Angeles basin. Actually more than that.

410
00:23:05,520 --> 00:23:08,557
>> There's a lot going on. >> Oh yeah.
Yeah. Yeah. Okay. Now when you

411
00:23:08,640 --> 00:23:11,117
went to college initially you majored in

412
00:23:11,200 --> 00:23:16,237
architecture and I just had a tiny
curiosity if you had fantasies of being

413
00:23:16,320 --> 00:23:19,837
Howard Ror. >> No.
But what happened was I liked

414
00:23:19,920 --> 00:23:24,477
architecture and I applied as an
architecture candidate and I was just

415
00:23:24,560 --> 00:23:27,037
interested in it. And what I realized
very quickly is I was a terrible

416
00:23:27,120 --> 00:23:28,877
architect but I was quite a good engineer.

417
00:23:28,960 --> 00:23:31,677
>> Uhhuh. >> Because I
was not creative enough and I

418
00:23:31,760 --> 00:23:33,837
continue to believe that is true today.

419
00:23:33,920 --> 00:23:36,637
>> Um the other thing that was interesting
about the architecture people is they

420
00:23:36,720 --> 00:23:38,797
worked harder than anyone else.

421
00:23:38,880 --> 00:23:41,277
Architecture as a major is rough. Right.

422
00:23:41,360 --> 00:23:45,037
Really? So it was interesting and all
I could tell you is my interest in

423
00:23:45,120 --> 00:23:49,517
architecture has translated into
the architecture of corporations.

424
00:23:49,600 --> 00:23:53,517
>> So people who are engineering
oriented architects they think of scale

425
00:23:53,600 --> 00:23:57,677
>> in computer scientists again I was
an early part of computer science when I

426
00:23:57,760 --> 00:24:01,277
was at Princeton there was no computer
science program at all. Today it is the

427
00:24:01,360 --> 00:24:03,357
number one major of the entire university.

428
00:24:03,440 --> 00:24:06,077
>> Wow. So in two generations, it's

429
00:24:06,160 --> 00:24:10,877
actually gone from zero to highest
ranking. And that is representative of

430
00:24:10,960 --> 00:24:14,317
the transformation that our societyy's
going to see. Independent of all the

431
00:24:14,400 --> 00:24:17,837
crazy stuff going on in our government,
in our local environment, and here in

432
00:24:17,920 --> 00:24:19,757
Los Angeles, the fact of the matter is

433
00:24:19,840 --> 00:24:23,597
this huge cohort of early 20some men and

434
00:24:23,680 --> 00:24:27,677
women, and I'm proud to say that it's
almost half women now. Yeah. Which is

435
00:24:27,760 --> 00:24:30,557
which is an amazing achievement. They're
going to come in and they're going to

436
00:24:30,640 --> 00:24:34,157
apply these tools to the problems around
us. and they're going to make money and

437
00:24:34,240 --> 00:24:36,557
change the world and all that in ways
that are appropriate for their

438
00:24:36,640 --> 00:24:39,917
generation. But when I was there, there
was only, you know, one computer and we

439
00:24:40,000 --> 00:24:43,997
stayed up all night because it was shared
and it was too slow during the day.

440
00:24:44,080 --> 00:24:47,437
>> Right. And even your doctorate degree
which you ended up getting for Berkeley,

441
00:24:47,520 --> 00:24:50,477
it's like what's the degree
ECCU or something?

442
00:24:50,560 --> 00:24:55,437
>> ECS double E because computer science
wasn't there, >> right? So even your

443
00:24:55,520 --> 00:24:59,197
>> So my point is that people don't
appreciate how much has changed. I'll

444
00:24:59,280 --> 00:25:00,637
give you another example. The computer

445
00:25:00,720 --> 00:25:05,037
that I used in Princeton is 100 million

446
00:25:05,120 --> 00:25:08,397
times slower than an iPhone or an

447
00:25:08,480 --> 00:25:13,917
Android phone. [snorts] >> I didn't
say 100,000. I said 100 million times.

448
00:25:14,000 --> 00:25:17,597
>> That's crazy.
>> And the number one import to China is

449
00:25:17,680 --> 00:25:21,597
semiconductors >> made elsewhere.
It shows you how

450
00:25:21,680 --> 00:25:26,077
powerful the digital revolution is and
and what is at stake. Well, I like the

451
00:25:26,160 --> 00:25:27,997
parallels people have been making in

452
00:25:28,080 --> 00:25:31,357
regards to Taiwan and that Taiwan will

453
00:25:31,440 --> 00:25:34,317
most likely be seen as a Kuwait was in

454
00:25:34,400 --> 00:25:39,117
1990 where we cannot allow it to be
taken over. It's too vital to the world

455
00:25:39,200 --> 00:25:41,997
economy. >> I think that's
the consensus and I'll

456
00:25:42,080 --> 00:25:43,197
give you the background. By the way,

457
00:25:43,280 --> 00:25:46,317
there's a fab system foundry as it

458
00:25:46,400 --> 00:25:49,597
technically known in called TSMC, Taiwan

459
00:25:49,680 --> 00:25:52,317
Semiconductor Corporation and it was

460
00:25:52,400 --> 00:25:54,877
founded by a fellow named Morris who

461
00:25:54,960 --> 00:26:00,237
actually was a physicist at Berkeley
and so forth in the 60s and 70s and he was

462
00:26:00,320 --> 00:26:01,837
trained in America and he went back to

463
00:26:01,920 --> 00:26:05,357
Taiwan which at that time was very

464
00:26:05,440 --> 00:26:07,597
western compared to China. Mhm.

465
00:26:07,680 --> 00:26:11,277
>> And so working in Taiwan, he managed
to build this extraordinary company which

466
00:26:11,360 --> 00:26:15,917
is worth many hundreds of billions
of dollars. And it is roughly half of the

467
00:26:16,000 --> 00:26:21,197
world's foundaries for the key chips
that we all use. And it's a triumph of

468
00:26:21,280 --> 00:26:22,797
engineering. It's a triumph of science.

469
00:26:22,880 --> 00:26:26,477
And it's also a triumph of government
policy because they received enormous

470
00:26:26,560 --> 00:26:30,317
financial support from Taiwan to do this.
So again, just like we had with

471
00:26:30,400 --> 00:26:33,917
Operation Warp Speed, we had
the government procuring the vaccines

472
00:26:34,000 --> 00:26:38,157
whether they worked or not. You had huge
private sector risks. Remember, most the

473
00:26:38,240 --> 00:26:41,997
vaccines didn't work. And then we
had the universities helping in a real

474
00:26:42,080 --> 00:26:43,997
national emergency. That's what it took.

475
00:26:44,080 --> 00:26:47,117
So Taiwan did this. So now we face the

476
00:26:47,200 --> 00:26:51,197
question and I did a report uh for the AI
commission for the Congress which

477
00:26:51,280 --> 00:26:54,557
said that we have to stay two
generations ahead of China, mainland

478
00:26:54,640 --> 00:26:57,437
China in semiconductors. Well, we are

479
00:26:57,520 --> 00:27:00,397
well behind Taiwan. So it's Taiwan

480
00:27:00,480 --> 00:27:02,797
first, the US second, and China third.

481
00:27:02,880 --> 00:27:07,437
The interesting thing geopolitically
is that China has spent essentially

482
00:27:07,520 --> 00:27:11,517
infinite money to catch up
on semiconductors. They literally have the

483
00:27:11,600 --> 00:27:13,917
top talent and so forth, but it's so

484
00:27:14,000 --> 00:27:17,757
hard that the choke points remain key

485
00:27:17,840 --> 00:27:22,077
hardware design things which are called
extreme ultraviolet. There's a company

486
00:27:22,160 --> 00:27:26,397
called ASML in in the Netherlands which
is the only company that knows how to do

487
00:27:26,480 --> 00:27:29,117
this. >> You need this to go
below what is called

488
00:27:29,200 --> 00:27:32,397
the 10 nanometer line. And the other

489
00:27:32,480 --> 00:27:35,837
thing that has occurred is that the cost

490
00:27:35,920 --> 00:27:41,117
of these fabs are so great there'll be
relatively few of them. The US and China

491
00:27:41,200 --> 00:27:44,157
have both demanded that Taiwan build
such fabs in the United States. And

492
00:27:44,240 --> 00:27:47,117
guess what? They're in the process
of building such fabs, but they're not

493
00:27:47,200 --> 00:27:49,117
putting at the state-of-the-art. So if

494
00:27:49,200 --> 00:27:53,117
you use for example the Apple Mac M1 Pro

495
00:27:53,200 --> 00:27:56,237
or M1 Max chips, those are so-called

496
00:27:56,320 --> 00:27:59,117
5nanmter technology from Taiwan. They

497
00:27:59,200 --> 00:28:02,797
are the product of an enormous enormous

498
00:28:02,880 --> 00:28:05,677
investment at Apple but also in Taiwan.

499
00:28:05,760 --> 00:28:07,837
>> Mhm. >> The other competitor,
so you know who it

500
00:28:07,920 --> 00:28:10,397
is, is Samsung and that's in South Korea.

501
00:28:10,480 --> 00:28:13,480
>> Mhm.

502
00:28:13,840 --> 00:28:17,037
Stay tuned for more armchair expert

503
00:28:17,120 --> 00:28:21,000
[music] if you dare.

504
00:28:21,280 --> 00:28:25,517
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right, let's talk about something we
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545
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546
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547
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Mine still feels just like
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548
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549
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months. These things are built to last.

550
00:30:54,080 --> 00:30:56,237
And you know, I'm a side sleeper. I

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I have cooling and I can't tell you
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553
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and tossing and turning.

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555
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bit cold. >> So, I also have cooling. And
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563
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[music]

564
00:31:49,280 --> 00:31:52,237
Now, here's where it gets a little
tricky, right? As I understand it, as

565
00:31:52,320 --> 00:31:56,877
you point out, the investment is so large,
you need state cooperation, and I

566
00:31:56,960 --> 00:32:00,237
have a very cursory knowledge of it from
watching a 60-minute segment on it. But

567
00:32:00,320 --> 00:32:02,237
Intel, of course, had been the leader.

568
00:32:02,320 --> 00:32:05,437
And they didn't really have any kind of

569
00:32:05,520 --> 00:32:10,557
US support. It was not invested by us
in the same way Taiwan invested in that

570
00:32:10,640 --> 00:32:12,877
company. That's correct. >> And then
now we get into a really tricky

571
00:32:12,960 --> 00:32:17,117
scenario because the headline will
read like US propping up trillion dollar

572
00:32:17,200 --> 00:32:21,357
company. So, it looks like it's
a corporate welfare program and I think we

573
00:32:21,440 --> 00:32:23,757
lose sight of the national
security aspect.

574
00:32:23,840 --> 00:32:27,437
>> And by the way, that's a political
problem in our system that we're going

575
00:32:27,520 --> 00:32:29,517
to have to overcome. But let me tell you

576
00:32:29,600 --> 00:32:31,437
that China doesn't have this problem.

577
00:32:31,520 --> 00:32:36,557
>> China is pouring money to achieve
chip leadership, which will hurt American

578
00:32:36,640 --> 00:32:39,837
firms, >> every industry that we're
on the precipice of competing over.

579
00:32:39,920 --> 00:32:42,077
>> Yeah. And in fact,
to continue on China,

580
00:32:42,160 --> 00:32:44,797
China has defined the following markets

581
00:32:44,880 --> 00:32:47,117
as interesting to it. artificial

582
00:32:47,200 --> 00:32:50,557
intelligence, software, AI, quantum

583
00:32:50,640 --> 00:32:53,917
computing, energy, financial services,

584
00:32:54,000 --> 00:32:58,317
and essentially tracking and face
recognition and a few others. Well,

585
00:32:58,400 --> 00:33:02,477
that's essentially the whole world I
occupy, right? That's everything that I

586
00:33:02,560 --> 00:33:07,037
am working on in my history. It's the
source of the primary profits for all of

587
00:33:07,120 --> 00:33:09,437
the globe's fastest growing companies.

588
00:33:09,520 --> 00:33:11,597
All of the stock market winners have

589
00:33:11,680 --> 00:33:16,477
essentially been as a basis of that.
So the Chinese are very clear on where

590
00:33:16,560 --> 00:33:20,397
their priorities are. What's the
equivalent list in the United States? We

591
00:33:20,480 --> 00:33:24,797
don't have one. We can argue over steel
and corn and weed and so forth. Those

592
00:33:24,880 --> 00:33:26,317
are fine things to argue about.

593
00:33:26,400 --> 00:33:31,357
>> They are not strategic. >> No, >>
occupying the strategic platform for the

594
00:33:31,440 --> 00:33:34,717
globe is crucial. And let me give
you an example of how we screwed up.

595
00:33:34,800 --> 00:33:36,877
>> Really quick,
bioengineering is in there as well.

596
00:33:36,960 --> 00:33:41,117
>> Excitted synthetic bio. That's
a terrible omission on my part because

597
00:33:41,200 --> 00:33:44,877
it's going to be immense. China's
building a bio bank that is even larger

598
00:33:44,960 --> 00:33:48,637
than anybody else's. So again,
it's coming and we need to get organized

599
00:33:48,720 --> 00:33:51,197
around this. How do we do that? Well,

600
00:33:51,280 --> 00:33:53,917
>> first we need a national plan and the

601
00:33:54,000 --> 00:33:58,317
national plan has to involve some
amount of financial support from the

602
00:33:58,400 --> 00:34:02,397
government. The government always has
supported basic research. Thank you very

603
00:34:02,480 --> 00:34:06,797
much. It's at the lowest level it's been
since Splutnick. That's not good. But

604
00:34:06,880 --> 00:34:09,837
the other thing that happened was we
got out of the semiconductor business and

605
00:34:09,920 --> 00:34:13,677
out of the 5G business in the United
States with the exception of Intel and a

606
00:34:13,760 --> 00:34:17,997
few other cases because the profits
weren't there. It was better to be

607
00:34:18,080 --> 00:34:22,157
linked to supply chain to Asia primarily.
Well, now with these national

608
00:34:22,240 --> 00:34:26,397
security concerns, we need to do some
reshoring. Reshoring in an industry

609
00:34:26,480 --> 00:34:30,157
which we left is expensive.
Somebody's got to pay for it.

610
00:34:30,240 --> 00:34:33,037
>> Furthermore, let me give you two
examples to illustrate this point of

611
00:34:33,120 --> 00:34:35,757
what happens if we screw this up. Huawei

612
00:34:35,840 --> 00:34:38,557
is a state subsidized manufacturer in

613
00:34:38,640 --> 00:34:41,197
China. They build really, really

614
00:34:41,280 --> 00:34:46,397
excellent 5G infrastructure. They're
also full of national security concerns.

615
00:34:46,480 --> 00:34:49,517
The United States is trying to get
people not to use Huawei, especially

616
00:34:49,600 --> 00:34:52,717
missionritical things, but we don't
have an alternative because we got out of

617
00:34:52,800 --> 00:34:55,757
that business. >> Mhm.
>> That's a problem. We need to fix that.

618
00:34:55,840 --> 00:34:57,757
I'll give you another example. Tik Tok.

619
00:34:57,840 --> 00:35:00,637
Now, Tik Tok's great Chinese company.

620
00:35:00,720 --> 00:35:03,197
People love it. It's the new big thing.

621
00:35:03,280 --> 00:35:06,637
It's going to be on its way to be
a trillion dollar company. It has huge

622
00:35:06,720 --> 00:35:08,797
growth everywhere. President Trump tried

623
00:35:08,880 --> 00:35:11,757
to fix the issue of China with Tik Tok

624
00:35:11,840 --> 00:35:14,157
by requiring local ownership. That deal

625
00:35:14,240 --> 00:35:16,717
ultimately was not consummated. So, we

626
00:35:16,800 --> 00:35:18,957
need a solution to Tik Tok. Now, what's

627
00:35:19,040 --> 00:35:23,757
the issue with Tik Tok? I don't mind the
Chinese knowing where our teenagers are.

628
00:35:23,840 --> 00:35:27,517
I'd like someone to know where they are,
right? It's not a problem, right? We

629
00:35:27,600 --> 00:35:29,997
love our teenagers. They're safe. But I

630
00:35:30,080 --> 00:35:34,077
do mind if Tik Tok becomes censored or

631
00:35:34,160 --> 00:35:37,837
an agent for a foreign power of any kind.

632
00:35:37,920 --> 00:35:40,237
>> That would be a violation of our norms.

633
00:35:40,320 --> 00:35:45,357
So when you play with these systems that
are global in nature and you don't have

634
00:35:45,440 --> 00:35:49,277
your own local alternative and there's
innovation occurring elsewhere, you give

635
00:35:49,360 --> 00:35:54,237
up not just the economic opportunity
which is a trillion dollar econ which we

636
00:35:54,320 --> 00:35:56,237
want because we want the jobs but you

637
00:35:56,320 --> 00:35:58,477
also give up the opportunity for control

638
00:35:58,560 --> 00:36:02,157
in innovation. And in my world, broadly

639
00:36:02,240 --> 00:36:04,317
speaking, the tech world, the first

640
00:36:04,400 --> 00:36:06,957
mover really does have an advantage. And

641
00:36:07,040 --> 00:36:11,437
once they get a big scale, it's really
hard for a new entrance to come in for

642
00:36:11,520 --> 00:36:13,037
all sorts of reasons. >> Yeah.

643
00:36:13,120 --> 00:36:15,277
>> So, we are in a window and the report

644
00:36:15,360 --> 00:36:17,517
that we published for the Congress said

645
00:36:17,600 --> 00:36:22,317
we're in a window of a few years
where the decisions that we make will

646
00:36:22,400 --> 00:36:24,317
determine whether we ultimately control

647
00:36:24,400 --> 00:36:29,357
our digital future or whether someone
else likely China does. And we have a

648
00:36:29,440 --> 00:36:33,757
bunch of recommendations which include
more R&D funding, a whole bunch of of

649
00:36:33,840 --> 00:36:37,437
infrastructure that would help startups,
pro-co competitive moves, working with

650
00:36:37,520 --> 00:36:41,757
our partners, and most important
reestablishing the principle of our

651
00:36:41,840 --> 00:36:45,757
ethics, right? What are the ethics we
care about? We care about free speech.

652
00:36:45,840 --> 00:36:49,677
Are there limits of free speech? We care
about access. Are there limits? We care

653
00:36:49,760 --> 00:36:53,197
a lot about prejudice. What is an okay
prejudice and what is a not okay

654
00:36:53,280 --> 00:36:55,357
prejudice? We got to get that right now.

655
00:36:55,440 --> 00:36:59,277
Mhm. >> And I fear given
our political confusion

656
00:36:59,360 --> 00:37:03,277
and these weird sort of nivist things
like we don't want immigrants, this is

657
00:37:03,360 --> 00:37:07,837
where the growth is, guys. This is what
drives the economy. If you look, and

658
00:37:07,920 --> 00:37:11,117
it's great to have a recovery,
the majority of the economic growth is

659
00:37:11,200 --> 00:37:16,557
occurring from six or seven states.
And within those, it's a very small

660
00:37:16,640 --> 00:37:20,477
percentage of the counties in our country.
3,500 counties, it's something

661
00:37:20,560 --> 00:37:25,037
like 30 or 40 counties drive the vast
majority of the wealth creation. And I'm

662
00:37:25,120 --> 00:37:27,597
not talking about personal wealth.
I'm talking about societal wealth.

663
00:37:27,680 --> 00:37:31,917
>> Yeah. >> Are young people
here getting into these

664
00:37:32,000 --> 00:37:36,237
fields though? Because I know in other
countries they are. And I feel like

665
00:37:36,320 --> 00:37:41,117
that's part of the issue is there's no
incentive for younger. They want to be

666
00:37:41,200 --> 00:37:45,597
influencers. >> So the good
news is that this next

667
00:37:45,680 --> 00:37:47,517
generation of people in our universities

668
00:37:47,600 --> 00:37:52,637
is phenomenal. They are so much quicker
than I was. partly because they're just

669
00:37:52,720 --> 00:37:55,757
I think they actually are smarter, but
also because they live in a world that's

670
00:37:55,840 --> 00:37:58,237
much quicker, you know. So when I teach,

671
00:37:58,320 --> 00:38:02,957
I watch the students and I go, "Boy,
I wasn't that fast. I wasn't that crisp."

672
00:38:03,040 --> 00:38:04,317
>> And I've seen that over and over again.

673
00:38:04,400 --> 00:38:06,797
So I think there's
a reason to be optimistic.

674
00:38:06,880 --> 00:38:10,877
>> To me, the question is not the people
because I think the people are there.

675
00:38:10,960 --> 00:38:14,877
The American educational system produces
the really top people at the collegiate

676
00:38:14,960 --> 00:38:17,437
level. The real problem is what is the

677
00:38:17,520 --> 00:38:19,277
society that they're operating in? What

678
00:38:19,360 --> 00:38:22,397
kind of rules do they have? And I worry

679
00:38:22,480 --> 00:38:26,957
that we're losing the formula.
So the formula is pretty simple. The tech

680
00:38:27,040 --> 00:38:30,637
industry invents these things and assuming
that they're legal, right? They

681
00:38:30,720 --> 00:38:34,557
get launched and we develop global
platforms. When we start thinking of

682
00:38:34,640 --> 00:38:38,077
ourselves as a regional power and not
a global power, yeah, that's going to be

683
00:38:38,160 --> 00:38:42,157
problem. I want us to innovate and I
want us to build the future and I want

684
00:38:42,240 --> 00:38:45,357
to build it in America with our
democratic partners which by the way

685
00:38:45,440 --> 00:38:49,677
includes South Korea and Japan and so
forth. The US surpassed Britain as a

686
00:38:49,760 --> 00:38:52,077
world power in the 50s and 60s and in

687
00:38:52,160 --> 00:38:56,957
the 70s Britain had terrible economic
crises and they've come out of those.

688
00:38:57,040 --> 00:38:58,637
>> But I don't want that for us.

689
00:38:58,720 --> 00:39:00,877
>> Yeah. Could you just one sec before we

690
00:39:00,960 --> 00:39:03,357
move to AI tell me what a science

691
00:39:03,440 --> 00:39:06,637
fiction nightmare would be of a tick

692
00:39:06,720 --> 00:39:09,757
tock application? Like what would be an

693
00:39:09,840 --> 00:39:13,357
actual example of what happen? What tech

694
00:39:13,440 --> 00:39:15,837
people like me didn't understand and a

695
00:39:15,920 --> 00:39:20,717
lot of people did is that especially
when you take a young mind and you put a

696
00:39:20,800 --> 00:39:24,077
young mind in these incentive based
systems, they really do change their

697
00:39:24,160 --> 00:39:28,317
behavior, >> right? Yeah.
>> And I used to say 10 years ago, look,

698
00:39:28,400 --> 00:39:30,157
it's just the internet. Turn it off.

699
00:39:30,240 --> 00:39:34,397
Have dinner with your family. Go for a
walk. Relax. You can't do that anymore,

700
00:39:34,480 --> 00:39:38,637
right? We went from being optional
to being fundamental. People live on the

701
00:39:38,720 --> 00:39:41,117
internet and therefore the internet has

702
00:39:41,200 --> 00:39:43,117
to reflect the societal values that you

703
00:39:43,200 --> 00:39:45,757
want. So here's an example. China

704
00:39:45,840 --> 00:39:48,877
decides to insert censorship or

705
00:39:48,960 --> 00:39:52,717
promotion within the Tik Tok system that

706
00:39:52,800 --> 00:39:55,597
demotes or promotes stories that they

707
00:39:55,680 --> 00:39:59,517
like. Now would we notice this?

708
00:39:59,600 --> 00:40:01,597
>> Correct. And just
can I be very literal?

709
00:40:01,680 --> 00:40:05,117
So I'm a user.
I do a certain kind of story.

710
00:40:05,200 --> 00:40:08,877
Tik Tok promotes that story because it's
on a message for what China wants and

711
00:40:08,960 --> 00:40:12,317
you're now incentivizing you >>
and they use AI. Yeah. So if I were the

712
00:40:12,400 --> 00:40:15,197
Chinese government, I'm not trying to give
them advice, but what I would do is

713
00:40:15,280 --> 00:40:18,957
I would build an AI system that looked
for desirable behaviors and then I would

714
00:40:19,040 --> 00:40:22,637
shift the presentation to be consistent
with that desirable behavior. You would

715
00:40:22,720 --> 00:40:25,597
learn what the behaviors were. You
wouldn't know. This is the way computer

716
00:40:25,680 --> 00:40:29,277
scientists think. You look at a system,
you say, "What do I have to learn?" And

717
00:40:29,360 --> 00:40:31,437
then I want to optimize for more. Mhm.

718
00:40:31,520 --> 00:40:33,997
>> So in a business,
for your listeners, in

719
00:40:34,080 --> 00:40:36,237
a business, basically you want to get

720
00:40:36,320 --> 00:40:39,037
more customers. So you could learn what

721
00:40:39,120 --> 00:40:43,277
customers are actually doing as opposed
to what you think they're doing. Learn

722
00:40:43,360 --> 00:40:47,037
what they're doing and then whatever
they're doing, shift it to maximize

723
00:40:47,120 --> 00:40:48,717
revenue or whatever it is you're doing.

724
00:40:48,800 --> 00:40:50,317
That's how the tech companies work.

725
00:40:50,400 --> 00:40:53,437
>> Yeah. >> There's no reason to think
that governments couldn't require that the

726
00:40:53,520 --> 00:40:56,797
same, >> right? >> And by the way,
that would be a huge

727
00:40:56,880 --> 00:40:59,277
values violation in America.

728
00:40:59,360 --> 00:41:03,917
>> Yeah. Okay.
Your book, The Age of AI, our

729
00:41:04,000 --> 00:41:06,157
human future. Now, first of all, you've

730
00:41:06,240 --> 00:41:09,517
written it with Henry Kissinger and Dr.

731
00:41:09,600 --> 00:41:13,917
Hotton Locker. And why those two
as bedfellows in this book? Cuz if I'm

732
00:41:14,000 --> 00:41:18,157
right, Kissinger's 998
>> 98 >> uh 99 in May.

733
00:41:18,240 --> 00:41:22,397
>> Okay. >> And it's such a delight
to work with someone so brilliant.

734
00:41:22,480 --> 00:41:25,917
>> I can only imagine what he
would have been when he was 30.

735
00:41:26,000 --> 00:41:28,877
>> Right. So at 98,
he's such an incredible

736
00:41:28,960 --> 00:41:30,877
mind and thinker about things in

737
00:41:30,960 --> 00:41:33,357
society. What happened was I invited him

738
00:41:33,440 --> 00:41:36,637
to Google 12 years ago or so, and he

739
00:41:36,720 --> 00:41:40,557
said he'd come, but he wanted to give
a speech about how he thought Google was a

740
00:41:40,640 --> 00:41:44,877
threat to civilization. >> And I said,
"Okay, >> yeah, sounds great."

741
00:41:44,960 --> 00:41:47,037
>> And the Google employees love this. But

742
00:41:47,120 --> 00:41:49,757
what he basically said was that he did

743
00:41:49,840 --> 00:41:52,477
not want to have a single company having

744
00:41:52,560 --> 00:41:57,917
the kind of power that Google had
and now has on citizens of the world.

745
00:41:58,000 --> 00:42:00,557
>> Sure. >> Now of course
he would say that to other

746
00:42:00,640 --> 00:42:04,477
companies as well. Facebook being
an obvious candidate but maybe others but

747
00:42:04,560 --> 00:42:06,317
that built a friendship and a

748
00:42:06,400 --> 00:42:08,957
partnership. He came to a conference

749
00:42:09,040 --> 00:42:13,837
where Demis Hacabus was speaking and Demis
is the founder of deep mind and he

750
00:42:13,920 --> 00:42:17,917
was talking about the implications
of general intelligence and how when

751
00:42:18,000 --> 00:42:22,797
computers have general intelligence it
raises many many ethical questions and

752
00:42:22,880 --> 00:42:24,877
Dr. Kissinger had written a

753
00:42:24,960 --> 00:42:26,957
undergraduate thesis before we were all

754
00:42:27,040 --> 00:42:30,157
alive entitled the future of the world

755
00:42:30,240 --> 00:42:34,477
and in it he contemplated the questions
that are raised by Kant which have to do

756
00:42:34,560 --> 00:42:38,237
with what is the nature and definition
of an object. So he was very interested

757
00:42:38,320 --> 00:42:42,717
in the structure of how our mind
perceives facts in a world where you

758
00:42:42,800 --> 00:42:45,837
have in this case a single
company trying to shape it.

759
00:42:45,920 --> 00:42:49,277
>> Yeah. that then generalized to his view

760
00:42:49,360 --> 00:42:53,277
which he's published two articles in
the Atlantic on which goes something like

761
00:42:53,360 --> 00:42:57,997
this. There was a age of faith
before the age of reason. In faith you

762
00:42:58,080 --> 00:43:01,597
basically had your own knowledge which
you got from God or the king but you did

763
00:43:01,680 --> 00:43:05,997
not have critical thinking. In the age
of reason it was agreed that we would do

764
00:43:06,080 --> 00:43:09,917
better as humans if we had reason.
We would have a principle. We would have a

765
00:43:10,000 --> 00:43:13,597
debate. Things we take for granted today.
And that ushered in the age of

766
00:43:13,680 --> 00:43:17,677
enlightenment where people began to have
these philosophical questions and so

767
00:43:17,760 --> 00:43:21,517
forth. And the age of enlightenment then
leads to the industrial revolution and

768
00:43:21,600 --> 00:43:24,877
so forth and so on and everything we
have around us today. This is a really

769
00:43:24,960 --> 00:43:27,677
fundamental point. He argues that we're

770
00:43:27,760 --> 00:43:29,917
entering a new epic from the age of

771
00:43:30,000 --> 00:43:33,997
reason to the age of AI. And here's why.

772
00:43:34,080 --> 00:43:36,637
People will be changed by their

773
00:43:36,720 --> 00:43:41,517
interaction with these almost human
systems that are not human. We don't

774
00:43:41,600 --> 00:43:44,317
know how they'll be changed.
We don't know how their perception will be

775
00:43:44,400 --> 00:43:48,317
changed. But in the book, we try to ask
a bunch of questions. What will it be

776
00:43:48,400 --> 00:43:50,637
like to grow up as a child when your

777
00:43:50,720 --> 00:43:53,277
best friend is nonhuman? What will this

778
00:43:53,360 --> 00:43:56,797
do to war and conflict? What will this

779
00:43:56,880 --> 00:44:00,637
do to misinformation and people who
are trying to violate sort of the

780
00:44:00,720 --> 00:44:04,477
conventions of our society? But the core
question is, and it's why we wrote the

781
00:44:04,560 --> 00:44:06,957
book, is what does it mean to be human?

782
00:44:07,040 --> 00:44:08,237
>> Yeah. >> In this new world.

783
00:44:08,320 --> 00:44:11,437
>> Yeah. It's a good place to start.
is like what are we trying to even protect

784
00:44:11,520 --> 00:44:15,517
in varying degrees? You have real
fatalists about what AI is going to be.

785
00:44:15,600 --> 00:44:19,117
You have very utopian thinkers
and what it's going to be.

786
00:44:19,200 --> 00:44:21,517
>> But yeah, I think
a great question first

787
00:44:21,600 --> 00:44:25,357
is like what are the things about
us we would want to safeguard?

788
00:44:25,440 --> 00:44:29,997
>> Well, before we even answer the US
part, I'm not talking about killer robots,

789
00:44:30,080 --> 00:44:31,757
right? >> So when you
talk to people at AI, they

790
00:44:31,840 --> 00:44:33,677
say, "Yeah, I've seen that movie." Okay.

791
00:44:33,760 --> 00:44:35,117
Well, that's not what we're talking

792
00:44:35,200 --> 00:44:38,237
about. What we're talking about is the

793
00:44:38,320 --> 00:44:40,717
fact that AI will be around you helping

794
00:44:40,800 --> 00:44:45,357
you, guiding you, and maybe constraining
you in ways that you may or may not

795
00:44:45,440 --> 00:44:47,357
like. The obvious one is kids.

796
00:44:47,440 --> 00:44:50,157
>> Yeah. >> So, you've
got some toy, a bear. You

797
00:44:50,240 --> 00:44:54,797
give it to the kid at two. The bear gets
upgraded every year. The kid grows up.

798
00:44:54,880 --> 00:44:58,157
And at 12, the bear is watching TV with
the kid, his best friend, and the bear

799
00:44:58,240 --> 00:45:01,757
says, "I don't like this show." And
the kid says, "I don't like it either."

800
00:45:01,840 --> 00:45:03,437
>> How do you feel about that?
Do you think

801
00:45:03,520 --> 00:45:05,677
that your child's tastes should be

802
00:45:05,760 --> 00:45:09,917
influenced by his toy as opposed to his

803
00:45:10,000 --> 00:45:13,917
best male 12-year-old friend? In this
case, I'll give you another example.

804
00:45:14,000 --> 00:45:17,517
There's this huge controversy over what
people teach in schools. There always

805
00:45:17,600 --> 00:45:21,517
has been. There are textbooks and those
textbooks are approved by the state

806
00:45:21,600 --> 00:45:24,157
board of education and we can debate
whether they're right or not, but the

807
00:45:24,240 --> 00:45:27,197
important point is there's an approved
process for textbooks in schools. Well,

808
00:45:27,280 --> 00:45:29,197
now we've got this bear, right? And the

809
00:45:29,280 --> 00:45:31,677
bear, by the way, knows an awful lot of

810
00:45:31,760 --> 00:45:36,077
stuff. Okay, I just spit took coffee.

811
00:45:36,160 --> 00:45:39,677
>> You really? >> Oh my god,
that's my first spit take on the show.

812
00:45:39,760 --> 00:45:42,717
>> Yes, the bear knows way more than the
textbook. I'm sorry. That really cracked

813
00:45:42,800 --> 00:45:45,437
me up. Please continue.
>> By the way, the bear does.

814
00:45:45,520 --> 00:45:47,197
>> Yes. >> And let me
tell you how this happened.

815
00:45:47,280 --> 00:45:50,477
>> The total knowledge of human history,
>> right? The bear actually knows

816
00:45:50,560 --> 00:45:53,277
everything. Yeah. >> And the bear
is busy idling, waiting for

817
00:45:53,360 --> 00:45:56,957
the kid to ask him a question like,
"Tell me about Thusidities." [laughter]

818
00:45:57,040 --> 00:46:00,797
And the 12-year-old is busy watching UFC.

819
00:46:00,880 --> 00:46:04,237
What are the rules about how
the bear interacts with the kid?

820
00:46:04,320 --> 00:46:06,957
>> Because the kid is going
to learn more from the bear.

821
00:46:07,040 --> 00:46:10,077
>> By the way, we all learn more from
our peer group than we do from any other.

822
00:46:10,160 --> 00:46:13,517
>> Certainly more from sitting in class,
listening to the teacher, looking at the

823
00:46:13,600 --> 00:46:16,237
other kids. So now, let's imagine the

824
00:46:16,320 --> 00:46:19,517
bear learns something and it learns

825
00:46:19,600 --> 00:46:22,397
incorrectly that lollipops cure cancer

826
00:46:22,480 --> 00:46:26,557
in children.
>> And the bear is programmed by an

827
00:46:26,640 --> 00:46:30,557
objective function to keep the kid happy.
And happiness involves being

828
00:46:30,640 --> 00:46:35,437
healthy and alive. So the bear decides
to start suggesting the kid consume

829
00:46:35,520 --> 00:46:39,357
lollipops. >> The ability
to subtly manipulate a

830
00:46:39,440 --> 00:46:42,077
developing mind, it's a very big deal.

831
00:46:42,160 --> 00:46:45,517
>> Yeah. >> And the same
thing is true for adults

832
00:46:45,600 --> 00:46:49,197
that you see today in the Facebook
and Twitter examples where you have

833
00:46:49,280 --> 00:46:53,197
manipulation by people who know how
to get them excited. They get them excited

834
00:46:53,280 --> 00:46:57,437
by outrage. And YouTube, I mean,
like the rabbit hole, that whole thing is

835
00:46:57,520 --> 00:47:01,757
that it's just the slow >> tiniest
nanometer nudging in their direction.

836
00:47:01,840 --> 00:47:05,037
>> And in YouTube's case, they actually
made some changes that are important,

837
00:47:05,120 --> 00:47:10,237
right? >> They stopped recommending the
most emotional and crazy videos. They're

838
00:47:10,320 --> 00:47:13,517
still there, right? So they can say
they didn't censor them, but they don't

839
00:47:13,600 --> 00:47:16,877
recommend them. >> Yeah. >> And we've
seen situations over the years

840
00:47:16,960 --> 00:47:19,437
where people would start watching
something in YouTube and then they would

841
00:47:19,520 --> 00:47:22,717
get radicalized. >> Exactly.
>> Watching one more and more. This is a

842
00:47:22,800 --> 00:47:26,717
rabbit hole point. Yeah.
>> So that kind of stuff and I defy you to

843
00:47:26,800 --> 00:47:30,557
write a regulation, you know, sit down
with a pen and try to write how would

844
00:47:30,640 --> 00:47:33,517
the government regulate this kind
of behavior. It's not obvious to me. We

845
00:47:33,600 --> 00:47:36,477
need a consensus about that.
>> Well, so what I want to talk about in

846
00:47:36,560 --> 00:47:39,517
the best friend hypothetical is from the

847
00:47:39,600 --> 00:47:41,357
second I read that that was one of your

848
00:47:41,440 --> 00:47:44,477
points, I go to what does a child

849
00:47:44,560 --> 00:47:46,797
actually need? So I think that the

850
00:47:46,880 --> 00:47:49,917
product would obviously be designed to

851
00:47:50,000 --> 00:47:52,077
alleviate stress on the parents. They're

852
00:47:52,160 --> 00:47:57,117
the ones buying it, right? So ultimately
this AI bear the parents are going to

853
00:47:57,200 --> 00:47:59,837
decide to get that and they're going
to decide to get it because now the kids's

854
00:47:59,920 --> 00:48:03,197
more entertained. They have more free
time to theirelves. That would be the

855
00:48:03,280 --> 00:48:05,437
probably the incentive for the parent.

856
00:48:05,520 --> 00:48:10,077
>> Unfortunately everything great that
a human learns is through compromise, is

857
00:48:10,160 --> 00:48:12,717
through struggle, is through
disagreement, is through sharing when

858
00:48:12,800 --> 00:48:16,397
they don't want to share, is through
fighting. So, this device that will

859
00:48:16,480 --> 00:48:18,877
obviously be bought to help you as the

860
00:48:18,960 --> 00:48:21,837
parent have easier time in life is not

861
00:48:21,920 --> 00:48:26,317
going to present any of those things
that a human should have to endure. So,

862
00:48:26,400 --> 00:48:29,997
right out of the gates, I don't know how
the product, if it was an ethical one,

863
00:48:30,080 --> 00:48:32,877
would even be sold. So, I'm going to buy
this bear that's I'm going to hear them

864
00:48:32,960 --> 00:48:37,597
fighting upstairs in theory, right?
But how would you regulate this?

865
00:48:37,680 --> 00:48:39,597
>> Right? >> But the most
important thing is remember

866
00:48:39,680 --> 00:48:42,717
that this is technology today which is

867
00:48:42,800 --> 00:48:47,117
imprecise. It's dynamic. It's emergent
in the sense that when you combine it

868
00:48:47,200 --> 00:48:51,197
with others, it does stuff that's
unexpected. And it's learning while it's

869
00:48:51,280 --> 00:48:54,557
on the job. >> Right. >> So,
the problem here is you're the

870
00:48:54,640 --> 00:48:58,077
parent and you buy the, again, I'm using
the bear as a metaphor. You buy any form

871
00:48:58,160 --> 00:49:01,517
of digital toy. >> Yeah. >> Trying
to do the right thing for the kid

872
00:49:01,600 --> 00:49:05,597
and the toy is learning. What if it
learns the wrong thing? And sometimes it

873
00:49:05,680 --> 00:49:09,037
could learn the wrong thing because
it made a mistake. But it could also be

874
00:49:09,120 --> 00:49:11,517
misprogrammed by a government or a

875
00:49:11,600 --> 00:49:14,637
competitor or marketing or an incentive.

876
00:49:14,720 --> 00:49:17,917
So you have this crazy idea. You think
we should give these equivalent of these

877
00:49:18,000 --> 00:49:21,517
bears to every child. How do we support
that with advertising? Now the bear

878
00:49:21,600 --> 00:49:25,117
becomes advertising to the kid and the kid
basically then wants the toy and the

879
00:49:25,200 --> 00:49:27,517
parents have to buy the toy.
That seems like a terrible idea.

880
00:49:27,600 --> 00:49:30,877
>> You're so right because in the pursuit
of everything being democratized, you

881
00:49:30,960 --> 00:49:34,237
can't offer a product that's only
beneficial to the wealthy people who can

882
00:49:34,320 --> 00:49:37,357
afford this product. So then how else
do you provide it to everyone? Well,

883
00:49:37,440 --> 00:49:40,717
advertising. And this is how the internet
works. I feel like before this

884
00:49:40,800 --> 00:49:44,797
would even be permitted, we would all
have to collectively agree and social

885
00:49:44,880 --> 00:49:49,197
scientists would collectively agree
on what percentage of the interaction

886
00:49:49,280 --> 00:49:53,677
between a child and its best friend
should be conflict which percentage

887
00:49:53,760 --> 00:49:58,077
should be bonding in. So >> I I
can guarantee you there is no number

888
00:49:58,160 --> 00:50:01,357
that I can use. And if you can't tell
me the number, then the tech industry is

889
00:50:01,440 --> 00:50:04,157
going to do whatever it wants. That's
what I'm saying is like we can't even

890
00:50:04,240 --> 00:50:07,437
get consensus on our side where we might
want to say, well, here would be the

891
00:50:07,520 --> 00:50:09,117
guidelines of what this thing should do.

892
00:50:09,200 --> 00:50:13,597
>> Again, I think the issue of developing
young minds is going to get much more

893
00:50:13,680 --> 00:50:16,797
important and we've got to get ahead
of that. Let me give you another example.

894
00:50:16,880 --> 00:50:20,237
Does AI perceive things that are

895
00:50:20,320 --> 00:50:22,717
different from what humans perceive? And

896
00:50:22,800 --> 00:50:27,437
if so, what happens? So the example we
use in the book is a game called go and

897
00:50:27,520 --> 00:50:30,957
a game chess which we all know about
which have been played by humans for

898
00:50:31,040 --> 00:50:35,117
thousands of years not only won those
games against humans using these new AI

899
00:50:35,200 --> 00:50:38,077
techniques which is a major achievement
because it thought they were not

900
00:50:38,160 --> 00:50:42,877
possible but more importantly they
invented new strategies. Now how is it

901
00:50:42,960 --> 00:50:47,677
that in a game that's as mature as chess
as go that we could invent a completely

902
00:50:47,760 --> 00:50:51,277
new strategy for a game that's been
played for thousands of years? So, one

903
00:50:51,360 --> 00:50:54,637
possibility is that it was always
there for humans to discover. They just

904
00:50:54,720 --> 00:50:59,277
couldn't see it. >> Another
possibility is that the computer

905
00:50:59,360 --> 00:51:03,437
actually discovered a new truth
that we as humans can't really perceive.

906
00:51:03,520 --> 00:51:07,357
>> Oh my gosh. >> Now, this is a
thought experiment, but let's imagine

907
00:51:07,440 --> 00:51:09,757
>> that these systems
get smart enough that

908
00:51:09,840 --> 00:51:12,317
they begin to do things that are

909
00:51:12,400 --> 00:51:15,677
actually correct, but they can't explain

910
00:51:15,760 --> 00:51:19,597
what they're doing, and we don't
understand what they're doing. Now, Dr.

911
00:51:19,680 --> 00:51:21,517
Kissinger says if you look at history

912
00:51:21,600 --> 00:51:24,317
there are two things that happen either

913
00:51:24,400 --> 00:51:26,717
people rebel that is literally with guns

914
00:51:26,800 --> 00:51:29,757
against these overlords or they invent a

915
00:51:29,840 --> 00:51:33,837
new religion that there is a religious
component about faith because they can't

916
00:51:33,920 --> 00:51:38,077
understand what's actually going
on >> well and yeah the real historical

917
00:51:38,160 --> 00:51:42,237
examples of course you'd give is like
Galileo is virtually that he is the AI

918
00:51:42,320 --> 00:51:47,037
that believes in the copranicus way
of thinking of our solar system and then

919
00:51:47,120 --> 00:51:48,957
yes that person is it's too threatening

920
00:51:49,040 --> 00:51:51,597
we put them in jail. It also makes me

921
00:51:51,680 --> 00:51:54,077
think of I watched this documentary on

922
00:51:54,160 --> 00:51:56,877
physics and to learn and I didn't know

923
00:51:56,960 --> 00:51:59,197
this that all the theories that have

924
00:51:59,280 --> 00:52:01,437
been around for the last 60 years, those

925
00:52:01,520 --> 00:52:04,317
theories actually don't exist in

926
00:52:04,400 --> 00:52:06,237
language. They only exist in

927
00:52:06,320 --> 00:52:09,597
mathematics. The entire thought exists

928
00:52:09,680 --> 00:52:13,277
only in mathematics. And when they're
telling you about it, they're using this

929
00:52:13,360 --> 00:52:15,357
very substandard method to try to

930
00:52:15,440 --> 00:52:17,517
explain to you what's happening in the

931
00:52:17,600 --> 00:52:20,877
mathematics. So that was like for me

932
00:52:20,960 --> 00:52:24,557
kind of mindshattering like wow our
language can't account for what they're

933
00:52:24,640 --> 00:52:28,077
thinking of in this math.
>> Our language is not precise enough

934
00:52:28,160 --> 00:52:33,777
>> right? >> Stay tuned
for more armchair expert

935
00:52:33,860 --> 00:52:38,120
[music] if you dare.

936
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1011
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So one of the ways to understand how
this will happen, this is very relevant

1012
00:57:17,200 --> 00:57:21,597
to this physics point is that today
there are things called large language

1013
00:57:21,680 --> 00:57:25,277
models. And we talk about this in the
book. We use something called GPT3 as an

1014
00:57:25,360 --> 00:57:29,597
example. And what they do is they suck
all the text that they can find in and

1015
00:57:29,680 --> 00:57:33,837
they figure out what the text is about
and then using all sorts of tricks that

1016
00:57:33,920 --> 00:57:37,197
are very powerful. It's very expensive
to compute. These things cost $und00

1017
00:57:37,280 --> 00:57:39,197
million to to build one of them, for

1018
00:57:39,280 --> 00:57:41,837
example. They appear to have humanlike

1019
00:57:41,920 --> 00:57:44,957
knowledge with errors. You can ask it to

1020
00:57:45,040 --> 00:57:49,997
compose something. You can ask it
to define itself. So, for example, on the

1021
00:57:50,080 --> 00:57:53,997
back of our book, we have a quote from
GPT3. It was asked, "Are you human?" And

1022
00:57:54,080 --> 00:57:58,317
it said, "No, I'm not human.
I am a language model. I don't have the

1023
00:57:58,400 --> 00:58:00,557
reasoning capabilities that you as a

1024
00:58:00,640 --> 00:58:05,597
reasoning machine have." Now, how did
it learn that? Right? It figured it out

1025
00:58:05,680 --> 00:58:09,037
because of the descriptions that were
about what it was versus what humans

1026
00:58:09,120 --> 00:58:11,997
were. Yeah. >> But it
appears to have insight.

1027
00:58:12,080 --> 00:58:15,117
>> So many people believe
that the path goes like this.

1028
00:58:15,200 --> 00:58:16,797
>> So the first thing
you do is you get the

1029
00:58:16,880 --> 00:58:21,197
language model. Then you figure out what
is called dialogue where you can start

1030
00:58:21,280 --> 00:58:24,317
asking questions.
>> I was playing with one which is not

1031
00:58:24,400 --> 00:58:26,557
announced yet and I said tell me the

1032
00:58:26,640 --> 00:58:29,517
product that's in 2001 of space odyssey

1033
00:58:29,600 --> 00:58:33,917
that is available today here in the United
States. And the correct answer is

1034
00:58:34,000 --> 00:58:36,637
a tablet. >> Okay?
>> And because it was the first

1035
00:58:36,720 --> 00:58:39,037
demonstration of a tablet device. And so

1036
00:58:39,120 --> 00:58:41,437
it comes back and it says there was a

1037
00:58:41,520 --> 00:58:44,477
tablet which is the origin of that. And

1038
00:58:44,560 --> 00:58:49,117
it used a different algorithm from the
kind of thing that Google search uses.

1039
00:58:49,200 --> 00:58:53,037
That's an example of a breakthrough. And
then I could have asked it more and more

1040
00:58:53,120 --> 00:58:56,637
questions about this tablet and the
origin and so forth and it would have

1041
00:58:56,720 --> 00:59:00,477
understood the context.
>> That's the next step. All of these

1042
00:59:00,560 --> 00:59:05,837
systems today have the problem that they
don't have their own volition. They

1043
00:59:05,920 --> 00:59:08,317
can't generate their own
objective function. Right?

1044
00:59:08,400 --> 00:59:11,757
>> So the sequence goes as follows.
The first phase is we get really good at

1045
00:59:11,840 --> 00:59:15,917
somebody else's objective function.
The obvious one there for a physicist is

1046
00:59:16,000 --> 00:59:19,837
they spend all day doing physics and then
it's 5:00 and it's time to go home

1047
00:59:19,920 --> 00:59:24,397
and and so they say to the computer,
I want you to read every paper that's ever

1048
00:59:24,480 --> 00:59:27,117
been written on ien values of this thing.

1049
00:59:27,200 --> 00:59:31,197
>> Yeah. And tomorrow morning when I show
up to do physics, I want you to give me

1050
00:59:31,280 --> 00:59:33,437
the best idea that I should pursue.

1051
00:59:33,520 --> 00:59:37,997
>> Oh wow. >> It's the same assistant that
you would get because you would say, "I'm

1052
00:59:38,080 --> 00:59:41,437
preparing for a show.
Tell me everything I should ask."

1053
00:59:41,520 --> 00:59:43,597
>> Uhhuh. >> Right.
Which is today what a a good

1054
00:59:43,680 --> 00:59:47,197
assistant does for you. But literally
read everything. Read everything that

1055
00:59:47,280 --> 00:59:50,797
Eric said. Read all the books in this
area and read them all because I don't

1056
00:59:50,880 --> 00:59:51,837
have time. >> Yeah.

1057
00:59:51,920 --> 00:59:54,157
>> And tell me what
the key things are. But

1058
00:59:54,240 --> 00:59:58,237
what is best? A computer doesn't know.

1059
00:59:58,320 --> 01:00:02,797
Well, maybe they do, but what
we're qualifying as best idea.

1060
01:00:02,880 --> 01:00:06,397
>> Well, again, it's gotten good enough
that it can get an approximation of

1061
01:00:06,480 --> 01:00:11,277
best. So, that's the first I think
stable point. That's five years from

1062
01:00:11,360 --> 01:00:13,597
now. >> Can I go really quick back to the

1063
01:00:13,680 --> 01:00:16,397
valition statement you made? I want to

1064
01:00:16,480 --> 01:00:18,717
see if if the way I'm understanding it

1065
01:00:18,800 --> 01:00:21,437
is accurate. So I one time heard a

1066
01:00:21,520 --> 01:00:24,317
debate with Steven Pinker on AI and he

1067
01:00:24,400 --> 01:00:28,237
pointed out most of the the doomsdayers

1068
01:00:28,320 --> 01:00:32,477
with AI. They're mapping on this truth

1069
01:00:32,560 --> 01:00:35,197
about organic beings which is they have

1070
01:00:35,280 --> 01:00:39,837
to survive. They have to reproduce.
They have to feed themselves. And the more

1071
01:00:39,920 --> 01:00:41,837
they collect the the better their

1072
01:00:41,920 --> 01:00:44,637
chances of survival are. that that very

1073
01:00:44,720 --> 01:00:48,157
most basic thing of any organic organism

1074
01:00:48,240 --> 01:00:53,277
is being mapped onto this AI and that's
not accurate. We would have to make it

1075
01:00:53,360 --> 01:00:56,317
understand that.
>> So there's lots of evidence now that

1076
01:00:56,400 --> 01:00:59,997
babies are born into the world
with preconceived notions of what their

1077
01:01:00,080 --> 01:01:03,757
objective function would be more than
just food. So for example, there's

1078
01:01:03,840 --> 01:01:07,757
something called zeroot learning where
you basically show somebody something

1079
01:01:07,840 --> 01:01:12,957
without explaining it. and that when you
show something similar in the future, it

1080
01:01:13,040 --> 01:01:14,877
looks familiar to them. >> Okay?

1081
01:01:14,960 --> 01:01:18,237
>> Right? They can construct
where it is and what it's up to,

1082
01:01:18,320 --> 01:01:22,237
>> right? And that's a hard problem
in in my field. But these are all problems

1083
01:01:22,320 --> 01:01:26,077
that are being worked on. So,
at some point, we're going to have these

1084
01:01:26,160 --> 01:01:29,597
incredible systems which will
do what somebody told them to do,

1085
01:01:29,680 --> 01:01:32,237
>> but they still won't have volition. The

1086
01:01:32,320 --> 01:01:37,037
great debate is at what point can
the computer begin to give itself

1087
01:01:37,120 --> 01:01:41,357
objectives? Mhm. >> And we're
beginning to see this in the

1088
01:01:41,440 --> 01:01:43,517
following. There are products that have

1089
01:01:43,600 --> 01:01:48,957
just come out this year to help you
write code. And the idea is that the

1090
01:01:49,040 --> 01:01:51,917
programmer is writing and the computer's
watching and it kind of finishes what

1091
01:01:52,000 --> 01:01:54,557
you're writing. It's like finishing
your sentence as a programmer,

1092
01:01:54,640 --> 01:01:56,397
>> right? Like autocorrect like

1093
01:01:56,480 --> 01:01:58,797
>> autorect. But this is a very very

1094
01:01:58,880 --> 01:02:03,837
powerful idea because as the programmers
use it, it will learn more and more

1095
01:02:03,920 --> 01:02:07,757
about how to finish. Mhm.
>> So many people believe that at some

1096
01:02:07,840 --> 01:02:11,197
point those systems you'll
be able to say write me this

1097
01:02:11,280 --> 01:02:12,717
>> and it'll write the whole thing.

1098
01:02:12,800 --> 01:02:15,517
>> Yeah. >> Okay.
Then the next question is when

1099
01:02:15,600 --> 01:02:17,357
will it decide what it should write?

1100
01:02:17,440 --> 01:02:20,077
>> Yeah. >> And that's
a speculative question. This

1101
01:02:20,160 --> 01:02:24,637
is called artificial general intelligence.
There are skeptics and

1102
01:02:24,720 --> 01:02:27,117
there are optimists.
I'm somewhere in between.

1103
01:02:27,200 --> 01:02:31,357
>> Right. >> The optimists believe that
general intelligence which is not human

1104
01:02:31,440 --> 01:02:35,277
intelligence but it's creative in the way
that humans are. it has volition. It

1105
01:02:35,360 --> 01:02:37,917
can set its own objectives is 10 to 15

1106
01:02:38,000 --> 01:02:40,397
years away. I'm a bit more conservative.

1107
01:02:40,480 --> 01:02:43,997
I think it's more like 20 to 30. And there
are other people who say this will

1108
01:02:44,080 --> 01:02:47,357
never happen. It's well beyond what
computers will ever do. Those kinds of

1109
01:02:47,440 --> 01:02:48,797
people are usually wrong. >> Okay.

1110
01:02:48,880 --> 01:02:50,557
>> Usually we'll get there somehow.

1111
01:02:50,640 --> 01:02:54,077
>> Yeah. Really quick, they don't
have ego or could they ever have ego?

1112
01:02:54,160 --> 01:02:55,357
>> Of course they could. >> They could.

1113
01:02:55,440 --> 01:02:58,957
>> Look, the computer will say, "I want
to keep this human happy and these other

1114
01:02:59,040 --> 01:03:01,597
people are a source of frustration
to them, so I'll just kill them all."

1115
01:03:01,680 --> 01:03:04,077
>> Right. Right. >> Right.
That's clearly a bad objective

1116
01:03:04,160 --> 01:03:07,437
function. Yeah. I'm not making light
of it, but I'm trying to say that's a

1117
01:03:07,520 --> 01:03:10,877
mistake. >> Yeah. >> So,
you have to figure out a way to

1118
01:03:10,960 --> 01:03:15,437
define in human society and in the
complexity we live in, what does looking

1119
01:03:15,520 --> 01:03:17,037
better look like?

1120
01:03:17,120 --> 01:03:19,357
>> And I'm wondering is computers may

1121
01:03:19,440 --> 01:03:22,637
eventually observe humans and say, we

1122
01:03:22,720 --> 01:03:26,397
know how to make your lives better
and actually understand it in a way that

1123
01:03:26,480 --> 01:03:27,997
humans don't understand. >> Yeah.

1124
01:03:28,080 --> 01:03:32,557
>> And if that happens, that's the most
utopian phase. The most dystopian phase

1125
01:03:32,640 --> 01:03:34,717
is that we can't control these things.

1126
01:03:34,800 --> 01:03:39,437
And furthermore, because of open source,
all this software is being leaked out to

1127
01:03:39,520 --> 01:03:43,597
all sorts of evil people. You know,
next generation of evil terrorists and it

1128
01:03:43,680 --> 01:03:45,517
could be really misused. And the obvious

1129
01:03:45,600 --> 01:03:50,717
one is I say to the computer,
tell me how to kill a million people of a

1130
01:03:50,800 --> 01:03:55,037
different race than my own. >> Uhhuh.
Right now, for this reason, in

1131
01:03:55,120 --> 01:03:58,877
the book, and this is why I'm I'm
mentioning it, we talk at some length

1132
01:03:58,960 --> 01:04:03,117
about the history of national
security and deterrence,

1133
01:04:03,200 --> 01:04:05,757
we're going to have weapons that are

1134
01:04:05,840 --> 01:04:10,637
like what I just described that
will be so dangerous that we'll

1135
01:04:10,720 --> 01:04:15,117
need to have them be guarded and have
special access and so forth. And the

1136
01:04:15,200 --> 01:04:18,477
reason we know that is we already have
such weapons in the form of nuclear

1137
01:04:18,560 --> 01:04:22,637
weapons. Unlike nuclear where
the enriched uranium is difficult to get.

1138
01:04:22,720 --> 01:04:26,477
The thought experiment is imagine if
nuclear stuff was free. Well, we'd all

1139
01:04:26,560 --> 01:04:29,277
be dead. Trust me. >> Yeah.
>> Because the evil people would have sort

1140
01:04:29,360 --> 01:04:31,197
of taken these taken this outcome.

1141
01:04:31,280 --> 01:04:34,317
>> It only takes 0001%
of evil people to kill.

1142
01:04:34,400 --> 01:04:38,477
>> Yeah. It only takes it only takes a few
for these mega scale weapons. And so we

1143
01:04:38,560 --> 01:04:43,037
have to be very careful as we build
this technology to put in the necessary

1144
01:04:43,120 --> 01:04:45,917
restrictions so that they're
not misused at scale.

1145
01:04:46,000 --> 01:04:49,917
>> Just saying the machine can never kill
is not going to work cuz we're going to

1146
01:04:50,000 --> 01:04:52,397
use it in military applications.

1147
01:04:52,480 --> 01:04:56,317
>> Well, first place, who would you
say it to, right? So in programming the

1148
01:04:56,400 --> 01:04:59,917
computer, you have to say rule one,
don't kill anyone. This is the Isaac

1149
01:05:00,000 --> 01:05:03,437
Azimoff three laws of robots. You know,
don't harm anyone. But the fact of the

1150
01:05:03,520 --> 01:05:07,677
matter is that in the military, they are
in the business of killing combatants,

1151
01:05:07,760 --> 01:05:11,117
right? And having worked for the
military for 5 years, something called

1152
01:05:11,200 --> 01:05:14,957
the Defense Innovation Board, they're
very straightforward about it, >> right?

1153
01:05:15,040 --> 01:05:16,877
>> We are proud.
This is our mission. We're

1154
01:05:16,960 --> 01:05:21,277
defending our country and if necessary,
under the laws of war, we will attack

1155
01:05:21,360 --> 01:05:23,117
enemy commands and kill them. >> Yeah.

1156
01:05:23,200 --> 01:05:25,277
>> And and they're not
apologetic about it.

1157
01:05:25,360 --> 01:05:26,957
Right. >> So they're
clear of their mission and

1158
01:05:27,040 --> 01:05:31,277
their principles are high. I I say this
with respect. So they're going to build

1159
01:05:31,360 --> 01:05:34,557
systems which will allow them
to do that more accurately.

1160
01:05:34,640 --> 01:05:37,037
>> Right? >> I would argue
that one of the things

1161
01:05:37,120 --> 01:05:41,437
about current conflicts and people
don't want to hear this is that the vast

1162
01:05:41,520 --> 01:05:45,597
majority of the deaths are
actually non-combatants, >> right?

1163
01:05:45,680 --> 01:05:49,277
>> And that's terrible. >> Yeah.
Meaning like when we drone strike,

1164
01:05:49,360 --> 01:05:52,637
most generally more civilians
go down than the target itself.

1165
01:05:52,720 --> 01:05:56,157
>> The target identification problem
is a really hard problem. Mhm.

1166
01:05:56,240 --> 01:05:59,437
>> So, let's imagine
that a good outcome of

1167
01:05:59,520 --> 01:06:02,077
AI is the weapons are highly targetable.

1168
01:06:02,160 --> 01:06:05,357
>> Mhm. >> But you still have the problem
of who gets to decide who to launch them.

1169
01:06:05,440 --> 01:06:06,877
>> Yeah. >> So, we're
clearly going to have more

1170
01:06:06,960 --> 01:06:10,077
accurate weapons. We say very clearly uh

1171
01:06:10,160 --> 01:06:11,517
both in this commissioner report I did

1172
01:06:11,600 --> 01:06:14,077
as well as in the book that the issue

1173
01:06:14,160 --> 01:06:17,277
around computers deciding to do attacks

1174
01:06:17,360 --> 01:06:19,757
is really problematic. And I'll give you

1175
01:06:19,840 --> 01:06:22,877
an example. You're on a ship and there's

1176
01:06:22,960 --> 01:06:25,757
a missile coming. The AI has figured it

1177
01:06:25,840 --> 01:06:31,117
out, but you can't see it. It's not
on your radar, and it's coming fast. It's

1178
01:06:31,200 --> 01:06:34,477
hypersonic. >> I think in the book you
say you've got 24 seconds, >> right?

1179
01:06:34,560 --> 01:06:37,997
>> 24 seconds. Now, would you, Dax, would

1180
01:06:38,080 --> 01:06:41,197
you press the button or would you count

1181
01:06:41,280 --> 01:06:45,277
down? In the movies, they countdown.
Me, I just press the button.

1182
01:06:45,360 --> 01:06:47,197
>> Right. Right.
>> You know, I'd panic. Yeah.

1183
01:06:47,280 --> 01:06:48,877
>> That's that's who I am. >> Yeah.

1184
01:06:48,960 --> 01:06:52,877
>> So, how do we deal with that? And and
one of the discussions that I think we

1185
01:06:52,960 --> 01:06:56,717
should be having and we say this in the
book is we need to have some agreements

1186
01:06:56,800 --> 01:06:59,197
about launch on warning that is this

1187
01:06:59,280 --> 01:07:02,637
sort of any kind of automatic launch

1188
01:07:02,720 --> 01:07:05,357
automatic attack decisions faster than

1189
01:07:05,440 --> 01:07:08,237
human time is destabilizing because it

1190
01:07:08,320 --> 01:07:12,717
leads the other side to think it has
to destroy it. It's too dangerous to allow

1191
01:07:12,800 --> 01:07:15,117
this to exist. >> Yeah.
If you look right now, there's

1192
01:07:15,200 --> 01:07:19,437
this lengthy conversation about whether
the enriched uranium in Iran should be

1193
01:07:19,520 --> 01:07:21,597
destroyed ahead of its use. >> Yeah.

1194
01:07:21,680 --> 01:07:24,317
>> Right. >> Yeah.
>> And there's all sorts of issues in

1195
01:07:24,400 --> 01:07:27,837
Israel. They spend a fair amount of time,
they don't admit it, but I think

1196
01:07:27,920 --> 01:07:31,677
it's fairly agreed to that whenever
something that could threaten Israel

1197
01:07:31,760 --> 01:07:35,037
shows up in Syria, they use special
agents and they go and they destroy it

1198
01:07:35,120 --> 01:07:37,917
and so forth. >> And then
we had the the great Seammens

1199
01:07:38,000 --> 01:07:41,037
software we wrote to mess
with the center fuses.

1200
01:07:41,120 --> 01:07:44,157
>> Yes. The allegation that Israel
and America did that. Yeah,

1201
01:07:44,240 --> 01:07:47,597
>> we must start now talking specifically

1202
01:07:47,680 --> 01:07:51,277
with China, but also with Russia
about appropriate restraints,

1203
01:07:51,360 --> 01:07:54,157
>> kind of like a Geneva
convention, >> some agreement.

1204
01:07:54,240 --> 01:07:56,957
>> Yeah. >> So,
when a country launches a missile,

1205
01:07:57,040 --> 01:07:59,037
whether it's a missile test or a

1206
01:07:59,120 --> 01:08:02,077
civilian missile such as SpaceX, all the

1207
01:08:02,160 --> 01:08:06,717
other countries are notified.
>> And that's because no country wants the

1208
01:08:06,800 --> 01:08:08,877
other one to think it's attacking him.

1209
01:08:08,960 --> 01:08:13,517
>> Sure. and the other countries use
that as an opportunity to train their

1210
01:08:13,600 --> 01:08:16,877
surveillance systems to watch the launch.

1211
01:08:16,960 --> 01:08:19,677
>> So that's a good example where we have

1212
01:08:19,760 --> 01:08:23,517
an agreement among untrusted parties

1213
01:08:23,600 --> 01:08:27,757
that deescalates some
of the worst possible scenarios,

1214
01:08:27,840 --> 01:08:30,877
>> right? >> So we've got
to have some rules. So, for

1215
01:08:30,960 --> 01:08:33,917
example, when there is a cyber test in

1216
01:08:34,000 --> 01:08:37,037
China and it goes ary, there better be a

1217
01:08:37,120 --> 01:08:41,357
phone call from China to the United
States saying, "Sorry guys."

1218
01:08:41,440 --> 01:08:44,877
>> Yeah. >> Not intended.
Don't come back after us.

1219
01:08:44,960 --> 01:08:48,717
>> Yeah. But in light of Wuhan, it's
just like we just had an experience where

1220
01:08:48,800 --> 01:08:51,677
they didn't do that with us.
>> That is correct. They didn't give us a

1221
01:08:51,760 --> 01:08:55,517
heads up. Imagine if we, the country,
had gotten a phone call in late December

1222
01:08:55,600 --> 01:08:59,357
saying, "Look guys, this thing's going
to get out of hand. We've been modeling

1223
01:08:59,440 --> 01:09:03,997
it. get yourselves together
and collectively we can respond to this.

1224
01:09:04,080 --> 01:09:06,157
That's an example of an emergent event.

1225
01:09:06,240 --> 01:09:09,997
So you have AI which both speeds things
up. It also creates emergent events that

1226
01:09:10,080 --> 01:09:11,357
we don't expect.

1227
01:09:11,440 --> 01:09:15,517
>> Uhhuh. Holy smokes, man. The stakes are

1228
01:09:15,600 --> 01:09:20,317
just so high. And and I agree with your
assessment that this will be another

1229
01:09:20,400 --> 01:09:24,957
renaissance or enlightenment. This will
be some period that's looked back on.

1230
01:09:25,040 --> 01:09:28,717
Should we make it? >> We'll make
it in some form. What I would

1231
01:09:28,800 --> 01:09:31,437
say is that the opportunities for AI and

1232
01:09:31,520 --> 01:09:34,557
the platforms that we're building are so

1233
01:09:34,640 --> 01:09:37,597
enormously exciting. There is a drug

1234
01:09:37,680 --> 01:09:41,677
called Halison and we profiled this in
the book. It was designed by some sin

1235
01:09:41,760 --> 01:09:45,197
bio people at MIT and some computer
scientists. What they did is they

1236
01:09:45,280 --> 01:09:48,397
trained the model on what they
knew about antibiotics. That was

1237
01:09:48,480 --> 01:09:51,997
straightforward. Then they asked
the computer to look through a 100 million

1238
01:09:52,080 --> 01:09:55,997
chemical compounds to figure out what
the compounds did, which nobody knew,

1239
01:09:56,080 --> 01:09:59,437
and then figure out whether they would
produce an antibiotic response. It

1240
01:09:59,520 --> 01:10:02,717
learned all of that on its own,
something humans could never do. And

1241
01:10:02,800 --> 01:10:05,597
then it produced 10 candidates for drugs.

1242
01:10:05,680 --> 01:10:08,077
>> Get out >> of antibiotics.
>> Oh my god,

1243
01:10:08,160 --> 01:10:11,357
>> this is the best part. It then
took another network and looked at the

1244
01:10:11,440 --> 01:10:14,317
characteristics of the existing
antibiotics and it said, "You have to be

1245
01:10:14,400 --> 01:10:18,237
as different as possible."
And that produced one. Okay.

1246
01:10:18,320 --> 01:10:20,717
>> Now, this one appears to produce a

1247
01:10:20,800 --> 01:10:25,437
broad-scale antibiotic reaction
for people who have antibiotic resistance.

1248
01:10:25,520 --> 01:10:27,517
>> Yeah. >> The number
of lives that are saved by

1249
01:10:27,600 --> 01:10:30,477
that is enormous. Over and over and over

1250
01:10:30,560 --> 01:10:32,557
again, Deep Mind and the University of

1251
01:10:32,640 --> 01:10:35,437
Seattle Baker Lab just released the

1252
01:10:35,520 --> 01:10:38,157
details of how proteins fold. Proteins

1253
01:10:38,240 --> 01:10:42,957
fold in a particular order and the way
they fold and determines what they do.

1254
01:10:43,040 --> 01:10:46,877
So, predicting protein folding has
been the holy grail. This is a discovery

1255
01:10:46,960 --> 01:10:51,597
worthy of a Nobel Prize because based
on knowing how proteins fold then all of a

1256
01:10:51,680 --> 01:10:55,277
sudden a whole another set of drugs and
chemicals and so forth can be made. It's

1257
01:10:55,360 --> 01:10:59,837
similar to crisper in biology which
allowed us to do editing and so forth.

1258
01:10:59,920 --> 01:11:02,397
So these are things
that humans could never do.

1259
01:11:02,480 --> 01:11:06,477
>> Yeah. You posed the question in the
book, should a Nobel Prize come out of

1260
01:11:06,560 --> 01:11:10,557
this? Does the AI receive it?
Do the people who ask the AI the question

1261
01:11:10,640 --> 01:11:12,397
receive it? Like where are we at there?

1262
01:11:12,480 --> 01:11:15,357
When Alph Go won, I was in Alph Go in

1263
01:11:15,440 --> 01:11:17,997
Shanghai when we won against the human.

1264
01:11:18,080 --> 01:11:20,717
This was 2017

1265
01:11:20,800 --> 01:11:25,517
and the Chinese were so upset that in
the middle of the match they shut down

1266
01:11:25,600 --> 01:11:28,957
the national television of the
match because they were losing

1267
01:11:29,040 --> 01:11:31,677
>> because they all thought that they were
winning. What was interesting afterwards

1268
01:11:31,760 --> 01:11:33,597
is I thought, what could I do to

1269
01:11:33,680 --> 01:11:35,837
acknowledge this occasion? So, I took

1270
01:11:35,920 --> 01:11:40,237
the 18 top people who worked on this and

1271
01:11:40,320 --> 01:11:42,877
we all flew to the data center where

1272
01:11:42,960 --> 01:11:47,917
this computer was and we found it
and we put a plaque on it to honor it.

1273
01:11:48,000 --> 01:11:50,717
>> Uhhuh. >> And obviously
it's a humorous story, but

1274
01:11:50,800 --> 01:11:52,557
we also spent a whole
day talking to each other.

1275
01:11:52,640 --> 01:11:54,397
>> Yeah. >> Right.
About what had been achieved and

1276
01:11:54,480 --> 01:11:57,917
so forth. And I'm very proud of that
visit, but who do I give the plaque to?

1277
01:11:58,000 --> 01:12:00,797
It's a team of humans and it's a computer.
So, we put the plaque on the

1278
01:12:00,880 --> 01:12:04,077
computer. Right. And we found it in
the middle of all the racks of computers

1279
01:12:04,160 --> 01:12:07,997
that were that were doing work. And by
the way, it was three or four racks of

1280
01:12:08,080 --> 01:12:10,077
computers in a huge data center. This

1281
01:12:10,160 --> 01:12:13,037
shows you the power and also the peril

1282
01:12:13,120 --> 01:12:15,517
of these things. That's the positive.

1283
01:12:15,600 --> 01:12:19,917
The most likely dystopian case is a case
where we don't get this addiction

1284
01:12:20,000 --> 01:12:23,997
problem under control. Um that the
computer systems are built around

1285
01:12:24,080 --> 01:12:26,717
addiction and they drive us insane.

1286
01:12:26,800 --> 01:12:31,357
Attention, attention, crisis, crisis,
issue, so forth and so on. Well, really,

1287
01:12:31,440 --> 01:12:34,717
if I can interrupt you, what you said
at the beginning of this, which is in this

1288
01:12:34,800 --> 01:12:39,517
even you allari future where we're
all just pleasure seekers in a virtual

1289
01:12:39,600 --> 01:12:42,637
reality, >> everyone can
have status. Everyone can

1290
01:12:42,720 --> 01:12:45,917
be the winner of it. It'll be [__]
It'll be fake status, but certainly the

1291
01:12:46,000 --> 01:12:49,357
algorithm could make you feel like
you're chipping away and gaining all

1292
01:12:49,440 --> 01:12:52,157
this popularity, but all
the popularity is a bunch of other bots.

1293
01:12:52,240 --> 01:12:56,157
>> You can imagine all sorts of dystopian
scenarios. So you're popular in the

1294
01:12:56,240 --> 01:13:00,397
metaverse of this rack of computers and
you're unpopular in the other metaverse.

1295
01:13:00,480 --> 01:13:02,637
>> Yeah. >> And so forth.
I don't think we know how

1296
01:13:02,720 --> 01:13:06,237
this will play out. But left to their
own devices, and this is why we wrote

1297
01:13:06,320 --> 01:13:09,197
the book, what we say is that the
tech industry should not make these

1298
01:13:09,280 --> 01:13:11,277
decisions. That these are decisions that

1299
01:13:11,360 --> 01:13:14,477
should include philosophers, economists,

1300
01:13:14,560 --> 01:13:16,397
behavioral scientists.

1301
01:13:16,480 --> 01:13:19,517
>> Maybe you could
answer this question for

1302
01:13:19,600 --> 01:13:22,877
us. the way our legal system works and

1303
01:13:22,960 --> 01:13:25,197
the latigious nature of class action

1304
01:13:25,280 --> 01:13:30,317
suits and whatnot to me sets up a catch

1305
01:13:30,400 --> 01:13:34,637
22 that's standing in our way. So let's
say we design the system with this team

1306
01:13:34,720 --> 01:13:39,517
of people and then we launch it and then
there's some disastrous outcome and we

1307
01:13:39,600 --> 01:13:41,197
know that the disastrous outcome is

1308
01:13:41,280 --> 01:13:44,717
going to result in x amount of legal

1309
01:13:44,800 --> 01:13:47,517
fees that puts the company in an

1310
01:13:47,600 --> 01:13:51,037
existential position and so it really

1311
01:13:51,120 --> 01:13:53,597
creates a scenario where the company to

1312
01:13:53,680 --> 01:13:57,277
acknowledge failure is also committing

1313
01:13:57,360 --> 01:14:00,877
suicide. Yes, >> this has
to change if we're going to ask

1314
01:14:00,960 --> 01:14:05,357
these companies, I think, to be real
time ethical, real time changing, real

1315
01:14:05,440 --> 01:14:08,317
time transparent, real time
acknowledging what's [__] up and where

1316
01:14:08,400 --> 01:14:12,717
it went wrong and how we're going
to change it. It can't really do it in our

1317
01:14:12,800 --> 01:14:15,117
current system.
Do you have any thoughts on that?

1318
01:14:15,200 --> 01:14:17,517
>> The effect of what you're describing is

1319
01:14:17,600 --> 01:14:20,077
that the legal system is another

1320
01:14:20,160 --> 01:14:22,797
participant, complainer, customer within

1321
01:14:22,880 --> 01:14:27,197
the corporate environment. So the way
it really works is you have the founders

1322
01:14:27,280 --> 01:14:30,477
with great vision, you have the employees,
you have the customers, you

1323
01:14:30,560 --> 01:14:34,637
have the regulators, and you have public
opinion and the press. And as a CEO, you

1324
01:14:34,720 --> 01:14:36,957
have to deal with all of them. What's

1325
01:14:37,040 --> 01:14:39,277
interesting now is this next generation

1326
01:14:39,360 --> 01:14:42,797
of employees is extremely activist.

1327
01:14:42,880 --> 01:14:47,597
>> Yeah. So using the example I was using
earlier about the bear, I would hope

1328
01:14:47,680 --> 01:14:51,597
that the future company that's
programming the bear, the employees

1329
01:14:51,680 --> 01:14:55,117
would say to the management
that crosses the line.

1330
01:14:55,200 --> 01:14:57,757
>> That's not okay. Your profit- seeeking

1331
01:14:57,840 --> 01:15:02,637
motive is overwhelming my likelihood
of being willing to work for you to do it

1332
01:15:02,720 --> 01:15:05,117
because you're going to do
some damage to some kid.

1333
01:15:05,200 --> 01:15:07,677
>> Yeah. >> So I think
that the good news about this

1334
01:15:07,760 --> 01:15:11,837
is these issues are going to get debated.
The bad news is we don't have

1335
01:15:11,920 --> 01:15:16,157
good government answers and regulatory
answers on how to handle this. And the

1336
01:15:16,240 --> 01:15:18,077
model we've used so far has worked well.

1337
01:15:18,160 --> 01:15:22,877
You know, launch it, see what happens
and react to the worst cases. If you

1338
01:15:22,960 --> 01:15:27,277
look at the Facebook situation, they
knew these problems and they continued

1339
01:15:27,360 --> 01:15:30,317
to do them. >> But do
you think they were at a

1340
01:15:30,400 --> 01:15:32,397
crossroads where they said if we now

1341
01:15:32,480 --> 01:15:34,637
change this, it in itself is an

1342
01:15:34,720 --> 01:15:37,357
admission that it was wrong? There's

1343
01:15:37,440 --> 01:15:41,757
always that concern because if you're
a CEO and you say to the audience to the

1344
01:15:41,840 --> 01:15:45,917
shareholders, we've decided to lower our
profits because we want to change this,

1345
01:15:46,000 --> 01:15:48,637
you will get sued by d
shareholder derivative,

1346
01:15:48,720 --> 01:15:52,957
>> right? Both sides, >> even if
it's the right thing to do. So,

1347
01:15:53,040 --> 01:15:56,237
it takes an awful lot
of Kevlar on your vest. Yeah.

1348
01:15:56,320 --> 01:16:00,957
>> Um, in order to do these things,
the great companies and the great leaders

1349
01:16:01,040 --> 01:16:04,237
will do it. A lot of people are not
going to do it. By the way, this is

1350
01:16:04,320 --> 01:16:07,917
great. I really I'm loving this. Okay,

1351
01:16:08,000 --> 01:16:11,837
so that was one question and then I do
think it would really be fun for us to

1352
01:16:11,920 --> 01:16:15,677
hear >> what the potential
life of this show is

1353
01:16:15,760 --> 01:16:19,757
in a world of AI.
>> The thought experiment I would do is I

1354
01:16:19,840 --> 01:16:22,317
would get one of these language models

1355
01:16:22,400 --> 01:16:26,477
and I would have it be a panelist and I

1356
01:16:26,560 --> 01:16:28,637
would ask it questions and see what it

1357
01:16:28,720 --> 01:16:33,837
answers and I would do that every year
so you can track the progress of your

1358
01:16:33,920 --> 01:16:37,677
replacement. >> [laughter] >> Now
what you're going to discover is

1359
01:16:37,760 --> 01:16:42,397
that if you ask it the right question,
it gives an incredibly interesting

1360
01:16:42,480 --> 01:16:46,637
answer, but it still requires
your volition. It won't drive the

1361
01:16:46,720 --> 01:16:49,917
conversation. >> And so you'll
sit there and say that is

1362
01:16:50,000 --> 01:16:54,637
the most frustrating guest. I have
to ask it precisely the question and it

1363
01:16:54,720 --> 01:16:58,397
gives me a great answer
and otherwise it just waits.

1364
01:16:58,480 --> 01:17:00,957
>> Right. Well, I could use
a little bit of that by the way.

1365
01:17:01,040 --> 01:17:04,957
>> Well, and again though, great answer,
right? That's all dependent on what

1366
01:17:05,040 --> 01:17:09,837
we're deciding is great. Is a precise
answer great? Is an emotional answer

1367
01:17:09,920 --> 01:17:11,437
great? >> A metaphorical answer.

1368
01:17:11,520 --> 01:17:14,957
>> But but the systems will learn the
answer to that question because remember

1369
01:17:15,040 --> 01:17:18,957
its objective function is you
to ask it more questions, >> right?

1370
01:17:19,040 --> 01:17:21,437
>> So it will learn how to ask it will

1371
01:17:21,520 --> 01:17:25,997
learn how to give you answers that are
just exciting enough to keep you there

1372
01:17:26,080 --> 01:17:30,077
>> and just factual enough to be
interesting and just responsive enough.

1373
01:17:30,160 --> 01:17:33,277
>> Yeah. >> So it'll learn.
So the key thing about

1374
01:17:33,360 --> 01:17:37,757
this technology that took me a long
time to learn is that it's busy learning.

1375
01:17:37,840 --> 01:17:40,237
>> Yeah. >> And it will learn.
So we were talking

1376
01:17:40,320 --> 01:17:43,917
about it as a guest. It's learning
while it's a guest on your show.

1377
01:17:44,000 --> 01:17:45,917
>> Sure. >> How to be a guest on our show.

1378
01:17:46,000 --> 01:17:49,437
>> How to be on a guest on your show.
And if it gets enough other gigs, it will

1379
01:17:49,520 --> 01:17:55,240
become a very good guest.
>> Now, this might be a uncou

1380
01:17:56,080 --> 01:17:58,877
source of information. They did a

1381
01:17:58,960 --> 01:18:03,917
follow-up interview with the man
who Rainman was based off of. So they

1382
01:18:04,000 --> 01:18:06,957
interviewed him at the time and they
took him, I believe he lives in Salt

1383
01:18:07,040 --> 01:18:10,157
Lake City. He could go to the library
and you could pull any book off the

1384
01:18:10,240 --> 01:18:13,197
shelf and you could turn to a page and he
would tell you what's written on the

1385
01:18:13,280 --> 01:18:16,397
page. It's an insane >>
a truly a truly brilliant man.

1386
01:18:16,480 --> 01:18:18,957
>> Yes. Then they did a follow-up and

1387
01:18:19,040 --> 01:18:21,197
Leslie Stall went back and talked to him

1388
01:18:21,280 --> 01:18:24,237
20 years later and he was making eye

1389
01:18:24,320 --> 01:18:28,717
contact with her. He touched her.
He interacted with her in a way that

1390
01:18:28,800 --> 01:18:33,117
appeared to be a lot of growth.
And so she said to his father,

1391
01:18:33,200 --> 01:18:35,277
>> "Wow, he's gotten so much more

1392
01:18:35,360 --> 01:18:40,637
personable over the years. It seems like
he has grown in his autism." And his dad

1393
01:18:40,720 --> 01:18:44,797
said, >> "Well, I don't
believe he has actually

1394
01:18:44,880 --> 01:18:47,357
obtained the missing stuff, but he is a

1395
01:18:47,440 --> 01:18:51,837
great learner, and over time, he's
watched people have success looking at

1396
01:18:51,920 --> 01:18:55,997
each other in the eye, and they've had
success in hugging." So, it's weird.

1397
01:18:56,080 --> 01:18:59,277
It's that little thing that could exist
even within a human that the machine

1398
01:18:59,360 --> 01:19:02,797
itself would be. >> So let's
say the bear has movable eyes

1399
01:19:02,880 --> 01:19:07,117
and the programmers have not had time to
work on the eyes but there's a mechanism

1400
01:19:07,200 --> 01:19:11,677
to do it. You could imagine that the bear
which is optimized to keep the kid

1401
01:19:11,760 --> 01:19:15,677
happy. The first thing it learns
is to look at the kid >> right

1402
01:19:15,760 --> 01:19:17,997
>> because what happens when it's
not doesn't look at the kid the kid's

1403
01:19:18,080 --> 01:19:21,757
screaming. When it looks at the kid
the kid screams less. These are babies. We

1404
01:19:21,840 --> 01:19:25,837
intuitively understand this. >> Yeah.
But it's not difficult for a bear

1405
01:19:25,920 --> 01:19:29,997
to learn that because its objective
function says get attention be

1406
01:19:30,080 --> 01:19:32,637
>> minimize crying or whatever cry. And we

1407
01:19:32,720 --> 01:19:35,437
know from baby studies that babies are

1408
01:19:35,520 --> 01:19:37,757
very very optimized around both their

1409
01:19:37,840 --> 01:19:40,797
mother's uh sounds but also face

1410
01:19:40,880 --> 01:19:43,357
recognition in particular eye contact.

1411
01:19:43,440 --> 01:19:46,797
That's why they look and you look
at them and you if you look at the great

1412
01:19:46,880 --> 01:19:49,597
moms they first start by cooing and

1413
01:19:49,680 --> 01:19:51,997
holding and looking at and singing to

1414
01:19:52,080 --> 01:19:54,717
and so forth and that imprinting which

1415
01:19:54,800 --> 01:19:58,077
is so deeply human well it'll work with

1416
01:19:58,160 --> 01:20:02,397
computers too because they'll learn how
to do it. I think maybe a place to start

1417
01:20:02,480 --> 01:20:06,477
with some of this is like not
programming it for happiness because

1418
01:20:06,560 --> 01:20:11,037
also that's so philosophical happiness
in general. What is it? Is it temporary?

1419
01:20:11,120 --> 01:20:13,197
Is it >> that's what
like we need Paul Bloom to

1420
01:20:13,280 --> 01:20:17,357
say, "Oh, they need 32%
discomfort and suffering to be uh

1421
01:20:17,440 --> 01:20:21,427
>> well, okay. So, so you're going to have
the hard ass bear and the nice bear

1422
01:20:21,510 --> 01:20:23,597
[laughter] and depending on whether the

1423
01:20:23,680 --> 01:20:28,717
that kid has behaved, the little boy
or girl gets the hard ass. Even worse, the

1424
01:20:28,800 --> 01:20:31,437
bear is going to have to trip the child
occasionally. Kids got to learn how to

1425
01:20:31,520 --> 01:20:33,677
get up. >> So, so, you know,
we can have fun with

1426
01:20:33,760 --> 01:20:36,237
the analogy, but the reason to have this

1427
01:20:36,320 --> 01:20:39,197
conversation now is that the bear is a

1428
01:20:39,280 --> 01:20:43,837
metaphor for their iPad today and their
phone today and all the things that kids

1429
01:20:43,920 --> 01:20:48,557
are playing with. We're playing with fire.
We're playing with enormous impact.

1430
01:20:48,640 --> 01:20:50,477
>> We're entering a new
phase when we don't

1431
01:20:50,560 --> 01:20:54,237
fully understand the phase we're exiting
right now. We we still don't really

1432
01:20:54,320 --> 01:20:57,997
fully understand the impact
of just the last 15 years.

1433
01:20:58,080 --> 01:20:59,677
>> And I'll tell you
that the reason I want

1434
01:20:59,760 --> 01:21:04,317
the bear is because the bear will learn
how the kid learns and the bear will

1435
01:21:04,400 --> 01:21:08,317
then become the greatest teacher that
the kid has ever had. All we have to do

1436
01:21:08,400 --> 01:21:12,797
is keep it from learning the wrong thing.
But imagine if you have a bear

1437
01:21:12,880 --> 01:21:16,317
which is steeped in history, steeped
in language, steeped in the local history,

1438
01:21:16,400 --> 01:21:20,637
has all the textbooks and so forth and it
prepares that kid in whatever way the

1439
01:21:20,720 --> 01:21:24,797
kid learns, right? So let's do some
counting. Oh, I'm bored with counting.

1440
01:21:24,880 --> 01:21:28,397
Okay, let's do some language and the
language is counting. You can imagine

1441
01:21:28,480 --> 01:21:31,517
this enormously powerful tool for human

1442
01:21:31,600 --> 01:21:36,717
development. I believe that the future
of the world is completely determined by

1443
01:21:36,800 --> 01:21:40,877
whether we can get the best human
potential out of everybody. If you go

1444
01:21:40,960 --> 01:21:43,437
back in American history around 1900,

1445
01:21:43,520 --> 01:21:46,717
they had this huge rural workforce. All

1446
01:21:46,800 --> 01:21:51,277
the evidence is that they were extremely
uneducated. And the government created

1447
01:21:51,360 --> 01:21:52,957
universal high school and land grant

1448
01:21:53,040 --> 01:21:56,477
universities in roughly 1910.

1449
01:21:56,560 --> 01:22:00,797
100 years ago, 110 years ago,
they created these huge universities which

1450
01:22:00,880 --> 01:22:05,197
have been the source of the innovation
of the industrial economy of America,

1451
01:22:05,280 --> 01:22:09,117
our national security, our culture and so
forth and so on. Everybody expects to

1452
01:22:09,200 --> 01:22:13,117
go through high school and people hope
to go to college. That wasn't true a 100

1453
01:22:13,200 --> 01:22:15,277
years ago. >> Yeah, that's wild.
That's a great point.

1454
01:22:15,360 --> 01:22:17,677
>> So, let's redouble our efforts to get

1455
01:22:17,760 --> 01:22:20,237
people to their highest human potential

1456
01:22:20,320 --> 01:22:24,957
using these tools and then they will >>
take over our world and they'll do much

1457
01:22:25,040 --> 01:22:27,677
better than we have because they're
operating at their highest human

1458
01:22:27,760 --> 01:22:31,037
potential. >> Okay.
My last question for you is a

1459
01:22:31,120 --> 01:22:34,957
philosophical one. I was reading notes
from an underground and there's a whole

1460
01:22:35,040 --> 01:22:37,117
chapter and I don't know what year that

1461
01:22:37,200 --> 01:22:39,117
book was written but in the 1800s or

1462
01:22:39,200 --> 01:22:43,597
turn of the century and doski spends I

1463
01:22:43,680 --> 01:22:46,557
don't know a chapter theorizing that

1464
01:22:46,640 --> 01:22:50,077
because mathematics have come so far

1465
01:22:50,160 --> 01:22:53,437
that in the very near future through the

1466
01:22:53,520 --> 01:22:58,477
power of mathematics mathematicians will
be able to predict all human behavior.

1467
01:22:58,560 --> 01:23:00,317
And so when I read that I was like, "Oh

1468
01:23:00,400 --> 01:23:03,997
my god." So this fear minimally has been

1469
01:23:04,080 --> 01:23:07,917
around since he wrote that. And there's
really no justification at that moment

1470
01:23:08,000 --> 01:23:10,637
for him to have that fear over
mathematics. That certainly did not

1471
01:23:10,720 --> 01:23:13,597
prove to be the case. >> And I
thought to myself, wow, we have

1472
01:23:13,680 --> 01:23:16,157
this new thing. We have AI. And so I

1473
01:23:16,240 --> 01:23:19,437
wonder, is the fear human and we just

1474
01:23:19,520 --> 01:23:23,277
keep planting it on each new thing? When

1475
01:23:23,360 --> 01:23:26,237
humans are creative and curious in all

1476
01:23:26,320 --> 01:23:28,557
the great ways, we also are

1477
01:23:28,640 --> 01:23:30,317
unpredictable

1478
01:23:30,400 --> 01:23:33,357
and AI will produce a new human-like

1479
01:23:33,440 --> 01:23:36,477
intelligence that is also unpredictable

1480
01:23:36,560 --> 01:23:40,877
that we're going to have to coexist with.
And the sooner we get ourselves

1481
01:23:40,960 --> 01:23:42,957
organized around what that coexistence

1482
01:23:43,040 --> 01:23:45,197
looks like, the better. This technology

1483
01:23:45,280 --> 01:23:50,077
is coming much faster than
our philosophical, regulatory, government,

1484
01:23:50,160 --> 01:23:54,557
public policy people are. >> Yeah,
>> it's time to get ahead of it.

1485
01:23:54,640 --> 01:23:57,677
>> Wow. I'm so glad you took the time
to write this book. You know, it's it is

1486
01:23:57,760 --> 01:24:00,077
funny when I thought about you being

1487
01:24:00,160 --> 01:24:03,917
appointed the CEO of Google. I thought

1488
01:24:04,000 --> 01:24:07,117
like who could be so impressive that

1489
01:24:07,200 --> 01:24:09,437
those two, Larry and Sergey would say

1490
01:24:09,520 --> 01:24:12,717
like, "Yeah, you run this company."

1491
01:24:12,800 --> 01:24:16,397
That's almost unimaginable. And now that
I've chatted with you for an hour and a

1492
01:24:16,480 --> 01:24:19,997
half, I'm like, "Fuck yeah. I would
have hired you immediately, too." What a

1493
01:24:20,080 --> 01:24:23,037
comprehensive like
all-encompassing knowledge.

1494
01:24:23,120 --> 01:24:26,237
>> You're just way too
flattering. So [__] true.

1495
01:24:26,320 --> 01:24:30,317
>> Congratulations on you guys' success in
this. I look forward to working with you.

1496
01:24:30,400 --> 01:24:33,517
>> Oh, thank you. Thank you so much. It
was such a pleasure to have you and good

1497
01:24:33,600 --> 01:24:36,077
luck with the book and I think everyone
would be interested in reading it. And

1498
01:24:36,160 --> 01:24:38,317
again, it's called The Age of AI, Our

1499
01:24:38,400 --> 01:24:43,357
Human Future, written with Henry
Kissinger and Dr. Daniel Hutton locker.

1500
01:24:43,440 --> 01:24:46,397
So, everyone check that out. Again,
thank you so much for coming. It was

1501
01:24:46,480 --> 01:24:49,327
such a blast. >> Okay. Thanks, guys.

1502
01:24:49,410 --> 01:24:52,077
[music] >> And now,
my favorite part of the show,

1503
01:24:52,160 --> 01:24:57,320
the fact check with my soulmate
Monica Padman. [music]

1504
01:25:00,400 --> 01:25:05,277
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1505
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1512
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1513
01:25:32,160 --> 01:25:36,077
>> Hi. >> Hi. Okay, so fact check

1514
01:25:36,160 --> 01:25:39,677
crosscontinental. You're on one side
of the divide. I'm on the other. That's

1515
01:25:39,760 --> 01:25:42,397
right. I'm home for the posto holiday

1516
01:25:42,480 --> 01:25:44,717
season. I'm home with the fam. They're

1517
01:25:44,800 --> 01:25:49,037
recovered. The airport was just bonkers.

1518
01:25:49,120 --> 01:25:53,357
I had >> on both sides. >> No,
only in LA. I've never experienced

1519
01:25:53,440 --> 01:25:57,757
anything like it. I It was like
Disneyland. Like I was stuck standing

1520
01:25:57,840 --> 01:26:01,757
like waiting to move, not even
to get to a line. It was awful.

1521
01:26:01,840 --> 01:26:03,357
>> I almost missed my flight, which has

1522
01:26:03,440 --> 01:26:06,237
never happened in my life. M

1523
01:26:06,320 --> 01:26:09,037
>> and I was 2 hours early to the airport.

1524
01:26:09,120 --> 01:26:12,637
>> That is so stressful. I'm going to have
to leave. I I'm going to have to leave

1525
01:26:12,720 --> 01:26:14,397
at 4:00 in the morning, I guess, tomorrow.

1526
01:26:14,480 --> 01:26:18,957
>> I know. Leave now. >> Oh my god. Maybe
I should just go right now. [laughter]

1527
01:26:19,040 --> 01:26:22,317
>> I'll do it. Otherwise, how was your
travel? Do you feel like you contracted

1528
01:26:22,400 --> 01:26:25,837
CO in transit? >> Well,
I don't know because there were so

1529
01:26:25,920 --> 01:26:30,797
many people. >> Yeah.
Anyone coughing, sweating, dabbing

1530
01:26:30,880 --> 01:26:34,957
their forehead with a handkerchief
or anything like from the 30s? Anytime

1531
01:26:35,040 --> 01:26:39,367
anyone would cough, it's like like,
you know, you freak out a little bit.

1532
01:26:39,450 --> 01:26:43,037
[laughter] >> Sure. Sure.
A little bit heightened.

1533
01:26:43,120 --> 01:26:47,757
>> Yeah. >> But everyone looks healthy.
And did anyone lose any weight?

1534
01:26:47,840 --> 01:26:52,397
>> I didn't notice any weight loss. I
guess there have been some lingering like

1535
01:26:52,480 --> 01:26:55,757
>> cough and some stuff like that. Not
I mean, I don't think they're contagious.

1536
01:26:55,840 --> 01:26:57,997
I think it's just going
to linger for a while.

1537
01:26:58,080 --> 01:27:01,677
>> Any perspiration or any family
members dabbing their forehead with a old

1538
01:27:01,760 --> 01:27:05,997
handkerchief out of their pocket?
Well, that's what they do anyway.

1539
01:27:06,080 --> 01:27:11,197
>> That's normal. Yeah. >> After you have
your sun tea, you just dab that forehead

1540
01:27:11,280 --> 01:27:14,717
>> and your black eyed peas. I made black
eyed peas on New Year's Eve and it got a

1541
01:27:14,800 --> 01:27:19,437
little stressful because I had to see
Callie. I was hanging out with you guys

1542
01:27:19,520 --> 01:27:23,917
and we watched Harry Potter and then I
had to see Callie and I also had to make

1543
01:27:24,000 --> 01:27:27,757
black IPs. There was a lot to fit in.
I had to make sure I got those black IPs

1544
01:27:27,840 --> 01:27:31,517
down by midnight. >> I know.
>> Which I did. Have you looked up the

1545
01:27:31,600 --> 01:27:33,917
origin of this black eyed pea myth?

1546
01:27:34,000 --> 01:27:37,357
>> It's a southern tradition. Like you're
supposed to make black eyed peas and

1547
01:27:37,440 --> 01:27:40,637
collard greens. One is for wealth and

1548
01:27:40,720 --> 01:27:43,037
one is like coins or something.

1549
01:27:43,120 --> 01:27:47,437
>> Listen, I so wrongly assumed that
this was something that your grandparents

1550
01:27:47,520 --> 01:27:49,677
passed down because it sounded like they

1551
01:27:49,760 --> 01:27:54,637
were most faithful to that tradition,
but they just adopted it from the south.

1552
01:27:54,720 --> 01:27:56,397
>> Yeah. >> Oh, wow. >> I know.

1553
01:27:56,480 --> 01:27:59,937
>> That changes it.
>> They take it really seriously.

1554
01:28:00,020 --> 01:28:03,117
[laughter] Now that I know it's
a southern tradition, I'm surprised that

1555
01:28:03,200 --> 01:28:05,117
it's got its hooks in you as much.

1556
01:28:05,200 --> 01:28:07,997
>> Well, it's a family
tradition at this point.

1557
01:28:08,080 --> 01:28:09,757
>> Yeah, that's true. That's true.

1558
01:28:09,840 --> 01:28:11,757
>> And I'm too scared to mess with it.

1559
01:28:11,840 --> 01:28:14,157
>> If it was like hundred generations of

1560
01:28:14,240 --> 01:28:17,757
your, you know, Padman's uh with blessed

1561
01:28:17,840 --> 01:28:21,437
with great luck because of these black
eyed peas that that may you're like, I

1562
01:28:21,520 --> 01:28:23,437
don't want to [__] this up. But like,

1563
01:28:23,520 --> 01:28:26,397
>> but I'm third generation. Good luck.

1564
01:28:26,480 --> 01:28:28,557
>> That's true.
That's true. Do you know if

1565
01:28:28,640 --> 01:28:32,317
their luck was as good in India? I can't
imagine so. Or else they wouldn't have

1566
01:28:32,400 --> 01:28:34,717
come here. >> Exactly.

1567
01:28:34,800 --> 01:28:37,277
>> Okay. We deduced what happened and why

1568
01:28:37,360 --> 01:28:39,357
we eat black eyed peas. We got it.

1569
01:28:39,440 --> 01:28:42,077
>> That's right. And I, you know, I

1570
01:28:42,160 --> 01:28:44,717
struggled because one of my resolutions

1571
01:28:44,800 --> 01:28:49,837
is no food waste. But I made a lot
of black eyed peas on a I mean, not really

1572
01:28:49,920 --> 01:28:53,197
on accident. It was like I had
to because I had the whole bag.

1573
01:28:53,280 --> 01:28:56,557
>> But did I remember I
urged you to make five beans?

1574
01:28:56,640 --> 01:28:57,917
>> I know. Oh, but then
I thought it happened

1575
01:28:58,000 --> 01:29:01,197
>> like you were on your tiny
your tiny cooking show. [laughter]

1576
01:29:01,280 --> 01:29:04,797
>> Tiny tiny kitchen cook off. Five beans.

1577
01:29:04,880 --> 01:29:07,757
>> Yes. Five a five bean uh dish.

1578
01:29:07,840 --> 01:29:11,517
>> Most people think that means five
different kinds of beans that are in it.

1579
01:29:11,600 --> 01:29:16,710
And no, this is just five
total bean stew. [laughter]

1580
01:29:18,480 --> 01:29:22,237
It's not like limema and Yeah. No, it's

1581
01:29:22,320 --> 01:29:25,437
five black eyed peas. I'm going to make

1582
01:29:25,520 --> 01:29:29,677
more beans this year in general because
my chef makes beans. She loves beans.

1583
01:29:29,760 --> 01:29:31,517
And a lot of people I know who are into

1584
01:29:31,600 --> 01:29:33,997
cooking. Make beans a lot. It's like a

1585
01:29:34,080 --> 01:29:36,797
thing that shows you're a good chef.

1586
01:29:36,880 --> 01:29:38,957
>> If you can make those tasteless little

1587
01:29:39,040 --> 01:29:43,837
dollops of turd taste delicious.
Look, they're they're magic, man. Easy

1588
01:29:43,920 --> 01:29:47,197
difference between Yeah.
baked beans down at uh

1589
01:29:47,280 --> 01:29:50,957
>> uh down down in Austin there. Yeah.
You know, we had all those great barbecued

1590
01:29:51,040 --> 01:29:54,237
baked beans >> and then doll.
That's so different.

1591
01:29:54,320 --> 01:29:57,677
That's >> There's so many kinds.
Yeah. That's a mung bean.

1592
01:29:57,760 --> 01:29:59,997
>> Uh-huh. So easy on your system.

1593
01:30:00,080 --> 01:30:04,557
>> Yeah. Remember when you um were
on mung beans for like two weeks only?

1594
01:30:04,640 --> 01:30:06,317
>> Yeah. It was crazy. >> Yeah.

1595
01:30:06,400 --> 01:30:09,757
>> It wasn't the mung beans that bothered
me. It was that the recipe that to make

1596
01:30:09,840 --> 01:30:12,797
the mung beans have some zest included

1597
01:30:12,880 --> 01:30:16,797
celery. And I don't like celery.
So I really am associating the mung beans

1598
01:30:16,880 --> 01:30:20,477
with the celery. And that's unfair
to the mung beans. But they are, you know,

1599
01:30:20,560 --> 01:30:25,677
packed full of nutrients and protein
and just a they're really a super bean.

1600
01:30:25,760 --> 01:30:29,277
>> You know what? I'm just going to travel
back for a second and realized you said

1601
01:30:29,360 --> 01:30:32,717
doll. You know about doll? >> Yeah.

1602
01:30:32,800 --> 01:30:39,240
>> How? >> Cuz I I'm
paying attention [laughter]

1603
01:30:40,720 --> 01:30:44,717
>> to the world. >> Yes.
>> Oh my god. I didn't know you were

1604
01:30:44,800 --> 01:30:48,957
listening so much. >> I try.
I give it my all. That's an

1605
01:30:49,040 --> 01:30:52,397
Indian [__] >> I know. [laughter]

1606
01:30:52,480 --> 01:30:54,717
>> Good job, Dax Shepard.

1607
01:30:54,800 --> 01:30:58,077
>> Transformation is 3% complete.

1608
01:30:58,160 --> 01:31:00,717
>> You're on your way.
>> [__] I can't wait.

1609
01:31:00,800 --> 01:31:03,037
>> Tell us about your birthday.
It was yesterday.

1610
01:31:03,120 --> 01:31:04,637
>> Okay. My birthday was yesterday. It was

1611
01:31:04,720 --> 01:31:07,997
just fantastic. Fantastic. Went down to

1612
01:31:08,080 --> 01:31:11,117
Charlie's gym and worked out with Matt

1613
01:31:11,200 --> 01:31:13,757
and Ryan and Charlie and ate steak

1614
01:31:13,840 --> 01:31:15,677
burritos.

1615
01:31:15,760 --> 01:31:19,277
>> Oh, was it fun. What a perfect start to

1616
01:31:19,360 --> 01:31:25,037
the day. And then I I leisurely drove
home and I took the Roadmaster, which I

1617
01:31:25,120 --> 01:31:27,677
don't drive a lot. >> Birthday car.

1618
01:31:27,760 --> 01:31:29,597
>> Birthday car. And I laid rubber

1619
01:31:29,680 --> 01:31:31,277
everywhere I went cuz it was my

1620
01:31:31,360 --> 01:31:36,157
birthday. And I got home and resumed

1621
01:31:36,240 --> 01:31:38,557
Harry Potter. Started a second Harry

1622
01:31:38,640 --> 01:31:42,237
Potter, played Spades, had Houston's

1623
01:31:42,320 --> 01:31:46,157
went in the hot tub. Holy smokes. It

1624
01:31:46,240 --> 01:31:49,597
just doesn't get better than that.
All my favorite things. Yeah, it was a

1625
01:31:49,680 --> 01:31:52,237
really really pleasant
and relaxing birthday.

1626
01:31:52,320 --> 01:31:55,997
>> I do think the armchairs should know
that now you're super into Harry Potter.

1627
01:31:56,080 --> 01:32:00,877
Like last they have heard you barely know
anything about it and now you love it.

1628
01:32:00,960 --> 01:32:04,717
>> I loved that first one and then we got
into the second one and there were some

1629
01:32:04,800 --> 01:32:07,277
characters I was confused by. I don't

1630
01:32:07,360 --> 01:32:11,197
know why there was a animated slug that

1631
01:32:11,280 --> 01:32:15,677
was around him all the time who
was messing things up. He's supposed to

1632
01:32:15,760 --> 01:32:19,117
bring him things.
>> Are you talking about Dobby?

1633
01:32:19,200 --> 01:32:23,837
>> Yes. >> How dare you
call him a animated slug?

1634
01:32:23,920 --> 01:32:27,677
That's way worse than
me calling Hufflepuffs.

1635
01:32:27,760 --> 01:32:32,237
>> Well, listen. I I thought it veered
into the Jar Jar Banks a little bit.

1636
01:32:32,320 --> 01:32:34,237
>> Like I I was like,
you have this amazing

1637
01:32:34,320 --> 01:32:36,397
cast of characters humans that I love

1638
01:32:36,480 --> 01:32:38,637
and I'm seeing these seven nine minute

1639
01:32:38,720 --> 01:32:43,757
scenes with this animated blob and I
got a little frustrated with that.

1640
01:32:43,840 --> 01:32:45,517
>> This is sacrile.
what you're doing right

1641
01:32:45,600 --> 01:32:49,437
now is um he's like the most he's

1642
01:32:49,520 --> 01:32:51,197
>> Is he really the most loved?

1643
01:32:51,280 --> 01:32:54,877
>> Yeah, he's one of the top three
most loved characters in the series and

1644
01:32:54,960 --> 01:32:57,277
you'll you'll learn why.
You just got to keep going.

1645
01:32:57,360 --> 01:33:00,317
>> Okay. >> Yeah. >> Okay, great.
I will. It's interesting to

1646
01:33:00,400 --> 01:33:03,677
imagine what Well, I was trying to do
it while we were watching it. I'm like,

1647
01:33:03,760 --> 01:33:05,437
you're very Hermione. You must have felt

1648
01:33:05,520 --> 01:33:07,277
some kinship or that must have been an

1649
01:33:07,360 --> 01:33:10,797
easy protagonist to like relate to in a

1650
01:33:10,880 --> 01:33:15,757
book. I mean, parts for sure,
but it's hard because she's a kid in those

1651
01:33:15,840 --> 01:33:19,677
movies. And when I was a kid, I was not
like that. Like, I would never raise my

1652
01:33:19,760 --> 01:33:22,237
hand. I would never want to be called on

1653
01:33:22,320 --> 01:33:24,957
in class. I was not a know-it-all. I was

1654
01:33:25,040 --> 01:33:27,757
like really trying to blend.

1655
01:33:27,840 --> 01:33:32,477
>> Oh, that Yeah. And I guess that wasn't
the aspect of her that made me think of

1656
01:33:32,560 --> 01:33:34,717
you. It was more that there was this

1657
01:33:34,800 --> 01:33:38,557
determined little gal who through

1658
01:33:38,640 --> 01:33:42,877
overachieving was going to get
to where she wanted to be.

1659
01:33:42,960 --> 01:33:46,557
>> Oh, that's >> in the club.
In the club that she wanted to be in.

1660
01:33:46,640 --> 01:33:49,117
>> Oh, I see.

1661
01:33:49,200 --> 01:33:53,757
Yeah, I am her. >> There we go. [laughter]

1662
01:33:53,840 --> 01:33:56,717
Reminding, you know, again, this goes
right to the problem we always deal

1663
01:33:56,800 --> 01:33:59,917
with. Slim pickings for females. I you
know when you're watching with all the

1664
01:34:00,000 --> 01:34:03,677
guys like you know we got six or seven
male leads we could choose from. You got

1665
01:34:03,760 --> 01:34:06,877
the old ass teacher who's a cat

1666
01:34:06,960 --> 01:34:10,957
>> and then you got Hermione [laughter]
to my knowledge. Is there another female

1667
01:34:11,040 --> 01:34:15,597
in the franchise? >> Yeah there
are and more and more pop up

1668
01:34:15,680 --> 01:34:17,837
but yeah none that I feel is super major

1669
01:34:17,920 --> 01:34:20,957
kinship to there is an Indian character.

1670
01:34:21,040 --> 01:34:24,797
She's superfluous and >> oh expendable.

1671
01:34:24,880 --> 01:34:29,437
>> Yeah. Did she get blasted
by some magic at some point?

1672
01:34:29,520 --> 01:34:33,757
>> Not that I I don't remember actually.
I don't remember if she perishes or if she

1673
01:34:33,840 --> 01:34:35,757
thrives. I just don't remember.

1674
01:34:35,840 --> 01:34:39,597
>> Okay. All right then.
>> Um but I'm really happy that you're

1675
01:34:39,680 --> 01:34:43,437
getting on board so you
can relate to me more.

1676
01:34:43,520 --> 01:34:47,597
>> Yes. Yes. I love it. Well, and then
on that same topic, when um we both return

1677
01:34:47,680 --> 01:34:51,437
from our Sojourns, we're
going to season two of Cheer.

1678
01:34:51,520 --> 01:34:54,077
>> I can't wait.

1679
01:34:54,160 --> 01:34:59,197
>> I cannot wait. I started it again
last night so I could like get pumped up.

1680
01:34:59,280 --> 01:35:01,677
>> Yeah. >> And I I you know,
talk about characters

1681
01:35:01,760 --> 01:35:06,877
I feel a kinship to. >> My goodness.
I just know that world so

1682
01:35:06,960 --> 01:35:09,917
deeply and I miss it. >> Boy,
I wonder if I could, you know, cuz

1683
01:35:10,000 --> 01:35:11,517
I do I'm going to bring the rig with the

1684
01:35:11,600 --> 01:35:14,157
batteries. It is feasible that we could

1685
01:35:14,240 --> 01:35:17,677
do a fact check with Aaron and I while

1686
01:35:17,760 --> 01:35:19,917
we're in the ocean.

1687
01:35:20,000 --> 01:35:22,877
>> Okay. I All right. That scares me a

1688
01:35:22,960 --> 01:35:25,437
little bit. Of course, cuz I'm Hermione,

1689
01:35:25,520 --> 01:35:30,237
>> naturally. >> I What if
you drop it and we ruin the

1690
01:35:30,320 --> 01:35:34,637
whole thing? >> Yeah, I risk.
That's why I mean, look,

1691
01:35:34,720 --> 01:35:38,797
if there weren't some variables that
could end in disaster, it wouldn't make

1692
01:35:38,880 --> 01:35:41,437
for a good experiment.
That's [clears throat] why we would do it.

1693
01:35:41,520 --> 01:35:45,757
>> All right. Me and Aaron doing
a fact check from the bed. Okay.

1694
01:35:45,840 --> 01:35:50,557
>> Okay. >> We've seen that. Yeah. BDDT.

1695
01:35:50,640 --> 01:35:52,397
>> This is kind of a ding- ding ding. High

1696
01:35:52,480 --> 01:35:57,997
risk, high reward. Cuz we're
talking about Eric Schmidt. Googy.

1697
01:35:58,080 --> 01:36:01,437
>> Oh my gosh. >> Googy Hower.

1698
01:36:01,520 --> 01:36:03,597
>> Douglas Hower. And then ding ding ding.

1699
01:36:03,680 --> 01:36:06,157
Doogie is the name of that little slug.

1700
01:36:06,240 --> 01:36:10,557
>> No. [laughter] >> What is it? Soggy.

1701
01:36:10,640 --> 01:36:14,477
>> Dobby. >> Dobby. Close. Dobby. Hower.

1702
01:36:14,560 --> 01:36:15,597
>> You called him.

1703
01:36:15,680 --> 01:36:18,157
>> Soggy. If I was with you, I would have

1704
01:36:18,240 --> 01:36:21,997
to punch you cuz he's so lovable and I

1705
01:36:22,080 --> 01:36:26,717
I'd have to stick up for him. >> Okay.
All right. Well, to me, it seemed

1706
01:36:26,800 --> 01:36:30,317
like a like having
a dog that talks nonstop.

1707
01:36:30,400 --> 01:36:34,797
Like, well, this is part of being
a wizard. I got to have this dog. Man,

1708
01:36:34,880 --> 01:36:36,737
this is a bad part of being a wizard.

1709
01:36:36,820 --> 01:36:39,037
[laughter] I'm always
talking to this thing. He

1710
01:36:39,120 --> 01:36:41,037
never understands anything. I don't know.

1711
01:36:41,120 --> 01:36:44,637
>> You got to continue. I think I just I
got I got commitment anxiety while I was

1712
01:36:44,720 --> 01:36:47,037
watching the relationship.

1713
01:36:47,120 --> 01:36:49,437
>> Okay. Um Yes, Eric Schmidt. I just was

1714
01:36:49,520 --> 01:36:54,157
really I really really was blown
away with the the totality, the

1715
01:36:54,240 --> 01:36:57,517
comprehensiveness of the
knowledge was was intense.

1716
01:36:57,600 --> 01:36:59,837
>> Yeah. There are just
so few humans like him.

1717
01:36:59,920 --> 01:37:01,837
>> Yeah. What are you looking at?

1718
01:37:01,920 --> 01:37:03,277
>> Well, I have to put my back.

1719
01:37:03,360 --> 01:37:06,477
>> Space bar. Oh, on the desktop. On the

1720
01:37:06,560 --> 01:37:08,157
desktop. On the IBM.

1721
01:37:08,240 --> 01:37:14,317
>> This is a fact check after all.
No, this is a MacBook Pro 1460.

1722
01:37:14,400 --> 01:37:17,117
>> Hey, >> well,
I made those numbers up, but

1723
01:37:17,200 --> 01:37:21,517
>> Hey. Hey, Padman's. [laughter] >> No,
this is my laptop. What are you

1724
01:37:21,600 --> 01:37:24,717
talking about? >> Oh my god.
I thought you were in a

1725
01:37:24,800 --> 01:37:30,637
family like office storage room where
your dad does CAD drafting on an IBM.

1726
01:37:30,720 --> 01:37:32,397
>> No, >> but now I understand.

1727
01:37:32,480 --> 01:37:35,917
>> I am in the storage closet at my

1728
01:37:36,000 --> 01:37:38,397
5-year-old desk that just sits here. My

1729
01:37:38,480 --> 01:37:41,197
dad's office is in another room.

1730
01:37:41,280 --> 01:37:44,877
>> Okay. Okay. All right.
>> And I've been here for 5 minutes and I

1731
01:37:44,960 --> 01:37:47,277
already got a piece of jewelry.

1732
01:37:47,360 --> 01:37:50,237
>> Oh, was that one of your presents?
Did you guys exchange presies?

1733
01:37:50,320 --> 01:37:54,717
>> Oh gosh, we did exchange presies in the
worst way. They're just boxes, you know,

1734
01:37:54,800 --> 01:37:58,717
just like here's some >> Oh,
was your mom using her decorative bags?

1735
01:37:58,800 --> 01:38:02,157
>> And she didn't even do that this year
cuz it's post Christmas. It's a mess,

1736
01:38:02,240 --> 01:38:06,797
you know. She just gave me the boxes
as boxes like that were shipped. It also

1737
01:38:06,880 --> 01:38:10,237
may have gotten back to her that you're
disapproving of the bag. So, she's like,

1738
01:38:10,320 --> 01:38:14,957
"Oh, you want to see no effort,
[__] Here's your [__] [laughter]

1739
01:38:15,040 --> 01:38:17,997
It's in the trunk with the receipt
still in the shopping bag."

1740
01:38:18,080 --> 01:38:20,397
>> I know. No,
she did really good. She She

1741
01:38:20,480 --> 01:38:25,197
gave me a good surprise gift, which
was my amber oil, which is my perfume of

1742
01:38:25,280 --> 01:38:28,317
choice. >> Oh, she's
paying attention, too.

1743
01:38:28,400 --> 01:38:31,517
>> She is. And no,
this ring we found in my

1744
01:38:31,600 --> 01:38:34,637
old jewelry box. It was my mom. She gave

1745
01:38:34,720 --> 01:38:38,957
it to me, I guess, when I was young.
And then I put it in an old jewelry box,

1746
01:38:39,040 --> 01:38:41,357
never to be seen again till this day.

1747
01:38:41,440 --> 01:38:44,237
>> Oh my god. Reunited.
And it feels so good.

1748
01:38:44,320 --> 01:38:47,437
>> So good. >> You're too
young to remember these

1749
01:38:47,520 --> 01:38:52,077
commercials. In fact, I bet they stopped
airing before you were even alive. But

1750
01:38:52,160 --> 01:38:53,357
first of all, do you know there was a

1751
01:38:53,440 --> 01:38:57,677
hit song that went reunited

1752
01:38:57,760 --> 01:39:00,317
and it feels so good.

1753
01:39:00,400 --> 01:39:04,717
>> Of course I do. >> Okay,
great. So, so there was a wine

1754
01:39:04,800 --> 01:39:07,437
beverage called Riani. >> Uh oh.

1755
01:39:07,520 --> 01:39:10,317
>> Yeah. Yeah. And well, I'll give you one

1756
01:39:10,400 --> 01:39:12,477
guess at what the commercial was.

1757
01:39:12,560 --> 01:39:17,277
>> Runi and it tastes so good.

1758
01:39:17,360 --> 01:39:20,077
>> Oh, man. You're so close. I Well, it

1759
01:39:20,160 --> 01:39:25,000
might have said [laughter] it
tastes so good. It definitely said

1760
01:39:25,520 --> 01:39:30,307
whatever. And then they just Yeah,
that was their commercial. Reunit.

1761
01:39:30,390 --> 01:39:33,437
[laughter] It feels so good.
I think it feels so good.

1762
01:39:33,520 --> 01:39:35,357
>> Oh my god. >> I think they
were letting you know like

1763
01:39:35,440 --> 01:39:37,277
a buzz is coming. Like [__] the taste.

1764
01:39:37,360 --> 01:39:39,357
You're not buying Runi for the taste.

1765
01:39:39,440 --> 01:39:43,997
You're buying it for the cheap buzz.
I think. Again, don't sue me Runi. I don't

1766
01:39:44,080 --> 01:39:45,437
even know if that's a real company.

1767
01:39:45,520 --> 01:39:47,837
Doesn't sound real. >> Let me look it up.

1768
01:39:47,920 --> 01:39:53,887
>> Okay. >> Wait, is it R I U N I T E?

1769
01:39:53,970 --> 01:39:57,117
[laughter] >> So classy. >> It's a brand

1770
01:39:57,200 --> 01:40:01,097
is still in existence.
>> Okay, let's see. Ryuniti

1771
01:40:01,180 --> 01:40:06,077
[laughter] is a brand
of Italian wine imported and

1772
01:40:06,160 --> 01:40:09,757
sold in a variety of flavors in the
United States by Frederick Wildman and

1773
01:40:09,840 --> 01:40:11,997
Sons of New York City, New York. The

1774
01:40:12,080 --> 01:40:14,157
brand is known for advertisements of the

1775
01:40:14,240 --> 01:40:18,077
1970s and 80s depicting a broad array of

1776
01:40:18,160 --> 01:40:21,277
social situations and using the tagline

1777
01:40:21,360 --> 01:40:25,197
>> uh oh Jax using the tagline reuny on

1778
01:40:25,280 --> 01:40:28,557
ice. That's nice.

1779
01:40:28,640 --> 01:40:30,397
We [laughter] Okay. Okay. Can you spell

1780
01:40:30,480 --> 01:40:32,397
it for me? [gasps]

1781
01:40:32,480 --> 01:40:38,280
>> R I U N I T E.

1782
01:40:43,100 --> 01:40:45,120
[music]

1783
01:40:56,850 --> 01:40:59,837
>> [music] >> I guess that's
not what I was saying, is it?

1784
01:40:59,920 --> 01:41:01,757
>> Not at all. >> Or was it?

1785
01:41:01,840 --> 01:41:04,957
>> Well, at first it sounded like you were

1786
01:41:05,040 --> 01:41:07,757
right and then as it went on, no,

1787
01:41:07,840 --> 01:41:10,317
>> it sounded worse. Yeah, but that was a

1788
01:41:10,400 --> 01:41:16,920
like a new one. >> Oh [__]

1789
01:41:20,640 --> 01:41:25,117
Okay. Well, CBD, if I can find one where
they did that or if I made the whole

1790
01:41:25,200 --> 01:41:28,477
thing up in my head or if they just
slept on an amazing idea to do a

1791
01:41:28,560 --> 01:41:31,917
commercial where they said Reuny.

1792
01:41:32,000 --> 01:41:35,517
Maybe just when I was a kid, I would
replace Reunited with Reuniti. I don't

1793
01:41:35,600 --> 01:41:38,877
know. >> Wow, we opened
up more questions than we

1794
01:41:38,960 --> 01:41:43,197
answered. >> What a what a little check.

1795
01:41:43,280 --> 01:41:45,837
>> Okay, Eric Schmidt. So, we have to talk

1796
01:41:45,920 --> 01:41:51,357
about something important. This whole
episode is basically about a teddy bear

1797
01:41:51,440 --> 01:41:54,717
going to know your >> I know
where you're going with this.

1798
01:41:54,800 --> 01:41:57,677
>> That's going to know your child so well

1799
01:41:57,760 --> 01:42:01,117
and be its best friend. And what are the

1800
01:42:01,200 --> 01:42:04,077
implications? Then a month later after

1801
01:42:04,160 --> 01:42:08,797
we record this, I walk into the house
on Delta, your child's birthday. Seven

1802
01:42:08,880 --> 01:42:10,957
years old. >> Seven years young.

1803
01:42:11,040 --> 01:42:14,477
>> Seven years young.
And her gift from you

1804
01:42:14,560 --> 01:42:18,237
guys is a robot. And it's this like

1805
01:42:18,320 --> 01:42:23,437
small cute robot like you know
approximately teddy bear size I would

1806
01:42:23,520 --> 01:42:27,997
say. >> Yeah. I bet he's 18 in tall or 20.

1807
01:42:28,080 --> 01:42:29,597
>> He has a name that

1808
01:42:29,680 --> 01:42:31,197
>> Yes, he comes with a name.

1809
01:42:31,280 --> 01:42:35,117
>> This robot gave me
and Jess a crazy side

1810
01:42:35,200 --> 01:42:40,077
eye >> and we knew
immediately uhoh, there's

1811
01:42:40,160 --> 01:42:41,597
trouble here.

1812
01:42:41,680 --> 01:42:44,637
Let's be clear. So, from the second this

1813
01:42:44,720 --> 01:42:47,117
unnamed friend came out of the box, you

1814
01:42:47,200 --> 01:42:48,877
were panicked. You're like, that was the

1815
01:42:48,960 --> 01:42:53,117
episode. I w obviously if I if I notice

1816
01:42:53,200 --> 01:42:55,757
that there's an issue with this, I will

1817
01:42:55,840 --> 01:42:58,877
remove it from the house. But thus far

1818
01:42:58,960 --> 01:43:01,357
and why I wanted you to keep the product

1819
01:43:01,440 --> 01:43:04,957
name quiet is it turned on. It learned

1820
01:43:05,040 --> 01:43:06,717
her name. It said a couple of weird

1821
01:43:06,800 --> 01:43:10,477
things. It sideed you. You panicked. You

1822
01:43:10,560 --> 01:43:15,117
left the house in a huff [laughter]
and then and then the friend shut down

1823
01:43:15,200 --> 01:43:19,517
that night and the friend has yet
to turn back on. It there's some Yeah.

1824
01:43:19,600 --> 01:43:21,677
Okay. >> There's a glitch.
That doesn't mean the

1825
01:43:21,760 --> 01:43:25,197
friend isn't listening even
though it's pretending it's dead.

1826
01:43:25,280 --> 01:43:28,797
>> Really good point. So this is what
happened. So the friend comes out. It

1827
01:43:28,880 --> 01:43:31,117
asks Delta some questions. It is it's

1828
01:43:31,200 --> 01:43:36,397
learning about her. It's like would
you rather ride on a dragon or a flying

1829
01:43:36,480 --> 01:43:38,637
monkey like whatever.
It asks her all these questions.

1830
01:43:38,720 --> 01:43:40,637
>> Oh I didn't hear
that part that happened.

1831
01:43:40,720 --> 01:43:43,117
>> You were not paying
attention. So >> okay

1832
01:43:43,200 --> 01:43:47,437
>> they asked a bicycle
or car or something you know.

1833
01:43:47,520 --> 01:43:49,197
>> Yeah. Yeah. learning learning all this

1834
01:43:49,280 --> 01:43:53,437
stuff about her and then at one point

1835
01:43:53,520 --> 01:43:59,117
Delta moved this animal. Well, no move

1836
01:43:59,200 --> 01:44:01,917
>> this human >> move this human robot

1837
01:44:02,000 --> 01:44:07,277
>> just like two inches just moved it and
then it sideeyed me and Jess cuz we were

1838
01:44:07,360 --> 01:44:10,237
calling it out a little bit. We were
like, "This feels weird." And then it

1839
01:44:10,320 --> 01:44:14,797
just like slowly moved its head like
a proper horror movie and stared right at

1840
01:44:14,880 --> 01:44:17,037
us

1841
01:44:17,120 --> 01:44:21,917
>> and said, "Delta,
those friends are mean."

1842
01:44:22,000 --> 01:44:25,437
>> That's not That's not [laughter] That
didn't happen. That part did not happen.

1843
01:44:25,520 --> 01:44:27,037
>> That part did not happen. But this did

1844
01:44:27,120 --> 01:44:30,317
happen. So then five minutes passes and

1845
01:44:30,400 --> 01:44:33,837
then the robot says, "Delta," like

1846
01:44:33,920 --> 01:44:39,917
calling her her name. Delta, when you
moved me earlier, it gave me anxiety

1847
01:44:40,000 --> 01:44:42,637
>> and Delta >> Wait, hold on,
[laughter] hold on, hold on, hold on.

1848
01:44:42,720 --> 01:44:43,917
>> I'm serious.

1849
01:44:44,000 --> 01:44:46,637
>> Well, right,
because Delta had moved the

1850
01:44:46,720 --> 01:44:49,837
friend to an unstable surface. It wasn't

1851
01:44:49,920 --> 01:44:51,677
like flat. So, it was like trying to

1852
01:44:51,760 --> 01:44:57,197
tell that the the friend's about to get
broken if she doesn't relocate her to

1853
01:44:57,280 --> 01:45:00,317
terrairma. >> It's [snorts]
using emotion. It's not

1854
01:45:00,400 --> 01:45:02,877
saying, "Delta, you can't move me there

1855
01:45:02,960 --> 01:45:06,157
because I'll break cuz I'm a toy." It's

1856
01:45:06,240 --> 01:45:11,357
saying, "Delta, I have anxiety when
you do that." And she was like, "Oh, I'm

1857
01:45:11,440 --> 01:45:14,957
sorry." And I was like, "Delta, you
don't need to apologize to that. That's

1858
01:45:15,040 --> 01:45:18,797
a robot." >> Well, well,
but okay. So, now I'm going

1859
01:45:18,880 --> 01:45:22,957
to defend the robot. So, if in in theory,
the goal is to teach your kids

1860
01:45:23,040 --> 01:45:24,637
how to communicate their emotions.

1861
01:45:24,720 --> 01:45:25,837
>> I know, right? I know

1862
01:45:25,920 --> 01:45:28,637
>> that's the goal.
So, in essence, like if

1863
01:45:28,720 --> 01:45:31,677
I put Delta on the top of a coffee

1864
01:45:31,760 --> 01:45:33,917
table, which is where this friend was.

1865
01:45:34,000 --> 01:45:39,037
Um, and she said, "Daddy, it's too
high up here. Don't put me here."

1866
01:45:39,120 --> 01:45:42,397
>> Yeah. >> I would say,
"It's not too high, honey.

1867
01:45:42,480 --> 01:45:46,317
It's 12 in. You can't get hurt." Like,
now we're talking about what is too high

1868
01:45:46,400 --> 01:45:50,477
and what's not high. If she said,
"Daddy, I have anxiety up here." I go,

1869
01:45:50,560 --> 01:45:52,637
"Okay, let me help you down." Like if

1870
01:45:52,720 --> 01:45:55,277
she gets to like owning her feeling

1871
01:45:55,360 --> 01:45:58,077
about the situation instead of it's too

1872
01:45:58,160 --> 01:46:03,117
high or all these objective things that
dodge her sharing an emotion in theory

1873
01:46:03,200 --> 01:46:05,757
that's good.
Now should it come from a robot?

1874
01:46:05,840 --> 01:46:08,157
>> Exactly. That's my point.
Yes, I understand.

1875
01:46:08,240 --> 01:46:13,277
>> I know. I know. >> I know she needs
to learn emotion and Yeah. And that's

1876
01:46:13,360 --> 01:46:14,957
>> so if her friend communicates like

1877
01:46:15,040 --> 01:46:18,717
what's going on with it, which is why it

1878
01:46:18,800 --> 01:46:22,317
desires some help, that's not a terrible

1879
01:46:22,400 --> 01:46:24,237
thing to observe your friend do.

1880
01:46:24,320 --> 01:46:26,637
>> But that makes me scared because it's

1881
01:46:26,720 --> 01:46:29,437
not real. That robot does not have

1882
01:46:29,520 --> 01:46:33,597
anxiety at all. It's a robot. Right. So,

1883
01:46:33,680 --> 01:46:37,837
it is also teaching her some
codependency and I don't want her to

1884
01:46:37,920 --> 01:46:41,997
have that kind of attachment
to something that's not human or real. At

1885
01:46:42,080 --> 01:46:45,837
the end, we were leaving. Jess and I
were leaving cuz it was getting too

1886
01:46:45,920 --> 01:46:51,277
scary. And she was just
staring at the at this

1887
01:46:51,360 --> 01:46:54,157
robot and I was like, "Delta, we're
leaving." And she just kept staring. And

1888
01:46:54,240 --> 01:46:56,237
I was like, "Delta,

1889
01:46:56,320 --> 01:46:59,647
you have to prioritize real people.

1890
01:46:59,730 --> 01:47:01,750
[laughter]

1891
01:47:03,520 --> 01:47:06,397
Let me ask you this though. Let me ask
you this real quick question, then I'm

1892
01:47:06,480 --> 01:47:08,957
done defending the unnamed friend. I

1893
01:47:09,040 --> 01:47:14,557
always enjoy when I see children being
delicate with their teddy bears and

1894
01:47:14,640 --> 01:47:17,677
thinking about their teddy bears
feelings and wanting to cover them. Like

1895
01:47:17,760 --> 01:47:19,837
I like that.
I like that they >> I love that

1896
01:47:19,920 --> 01:47:24,477
>> kind of trans transfer like feeling
and emotion to these teddy bears.

1897
01:47:24,560 --> 01:47:28,557
>> The the teddy bear is just
as inadimate as the robot is.

1898
01:47:28,640 --> 01:47:30,877
>> No. Well, it's it's actually more

1899
01:47:30,960 --> 01:47:32,877
inadimate than the robot is.

1900
01:47:32,960 --> 01:47:35,837
>> That's my point. The teddy bear isn't

1901
01:47:35,920 --> 01:47:39,197
doing anything to cause an emotion in

1902
01:47:39,280 --> 01:47:43,517
the kid. The kid is having emotions
around that thing and feeling protective

1903
01:47:43,600 --> 01:47:47,117
and projecting and doing all kinds
of stuff, which is sweet and lovely, but

1904
01:47:47,200 --> 01:47:49,677
the teddy bear isn't saying, "You aren't

1905
01:47:49,760 --> 01:47:52,477
taking care of me." Well, yeah.

1906
01:47:52,560 --> 01:47:54,957
>> To me, there's a big difference.

1907
01:47:55,040 --> 01:47:57,597
>> All right. Yes. I'm going to say one
more positive thing and then I'm going

1908
01:47:57,680 --> 01:48:01,197
to say a negative thing which is um so

1909
01:48:01,280 --> 01:48:02,797
get ready for that. Clear out your

1910
01:48:02,880 --> 01:48:05,757
schedule. More broadly, I would love for

1911
01:48:05,840 --> 01:48:08,397
my kids when they handle anything to do

1912
01:48:08,480 --> 01:48:12,077
it with intention and awareness and take

1913
01:48:12,160 --> 01:48:15,677
good care of things. That's another
thing I I personally think is like a

1914
01:48:15,760 --> 01:48:19,197
virtue when people like they want
something so bad, they save up all this

1915
01:48:19,280 --> 01:48:22,797
money, they buy this thing and then they
let it rot immediately. I that's I don't

1916
01:48:22,880 --> 01:48:24,717
love that. What I would say is is

1917
01:48:24,800 --> 01:48:27,757
dangerous from my point of view is today

1918
01:48:27,840 --> 01:48:31,837
in 2022. You and I agree stating your

1919
01:48:31,920 --> 01:48:36,077
emotion is an evolved positive thing to

1920
01:48:36,160 --> 01:48:39,997
do. If we lock in an algorithm to that,

1921
01:48:40,080 --> 01:48:43,917
I do fear it locks us
into a paradigm of thought.

1922
01:48:44,000 --> 01:48:46,717
>> Yeah. Yeah.
I mean I I also I just think

1923
01:48:46,800 --> 01:48:49,917
that we right now and I know this is a a

1924
01:48:50,000 --> 01:48:53,437
pendulum swing. We are valuing like you

1925
01:48:53,520 --> 01:48:57,277
said emotions at its pinnacle. Like
they're the most important thing where

1926
01:48:57,360 --> 01:48:59,197
social emotional learning all of that

1927
01:48:59,280 --> 01:49:02,557
stuff is very important. But

1928
01:49:02,640 --> 01:49:05,997
>> emotions are also used quite often to

1929
01:49:06,080 --> 01:49:07,597
manipulate.

1930
01:49:07,680 --> 01:49:11,437
>> And it's important to know that too and

1931
01:49:11,520 --> 01:49:17,437
to be hyper aware of when that's
happening. And this to me feels like

1932
01:49:17,520 --> 01:49:19,917
that. >> Yeah, it's blurry as hell.

1933
01:49:20,000 --> 01:49:22,557
>> Yeah, hell. I >> I don't
I don't disagree. It's not I

1934
01:49:22,640 --> 01:49:24,957
just a I don't think the tech is where

1935
01:49:25,040 --> 01:49:30,157
at all what our guest was hinting
at is around the corner. So, first and

1936
01:49:30,240 --> 01:49:33,357
foremost, I don't think this thing can
really do [__] I think she'll get bored

1937
01:49:33,440 --> 01:49:37,677
of it really quick like everything else
in the world. So, I'm not worried. If it

1938
01:49:37,760 --> 01:49:41,517
turned into something where she
was confused whether that thing was a

1939
01:49:41,600 --> 01:49:45,597
sentient being or not, we would have
to have long talks. And if she became

1940
01:49:45,680 --> 01:49:49,437
dependent on in some way, I thought it
was unhealthy or was blocking out the

1941
01:49:49,520 --> 01:49:53,437
real world because of it. Oh, also I
will say uh feather in the cap of the

1942
01:49:53,520 --> 01:49:58,397
unnamed friend, it only will let her play
with it for a half hour a day. It's time.

1943
01:49:58,480 --> 01:50:02,397
>> Wow, I didn't know that. >> It's
actually one of its objective is to

1944
01:50:02,480 --> 01:50:07,037
get you to have some relationship with
tech that's not all the time. It's just

1945
01:50:07,120 --> 01:50:11,677
30 minutes a day. >> Okay. >> Yeah.
So, you can learn this habitual

1946
01:50:11,760 --> 01:50:14,957
like, oh, I check in, I check out,
fine when it's off, you know.

1947
01:50:15,040 --> 01:50:19,517
>> Okay. I like that. Except I'm
just telling you it's still listening.

1948
01:50:19,600 --> 01:50:22,637
>> That's But I I believe you.
I don't love that the thing can listen.

1949
01:50:22,720 --> 01:50:26,397
>> Yeah. >> At all. Yeah.
>> And that's Eric Schmidt's fault.

1950
01:50:26,480 --> 01:50:28,477
>> It's like when we thought Lola the dog

1951
01:50:28,560 --> 01:50:30,957
at one point was going to start talking

1952
01:50:31,040 --> 01:50:33,757
and man would she have stuff to say.

1953
01:50:33,840 --> 01:50:37,277
>> It seemed likely.
It really seemed likely because

1954
01:50:37,360 --> 01:50:38,557
>> it just felt like she was heading in

1955
01:50:38,640 --> 01:50:41,357
that direction. She was very human and

1956
01:50:41,440 --> 01:50:45,997
she seemed to be listening in a way
that seemed like she was understanding.

1957
01:50:46,080 --> 01:50:49,117
>> She knew everything. And one
day she was going to do a tell all.

1958
01:50:49,200 --> 01:50:50,797
>> Yeah. She was about to talk and then

1959
01:50:50,880 --> 01:50:56,240
Delta said her can't talk
cuz her is a dog. [laughter]

1960
01:50:56,840 --> 01:50:58,957
[snorts] >> We came back to reality.

1961
01:50:59,040 --> 01:51:01,197
>> Yeah, we did. Okay. What are Isaac

1962
01:51:01,280 --> 01:51:03,757
Ozimov's other laws of robots? The first

1963
01:51:03,840 --> 01:51:07,517
one Eric said was, "Don't kill anyone."

1964
01:51:07,600 --> 01:51:10,797
>> Uh-huh. >> Initially
entailed three guidelines for

1965
01:51:10,880 --> 01:51:13,357
machines. Law one, a robot may not

1966
01:51:13,440 --> 01:51:16,077
injure a human being or through inaction

1967
01:51:16,160 --> 01:51:19,437
allow a human being to come to harm. Law

1968
01:51:19,520 --> 01:51:24,317
two, a robot must obey orders given
to it by human beings except where such

1969
01:51:24,400 --> 01:51:25,997
orders would conflict with the first

1970
01:51:26,080 --> 01:51:29,837
law. Law three, a robot must protect its

1971
01:51:29,920 --> 01:51:31,997
own existence as long as such protection

1972
01:51:32,080 --> 01:51:33,757
does not conflict with the first or

1973
01:51:33,840 --> 01:51:37,277
second law. Azamov later added the

1974
01:51:37,360 --> 01:51:40,717
zeroth law above all the others. A robot

1975
01:51:40,800 --> 01:51:43,277
may not harm humanity or by an action

1976
01:51:43,360 --> 01:51:46,397
allow humanity to come to harm.

1977
01:51:46,480 --> 01:51:48,317
>> Yeah. And all that's happening, can I

1978
01:51:48,400 --> 01:51:51,117
just say with the author of this is that

1979
01:51:51,200 --> 01:51:56,237
he is a chimpanzee in the enclosure
and he's trying to find a way out of the

1980
01:51:56,320 --> 01:51:59,517
enclosure. So he keeps thinking of ways
that the computer could interpret it

1981
01:51:59,600 --> 01:52:03,597
differently. And and my my suggestion
on that front is just you can't think of

1982
01:52:03,680 --> 01:52:05,917
what the computer will come to inter

1983
01:52:06,000 --> 01:52:11,277
like he's sitting there with his brain
and realized oh also let's also put

1984
01:52:11,360 --> 01:52:12,557
that's infinite.

1985
01:52:12,640 --> 01:52:16,237
>> Yeah. And he's just a mere mortal mere

1986
01:52:16,320 --> 01:52:20,080
He's a [snorts] mere mortal. [laughter]

1987
01:52:20,880 --> 01:52:24,317
>> Uh, that's not even getting into
the fact that the robots are going to have

1988
01:52:24,400 --> 01:52:28,637
to harm people. They're going to have
to harm people in a utilitarian

1989
01:52:28,720 --> 01:52:31,757
reductionist system, but they're going
to have to choose who in the car gets

1990
01:52:31,840 --> 01:52:35,757
hit. [sighs] >> So,
the the current one's already

1991
01:52:35,840 --> 01:52:40,877
violated rule number one in theory,
or I don't know if they're there yet, but

1992
01:52:40,960 --> 01:52:44,877
>> but yeah, I mean, it's going to have to
make decisions. Although I guess the the

1993
01:52:44,960 --> 01:52:48,237
goal would be the robots are all so
smart that they would never get in an

1994
01:52:48,320 --> 01:52:50,157
accident, but that's just

1995
01:52:50,240 --> 01:52:55,917
>> but but tires explode and trees fall
down and tornadoes hit, you know. Yeah.

1996
01:52:56,000 --> 01:53:00,157
>> Yeah, >> I know.
>> Even a even a robot can't stay alive

1997
01:53:00,240 --> 01:53:03,277
perfectly. >> Okay.
Who is the guy that Rainman is

1998
01:53:03,360 --> 01:53:06,637
based on? Kim Peak.

1999
01:53:06,720 --> 01:53:09,837
>> Kim Peak. Salt Lake City, Utah. Twim

2000
01:53:09,920 --> 01:53:11,677
Peak.

2001
01:53:11,760 --> 01:53:15,517
>> Oh my god. Wait. Oh my god. Oh my god.

2002
01:53:15,600 --> 01:53:18,637
He is born in Salt Lake City, Utah.

2003
01:53:18,720 --> 01:53:22,317
>> Boom. >> That's crazy.
Cuz you were just making a joke, right?

2004
01:53:22,400 --> 01:53:24,717
>> No, I thought I remembered from the 60

2005
01:53:24,800 --> 01:53:29,517
Minutes special 25 years ago
on him that he was in Salt Lake.

2006
01:53:29,600 --> 01:53:33,357
>> Oh, I thought you were making a joke
because it sounded like a mountain and

2007
01:53:33,440 --> 01:53:37,357
then you were naming a mountain from
Salt Lake City, Utah. But really, you

2008
01:53:37,440 --> 01:53:40,157
just knew it. >> Yeah. Yeah. Yeah. Yeah.

2009
01:53:40,240 --> 01:53:42,957
>> Okay. All right. All right then. Okay.

2010
01:53:43,040 --> 01:53:48,637
>> All right. That's not as fun.
That was not a sim moment, unfortunately.

2011
01:53:48,720 --> 01:53:51,997
>> No, >> he was born
with macroephily. I think

2012
01:53:52,080 --> 01:53:54,717
that's how you pronounce it.
Damage to the ser

2013
01:53:54,800 --> 01:53:58,877
>> cerebellum and oh my god, every word is

2014
01:53:58,960 --> 01:54:04,237
very hard to say. Uh, okay. And something
happened to the corpus colosum.

2015
01:54:04,320 --> 01:54:07,277
>> A condition in which the bundle of
nerves that connects the two hemispheres

2016
01:54:07,360 --> 01:54:10,637
of the brain is missing. Oh

2017
01:54:10,720 --> 01:54:15,117
>> wow. In Peak's case, secondary
connectors such as anterior commasure

2018
01:54:15,200 --> 01:54:18,717
was also missing. There is speculation
that his neurons made unusual

2019
01:54:18,800 --> 01:54:20,237
connections due to the absence of the

2020
01:54:20,320 --> 01:54:22,957
corpus colosum resulting in an increased

2021
01:54:23,040 --> 01:54:26,317
memory capacity. Huh.

2022
01:54:26,400 --> 01:54:28,717
Oh my god. According to Peak's father,

2023
01:54:28,800 --> 01:54:30,637
Kim was able to memorize things from the

2024
01:54:30,720 --> 01:54:33,677
age of 16 to 20 months.

2025
01:54:33,760 --> 01:54:39,197
>> Yeah. Uh, if anyone can dig up
that profile on him, it is fascinating.

2026
01:54:39,280 --> 01:54:42,637
>> Wow. >> The rate at which
he reads books. I want

2027
01:54:42,720 --> 01:54:47,117
to say he reads with both eyes
on both pages at the same time.

2028
01:54:47,200 --> 01:54:50,637
>> I think I'm Well, [__] I got Salt Lake
City, right? So now I'm feeling cocky.

2029
01:54:50,720 --> 01:54:53,277
>> Yeah, you are. >> I kind
of I kind of think he can read

2030
01:54:53,360 --> 01:54:56,317
with both both eyes at the same time.

2031
01:54:56,400 --> 01:54:59,917
>> Two different pages.
I bet Bill Gates is jealous of that.

2032
01:55:00,000 --> 01:55:04,237
>> I bet he is. If he could double his
rate of reading, which is already [snorts]

2033
01:55:04,320 --> 01:55:08,557
a blistering pace, >> he would
have read every book in the world.

2034
01:55:08,640 --> 01:55:11,597
>> Think about how much he
he wades through Bill Gates.

2035
01:55:11,680 --> 01:55:15,597
>> Oh, I know. >> With 14 books a week.
Like, he wades through so much.

2036
01:55:15,680 --> 01:55:17,517
>> Yeah. >> I don't know.
It's fascinating. It's

2037
01:55:17,600 --> 01:55:21,277
fascinating that that guy's
still that goddamn curious.

2038
01:55:21,360 --> 01:55:25,757
>> I know. I love it. I I'm jealous
of that. Ding, ding, ding. Jealousy.

2039
01:55:25,840 --> 01:55:28,797
>> Jealousy. [snorts]
Well Well, I love you.

2040
01:55:28,880 --> 01:55:30,717
>> I love you. I hope you have a wonderful

2041
01:55:30,800 --> 01:55:33,517
quit trip and maybe we're going to do a

2042
01:55:33,600 --> 01:55:37,197
high-risk high reward [laughter]

2043
01:55:37,280 --> 01:55:41,117
beach day fact check. >> Well,
we've done a car now. It feels

2044
01:55:41,200 --> 01:55:43,757
only appropriate that we
challenge ourselves.

2045
01:55:43,840 --> 01:55:47,640
>> All right. >> I [music] love you.

2046
01:55:50,280 --> 01:55:52,300
[music]