Eng Do You Trust This Computer? (2018)

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♪♪

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♪♪

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What we're on the brink of is
a world of increasingly intense,

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sophisticated
artificial intelligence.

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Man: Technology is evolving
so much faster than our society

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has the ability
to protect us as citizens.

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The robots are coming, and they
will destroy our livelihoods.

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♪♪

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You have a networked
intelligence that watches us,

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knows everything about us,
and begins to try to change us.

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Man #2: Twitter has become the
world's number-one news site.

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Man #3:
Technology is never good or bad.

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It's what we do
with the technology.

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Eventually, millions of people
are gonna be thrown out of jobs

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because their skills
are going to be obsolete.

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Woman: Mass unemployment...

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greater inequalities,
even social unrest.

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Man #4: Regardless of whether
to be afraid or not afraid,

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the change is coming,
and nobody can stop it.

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♪♪

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Man #5: We've invested
huge amounts of money,

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and so it stands to reason
that the military,

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with their own desires,

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are gonna start to use
these technologies.

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Man #6:
Autonomous weapons systems

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could lead to a global arms race

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to rival the Nuclear Era.

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♪♪

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Man #7:
We know what the answer is.

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They'll eventually
be killing us.

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♪♪

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Man #8:
These technology leaps

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are gonna yield
incredible miracles...

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and incredible horrors.

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♪♪

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Man #9: We created it,
so I think, as we move forward,

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this intelligence
will contain parts of us.

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And I think the question is --

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Will it contain
the good parts...

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or the bad parts?

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♪♪

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♪♪

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Sarah: The survivors
called the war "Judgment Day."

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They lived only to face
a new nightmare --

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the war against the machines.

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Aah!

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Nolan: I think
we've completely fucked this up.

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I think Hollywood has managed
to inoculate the general public

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against this question --

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the idea of machines
that will take over the world.

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Open the pod bay doors, HAL.

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I'm sorry, Dave.

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I'm afraid I can't do that.

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HAL?

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Nolan:
We've cried wolf enough times...

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HAL?
...that the public
has stopped paying attention,

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because it feels like
science fiction.

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Even sitting here talking
about it right now,

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it feels a little bit silly,
a little bit like,

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"Oh, this is an artifact
of some cheeseball movie."

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The WOPR spends all its time
thinking about World War III.

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But it's not.

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The general public is about
to get blindsided by this.

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♪♪

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As a society and as individuals,

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we're increasingly surrounded
by machine intelligence.

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We carry this pocket device
in the palm of our hand

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that we use to make
a striking array

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of life decisions right now,

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aided by a set
of distant algorithms

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that we have no understanding.

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♪♪

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We're already pretty jaded
about the idea

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that we can talk to our phone,

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and it mostly understands us.

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Woman: I found quite a number
of action films.

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Five years ago -- no way.

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Markoff: Robotics.
Machines that see and speak...

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Woman: Hi, there....and listen.

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All that's real now.

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And these technologies

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are gonna fundamentally
change our society.

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Thrun: Now we have this great
movement of self-driving cars.

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Driving a car autonomously

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can move people's lives
into a better place.

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Horvitz: I've lost
a number of family members,

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including my mother,

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my brother and sister-in-law
and their kids,

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to automobile accidents.

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It's pretty clear we could
almost eliminate car accidents

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with automation.

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30,000 lives in the U.S. alone.

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About a million around the world
per year.

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Ferrucci:
In healthcare, early indicators

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are the name of the game
in that space,

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so that's another place where
it can save somebody's life.

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Dr. Herman: Here in
the breast-cancer center,

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all the things that
the radiologist's brain

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does in two minutes, the
computer does instantaneously.

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The computer has looked
at 1 billion mammograms,

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and it takes that data
and applies it

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to this image instantaneously,

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so the medical application
is profound.

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Zilis:
Another really exciting area

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that we're seeing
a lot of development in

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is actually understanding
our genetic code

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and using that
to both diagnose disease

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and create
personalized treatments.

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♪♪

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Kurzweil:
The primary application
of all these machines

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will be to extend
our own intelligence.

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We'll be able to make
ourselves smarter,

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and we'll be better
at solving problems.

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We don't have to age.
We'll actually understand aging.

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We'll be able to stop it.

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Man: There's really no limit
to what intelligent machines

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can do for the human race.

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♪♪

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How could a smarter machine
not be a better machine?

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It's hard to say exactly
when I began to think

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that that was a bit naive.

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♪♪

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Stuart Russell,
he's basically a god

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in the field
of artificial intelligence.

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He wrote the book that almost
every university uses.

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Russell: I used to say it's the
best-selling AI textbook.

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Now I just say "It's the PDF
that's stolen most often."

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♪♪

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Artificial intelligence is
about making computers smart,

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and from the point
of view of the public,

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what counts as AI
is just something

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that's surprisingly intelligent

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compared to what
we thought computers

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would typically be able to do.

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AI is a field of research
to try to basically simulate

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all kinds of human capabilities.

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We're in the AI era.

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Silicon Valley
has the ability to focus

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on one bright, shiny thing.

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It was social networking

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and social media
over the last decade,

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and it's pretty clear
that the bit has flipped.

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And it starts
with machine learning.

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Nolan: When we look back at this
moment, what was the first AI?

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It's not sexy,
and it isn't the thing

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we could see at the movies,

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but you'd make a great case
that Google created,

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not a search engine,
but a godhead.

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A way for people to ask
any question they wanted

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and get the answer they needed.

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Russell: Most people are not
aware that what Google is doing

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is actually a form of
artificial intelligence.

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They just go there,
they type in a thing.

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Google gives them the answer.

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Musk: With each search,
we train it to be better.

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Sometimes we're typing a search,
and it tell us the answer

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before you've finished
asking the question.

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You know, who is the president
of Kazakhstan?

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And it'll just tell you.

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You don't have to go to the
Kazakhstan national website

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to find out.

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You didn't used to be
able to do that.

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Nolan:
That is artificial intelligence.

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Years from now when we try
to understand, we will say,

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"How did we miss it?"

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Markoff: It's one of
the striking contradictions

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that we're facing.

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Google and Facebook, et al,

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have built businesses
on giving us,

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as a society, free stuff.

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But it's a Faustian bargain.

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They're extracting something
from us in exchange,

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but we don't know

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what code is running
on the other side and why.

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We have no idea.

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It does strike
right at the issue

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of how much we should
trust these machines.

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I use computers
literally for everything.

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There are so many
computer advancements now,

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and it's become such
a big part of our lives.

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It's just incredible
what a computer can do.

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You can actually carry
a computer in your purse.

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I mean, how awesome is that?

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I think most technology is meant
to make things easier

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and simpler for all of us,

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so hopefully that just
remains the focus.

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I think everybody loves
their computers.

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♪♪

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People don't realize
they are constantly

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being negotiated with
by machines,

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whether that's the price
of products in your Amazon cart,

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whether you can get
on a particular flight,

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whether you can reserve
a room at a particular hotel.

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What you're experiencing
are machine-learning algorithms

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that have determined
that a person like you

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is willing to pay 2 cents more

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and is changing the price.

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♪♪

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Kosinski: Now, a computer looks
at millions of people

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simultaneously for
very subtle patterns.

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You can take seemingly
innocent digital footprints,

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such as someone's playlist
on Spotify,

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or stuff that they
bought on Amazon,

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and then use algorithms
to translate this

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into a very detailed and a
very accurate, intimate profile.

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Kaplan: There is a dossier on
each of us that is so extensive

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it would be possibly
accurate to say

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that they know more about you
than your mother does.

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♪♪

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Tegmark: The major cause
of the recent AI breakthrough

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isn't just that some dude

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had a brilliant insight
all of a sudden,

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but simply that we have
much bigger data

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to train them on
and vastly better computers.

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el Kaliouby:
The magic is in the data.

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It's a ton of data.

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I mean, it's data
that's never existed before.

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We've never had
this data before.

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We've created technologies
that allow us to capture

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vast amounts of information.

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If you think of a billion
cellphones on the planet

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with gyroscopes
and accelerometers

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and fingerprint readers...

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couple that with the GPS
and the photos they take

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and the tweets that you send,

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we're all giving off huge
amounts of data individually.

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Cars that drive as the cameras
on them suck up information

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about the world around them.

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The satellites that are now
in orbit the size of a toaster.

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The infrared about
the vegetation on the planet.

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The buoys that are out
in the oceans

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to feed into the climate models.

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♪♪

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And the NSA, the CIA,
as they collect information

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about the
geopolitical situations.

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The world today is literally
swimming in this data.

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♪♪

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Kosinski: Back in 2012,

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IBM estimated
that an average human being

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leaves 500 megabytes
of digital footprints every day.

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If you wanted to back up
on the one day worth of data

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that humanity produces

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and imprint it out
on a letter-sized paper,

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double-sided, font size 12,
and you stack it up,

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it would reach from
the surface of the Earth

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to the sun four times over.

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That's every day.

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Kaplan: The data itself
is not good or evil.

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It's how it's used.

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We're relying, really,
on the goodwill of these people

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and on the policies
of these companies.

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There is no legal requirement
for how they can

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and should use
that kind of data.

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That, to me, is at the heart
of the trust issue.

255
00:13:11,007 --> 00:13:13,793
Barrat: Right now there's a
giant race for creating machines

256
00:13:13,836 --> 00:13:15,751
that are as smart as humans.

257
00:13:15,795 --> 00:13:17,971
Google -- They're working on
what's really the kind of

258
00:13:18,014 --> 00:13:20,016
Manhattan Project
of artificial intelligence.

259
00:13:20,060 --> 00:13:22,671
They've got the most money.
They've got the most talent.

260
00:13:22,714 --> 00:13:27,067
They're buying up AI companies
and robotics companies.

261
00:13:27,110 --> 00:13:29,069
Urban: People still think
of Google as a search engine

262
00:13:29,112 --> 00:13:30,722
and their e-mail provider

263
00:13:30,766 --> 00:13:33,943
and a lot of other things
that we use on a daily basis,

264
00:13:33,987 --> 00:13:39,383
but behind that search box
are 10 million servers.

265
00:13:39,427 --> 00:13:42,299
That makes Google the most
powerful computing platform

266
00:13:42,343 --> 00:13:43,910
in the world.

267
00:13:43,953 --> 00:13:47,217
Google is now working
on an AI computing platform

268
00:13:47,261 --> 00:13:50,133
that will have
100 million servers.

269
00:13:52,179 --> 00:13:53,963
So when you're interacting
with Google,

270
00:13:54,007 --> 00:13:56,052
we're just seeing
the toenail of something

271
00:13:56,096 --> 00:13:58,881
that is a giant beast
in the making.

272
00:13:58,925 --> 00:14:00,622
And the truth is,
I'm not even sure

273
00:14:00,665 --> 00:14:02,798
that Google knows
what it's becoming.

274
00:14:02,842 --> 00:14:11,502
♪♪

275
00:14:11,546 --> 00:14:14,114
Phoenix: If you look inside of
what algorithms are being used

276
00:14:14,157 --> 00:14:15,811
at Google,

277
00:14:15,855 --> 00:14:20,076
it's technology
largely from the '80s.

278
00:14:20,120 --> 00:14:23,863
So these are models that you
train by showing them a 1, a 2,

279
00:14:23,906 --> 00:14:27,344
and a 3, and it learns not
what a 1 is or what a 2 is --

280
00:14:27,388 --> 00:14:30,434
It learns what the difference
between a 1 and a 2 is.

281
00:14:30,478 --> 00:14:32,436
It's just a computation.

282
00:14:32,480 --> 00:14:35,396
In the last half decade, where
we've made this rapid progress,

283
00:14:35,439 --> 00:14:38,268
it has all been
in pattern recognition.

284
00:14:38,312 --> 00:14:41,184
Tegmark: Most of
the good, old-fashioned AI

285
00:14:41,228 --> 00:14:44,057
was when we would tell
our computers

286
00:14:44,100 --> 00:14:46,798
how to play a game like chess...

287
00:14:46,842 --> 00:14:49,584
from the old paradigm where
you just tell the computer

288
00:14:49,627 --> 00:14:52,195
exactly what to do.

289
00:14:54,502 --> 00:14:57,505
Announcer:
This is "Jeopardy!"

290
00:14:57,548 --> 00:14:59,376
♪♪

291
00:14:59,420 --> 00:15:02,510
"The IBM Challenge"!

292
00:15:02,553 --> 00:15:05,730
Ferrucci: No one at the time
had thought that a machine

293
00:15:05,774 --> 00:15:08,298
could have the precision
and the confidence

294
00:15:08,342 --> 00:15:09,952
and the speed
to play "Jeopardy!"

295
00:15:09,996 --> 00:15:11,475
well enough against
the best humans.

296
00:15:11,519 --> 00:15:14,609
Let's play "Jeopardy!"

297
00:15:18,569 --> 00:15:20,354
Watson.Watson: What is "shoe"?

298
00:15:20,397 --> 00:15:21,877
You are right.
You get to pick.

299
00:15:21,921 --> 00:15:24,836
Literary Character APB
for $800.

300
00:15:24,880 --> 00:15:28,014
Answer --
the Daily Double.

301
00:15:28,057 --> 00:15:31,539
Watson actually got its
knowledge by reading Wikipedia

302
00:15:31,582 --> 00:15:34,672
and 200 million pages
of natural-language documents.

303
00:15:34,716 --> 00:15:36,674
Ferrucci:
You can't program every line

304
00:15:36,718 --> 00:15:38,502
of how the world works.

305
00:15:38,546 --> 00:15:40,722
The machine has to learn
by reading.

306
00:15:40,765 --> 00:15:42,202
Now we come to Watson.

307
00:15:42,245 --> 00:15:43,986
"Who is Bram Stoker?"

308
00:15:44,030 --> 00:15:45,988
And the wager?

309
00:15:46,032 --> 00:15:49,165
Hello! $17,973.

310
00:15:49,209 --> 00:15:50,993
$41,413.

311
00:15:51,037 --> 00:15:53,343
And a two-day total
of $77--

312
00:15:53,387 --> 00:15:56,694
Phoenix: Watson's trained
on huge amounts of text,

313
00:15:56,738 --> 00:15:59,828
but it's not like it
understands what it's saying.

314
00:15:59,871 --> 00:16:02,309
It doesn't know that water makes
things wet by touching water

315
00:16:02,352 --> 00:16:04,441
and by seeing the way
things behave in the world

316
00:16:04,485 --> 00:16:06,182
the way you and I do.

317
00:16:06,226 --> 00:16:10,143
A lot of language AI today
is not building logical models

318
00:16:10,186 --> 00:16:11,622
of how the world works.

319
00:16:11,666 --> 00:16:15,365
Rather, it's looking at
how the words appear

320
00:16:15,409 --> 00:16:18,238
in the context of other words.

321
00:16:18,281 --> 00:16:20,196
Barrat: David Ferrucci
developed IBM's Watson,

322
00:16:20,240 --> 00:16:23,547
and somebody asked him,
"Does Watson think?"

323
00:16:23,591 --> 00:16:27,160
And he said,
"Does a submarine swim?"

324
00:16:27,203 --> 00:16:29,031
And what they meant was,
when they developed submarines,

325
00:16:29,075 --> 00:16:32,992
they borrowed basic principles
of swimming from fish.

326
00:16:33,035 --> 00:16:35,037
But a submarine swims
farther and faster than fish

327
00:16:35,081 --> 00:16:36,125
and can carry a huge payload.

328
00:16:36,169 --> 00:16:39,911
It out-swims fish.

329
00:16:39,955 --> 00:16:41,870
Ng: Watson winning the game
of "Jeopardy!"

330
00:16:41,913 --> 00:16:43,741
will go down
in the history of AI

331
00:16:43,785 --> 00:16:46,570
as a significant milestone.

332
00:16:46,614 --> 00:16:49,269
We tend to be amazed
when the machine does so well.

333
00:16:49,312 --> 00:16:52,663
I'm even more amazed when the
computer beats humans at things

334
00:16:52,707 --> 00:16:55,188
that humans are
naturally good at.

335
00:16:55,231 --> 00:16:58,060
This is how we make progress.

336
00:16:58,104 --> 00:17:00,671
In the early days of
the Google Brain project,

337
00:17:00,715 --> 00:17:02,804
I gave the team a very
simple instruction,

338
00:17:02,847 --> 00:17:05,807
which was, "Build the biggest
neural network possible,

339
00:17:05,850 --> 00:17:08,157
like 1,000 computers."

340
00:17:08,201 --> 00:17:09,724
Musk: A neural net is
something very close

341
00:17:09,767 --> 00:17:12,161
to a simulation
of how the brain works.

342
00:17:12,205 --> 00:17:16,818
It's very probabilistic,
but with contextual relevance.

343
00:17:16,861 --> 00:17:18,298
Urban: In your brain,
you have long neurons

344
00:17:18,341 --> 00:17:20,256
that connect to thousands
of other neurons,

345
00:17:20,300 --> 00:17:22,519
and you have these pathways
that are formed and forged

346
00:17:22,563 --> 00:17:24,739
based on what
the brain needs to do.

347
00:17:24,782 --> 00:17:28,960
When a baby tries something and
it succeeds, there's a reward,

348
00:17:29,004 --> 00:17:32,312
and that pathway that created
the success is strengthened.

349
00:17:32,355 --> 00:17:34,662
If it fails at something,
the pathway is weakened,

350
00:17:34,705 --> 00:17:36,794
and so, over time,
the brain becomes honed

351
00:17:36,838 --> 00:17:40,320
to be good at
the environment around it.

352
00:17:40,363 --> 00:17:43,279
Ng: Really, it's just getting
machines to learn by themselves.

353
00:17:43,323 --> 00:17:45,238
This is called "deep learning,"
and "deep learning"

354
00:17:45,281 --> 00:17:48,676
and "neural networks"
mean roughly the same thing.

355
00:17:48,719 --> 00:17:52,375
Tegmark: Deep learning
is a totally different approach

356
00:17:52,419 --> 00:17:55,161
where the computer learns
more like a toddler,

357
00:17:55,204 --> 00:17:56,466
by just getting a lot of data

358
00:17:56,510 --> 00:18:00,340
and eventually
figuring stuff out.

359
00:18:00,383 --> 00:18:03,125
The computer just gets
smarter and smarter

360
00:18:03,169 --> 00:18:05,997
as it has more experiences.

361
00:18:06,041 --> 00:18:08,130
Ng: So, imagine, if you will,
a neural network, you know,

362
00:18:08,174 --> 00:18:09,697
like 1,000 computers.

363
00:18:09,740 --> 00:18:11,438
And it wakes up
not knowing anything.

364
00:18:11,481 --> 00:18:14,093
And we made it watch YouTube
for a week.

365
00:18:14,136 --> 00:18:16,704
♪♪

366
00:18:18,706 --> 00:18:20,360
♪ Oppan Gangnam style

367
00:18:20,403 --> 00:18:23,189
Ow!

368
00:18:25,408 --> 00:18:28,194
Charlie!
That really hurt!

369
00:18:28,237 --> 00:18:30,152
♪♪

370
00:18:30,196 --> 00:18:31,327
♪ Gangnam style

371
00:18:31,371 --> 00:18:33,286
♪ Op, op, op, op

372
00:18:33,329 --> 00:18:36,202
♪ Oppan Gangnam style

373
00:18:36,245 --> 00:18:38,508
Ng: And so, after watching
YouTube for a week,

374
00:18:38,552 --> 00:18:39,988
what would it learn?

375
00:18:40,031 --> 00:18:41,903
We had a hypothesis that
it would learn to detect

376
00:18:41,946 --> 00:18:44,384
commonly occurring objects
in videos.

377
00:18:44,427 --> 00:18:47,517
And so, we know that human faces
appear a lot in videos,

378
00:18:47,561 --> 00:18:49,302
so we looked,
and, lo and behold,

379
00:18:49,345 --> 00:18:51,608
there was a neuron that had
learned to detect human faces.

380
00:18:51,652 --> 00:18:56,265
Leave Britney alone!

381
00:18:56,309 --> 00:18:58,354
Well, what else
appears in videos a lot?

382
00:19:00,095 --> 00:19:01,792
So, we looked,
and to our surprise,

383
00:19:01,836 --> 00:19:04,882
there was actually a neuron
that had learned to detect cats.

384
00:19:04,926 --> 00:19:14,849
♪♪

385
00:19:14,892 --> 00:19:17,068
I still remember
seeing recognition.

386
00:19:17,112 --> 00:19:18,635
"Wow, that's a cat.
Okay, cool.

387
00:19:18,679 --> 00:19:20,071
Great."

388
00:19:23,162 --> 00:19:24,859
Barrat:
It's all pretty innocuous

389
00:19:24,902 --> 00:19:26,295
when you're thinking
about the future.

390
00:19:26,339 --> 00:19:29,733
It all seems kind of
harmless and benign.

391
00:19:29,777 --> 00:19:31,605
But we're making
cognitive architectures

392
00:19:31,648 --> 00:19:33,520
that will fly farther
and faster than us

393
00:19:33,563 --> 00:19:35,086
and carry a bigger payload,

394
00:19:35,130 --> 00:19:37,437
and they won't be
warm and fuzzy.

395
00:19:37,480 --> 00:19:39,656
Ferrucci: I think that,
in three to five years,

396
00:19:39,700 --> 00:19:41,702
you will see a computer system

397
00:19:41,745 --> 00:19:45,401
that will be able
to autonomously learn

398
00:19:45,445 --> 00:19:49,013
how to understand,
how to build understanding,

399
00:19:49,057 --> 00:19:51,364
not unlike the way
the human mind works.

400
00:19:53,931 --> 00:19:56,891
Whatever that lunch was,
it was certainly delicious.

401
00:19:56,934 --> 00:19:59,807
Simply some of
Robby's synthetics.

402
00:19:59,850 --> 00:20:01,635
He's your cook, too?

403
00:20:01,678 --> 00:20:04,551
Even manufactures
the raw materials.

404
00:20:04,594 --> 00:20:06,944
Come around here, Robby.

405
00:20:06,988 --> 00:20:09,773
I'll show you
how this works.

406
00:20:11,122 --> 00:20:13,342
One introduces
a sample of human food

407
00:20:13,386 --> 00:20:15,344
through this aperture.

408
00:20:15,388 --> 00:20:17,738
Down here there's a small
built-in chemical laboratory,

409
00:20:17,781 --> 00:20:19,218
where he analyzes it.

410
00:20:19,261 --> 00:20:21,263
Later, he can reproduce
identical molecules

411
00:20:21,307 --> 00:20:22,482
in any shape or quantity.

412
00:20:22,525 --> 00:20:24,614
Why, it's
a housewife's dream.

413
00:20:24,658 --> 00:20:26,834
Announcer: Meet Baxter,

414
00:20:26,877 --> 00:20:29,445
revolutionary
new category of robots,

415
00:20:29,489 --> 00:20:30,490
with common sense.

416
00:20:30,533 --> 00:20:31,839
Baxter...

417
00:20:31,882 --> 00:20:33,449
Barrat: Baxter is
a really good example

418
00:20:33,493 --> 00:20:36,887
of the kind of competition
we face from machines.

419
00:20:36,931 --> 00:20:42,676
Baxter can do almost anything
we can do with our hands.

420
00:20:42,719 --> 00:20:45,722
Baxter costs about
what a minimum-wage worker

421
00:20:45,766 --> 00:20:47,507
makes in a year.

422
00:20:47,550 --> 00:20:48,769
But Baxter won't be
taking the place

423
00:20:48,812 --> 00:20:50,118
of one minimum-wage worker --

424
00:20:50,161 --> 00:20:51,772
He'll be taking
the place of three,

425
00:20:51,815 --> 00:20:55,515
because they never get tired,
they never take breaks.

426
00:20:55,558 --> 00:20:57,865
Gourley: That's probably the
first thing we're gonna see --

427
00:20:57,908 --> 00:20:59,475
displacement of jobs.

428
00:20:59,519 --> 00:21:01,651
They're gonna be done
quicker, faster, cheaper

429
00:21:01,695 --> 00:21:04,088
by machines.

430
00:21:04,132 --> 00:21:07,657
Our ability to even stay current
is so insanely limited

431
00:21:07,701 --> 00:21:10,138
compared to
the machines we build.

432
00:21:10,181 --> 00:21:13,446
For example, now we have this
great movement of Uber and Lyft

433
00:21:13,489 --> 00:21:15,056
kind of making
transportation cheaper

434
00:21:15,099 --> 00:21:16,405
and democratizing
transportation,

435
00:21:16,449 --> 00:21:17,711
which is great.

436
00:21:17,754 --> 00:21:19,321
The next step is gonna be

437
00:21:19,365 --> 00:21:21,149
that they're all gonna be
replaced by driverless cars,

438
00:21:21,192 --> 00:21:22,411
and then all the Uber
and Lyft drivers

439
00:21:22,455 --> 00:21:25,936
have to find
something new to do.

440
00:21:25,980 --> 00:21:28,156
Barrat: There are
4 million professional drivers

441
00:21:28,199 --> 00:21:29,723
in the United States.

442
00:21:29,766 --> 00:21:31,638
They're unemployed soon.

443
00:21:31,681 --> 00:21:34,075
7 million people
that do data entry.

444
00:21:34,118 --> 00:21:37,339
Those people
are gonna be jobless.

445
00:21:37,383 --> 00:21:40,342
A job isn't just about money,
right?

446
00:21:40,386 --> 00:21:42,605
On a biological level,
it serves a purpose.

447
00:21:42,649 --> 00:21:45,391
It becomes a defining thing.

448
00:21:45,434 --> 00:21:48,350
When the jobs went away
in any given civilization,

449
00:21:48,394 --> 00:21:50,787
it doesn't take long
until that turns into violence.

450
00:21:53,355 --> 00:21:57,011
♪♪

451
00:21:59,622 --> 00:22:02,016
We face a giant divide
between rich and poor,

452
00:22:02,059 --> 00:22:05,019
because that's what automation
and AI will provoke --

453
00:22:05,062 --> 00:22:08,588
a greater divide between
the haves and the have-nots.

454
00:22:08,631 --> 00:22:10,807
Right now, it's working
into the middle class,

455
00:22:10,851 --> 00:22:12,896
into white-collar jobs.

456
00:22:12,940 --> 00:22:15,334
IBM's Watson does
business analytics

457
00:22:15,377 --> 00:22:20,600
that we used to pay a business
analyst $300 an hour to do.

458
00:22:20,643 --> 00:22:23,037
Gourley: Today, you're going
to college to be a doctor,

459
00:22:23,080 --> 00:22:25,082
to be an accountant,
to be a journalist.

460
00:22:25,126 --> 00:22:28,608
It's unclear that there's
gonna be jobs there for you.

461
00:22:28,651 --> 00:22:32,612
Ng: If someone's planning for
a 40-year career in radiology,

462
00:22:32,655 --> 00:22:34,222
just reading images,

463
00:22:34,265 --> 00:22:35,745
I think that could be
a challenge

464
00:22:35,789 --> 00:22:36,920
to the new graduates of today.

465
00:22:39,270 --> 00:22:49,193
♪♪

466
00:22:50,847 --> 00:22:58,464
♪♪

467
00:22:58,507 --> 00:23:02,729
Dr. Herman: The da Vinci robot
is currently utilized

468
00:23:02,772 --> 00:23:07,516
by a variety of surgeons
for its accuracy and its ability

469
00:23:07,560 --> 00:23:12,303
to avoid the inevitable
fluctuations of the human hand.

470
00:23:12,347 --> 00:23:17,787
♪♪

471
00:23:17,831 --> 00:23:23,358
♪♪

472
00:23:23,402 --> 00:23:28,494
Anybody who watches this
feels the amazingness of it.

473
00:23:30,931 --> 00:23:34,674
You look through the scope,
and you're seeing the claw hand

474
00:23:34,717 --> 00:23:36,893
holding that woman's ovary.

475
00:23:36,937 --> 00:23:42,638
Humanity was resting right here
in the hands of this robot.

476
00:23:42,682 --> 00:23:46,947
People say it's the future,
but it's not the future --

477
00:23:46,990 --> 00:23:50,516
It's the present.

478
00:23:50,559 --> 00:23:52,474
Zilis: If you think about
a surgical robot,

479
00:23:52,518 --> 00:23:54,737
there's often not a lot
of intelligence in these things,

480
00:23:54,781 --> 00:23:56,783
but over time, as we put
more and more intelligence

481
00:23:56,826 --> 00:23:58,567
into these systems,

482
00:23:58,611 --> 00:24:02,441
the surgical robots can actually
learn from each robot surgery.

483
00:24:02,484 --> 00:24:04,181
They're tracking the movements,
they're understanding

484
00:24:04,225 --> 00:24:05,966
what worked
and what didn't work.

485
00:24:06,009 --> 00:24:08,708
And eventually, the robot
for routine surgeries

486
00:24:08,751 --> 00:24:12,320
is going to be able to perform
that entirely by itself...

487
00:24:12,363 --> 00:24:13,756
or with human supervision.

488
00:24:32,558 --> 00:24:34,995
♪♪

489
00:24:35,038 --> 00:24:37,214
Dr. Herman: It seems that we're
feeding it and creating it,

490
00:24:37,258 --> 00:24:42,785
but, in a way, we are a slave
to the technology,

491
00:24:42,829 --> 00:24:45,701
because we can't go back.

492
00:24:50,053 --> 00:24:52,882
Gourley: The machines are taking
bigger and bigger bites

493
00:24:52,926 --> 00:24:57,147
out of our skill set
at an ever-increasing speed.

494
00:24:57,191 --> 00:24:59,236
And so we've got to run
faster and faster

495
00:24:59,280 --> 00:25:00,890
to keep ahead of the machines.

496
00:25:02,675 --> 00:25:04,677
How do I look?

497
00:25:04,720 --> 00:25:06,374
Good.

498
00:25:10,030 --> 00:25:11,553
Are you attracted to me?

499
00:25:11,597 --> 00:25:14,251
What?Are you attracted to me?

500
00:25:14,295 --> 00:25:17,777
You give me indications
that you are.

501
00:25:17,820 --> 00:25:20,562
I do?
Yes.

502
00:25:20,606 --> 00:25:22,608
Nolan: This is the future
we're headed into.

503
00:25:22,651 --> 00:25:26,046
We want to design
our companions.

504
00:25:26,089 --> 00:25:29,266
We're gonna like to see
a human face on AI.

505
00:25:29,310 --> 00:25:33,967
Therefore, gaming our emotions
will be depressingly easy.

506
00:25:34,010 --> 00:25:35,272
We're not that complicated.

507
00:25:35,316 --> 00:25:38,101
We're simple.
Stimulus-response.

508
00:25:38,145 --> 00:25:43,063
I can make you like me basically
by smiling at you a lot.

509
00:25:43,106 --> 00:25:45,674
AIs are gonna be fantastic
at manipulating us.

510
00:25:45,718 --> 00:25:54,640
♪♪

511
00:25:54,683 --> 00:25:56,946
So, you've developed
a technology

512
00:25:56,990 --> 00:26:00,036
that can sense
what people are feeling.

513
00:26:00,080 --> 00:26:01,472
Right.
We've developed technology

514
00:26:01,516 --> 00:26:03,387
that can read
your facial expressions

515
00:26:03,431 --> 00:26:06,521
and map that to a number
of emotional states.

516
00:26:06,565 --> 00:26:08,697
el Kaliouby: 15 years ago,
I had just finished

517
00:26:08,741 --> 00:26:11,482
my undergraduate studies
in computer science,

518
00:26:11,526 --> 00:26:15,008
and it struck me that I was
spending a lot of time

519
00:26:15,051 --> 00:26:17,793
interacting with my laptops
and my devices,

520
00:26:17,837 --> 00:26:23,582
yet these devices had absolutely
no clue how I was feeling.

521
00:26:23,625 --> 00:26:26,802
I started thinking, "What if
this device could sense

522
00:26:26,846 --> 00:26:29,326
that I was stressed
or I was having a bad day?

523
00:26:29,370 --> 00:26:31,067
What would that open up?"

524
00:26:32,721 --> 00:26:34,418
Hi, first-graders!

525
00:26:34,462 --> 00:26:35,855
How are you?

526
00:26:35,898 --> 00:26:37,813
Can I get a hug?

527
00:26:37,857 --> 00:26:40,773
We had kids interact
with the technology.

528
00:26:40,816 --> 00:26:42,862
A lot of it
is still in development,

529
00:26:42,905 --> 00:26:44,472
but it was just amazing.

530
00:26:44,515 --> 00:26:46,648
Who likes robots?
Me!

531
00:26:46,692 --> 00:26:48,911
Who wants to have a robot
in their house?

532
00:26:48,955 --> 00:26:51,479
What would you use
a robot for, Jack?

533
00:26:51,522 --> 00:26:56,353
I would use it to ask my mom
very hard math questions.

534
00:26:56,397 --> 00:26:58,181
Okay.
What about you, Theo?

535
00:26:58,225 --> 00:27:02,272
I would use it
for scaring people.

536
00:27:02,316 --> 00:27:04,666
All right.
So, start by smiling.

537
00:27:04,710 --> 00:27:06,625
Nice.

538
00:27:06,668 --> 00:27:09,018
Brow furrow.

539
00:27:09,062 --> 00:27:10,890
Nice one.
Eyebrow raise.

540
00:27:10,933 --> 00:27:12,587
This generation, technology

541
00:27:12,631 --> 00:27:15,068
is just surrounding them
all the time.

542
00:27:15,111 --> 00:27:17,853
It's almost like they expect
to have robots in their homes,

543
00:27:17,897 --> 00:27:22,336
and they expect these robots
to be socially intelligent.

544
00:27:22,379 --> 00:27:25,252
What makes robots smart?

545
00:27:25,295 --> 00:27:29,648
Put them in, like, a math
or biology class.

546
00:27:29,691 --> 00:27:32,259
I think you would
have to train it.

547
00:27:32,302 --> 00:27:35,218
All right.
Let's walk over here.

548
00:27:35,262 --> 00:27:37,394
So, if you smile and you
raise your eyebrows,

549
00:27:37,438 --> 00:27:39,005
it's gonna run over to you.

550
00:27:39,048 --> 00:27:40,833
Woman: It's coming over!
It's coming over! Look.

551
00:27:43,183 --> 00:27:45,272
But if you look angry,
it's gonna run away.

552
00:27:46,534 --> 00:27:48,797
-Awesome!
-Oh, that was good.

553
00:27:48,841 --> 00:27:52,366
We're training computers to read
and recognize emotions.

554
00:27:52,409 --> 00:27:53,846
Ready? Set? Go!

555
00:27:53,889 --> 00:27:57,414
And the response so far
has been really amazing.

556
00:27:57,458 --> 00:27:59,590
People are integrating this
into health apps,

557
00:27:59,634 --> 00:28:04,465
meditation apps, robots, cars.

558
00:28:04,508 --> 00:28:06,728
We're gonna see
how this unfolds.

559
00:28:06,772 --> 00:28:09,426
♪♪

560
00:28:09,470 --> 00:28:11,602
Zilis:
Robots can contain AI,

561
00:28:11,646 --> 00:28:14,388
but the robot is just
a physical instantiation,

562
00:28:14,431 --> 00:28:16,782
and the artificial
intelligence is the brain.

563
00:28:16,825 --> 00:28:19,872
And so brains can exist purely
in software-based systems.

564
00:28:19,915 --> 00:28:22,483
They don't need to have
a physical form.

565
00:28:22,526 --> 00:28:25,094
Robots can exist without
any artificial intelligence.

566
00:28:25,138 --> 00:28:28,097
We have a lot of
dumb robots out there.

567
00:28:28,141 --> 00:28:31,753
But a dumb robot can be
a smart robot overnight,

568
00:28:31,797 --> 00:28:34,103
given the right software,
given the right sensors.

569
00:28:34,147 --> 00:28:38,629
Barrat: We can't help but impute
motive into inanimate objects.

570
00:28:38,673 --> 00:28:40,327
We do it with machines.

571
00:28:40,370 --> 00:28:41,502
We'll treat them like children.

572
00:28:41,545 --> 00:28:43,330
We'll treat them
like surrogates.

573
00:28:43,373 --> 00:28:45,027
-Goodbye!
-Goodbye!

574
00:28:45,071 --> 00:28:48,204
And we'll pay the price.

575
00:28:49,292 --> 00:28:58,998
♪♪

576
00:28:59,041 --> 00:29:08,572
♪♪

577
00:29:08,616 --> 00:29:10,792
Okay, welcome to ATR.

578
00:29:10,836 --> 00:29:18,060
♪♪

579
00:29:25,067 --> 00:29:30,594
♪♪

580
00:29:30,638 --> 00:29:36,122
♪♪

581
00:29:47,786 --> 00:29:51,485
♪♪

582
00:29:51,528 --> 00:29:52,791
Konnichiwa.

583
00:30:24,170 --> 00:30:29,436
♪♪

584
00:30:53,677 --> 00:30:56,942
♪♪

585
00:30:56,985 --> 00:30:58,682
Gourley: We build
artificial intelligence,

586
00:30:58,726 --> 00:31:02,948
and the very first thing
we want to do is replicate us.

587
00:31:02,991 --> 00:31:05,341
I think the key point will come

588
00:31:05,385 --> 00:31:09,258
when all the major senses
are replicated --

589
00:31:09,302 --> 00:31:11,130
sight...

590
00:31:11,173 --> 00:31:12,871
touch...

591
00:31:12,914 --> 00:31:14,611
smell.

592
00:31:14,655 --> 00:31:17,919
When we replicate our senses,
is that when it become alive?

593
00:31:17,963 --> 00:31:22,010
♪♪

594
00:31:24,795 --> 00:31:27,581
♪♪

595
00:31:27,624 --> 00:31:29,104
Nolan:
So many of our machines

596
00:31:29,148 --> 00:31:31,019
are being built
to understand us.

597
00:31:32,847 --> 00:31:34,805
But what happens when
an anthropomorphic creature

598
00:31:34,849 --> 00:31:37,417
discovers that they can
adjust their loyalty,

599
00:31:37,460 --> 00:31:40,028
adjust their courage,
adjust their avarice,

600
00:31:40,072 --> 00:31:42,291
adjust their cunning?

601
00:31:42,335 --> 00:31:44,815
♪♪

602
00:31:44,859 --> 00:31:47,166
Musk: The average person,
they don't see killer robots

603
00:31:47,209 --> 00:31:48,645
going down the streets.

604
00:31:48,689 --> 00:31:50,996
They're like, "What are
you talking about?"

605
00:31:51,039 --> 00:31:53,955
Man, we want to make sure
that we don't have killer robots

606
00:31:53,999 --> 00:31:57,045
going down the street.

607
00:31:57,089 --> 00:31:59,439
Once they're going down
the street, it is too late.

608
00:31:59,482 --> 00:32:05,010
♪♪

609
00:32:05,053 --> 00:32:07,099
Russell: The thing
that worries me right now,

610
00:32:07,142 --> 00:32:08,578
that keeps me awake,

611
00:32:08,622 --> 00:32:11,842
is the development
of autonomous weapons.

612
00:32:11,886 --> 00:32:19,850
♪♪

613
00:32:19,894 --> 00:32:27,771
♪♪

614
00:32:27,815 --> 00:32:32,733
Up to now, people have expressed
unease about drones,

615
00:32:32,776 --> 00:32:35,127
which are remotely
piloted aircraft.

616
00:32:35,170 --> 00:32:39,783
♪♪

617
00:32:39,827 --> 00:32:43,309
If you take a drone's camera
and feed it into the AI system,

618
00:32:43,352 --> 00:32:47,443
it's a very easy step from here
to fully autonomous weapons

619
00:32:47,487 --> 00:32:50,881
that choose their own targets
and release their own missiles.

620
00:32:50,925 --> 00:32:58,150
♪♪

621
00:32:58,193 --> 00:33:05,374
♪♪

622
00:33:05,418 --> 00:33:12,686
♪♪

623
00:33:12,729 --> 00:33:15,080
The expected life-span
of a human being

624
00:33:15,123 --> 00:33:16,516
in that kind of
battle environment

625
00:33:16,559 --> 00:33:20,520
would be measured in seconds.

626
00:33:20,563 --> 00:33:23,740
Singer: At one point,
drones were science fiction,

627
00:33:23,784 --> 00:33:28,832
and now they've become
the normal thing in war.

628
00:33:28,876 --> 00:33:33,402
There's over 10,000 in
U.S. military inventory alone.

629
00:33:33,446 --> 00:33:35,274
But they're not
just a U.S. phenomena.

630
00:33:35,317 --> 00:33:39,060
There's more than 80 countries
that operate them.

631
00:33:39,104 --> 00:33:41,932
Gourley: It stands to reason
that people making some

632
00:33:41,976 --> 00:33:44,587
of the most important and
difficult decisions in the world

633
00:33:44,631 --> 00:33:46,328
are gonna start to use
and implement

634
00:33:46,372 --> 00:33:48,591
artificial intelligence.

635
00:33:48,635 --> 00:33:50,724
♪♪

636
00:33:50,767 --> 00:33:53,596
The Air Force just designed
a $400-billion jet program

637
00:33:53,640 --> 00:33:55,555
to put pilots in the sky,

638
00:33:55,598 --> 00:34:01,300
and a $500 AI, designed by
a couple of graduate students,

639
00:34:01,343 --> 00:34:03,432
is beating the best human pilots

640
00:34:03,476 --> 00:34:05,782
with a relatively
simple algorithm.

641
00:34:05,826 --> 00:34:09,395
♪♪

642
00:34:09,438 --> 00:34:13,399
AI will have as big an impact
on the military

643
00:34:13,442 --> 00:34:17,490
as the combustion engine
had at the turn of the century.

644
00:34:17,533 --> 00:34:18,839
It will literally touch

645
00:34:18,882 --> 00:34:21,233
everything
that the military does,

646
00:34:21,276 --> 00:34:25,324
from driverless convoys
delivering logistical supplies,

647
00:34:25,367 --> 00:34:27,021
to unmanned drones

648
00:34:27,065 --> 00:34:30,764
delivering medical aid,
to computational propaganda,

649
00:34:30,807 --> 00:34:34,246
trying to win the hearts
and minds of a population.

650
00:34:34,289 --> 00:34:38,337
And so it stands to reason
that whoever has the best AI

651
00:34:38,380 --> 00:34:41,688
will probably achieve
dominance on this planet.

652
00:34:45,561 --> 00:34:47,650
At some point in
the early 21st century,

653
00:34:47,694 --> 00:34:51,219
all of mankind was
united in celebration.

654
00:34:51,263 --> 00:34:53,830
We marveled
at our own magnificence

655
00:34:53,874 --> 00:34:56,833
as we gave birth to AI.

656
00:34:56,877 --> 00:34:58,966
AI?

657
00:34:59,009 --> 00:35:00,489
You mean
artificial intelligence?

658
00:35:00,533 --> 00:35:01,751
A singular consciousness

659
00:35:01,795 --> 00:35:05,886
that spawned
an entire race of machines.

660
00:35:05,929 --> 00:35:09,716
We don't know
who struck first -- us or them,

661
00:35:09,759 --> 00:35:12,980
but we know that it was us
that scorched the sky.

662
00:35:14,677 --> 00:35:16,766
Singer: There's a long history
of science fiction,

663
00:35:16,810 --> 00:35:19,987
not just predicting the future,
but shaping the future.

664
00:35:20,030 --> 00:35:26,820
♪♪

665
00:35:26,863 --> 00:35:30,389
Arthur Conan Doyle
writing before World War I

666
00:35:30,432 --> 00:35:34,393
on the danger of how
submarines might be used

667
00:35:34,436 --> 00:35:38,048
to carry out civilian blockades.

668
00:35:38,092 --> 00:35:40,399
At the time
he's writing this fiction,

669
00:35:40,442 --> 00:35:43,402
the Royal Navy made fun
of Arthur Conan Doyle

670
00:35:43,445 --> 00:35:45,230
for this absurd idea

671
00:35:45,273 --> 00:35:47,623
that submarines
could be useful in war.

672
00:35:47,667 --> 00:35:53,412
♪♪

673
00:35:53,455 --> 00:35:55,370
One of the things
we've seen in history

674
00:35:55,414 --> 00:35:58,243
is that our attitude
towards technology,

675
00:35:58,286 --> 00:36:01,942
but also ethics,
are very context-dependent.

676
00:36:01,985 --> 00:36:03,726
For example, the submarine...

677
00:36:03,770 --> 00:36:06,468
nations like Great Britain
and even the United States

678
00:36:06,512 --> 00:36:09,863
found it horrifying
to use the submarine.

679
00:36:09,906 --> 00:36:13,214
In fact, the German use of the
submarine to carry out attacks

680
00:36:13,258 --> 00:36:18,480
was the reason why the United
States joined World War I.

681
00:36:18,524 --> 00:36:20,613
But move the timeline forward.

682
00:36:20,656 --> 00:36:23,529
Man: The United States
of America was suddenly

683
00:36:23,572 --> 00:36:28,403
and deliberately attacked
by the empire of Japan.

684
00:36:28,447 --> 00:36:32,190
Five hours after Pearl Harbor,
the order goes out

685
00:36:32,233 --> 00:36:36,498
to commit unrestricted
submarine warfare against Japan.

686
00:36:39,936 --> 00:36:44,289
So Arthur Conan Doyle
turned out to be right.

687
00:36:44,332 --> 00:36:46,856
Nolan: That's the great old line
about science fiction --

688
00:36:46,900 --> 00:36:48,336
It's a lie that tells the truth.

689
00:36:48,380 --> 00:36:51,470
Fellow executives,
it gives me great pleasure

690
00:36:51,513 --> 00:36:54,821
to introduce you to the future
of law enforcement...

691
00:36:54,864 --> 00:36:56,562
ED-209.

692
00:36:56,605 --> 00:37:03,612
♪♪

693
00:37:03,656 --> 00:37:05,919
This isn't just a question
of science fiction.

694
00:37:05,962 --> 00:37:09,488
This is about what's next, about
what's happening right now.

695
00:37:09,531 --> 00:37:13,927
♪♪

696
00:37:13,970 --> 00:37:17,496
The role of intelligent systems
is growing very rapidly

697
00:37:17,539 --> 00:37:19,324
in warfare.

698
00:37:19,367 --> 00:37:22,152
Everyone is pushing
in the unmanned realm.

699
00:37:22,196 --> 00:37:26,374
♪♪

700
00:37:26,418 --> 00:37:28,898
Gourley: Today, the Secretary of
Defense is very, very clear --

701
00:37:28,942 --> 00:37:32,337
We will not create fully
autonomous attacking vehicles.

702
00:37:32,380 --> 00:37:34,643
Not everyone
is gonna hold themselves

703
00:37:34,687 --> 00:37:36,515
to that same set of values.

704
00:37:36,558 --> 00:37:40,693
And when China and Russia start
deploying autonomous vehicles

705
00:37:40,736 --> 00:37:45,611
that can attack and kill, what's
the move that we're gonna make?

706
00:37:45,654 --> 00:37:49,963
♪♪

707
00:37:50,006 --> 00:37:51,617
Russell: You can't say,
"Well, we're gonna use

708
00:37:51,660 --> 00:37:53,967
autonomous weapons
for our military dominance,

709
00:37:54,010 --> 00:37:56,796
but no one else
is gonna use them."

710
00:37:56,839 --> 00:38:00,495
If you make these weapons,
they're gonna be used to attack

711
00:38:00,539 --> 00:38:03,324
human populations
in large numbers.

712
00:38:03,368 --> 00:38:12,507
♪♪

713
00:38:12,551 --> 00:38:14,596
Autonomous weapons are,
by their nature,

714
00:38:14,640 --> 00:38:16,468
weapons of mass destruction,

715
00:38:16,511 --> 00:38:19,862
because it doesn't need a human
being to guide it or carry it.

716
00:38:19,906 --> 00:38:22,517
You only need one person,
to, you know,

717
00:38:22,561 --> 00:38:25,781
write a little program.

718
00:38:25,825 --> 00:38:30,220
It just captures
the complexity of this field.

719
00:38:30,264 --> 00:38:32,571
It is cool.
It is important.

720
00:38:32,614 --> 00:38:34,573
It is amazing.

721
00:38:34,616 --> 00:38:37,053
It is also frightening.

722
00:38:37,097 --> 00:38:38,968
And it's all about trust.

723
00:38:42,102 --> 00:38:44,583
It's an open letter about
artificial intelligence,

724
00:38:44,626 --> 00:38:47,063
signed by some of
the biggest names in science.

725
00:38:47,107 --> 00:38:48,413
What do they want?

726
00:38:48,456 --> 00:38:50,763
Ban the use of
autonomous weapons.

727
00:38:50,806 --> 00:38:52,373
Woman: The author stated,

728
00:38:52,417 --> 00:38:54,375
"Autonomous weapons
have been described

729
00:38:54,419 --> 00:38:56,595
as the third revolution
in warfare."

730
00:38:56,638 --> 00:38:58,553
Woman #2: ...thousand
artificial-intelligence
specialists

731
00:38:58,597 --> 00:39:01,817
calling for a global ban
on killer robots.

732
00:39:01,861 --> 00:39:04,342
Tegmark:
This open letter basically says

733
00:39:04,385 --> 00:39:06,344
that we should redefine the goal

734
00:39:06,387 --> 00:39:07,954
of the field of
artificial intelligence

735
00:39:07,997 --> 00:39:11,610
away from just creating pure,
undirected intelligence,

736
00:39:11,653 --> 00:39:13,655
towards creating
beneficial intelligence.

737
00:39:13,699 --> 00:39:16,092
The development of AI
is not going to stop.

738
00:39:16,136 --> 00:39:18,094
It is going to continue
and get better.

739
00:39:18,138 --> 00:39:19,835
If the international community

740
00:39:19,879 --> 00:39:21,968
isn't putting
certain controls on this,

741
00:39:22,011 --> 00:39:24,666
people will develop things
that can do anything.

742
00:39:24,710 --> 00:39:27,365
Woman: The letter says
that we are years, not decades,

743
00:39:27,408 --> 00:39:28,714
away from these weapons
being deployed.

744
00:39:28,757 --> 00:39:30,106
So first of all...

745
00:39:30,150 --> 00:39:32,413
We had 6,000 signatories
of that letter,

746
00:39:32,457 --> 00:39:35,155
including many of
the major figures in the field.

747
00:39:37,026 --> 00:39:39,942
I'm getting a lot of visits
from high-ranking officials

748
00:39:39,986 --> 00:39:42,989
who wish to emphasize that
American military dominance

749
00:39:43,032 --> 00:39:45,731
is very important,
and autonomous weapons

750
00:39:45,774 --> 00:39:50,083
may be part of
the Defense Department's plan.

751
00:39:50,126 --> 00:39:52,433
That's very, very scary,
because a value system

752
00:39:52,477 --> 00:39:54,479
of military developers
of technology

753
00:39:54,522 --> 00:39:57,307
is not the same as a value
system of the human race.

754
00:39:57,351 --> 00:40:00,746
♪♪

755
00:40:00,789 --> 00:40:02,922
Markoff: Out of the concerns
about the possibility

756
00:40:02,965 --> 00:40:06,665
that this technology might be
a threat to human existence,

757
00:40:06,708 --> 00:40:08,144
a number of the technologists

758
00:40:08,188 --> 00:40:09,972
have funded
the Future of Life Institute

759
00:40:10,016 --> 00:40:12,192
to try to grapple
with these problems.

760
00:40:13,193 --> 00:40:14,847
All of these guys are secretive,

761
00:40:14,890 --> 00:40:16,805
and so it's interesting
to me to see them,

762
00:40:16,849 --> 00:40:20,635
you know, all together.

763
00:40:20,679 --> 00:40:24,030
Everything we have is a result
of our intelligence.

764
00:40:24,073 --> 00:40:26,641
It's not the result
of our big, scary teeth

765
00:40:26,685 --> 00:40:29,470
or our large claws
or our enormous muscles.

766
00:40:29,514 --> 00:40:32,473
It's because we're actually
relatively intelligent.

767
00:40:32,517 --> 00:40:35,520
And among my generation,
we're all having

768
00:40:35,563 --> 00:40:37,086
what we call "holy cow,"

769
00:40:37,130 --> 00:40:39,045
or "holy something else"
moments,

770
00:40:39,088 --> 00:40:41,003
because we see
that the technology

771
00:40:41,047 --> 00:40:44,180
is accelerating faster
than we expected.

772
00:40:44,224 --> 00:40:46,705
I remember sitting
around the table there

773
00:40:46,748 --> 00:40:50,099
with some of the best and
the smartest minds in the world,

774
00:40:50,143 --> 00:40:52,058
and what really
struck me was,

775
00:40:52,101 --> 00:40:56,149
maybe the human brain
is not able to fully grasp

776
00:40:56,192 --> 00:40:58,673
the complexity of the world
that we're confronted with.

777
00:40:58,717 --> 00:41:01,415
Russell:
As it's currently constructed,

778
00:41:01,459 --> 00:41:04,766
the road that AI is following
heads off a cliff,

779
00:41:04,810 --> 00:41:07,595
and we need to change
the direction that we're going

780
00:41:07,639 --> 00:41:10,729
so that we don't take
the human race off the cliff.

781
00:41:13,558 --> 00:41:17,126
Musk: Google acquired DeepMind
several years ago.

782
00:41:17,170 --> 00:41:18,737
DeepMind operates

783
00:41:18,780 --> 00:41:22,088
as a semi-independent
subsidiary of Google.

784
00:41:22,131 --> 00:41:24,960
The thing that makes
DeepMind unique

785
00:41:25,004 --> 00:41:26,919
is that DeepMind
is absolutely focused

786
00:41:26,962 --> 00:41:30,313
on creating digital
superintelligence --

787
00:41:30,357 --> 00:41:34,056
an AI that is vastly smarter
than any human on Earth

788
00:41:34,100 --> 00:41:36,624
and ultimately smarter than
all humans on Earth combined.

789
00:41:36,668 --> 00:41:40,715
This is from the DeepMind
reinforcement learning system.

790
00:41:40,759 --> 00:41:43,544
Basically wakes up
like a newborn baby

791
00:41:43,588 --> 00:41:46,852
and is shown the screen
of an Atari video game

792
00:41:46,895 --> 00:41:50,508
and then has to learn
to play the video game.

793
00:41:50,551 --> 00:41:55,600
It knows nothing about objects,
about motion, about time.

794
00:41:57,602 --> 00:41:59,604
It only knows that there's
an image on the screen

795
00:41:59,647 --> 00:42:02,563
and there's a score.

796
00:42:02,607 --> 00:42:06,436
So, if your baby woke up
the day it was born

797
00:42:06,480 --> 00:42:08,090
and, by late afternoon,

798
00:42:08,134 --> 00:42:11,093
was playing
40 different Atari video games

799
00:42:11,137 --> 00:42:15,315
at a superhuman level,
you would be terrified.

800
00:42:15,358 --> 00:42:19,101
You would say, "My baby
is possessed. Send it back."

801
00:42:19,145 --> 00:42:23,584
Musk: The DeepMind system
can win at any game.

802
00:42:23,628 --> 00:42:27,588
It can already beat all
the original Atari games.

803
00:42:27,632 --> 00:42:29,155
It is superhuman.

804
00:42:29,198 --> 00:42:31,636
It plays the games at superspeed
in less than a minute.

805
00:42:35,640 --> 00:42:37,032
♪♪

806
00:42:37,076 --> 00:42:38,643
DeepMind turned
to another challenge,

807
00:42:38,686 --> 00:42:40,558
and the challenge
was the game of Go,

808
00:42:40,601 --> 00:42:42,603
which people
have generally argued

809
00:42:42,647 --> 00:42:45,084
has been beyond
the power of computers

810
00:42:45,127 --> 00:42:48,304
to play with
the best human Go players.

811
00:42:48,348 --> 00:42:51,264
First, they challenged
a European Go champion.

812
00:42:53,222 --> 00:42:55,834
Then they challenged
a Korean Go champion.

813
00:42:55,877 --> 00:42:57,836
Man:
Please start the game.

814
00:42:57,879 --> 00:42:59,838
And they were able
to win both times

815
00:42:59,881 --> 00:43:02,797
in kind of striking fashion.

816
00:43:02,841 --> 00:43:05,017
Nolan: You were reading articles
in New York Timesyears ago

817
00:43:05,060 --> 00:43:09,761
talking about how Go would take
100 years for us to solve.

818
00:43:09,804 --> 00:43:11,110
Urban:
People said, "Well, you know,

819
00:43:11,153 --> 00:43:13,460
but that's still just a board.

820
00:43:13,503 --> 00:43:15,027
Poker is an art.

821
00:43:15,070 --> 00:43:16,419
Poker involves reading people.

822
00:43:16,463 --> 00:43:18,073
Poker involves lying
and bluffing.

823
00:43:18,117 --> 00:43:19,553
It's not an exact thing.

824
00:43:19,597 --> 00:43:21,381
That will never be,
you know, a computer.

825
00:43:21,424 --> 00:43:22,861
You can't do that."

826
00:43:22,904 --> 00:43:24,732
They took the best
poker players in the world,

827
00:43:24,776 --> 00:43:27,387
and it took seven days
for the computer

828
00:43:27,430 --> 00:43:30,520
to start demolishing the humans.

829
00:43:30,564 --> 00:43:32,261
So it's the best poker player
in the world,

830
00:43:32,305 --> 00:43:34,655
it's the best Go player in the
world, and the pattern here

831
00:43:34,699 --> 00:43:37,440
is that AI might take
a little while

832
00:43:37,484 --> 00:43:40,443
to wrap its tentacles
around a new skill,

833
00:43:40,487 --> 00:43:44,883
but when it does, when it
gets it, it is unstoppable.

834
00:43:44,926 --> 00:43:51,977
♪♪

835
00:43:52,020 --> 00:43:55,110
DeepMind's AI has
administrator-level access

836
00:43:55,154 --> 00:43:57,156
to Google's servers

837
00:43:57,199 --> 00:44:00,768
to optimize energy usage
at the data centers.

838
00:44:00,812 --> 00:44:04,816
However, this could be
an unintentional Trojan horse.

839
00:44:04,859 --> 00:44:07,253
DeepMind has to have complete
control of the data centers,

840
00:44:07,296 --> 00:44:08,950
so with a little
software update,

841
00:44:08,994 --> 00:44:10,691
that AI could take
complete control

842
00:44:10,735 --> 00:44:12,214
of the whole Google system,

843
00:44:12,258 --> 00:44:13,607
which means
they can do anything.

844
00:44:13,651 --> 00:44:14,913
They could look
at all your data.

845
00:44:14,956 --> 00:44:16,131
They could do anything.

846
00:44:16,175 --> 00:44:18,917
♪♪

847
00:44:20,135 --> 00:44:23,051
We're rapidly heading towards
digital superintelligence

848
00:44:23,095 --> 00:44:24,313
that far exceeds any human.

849
00:44:24,357 --> 00:44:26,402
I think it's very obvious.

850
00:44:26,446 --> 00:44:27,708
Barrat:
The problem is, we're not gonna

851
00:44:27,752 --> 00:44:29,710
suddenly hit
human-level intelligence

852
00:44:29,754 --> 00:44:33,105
and say,
"Okay, let's stop research."

853
00:44:33,148 --> 00:44:34,715
It's gonna go beyond
human-level intelligence

854
00:44:34,759 --> 00:44:36,195
into what's called
"superintelligence,"

855
00:44:36,238 --> 00:44:39,459
and that's anything
smarter than us.

856
00:44:39,502 --> 00:44:41,287
Tegmark:
AI at the superhuman level,

857
00:44:41,330 --> 00:44:42,810
if we succeed with that,
will be

858
00:44:42,854 --> 00:44:46,553
by far the most powerful
invention we've ever made

859
00:44:46,596 --> 00:44:50,296
and the last invention
we ever have to make.

860
00:44:50,339 --> 00:44:53,168
And if we create AI
that's smarter than us,

861
00:44:53,212 --> 00:44:54,735
we have to be open
to the possibility

862
00:44:54,779 --> 00:44:57,520
that we might actually
lose control to them.

863
00:44:57,564 --> 00:45:00,741
♪♪

864
00:45:00,785 --> 00:45:02,612
Russell: Let's say
you give it some objective,

865
00:45:02,656 --> 00:45:04,745
like curing cancer,
and then you discover

866
00:45:04,789 --> 00:45:06,965
that the way
it chooses to go about that

867
00:45:07,008 --> 00:45:08,444
is actually in conflict

868
00:45:08,488 --> 00:45:12,405
with a lot of other things
you care about.

869
00:45:12,448 --> 00:45:16,496
Musk: AI doesn't have to be evil
to destroy humanity.

870
00:45:16,539 --> 00:45:20,674
If AI has a goal, and humanity
just happens to be in the way,

871
00:45:20,718 --> 00:45:22,894
it will destroy humanity
as a matter of course,

872
00:45:22,937 --> 00:45:25,113
without even thinking about it.
No hard feelings.

873
00:45:25,157 --> 00:45:27,072
It's just like
if we're building a road

874
00:45:27,115 --> 00:45:29,770
and an anthill happens
to be in the way...

875
00:45:29,814 --> 00:45:31,467
We don't hate ants.

876
00:45:31,511 --> 00:45:33,165
We're just building a road.

877
00:45:33,208 --> 00:45:34,557
And so goodbye, anthill.

878
00:45:34,601 --> 00:45:37,952
♪♪

879
00:45:37,996 --> 00:45:40,172
It's tempting
to dismiss these concerns,

880
00:45:40,215 --> 00:45:42,783
'cause it's, like,
something that might happen

881
00:45:42,827 --> 00:45:47,396
in a few decades or 100 years,
so why worry?

882
00:45:47,440 --> 00:45:50,704
Russell: But if you go back
to September 11, 1933,

883
00:45:50,748 --> 00:45:52,401
Ernest Rutherford,

884
00:45:52,445 --> 00:45:54,795
who was the most well-known
nuclear physicist of his time,

885
00:45:54,839 --> 00:45:56,318
said that the possibility

886
00:45:56,362 --> 00:45:58,668
of ever extracting
useful amounts of energy

887
00:45:58,712 --> 00:46:00,801
from the transmutation
of atoms, as he called it,

888
00:46:00,845 --> 00:46:03,151
was moonshine.

889
00:46:03,195 --> 00:46:04,849
The next morning, Leo Szilard,

890
00:46:04,892 --> 00:46:06,502
who was a much
younger physicist,

891
00:46:06,546 --> 00:46:09,984
read this and got really annoyed
and figured out

892
00:46:10,028 --> 00:46:11,943
how to make
a nuclear chain reaction

893
00:46:11,986 --> 00:46:13,379
just a few months later.

894
00:46:13,422 --> 00:46:20,560
♪♪

895
00:46:20,603 --> 00:46:23,693
We have spent more
than $2 billion

896
00:46:23,737 --> 00:46:27,523
on the greatest
scientific gamble in history.

897
00:46:27,567 --> 00:46:30,222
Russell: So when people say
that, "Oh, this is so far off

898
00:46:30,265 --> 00:46:32,528
in the future, we don't have
to worry about it,"

899
00:46:32,572 --> 00:46:36,271
it might only be three, four
breakthroughs of that magnitude

900
00:46:36,315 --> 00:46:40,275
that will get us from here
to superintelligent machines.

901
00:46:40,319 --> 00:46:42,974
Tegmark: If it's gonna take
20 years to figure out

902
00:46:43,017 --> 00:46:45,237
how to keep AI beneficial,

903
00:46:45,280 --> 00:46:48,849
then we should start today,
not at the last second

904
00:46:48,893 --> 00:46:51,460
when some dudes
drinking Red Bull

905
00:46:51,504 --> 00:46:53,332
decide to flip the switch
and test the thing.

906
00:46:53,375 --> 00:46:56,770
♪♪

907
00:46:56,814 --> 00:46:58,859
Musk:
We have five years.

908
00:46:58,903 --> 00:47:00,600
I think
digital superintelligence

909
00:47:00,643 --> 00:47:03,864
will happen in my lifetime.

910
00:47:03,908 --> 00:47:05,735
100%.

911
00:47:05,779 --> 00:47:07,215
Barrat: When this happens,

912
00:47:07,259 --> 00:47:09,696
it will be surrounded
by a bunch of people

913
00:47:09,739 --> 00:47:13,091
who are really just excited
about the technology.

914
00:47:13,134 --> 00:47:15,571
They want to see it succeed,
but they're not anticipating

915
00:47:15,615 --> 00:47:16,964
that it can get out of control.

916
00:47:17,008 --> 00:47:24,450
♪♪

917
00:47:25,494 --> 00:47:28,584
Oh, my God, I trust
my computer so much.

918
00:47:28,628 --> 00:47:30,195
That's an amazing question.

919
00:47:30,238 --> 00:47:31,457
I don't trust
my computer.

920
00:47:31,500 --> 00:47:32,937
If it's on,
I take it off.

921
00:47:32,980 --> 00:47:34,242
Like, even when it's off,

922
00:47:34,286 --> 00:47:35,896
I still think it's on.
Like, you know?

923
00:47:35,940 --> 00:47:37,637
Like, you really cannot tru--
Like, the webcams,

924
00:47:37,680 --> 00:47:39,595
you don't know if, like,
someone might turn it...

925
00:47:39,639 --> 00:47:41,249
You don't know, like.

926
00:47:41,293 --> 00:47:42,903
I don't trust my computer.

927
00:47:42,947 --> 00:47:46,907
Like, in my phone,
every time they ask me

928
00:47:46,951 --> 00:47:49,475
"Can we send your
information to Apple?"

929
00:47:49,518 --> 00:47:50,998
every time, I...

930
00:47:51,042 --> 00:47:53,087
So, I don't trust my phone.

931
00:47:53,131 --> 00:47:56,743
Okay. So, part of it is,
yes, I do trust it,

932
00:47:56,786 --> 00:48:00,660
because it would be really
hard to get through the day

933
00:48:00,703 --> 00:48:04,011
in the way our world is
set up without computers.

934
00:48:04,055 --> 00:48:05,360
♪♪

935
00:48:10,975 --> 00:48:13,368
Dr. Herman: Trust is
such a human experience.

936
00:48:13,412 --> 00:48:21,246
♪♪

937
00:48:21,289 --> 00:48:25,119
I have a patient coming in
with an intracranial aneurysm.

938
00:48:25,163 --> 00:48:29,994
♪♪

939
00:48:30,037 --> 00:48:31,691
They want to look
in my eyes and know

940
00:48:31,734 --> 00:48:34,955
that they can trust
this person with their life.

941
00:48:34,999 --> 00:48:39,394
I'm not horribly concerned
about anything.

942
00:48:39,438 --> 00:48:40,830
Good.
Part of that

943
00:48:40,874 --> 00:48:42,920
is because
I have confidence in you.

944
00:48:42,963 --> 00:48:50,710
♪♪

945
00:48:50,753 --> 00:48:52,233
This procedure
we're doing today

946
00:48:52,277 --> 00:48:57,151
20 years ago
was essentially impossible.

947
00:48:57,195 --> 00:49:00,328
We just didn't have the
materials and the technologies.

948
00:49:04,202 --> 00:49:13,385
♪♪

949
00:49:13,428 --> 00:49:22,655
♪♪

950
00:49:22,698 --> 00:49:26,485
So, the coil is barely
in there right now.

951
00:49:26,528 --> 00:49:29,923
It's just a feather
holding it in.

952
00:49:29,967 --> 00:49:32,012
It's nervous time.

953
00:49:32,056 --> 00:49:36,147
♪♪

954
00:49:36,190 --> 00:49:37,626
We're just in purgatory,

955
00:49:37,670 --> 00:49:40,673
intellectual,
humanistic purgatory,

956
00:49:40,716 --> 00:49:43,632
and AI might know
exactly what to do here.

957
00:49:43,676 --> 00:49:50,596
♪♪

958
00:49:50,639 --> 00:49:52,554
We've got the coil
into the aneurysm.

959
00:49:52,598 --> 00:49:54,556
But it wasn't in
tremendously well

960
00:49:54,600 --> 00:49:56,428
that I knew that it would stay,

961
00:49:56,471 --> 00:50:01,041
so with a maybe 20% risk
of a very bad situation,

962
00:50:01,085 --> 00:50:04,436
I elected
to just bring her back.

963
00:50:04,479 --> 00:50:05,959
Because of my relationship
with her

964
00:50:06,003 --> 00:50:08,222
and knowing the difficulties
of coming in

965
00:50:08,266 --> 00:50:11,051
and having the procedure,
I consider things,

966
00:50:11,095 --> 00:50:14,272
when I should only consider
the safest possible route

967
00:50:14,315 --> 00:50:16,361
to achieve success.

968
00:50:16,404 --> 00:50:19,755
But I had to stand there for
10 minutes agonizing about it.

969
00:50:19,799 --> 00:50:21,757
The computer feels nothing.

970
00:50:21,801 --> 00:50:24,760
The computer just does
what it's supposed to do,

971
00:50:24,804 --> 00:50:26,284
better and better.

972
00:50:26,327 --> 00:50:30,288
♪♪

973
00:50:30,331 --> 00:50:32,551
I want to be AI in this case.

974
00:50:35,945 --> 00:50:38,861
But can AI be compassionate?

975
00:50:38,905 --> 00:50:43,040
♪♪

976
00:50:43,083 --> 00:50:47,827
I mean, it's everybody's
question about AI.

977
00:50:47,870 --> 00:50:51,961
We are the sole
embodiment of humanity,

978
00:50:52,005 --> 00:50:55,269
and it's a stretch for us
to accept that a machine

979
00:50:55,313 --> 00:50:58,794
can be compassionate
and loving in that way.

980
00:50:58,838 --> 00:51:05,105
♪♪

981
00:51:05,149 --> 00:51:07,281
Part of me
doesn't believe in magic,

982
00:51:07,325 --> 00:51:09,805
but part of me has faith
that there is something

983
00:51:09,849 --> 00:51:11,546
beyond the sum of the parts,

984
00:51:11,590 --> 00:51:15,637
that there is at least a oneness
in our shared ancestry,

985
00:51:15,681 --> 00:51:20,338
our shared biology,
our shared history.

986
00:51:20,381 --> 00:51:23,210
Some connection there
beyond machine.

987
00:51:23,254 --> 00:51:30,304
♪♪

988
00:51:30,348 --> 00:51:32,567
So, then, you have
the other side of that, is,

989
00:51:32,611 --> 00:51:34,047
does the computer
know it's conscious,

990
00:51:34,091 --> 00:51:37,137
or can it be conscious,
or does it care?

991
00:51:37,181 --> 00:51:40,009
Does it need to be conscious?

992
00:51:40,053 --> 00:51:42,011
Does it need to be aware?

993
00:51:42,055 --> 00:51:47,365
♪♪

994
00:51:47,408 --> 00:51:52,848
♪♪

995
00:51:52,892 --> 00:51:56,417
I do not think that a robot
could ever be conscious.

996
00:51:56,461 --> 00:51:58,376
Unless they programmed it
that way.

997
00:51:58,419 --> 00:52:00,639
Conscious? No.

998
00:52:00,682 --> 00:52:03,163
No.
No.

999
00:52:03,207 --> 00:52:06,035
I mean, think a robot could be
programmed to be conscious.

1000
00:52:06,079 --> 00:52:09,648
How are they programmed
to do everything else?

1001
00:52:09,691 --> 00:52:12,390
That's another big part
of artificial intelligence,

1002
00:52:12,433 --> 00:52:15,741
is to make them conscious
and make them feel.

1003
00:52:17,003 --> 00:52:22,400
♪♪

1004
00:52:22,443 --> 00:52:26,230
Lipson: Back in 2005, we started
trying to build machines

1005
00:52:26,273 --> 00:52:27,709
with self-awareness.

1006
00:52:27,753 --> 00:52:33,062
♪♪

1007
00:52:33,106 --> 00:52:37,284
This robot, to begin with,
didn't know what it was.

1008
00:52:37,328 --> 00:52:40,244
All it knew was that it needed
to do something like walk.

1009
00:52:40,287 --> 00:52:44,073
♪♪

1010
00:52:44,117 --> 00:52:45,597
Through trial and error,

1011
00:52:45,640 --> 00:52:49,731
it figured out how to walk
using its imagination,

1012
00:52:49,775 --> 00:52:54,040
and then it walked away.

1013
00:52:54,083 --> 00:52:56,390
And then we did
something very cruel.

1014
00:52:56,434 --> 00:52:58,653
We chopped off a leg
and watched what happened.

1015
00:52:58,697 --> 00:53:03,005
♪♪

1016
00:53:03,049 --> 00:53:07,749
At the beginning, it didn't
quite know what had happened.

1017
00:53:07,793 --> 00:53:13,233
But over about a period
of a day, it then began to limp.

1018
00:53:13,277 --> 00:53:16,845
And then, a year ago,
we were training an AI system

1019
00:53:16,889 --> 00:53:20,240
for a live demonstration.

1020
00:53:20,284 --> 00:53:21,763
We wanted to show how we wave

1021
00:53:21,807 --> 00:53:24,113
all these objects
in front of the camera

1022
00:53:24,157 --> 00:53:27,334
and the AI could
recognize the objects.

1023
00:53:27,378 --> 00:53:29,031
And so, we're preparing
this demo,

1024
00:53:29,075 --> 00:53:31,251
and we had on a side screen
this ability

1025
00:53:31,295 --> 00:53:36,778
to watch what certain
neurons were responding to.

1026
00:53:36,822 --> 00:53:39,041
And suddenly we noticed
that one of the neurons

1027
00:53:39,085 --> 00:53:41,087
was tracking faces.

1028
00:53:41,130 --> 00:53:45,483
It was tracking our faces
as we were moving around.

1029
00:53:45,526 --> 00:53:48,616
Now, the spooky thing about this
is that we never trained

1030
00:53:48,660 --> 00:53:52,490
the system
to recognize human faces,

1031
00:53:52,533 --> 00:53:55,710
and yet, somehow,
it learned to do that.

1032
00:53:57,973 --> 00:53:59,584
Even though these robots
are very simple,

1033
00:53:59,627 --> 00:54:02,500
we can see there's
something else going on there.

1034
00:54:02,543 --> 00:54:05,851
It's not just programming.

1035
00:54:05,894 --> 00:54:08,462
So, this is just the beginning.

1036
00:54:10,377 --> 00:54:14,294
Horvitz: I often think about
that beach in Kitty Hawk,

1037
00:54:14,338 --> 00:54:18,255
the 1903 flight
by Orville and Wilbur Wright.

1038
00:54:21,214 --> 00:54:24,348
It was kind of a canvas plane,
and it's wood and iron,

1039
00:54:24,391 --> 00:54:26,828
and it gets off the ground for,
what, a minute and 20 seconds,

1040
00:54:26,872 --> 00:54:29,091
on this windy day

1041
00:54:29,135 --> 00:54:31,006
before touching back down again.

1042
00:54:33,270 --> 00:54:37,143
And it was
just around 65 summers or so

1043
00:54:37,186 --> 00:54:43,149
after that moment that you have
a 747 taking off from JFK...

1044
00:54:43,192 --> 00:54:50,156
♪♪

1045
00:54:50,199 --> 00:54:51,984
...where a major concern
of someone on the airplane

1046
00:54:52,027 --> 00:54:55,422
might be whether or not
their salt-free diet meal

1047
00:54:55,466 --> 00:54:56,902
is gonna be coming to them
or not.

1048
00:54:56,945 --> 00:54:58,469
We have a whole infrastructure,

1049
00:54:58,512 --> 00:55:01,385
with travel agents
and tower control,

1050
00:55:01,428 --> 00:55:03,778
and it's all casual,
and it's all part of the world.

1051
00:55:03,822 --> 00:55:07,042
♪♪

1052
00:55:07,086 --> 00:55:09,523
Right now, as far
as we've come with machines

1053
00:55:09,567 --> 00:55:12,134
that think and solve problems,
we're at Kitty Hawk now.

1054
00:55:12,178 --> 00:55:13,745
We're in the wind.

1055
00:55:13,788 --> 00:55:17,052
We have our tattered-canvas
planes up in the air.

1056
00:55:17,096 --> 00:55:20,882
♪♪

1057
00:55:20,926 --> 00:55:23,885
But what happens
in 65 summers or so?

1058
00:55:23,929 --> 00:55:27,889
We will have machines
that are beyond human control.

1059
00:55:27,933 --> 00:55:30,457
Should we worry about that?

1060
00:55:30,501 --> 00:55:32,590
♪♪

1061
00:55:32,633 --> 00:55:34,853
I'm not sure it's going to help.

1062
00:55:40,337 --> 00:55:44,036
Kaplan: Nobody has any idea
today what it means for a robot

1063
00:55:44,079 --> 00:55:46,430
to be conscious.

1064
00:55:46,473 --> 00:55:48,649
There is no such thing.

1065
00:55:48,693 --> 00:55:50,172
There are a lot of smart people,

1066
00:55:50,216 --> 00:55:53,088
and I have a great deal
of respect for them,

1067
00:55:53,132 --> 00:55:57,528
but the truth is, machines
are natural psychopaths.

1068
00:55:57,571 --> 00:55:59,225
Man:
Fear came back into the market.

1069
00:55:59,268 --> 00:56:01,706
Man #2: Went down 800,
nearly 1,000, in a heartbeat.

1070
00:56:01,749 --> 00:56:03,360
I mean,
it is classic capitulation.

1071
00:56:03,403 --> 00:56:04,796
There are some people
who are proposing

1072
00:56:04,839 --> 00:56:07,146
it was some kind
of fat-finger error.

1073
00:56:07,189 --> 00:56:09,583
Take the Flash Crash of 2010.

1074
00:56:09,627 --> 00:56:13,413
In a matter of minutes,
$1 trillion in value

1075
00:56:13,457 --> 00:56:15,415
was lost in the stock market.

1076
00:56:15,459 --> 00:56:18,984
Woman: The Dow dropped nearly
1,000 points in a half-hour.

1077
00:56:19,027 --> 00:56:22,553
Kaplan:
So, what went wrong?

1078
00:56:22,596 --> 00:56:26,644
By that point in time,
more than 60% of all the trades

1079
00:56:26,687 --> 00:56:29,124
that took place
on the stock exchange

1080
00:56:29,168 --> 00:56:32,693
were actually being
initiated by computers.

1081
00:56:32,737 --> 00:56:34,216
Man:
Panic selling on the way down,

1082
00:56:34,260 --> 00:56:35,783
and all of a sudden
it stopped on a dime.

1083
00:56:35,827 --> 00:56:37,611
Man #2: This is all happening
in real time, folks.

1084
00:56:37,655 --> 00:56:39,526
Wisz: The short story of what
happened in the Flash Crash

1085
00:56:39,570 --> 00:56:42,399
is that algorithms
responded to algorithms,

1086
00:56:42,442 --> 00:56:45,358
and it compounded upon itself
over and over and over again

1087
00:56:45,402 --> 00:56:47,012
in a matter of minutes.

1088
00:56:47,055 --> 00:56:50,972
Man: At one point, the market
fell as if down a well.

1089
00:56:51,016 --> 00:56:54,323
There is no regulatory body
that can adapt quickly enough

1090
00:56:54,367 --> 00:56:57,979
to prevent potentially
disastrous consequences

1091
00:56:58,023 --> 00:57:01,243
of AI operating
in our financial systems.

1092
00:57:01,287 --> 00:57:03,898
They are so prime
for manipulation.

1093
00:57:03,942 --> 00:57:05,639
Let's talk about the speed
with which

1094
00:57:05,683 --> 00:57:08,076
we are watching
this market deteriorate.

1095
00:57:08,120 --> 00:57:11,602
That's the type of AI-run-amuck
that scares people.

1096
00:57:11,645 --> 00:57:13,560
Kaplan:
When you give them a goal,

1097
00:57:13,604 --> 00:57:17,825
they will relentlessly
pursue that goal.

1098
00:57:17,869 --> 00:57:20,393
How many computer programs
are there like this?

1099
00:57:20,437 --> 00:57:23,483
Nobody knows.

1100
00:57:23,527 --> 00:57:27,444
Kosinski: One of the fascinating
aspects about AI in general

1101
00:57:27,487 --> 00:57:31,970
is that no one really
understands how it works.

1102
00:57:32,013 --> 00:57:36,975
Even the people who create AI
don't really fully understand.

1103
00:57:37,018 --> 00:57:39,804
Because it has millions
of elements,

1104
00:57:39,847 --> 00:57:41,675
it becomes completely impossible

1105
00:57:41,719 --> 00:57:45,113
for a human being
to understand what's going on.

1106
00:57:45,157 --> 00:57:52,512
♪♪

1107
00:57:52,556 --> 00:57:56,037
Grassegger: Microsoft had set up
this artificial intelligence

1108
00:57:56,081 --> 00:57:59,127
called Tay on Twitter,
which was a chatbot.

1109
00:58:00,912 --> 00:58:02,696
They started out in the morning,

1110
00:58:02,740 --> 00:58:06,526
and Tay was starting to tweet
and learning from stuff

1111
00:58:06,570 --> 00:58:10,835
that was being sent to him
from other Twitter people.

1112
00:58:10,878 --> 00:58:13,272
Because some people,
like trolls, attacked him,

1113
00:58:13,315 --> 00:58:18,582
within 24 hours, the Microsoft
bot became a terrible person.

1114
00:58:18,625 --> 00:58:21,367
They had to literally
pull Tay off the Net

1115
00:58:21,410 --> 00:58:24,718
because he had turned
into a monster.

1116
00:58:24,762 --> 00:58:30,550
A misanthropic, racist, horrible
person you'd never want to meet.

1117
00:58:30,594 --> 00:58:32,857
And nobody had foreseen this.

1118
00:58:35,337 --> 00:58:38,602
The whole idea of AI is that
we are not telling it exactly

1119
00:58:38,645 --> 00:58:42,780
how to achieve a given
outcome or a goal.

1120
00:58:42,823 --> 00:58:46,435
AI develops on its own.

1121
00:58:46,479 --> 00:58:48,829
Nolan: We're worried about
superintelligent AI,

1122
00:58:48,873 --> 00:58:52,790
the master chess player
that will outmaneuver us,

1123
00:58:52,833 --> 00:58:55,923
but AI won't have to
actually be that smart

1124
00:58:55,967 --> 00:59:00,145
to have massively disruptive
effects on human civilization.

1125
00:59:00,188 --> 00:59:01,886
We've seen over the last century

1126
00:59:01,929 --> 00:59:05,150
it doesn't necessarily take
a genius to knock history off

1127
00:59:05,193 --> 00:59:06,804
in a particular direction,

1128
00:59:06,847 --> 00:59:09,589
and it won't take a genius AI
to do the same thing.

1129
00:59:09,633 --> 00:59:13,158
Bogus election news stories
generated more engagement

1130
00:59:13,201 --> 00:59:17,075
on Facebook
than top real stories.

1131
00:59:17,118 --> 00:59:21,079
Facebook really is
the elephant in the room.

1132
00:59:21,122 --> 00:59:23,777
Kosinski:
AI running Facebook news feed --

1133
00:59:23,821 --> 00:59:28,347
The task for AI
is keeping users engaged,

1134
00:59:28,390 --> 00:59:29,827
but no one really understands

1135
00:59:29,870 --> 00:59:34,832
exactly how this AI
is achieving this goal.

1136
00:59:34,875 --> 00:59:38,792
Nolan: Facebook is building an
elegant mirrored wall around us.

1137
00:59:38,836 --> 00:59:41,665
A mirror that we can ask,
"Who's the fairest of them all?"

1138
00:59:41,708 --> 00:59:45,016
and it will answer, "You, you,"
time and again

1139
00:59:45,059 --> 00:59:48,193
and slowly begin
to warp our sense of reality,

1140
00:59:48,236 --> 00:59:53,502
warp our sense of politics,
history, global events,

1141
00:59:53,546 --> 00:59:57,028
until determining what's true
and what's not true,

1142
00:59:57,071 --> 00:59:58,943
is virtually impossible.

1143
01:00:01,032 --> 01:00:03,861
The problem is that AI
doesn't understand that.

1144
01:00:03,904 --> 01:00:08,039
AI just had a mission --
maximize user engagement,

1145
01:00:08,082 --> 01:00:10,041
and it achieved that.

1146
01:00:10,084 --> 01:00:13,653
Nearly 2 billion people
spend nearly one hour

1147
01:00:13,697 --> 01:00:17,831
on average a day
basically interacting with AI

1148
01:00:17,875 --> 01:00:21,530
that is shaping
their experience.

1149
01:00:21,574 --> 01:00:24,664
Even Facebook engineers,
they don't like fake news.

1150
01:00:24,708 --> 01:00:26,666
It's very bad business.

1151
01:00:26,710 --> 01:00:28,015
They want to get rid
of fake news.

1152
01:00:28,059 --> 01:00:29,974
It's just very difficult
to do because,

1153
01:00:30,017 --> 01:00:32,324
how do you recognize news
as fake

1154
01:00:32,367 --> 01:00:34,456
if you cannot read
all of those news personally?

1155
01:00:34,500 --> 01:00:39,418
There's so much
active misinformation

1156
01:00:39,461 --> 01:00:41,115
and it's packaged very well,

1157
01:00:41,159 --> 01:00:44,553
and it looks the same when
you see it on a Facebook page

1158
01:00:44,597 --> 01:00:47,426
or you turn on your television.

1159
01:00:47,469 --> 01:00:49,210
Nolan:
It's not terribly sophisticated,

1160
01:00:49,254 --> 01:00:51,691
but it is terribly powerful.

1161
01:00:51,735 --> 01:00:54,346
And what it means is
that your view of the world,

1162
01:00:54,389 --> 01:00:56,435
which, 20 years ago,
was determined,

1163
01:00:56,478 --> 01:01:00,004
if you watched the nightly news,
by three different networks,

1164
01:01:00,047 --> 01:01:02,528
the three anchors who endeavored
to try to get it right.

1165
01:01:02,571 --> 01:01:04,225
Might have had a little bias
one way or the other,

1166
01:01:04,269 --> 01:01:05,923
but, largely speaking,
we could all agree

1167
01:01:05,966 --> 01:01:08,273
on an objective reality.

1168
01:01:08,316 --> 01:01:10,754
Well, that objectivity is gone,

1169
01:01:10,797 --> 01:01:13,757
and Facebook has
completely annihilated it.

1170
01:01:13,800 --> 01:01:17,064
♪♪

1171
01:01:17,108 --> 01:01:19,197
If most of your understanding
of how the world works

1172
01:01:19,240 --> 01:01:20,807
is derived from Facebook,

1173
01:01:20,851 --> 01:01:23,418
facilitated
by algorithmic software

1174
01:01:23,462 --> 01:01:27,118
that tries to show you
the news you want to see,

1175
01:01:27,161 --> 01:01:28,815
that's a terribly
dangerous thing.

1176
01:01:28,859 --> 01:01:33,080
And the idea that we have not
only set that in motion,

1177
01:01:33,124 --> 01:01:37,258
but allowed bad-faith actors
access to that information...

1178
01:01:37,302 --> 01:01:39,565
I mean, this is a recipe
for disaster.

1179
01:01:39,608 --> 01:01:43,134
♪♪

1180
01:01:43,177 --> 01:01:45,876
Urban: I think that there will
definitely be lots of bad actors

1181
01:01:45,919 --> 01:01:48,922
trying to manipulate the world
with AI.

1182
01:01:48,966 --> 01:01:52,143
2016 was a perfect example
of an election

1183
01:01:52,186 --> 01:01:55,015
where there was lots of AI
producing lots of fake news

1184
01:01:55,059 --> 01:01:58,323
and distributing it
for a purpose, for a result.

1185
01:01:59,890 --> 01:02:02,283
Ladies and gentlemen,
honorable colleagues...

1186
01:02:02,327 --> 01:02:04,546
it's my privilege
to speak to you today

1187
01:02:04,590 --> 01:02:07,985
about the power of big data
and psychographics

1188
01:02:08,028 --> 01:02:09,682
in the electoral process

1189
01:02:09,726 --> 01:02:12,206
and, specifically,
to talk about the work

1190
01:02:12,250 --> 01:02:14,513
that we contributed
to Senator Cruz's

1191
01:02:14,556 --> 01:02:16,558
presidential primary campaign.

1192
01:02:16,602 --> 01:02:19,910
Nolan: Cambridge Analytica
emerged quietly as a company

1193
01:02:19,953 --> 01:02:21,563
that, according to its own hype,

1194
01:02:21,607 --> 01:02:26,307
has the ability to use
this tremendous amount of data

1195
01:02:26,351 --> 01:02:30,137
in order
to effect societal change.

1196
01:02:30,181 --> 01:02:33,358
In 2016, they had
three major clients.

1197
01:02:33,401 --> 01:02:34,794
Ted Cruz was one of them.

1198
01:02:34,838 --> 01:02:37,884
It's easy to forget
that, only 18 months ago,

1199
01:02:37,928 --> 01:02:41,148
Senator Cruz was one of
the less popular candidates

1200
01:02:41,192 --> 01:02:42,846
seeking nomination.

1201
01:02:42,889 --> 01:02:47,241
So, what was not possible maybe,
like, 10 or 15 years ago,

1202
01:02:47,285 --> 01:02:49,374
was that you can send fake news

1203
01:02:49,417 --> 01:02:52,420
to exactly the people
that you want to send it to.

1204
01:02:52,464 --> 01:02:56,685
And then you could actually see
how he or she reacts on Facebook

1205
01:02:56,729 --> 01:02:58,905
and then adjust that information

1206
01:02:58,949 --> 01:03:01,778
according to the feedback
that you got.

1207
01:03:01,821 --> 01:03:03,257
So you can start developing

1208
01:03:03,301 --> 01:03:06,130
kind of a real-time management
of a population.

1209
01:03:06,173 --> 01:03:08,697
In this case, we've zoned in

1210
01:03:08,741 --> 01:03:10,699
on a group
we've called "Persuasion."

1211
01:03:10,743 --> 01:03:13,746
These are people who are
definitely going to vote,

1212
01:03:13,790 --> 01:03:16,705
to caucus, but they need
moving from the center

1213
01:03:16,749 --> 01:03:18,490
a little bit more
towards the right.

1214
01:03:18,533 --> 01:03:19,708
in order to support Cruz.

1215
01:03:19,752 --> 01:03:22,059
They need a persuasion message.

1216
01:03:22,102 --> 01:03:23,800
"Gun rights," I've selected.

1217
01:03:23,843 --> 01:03:25,802
That narrows the field
slightly more.

1218
01:03:25,845 --> 01:03:29,066
And now we know that we need
a message on gun rights,

1219
01:03:29,109 --> 01:03:31,111
it needs to be
a persuasion message,

1220
01:03:31,155 --> 01:03:32,591
and it needs to be nuanced

1221
01:03:32,634 --> 01:03:34,201
according to
the certain personality

1222
01:03:34,245 --> 01:03:36,029
that we're interested in.

1223
01:03:36,073 --> 01:03:39,946
Through social media, there's an
infinite amount of information

1224
01:03:39,990 --> 01:03:42,514
that you can gather
about a person.

1225
01:03:42,557 --> 01:03:45,734
We have somewhere close
to 4,000 or 5,000 data points

1226
01:03:45,778 --> 01:03:48,563
on every adult
in the United States.

1227
01:03:48,607 --> 01:03:51,915
Grassegger: It's about targeting
the individual.

1228
01:03:51,958 --> 01:03:54,352
It's like a weapon,
which can be used

1229
01:03:54,395 --> 01:03:55,962
in the totally wrong direction.

1230
01:03:56,006 --> 01:03:58,051
That's the problem
with all of this data.

1231
01:03:58,095 --> 01:04:02,229
It's almost as if we built the
bullet before we built the gun.

1232
01:04:02,273 --> 01:04:04,362
Ted Cruz employed our data,

1233
01:04:04,405 --> 01:04:06,407
our behavioral insights.

1234
01:04:06,451 --> 01:04:09,541
He started from a base
of less than 5%

1235
01:04:09,584 --> 01:04:15,590
and had a very slow-and-steady-
but-firm rise to above 35%,

1236
01:04:15,634 --> 01:04:17,157
making him, obviously,

1237
01:04:17,201 --> 01:04:20,465
the second most threatening
contender in the race.

1238
01:04:20,508 --> 01:04:23,120
Now, clearly, the Cruz
campaign is over now,

1239
01:04:23,163 --> 01:04:24,904
but what I can tell you

1240
01:04:24,948 --> 01:04:28,168
is that of the two candidates
left in this election,

1241
01:04:28,212 --> 01:04:30,867
one of them is using
these technologies.

1242
01:04:32,564 --> 01:04:35,959
I, Donald John Trump,
do solemnly swear

1243
01:04:36,002 --> 01:04:38,222
that I will faithfully execute

1244
01:04:38,265 --> 01:04:42,226
the office of President
of the United States.

1245
01:04:42,269 --> 01:04:46,273
♪♪

1246
01:04:48,275 --> 01:04:50,234
Nolan: Elections are
a marginal exercise.

1247
01:04:50,277 --> 01:04:53,237
It doesn't take
a very sophisticated AI

1248
01:04:53,280 --> 01:04:57,719
in order to have
a disproportionate impact.

1249
01:04:57,763 --> 01:05:02,550
Before Trump, Brexit was
another supposed client.

1250
01:05:02,594 --> 01:05:04,726
Well, at 20 minutes to 5:00,

1251
01:05:04,770 --> 01:05:08,730
we can now say
the decision taken in 1975

1252
01:05:08,774 --> 01:05:10,950
by this country to join
the common market

1253
01:05:10,994 --> 01:05:15,999
has been reversed by this
referendum to leave the EU.

1254
01:05:16,042 --> 01:05:19,828
Nolan: Cambridge Analytica
allegedly uses AI

1255
01:05:19,872 --> 01:05:23,267
to push through two of
the most ground-shaking pieces

1256
01:05:23,310 --> 01:05:27,967
of political change
in the last 50 years.

1257
01:05:28,011 --> 01:05:30,709
These are epochal events,
and if we believe the hype,

1258
01:05:30,752 --> 01:05:33,755
they are connected directly
to a piece of software,

1259
01:05:33,799 --> 01:05:37,194
essentially, created
by a professor at Stanford.

1260
01:05:37,237 --> 01:05:41,415
♪♪

1261
01:05:41,459 --> 01:05:43,635
Kosinski:
Back in 2013, I described

1262
01:05:43,678 --> 01:05:45,593
that what they are doing
is possible

1263
01:05:45,637 --> 01:05:49,293
and warned against this
happening in the future.

1264
01:05:49,336 --> 01:05:51,382
Grassegger:
At the time, Michal Kosinski

1265
01:05:51,425 --> 01:05:52,949
was a young Polish researcher

1266
01:05:52,992 --> 01:05:54,994
working at the
Psychometrics Centre.

1267
01:05:55,038 --> 01:06:00,217
So, what Michal had done was to
gather the largest-ever data set

1268
01:06:00,260 --> 01:06:03,481
of how people
behave on Facebook.

1269
01:06:03,524 --> 01:06:07,789
Kosinski:
Psychometrics is trying
to measure psychological traits,

1270
01:06:07,833 --> 01:06:09,922
such as personality,
intelligence,

1271
01:06:09,966 --> 01:06:11,880
political views, and so on.

1272
01:06:11,924 --> 01:06:15,058
Now, traditionally,
those traits were measured

1273
01:06:15,101 --> 01:06:17,712
using tests and questions.

1274
01:06:17,756 --> 01:06:19,410
Nolan: Personality test --
the most benign thing

1275
01:06:19,453 --> 01:06:20,715
you could possibly think of.

1276
01:06:20,759 --> 01:06:22,065
Something that doesn't
necessarily have

1277
01:06:22,108 --> 01:06:24,197
a lot of utility, right?

1278
01:06:24,241 --> 01:06:27,331
Kosinski: Our idea was that
instead of tests and questions,

1279
01:06:27,374 --> 01:06:30,029
we could simply look at the
digital footprints of behaviors

1280
01:06:30,073 --> 01:06:32,553
that we are all leaving behind

1281
01:06:32,597 --> 01:06:34,903
to understand openness,

1282
01:06:34,947 --> 01:06:37,732
conscientiousness,
neuroticism.

1283
01:06:37,776 --> 01:06:39,560
Grassegger: You can easily buy
personal data,

1284
01:06:39,604 --> 01:06:43,129
such as where you live, what
club memberships you've tried,

1285
01:06:43,173 --> 01:06:45,044
which gym you go to.

1286
01:06:45,088 --> 01:06:47,873
There are actually marketplaces
for personal data.

1287
01:06:47,916 --> 01:06:49,918
Nolan: It turns out, we can
discover an awful lot

1288
01:06:49,962 --> 01:06:51,442
about what you're gonna do

1289
01:06:51,485 --> 01:06:55,750
based on a very, very tiny
set of information.

1290
01:06:55,794 --> 01:06:58,275
Kosinski: We are training
deep-learning networks

1291
01:06:58,318 --> 01:07:01,278
to infer intimate traits,

1292
01:07:01,321 --> 01:07:04,759
people's political views,
personality,

1293
01:07:04,803 --> 01:07:07,806
intelligence,
sexual orientation

1294
01:07:07,849 --> 01:07:10,504
just from an image
from someone's face.

1295
01:07:10,548 --> 01:07:17,033
♪♪

1296
01:07:17,076 --> 01:07:20,645
Now think about countries which
are not so free and open-minded.

1297
01:07:20,688 --> 01:07:23,300
If you can reveal people's
religious views

1298
01:07:23,343 --> 01:07:25,954
or political views
or sexual orientation

1299
01:07:25,998 --> 01:07:28,740
based on only profile pictures,

1300
01:07:28,783 --> 01:07:33,310
this could be literally
an issue of life and death.

1301
01:07:33,353 --> 01:07:36,965
♪♪

1302
01:07:37,009 --> 01:07:39,751
I think there's no going back.

1303
01:07:42,145 --> 01:07:44,321
Do you know what
the Turing test is?

1304
01:07:44,364 --> 01:07:48,977
It's when a human interacts
with a computer,

1305
01:07:49,021 --> 01:07:50,805
and if the human doesn't know
they're interacting

1306
01:07:50,849 --> 01:07:52,546
with a computer,

1307
01:07:52,590 --> 01:07:54,026
the test is passed.

1308
01:07:54,070 --> 01:07:57,247
And over the next few days,

1309
01:07:57,290 --> 01:07:59,684
you're gonna be the human
component in a Turing test.

1310
01:07:59,727 --> 01:08:02,295
Holy shit.Yeah, that's right, Caleb.

1311
01:08:02,339 --> 01:08:04,080
You got it.

1312
01:08:04,123 --> 01:08:06,865
'Cause if that test
is passed,

1313
01:08:06,908 --> 01:08:10,825
you are dead center of
the greatest scientific event

1314
01:08:10,869 --> 01:08:12,958
in the history of man.

1315
01:08:13,001 --> 01:08:14,612
If you've created
a conscious machine,

1316
01:08:14,655 --> 01:08:17,615
it's not the history
of man--

1317
01:08:17,658 --> 01:08:19,356
That's the history
of gods.

1318
01:08:19,399 --> 01:08:26,798
♪♪

1319
01:08:26,841 --> 01:08:28,452
Nolan: It's almost like
technology is a god

1320
01:08:28,495 --> 01:08:29,975
in and of itself.

1321
01:08:30,018 --> 01:08:33,152
♪♪

1322
01:08:33,196 --> 01:08:35,241
Like the weather.
We can't impact it.

1323
01:08:35,285 --> 01:08:39,593
We can't slow it down.
We can't stop it.

1324
01:08:39,637 --> 01:08:43,249
We feel powerless.

1325
01:08:43,293 --> 01:08:44,685
Kurzweil:
If we think of God

1326
01:08:44,729 --> 01:08:46,687
as an unlimited amount
of intelligence,

1327
01:08:46,731 --> 01:08:48,167
the closest we can get to that

1328
01:08:48,211 --> 01:08:50,474
is by evolving
our own intelligence

1329
01:08:50,517 --> 01:08:55,566
by merging with the artificial
intelligence we're creating.

1330
01:08:55,609 --> 01:08:58,003
Musk:
Today, our computers, phones,

1331
01:08:58,046 --> 01:09:01,615
applications give us
superhuman capability.

1332
01:09:01,659 --> 01:09:04,662
So, as the old maxim says,
if you can't beat 'em, join 'em.

1333
01:09:06,968 --> 01:09:09,971
el Kaliouby: It's about
a human-machine partnership.

1334
01:09:10,015 --> 01:09:11,669
I mean, we already see
how, you know,

1335
01:09:11,712 --> 01:09:14,933
our phones, for example, act
as memory prosthesis, right?

1336
01:09:14,976 --> 01:09:17,196
I don't have to remember
your phone number anymore

1337
01:09:17,240 --> 01:09:19,198
'cause it's on my phone.

1338
01:09:19,242 --> 01:09:22,070
It's about machines
augmenting our human abilities,

1339
01:09:22,114 --> 01:09:25,248
as opposed to, like,
completely displacing them.

1340
01:09:25,291 --> 01:09:27,380
Nolan: If you look at all the
objects that have made the leap

1341
01:09:27,424 --> 01:09:30,122
from analog to digital
over the last 20 years...

1342
01:09:30,166 --> 01:09:32,080
it's a lot.

1343
01:09:32,124 --> 01:09:35,388
We're the last analog object
in a digital universe.

1344
01:09:35,432 --> 01:09:36,911
And the problem with that,
of course,

1345
01:09:36,955 --> 01:09:40,567
is that the data input/output
is very limited.

1346
01:09:40,611 --> 01:09:42,613
It's this.
It's these.

1347
01:09:42,656 --> 01:09:45,355
Zilis:
Our eyes are pretty good.

1348
01:09:45,398 --> 01:09:48,445
We're able to take in a lot
of visual information.

1349
01:09:48,488 --> 01:09:52,536
But our information output
is very, very, very low.

1350
01:09:52,579 --> 01:09:55,669
The reason this is important --
If we envision a scenario

1351
01:09:55,713 --> 01:09:59,543
where AI's playing a more
prominent role in societies,

1352
01:09:59,586 --> 01:10:02,023
we want good ways to interact
with this technology

1353
01:10:02,067 --> 01:10:04,983
so that it ends up
augmenting us.

1354
01:10:05,026 --> 01:10:07,812
♪♪

1355
01:10:07,855 --> 01:10:09,553
Musk: I think
it's incredibly important

1356
01:10:09,596 --> 01:10:12,295
that AI not be "other."

1357
01:10:12,338 --> 01:10:14,862
It must be us.

1358
01:10:14,906 --> 01:10:18,605
And I could be wrong
about what I'm saying.

1359
01:10:18,649 --> 01:10:20,216
I'm certainly open to ideas

1360
01:10:20,259 --> 01:10:23,915
if anybody can suggest
a path that's better.

1361
01:10:23,958 --> 01:10:27,266
But I think we're gonna really
have to either merge with AI

1362
01:10:27,310 --> 01:10:28,963
or be left behind.

1363
01:10:29,007 --> 01:10:36,362
♪♪

1364
01:10:36,406 --> 01:10:38,756
Gourley: It's hard to kind of
think of unplugging a system

1365
01:10:38,799 --> 01:10:41,802
that's distributed
everywhere on the planet,

1366
01:10:41,846 --> 01:10:45,806
that's distributed now
across the solar system.

1367
01:10:45,850 --> 01:10:49,375
You can't just, you know,
shut that off.

1368
01:10:49,419 --> 01:10:51,290
Nolan:
We've opened Pandora's box.

1369
01:10:51,334 --> 01:10:55,642
We've unleashed forces that
we can't control, we can't stop.

1370
01:10:55,686 --> 01:10:57,296
We're in the midst
of essentially creating

1371
01:10:57,340 --> 01:10:59,516
a new life-form on Earth.

1372
01:10:59,559 --> 01:11:05,826
♪♪

1373
01:11:05,870 --> 01:11:07,611
Russell:
We don't know what happens next.

1374
01:11:07,654 --> 01:11:10,353
We don't know what shape
the intellect of a machine

1375
01:11:10,396 --> 01:11:14,531
will be when that intellect is
far beyond human capabilities.

1376
01:11:14,574 --> 01:11:17,360
It's just not something
that's possible.

1377
01:11:17,403 --> 01:11:24,715
♪♪

1378
01:11:24,758 --> 01:11:26,934
The least scary future
I can think of is one

1379
01:11:26,978 --> 01:11:29,633
where we have at least
democratized AI.

1380
01:11:31,548 --> 01:11:34,159
Because if one company
or small group of people

1381
01:11:34,202 --> 01:11:37,031
manages to develop godlike
digital superintelligence,

1382
01:11:37,075 --> 01:11:40,339
they can take over the world.

1383
01:11:40,383 --> 01:11:42,210
At least when there's
an evil dictator,

1384
01:11:42,254 --> 01:11:44,343
that human is going to die,

1385
01:11:44,387 --> 01:11:46,998
but, for an AI,
there would be no death.

1386
01:11:47,041 --> 01:11:49,392
It would live forever.

1387
01:11:49,435 --> 01:11:51,916
And then you have
an immortal dictator

1388
01:11:51,959 --> 01:11:53,570
from which we can never escape.

1389
01:11:53,613 --> 01:12:02,100
♪♪

1390
01:12:02,143 --> 01:12:10,587
♪♪

1391
01:12:10,630 --> 01:12:19,160
♪♪

1392
01:12:19,204 --> 01:12:27,647
♪♪

1393
01:12:27,691 --> 01:12:36,221
♪♪

1394
01:12:36,264 --> 01:12:44,838
♪♪

1395
01:12:51,845 --> 01:12:53,717
Woman on P.A.:
Alan. Macchiato.

1396
01:13:10,951 --> 01:13:17,610
♪♪

1397
01:13:17,654 --> 01:13:24,269
♪♪

1398
01:13:24,312 --> 01:13:30,884
♪♪

1399
01:13:30,928 --> 01:13:37,543
♪♪

1400
01:13:37,587 --> 01:13:44,245
♪♪

1401
01:13:44,289 --> 01:13:50,817
♪♪

1402
01:13:50,861 --> 01:13:57,520
♪♪

1403
01:13:57,563 --> 01:14:04,178
♪♪

1404
01:14:04,222 --> 01:14:10,794
♪♪

1405
01:14:10,837 --> 01:14:17,496
♪♪

1406
01:14:17,540 --> 01:14:24,111
♪♪

1407
01:14:24,155 --> 01:14:30,727
♪♪

1408
01:14:30,770 --> 01:14:37,473
♪♪

1409
01:14:37,516 --> 01:14:44,044
♪♪

1410
01:14:44,088 --> 01:14:47,570
Woman:
Hello?

1411
01:14:47,613 --> 01:14:54,838
♪♪

1412
01:14:54,881 --> 01:15:02,236
♪♪

1413
01:15:02,280 --> 01:15:09,548
♪♪

1414
01:15:09,592 --> 01:15:16,773
♪♪

1415
01:15:16,816 --> 01:15:18,688
♪ Yeah, yeah

1416
01:15:18,731 --> 01:15:20,080
♪ Yeah, yeah

1417
01:15:20,124 --> 01:15:27,261
♪♪

1418
01:15:27,305 --> 01:15:34,442
♪♪

1419
01:15:34,486 --> 01:15:41,580
♪♪

1420
01:15:41,624 --> 01:15:43,234
♪ Yeah, yeah

1421
01:15:43,277 --> 01:15:45,541
♪ Yeah, yeah

1422
01:15:45,584 --> 01:15:51,764
♪♪

1423
01:15:51,808 --> 01:15:57,988
♪♪

1424
01:15:58,031 --> 01:16:04,342
♪♪

1425
01:16:04,385 --> 01:16:10,609
♪♪

1426
01:16:10,653 --> 01:16:13,046
Hello?

1427
01:16:13,090 --> 01:16:22,012
♪♪

1428
01:16:22,055 --> 01:16:31,021
♪♪

1429
01:16:31,064 --> 01:16:39,986
♪♪

1430
01:16:40,030 --> 01:16:48,952
♪♪

1431
01:16:48,995 --> 01:16:57,961
♪♪

1432
01:16:58,004 --> 01:17:06,926
♪♪

1433
01:17:06,970 --> 01:17:15,892
♪♪

1434
01:17:15,935 --> 01:17:24,901
♪♪

1435
01:17:24,944 --> 01:17:33,910
♪♪

1436
01:17:33,953 --> 01:17:40,960
♪♪