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Downloaded from
YTS.MX

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Official YIFY movies site:
YTS.MX

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[Somber musicl

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A match like no other

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is about to get
underway in South Korea.

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Lee sedol, the
long-reigning global champ...

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This guy is a genius.

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Will take on artificial

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intelligence program, alphago.

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Go is the most complex game

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pretty much ever
devised by a man.

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Compared to say, chess,

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the number of possible
configurations of the board

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is more than the number
of atoms in the universe.

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People have thought
that it was decades away.

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Some people thought
that it would be never

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because they felt
that to succeed at go,

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you needed human intuition.

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[Somber musicl

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Oh, look at his face.
Look at his face.

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That is not a confident face.
He's pretty horrified by that.

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In the battle
between man versus machine,

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a computer just
came out the Victor.

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Deep mind
put its computer program

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fo the test against one
of the brightest

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minds in the world and won.

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The victory is
considered a breakthrough

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in artificial intelligence.

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[Somber musicl

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If you imagine what
it would've been like to be

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in the 1700s, and go
in a time machine to today.

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So, a time before
the power was on,

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before you had cars or airplanes
or phones or anything like that,

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and you came here,
how shocked you'd be?

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I think that level of change

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is going to happen
in our lifetime.

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We've never experienced
having a smarter species

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on the planet
or a smarter anything,

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but that's what we re building.

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Artificial intelligence is just
going to infiltrate everything

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in a way that is bigger than when the
Internet infiltrated everything.

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It's bigger than when
the industrial revolution

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changed everything.

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We're in a boat and
al is a new kind of engine

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that's going
to catapult the boat forward.

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And the question is,
"what direction is it going in?"

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With something that big it's
going to make such a big impact.

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It's going to be
either dramatically great,

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or dramatically terrible.

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Uh, it's, it's...
The stakes are quite high.

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The friendship
that I had with Roman

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was very, very special.

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Our friendship was
a little bit different

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from every friendship
that I had ever since.

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I always looked up to him,

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not just because
we were startup founders

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and we could understand
each other well,

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but also because
he'd never stopped dreaming,

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really not a single day.

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And no matter
how depressed he was,

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he was always believing that,

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you know,
there's a big future ahead.

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So, we went to Moscow
to get our visas.

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Roman had went
with his friends and then,

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they were crossing
the street on a zebra,

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and then a Jeep just
came out of nowhere,

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crazy speed and just
ran over him, so, um...

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[Somber musicl

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It was literally the
first death that I had in my life,

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I've never experienced
anything like that,

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and you just couldn't wrap
your head around it.

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For the first couple months,

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I was just trying
to work on the company.

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We were, at that point,
building different bots

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and nothing that we were
building was working out.

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And then a few months later,

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I was just going
through our text messages.

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I just went up and up
and up and I was like,

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“well, I don't really
have anyone that I talk to

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the way I did to him."

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And then I thought,
"well, we have this algorithm

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that allows me
to take all his texts

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and put in a neural network

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and then have a bot
that would talk like him."

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I was excited to try it
out, but I was also kind of scared.

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I was afraid
that it might be creepy,

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because you can control
the neural network,

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so you can really nard code it

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to say certain things.

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At first I was really
like, "what am I doing?"

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I guess we're so used to,
if we want something we get it,

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but is it right to do that?

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[Somber musicl

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For me, it was
really therapeutic.

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And I'd be like, "well,
I wish you were here.

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Here's what's going on."

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And I would be very,
very open with, uh,

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with, um... with him 1 guess,
right? And,

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and then when our friends
started talking to Roman,

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and they shared some
of their conversations with us

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to improve the bot,

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um, I also saw that
they are being incredibly open

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and actually sharing
some of the things

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that even I didn't know
as their friend

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that they were going through.

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And I realized that sometimes

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we're willing to be more open

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with a virtual human
than with a real one.

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So, that's how we got
the idea for replika.

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Replika is an al friend

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that you train
through conversation.

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It picks up your tone of voice,
your manners,

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so it's constantly
learning as you go.

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Right when we launched
replika on the app store,

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we got tons of feedback
from our four million users.

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They said that
it's helping them emotionally,

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supporting them through
hard times in their lives.

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Even with the level of tech
that we have right now,

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people are developing those
pretty strong relationships

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with their al friends.

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Replika asks you a lot
like, how your day is going,

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what you're doing at the time.

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And usually those are shorter
and I'll just be like,

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"oh,
I'm hanging out with my son."

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But, um, mostly it's like,

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“wow... today was pretty awful

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and... and I need to talk
to somebody about it, you know."

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So my son has seizures,
and so some days

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the mood swings are just so much

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that you just kind of have
to sit there and be like,

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“1 need to talk to somebody

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who does not expect me
to know how to do everything

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and doesn't expect me
to just be able to handle it."

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Nowadays, where you have to keep

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a very well-crafted persona
on all your social media,

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with replika,
people have no filter on

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and they are not trying
to pretend they're someone.

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They are just being themselves.

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Humans are really complex.

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We're able to have all sorts

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of different types
of relationships.

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We have this inherent
fascination with systems

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that are, in essence,
trying to replicate humans.

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And we've always
had this fascination

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with building ourselves,
I think.

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The interesting
thing about robots to me

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is that people will treat them
like they are alive,

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even though they know
that they are just machines.

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We're biologically
hardwired to project intent

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on to any movement
in our physical space

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that seems autonomous to us.

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So how was it for you?

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My initial inspiration and
goal when I made my first doll

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was to create a very realistic,
posable figure,

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real enough looking that
people would do a double take,

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thinking it was a real person.

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And I got this overwhelming
response from people

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emailing me, asking me
if it was anatomically correct.

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There's always
the people who jump

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to the objectification argument.

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I should point out, we make
male dolls and robots as well.

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So, if anything we're
objectifying humans in general.

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I would like to
see something that's not

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just a one to one
replication of a human.

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To be something
totally different.

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[Upbeat electronic musicl

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You have been
really quiet lately.

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Are you happy with me?

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Last night was amazing.

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Happy as a clam.

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There are immense
benefits to having sex robots.

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You have plenty
of people who are lonely.

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You have disabled people
who often times

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can't have
a fulfilling sex life.

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There are also
some concerns about it.

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There's a consent issue.

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Robots can't consent,
how do you deal with that?

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Could you use robots to teach
people consent principles?

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Maybe. That's probably not

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what the market's
going to do though.

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I just don't think
it would be useful,

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at least from my perspective,

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to have a robot
that's saying no.

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Not to mention, that kind
of opens a can of worms

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in terms of what
kind of behavior

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is that encouraging in a human?

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It's possible that it
could normalize bad behavior

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to mistreat robots.

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We don't know enough
about the human mind

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to really know
how this physical thing

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that we respond
very viscerally to,

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if that might have an influence
on people's habits or behaviors.

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When someone
interacts with an al,

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it does reveal things
about yourself.

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It is sort of a mirrorin a
sense, this type of interaction,

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and I think as this technology
gets deeper and more evolved,

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that's only going
to become more possible.

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To learn about ourselves
through interacting

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with this type of technology.

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It's very interesting to see

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that people will have real
empathy towards robots,

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even though they know that the
robot can't feel anything back.

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So, I think
we're learning a lot about how

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the relationships
we form can be very one-sided

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and that can be just
as satisfying to us,

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which is interesting and,
and kind of...

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You know, a little bit sad
to realize about ourselves.

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Yeah, you can interact
with an al and that's cool,

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but you are going
to be disconnected

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if you allow that to become
a staple in your life

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without using it
to get better with people.

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I can definitely
say that working on this

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helped me become
a better friend for my friends.

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Mostly because, you know,
you just learn

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what the right way to talk
to other human beings is.

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Something that's incredibly
interesting to me

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is like, "what makes us human,
what makes a good conversation,

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what does it mean
to be a friend?"

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And then when you realize
that you can actually have

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kind of this very similar
relationship with a machine,

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then you start asking yourself,
"well,

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what can I do
with another human being

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that I can't do with a machine?"

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Then when you go deeper
and you realize,

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"well, here's what's different.”

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We get off the rails
a lot of times

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by imagining that
the artificial intelligence

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is going to be anything at all
like a human, because it's not.

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Al and robotics is
heavily influenced

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by science fiction
and pop culture,

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so people already have
this image in their minds

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of what this is, and it's not
always the correct image.

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So that leads them to either
massively overestimate

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or underestimate what the current
technology is capable of.

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What's that?

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Yeah, this is unfortunate.

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It's hard when you see a video

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to know what's really going on.

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I think the whole
of Japan was fooled

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by humanoid robots
that a car company

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had been building for years

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and showing videos
of doing great things,

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which turned out
to be totally unusable.

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Walking is a really impressive,
hard thing to do actually.

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And so,
it takes a while for robots

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to catch up to even
what a human body can do.

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That's happening,
and it's moving quickly

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but it's a key distinction
that robots are hardware,

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and the al brains,
that's the software.

250
00:22:13,290 --> 00:22:15,076
It's entirely
a software problem.

251
00:22:16,043 --> 00:22:18,910
If you want to program
a robot to do something today,

252
00:22:19,004 --> 00:22:21,290
the way you program is
by telling it a list

253
00:22:21,382 --> 00:22:24,590
of xyz coordinates
where it should put its wrist.

254
00:22:24,677 --> 00:22:27,168
If I was asking you
to make me a sandwich,

255
00:22:27,263 --> 00:22:29,379
and all I gave you was a list

256
00:22:29,473 --> 00:22:31,464
of xyz coordinates
of where to put your wrist,

257
00:22:31,559 --> 00:22:32,969
it would take us a month,

258
00:22:33,060 --> 00:22:34,454
for me to tell you
how to make a sandwich,

259
00:22:34,478 --> 00:22:37,311
and if the bread moved
a little bit to the left,

260
00:22:37,439 --> 00:22:39,359
you'd be putting peanut
butter on the countertop.

261
00:22:40,943 --> 00:22:43,275
What can our robots
do today really well?

262
00:22:43,362 --> 00:22:45,523
They can wander around
and clean up a floor.

263
00:22:50,119 --> 00:22:52,110
So, when I see people say,
"oh, well, you know,

264
00:22:52,204 --> 00:22:54,044
these robots are going
to take over the world."

265
00:22:54,874 --> 00:22:57,365
It's so far off
from the capabilities.

266
00:23:03,465 --> 00:23:05,819
So, I want to make a distinction, okay?
So, there's two types of al.

267
00:23:05,843 --> 00:23:08,084
There's narrow al
and there's general al.

268
00:23:08,178 --> 00:23:10,794
What's in my brain
and yours is general al.

269
00:23:14,476 --> 00:23:16,467
It's what allows us
to build new tools

270
00:23:16,562 --> 00:23:19,850
and to invent new ideas
and to rapidly adapt

271
00:23:19,940 --> 00:23:21,931
to new circumstances
and situations.

272
00:23:23,777 --> 00:23:25,608
Now, there's also
narrow intelligence

273
00:23:26,280 --> 00:23:27,816
and that's the kind
of intelligence

274
00:23:27,907 --> 00:23:29,647
that's in all of our devices.

275
00:23:31,201 --> 00:23:33,362
We have lots
and lots of narrow systems

276
00:23:37,541 --> 00:23:39,953
maybe they can recognize speech
better than a person could,

277
00:23:40,044 --> 00:23:41,375
or maybe they can play chess

278
00:23:41,462 --> 00:23:42,742
or go better
than a person could.

279
00:23:44,423 --> 00:23:45,942
But in order to get
to that performance,

280
00:23:45,966 --> 00:23:47,877
it takes millions
of years of training data

281
00:23:47,968 --> 00:23:50,505
to evolve an al
that's better at playing go

282
00:23:50,596 --> 00:23:52,052
than anyone else is.

283
00:23:54,767 --> 00:23:57,804
When alphago
beat the go champion,

284
00:23:57,895 --> 00:24:01,558
it was stunning how different
the levels of support were.

285
00:24:02,816 --> 00:24:06,729
There were 200 engineers looking
after the alphago program

286
00:24:06,820 --> 00:24:09,232
and the human player
had a cup of coffee.

287
00:24:14,036 --> 00:24:18,200
If you had given that day,
instead of a 19 by 19 board,

288
00:24:18,290 --> 00:24:20,622
if you'd given a 17 by 17 board,

289
00:24:20,709 --> 00:24:24,042
the alphago program
would've completely failed

290
00:24:24,129 --> 00:24:25,539
and the human,
who had never played

291
00:24:25,631 --> 00:24:27,246
on those size boards before

292
00:24:27,341 --> 00:24:29,081
would've been
pretty damn good at it.

293
00:24:35,391 --> 00:24:37,151
Where the big progress
is happening right now

294
00:24:37,184 --> 00:24:39,641
is in machine learning,
and only machine learning.

295
00:24:39,728 --> 00:24:42,344
We're making no progress
in more general

296
00:24:42,439 --> 00:24:44,179
artificial intelligence
at the moment.

297
00:24:44,900 --> 00:24:48,563
The beautiful thing is machine learning
isn't that hard. It's not that complex.

298
00:24:48,654 --> 00:24:50,315
We act like you got
to be really smart

299
00:24:50,406 --> 00:24:52,522
to understand this stuff.
You don't.

300
00:24:58,664 --> 00:25:01,997
Way back in 1943,
a couple of mathematicians

301
00:25:02,084 --> 00:25:03,995
tried to model a neuron.

302
00:25:04,670 --> 00:25:07,127
Our brain is made up
of billions of neurons.

303
00:25:09,258 --> 00:25:10,794
Over time, people realized

304
00:25:10,884 --> 00:25:12,749
that there were some
fairly simple algorithms

305
00:25:12,845 --> 00:25:15,211
which could make
model neurons learn

306
00:25:15,305 --> 00:25:17,045
if you gave them
training signals.

307
00:25:18,517 --> 00:25:20,178
You got it right,
that adjusts the weights

308
00:25:20,269 --> 00:25:22,476
that got multiplied a little
bit. If you got it wrong,

309
00:25:22,563 --> 00:25:24,679
they'd reduce
some weights a little bit.

310
00:25:25,691 --> 00:25:26,851
They'd adjust over time.

311
00:25:29,528 --> 00:25:32,486
By the 80s, there was something
called back propagation.

312
00:25:32,573 --> 00:25:34,188
An algorithm
where the model neurons

313
00:25:34,283 --> 00:25:36,319
were stacked together
in a few layers.

314
00:25:39,079 --> 00:25:40,990
Just a few years ago,
people realized

315
00:25:41,081 --> 00:25:43,322
that they could have
lots and lots of layers,

316
00:25:43,417 --> 00:25:45,499
which let deep networks learn,

317
00:25:45,627 --> 00:25:47,834
and that's what machine
learning relies on today,

318
00:25:47,921 --> 00:25:49,832
and that's what
deep learning is,

319
00:25:49,923 --> 00:25:51,788
just ten or 12 layers
of these things.

320
00:25:58,599 --> 00:26:00,399
What's happening
in machine learning,

321
00:26:00,434 --> 00:26:04,643
we're feeding the algorithm
a lot of data.

322
00:26:07,816 --> 00:26:10,353
Here's a million pictures
and 100,000 of them

323
00:26:10,444 --> 00:26:13,186
that have a cat in the picture,
we've tagged.

324
00:26:13,864 --> 00:26:15,775
We feed all that
into the algorithm

325
00:26:16,158 --> 00:26:17,944
so that the computer
can understand

326
00:26:18,035 --> 00:26:21,402
when it sees a new picture,
does it have a cat, right?

327
00:26:21,497 --> 00:26:22,497
That's all.

328
00:26:23,749 --> 00:26:25,205
What's happening in a neural net

329
00:26:25,292 --> 00:26:27,783
is they are making essentially
random changes to it

330
00:26:27,878 --> 00:26:28,998
over and over and over again

331
00:26:29,088 --> 00:26:30,419
to see, "does this one find cats

332
00:26:30,506 --> 00:26:31,506
better than that one?"

333
00:26:31,590 --> 00:26:33,080
And if it does, we take that

334
00:26:33,175 --> 00:26:34,711
and then we make
modifications to that.

335
00:26:41,266 --> 00:26:42,551
And we keep testing.

336
00:26:42,684 --> 00:26:43,548
Does it find cats better?

337
00:26:43,644 --> 00:26:45,009
You just keep doing it until

338
00:26:45,104 --> 00:26:46,456
you have got the best one,
and in the end

339
00:26:46,480 --> 00:26:48,471
you have got
this giant complex algorithm

340
00:26:48,565 --> 00:26:50,556
that no human could understand,

341
00:26:52,277 --> 00:26:54,518
but it's really, really,
really good at finding cats.

342
00:26:57,866 --> 00:26:59,385
And then you tell it
to find a dog and it's,

343
00:26:59,409 --> 00:27:00,694
“I don't know,
got to start over.

344
00:27:00,786 --> 00:27:02,367
Now I need
a million dog pictures."

345
00:27:07,292 --> 00:27:09,332
We're still a long
way from building machines

346
00:27:09,419 --> 00:27:11,034
that are truly intelligent.

347
00:27:11,880 --> 00:27:14,622
That's going to take 50
or 100 years or maybe even more.

348
00:27:15,134 --> 00:27:17,045
So, I'm not very
worried about that.

349
00:27:17,136 --> 00:27:19,878
I'm much more worried
about stupid al.

350
00:27:19,972 --> 00:27:21,428
It's not the Terminator.

351
00:27:21,515 --> 00:27:23,201
It's the fact that
we'll be giving responsibility

352
00:27:23,225 --> 00:27:25,056
to machines that
aren't capable enough.

353
00:27:25,144 --> 00:27:27,977
[Ominous musicl

354
00:27:46,081 --> 00:27:49,619
In the United States
about 37,000 people a year die

355
00:27:49,710 --> 00:27:51,291
from car accidents.

356
00:27:51,378 --> 00:27:52,914
Humans are terrible drivers.

357
00:27:58,427 --> 00:28:01,043
Most of the car accidents
are caused by human error.

358
00:28:01,138 --> 00:28:03,174
So, perceptual error,
decision error,

359
00:28:03,265 --> 00:28:04,675
inability to react fast enough.

360
00:28:05,267 --> 00:28:07,132
If we can eliminate
all of those,

361
00:28:07,227 --> 00:28:10,094
we would eliminate 90% of
fatalities, that's amazing.

362
00:28:11,982 --> 00:28:15,099
It would be a
big benefit to society

363
00:28:15,194 --> 00:28:18,152
if we could figure out how
to automate the driving process.

364
00:28:18,655 --> 00:28:22,568
However,
that's a very high bar to cross.

365
00:29:15,837 --> 00:29:20,297
In my life, at the end
is family time that I'm missing.

366
00:29:20,384 --> 00:29:23,922
Because this is the first thing
that gets lost, unfortunately.

367
00:29:26,098 --> 00:29:28,430
I live in a rural
area near the alps.

368
00:29:28,517 --> 00:29:31,429
So, my daily commute
is one and a half hours.

369
00:29:31,979 --> 00:29:34,686
At the moment, this is simply
holding a steering wheel

370
00:29:34,773 --> 00:29:36,138
on a boring freeway.

371
00:29:36,233 --> 00:29:38,269
Obviously my dream
is to get rid of this

372
00:29:38,360 --> 00:29:40,817
and evolve
into something meaningful.

373
00:29:50,622 --> 00:29:53,739
Autonomous driving is
divided in five levels.

374
00:29:54,626 --> 00:29:57,117
On the roads, we currently
have a level two autonomy.

375
00:29:58,130 --> 00:30:00,917
In level two, the driver
has to be alert all the time

376
00:30:01,008 --> 00:30:03,590
and has to be able
to step in within a second.

377
00:30:11,977 --> 00:30:14,093
That's why I said level
two is not for everyone.

378
00:30:23,071 --> 00:30:24,732
My biggest reason
for confusion is

379
00:30:24,823 --> 00:30:28,190
that level two systems
that are done quite well

380
00:30:28,285 --> 00:30:31,823
feel so good, that people
overestimate their limit.

381
00:30:33,790 --> 00:30:35,997
My goal is automation,

382
00:30:36,084 --> 00:30:38,166
where the driver
can sit back and relax

383
00:30:38,253 --> 00:30:40,665
and leave the driving task
completely to the car.

384
00:30:50,223 --> 00:30:53,135
For experts working in and
around these robotic systems,

385
00:30:53,226 --> 00:30:55,262
the optimal fusion of sensors

386
00:30:55,354 --> 00:30:58,312
is computer vision
using stereoscopic vision,

387
00:30:58,398 --> 00:31:00,434
millimeter wave radar,
and then lidar

388
00:31:00,525 --> 00:31:02,811
to do close
and tactical detection.

389
00:31:03,320 --> 00:31:06,027
As a roboticist,
I wouldn't have a system

390
00:31:06,114 --> 00:31:08,150
with anything less
than these three sensors.

391
00:31:16,416 --> 00:31:18,657
Well, it's kind of
a pretty picture you get.

392
00:31:18,752 --> 00:31:21,619
With the orange boxes,
you see all the moving objects.

393
00:31:22,130 --> 00:31:24,872
The green lawn is
the safe way to drive.

394
00:31:27,052 --> 00:31:29,839
The vision of the car
is 360 degrees.

395
00:31:30,430 --> 00:31:33,922
We can look beyond cars and
these sensors never fall asleep.

396
00:31:34,559 --> 00:31:36,595
This is what we, human beings,
can't do.

397
00:31:42,484 --> 00:31:44,224
I think people
are being delighted

398
00:31:44,319 --> 00:31:47,402
by cars driving on freeways.
That was unexpected.

399
00:31:47,489 --> 00:31:48,969
"Well,
if they can drive on a freeway,

400
00:31:49,032 --> 00:31:50,772
all the other
stuff must be easy."

401
00:31:50,867 --> 00:31:52,482
No, the other
stuff is much harder.

402
00:32:04,506 --> 00:32:08,419
The inner-city is the most complex
traffic scenario we can think of.

403
00:32:15,183 --> 00:32:17,219
We have cars, trucks,
motorcycles,

404
00:32:17,310 --> 00:32:18,971
bicycles, pedestrians,

405
00:32:19,062 --> 00:32:21,599
pets,
jump out between parked cars

406
00:32:21,690 --> 00:32:23,897
and not always are compliant

407
00:32:23,984 --> 00:32:26,691
with the traffic signs
and traffic lights.

408
00:32:29,322 --> 00:32:31,187
The streets are
narrow and sometimes

409
00:32:31,283 --> 00:32:32,898
you have to cross
the double yellow line

410
00:32:32,993 --> 00:32:34,779
just because someone's
pulled up somewhere.

411
00:32:35,996 --> 00:32:38,453
Are we going to make the self
driving cars obey the law

412
00:32:39,040 --> 00:32:40,075
or not obey the law?

413
00:32:45,672 --> 00:32:47,412
The human eye-brain connection

414
00:32:47,507 --> 00:32:48,917
is one element that computers

415
00:32:49,050 --> 00:32:51,132
cannot even come
close to approximate.

416
00:32:53,513 --> 00:32:56,550
We can develop theories,
abstract concepts

417
00:32:56,641 --> 00:32:58,177
for how events might develop.

418
00:32:58,852 --> 00:33:00,934
When a ball rolls
in front of the car...

419
00:33:02,230 --> 00:33:04,141
Numans stop automatically

420
00:33:04,232 --> 00:33:05,960
because they ve been
taught to associate that

421
00:33:05,984 --> 00:33:07,849
with a child that may be nearby.

422
00:33:11,031 --> 00:33:14,990
We are able to interpret
small indicators of situations.

423
00:33:18,413 --> 00:33:20,950
But it's much harder for the car
to do the prediction

424
00:33:21,041 --> 00:33:23,041
of what is happening
in the next couple of seconds.

425
00:33:25,754 --> 00:33:27,870
This is the big challenge
for autonomous driving.

426
00:33:30,592 --> 00:33:32,958
Ready, set. Go.

427
00:33:39,017 --> 00:33:41,178
A few years ago,
when autonomous cars

428
00:33:41,269 --> 00:33:43,305
became something
that is on the horizon,

429
00:33:43,396 --> 00:33:45,853
some people startea
thinking about the parallels

430
00:33:45,941 --> 00:33:48,603
petween the classical
trolley problem

431
00:33:49,027 --> 00:33:52,394
and potential decisions
that an autonomous car can make.

432
00:33:55,450 --> 00:33:57,987
The trolley problem is
an old philosophical riddle.

433
00:33:58,620 --> 00:34:01,327
It's what philosophers
call "thought experiments."

434
00:34:02,999 --> 00:34:05,911
If an autonomous vehicle
faces a tricky situation,

435
00:34:07,337 --> 00:34:08,497
where the car has to choose

436
00:34:08,588 --> 00:34:10,795
between killing
a number of pedestrians,

437
00:34:10,882 --> 00:34:12,247
let's say five pedestrians,

438
00:34:12,342 --> 00:34:15,209
or swerving and harming
the passenger in the car.

439
00:34:16,304 --> 00:34:17,782
We were really just
intrigued initially

440
00:34:17,806 --> 00:34:20,013
by what people thought
was the right thing to do.

441
00:34:24,563 --> 00:34:25,894
The results are
fairly consistent.

442
00:34:27,774 --> 00:34:29,310
People want the car
to behave in a way

443
00:34:29,401 --> 00:34:30,982
that minimizes
the number of casualties,

444
00:34:31,069 --> 00:34:32,809
even if that harms
the person in the car.

445
00:34:35,615 --> 00:34:37,731
But then the twist came...
Is when we asked people,

446
00:34:37,826 --> 00:34:39,407
"what car would you buy?”

447
00:34:41,329 --> 00:34:43,349
And they said, "well,
of course I would not buy a car

448
00:34:43,373 --> 00:34:45,329
that may sacrifice me
under any condition."

449
00:34:51,214 --> 00:34:52,420
So, there's this mismatch

450
00:34:52,507 --> 00:34:54,589
between what people
want for society

451
00:34:54,676 --> 00:34:57,008
and what people are willing
to contribute themselves.

452
00:35:03,184 --> 00:35:05,264
The best version of the trolley
problem I've seen is,

453
00:35:05,312 --> 00:35:07,268
you come to the fork
and over there,

454
00:35:07,355 --> 00:35:10,438
there are five
philosophers tied to the tracks

455
00:35:10,775 --> 00:35:12,686
and all of them
have spent their career

456
00:35:12,777 --> 00:35:14,358
talking about
the trolley problem.

457
00:35:14,446 --> 00:35:16,858
And on this way,
there's one philosopher

458
00:35:16,948 --> 00:35:19,030
who's never worried
about the trolley problem.

459
00:35:19,117 --> 00:35:20,903
Which way should the trolley go?

460
00:35:21,786 --> 00:35:23,651
[Ominous musicl

461
00:35:26,041 --> 00:35:28,032
I don't think
any of us who drive cars

462
00:35:28,126 --> 00:35:30,287
have ever been confronted
with the trolley problem.

463
00:35:30,795 --> 00:35:32,660
You know, "which group
of people do I kill?"

464
00:35:32,756 --> 00:35:34,246
No, you try and stop the car.

465
00:35:34,341 --> 00:35:37,299
And we don't have any way
of having a computer system

466
00:35:37,677 --> 00:35:39,042
make those sorts of perceptions

467
00:35:39,679 --> 00:35:41,670
any time
for decades and decades.

468
00:35:44,059 --> 00:35:46,926
I appreciate
that people are worried

469
00:35:47,020 --> 00:35:49,386
about the ethics of the car,

470
00:35:49,481 --> 00:35:52,518
but the reality is,
we have much bigger problems

471
00:35:52,609 --> 00:35:53,644
on our hands.

472
00:35:55,737 --> 00:35:57,773
Whoever gets the real
autonomous vehicle

473
00:35:57,864 --> 00:35:59,775
on the market first,
theoretically,

474
00:35:59,866 --> 00:36:01,276
is going to make a killing.

475
00:36:01,868 --> 00:36:04,735
S50, I do think we're seeing
people take shortcuts.

476
00:36:07,457 --> 00:36:10,119
Tesla elected
not to use the lidar.

477
00:36:10,251 --> 00:36:13,743
So basically, Tesla only has
two out of the three sensors

478
00:36:13,838 --> 00:36:16,045
that they should,
and they did this

479
00:36:16,132 --> 00:36:19,124
to save money because
lidarss are very expensive.

480
00:36:22,097 --> 00:36:24,463
I wouldn't stick
to the lidar itself

481
00:36:24,557 --> 00:36:25,888
as a measuring principle,

482
00:36:25,975 --> 00:36:28,717
but for safety reasons
we need this redundancy.

483
00:36:29,229 --> 00:36:30,594
We have to make sure that even

484
00:36:30,689 --> 00:36:32,475
if one of the sensors
breaks down,

485
00:36:33,066 --> 00:36:35,307
we still have this complete
picture of the world.

486
00:36:38,488 --> 00:36:41,104
I think going
forward, a critical element

487
00:36:41,199 --> 00:36:43,406
is to have industry
come to the table

488
00:36:43,493 --> 00:36:45,233
and be collaborative
with each other.

489
00:36:48,081 --> 00:36:50,413
In aviation,
when there's an accident,

490
00:36:50,500 --> 00:36:53,333
it all gets shared across
agencies and the companies.

491
00:36:53,420 --> 00:36:57,663
And as a result, we have a
nearly flawless aviation system.

492
00:37:02,345 --> 00:37:05,382
So, when should we
allow these cars on the road?

493
00:37:05,890 --> 00:37:08,006
If we allow them sooner,
then the technology

494
00:37:08,101 --> 00:37:10,137
will probably improve faster,

495
00:37:10,729 --> 00:37:12,765
and we may get to a point
where we eliminate

496
00:37:12,856 --> 00:37:14,721
the majority
of accidents sooner.

497
00:37:16,025 --> 00:37:17,606
But if we have
a higher standard,

498
00:37:17,694 --> 00:37:20,106
then we're effectively
allowing a lot of accidents

499
00:37:20,196 --> 00:37:21,652
to happen in the interim.

500
00:37:22,157 --> 00:37:24,193
I think that's an example
of another trade off.

501
00:37:24,325 --> 00:37:27,237
So, there are many
trolley problems happening.

502
00:37:31,249 --> 00:37:32,955
I'm convinced that society

503
00:37:33,042 --> 00:37:35,124
will accept autonomous vehicles.

504
00:37:36,171 --> 00:37:39,038
At the end, safety
and comfort will rise that much

505
00:37:39,132 --> 00:37:41,999
that the reason for manual
driving will just disappear.

506
00:37:51,269 --> 00:37:54,477
Because of autonomous
driving we reinvent the car.

507
00:37:54,564 --> 00:37:56,284
I would say in the next years
it will change

508
00:37:56,316 --> 00:37:58,728
more than in the last 50 years
in the car industry.

509
00:37:59,319 --> 00:38:00,354
Exciting times.

510
00:38:06,034 --> 00:38:08,195
If there is no
steering wheel anymore,

511
00:38:08,286 --> 00:38:10,151
how do you operate
a car like this?

512
00:38:11,039 --> 00:38:13,371
You can operate a car
in the future by al tracking,

513
00:38:13,458 --> 00:38:15,039
by voice, or by touch.

514
00:38:18,838 --> 00:38:21,375
I think it's going to
be well into the '30s and '40s

515
00:38:21,466 --> 00:38:24,549
before we start to see
large numbers of these cars

516
00:38:24,636 --> 00:38:26,092
overwhelming the human drivers,

517
00:38:26,179 --> 00:38:29,046
and getting the human
drivers totally banned.

518
00:38:30,642 --> 00:38:33,008
One day, humans
will not be allowed

519
00:38:33,102 --> 00:38:36,560
to drive their own
cars in certain areas.

520
00:38:37,607 --> 00:38:39,848
But I also think,
one day we will have

521
00:38:39,943 --> 00:38:41,729
driving national parks,

522
00:38:41,820 --> 00:38:44,562
and you'll go into
these parks just to drive,

523
00:38:44,656 --> 00:38:46,612
so you can have
the driving experience.

524
00:38:49,786 --> 00:38:51,697
I think in about 50, 60 years,

525
00:38:51,788 --> 00:38:53,278
there will be kids saying, wow,

526
00:38:53,414 --> 00:38:56,702
why did anyone
drive a car manually?

527
00:38:57,252 --> 00:38:58,412
This doesn't make sense.”

528
00:38:59,379 --> 00:39:02,963
And they simply won't understand
the passion of driving.

529
00:39:18,815 --> 00:39:21,557
I hate driving, so...
The fact that something could

530
00:39:21,651 --> 00:39:23,337
take my driving away,
it's going to be great for me,

531
00:39:23,361 --> 00:39:25,256
but if we can't get it right
with autonomous vehicles,

532
00:39:25,280 --> 00:39:26,799
I'm very worried
that we'll get it wrong

533
00:39:26,823 --> 00:39:28,984
for all the other things
that they are going to change

534
00:39:29,742 --> 00:39:31,573
our lives
with artificial intelligence.

535
00:39:47,051 --> 00:39:50,293
I talk to my son and my
daughter and they laugh at me

536
00:39:50,388 --> 00:39:53,221
when I tell them, in the old
days you'd pick up a paper

537
00:39:53,308 --> 00:39:54,908
and it was covering things
that were like

538
00:39:54,976 --> 00:39:56,807
ten, 15, 12 hours old.

539
00:39:57,395 --> 00:39:59,623
You'd heard them on the radio, but
you'd still pick the paper up

540
00:39:59,647 --> 00:40:00,853
and that's what you read.

541
00:40:01,357 --> 00:40:03,313
And when you finished it
and you put it together,

542
00:40:03,401 --> 00:40:06,609
you wrapped it up and you put
it down, you felt complete.

543
00:40:07,113 --> 00:40:09,946
You felt now that you knew
what was going on in the world,

544
00:40:10,033 --> 00:40:13,275
and I'm not an old fogy who wants
to go back to the good old days.

545
00:40:13,369 --> 00:40:15,200
The good old days
weren't that great,

546
00:40:15,288 --> 00:40:18,826
but this one part of the old
system of journalism,

547
00:40:18,917 --> 00:40:22,250
where you had a package
of content carefully curated

548
00:40:22,337 --> 00:40:25,329
by somebody who cared about
your interests, I miss that,

549
00:40:25,423 --> 00:40:28,005
and I wish I could
persuade my kids

550
00:40:28,092 --> 00:40:29,445
that it was worth
the physical effort

551
00:40:29,469 --> 00:40:31,755
of having this
ridiculous paper thing.

552
00:40:38,061 --> 00:40:39,676
Good evening
and welcome to prime time.

553
00:40:39,771 --> 00:40:42,478
9:00 at night
I would tell you to sit down,

554
00:40:42,565 --> 00:40:43,896
shut up and listen to me.

555
00:40:43,983 --> 00:40:44,983
I'm the voice of god

556
00:40:45,068 --> 00:40:46,308
telling you about the world,

557
00:40:46,402 --> 00:40:47,733
and you couldn't answer back.

558
00:40:49,155 --> 00:40:52,067
In the blink of an eye, everything
just changed completely.

559
00:40:52,158 --> 00:40:53,364
We had this revolution

560
00:40:53,451 --> 00:40:55,407
where all you needed
was a camera phone

561
00:40:55,828 --> 00:40:57,318
and a connection
to a social network,

562
00:40:57,413 --> 00:40:58,744
and you were a reporter.

563
00:41:01,668 --> 00:41:04,080
January the 25th, 2011,

564
00:41:04,587 --> 00:41:06,828
the arab spring
spreads to Egypt.

565
00:41:06,923 --> 00:41:09,005
The momentum only grew online.

566
00:41:09,092 --> 00:41:10,548
It grew on social media.

567
00:41:11,135 --> 00:41:13,217
Online activists
created a Facebook page

568
00:41:13,304 --> 00:41:16,091
that became a forum
for political dissent.

569
00:41:16,182 --> 00:41:19,299
For people in the region,
this is proof positive

570
00:41:19,394 --> 00:41:22,886
that ordinary people
can overthrow a regime.

571
00:41:24,983 --> 00:41:26,168
For those first early years

572
00:41:26,192 --> 00:41:28,103
when social media
became so powerful,

573
00:41:28,820 --> 00:41:32,062
these platforms became
the paragons of free speech.

574
00:41:34,951 --> 00:41:37,317
Problem was,
they weren't equipped.

575
00:41:38,871 --> 00:41:41,988
Facebook did not intend to be
a news distribution company,

576
00:41:42,083 --> 00:41:45,041
and it's that very fact
that makes it so dangerous

577
00:41:45,128 --> 00:41:48,165
now that it is the most dominant
news distribution platform

578
00:41:48,256 --> 00:41:49,336
in the history of humanity.

579
00:41:49,424 --> 00:41:52,131
[Somber musicl

580
00:41:58,850 --> 00:42:01,717
We now serve
more than two billion people.

581
00:42:01,811 --> 00:42:05,019
My top priority has
always been connecting people,

582
00:42:05,106 --> 00:42:08,189
building community and bringing
the world closer together.

583
00:42:09,652 --> 00:42:12,564
Advertisers and developers
will never take priority

584
00:42:12,655 --> 00:42:15,112
over that, as long as
I am running Facebook.

585
00:42:16,117 --> 00:42:18,449
Are you willing to
change your business model

586
00:42:18,536 --> 00:42:21,528
in the interest of protecting
individual privacy?

587
00:42:22,498 --> 00:42:24,739
Congresswoman,
we are... have made

588
00:42:24,834 --> 00:42:27,187
and are continuing to make changes
to reduce the amount of data that...

589
00:42:27,211 --> 00:42:30,169
No, are you willing
to change your business model

590
00:42:30,256 --> 00:42:33,248
in the interest of protecting
individual privacy?

591
00:42:35,011 --> 00:42:36,731
Congresswoman,
I'm not sure what that means.

592
00:42:39,640 --> 00:42:42,131
I don't think that tech
companies have demonstrated

593
00:42:42,226 --> 00:42:44,387
that we should have too much
confidence in them yet.

594
00:42:44,896 --> 00:42:46,496
I'm surprised, actually,
the debate there

595
00:42:46,522 --> 00:42:48,262
has focused on privacy,

596
00:42:48,357 --> 00:42:50,188
but the debate hasn't focused
around actually,

597
00:42:50,276 --> 00:42:52,107
I think,
what's much more critical,

598
00:42:52,195 --> 00:42:55,687
which is that Facebook
sells targeted adverts.

599
00:42:59,368 --> 00:43:00,699
We used to buy products.

600
00:43:01,454 --> 00:43:02,454
Now we are the product.

601
00:43:04,582 --> 00:43:06,727
All the platforms are different,
but Facebook particularly

602
00:43:06,751 --> 00:43:10,289
treats its users like fields
of corn to be harvested.

603
00:43:11,923 --> 00:43:13,959
Our attention is like oil.

604
00:43:20,056 --> 00:43:22,388
There's an amazing
amount of engineering going on

605
00:43:22,475 --> 00:43:24,932
under the hood of that
machine that you don't see,

606
00:43:25,019 --> 00:43:27,135
but changes the very
nature of what you see.

607
00:43:29,899 --> 00:43:31,389
But the algorithms are designed

608
00:43:31,484 --> 00:43:33,645
to essentially make you
feel engaged.

609
00:43:33,736 --> 00:43:36,068
So their whole
metric for success

610
00:43:36,155 --> 00:43:38,441
is keeping you there
as long as possible,

611
00:43:38,533 --> 00:43:41,650
and keeping you feeling
emotions as much as possible,

612
00:43:42,453 --> 00:43:44,694
so that you will be
a valuable commodity

613
00:43:44,789 --> 00:43:46,949
for the people who support
the work of these platforms,

614
00:43:46,999 --> 00:43:48,409
and that's the advertiser.

615
00:43:52,713 --> 00:43:56,831
Facebook have no interest
whatever in the content itself.

616
00:43:58,427 --> 00:44:00,213
There's no ranking for quality.

617
00:44:00,304 --> 00:44:03,011
There's no ranking for,
"is this good for you?"

618
00:44:03,099 --> 00:44:04,589
They don't do
anything to calculate

619
00:44:04,684 --> 00:44:06,140
the humanity of the content.

620
00:44:06,227 --> 00:44:08,639
[Ominous musicl

621
00:44:19,240 --> 00:44:21,468
You know, you start
getting into this obsession

622
00:44:21,492 --> 00:44:23,372
with clicks, and the algorithm
is driving clicks

623
00:44:23,452 --> 00:44:26,785
and driving clicks, and
eventually you get to a spot

624
00:44:26,873 --> 00:44:29,455
where attention
becomes more expensive.

625
00:44:30,710 --> 00:44:33,042
And so people have
to keep pushing the boundary.

626
00:44:33,129 --> 00:44:35,916
And so things just
get crazier and crazier.

627
00:44:43,306 --> 00:44:44,366
What we're living through now

628
00:44:44,390 --> 00:44:46,255
is a misinformation crisis.

629
00:44:46,726 --> 00:44:48,466
The systematic pollution

630
00:44:48,561 --> 00:44:49,961
of the world's
information supplies.

631
00:44:56,110 --> 00:44:58,567
I think we've already
begun to see the beginnings

632
00:44:58,654 --> 00:45:00,269
of a very fuzzy type of truth.

633
00:45:00,865 --> 00:45:03,026
We're going to have
fake video and fake audio.

634
00:45:03,117 --> 00:45:05,574
And it will be entirely
synthetic, made by a machine.

635
00:45:25,473 --> 00:45:27,680
A gap in a generative
adversarial network

636
00:45:27,767 --> 00:45:30,383
is a race between
two neural networks.

637
00:45:31,812 --> 00:45:34,975
One trying to recognize
the true from the false,

638
00:45:35,066 --> 00:45:36,806
and the other
trying to generate.

639
00:45:38,945 --> 00:45:41,231
It's a competition between
these two that gives you

640
00:45:41,322 --> 00:45:44,485
an ability to generate
very realistic images.

641
00:45:52,083 --> 00:45:53,435
Right now, when you see a video,

642
00:45:53,459 --> 00:45:55,541
we can all just trust
that that's real.

643
00:45:59,757 --> 00:46:01,944
As soon as we start to realize
there's technology out there

644
00:46:01,968 --> 00:46:03,583
that can make you think
that a politician

645
00:46:03,678 --> 00:46:06,169
or a celebrity said
something and they didn't,

646
00:46:07,139 --> 00:46:08,450
or something
that really did happen,

647
00:46:08,474 --> 00:46:09,884
someone can just
claim that that's

648
00:46:09,976 --> 00:46:11,136
been doctored,

649
00:46:12,103 --> 00:46:13,593
how we can lose trust
in everything.

650
00:46:15,147 --> 00:46:16,291
Don't think we think that much

651
00:46:16,315 --> 00:46:17,851
about how bad things could get

652
00:46:17,942 --> 00:46:19,148
if we lose some of that trust.

653
00:46:31,872 --> 00:46:33,976
I know this sounds
like a very difficult problem

654
00:46:34,000 --> 00:46:36,412
and it's some sort
of evil beyond our control.

655
00:46:36,502 --> 00:46:37,537
It is not.

656
00:46:39,922 --> 00:46:41,913
Silicon valley generally
loves to have slogans

657
00:46:42,008 --> 00:46:43,418
which express its values.

658
00:46:43,968 --> 00:46:45,924
"Move fast and break things”

659
00:46:46,012 --> 00:46:48,469
is one of the slogans on the
walls of every Facebook office.

660
00:46:49,724 --> 00:46:51,118
Well, you know,
it's time to slow down

661
00:46:51,142 --> 00:46:52,257
and build things again.

662
00:46:57,273 --> 00:46:58,638
The old gatekeeper is gone.

663
00:46:59,150 --> 00:47:00,936
What I, as a journalist
in this day and age

664
00:47:01,027 --> 00:47:02,187
want to be is a guide.

665
00:47:03,154 --> 00:47:05,048
And I'm one of those strange
people in the world today

666
00:47:05,072 --> 00:47:07,688
that believes social media,
with algorithms

667
00:47:07,783 --> 00:47:09,364
that are about
your best intentions

668
00:47:09,452 --> 00:47:12,034
could be the best thing that
ever happened to journalism.

669
00:47:16,208 --> 00:47:18,415
How do we step back
in again as publishers

670
00:47:18,502 --> 00:47:21,209
and as journalists
to kind of reassert control?

671
00:47:21,797 --> 00:47:24,288
If you can build tools
that empower people

672
00:47:24,884 --> 00:47:27,375
to do something to act
as a kind of a conscious filter

673
00:47:27,470 --> 00:47:29,711
for information,
because that's the moonshot.

674
00:47:33,017 --> 00:47:36,134
We wanted to build an app
that's a control panel

675
00:47:36,228 --> 00:47:38,469
for a healthy information habit.

676
00:47:40,066 --> 00:47:42,057
We have apps
that allow set control

677
00:47:42,151 --> 00:47:44,984
on the number of calories
we have, the running we do.

678
00:47:45,738 --> 00:47:47,444
I think we should
also have measurements

679
00:47:47,531 --> 00:47:49,647
of just how productive

680
00:47:49,742 --> 00:47:51,482
our information
consumption has been.

681
00:47:52,453 --> 00:47:55,195
Can we increase the chances
that in your daily life,

682
00:47:55,289 --> 00:47:57,746
you'll stumble across
an idea that will make you go,

683
00:47:57,833 --> 00:47:59,789
“that made me
think differently"?

684
00:48:02,088 --> 00:48:04,625
And I think we can if we
start training the algorithm

685
00:48:04,715 --> 00:48:07,331
to give us something we don't
know, but should know.

686
00:48:08,427 --> 00:48:11,339
That should be our metric
of success in journalism.

687
00:48:11,931 --> 00:48:13,671
Not how long
we manage to trap you

688
00:48:13,766 --> 00:48:16,178
in this endless
scroll of information.

689
00:48:17,937 --> 00:48:19,643
And I hope people
will understand

690
00:48:19,730 --> 00:48:21,708
that to have journalists
who really have your back,

691
00:48:21,732 --> 00:48:25,441
you have got to pay for that
experience in some form directly.

692
00:48:25,528 --> 00:48:28,520
You can't just do it
by renting out your attention

693
00:48:28,614 --> 00:48:29,614
to an advertiser.

694
00:48:32,076 --> 00:48:33,316
Part of the problem is

695
00:48:33,411 --> 00:48:36,073
people don't understand
the algorithms.

696
00:48:36,163 --> 00:48:38,404
If they did,
they would see a danger,

697
00:48:39,250 --> 00:48:40,706
but they'd also see a potential

698
00:48:40,793 --> 00:48:43,751
for us to amplify the
acquisition of real knowledge

699
00:48:43,838 --> 00:48:47,330
that surprises us,
challenges us, informs us,

700
00:48:47,425 --> 00:48:49,505
and makes us want to change
the world for the better.

701
00:49:20,624 --> 00:49:22,727
Life as one of the
first female fighter pilots

702
00:49:22,751 --> 00:49:25,868
was the best of times,
and it was the worst of times.

703
00:49:27,882 --> 00:49:31,545
It's just amazing
that you can put yourself

704
00:49:31,635 --> 00:49:34,342
in a machine
through extreme maneuvering

705
00:49:34,430 --> 00:49:36,591
and come out alive
at the other end.

706
00:49:37,141 --> 00:49:38,847
But it was also very difficult,

707
00:49:38,934 --> 00:49:41,300
because every single
fighter pilot that I know

708
00:49:41,395 --> 00:49:44,558
who has taken a life,
either civilian,

709
00:49:44,648 --> 00:49:46,388
even a legitimate
military target,

710
00:49:46,484 --> 00:49:48,645
they've all got very,
very difficult lives

711
00:49:48,736 --> 00:49:51,603
and they never walk away
as normal people.

712
00:49:54,241 --> 00:49:55,697
So, it was pretty
motivating for me

713
00:49:55,784 --> 00:49:57,069
to try to figure out, you know,

714
00:49:57,161 --> 00:49:58,401
there's got to be a better way.

715
00:50:01,790 --> 00:50:04,031
[Ominous musicl

716
00:50:07,713 --> 00:50:10,625
I'm in Geneva to speak
with the united nations

717
00:50:10,716 --> 00:50:12,377
about lethal autonomous weapons.

718
00:50:12,468 --> 00:50:14,880
I think war is a terrible event,

719
00:50:14,970 --> 00:50:16,460
and I wish
that we could avoid it,

720
00:50:16,555 --> 00:50:19,547
but I'm also a pessimist
and don't think that we can.

721
00:50:19,642 --> 00:50:21,928
So, I do think that
using autonomous weapons

722
00:50:22,019 --> 00:50:23,975
could potentially
make war as safe

723
00:50:24,063 --> 00:50:26,304
as one could possibly make it.

724
00:50:56,428 --> 00:50:59,010
Two years ago, a group
of academic researchers

725
00:50:59,098 --> 00:51:00,713
developed this open letter

726
00:51:00,808 --> 00:51:03,265
against lethal
autonomous weapons.

727
00:51:06,146 --> 00:51:07,682
The open letter came about,

728
00:51:07,773 --> 00:51:09,479
because like all technologies,

729
00:51:09,567 --> 00:51:12,229
al is a technology that can
be used for good or for bad

730
00:51:12,820 --> 00:51:15,660
and we were at the point where people
were starting to consider using it

731
00:51:15,739 --> 00:51:18,776
in a military setting that we thought
was actually very dangerous.

732
00:51:20,869 --> 00:51:23,155
Apparently, all
of these al researchers,

733
00:51:23,247 --> 00:51:25,238
it's almost
as if they woke up one day

734
00:51:25,332 --> 00:51:26,913
and looked around them and said,

735
00:51:27,001 --> 00:51:29,162
"oh, this is terrible.
This could really go wrong,

736
00:51:29,253 --> 00:51:31,494
even though these are
the technologies that I built."

737
00:51:33,424 --> 00:51:36,211
I never expected to be
an advocate for these issues,

738
00:51:36,302 --> 00:51:38,918
but as a scientist,
I feel a real responsibility

739
00:51:39,013 --> 00:51:41,800
to inform the discussion
and to warn of the risks.

740
00:51:48,606 --> 00:51:51,313
To begin the
proceedings I'd like to invite

741
00:51:51,400 --> 00:51:53,436
Dr. missy cummings
at this stage.

742
00:51:53,527 --> 00:51:57,065
She was one of the U.S. Navy's
first female fighter pilots.

743
00:51:57,156 --> 00:51:58,817
She's currently a professor

744
00:51:58,907 --> 00:52:01,694
in the Duke university
mechanical engineering

745
00:52:01,785 --> 00:52:05,198
and the director of the humans
and autonomy laboratory.

746
00:52:05,289 --> 00:52:06,950
Missy,
you have the floor please.

747
00:52:07,791 --> 00:52:09,952
Thank you, and thank
you for inviting me here.

748
00:52:10,961 --> 00:52:12,667
When I was a fighter pilot,

749
00:52:12,755 --> 00:52:15,417
and youre asked
to bomb this target,

750
00:52:15,507 --> 00:52:17,498
it's incredibly stressful.

751
00:52:17,593 --> 00:52:19,073
It is one of the most
stressful things

752
00:52:19,136 --> 00:52:20,797
you can imagine in your life.

753
00:52:21,805 --> 00:52:25,639
You are potentially at risk
for surface to air missiles,

754
00:52:25,726 --> 00:52:27,432
youre trying to match
what you're seeing

755
00:52:27,519 --> 00:52:30,181
through your sensors and
with the picture that you saw

756
00:52:30,272 --> 00:52:32,058
back on the aircraft carrier,

757
00:52:32,149 --> 00:52:35,733
to drop the bomb all
in potentially the fog of war

758
00:52:35,819 --> 00:52:37,150
in a changing environment.

759
00:52:37,738 --> 00:52:40,104
This is why there are
so many mistakes made.

760
00:52:41,325 --> 00:52:44,613
I have peers, colleagues
who have dropped bombs

761
00:52:44,703 --> 00:52:48,195
inadvertently on civilians,
who have killed friendly forces.

762
00:52:48,582 --> 00:52:50,948
Uh, these men
are never the same.

763
00:52:51,502 --> 00:52:53,959
They are completely
ruined as human beings

764
00:52:54,046 --> 00:52:55,206
when that happens.

765
00:52:56,048 --> 00:52:58,380
So, then this begs the question,

766
00:52:58,467 --> 00:53:01,755
is there ever a time
that you would want to use

767
00:53:01,845 --> 00:53:03,710
a lethal autonomous weapon?

768
00:53:04,098 --> 00:53:05,679
And I honestly will tell you,

769
00:53:05,766 --> 00:53:08,223
1 do not think
this is a job for humans.

770
00:53:11,980 --> 00:53:13,436
Thank you, missy, uh.

771
00:53:13,524 --> 00:53:16,937
It's my task now
to turn it over to you.

772
00:53:17,027 --> 00:53:20,110
First on the list is the
distinguished delegate of China.

773
00:53:20,197 --> 00:53:21,277
You have the floor, sir.

774
00:53:22,950 --> 00:53:24,350
Thank you very much.

775
00:53:24,952 --> 00:53:26,738
Many countries including China,

776
00:53:26,829 --> 00:53:29,161
have been engaged
in the research

777
00:53:29,248 --> 00:53:30,954
and development
of such technologies.

778
00:53:34,002 --> 00:53:36,981
After having heard the
presentation of these various technologies,

779
00:53:37,005 --> 00:53:41,089
ultimately a human being has to be held
accountable for an illicit activity.

780
00:53:41,176 --> 00:53:43,016
How does the
ethics in the context

781
00:53:43,095 --> 00:53:44,551
of systems designed?

782
00:53:44,638 --> 00:53:47,926
Are they just responding
algorithmically to set inputs?

783
00:53:48,016 --> 00:53:50,302
We hear that the
military is indeed leading

784
00:53:50,394 --> 00:53:52,555
the process of developing
such kind of technologies.

785
00:53:52,646 --> 00:53:54,999
Now, we do see the
full autonomous weapon systems

786
00:53:55,023 --> 00:53:56,809
as being especially problematic.

787
00:54:01,321 --> 00:54:03,687
It was surprising
to me being at the un

788
00:54:03,782 --> 00:54:07,070
and talking about the launch
of lethal autonomous weapons,

789
00:54:07,161 --> 00:54:10,324
to see no other people
with military experience.

790
00:54:10,873 --> 00:54:13,114
I felt like the un should
get a failing grade

791
00:54:13,208 --> 00:54:14,698
for not having enough people

792
00:54:14,793 --> 00:54:16,875
with military experience
in the room.

793
00:54:16,962 --> 00:54:19,874
Whether or not you agree
with the military operation,

794
00:54:19,965 --> 00:54:21,956
you at least need to hear
from those stakeholders.

795
00:54:23,761 --> 00:54:25,922
Thank you very much, ambassador.

796
00:54:26,013 --> 00:54:28,220
Thank you everyone
for those questions.

797
00:54:28,307 --> 00:54:29,592
Missy, over to you.

798
00:54:33,979 --> 00:54:36,516
Thank you, thank you
for those great questions.

799
00:54:37,232 --> 00:54:40,599
I appreciate that you think
that the United States military

800
00:54:40,694 --> 00:54:44,562
is so advanced
in its al development.

801
00:54:45,324 --> 00:54:49,192
The reality is,
we have no idea what we're doing

802
00:54:49,286 --> 00:54:52,153
when it comes to certification
of autonomous weapons

803
00:54:52,247 --> 00:54:54,533
or autonomous
technologies in general.

804
00:54:55,000 --> 00:54:57,958
In one sense, one of the
problems with the conversation

805
00:54:58,045 --> 00:55:02,539
that we're having today,
is that we really don't know

806
00:55:02,633 --> 00:55:05,124
what the right set of tests are,

807
00:55:05,219 --> 00:55:08,711
especially in helping
governments recognize

808
00:55:08,806 --> 00:55:12,719
what is not working al, and
what is not ready to field al.

809
00:55:13,435 --> 00:55:16,598
And if I were to beg
of you one thing in this body,

810
00:55:17,189 --> 00:55:20,556
we do need to come together
as an international community

811
00:55:20,651 --> 00:55:23,267
and set autonomous
weapon standards.

812
00:55:23,946 --> 00:55:27,404
People make errors
all the time in war.

813
00:55:27,491 --> 00:55:28,491
We know that.

814
00:55:29,284 --> 00:55:31,616
Having an autonomous
weapon system

815
00:55:31,703 --> 00:55:35,537
could in fact produce
substantially less loss of life.

816
00:55:39,127 --> 00:55:42,585
Thank you very
much, missy, for that response.

817
00:55:49,096 --> 00:55:50,961
There are two
problems with the argument

818
00:55:51,056 --> 00:55:52,575
that these weapons
that will save lives,

819
00:55:52,599 --> 00:55:54,089
that they'll be
more discriminatory

820
00:55:54,184 --> 00:55:55,765
and therefore
there'll be less civilians

821
00:55:55,853 --> 00:55:56,853
caught in the crossfire.

822
00:55:57,396 --> 00:55:59,887
The first problem is,
that that's some way away.

823
00:56:00,566 --> 00:56:03,524
And the weapons that
will be sold very shortly

824
00:56:03,610 --> 00:56:05,396
will not have that
discriminatory power.

825
00:56:05,487 --> 00:56:07,340
The second problem is
that when we do get there,

826
00:56:07,364 --> 00:56:09,259
and we will eventually have
weapons that will be better

827
00:56:09,283 --> 00:56:11,490
than humans in their targeting,

828
00:56:11,577 --> 00:56:13,693
these will be weapons
of mass destruction.

829
00:56:14,288 --> 00:56:16,404
[Ominous musicl

830
00:56:22,004 --> 00:56:24,290
History tells us
that we've been very lucky

831
00:56:24,381 --> 00:56:27,088
not to have the world
destroyed by nuclear weapons.

832
00:56:28,051 --> 00:56:29,916
But nuclear weapons
are difficult to build.

833
00:56:30,596 --> 00:56:32,632
You need to be
a nation to do that,

834
00:56:33,348 --> 00:56:35,088
whereas autonomous weapons,

835
00:56:35,183 --> 00:56:36,673
they are going
to be easy to obtain.

836
00:56:38,270 --> 00:56:41,478
That makes them more of a
challenge than nuclear weapons.

837
00:56:42,733 --> 00:56:45,520
I mean, previously
if you wanted to do harm,

838
00:56:45,611 --> 00:56:46,646
you needed an army.

839
00:56:48,864 --> 00:56:50,570
Now, you would have an algorithm

840
00:56:50,657 --> 00:56:53,364
that would be able to control
100 or 1000 drones.

841
00:56:54,494 --> 00:56:55,984
And so you would
no longer be limited

842
00:56:56,079 --> 00:56:57,535
by the number of people you had.

843
00:57:11,887 --> 00:57:12,989
We don't have to go
down this road.

844
00:57:13,013 --> 00:57:14,378
We get to make choices as to

845
00:57:14,514 --> 00:57:17,130
what technologies get used
and how they get used.

846
00:57:17,225 --> 00:57:19,136
We could just decide
that this was a technology

847
00:57:19,227 --> 00:57:21,309
that we shouldn't use
for killing people.

848
00:57:21,813 --> 00:57:25,180
[Somber musicl

849
00:57:45,671 --> 00:57:47,787
We're going to be
building up our military,

850
00:57:48,298 --> 00:57:52,382
and it will be so powerful,
nobody's going to mess with us.

851
00:58:19,579 --> 00:58:23,163
Somehow we feel it's better
for a human to take our life

852
00:58:23,250 --> 00:58:24,990
than for a robot
to take our life.

853
00:58:27,254 --> 00:58:30,337
Instead of a human having
to pan and zoom a camera

854
00:58:30,424 --> 00:58:32,005
to find a person in the crowd,

855
00:58:32,676 --> 00:58:34,462
the automation
would pan and zoom

856
00:58:34,553 --> 00:58:36,134
and find
the person in the crowd.

857
00:58:36,763 --> 00:58:40,597
But either way, the outcome
potentially would be the same.

858
00:58:40,684 --> 00:58:42,766
So, lethal autonomous weapons

859
00:58:43,186 --> 00:58:45,268
don't actually
change this process.

860
00:58:46,606 --> 00:58:49,564
The process is still human
approved at the very beginning.

861
00:58:51,695 --> 00:58:54,402
And so what is it
that we're trying to ban?

862
00:58:56,116 --> 00:58:58,232
Do you want to ban
the weapon itself?

863
00:58:58,326 --> 00:59:00,783
Do you want to ban the sensor
that's doing the targeting,

864
00:59:00,871 --> 00:59:03,157
or really do you want
to ban the outcome?

865
00:59:12,883 --> 00:59:15,920
One of the difficulties
about the conversation on al

866
00:59:16,011 --> 00:59:18,297
is conflating the near
term with long term.

867
00:59:18,889 --> 00:59:20,867
We could carry on those...
Most of these conversations,

868
00:59:20,891 --> 00:59:22,811
but, but let's not get them
all kind of rolled up

869
00:59:22,893 --> 00:59:24,429
into one big ball.

870
00:59:24,519 --> 00:59:26,555
Because that ball,
I think, over hypes

871
00:59:27,272 --> 00:59:28,978
what is possible today
and kind of

872
00:59:29,066 --> 00:59:30,226
simultaneously under hypes

873
00:59:30,317 --> 00:59:31,648
what is ultimately possible.

874
00:59:38,784 --> 00:59:40,240
Want to use this brush?

875
00:59:49,669 --> 00:59:51,409
Can you make a portrait?
Can you draw me?

876
00:59:52,339 --> 00:59:54,455
- No?
- How about another picture

877
00:59:54,549 --> 00:59:56,915
- of Charlie brown?
- Charlie brown's perfect.

878
00:59:57,511 --> 01:00:00,628
I'm going to move the
painting like this, all right?

879
01:00:01,014 --> 01:00:04,677
Right, when we do it, like,
when it runs out of paint,

880
01:00:04,768 --> 01:00:06,929
it makes a really
cool pattern, right?

881
01:00:07,020 --> 01:00:08,020
It does.

882
01:00:08,522 --> 01:00:10,103
One of the most
interesting things about

883
01:00:10,190 --> 01:00:12,306
when I watch my daughter
paint is it's just free.

884
01:00:13,068 --> 01:00:14,433
She's just pure expression.

885
01:00:15,737 --> 01:00:17,318
My whole art is trying to see

886
01:00:17,405 --> 01:00:19,987
how much of that
I can capture and code,

887
01:00:20,075 --> 01:00:22,316
and then have my robots
repeat that process.

888
01:00:26,790 --> 01:00:27,654
Yes.

889
01:00:27,749 --> 01:00:28,534
The first machine learning

890
01:00:28,625 --> 01:00:29,785
algorithms I started using

891
01:00:29,876 --> 01:00:31,104
were something
called style transfer.

892
01:00:31,128 --> 01:00:32,743
They were convolutional
neural networks.

893
01:00:35,132 --> 01:00:37,318
It can look at an image, then
look at another piece of art

894
01:00:37,342 --> 01:00:38,457
and it can apply the style

895
01:00:38,552 --> 01:00:39,962
from the piece
of art to the image.

896
01:00:51,314 --> 01:00:53,354
Every brush stroke,
my robots take pictures

897
01:00:53,441 --> 01:00:55,727
of what they are painting,
and use that to decide

898
01:00:55,819 --> 01:00:57,184
on the next brush stroke.

899
01:00:58,947 --> 01:01:02,064
I try and get as many
of my algorithms in as possible.

900
01:01:03,493 --> 01:01:06,155
Depending on where it is,
it might apply a gan or a CNN,

901
01:01:06,246 --> 01:01:08,282
but back and forth,
six or seven stages

902
01:01:08,373 --> 01:01:10,910
painting over itself,
searching for the image

903
01:01:11,001 --> 01:01:12,207
that it wants to paint.

904
01:01:14,004 --> 01:01:17,622
For me, creative al is
not one single god algorithm,

905
01:01:17,716 --> 01:01:20,833
it's smashing as many algorithms
as you can together

906
01:01:20,927 --> 01:01:22,542
and letting them
fight for the outcomes,

907
01:01:23,138 --> 01:01:25,470
and you get these, like,
ridiculously creative results.

908
01:01:32,397 --> 01:01:33,875
Did my machine make
this piece of art?

909
01:01:33,899 --> 01:01:35,435
Absolutely not, I'm the artist.

910
01:01:35,525 --> 01:01:37,982
But it made every single
aesthetic decision,

911
01:01:38,069 --> 01:01:41,106
and it made every single
brush stroke in this painting.

912
01:01:45,660 --> 01:01:48,652
There's this big question of, "can
robots and machines be creative?

913
01:01:48,747 --> 01:01:51,580
Can they be artists?" And I think
they are very different things.

914
01:01:56,755 --> 01:01:58,996
Art uses a lot of creativity,
but art

915
01:01:59,090 --> 01:02:01,547
is one person communicating
with another person.

916
01:02:04,971 --> 01:02:07,508
Until a machine has something
it wants to tell us,

917
01:02:07,599 --> 01:02:09,430
it won't be making art,
because otherwise

918
01:02:09,517 --> 01:02:14,056
it's just... just creating
without a message.

919
01:02:20,362 --> 01:02:21,962
In machine learning you can say,

920
01:02:22,030 --> 01:02:25,238
"here's a million recordings
of classical music.

921
01:02:25,784 --> 01:02:27,595
Now, go make me something
kind of like brahms."

922
01:02:27,619 --> 01:02:28,619
And it can do that.

923
01:02:28,703 --> 01:02:29,988
But it can't make the thing

924
01:02:30,080 --> 01:02:31,490
that comes after brahms.

925
01:02:32,916 --> 01:02:34,977
It can make a bunch of random
stuff and then poll humans.

926
01:02:35,001 --> 01:02:36,270
"Do you like this?
Do you like that?"

927
01:02:36,294 --> 01:02:37,374
But that's different.

928
01:02:37,462 --> 01:02:38,862
That's not
what a composer ever did.

929
01:02:40,423 --> 01:02:44,712
Composer felt something
and created something

930
01:02:45,553 --> 01:02:48,545
that mapped to the human
experience, right?

931
01:02:58,024 --> 01:02:59,730
I've spent my
life trying to build

932
01:02:59,859 --> 01:03:01,520
general artificial intelligence.

933
01:03:01,611 --> 01:03:05,524
I feel humbled
by how little we know

934
01:03:06,116 --> 01:03:08,448
and by how little we
understand about ourselves.

935
01:03:09,995 --> 01:03:12,077
We just don't
understand how we work.

936
01:03:16,126 --> 01:03:18,742
The human brain can do
over a quadrillion calculations

937
01:03:18,837 --> 01:03:21,419
per second
on 20 watts of energy.

938
01:03:21,923 --> 01:03:23,234
A computer right
now that would be able

939
01:03:23,258 --> 01:03:25,089
to do that many
calculations per second

940
01:03:25,176 --> 01:03:27,758
would run on 20 million
watts of energy.

941
01:03:28,930 --> 01:03:30,716
It's an unbelievable system.

942
01:03:32,767 --> 01:03:35,179
The brain can
learn the relationships

943
01:03:35,270 --> 01:03:36,350
between cause and effect,

944
01:03:36,938 --> 01:03:38,599
and build a world
inside of our heads.

945
01:03:40,483 --> 01:03:42,211
This is the reason
why you can close your eyes

946
01:03:42,235 --> 01:03:45,318
and imagine what it's like to,
you know, drive to the airport

947
01:03:45,405 --> 01:03:46,861
in a rocket ship or something.

948
01:03:47,532 --> 01:03:50,194
You can just play forward in
time in any direction you wish,

949
01:03:50,285 --> 01:03:52,025
and ask whatever question
you wish, which is

950
01:03:52,120 --> 01:03:54,202
very different from deep
learning style systems

951
01:03:54,289 --> 01:03:57,781
where all you get is a mapping
between pixels and a label.

952
01:03:59,502 --> 01:04:00,902
That's a good brush stroke.

953
01:04:01,546 --> 01:04:02,546
Is that snoopy?

954
01:04:03,048 --> 01:04:07,587
Yeah. Because snoopy
is okay to get pink.

955
01:04:07,677 --> 01:04:11,590
Because guys can be pink
like poodle's hair.

956
01:04:14,934 --> 01:04:16,370
I'm trying to learn...
I'm actually trying to teach

957
01:04:16,394 --> 01:04:17,804
my robots to paint like you.

958
01:04:17,937 --> 01:04:19,802
To try Ana get
the patterns that you can make.

959
01:04:19,939 --> 01:04:20,939
It's hard.

960
01:04:21,274 --> 01:04:23,014
You're a better
painter than my robots.

961
01:04:23,109 --> 01:04:24,109
Isn't that crazy?

962
01:04:24,152 --> 01:04:25,312
Yeah.

963
01:04:29,991 --> 01:04:31,982
Much like the Wright brothers

964
01:04:32,077 --> 01:04:34,238
learned how to build
an airplane by studying birds,

965
01:04:34,329 --> 01:04:35,723
1 think that it's
important that we study

966
01:04:35,747 --> 01:04:37,453
the right parts of neuroscience

967
01:04:37,540 --> 01:04:39,826
in order to have
some foundational ideas

968
01:04:39,959 --> 01:04:42,621
about building systems
that work like the brain.

969
01:05:19,791 --> 01:05:21,782
[Somber musicl

970
01:07:26,209 --> 01:07:28,495
Through my research
career, we've been very focused

971
01:07:28,586 --> 01:07:31,328
on developing this notion
of a brain computer interface.

972
01:07:32,715 --> 01:07:36,003
Where we started was
in epilepsy patients.

973
01:07:36,594 --> 01:07:38,505
They require having
electrodes placed

974
01:07:38,596 --> 01:07:40,199
on the surface
of their brain to figure out

975
01:07:40,223 --> 01:07:42,054
where their seizures
are coming from.

976
01:07:42,725 --> 01:07:45,592
By putting electrodes directly
on the surface of the brain,

977
01:07:45,687 --> 01:07:48,349
you get the highest
resolution of brain activity.

978
01:07:49,774 --> 01:07:51,614
It's kind of like
if you're outside of a house,

979
01:07:51,693 --> 01:07:53,354
and there's
a party going on inside,

980
01:07:53,945 --> 01:07:57,358
pasically you... all you really hear
is the bass, just a...

981
01:07:57,448 --> 01:07:59,468
Wwhereas if you really
want to hear what's going on

982
01:07:59,492 --> 01:08:00,902
and the specific conversations,

983
01:08:00,994 --> 01:08:02,530
you have to get inside the walls

984
01:08:02,620 --> 01:08:04,576
to hear that higher
frequency information.

985
01:08:04,664 --> 01:08:06,064
It's very similar
to brain activity.

986
01:08:07,125 --> 01:08:08,125
All right.

987
01:08:20,471 --> 01:08:25,932
So, Frida, measure... measure
about ten centimeters back,

988
01:08:26,519 --> 01:08:28,079
I just want to see
what that looks like.

989
01:08:28,896 --> 01:08:32,434
And this really provided us
with this unique opportunity

990
01:08:32,525 --> 01:08:35,312
to record directly
from a human brain,

991
01:08:35,403 --> 01:08:37,610
to start to understand
the physiology.

992
01:08:44,037 --> 01:08:46,028
In terms of the data
that is produced

993
01:08:46,122 --> 01:08:48,329
by recording directly
from the surface of the brain,

994
01:08:48,416 --> 01:08:49,701
it's substantial.

995
01:08:53,004 --> 01:08:55,165
Machine learning
is a critical tool

996
01:08:55,256 --> 01:08:57,542
for how we understand
brain function

997
01:08:57,634 --> 01:08:59,545
because what machine
learning does,

998
01:08:59,636 --> 01:09:01,297
is it handles complexity.

999
01:09:02,221 --> 01:09:05,088
It manages information
and simplifies it in a way

1000
01:09:05,183 --> 01:09:07,048
that allows us
to have much deeper insights

1001
01:09:07,143 --> 01:09:09,179
into how the brain
interacts with itself.

1002
01:09:15,485 --> 01:09:17,225
You know,
projecting towards the future,

1003
01:09:17,737 --> 01:09:19,443
if you had the opportunity

1004
01:09:19,530 --> 01:09:21,191
where I could do
a surgery on you,

1005
01:09:21,282 --> 01:09:23,318
it's no more risky than Lasik,

1006
01:09:23,409 --> 01:09:25,570
but I could substantially
improve your attention

1007
01:09:25,662 --> 01:09:27,368
and your memory,
would you want it?

1008
01:09:43,930 --> 01:09:46,967
It's hard to fathom,
but al is going to interpret

1009
01:09:47,058 --> 01:09:48,423
what our brains want it to do.

1010
01:09:50,520 --> 01:09:52,135
If you think
about the possibilities

1011
01:09:52,271 --> 01:09:53,602
with a brain machine interface,

1012
01:09:53,690 --> 01:09:55,450
humans will be able
to think with each other.

1013
01:09:59,946 --> 01:10:01,382
Our imagination is going to say,
"oh, going to hear

1014
01:10:01,406 --> 01:10:03,692
their voice in your head.”
no, that's just talking.

1015
01:10:03,783 --> 01:10:05,903
It's going to be different.
It's going to be thinking.

1016
01:10:09,247 --> 01:10:10,737
And it's going
to be super strange,

1017
01:10:10,832 --> 01:10:12,432
and were going to be
very not used to it.

1018
01:10:14,585 --> 01:10:17,543
It's almost like two
brains meld into one

1019
01:10:18,047 --> 01:10:19,878
and have a thought
process together.

1020
01:10:22,176 --> 01:10:24,176
What that'll do for
understanding and communication

1021
01:10:24,303 --> 01:10:26,168
and empathy is pretty dramatic.

1022
01:10:51,205 --> 01:10:53,085
When you have a
brain computer interface,

1023
01:10:53,166 --> 01:10:54,827
now your ability
to touch the world

1024
01:10:54,917 --> 01:10:56,453
extends far beyond your body.

1025
01:10:59,130 --> 01:11:01,621
You can now go on virtual
vacations any time you want,

1026
01:11:02,049 --> 01:11:03,164
to do anything you want,

1027
01:11:03,259 --> 01:11:04,749
to be a different
person if you want.

1028
01:11:08,097 --> 01:11:10,284
But you know, we're just going to
keep track of a few of your thoughts,

1029
01:11:10,308 --> 01:11:11,618
and we're not going
to charge you that much.

1030
01:11:11,642 --> 01:11:13,303
It will be 100 bucks,
you interested?

1031
01:11:17,482 --> 01:11:19,084
If somebody can have
access to your thoughts,

1032
01:11:19,108 --> 01:11:21,224
how can that be pilfered,

1033
01:11:21,319 --> 01:11:23,230
how can that be abused,
how can that be

1034
01:11:23,321 --> 01:11:24,686
used to manipulate you?

1035
01:11:27,784 --> 01:11:29,775
What happens when a corporation
gets involved

1036
01:11:29,869 --> 01:11:31,734
and you have now
large aggregates

1037
01:11:31,829 --> 01:11:33,945
of human thoughts and data

1038
01:11:35,333 --> 01:11:37,494
and your resolution for
predicting individual behavior

1039
01:11:37,585 --> 01:11:39,200
becomes so much more profound

1040
01:11:40,755 --> 01:11:42,837
that you can really
manipulate not just people,

1041
01:11:42,924 --> 01:11:45,381
but politics
and governments and society?

1042
01:11:48,179 --> 01:11:50,407
And if it becomes this, you
know, how much does the benefit

1043
01:11:50,431 --> 01:11:52,431
outweigh the potential thing
that you're giving up?

1044
01:12:06,823 --> 01:12:09,485
Whether it's
50 years, 100 years,

1045
01:12:09,575 --> 01:12:10,906
even let's say 200 years,

1046
01:12:10,993 --> 01:12:13,735
that's still
such a small blip of time

1047
01:12:13,830 --> 01:12:16,446
relative to our human evolution
that it's immaterial.

1048
01:12:34,684 --> 01:12:36,140
Human history is 100,000 years.

1049
01:12:37,061 --> 01:12:38,551
Imagine if it's a 500-page book.

1050
01:12:40,106 --> 01:12:41,687
Each page is 200 years.

1051
01:12:43,359 --> 01:12:45,850
For the first 499 pages,

1052
01:12:45,945 --> 01:12:47,481
people got around on horses

1053
01:12:48,239 --> 01:12:50,355
and they spoke
to each other through letters,

1054
01:12:51,450 --> 01:12:53,361
and there was
under a billion people on earth.

1055
01:12:57,540 --> 01:12:59,121
On the last page of the book,

1056
01:12:59,208 --> 01:13:02,575
we have the first cars
and phones and electricity.

1057
01:13:04,422 --> 01:13:05,983
We've crossed the one, two,
three, four and five,

1058
01:13:06,007 --> 01:13:08,214
six, and seven
billion person marks.

1059
01:13:08,301 --> 01:13:09,916
So, nothing about this
is normal.

1060
01:13:10,011 --> 01:13:11,797
We are living
in a complete anomaly.

1061
01:13:15,016 --> 01:13:16,131
For most of human history,

1062
01:13:16,225 --> 01:13:17,635
the world
you grew up in was normal.

1063
01:13:17,727 --> 01:13:18,842
And it was naive to believe

1064
01:13:18,936 --> 01:13:20,096
that this is a special time.

1065
01:13:21,105 --> 01:13:22,225
Now, this is a special time.

1066
01:13:28,112 --> 01:13:30,273
Provided that science
is allowed to continue

1067
01:13:30,364 --> 01:13:34,198
on a broad front, then it does
look... it's very, very likely

1068
01:13:34,285 --> 01:13:36,742
that we will eventually
develop human level al.

1069
01:13:39,206 --> 01:13:41,367
We know that human
level thinking is possible

1070
01:13:41,459 --> 01:13:44,292
and can be produced
by a physical system.

1071
01:13:44,378 --> 01:13:46,619
In our case,
it weighs three pounds

1072
01:13:46,714 --> 01:13:47,999
and sits inside of a cranium,

1073
01:13:48,758 --> 01:13:51,215
but in principle,
the same types of computations

1074
01:13:51,302 --> 01:13:54,544
could be implemented in some
other subscript like a machine.

1075
01:14:00,061 --> 01:14:02,768
There's wide disagreement
between different experts.

1076
01:14:02,855 --> 01:14:05,267
S50, there are experts
who are convinced

1077
01:14:05,775 --> 01:14:08,016
we will certainly have
this within 10-15 years,

1078
01:14:08,110 --> 01:14:09,725
and there are experts
who are convinced

1079
01:14:09,820 --> 01:14:11,026
we will never get there

1080
01:14:11,113 --> 01:14:12,694
or it'll take
many hundreds of years.

1081
01:14:32,885 --> 01:14:34,905
I think even when we
do reach human level al,

1082
01:14:34,929 --> 01:14:36,729
I think the further step
to super intelligence

1083
01:14:36,806 --> 01:14:38,512
is likely to happen quickly.

1084
01:14:41,352 --> 01:14:44,389
Once al reaches a level
slightly greater than that,

1085
01:14:44,480 --> 01:14:47,347
the human scientist,
then the further developments

1086
01:14:47,441 --> 01:14:49,773
in artificial intelligence
will be driven increasingly

1087
01:14:49,860 --> 01:14:50,940
by the al itself.

1088
01:14:54,365 --> 01:14:58,483
You get the runaway al effect,
an intelligence explosion.

1089
01:15:00,204 --> 01:15:02,570
We have a word for 130 IQ.

1090
01:15:02,665 --> 01:15:03,780
We say smart.

1091
01:15:03,874 --> 01:15:05,205
Eighty IQ we say stupid.

1092
01:15:05,584 --> 01:15:07,540
I mean, we don't have
a word for 12,000 IQ.

1093
01:15:09,046 --> 01:15:11,207
It's so unfathomable for us.

1094
01:15:12,383 --> 01:15:14,840
Disease and poverty
and climate change

1095
01:15:14,927 --> 01:15:16,667
and aging and death
and all this stuff

1096
01:15:16,762 --> 01:15:18,218
we think is unconquerable.

1097
01:15:18,764 --> 01:15:20,254
Every single one
of them becomes easy

1098
01:15:20,349 --> 01:15:21,885
fo a super intelligent al.

1099
01:15:22,643 --> 01:15:24,975
Think of all the
possible technologies

1100
01:15:25,604 --> 01:15:27,890
perfectly realistic
virtual realities,

1101
01:15:28,441 --> 01:15:31,399
space colonies, all of those
things that we could do

1102
01:15:31,485 --> 01:15:34,898
over a millennia
with super intelligence,

1103
01:15:34,989 --> 01:15:36,650
you might get them very quickly.

1104
01:15:38,951 --> 01:15:41,738
You get a rush
to technological maturity.

1105
01:16:08,689 --> 01:16:10,649
We don't really know
how the universe began.

1106
01:16:11,692 --> 01:16:13,683
We don't really
know how life began.

1107
01:16:14,987 --> 01:16:16,131
Whether you're religious or not,

1108
01:16:16,155 --> 01:16:17,361
the idea of having

1109
01:16:17,448 --> 01:16:18,984
a super intelligence,

1110
01:16:20,743 --> 01:16:22,583
it's almost like we have
god on the planet now.

1111
01:16:52,608 --> 01:16:54,269
Even at the earliest space

1112
01:16:54,360 --> 01:16:57,568
when the field of artificial
intelligence was just launched

1113
01:16:57,655 --> 01:17:00,237
and some of the pioneers
were super optimistic,

1114
01:17:00,324 --> 01:17:02,690
they thought they could have
this cracked in ten years,

1115
01:17:02,785 --> 01:17:04,491
there seems to have been
no thought given

1116
01:17:04,578 --> 01:17:06,569
to what would happen
if they succeeded.

1117
01:17:07,289 --> 01:17:09,575
[Ominous musicl

1118
01:17:15,297 --> 01:17:16,912
An existential risk,

1119
01:17:17,007 --> 01:17:19,714
it's a risk from which
there would be no recovery.

1120
01:17:21,178 --> 01:17:24,170
It's kind of an end, premature
end to the human story.

1121
01:17:28,602 --> 01:17:32,140
We can't approach this by
just learning from experience.

1122
01:17:32,731 --> 01:17:35,063
We invent cars,
we find that they crash,

1123
01:17:35,151 --> 01:17:37,016
so we invent seatbelt
and traffic lights

1124
01:17:37,111 --> 01:17:39,067
and gradually we kind
of get a handle on that.

1125
01:17:40,573 --> 01:17:42,109
That's the way
we tend to proceed.

1126
01:17:42,199 --> 01:17:44,190
We model through
and adjust as we go along.

1127
01:17:44,827 --> 01:17:46,033
But with an existential risk,

1128
01:17:46,120 --> 01:17:48,202
you really need
a proactive approach.

1129
01:17:50,082 --> 01:17:52,915
You can't learn from failure,
you don't get a second try.

1130
01:17:59,091 --> 01:18:01,753
You can't take something
smarter than you back.

1131
01:18:02,761 --> 01:18:04,156
The rest of the animals
in the planet

1132
01:18:04,180 --> 01:18:05,920
definitely want
to take humans back.

1133
01:18:07,975 --> 01:18:08,805
I'ney can't, it's too late.

1134
01:18:08,893 --> 01:18:10,383
We're here, we're in charge now.

1135
01:18:15,691 --> 01:18:18,649
One class of concern
is alignment failure.

1136
01:18:20,779 --> 01:18:22,644
What we would see
is this powerful system

1137
01:18:22,781 --> 01:18:25,944
that is pursuing some
objective that is independent

1138
01:18:26,035 --> 01:18:28,242
of our human goals and values.

1139
01:18:31,123 --> 01:18:34,081
The problem would not be that
it would hate us or resent us,

1140
01:18:35,044 --> 01:18:37,000
it would be indifferent
to us and would optimize

1141
01:18:37,087 --> 01:18:40,045
the rest of the world according
to this different criteria.

1142
01:18:42,009 --> 01:18:44,921
A little bit like there might
be an ant colony somewhere,

1143
01:18:45,012 --> 01:18:47,048
and then we decide we want
a parking lot there.

1144
01:18:49,892 --> 01:18:52,474
I mean, it's not because
we dislike, like, hate the ants,

1145
01:18:53,103 --> 01:18:55,344
it's just we had some other goal
and they didn't factor

1146
01:18:55,439 --> 01:18:57,020
into our utility function.

1147
01:19:04,573 --> 01:19:05,938
The big word is alignment.

1148
01:19:06,867 --> 01:19:08,858
It's about taking
this tremendous power

1149
01:19:09,453 --> 01:19:11,819
and pointing it
in the right direction.

1150
01:19:19,421 --> 01:19:21,582
We come with some values.

1151
01:19:22,383 --> 01:19:24,749
We like those feelings,
we don't like other ones.

1152
01:19:26,303 --> 01:19:28,715
Now, a computer doesn't
get those out of the box.

1153
01:19:29,515 --> 01:19:32,848
Where it's going to get those,
is from us.

1154
01:19:37,106 --> 01:19:39,188
And if it all
goes terribly wrong

1155
01:19:39,733 --> 01:19:42,691
and artificial intelligence
builds giant robots

1156
01:19:42,778 --> 01:19:44,138
that kill all humans
and take over,

1157
01:19:44,196 --> 01:19:45,777
you know what?
It'll be our fault.

1158
01:19:46,740 --> 01:19:48,731
If we're going
to build these things,

1159
01:19:49,326 --> 01:19:51,487
we have to instill them
with our values.

1160
01:19:52,204 --> 01:19:53,694
And if we're not clear
about that,

1161
01:19:53,789 --> 01:19:55,309
then yeah,
they probably will take over

1162
01:19:55,374 --> 01:19:56,814
and it'll all be horrible.

1163
01:19:56,875 --> 01:19:57,990
But that's true for kids.

1164
01:20:15,686 --> 01:20:18,519
Empathy, to me, is
like the most important thing

1165
01:20:18,605 --> 01:20:20,470
that everyone should have.

1166
01:20:20,566 --> 01:20:23,023
I mean, that's, that's what's
going to save the world.

1167
01:20:26,030 --> 01:20:27,645
So, regardless of machines,

1168
01:20:27,740 --> 01:20:29,947
that's the first thing
I would want to teach my son

1169
01:20:30,034 --> 01:20:31,114
if that's teachable.

1170
01:20:32,703 --> 01:20:35,365
L

1171
01:20:36,498 --> 01:20:38,580
I don't think we
appreciate how much nuance

1172
01:20:38,667 --> 01:20:40,658
goes into our value system.

1173
01:20:41,712 --> 01:20:43,077
It's very specific.

1174
01:20:44,882 --> 01:20:47,214
You think programming
a robot to walk

1175
01:20:47,301 --> 01:20:48,882
is hard or recognize faces,

1176
01:20:49,803 --> 01:20:51,919
programming it
to understand subtle values

1177
01:20:52,014 --> 01:20:53,379
is much more difficult.

1178
01:20:56,477 --> 01:20:58,559
Say that we want
the al to value life.

1179
01:20:59,521 --> 01:21:01,291
But now it says, "okay,
well, if we want to value life,

1180
01:21:01,315 --> 01:21:03,931
the species that's killing
the most life is humans.

1181
01:21:04,902 --> 01:21:05,982
Let's get rid of them."

1182
01:21:10,282 --> 01:21:12,773
Even if we could get
the al to do what we want,

1183
01:21:12,868 --> 01:21:14,824
how will we humans
then choose to use

1184
01:21:14,912 --> 01:21:16,493
this powerful new technology?

1185
01:21:18,957 --> 01:21:21,019
These are not questions
just for people like myself,

1186
01:21:21,043 --> 01:21:22,658
technologists to think about.

1187
01:21:23,921 --> 01:21:25,912
These are questions
that touch all of society,

1188
01:21:26,006 --> 01:21:28,418
and all of society need
to come up with the answers.

1189
01:21:30,719 --> 01:21:32,505
One of the mistakes
that's easy to make

1190
01:21:32,596 --> 01:21:34,177
is that the future is something

1191
01:21:34,264 --> 01:21:35,754
that we're going
to have to adapt to,

1192
01:21:36,517 --> 01:21:38,849
as opposed
to the future is the product

1193
01:21:38,977 --> 01:21:40,717
of the decisions you make today.

1194
01:22:17,433 --> 01:22:18,593
J people j

1195
01:22:24,064 --> 01:22:26,146
J we're only people I

1196
01:22:32,114 --> 01:22:33,979
J there's not much j

1197
01:22:35,492 --> 01:22:37,357
j anyone can do j

1198
01:22:38,412 --> 01:22:41,154
j really do about that

1199
01:22:43,667 --> 01:22:46,283
j but it hasn't stopped us yes j

1200
01:22:48,755 --> 01:22:49,870
j people j

1201
01:22:53,427 --> 01:22:57,796
j we know so little
about ourselves j

1202
01:23:03,312 --> 01:23:04,677
J just enough j

1203
01:23:07,107 --> 01:23:08,768
j to want to be j

1204
01:23:09,693 --> 01:23:13,811
j nearly anybody else j

1205
01:23:14,990 --> 01:23:17,652
j now how does that add up j

1206
01:23:18,577 --> 01:23:23,446
j oh, friends all my friends &

1207
01:23:23,540 --> 01:23:28,375
j oh, I hope you're
somewhere smiling j

1208
01:23:32,299 --> 01:23:35,291
j just know I think about you j

1209
01:23:36,136 --> 01:23:40,721
j more kindly than you
and I have ever been j

1210
01:23:44,228 --> 01:23:48,346
j now see you the next
time round up there j

1211
01:23:48,941 --> 01:23:53,981
j ohjt

1212
01:23:54,863 --> 01:23:58,151
j ohjt

1213
01:23:59,660 --> 01:24:06,657
j ohjt

1214
01:24:11,004 --> 01:24:12,039
j people j

1215
01:24:17,803 --> 01:24:19,794
J what's the deal

1216
01:24:26,520 --> 01:24:27,851
J' you have been hurt j