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- Intelligence is
the ability to understand.
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We passed on what we know to machines.
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- The rise of
artificial intelligence
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is happening fast, but some
fear the new technology
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might have more problems than anticipated.
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- We will not control it.
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- Artificially
intelligent algorithms are here,
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but this is only the beginning.
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- In the age of AI,
data is the new oil.
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- Today, Amazon,
Google and Facebook
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are richer and more
powerful than any companies
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that have ever existed
throughout human history.
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- A handful of people working at
a handful of technology companies
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steer what a billion
people are thinking today.
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- This technology is changing:
What does it mean to be human?
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- Artificial intelligence is simply
non-biological intelligence.
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And intelligence itself is simply
the ability to accomplish goals.
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I'm convinced that AI
will ultimately be either
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the best thing ever to happen to humanity,
or the worst thing ever to happen.
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We can use it to solve all
of today's and tomorrow's
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greatest problems; cure diseases,
deal with climate change,
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lift everybody out of poverty.
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But, we could use exactly
the same technology
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to create a brutal global
dictatorship with unprecedented
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surveillance and inequality and suffering.
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That's why this is the most important
conversation of our time.
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- Artificial intelligence is everywhere
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because we now have thinking machines.
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If you go on social media or online,
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there's an artificial intelligence engine
that decides what to recommend.
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If you go on Facebook and you're just
scrolling through your friends' posts,-
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there's an AI engine that's picking
which one to show you first
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- and which one to bury.
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If you try to get insurance,
there is an AI engine
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trying to figure out how risky you are.
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And if you apply for a job,
it's quite possible
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that an AI engine looks at the resume.
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- We are made of data.
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Every one of us is made of data
-in terms of how we behave,
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how we talk, how we love,
what we do every day.
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So, computer scientists are
developing deep learning
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algorithms that can learn
to identify, classify,
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and predict patterns within
massive amounts of data.
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We are facing a form of
precision surveillance,
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you could call it algorithmic surveillance,
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and it means that you
cannot go unrecognized.
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You are always under
the watch of algorithms.
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- Almost all the AI
development on the planet today
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is done by a handful of
big technology companies
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or by a few large governments.
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If we look at what AI is
mostly being developed for,
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I would say it's killing,
spying, and brainwashing.
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So, I mean, we have military AI,
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we have a whole surveillance
apparatus being built
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using AI by major governments,
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and we have an advertising
industry which is oriented
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toward recognizing what ads
to try to sell to someone.
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- We humans have come to
a fork in the road now.
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The AI we have today is very narrow.
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The holy grail of AI research
ever since the beginning
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is to make AI that can do
everything better than us,
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and we've basically built a God.
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It's going to revolutionize
life as we know it.
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It's incredibly important
to take a step back
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and think carefully about this.
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What sort of society do we want?
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- So, we're in this
historic transformation.
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Like we're raising this new creature.
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We have a new offspring of sorts.
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But just like actual offspring,
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you don't get to control
everything it's going to do.
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We are living at
this privileged moment where,
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for the first time, we
will see probably that AI
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is really going to outcompete
humans in many, many,
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if not all, important fields.
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- Everything is going to change.
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A new form of life is emerging.
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When I was a boy, I thought,
how can I maximize my impact?
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And then it was clear that
I have to build something
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that learns to become smarter than myself,
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such that I can retire,
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and the smarter thing
can further self-improve
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and solve all the problems
that I cannot solve.
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Multiplying that tiny
little bit of creativity
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that I have into infinity,
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and that's what has been
driving me since then.
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How am I trying to build
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a general purpose artificial intelligence?
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If you want to be intelligent,
you have to recognize speech,
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video and handwriting, and
faces, and all kinds of things,
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and there we have made a lot of progress.
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See, LSTM, neural networks,
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which we developed in our labs
in Munich and in Switzerland,
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and it's now used for speech
recognition and translation,
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and video recognition.
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They are now in everybody's
smartphone, almost one billion
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iPhones and in over
two billion Android phones.
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So, we are generating all
kinds of useful by-products
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on the way to the general goal.
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The main goal, some Artificial
General Intelligence,
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an AGI that can learn to improve
the learning algorithm itself.
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So, it basically can learn
to improve the way it learns
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and it can also recursively
improve the way it learns,
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the way it learns without
any limitations except for
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the basic fundamental
limitations of computability.
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One of my favorite
robots is this one here.
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We use this robot for our
studies of artificial curiosity.
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Where we are trying to teach
this robot to teach itself.
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What is a baby doing?
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A baby is curiously exploring its world.
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That's how he learns how gravity works
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and how certain things topple, and so on.
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And as it learns to ask
questions about the world,
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and as it learns to
answer these questions,
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it becomes a more and more
general problem solver.
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And so, our artificial
systems are also learning
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to ask all kinds of
questions, not just slavishly
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try to answer the questions
given to them by humans.
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You have to give AI the freedom
to invent its own tasks.
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If you don't do that, it's not
going to become very smart.
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On the other hand, it's really hard
to predict what they are going to do.
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- I feel that technology
is a force of nature.
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I feel like there is a lot of similarity between
technology and biological evolution.
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Playing God.
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Scientists have been accused
of playing God for a while,
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- but there is a real sense in
which we are creating something
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very different from anything
we've created so far.
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I was interested in the concept of
AI from a relatively early age.
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At some point, I got especially
interested in machine learning.
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What is experience?
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What is learning?
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What is thinking?
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00:14:06,890 --> 00:14:08,150
How does the brain work?
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These questions are philosophical,
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but it looks like we can
come up with algorithms that
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both do useful things and help
us answer these questions.
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Like it's almost like applied philosophy.
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Artificial General Intelligence, AGI.
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A computer system that
can do any job or any task
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that a human does, but only better.
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Yeah, I mean, we definitely
will be able to create
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completely autonomous
beings with their own goals.
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And it will be very important,
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especially as these beings
become much smarter than humans,
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it's going to be important
to have these beings,
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that the goals of these beings
be aligned with our goals.
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That's what we're trying
to do at OpenAI.
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Be at the forefront of research
and steer the research,
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steer their initial conditions
so to maximize the chance
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that the future will be good for humans.
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Now, AI is a great thing
because AI will solve
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all the problems that we have today.
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It will solve employment,
it will solve disease,
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it will solve poverty,
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but it will also create new problems.
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I think that...
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The problem of fake news
is going to be a thousand,
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a million times worse.
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Cyberattacks will
become much more extreme.
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You will have totally
automated AI weapons.
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I think AI has the potential to create
infinitely stable dictatorships.
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You're gonna see dramatically
more intelligent systems
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in 10 or 15 years from now,
and I think it's highly likely
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that those systems will have
completely astronomical impact on society.
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Will humans actually benefit?
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00:17:22,860 --> 00:17:24,860
And who will benefit, who will not?
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- In 2012, IBM estimated that
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an average person is
generating 500 megabytes
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of digital footprints every single day.
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Imagine that you wanted
to back-up one day worth
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of data that humanity is
leaving behind, on paper.
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How tall will be the stack
of paper that contains
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just one day worth of data
that humanity is producing?
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It's like from the earth
to the sun, four times over.
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In 2025, we'll be generating
62 gigabytes of data
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per person, per day.
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We're leaving a ton of digital footprints
while going through our lives.
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They provide computer algorithms
with a fairly good idea about who we are,
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what we want, what we are doing.
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In my work, I looked at different
types of digital footprints.
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I looked at Facebook likes,
I looked at language,
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credit card records, web browsing
histories, search records.
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and each time I found that if
you get enough of this data,
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you can accurately predict future behavior
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and reveal important intimate traits.
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This can be used in great ways,
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but it can also be used
to manipulate people.
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Facebook is delivering daily information
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to two billion people or more.
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If you slightly change the
functioning of the Facebook engine,
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you can move the opinions and hence,
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the votes of millions of people.
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- Brexit!
- When do we want it?
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- Now!
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- A politician
wouldn't be able to figure out
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which message each one of
his or her voters would like,
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but a computer can see
what political message
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would be particularly convincing for you.
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- Ladies and gentlemen,
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it's my privilege to speak
to you today about the power
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of big data and psychographics
in the electoral process.
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- Data from Cambridge Analytica
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secretly harvested the
personal information
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of 50 million unsuspecting Facebook users.
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USA!
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- The data firm hired by Donald Trump's
presidential election campaign
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00:20:35,870 --> 00:20:41,080
used secretly obtained information
to directly target potential American voters.
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00:20:41,080 --> 00:20:42,940
- With that, they say they can predict
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the personality of every single
adult in the United States.
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00:20:47,470 --> 00:20:49,460
- Tonight we're hearing
from Cambridge Analytica
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00:20:49,460 --> 00:20:51,650
whistleblower, Christopher Wiley.
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00:20:51,650 --> 00:20:54,990
- What we worked on was
data harvesting programs
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00:20:54,990 --> 00:20:59,200
where we would pull data and run that
data through algorithms that could profile
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00:20:59,200 --> 00:21:01,930
their personality traits and
other psychological attributes
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00:21:01,930 --> 00:21:06,580
to exploit mental vulnerabilities
that our algorithms showed that they had.
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00:21:15,500 --> 00:21:17,570
- Cambridge Analytica mentioned once
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00:21:17,570 --> 00:21:20,460
or said that their models
were based on my work,
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00:21:22,510 --> 00:21:25,140
but Cambridge Analytica is
just one of the hundreds
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of companies that are using
such methods to target voters.
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00:21:32,130 --> 00:21:35,760
You know, I would be asked
questions by journalists such as,
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"So how do you feel about
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00:21:38,810 --> 00:21:41,990
"electing Trump and supporting Brexit?"
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How do you answer to such question?
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00:21:45,400 --> 00:21:51,740
I guess that I have to deal
with being blamed for all of it.
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00:22:07,700 --> 00:22:12,480
- How tech started was
as a democratizing force,
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00:22:12,480 --> 00:22:15,110
as a force for good, as
an ability for humans
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00:22:15,110 --> 00:22:17,900
to interact with each
other without gatekeepers.
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There's never been a bigger experiment
in communications for the human race.
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00:22:26,330 --> 00:22:29,060
What happens when everybody
gets to have their say?
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00:22:29,780 --> 00:22:31,850
You would assume that it
would be for the better,
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00:22:31,850 --> 00:22:34,540
that there would be more democracy,
there would be more discussion,
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00:22:34,540 --> 00:22:37,660
there would be more tolerance,
but what's happened is that
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00:22:37,660 --> 00:22:39,720
these systems have been hijacked.
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00:22:41,300 --> 00:22:43,860
- We stand for connecting every person.
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For a global community.
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00:22:47,780 --> 00:22:50,500
- One company,
Facebook, is responsible
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00:22:50,500 --> 00:22:53,540
for the communications of
a lot of the human race.
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00:22:55,650 --> 00:22:57,360
Same thing with Google.
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00:22:57,360 --> 00:23:00,540
Everything you want know about
the world comes from them.
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00:23:01,540 --> 00:23:05,120
This is global information economy
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00:23:05,120 --> 00:23:07,830
that is controlled by a
small group of people.
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00:23:14,400 --> 00:23:17,920
- The world's richest companies
are all technology companies.
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00:23:18,770 --> 00:23:24,500
Google, Apple, Microsoft,
Amazon, Facebook.
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It's staggering how,
247
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in probably just 10 years,
248
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that the entire corporate power structure
249
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are basically in the business
of trading electrons.
250
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These little bits and bytes
are really the new currency.
251
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- The way that data is monetized
252
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is happening all around us,
even if it's invisible to us.
253
00:24:00,960 --> 00:24:04,280
Google has every amount
of information available.
254
00:24:04,280 --> 00:24:07,110
They track people by their GPS location.
255
00:24:07,110 --> 00:24:10,080
They know exactly what your
search history has been.
256
00:24:10,080 --> 00:24:12,850
They know your political preferences.
257
00:24:12,850 --> 00:24:14,840
Your search history alone can tell you
258
00:24:14,840 --> 00:24:17,570
everything about an individual
from their health problems
259
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to their sexual preferences.
260
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So, Google's reach is unlimited.
261
00:24:28,750 --> 00:24:31,620
- So we've seen Google and Facebook
262
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rise into these large
surveillance machines
263
00:24:35,040 --> 00:24:38,200
and they're both actually ad brokers.
264
00:24:38,200 --> 00:24:42,040
It sounds really mundane,
but they're high tech ad brokers.
265
00:24:42,910 --> 00:24:46,080
And the reason they're so
profitable is that they're using
266
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artificial intelligence to
process all this data about you,
267
00:24:51,560 --> 00:24:54,660
and then to match you with the advertiser
268
00:24:54,660 --> 00:24:59,660
that wants to reach people
like you, - for whatever message.
269
00:25:03,000 --> 00:25:07,880
- One of the problems with technology is
that it's been developed to be addictive.
270
00:25:07,880 --> 00:25:09,910
The way these companies
design these things
271
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is in order to pull you in and engage you.
272
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They want to become essentially
a slot machine of attention.
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So you're always paying attention,
you're always jacked into the matrix,
274
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you're always checking.
275
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- When somebody controls what you read,
they also control what you think.
276
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You get more of what you've
seen before and liked before,
277
00:25:34,610 --> 00:25:37,890
because this gives more traffic
and that gives more ads,
278
00:25:39,410 --> 00:25:43,160
but it also locks you
into your echo chamber.
279
00:25:43,160 --> 00:25:46,350
And this is what leads
to this polarization that we see today.
280
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Jair Bolsonaro!
281
00:25:49,970 --> 00:25:52,590
- Jair Bolsonaro,
Brazil's right-wing
282
00:25:52,590 --> 00:25:56,000
populist candidate sometimes
likened to Donald Trump,
283
00:25:56,000 --> 00:25:58,640
winning the presidency Sunday
night in that country's
284
00:25:58,640 --> 00:26:01,840
most polarizing election in decades.
285
00:26:01,840 --> 00:26:02,840
- Bolsonaro!
286
00:26:04,160 --> 00:26:09,550
- What we are seeing around the world
is upheaval and polarization and conflict
287
00:26:10,770 --> 00:26:15,000
that is partially pushed by algorithms
288
00:26:15,000 --> 00:26:19,070
that's figured out that
political extremes,
289
00:26:19,070 --> 00:26:22,180
tribalism, and sort of
shouting for your team,
290
00:26:22,180 --> 00:26:25,290
and feeling good about it, is engaging.
291
00:26:30,420 --> 00:26:33,150
- Social media may
be adding to the attention
292
00:26:33,150 --> 00:26:35,490
to hate crimes around the globe.
293
00:26:35,490 --> 00:26:38,120
- It's about how
people can become radicalized
294
00:26:38,120 --> 00:26:41,740
by living in the fever
swamps of the Internet.
295
00:26:41,740 --> 00:26:45,960
- So is this a key moment for the tech giants?
Are they now prepared to take responsibility
296
00:26:45,960 --> 00:26:48,840
as publishers for what
they share with the world?
297
00:26:49,790 --> 00:26:52,750
- If you deploy a
powerful potent technology
298
00:26:52,750 --> 00:26:56,670
at scale, and if you're talking
about Google and Facebook,
299
00:26:56,670 --> 00:26:59,530
you're deploying things
at scale of billions.
300
00:26:59,530 --> 00:27:02,770
If your artificial intelligence
is pushing polarization,
301
00:27:02,770 --> 00:27:05,090
you have global upheaval potentially.
302
00:27:05,720 --> 00:27:09,240
White lives matter!
303
00:27:09,320 --> 00:27:13,620
Black lives matter!
304
00:28:00,620 --> 00:28:04,040
- Artificial General Intelligence, AGI.
305
00:28:06,350 --> 00:28:08,320
Imagine your smartest friend,
306
00:28:09,600 --> 00:28:11,950
with 1,000 friends, just as smart,
307
00:28:14,680 --> 00:28:17,580
and then run them at a 1,000
times faster than real time.
308
00:28:17,580 --> 00:28:19,940
So it means that in every day of our time,
309
00:28:19,940 --> 00:28:22,510
they will do three years of thinking.
310
00:28:22,510 --> 00:28:25,520
Can you imagine how much you could do
311
00:28:26,580 --> 00:28:31,480
if, for every day, you could
do three years' worth of work?
312
00:28:52,440 --> 00:28:55,720
It wouldn't be an unfair comparison to say
313
00:28:55,720 --> 00:28:59,780
that what we have right now
is even more exciting than
314
00:28:59,780 --> 00:29:02,510
the quantum physicists of
the early 20th century.
315
00:29:02,510 --> 00:29:04,240
They discovered nuclear power.
316
00:29:05,740 --> 00:29:08,380
I feel extremely lucky to
be taking part in this.
317
00:29:15,310 --> 00:29:18,820
Many machine learning experts,
who are very knowledgeable and experienced,
318
00:29:18,820 --> 00:29:20,820
have a lot of skepticism about AGI.
319
00:29:22,350 --> 00:29:25,900
About when it would happen,
and about whether it could happen at all.
320
00:29:31,460 --> 00:29:35,740
But right now, this is something that just
not that many people have realized yet.
321
00:29:36,500 --> 00:29:41,240
That the speed of computers,
for neural networks, for AI,
322
00:29:41,240 --> 00:29:45,240
are going to become maybe
100,000 times faster
323
00:29:45,240 --> 00:29:46,980
in a small number of years.
324
00:29:49,330 --> 00:29:52,100
The entire hardware
industry for a long time
325
00:29:52,100 --> 00:29:54,660
didn't really know what to do next,
326
00:29:55,660 --> 00:30:00,900
but with artificial neural networks,
now that they actually work,
327
00:30:00,900 --> 00:30:03,420
you have a reason to build huge computers.
328
00:30:04,530 --> 00:30:06,930
You can build a brain in
silicon, it's possible.
329
00:30:14,450 --> 00:30:18,710
The very first AGIs
will be basically very,
330
00:30:18,710 --> 00:30:22,850
very large data centers
packed with specialized
331
00:30:22,850 --> 00:30:25,660
neural network processors
working in parallel.
332
00:30:28,070 --> 00:30:30,920
Compact, hot, power hungry package,
333
00:30:32,140 --> 00:30:35,540
consuming like 10 million
homes' worth of energy.
334
00:30:54,140 --> 00:30:55,640
A roast beef sandwich.
335
00:30:55,640 --> 00:30:58,290
Yeah, something slightly different.
336
00:30:58,290 --> 00:30:59,200
Just this once.
337
00:31:04,060 --> 00:31:06,260
Even the very first AGIs
338
00:31:06,260 --> 00:31:09,060
will be dramatically
more capable than humans.
339
00:31:10,970 --> 00:31:14,730
Humans will no longer be
economically useful for nearly any task.
340
00:31:16,200 --> 00:31:17,880
Why would you want to hire a human,
341
00:31:17,880 --> 00:31:21,980
if you could just get a computer that's going to
do it much better and much more cheaply?
342
00:31:28,900 --> 00:31:31,020
AGI is going to be like, without question,
343
00:31:31,880 --> 00:31:34,660
the most important
technology in the history
344
00:31:34,660 --> 00:31:36,600
of the planet by a huge margin.
345
00:31:39,410 --> 00:31:42,550
It's going to be bigger
than electricity, nuclear,
346
00:31:42,550 --> 00:31:44,140
and the Internet combined.
347
00:31:45,740 --> 00:31:47,620
In fact, you could say
that the whole purpose
348
00:31:47,620 --> 00:31:49,660
of all human science, the
purpose of computer science,
349
00:31:49,660 --> 00:31:52,550
the End Game, this is the
End Game, to build this.
350
00:31:52,550 --> 00:31:54,130
And it's going to be built.
351
00:31:54,130 --> 00:31:56,000
It's going to be a new life form.
352
00:31:56,000 --> 00:31:57,090
It's going to be...
353
00:31:59,190 --> 00:32:00,740
It's going to make us obsolete.
354
00:32:22,070 --> 00:32:24,520
- European manufacturers
know the Americans
355
00:32:24,520 --> 00:32:27,460
have invested heavily in
the necessary hardware.
356
00:32:27,460 --> 00:32:29,780
- Step into a
brave new world of power,
357
00:32:29,780 --> 00:32:31,630
performance and productivity.
358
00:32:32,360 --> 00:32:35,480
- All of the images you are
about to see on the large screen
359
00:32:35,480 --> 00:32:39,030
will be generated by
what's in that Macintosh.
360
00:32:39,760 --> 00:32:42,210
- It's my honor and
privilege to introduce to you
361
00:32:42,210 --> 00:32:44,700
the Windows 95 Development Team.
362
00:32:45,640 --> 00:32:49,040
- Human physical labor has
been mostly obsolete for
363
00:32:49,040 --> 00:32:50,620
getting on for a century.
364
00:32:51,390 --> 00:32:55,300
Routine human mental labor
is rapidly becoming obsolete
365
00:32:55,300 --> 00:32:58,980
and that's why we're seeing a lot of
the middle class jobs disappearing.
366
00:33:01,010 --> 00:33:02,340
- Every once in a while,
367
00:33:02,340 --> 00:33:06,370
a revolutionary product comes
along that changes everything.
368
00:33:06,370 --> 00:33:09,190
Today, Apple is reinventing the phone.
369
00:33:20,730 --> 00:33:24,270
- Machine intelligence
is already all around us.
370
00:33:24,270 --> 00:33:27,620
The list of things that we humans
can do better than machines
371
00:33:27,620 --> 00:33:29,600
is actually
shrinking pretty fast.
372
00:33:35,730 --> 00:33:37,400
- Driverless cars are great.
373
00:33:37,400 --> 00:33:40,010
They probably will reduce accidents.
374
00:33:40,010 --> 00:33:43,670
Except, alongside with
that, in the United States,
375
00:33:43,670 --> 00:33:46,410
you're going to lose 10 million jobs.
376
00:33:46,480 --> 00:33:49,980
What are you going to do with
10 million unemployed people?
377
00:33:54,900 --> 00:33:58,660
- The risk for social
conflict and tensions,
378
00:33:58,660 --> 00:34:02,080
if you exacerbate inequalities,
is very, very high.
379
00:34:11,560 --> 00:34:13,870
- AGI can, by definition,
380
00:34:13,870 --> 00:34:16,700
do all jobs better than we can do.
381
00:34:16,700 --> 00:34:18,610
People who are saying,
"Oh, there will always be jobs
382
00:34:18,610 --> 00:34:21,120
"that humans can do better
than machines," are simply
383
00:34:21,120 --> 00:34:24,180
betting against science and
saying there will never be AGI.
384
00:34:30,860 --> 00:34:33,630
- What we are seeing
now is like a train hurtling
385
00:34:33,630 --> 00:34:37,880
down a dark tunnel at
breakneck speed and it looks like
386
00:34:37,880 --> 00:34:39,660
we're sleeping at the wheel.
387
00:35:29,170 --> 00:35:34,710
- A large fraction of the digital footprints
we're leaving behind are digital images.
388
00:35:35,690 --> 00:35:39,340
And specifically, what's really
interesting to me as a psychologist
389
00:35:39,340 --> 00:35:41,470
are digital images of our faces.
390
00:35:44,500 --> 00:35:47,260
Here you can see the difference
in the facial outline
391
00:35:47,260 --> 00:35:50,130
of an average gay and
an average straight face.
392
00:35:50,130 --> 00:35:52,660
And you can see that straight men
393
00:35:52,660 --> 00:35:55,380
have slightly broader jaws.
394
00:35:55,680 --> 00:36:00,580
Gay women have slightly larger jaws,
compared with straight women.
395
00:36:02,510 --> 00:36:05,670
Computer algorithms can
reveal our political views
396
00:36:05,670 --> 00:36:08,280
or sexual orientation, or intelligence,
397
00:36:08,280 --> 00:36:11,120
just based on the picture of our faces.
398
00:36:12,070 --> 00:36:16,760
Even a human brain can distinguish between
gay and straight men with some accuracy.
399
00:36:16,920 --> 00:36:21,700
Now it turns out that the computer
can do it with much higher accuracy.
400
00:36:21,700 --> 00:36:25,200
What you're seeing here is an accuracy of
401
00:36:25,200 --> 00:36:29,410
off-the-shelf facial recognition software.
402
00:36:29,410 --> 00:36:31,640
This is terrible news
403
00:36:31,640 --> 00:36:34,320
for gay men and women
all around the world.
404
00:36:34,320 --> 00:36:35,680
And not only gay men and women,
405
00:36:35,680 --> 00:36:38,300
because the same algorithms
can be used to detect other
406
00:36:38,300 --> 00:36:42,350
intimate traits, think being
a member of the opposition,
407
00:36:42,350 --> 00:36:45,090
or being a liberal, or being an atheist.
408
00:36:46,850 --> 00:36:50,070
Being an atheist is
also punishable by death
409
00:36:50,070 --> 00:36:52,610
in Saudi Arabia, for instance.
410
00:36:59,780 --> 00:37:04,440
My mission as an academic is to
warn people about the dangers of algorithms
411
00:37:04,440 --> 00:37:08,290
being able to reveal our intimate traits.
412
00:37:09,780 --> 00:37:14,060
The problem is that when
people receive bad news,
413
00:37:14,060 --> 00:37:16,260
they very often choose to dismiss them.
414
00:37:17,810 --> 00:37:22,250
Well, it's a bit scary when you start receiving
death threats from one day to another,
415
00:37:22,250 --> 00:37:24,830
and I've received quite a
few death threats, -
416
00:37:25,910 --> 00:37:30,870
-but as a scientist, I have to
basically show what is possible.
417
00:37:33,590 --> 00:37:36,900
So what I'm really interested
in now is to try to see
418
00:37:36,900 --> 00:37:41,080
whether we can predict other
traits from people's faces.
419
00:37:46,270 --> 00:37:48,580
Now, if you can detect
depression from a face,
420
00:37:48,580 --> 00:37:53,580
or suicidal thoughts, maybe a CCTV system
421
00:37:53,690 --> 00:37:56,970
on the train station can save some lives.
422
00:37:59,130 --> 00:38:03,830
What if we could predict that someone
is more prone to commit a crime?
423
00:38:04,960 --> 00:38:07,150
You probably had a school counselor,
424
00:38:07,150 --> 00:38:10,380
a psychologist hired
there to identify children
425
00:38:10,380 --> 00:38:14,830
that potentially may have
some behavioral problems.
426
00:38:17,330 --> 00:38:20,080
So now imagine if you could
predict with high accuracy
427
00:38:20,080 --> 00:38:22,590
that someone is likely to
commit a crime in the future
428
00:38:22,590 --> 00:38:24,780
from the language use, from the face,
429
00:38:24,780 --> 00:38:27,580
from the facial expressions,
from the likes on Facebook.
430
00:38:32,310 --> 00:38:35,210
I'm not developing new
methods, I'm just describing
431
00:38:35,210 --> 00:38:38,890
something or testing something
in an academic environment.
432
00:38:40,590 --> 00:38:42,790
But there obviously is a chance that,
433
00:38:42,790 --> 00:38:47,790
while warning people against
risks of new technologies,
434
00:38:47,960 --> 00:38:50,560
I may also give some people new ideas.
435
00:39:11,140 --> 00:39:13,830
- We haven't yet seen
the future in terms of
436
00:39:13,830 --> 00:39:18,780
the ways in which the
new data-driven society
437
00:39:18,780 --> 00:39:21,380
is going to really evolve.
438
00:39:23,540 --> 00:39:26,740
The tech companies want
to get every possible bit
439
00:39:26,740 --> 00:39:30,170
of information that they
can collect on everyone
440
00:39:30,170 --> 00:39:31,690
to facilitate business.
441
00:39:33,470 --> 00:39:37,090
The police and the military
want to do the same thing
442
00:39:37,090 --> 00:39:38,830
to facilitate security.
443
00:39:41,960 --> 00:39:46,440
The interests that the two
have in common are immense,
444
00:39:46,440 --> 00:39:51,220
and so the extent of collaboration
between what you might
445
00:39:51,220 --> 00:39:56,820
call the Military-Tech Complex
is growing dramatically.
446
00:40:00,400 --> 00:40:03,010
- The CIA, for a very long time,
447
00:40:03,010 --> 00:40:06,120
has maintained a close
connection with Silicon Valley.
448
00:40:07,110 --> 00:40:10,270
Their venture capital
firm known as In-Q-Tel,
449
00:40:10,270 --> 00:40:13,870
makes seed investments to
start-up companies developing
450
00:40:13,870 --> 00:40:17,490
breakthrough technology that
the CIA hopes to deploy.
451
00:40:18,660 --> 00:40:22,100
Palantir, the big data analytics firm,
452
00:40:22,100 --> 00:40:25,100
one of their first seed
investments was from In-Q-Tel.
453
00:40:28,950 --> 00:40:31,780
- In-Q-Tel has struck gold in Palantir
454
00:40:31,780 --> 00:40:35,990
in helping to create a private vendor
455
00:40:35,990 --> 00:40:40,850
that has intelligence and
artificial intelligence
456
00:40:40,850 --> 00:40:44,460
capabilities that the government
can't even compete with.
457
00:40:46,370 --> 00:40:48,720
- Good evening, I'm Peter Thiel.
458
00:40:49,740 --> 00:40:54,210
I'm not a politician, but
neither is Donald Trump.
459
00:40:54,210 --> 00:40:58,620
He is a builder and it's
time to rebuild America.
460
00:41:01,280 --> 00:41:03,940
- Peter Thiel,
the founder of Palantir,
461
00:41:03,940 --> 00:41:06,680
was a Donald Trump transition advisor
462
00:41:06,680 --> 00:41:09,180
and a close friend and donor.
463
00:41:11,080 --> 00:41:13,700
Trump was elected largely on the promise
464
00:41:13,700 --> 00:41:17,560
to deport millions of immigrants.
465
00:41:17,560 --> 00:41:22,240
The only way you can do that
is with a lot of intelligence
466
00:41:22,240 --> 00:41:25,010
and that's where Palantir comes in.
467
00:41:28,680 --> 00:41:33,340
They ingest huge troves
of data, which include,
468
00:41:33,340 --> 00:41:37,370
where you live, where
you work, who you know,
469
00:41:37,370 --> 00:41:40,930
who your neighbors are,
who your family is,
470
00:41:40,930 --> 00:41:44,480
where you have visited, where you stay,
471
00:41:44,480 --> 00:41:46,320
your social media profile.
472
00:41:49,250 --> 00:41:53,240
Palantir gets all of that
and is remarkably good
473
00:41:53,240 --> 00:41:58,240
at structuring it in a way
that helps law enforcement,
474
00:41:58,460 --> 00:42:02,450
immigration authorities
or intelligence agencies
475
00:42:02,450 --> 00:42:06,210
of any kind, track you, find you,
476
00:42:06,210 --> 00:42:09,550
and learn everything there
is to know about you.
477
00:42:48,430 --> 00:42:51,040
- We're putting AI in
charge now of evermore
478
00:42:51,040 --> 00:42:53,860
important decisions that
affect people's lives.
479
00:42:54,810 --> 00:42:58,050
Old-school AI used to have
its intelligence programmed in
480
00:42:58,050 --> 00:43:01,420
by humans who understood
how it worked, but today,
481
00:43:01,420 --> 00:43:04,080
powerful AI systems have
just learned for themselves,
482
00:43:04,080 --> 00:43:07,480
and we have no clue really how they work,
483
00:43:07,480 --> 00:43:09,680
which makes it really hard to trust them.
484
00:43:14,270 --> 00:43:17,820
- This isn't some futuristic
technology, this is now.
485
00:43:19,470 --> 00:43:23,220
AI might help determine
where a fire department
486
00:43:23,220 --> 00:43:25,900
is built in a community
or where a school is built.
487
00:43:25,900 --> 00:43:28,380
It might decide whether you get bail,
488
00:43:28,380 --> 00:43:30,680
or whether you stay in jail.
489
00:43:30,680 --> 00:43:32,930
It might decide where the
police are going to be.
490
00:43:32,930 --> 00:43:37,140
It might decide whether you're going to be
under additional police scrutiny.
491
00:43:43,340 --> 00:43:46,140
- It's popular now in the US
to do predictive policing.
492
00:43:46,980 --> 00:43:50,900
So what they do is they use an algorithm
to figure out where crime will be,
493
00:43:51,760 --> 00:43:55,240
- and they use that to tell where
we should send police officers.
494
00:43:56,430 --> 00:43:59,420
So that's based on a
measurement of crime rate.
495
00:44:00,640 --> 00:44:02,330
So we know that there is bias.
496
00:44:02,330 --> 00:44:05,090
Black people and Hispanic
people are pulled over,
497
00:44:05,090 --> 00:44:07,180
and stopped by the police
officers more frequently
498
00:44:07,180 --> 00:44:09,820
than white people are, so we
have this biased data going in,
499
00:44:09,820 --> 00:44:11,820
and then what happens
is you use that to say,
500
00:44:11,820 --> 00:44:13,640
"Oh, here's where the cops should go."
501
00:44:13,640 --> 00:44:17,320
Well, the cops go to those neighborhoods,
and guess what they do, they arrest people.
502
00:44:17,680 --> 00:44:21,160
And then it feeds back
biased data into the system,
503
00:44:21,160 --> 00:44:23,060
and that's called a feedback loop.
504
00:44:35,990 --> 00:44:40,650
- Predictive policing
leads at the extremes
505
00:44:41,590 --> 00:44:45,910
to experts saying, "Show me your baby,
506
00:44:45,910 --> 00:44:48,860
"and I will tell you whether
she's going to be a criminal."
507
00:44:51,300 --> 00:44:55,780
Now that we can predict it,
we're going to then surveil
508
00:44:55,780 --> 00:45:01,100
those kids much more closely
and we're going to jump on them
509
00:45:01,100 --> 00:45:03,700
at the first sign of a problem.
510
00:45:03,700 --> 00:45:06,700
And that's going to make
for more effective policing.
511
00:45:07,000 --> 00:45:11,250
It does, but it's going to
make for a really grim society
512
00:45:11,250 --> 00:45:16,250
and it's reinforcing
dramatically existing injustices.
513
00:45:22,960 --> 00:45:27,810
- Imagine a world in which
networks of CCTV cameras,
514
00:45:27,810 --> 00:45:30,930
drone surveillance
cameras, have sophisticated
515
00:45:30,930 --> 00:45:34,440
face recognition technologies
and are connected
516
00:45:34,440 --> 00:45:37,020
to other government
surveillance databases.
517
00:45:38,080 --> 00:45:41,190
We will have the
technology in place to have
518
00:45:41,190 --> 00:45:45,690
all of our movements comprehensively
tracked and recorded.
519
00:45:47,680 --> 00:45:50,330
What that also means is that we will have
520
00:45:50,330 --> 00:45:52,940
created a surveillance time machine
521
00:45:52,940 --> 00:45:56,620
that will allow governments
and powerful corporations
522
00:45:56,620 --> 00:45:58,710
to essentially hit rewind on our lives.
523
00:45:58,710 --> 00:46:01,780
We might not be under any suspicion now
524
00:46:01,780 --> 00:46:03,860
and five years from now,
they might want to know
525
00:46:03,860 --> 00:46:08,100
more about us, and can
then recreate granularly
526
00:46:08,100 --> 00:46:10,260
everything we've done,
everyone we've seen,
527
00:46:10,260 --> 00:46:13,050
everyone we've been around
over that entire period.
528
00:46:15,820 --> 00:46:19,320
That's an extraordinary amount of power
529
00:46:19,320 --> 00:46:21,660
for us to seed to anyone.
530
00:46:22,910 --> 00:46:25,370
And it's a world that I
think has been difficult
531
00:46:25,370 --> 00:46:29,180
for people to imagine,
but we've already built
532
00:46:29,180 --> 00:46:31,660
the architecture to enable that.
533
00:47:07,140 --> 00:47:10,290
- I'm a political reporter
and I'm very interested
534
00:47:10,290 --> 00:47:14,670
in the ways powerful industries
use their political power
535
00:47:14,670 --> 00:47:17,180
to influence the public policy process.
536
00:47:21,000 --> 00:47:24,420
The large tech companies and
their lobbyists get together
537
00:47:24,420 --> 00:47:27,530
behind closed doors and
are able to craft policies
538
00:47:27,530 --> 00:47:29,340
that we all have to live under.
539
00:47:30,790 --> 00:47:35,780
That's true for surveillance policies,
for policies in terms of data collection,
540
00:47:35,780 --> 00:47:40,540
but also increasingly important when it
comes to military and foreign policy.
541
00:47:43,930 --> 00:47:50,220
Starting in 2016, the Defense Department
formed the Defense Innovation Board.
542
00:47:50,220 --> 00:47:54,010
That's a special body created
to bring top tech executives
543
00:47:54,010 --> 00:47:56,420
into closer contact with the military.
544
00:47:59,140 --> 00:48:01,750
Eric Schmidt, former chairman of Alphabet,
545
00:48:01,750 --> 00:48:03,380
the parent company of Google,
546
00:48:03,380 --> 00:48:07,240
became the chairman of the
Defense Innovation Board,
547
00:48:07,240 --> 00:48:09,930
and one of their first
priorities was to say,
548
00:48:09,930 --> 00:48:13,850
"We need more artificial intelligence
integrated into the military."
549
00:48:15,930 --> 00:48:19,190
- I've worked with a group
of volunteers over the
550
00:48:19,190 --> 00:48:22,150
last couple of years to take
a look at innovation in the
551
00:48:22,150 --> 00:48:26,780
overall military, and my summary
conclusion is that we have
552
00:48:26,780 --> 00:48:30,460
fantastic people who are
trapped in a very bad system.
553
00:48:33,290 --> 00:48:35,700
- From the Department of
Defense's perspective,
554
00:48:35,700 --> 00:48:37,700
where I really started
to get interested in it
555
00:48:37,700 --> 00:48:40,980
was when we started thinking
about Unmanned Systems and
556
00:48:40,980 --> 00:48:45,980
how robotic and unmanned systems
would start to change war.
557
00:48:46,160 --> 00:48:50,320
The smarter you made the
Unmanned Systems and robots,
558
00:48:50,320 --> 00:48:53,570
the more powerful you might
be able to make your military.
559
00:48:55,540 --> 00:48:57,240
- Under Secretary of Defense,
560
00:48:57,240 --> 00:49:00,420
Robert Work put together
a major memo known as
561
00:49:00,420 --> 00:49:03,680
the Algorithmic Warfare
Cross-Functional Team,
562
00:49:03,680 --> 00:49:05,540
better known as Project Maven.
563
00:49:07,830 --> 00:49:10,760
Eric Schmidt gave a number of
speeches and media appearances
564
00:49:10,760 --> 00:49:14,310
where he said this effort
was designed to increase fuel
565
00:49:14,310 --> 00:49:18,240
efficiency in the Air Force,
to help with the logistics,
566
00:49:18,240 --> 00:49:21,300
but behind closed doors there
was another parallel effort.
567
00:49:26,710 --> 00:49:29,760
Late in 2017 as part of Project Maven,
568
00:49:29,760 --> 00:49:33,710
Google, Eric Schmidt's firm,
was tasked to secretly work
569
00:49:33,710 --> 00:49:36,100
on another part of Project Maven,
570
00:49:36,100 --> 00:49:41,480
and that was to take the vast
volumes of image data vacuumed up
571
00:49:41,480 --> 00:49:47,060
by drones operating in Iraq
and Afghanistan and to teach an AI
572
00:49:47,060 --> 00:49:49,690
to quickly identify
targets on the battlefield.
573
00:49:52,600 --> 00:49:58,470
- We have a sensor and the sensor
can do full motion video of an entire city.
574
00:49:58,470 --> 00:50:01,690
And we would have three
seven-person teams working
575
00:50:01,690 --> 00:50:06,360
constantly and they could
process 15% of the information.
576
00:50:06,360 --> 00:50:08,950
The other 85% of the
information was just left
577
00:50:08,950 --> 00:50:14,040
on the cutting room floor, so we said,
"Hey, AI and machine learning
578
00:50:14,040 --> 00:50:17,810
"would help us process
100% of the information."
579
00:50:25,380 --> 00:50:28,760
- Google has long had
the motto, "Don't be evil."
580
00:50:28,760 --> 00:50:30,630
They have created a public image
581
00:50:30,630 --> 00:50:35,080
that they are devoted
to public transparency.
582
00:50:35,080 --> 00:50:39,200
But for Google to slowly
transform into a defense contractor,
583
00:50:39,200 --> 00:50:41,720
they maintained
the utmost secrecy.
584
00:50:41,720 --> 00:50:45,280
And you had Google entering into
this contract with most of the employees,
585
00:50:45,280 --> 00:50:49,020
even employees who were working on
the program completely left in the dark.
586
00:51:01,840 --> 00:51:05,050
- Usually within Google,
anyone in the company
587
00:51:05,050 --> 00:51:08,100
is allowed to know about any
other project that's happening
588
00:51:08,100 --> 00:51:09,800
in some other part of the company.
589
00:51:11,180 --> 00:51:14,300
With Project Maven, the fact
that it was kept secret,
590
00:51:14,300 --> 00:51:18,740
I think was alarming to people
because that's not the norm at Google.
591
00:51:20,620 --> 00:51:22,940
- When this story was first revealed,
592
00:51:22,940 --> 00:51:25,540
it set off a firestorm within Google.
593
00:51:25,540 --> 00:51:28,420
You had a number of employees
quitting in protests,
594
00:51:28,420 --> 00:51:31,900
others signing a petition
objecting to this work.
595
00:51:34,130 --> 00:51:37,680
- You have to really say,
"I don't want to be part of this anymore."
596
00:51:38,820 --> 00:51:41,750
There are companies
called defense contractors
597
00:51:41,750 --> 00:51:45,830
and Google should just not
be one of those companies
598
00:51:45,830 --> 00:51:50,040
because people need to trust
Google for Google to work.
599
00:51:52,070 --> 00:51:55,710
- Good morning and welcome to Google I/O.
600
00:51:57,310 --> 00:52:00,160
- We've seen emails that
show that Google simply
601
00:52:00,160 --> 00:52:03,350
continued to mislead their
employees that the drone
602
00:52:03,350 --> 00:52:07,390
targeting program was only a
minor effort that could at most
603
00:52:07,390 --> 00:52:11,170
be worth $9 million to the firm,
which is drops in the bucket
604
00:52:11,170 --> 00:52:13,540
for a gigantic company like Google.
605
00:52:14,400 --> 00:52:17,300
But from internal emails that we obtained,
606
00:52:17,300 --> 00:52:21,530
Google was expecting Project
Maven would ramp up to as much
607
00:52:21,530 --> 00:52:26,530
as $250 million, and that this
entire effort would provide
608
00:52:26,630 --> 00:52:29,900
Google with Special Defense
Department certification to make
609
00:52:29,900 --> 00:52:32,660
them available for even
bigger defense contracts,
610
00:52:32,660 --> 00:52:34,980
some worth as much as $10 billion.
611
00:52:45,710 --> 00:52:49,750
The pressure for Google to
compete for military contracts
612
00:52:49,750 --> 00:52:53,780
has come at a time when its competitors
are also shifting their culture.
613
00:52:55,850 --> 00:53:00,070
Amazon, similarly pitching the
military and law enforcement.
614
00:53:00,070 --> 00:53:02,280
IBM and other leading firms,
615
00:53:02,280 --> 00:53:04,890
they're pitching law
enforcement and military.
616
00:53:05,840 --> 00:53:09,420
To stay competitive, Google
has slowly transformed.
617
00:53:15,150 --> 00:53:18,910
- The Defense Science
Board said of all of the
618
00:53:18,910 --> 00:53:22,170
technological advances that
are happening right now,
619
00:53:22,170 --> 00:53:26,780
the single most important thing
was artificial intelligence
620
00:53:26,780 --> 00:53:30,780
and the autonomous operations
that it would lead.
621
00:53:30,780 --> 00:53:32,330
Are we investing enough?
622
00:53:37,810 --> 00:53:41,220
- Once we develop what are known as
623
00:53:41,220 --> 00:53:45,820
autonomous lethal weapons, in other words,
624
00:53:45,820 --> 00:53:51,510
weapons that are not controlled at all,
they are genuinely autonomous,
625
00:53:51,510 --> 00:53:54,000
you've only got to get
a president who says,
626
00:53:54,000 --> 00:53:56,410
"The hell with international law,
we've got these weapons.
627
00:53:56,410 --> 00:53:58,560
"We're going to do what
we want with them."
628
00:54:02,540 --> 00:54:03,960
- We're very close.
629
00:54:03,960 --> 00:54:06,340
When you have the hardware
already set up
630
00:54:06,340 --> 00:54:10,360
and all you have to do is flip a switch
to make it fully autonomous,
631
00:54:10,360 --> 00:54:13,240
what is it there that's
stopping you from doing that?
632
00:54:14,670 --> 00:54:19,080
There's something really to be feared
in war at machine speed.
633
00:54:19,930 --> 00:54:24,370
What if you're a machine and you've run
millions and millions of different war scenarios
634
00:54:24,370 --> 00:54:28,060
and you have a team of drones and
you've delegated control to half of them,
635
00:54:28,060 --> 00:54:30,790
and you're collaborating in real time?
636
00:54:30,790 --> 00:54:35,600
What happens when that swarm of drones
is tasked with engaging a city?
637
00:54:37,490 --> 00:54:39,870
How will they take over that city?
638
00:54:39,870 --> 00:54:42,760
The answer is we won't
know until it happens.
639
00:54:50,020 --> 00:54:53,910
- We do not want an AI system to decide
640
00:54:53,910 --> 00:54:56,180
what human it would attack,
641
00:54:56,180 --> 00:54:59,500
but we're going up against
authoritarian competitors.
642
00:54:59,500 --> 00:55:02,450
So in my view, an authoritarian regime
643
00:55:02,450 --> 00:55:05,710
will have less problem
delegating authority
644
00:55:05,710 --> 00:55:08,460
to a machine to make lethal decisions.
645
00:55:09,410 --> 00:55:12,660
So how that plays out remains to be seen.
646
00:55:37,270 --> 00:55:41,020
- Almost the gift of AI
now is that it will force us
647
00:55:41,020 --> 00:55:44,410
collectively to think
through at a very basic level,
648
00:55:44,410 --> 00:55:46,310
what does it mean to be human?
649
00:55:48,620 --> 00:55:50,510
What do I do as a human better
650
00:55:50,510 --> 00:55:52,970
than a certain
super smart machine can do?
651
00:55:56,300 --> 00:56:01,000
First, we create our technology
and then it recreates us.
652
00:56:01,000 --> 00:56:05,630
We need to make sure that we
don't miss some of the things
653
00:56:05,630 --> 00:56:07,520
that make us so beautiful human.
654
00:56:11,840 --> 00:56:14,380
- Once we build intelligent machines,
655
00:56:14,380 --> 00:56:16,920
the philosophical vocabulary
we have available to think
656
00:56:16,920 --> 00:56:20,680
about ourselves as human
increasingly fails us.
657
00:56:23,160 --> 00:56:25,950
If I ask you to write up a
list of all the terms you have
658
00:56:25,950 --> 00:56:28,960
available to describe yourself as human,
659
00:56:28,960 --> 00:56:31,100
there are not so many terms.
660
00:56:31,100 --> 00:56:38,140
Culture, history, sociality,
maybe politics, civilization,
661
00:56:38,820 --> 00:56:45,090
subjectivity, all of these
terms ground in two positions
662
00:56:45,670 --> 00:56:48,020
that humans are more than mere animals
663
00:56:49,000 --> 00:56:52,200
and that humans are
more than mere machines.
664
00:56:55,140 --> 00:56:59,290
But if machines truly
think there is a large set
665
00:56:59,290 --> 00:57:03,520
of key philosophical questions
in which what is at stake is:
666
00:57:04,880 --> 00:57:09,080
Who are we? What is our place in the world?
What is the world? How is it structured?
667
00:57:09,080 --> 00:57:12,190
Do the categories that we have relied on
668
00:57:12,190 --> 00:57:14,510
- Do they still work? Were they wrong?
669
00:57:19,500 --> 00:57:22,220
- Many people think of intelligence
as something mysterious
670
00:57:22,220 --> 00:57:26,190
that can only exist inside of
biological organisms, like us,
671
00:57:26,190 --> 00:57:29,260
but intelligence is all
about information processing.
672
00:57:30,280 --> 00:57:32,230
It doesn't matter whether
the intelligence is processed
673
00:57:32,230 --> 00:57:35,940
by carbon atoms inside of
cells and brains, and people,
674
00:57:35,940 --> 00:57:38,020
or by silicon atoms in computers.
675
00:57:40,700 --> 00:57:43,320
Part of the success of
AI recently has come
676
00:57:43,320 --> 00:57:47,540
from stealing great ideas from evolution.
677
00:57:47,540 --> 00:57:49,320
We noticed that the brain, for example,
678
00:57:49,320 --> 00:57:52,920
has all these neurons inside
connected in complicated ways.
679
00:57:52,920 --> 00:57:55,660
So we stole that idea and abstracted it
680
00:57:55,660 --> 00:57:58,350
into artificial neural
networks in computers,
681
00:57:59,330 --> 00:58:02,800
and that's what has revolutionized
machine intelligence.
682
00:58:07,690 --> 00:58:10,300
If we one day get Artificial
General Intelligence,
683
00:58:10,300 --> 00:58:14,330
then by definition, AI can
also do better the job of AI
684
00:58:14,330 --> 00:58:18,930
programming and that means
that further progress in making
685
00:58:18,930 --> 00:58:22,950
AI will be dominated not by
human programmers, but by AI.
686
00:58:25,450 --> 00:58:29,090
Recursively self-improving AI
could leave human intelligence
687
00:58:29,090 --> 00:58:32,990
far behind,
creating super intelligence.
688
00:58:34,970 --> 00:58:37,970
It's gonna be the last
invention we ever need to make,
689
00:58:37,970 --> 00:58:40,520
because it can then invent everything else
690
00:58:40,520 --> 00:58:42,010
much faster than we could.
691
00:59:54,040 --> 00:59:59,040
- There is a future that
we all need to talk about.
692
00:59:59,110 --> 01:00:02,430
Some of the fundamental
questions about the future
693
01:00:02,430 --> 01:00:06,280
of artificial intelligence,
not just where it's going,
694
01:00:06,280 --> 01:00:09,630
but what it means for society to go there.
695
01:00:10,930 --> 01:00:14,620
It is not what computers can do,
696
01:00:14,620 --> 01:00:17,590
but what computers should do.
697
01:00:17,590 --> 01:00:22,210
As the generation of people
that is bringing AI to the future,
698
01:00:22,210 --> 01:00:27,690
we are the generation that will
answer this question first and foremost.
699
01:00:34,570 --> 01:00:37,380
- We haven't created the
human-level thinking machine yet,
700
01:00:37,380 --> 01:00:39,410
but we get closer and closer.
701
01:00:41,100 --> 01:00:44,780
Maybe we'll get to human-level
AI in five years from now
702
01:00:44,780 --> 01:00:47,410
or maybe it'll take 50
or 100 years from now.
703
01:00:47,660 --> 01:00:51,540
It almost doesn't matter.
Like these are all really, really soon,
704
01:00:51,540 --> 01:00:56,060
in terms of the overall
history of humanity.
705
01:01:01,200 --> 01:01:02,180
Very nice.
706
01:01:15,750 --> 01:01:19,350
So, the AI field is
extremely international.
707
01:01:19,350 --> 01:01:23,790
China is up and coming and it's
starting to rival the US,
708
01:01:23,790 --> 01:01:26,940
Europe and Japan in terms of putting a lot
709
01:01:26,940 --> 01:01:29,770
of processing power behind AI
710
01:01:29,770 --> 01:01:33,120
and gathering a lot of
data to help AI learn.
711
01:01:35,960 --> 01:01:40,540
We have a young generation
of Chinese researchers now.
712
01:01:40,540 --> 01:01:43,910
Nobody knows where the next
revolution is going to come from.
713
01:01:50,120 --> 01:01:54,290
- China always wanted to become
the superpower in the world.
714
01:01:56,120 --> 01:01:59,410
The Chinese government thinks AI
gave them the chance to become
715
01:01:59,410 --> 01:02:03,940
one of the most advanced
technology wise, business wise.
716
01:02:04,190 --> 01:02:07,780
So the Chinese government look at
this as a huge opportunity.
717
01:02:09,140 --> 01:02:13,670
Like they've raised a flag
and said, "That's a good field.
718
01:02:13,670 --> 01:02:16,300
"The companies should jump into it."
719
01:02:16,300 --> 01:02:18,300
Then China's commercial world
and companies say,
720
01:02:18,300 --> 01:02:20,800
"Okay, the government
raised a flag, that's good.
721
01:02:20,800 --> 01:02:22,500
"Let's put the money into it."
722
01:02:23,900 --> 01:02:28,090
Chinese tech giants, like Baidu,
like Tencent and like AliBaba,
723
01:02:28,090 --> 01:02:31,800
they put a lot of the
investment into the AI field.
724
01:02:33,410 --> 01:02:36,740
So we see that China's AI
development is booming.
725
01:02:43,430 --> 01:02:47,370
- In China, everybody
has Alipay and WeChat pay,
726
01:02:47,370 --> 01:02:49,500
so mobile payment is everywhere.
727
01:02:50,850 --> 01:02:54,850
And with that, they can
do a lot of AI analysis
728
01:02:54,850 --> 01:02:59,340
to know like your spending
habits, your credit rating.
729
01:03:01,150 --> 01:03:06,100
Face recognition technology
is widely adopted in China,
730
01:03:06,100 --> 01:03:08,200
in airports and train stations.
731
01:03:09,040 --> 01:03:11,570
So, in the future, maybe
in just a few months,
732
01:03:11,570 --> 01:03:15,140
you don't need a paper
ticket to board a train.
733
01:03:15,140 --> 01:03:15,970
Only your face.
734
01:03:24,620 --> 01:03:29,690
- We generate the world's biggest platform
of facial recognition.
735
01:03:31,260 --> 01:03:37,300
We have 300,000 developers
using our platform.
736
01:03:38,330 --> 01:03:41,550
A lot of it is selfie camera apps.
737
01:03:41,550 --> 01:03:43,930
It makes you look more beautiful.
738
01:03:46,550 --> 01:03:49,780
There are millions and millions
of cameras in the world,
739
01:03:50,760 --> 01:03:54,880
each camera from my point
is a data generator.
740
01:03:58,820 --> 01:04:02,810
In a machine's eye, your face
will change into the features
741
01:04:02,810 --> 01:04:06,660
and it will turn your face
into a paragraph of code.
742
01:04:07,720 --> 01:04:11,980
So we can detect how old you
are, if you're male or female,
743
01:04:11,980 --> 01:04:13,440
and your emotions.
744
01:04:16,640 --> 01:04:20,110
Shopping is about what kind
of thing you are looking at.
745
01:04:20,110 --> 01:04:25,650
We can track your eyeballs,
so if you are focusing on some product,
746
01:04:25,650 --> 01:04:28,690
we can track that so that we can know
747
01:04:28,690 --> 01:04:32,120
which kind of people like
which kind of product.
748
01:04:42,450 --> 01:04:45,820
Our mission is to create a platform
749
01:04:45,950 --> 01:04:50,300
that will enable millions of AI
developers in China.
750
01:04:51,280 --> 01:04:58,060
We study all the data we can get.
751
01:05:00,440 --> 01:05:04,350
Not just user profiles,
752
01:05:04,350 --> 01:05:08,380
but what you are doing at the moment,
753
01:05:09,460 --> 01:05:12,060
your geographical location.
754
01:05:15,380 --> 01:05:23,580
This platform will be so valuable that we don't
even worry about profit now,
755
01:05:23,580 --> 01:05:27,660
because it is definitely there.
756
01:05:29,660 --> 01:05:34,420
China's social credit system is
just one of the applications.
757
01:05:45,990 --> 01:05:48,040
- The Chinese government is using multiple
758
01:05:48,040 --> 01:05:50,810
different kinds of
technologies, whether it's AI,
759
01:05:50,810 --> 01:05:53,650
whether it's big data
platforms, facial recognition,
760
01:05:53,650 --> 01:05:57,030
voice recognition, essentially to monitor
761
01:05:57,030 --> 01:05:58,800
what the population is doing.
762
01:06:01,860 --> 01:06:04,240
I think the Chinese
government has made very clear
763
01:06:04,240 --> 01:06:09,240
its intent to gather massive
amounts of data about people
764
01:06:09,600 --> 01:06:13,300
to socially engineer a
dissent-free society.
765
01:06:16,040 --> 01:06:19,060
The logic behind the Chinese government's
766
01:06:19,060 --> 01:06:23,180
social credit system,
it's to take the idea that
767
01:06:23,180 --> 01:06:27,930
whether you are credit
worthy for a financial loan
768
01:06:27,930 --> 01:06:31,860
and adding to it a very
political dimension to say,
769
01:06:31,860 --> 01:06:34,270
"Are you a trustworthy human being?
770
01:06:36,530 --> 01:06:40,270
"What you've said online, have you ever been
critical of the authorities?
771
01:06:40,270 --> 01:06:42,190
"Do you have a criminal record?"
772
01:06:43,720 --> 01:06:47,340
And all that information is
packaged up together to rate
773
01:06:47,340 --> 01:06:51,820
you in ways that if you have
performed well in their view,
774
01:06:51,820 --> 01:06:56,490
you'll have easier access to certain kinds
of state services or benefits.
775
01:06:57,790 --> 01:06:59,710
But if you haven't done very well,
776
01:06:59,710 --> 01:07:02,130
you are going to be
penalized or restricted.
777
01:07:05,940 --> 01:07:09,020
There's no way for people to
challenge those designations
778
01:07:09,020 --> 01:07:12,100
or, in some cases, even know that
they've been put in that category,
779
01:07:12,100 --> 01:07:17,990
and it's not until they try to access some kind
of state service or buy a plane ticket,
780
01:07:17,990 --> 01:07:20,460
or get a passport, or
enroll their kid in school,
781
01:07:20,460 --> 01:07:23,590
that they come to learn that they've
been labeled in this way,
782
01:07:23,590 --> 01:07:27,550
and that there are negative
consequences for them as a result.
783
01:07:44,970 --> 01:07:48,660
We've spent the better part
of the last one or two years
784
01:07:48,660 --> 01:07:53,180
looking at abuses of surveillance
technology across China,
785
01:07:53,180 --> 01:07:56,030
and a lot of that work
has taken us to Xinjiang,
786
01:07:57,580 --> 01:08:01,220
the Northwestern region of
China that has a more than half
787
01:08:01,220 --> 01:08:05,870
population of Turkic Muslims,
Uyghurs, Kazakhs and Hui.
788
01:08:08,430 --> 01:08:10,980
This is a region and a
population the Chinese government
789
01:08:10,980 --> 01:08:14,900
has long considered to be
politically suspect or disloyal.
790
01:08:17,580 --> 01:08:20,160
We came to find information
about what's called
791
01:08:20,160 --> 01:08:23,130
the Integrated Joint Operations Platform,
792
01:08:23,130 --> 01:08:27,190
which is a predictive policing
program and that's one
793
01:08:27,190 --> 01:08:30,650
of the programs that has been
spitting out lists of names
794
01:08:30,650 --> 01:08:33,560
of people to be subjected
to political re-education.
795
01:08:39,090 --> 01:08:42,770
A number of our interviewees
for the report we just released
796
01:08:42,770 --> 01:08:46,110
about the political education
camps in Xinjiang just
797
01:08:46,110 --> 01:08:50,380
painted an extraordinary
portrait of a surveillance state.
798
01:08:53,460 --> 01:08:56,380
A region awash in surveillance cameras
799
01:08:56,380 --> 01:09:00,600
for facial recognition purposes,
checkpoints, body scanners,
800
01:09:00,600 --> 01:09:04,030
QR codes outside people's homes.
801
01:09:05,660 --> 01:09:10,690
Yeah, it really is the stuff of dystopian
movies that we've all gone to and thought,
802
01:09:10,690 --> 01:09:13,160
"Wow, that would be a
creepy world to live in."
803
01:09:13,160 --> 01:09:16,510
Yeah, well, 13 million
Turkic Muslims in China
804
01:09:16,510 --> 01:09:18,530
are living in that reality right now.
805
01:09:30,840 --> 01:09:33,060
- The Intercept reports
that Google is planning to
806
01:09:33,060 --> 01:09:36,490
launch a censored version of
its search engine in China.
807
01:09:36,490 --> 01:09:38,730
- Google's search
for new markets leads it
808
01:09:38,730 --> 01:09:42,170
to China, despite Beijing's
rules on censorship.
809
01:09:42,170 --> 01:09:44,080
- Tell us more
about why you felt it was
810
01:09:44,080 --> 01:09:46,650
your ethical responsibility to resign,
811
01:09:46,650 --> 01:09:51,060
because you talk about being complicit in
censorship and oppression, and surveillance.
812
01:09:51,060 --> 01:09:54,220
- There is a Chinese
venture company that has to be
813
01:09:54,220 --> 01:09:56,230
set up for Google to operate in China.
814
01:09:56,230 --> 01:09:58,920
And the question is, to what
degree did they get to control
815
01:09:58,920 --> 01:10:01,970
the blacklist and to what
degree would they have just
816
01:10:01,970 --> 01:10:05,220
unfettered access to
surveilling Chinese citizens?
817
01:10:05,220 --> 01:10:07,470
And the fact that Google
refuses to respond
818
01:10:07,470 --> 01:10:09,250
to human rights organizations on this,
819
01:10:09,250 --> 01:10:12,060
I think should be extremely
disturbing to everyone.
820
01:10:16,640 --> 01:10:21,120
Due to my conviction that dissent is
fundamental to functioning democracies
821
01:10:21,120 --> 01:10:25,560
and forced to resign in order to avoid
contributing to or profiting from the erosion
822
01:10:25,560 --> 01:10:27,510
of protections for dissidents.
823
01:10:28,560 --> 01:10:30,920
The UN is currently
reporting that between
824
01:10:30,920 --> 01:10:34,190
200,000 and one million
Uyghurs have been disappeared
825
01:10:34,190 --> 01:10:36,520
into re-education camps.
826
01:10:36,520 --> 01:10:39,290
And there is a serious argument
that Google would be complicit
827
01:10:39,290 --> 01:10:42,460
should it launch a surveilled
version of search in China.
828
01:10:45,180 --> 01:10:51,380
Dragonfly is a project meant
to launch search in China under
829
01:10:51,390 --> 01:10:55,810
Chinese government regulations,
which include censoring
830
01:10:55,810 --> 01:10:59,510
sensitive content, basic
queries on human rights,
831
01:10:59,510 --> 01:11:03,210
information about political
representatives is blocked,
832
01:11:03,210 --> 01:11:07,080
information about student
protests is blocked.
833
01:11:07,080 --> 01:11:09,180
And that's one small part of it.
834
01:11:09,180 --> 01:11:12,250
Perhaps a deeper concern is
the surveillance side of this.
835
01:11:16,070 --> 01:11:19,580
When I raised the issue with my managers,
with my colleagues,
836
01:11:19,580 --> 01:11:23,370
there was a lot of concern, but everyone
just said, "I don't know anything."
837
01:11:27,760 --> 01:11:30,200
And then when there was a meeting finally,
838
01:11:30,200 --> 01:11:35,080
there was essentially no addressing
the serious concerns associated with it.
839
01:11:37,010 --> 01:11:39,790
So then I filed my formal resignation,
840
01:11:39,790 --> 01:11:42,950
not just to my manager,
but I actually distributed it company-wide.
841
01:11:42,950 --> 01:11:45,440
And that's the letter
that I was reading from.
842
01:11:50,360 --> 01:11:55,960
Personally, I haven't slept well.
I've had pretty horrific headaches,
843
01:11:55,960 --> 01:11:58,520
wake up in the middle of
the night just sweating.
844
01:12:00,640 --> 01:12:03,340
With that said, what I
found since speaking out
845
01:12:03,340 --> 01:12:07,930
is just how positive the global
response to this has been.
846
01:12:10,350 --> 01:12:13,910
Engineers should demand
to know what the uses
847
01:12:13,910 --> 01:12:16,280
of their technical contributions are
848
01:12:16,280 --> 01:12:19,340
and to have a seat at the table
in those ethical decisions.
849
01:12:27,020 --> 01:12:29,590
Most citizens don't really
understand what it means to be
850
01:12:29,590 --> 01:12:32,250
in a very large scale
prescriptive technology.
851
01:12:33,720 --> 01:12:36,260
Where someone has already
pre-divided the work
852
01:12:36,260 --> 01:12:38,300
and all you know about
is your little piece,
853
01:12:38,300 --> 01:12:41,150
and almost certainly you don't
understand how it fits in.
854
01:12:44,630 --> 01:12:51,040
So, I think it's worth drawing the analogy
to physicists' work on the atomic bomb.
855
01:12:53,840 --> 01:12:56,610
In fact, that's actually
the community I came out of.
856
01:12:59,220 --> 01:13:03,460
I wasn't a nuclear scientist by any means,
but I was an applied mathematician
857
01:13:04,400 --> 01:13:09,400
and my PhD program was actually funded
to train people to work in weapons labs.
858
01:13:12,980 --> 01:13:17,100
One could certainly argue
that there is an existential threat
859
01:13:17,820 --> 01:13:22,060
and whoever is leading
in AI will lead militarily.
860
01:13:31,420 --> 01:13:34,250
- China fully expects to
pass the United States
861
01:13:34,250 --> 01:13:37,480
as the number one economy in
the world and it believes that
862
01:13:37,480 --> 01:13:42,450
AI will make that jump more
quickly and more dramatically.
863
01:13:43,900 --> 01:13:46,480
And they also see it as
being able to leapfrog
864
01:13:46,480 --> 01:13:49,820
the United States in
terms of military power.
865
01:13:57,580 --> 01:13:59,600
Their plan is very simple.
866
01:13:59,600 --> 01:14:03,620
We want to catch the United States
and these technologies by 2020,
867
01:14:03,620 --> 01:14:07,940
we want to surpass the United States
in these technologies by 2025,
868
01:14:07,940 --> 01:14:12,600
and we want to be the world leader in AI
and autonomous technologies by 2030.
869
01:14:15,110 --> 01:14:16,850
It is a national plan.
870
01:14:16,850 --> 01:14:21,790
It is backed up by at least
$150 billion in investments.
871
01:14:21,790 --> 01:14:25,060
So, this is definitely a race.
872
01:14:50,070 --> 01:14:52,070
- AI is a little bit like fire.
873
01:14:53,360 --> 01:14:56,270
Fire was invented 700,000 years ago,
874
01:14:57,480 --> 01:14:59,820
and it has its pros and cons.
875
01:15:01,960 --> 01:15:06,810
People realized you can use fire
to keep warm at night and to cook,
876
01:15:08,520 --> 01:15:14,420
but they also realized that you can
kill other people with that.
877
01:15:19,860 --> 01:15:24,300
Fire also has this
AI-like quality of growing
878
01:15:24,300 --> 01:15:27,400
in a wildfire without further human ado,
879
01:15:30,530 --> 01:15:35,970
but the advantages outweigh
the disadvantages by so much
880
01:15:35,970 --> 01:15:39,760
that we are not going
to stop its development.
881
01:15:49,540 --> 01:15:51,180
Europe is waking up.
882
01:15:52,160 --> 01:15:58,390
Lots of companies in Europe
are realizing that the next wave of AI
883
01:15:58,730 --> 01:16:01,540
will be much bigger than the current wave.
884
01:16:03,310 --> 01:16:08,000
The next wave of AI will be about robots.
885
01:16:09,810 --> 01:16:15,450
All these machines that make
things, that produce stuff,
886
01:16:15,450 --> 01:16:19,370
that build other machines,
they are going to become smart.
887
01:16:26,660 --> 01:16:29,950
In the not-so-distant future,
we will have robots
888
01:16:29,950 --> 01:16:33,290
that we can teach like we teach kids.
889
01:16:35,640 --> 01:16:38,660
For example, I will talk to a
little robot and I will say,
890
01:16:39,970 --> 01:16:42,550
"Look here, robot, look here.
891
01:16:42,550 --> 01:16:44,620
"Let's assemble a smartphone.
892
01:16:44,620 --> 01:16:48,720
"We take this slab of plastic like that
and we takes a screwdriver like that,
893
01:16:48,720 --> 01:16:52,310
"and now we screw in everything like this.
894
01:16:52,310 --> 01:16:54,310
"No, no, not like this.
895
01:16:54,310 --> 01:16:57,690
"Like this, look, robot, look, like this."
896
01:16:58,820 --> 01:17:01,890
And he will fail a couple
of times but rather quickly,
897
01:17:01,890 --> 01:17:05,860
he will learn to do the same thing
much better than I could do it.
898
01:17:06,710 --> 01:17:11,400
And then we stop the learning
and we make a million copies, and sell it.
899
01:17:32,410 --> 01:17:35,800
Regulation of AI sounds
like an attractive idea,
900
01:17:35,800 --> 01:17:38,210
but I don't think it's possible.
901
01:17:40,250 --> 01:17:42,720
One of the reasons why it won't work is
902
01:17:42,720 --> 01:17:46,360
the sheer curiosity of scientists.
903
01:17:47,260 --> 01:17:49,560
They don't give a damn for regulation.
904
01:17:52,640 --> 01:17:56,740
Military powers won't give a
damn for regulations, either.
905
01:17:56,740 --> 01:17:59,020
They will say,
"If we, the Americans don't do it,
906
01:17:59,020 --> 01:18:00,720
"then the Chinese will do it."
907
01:18:00,720 --> 01:18:03,150
And the Chinese will say,
"Oh, if we don't do it,
908
01:18:03,150 --> 01:18:04,810
"then the Russians will do it."
909
01:18:07,700 --> 01:18:10,930
No matter what kind of political
regulation is out there,
910
01:18:10,930 --> 01:18:14,660
all these military industrial complexes,
911
01:18:14,660 --> 01:18:17,570
they will almost by
definition have to ignore that
912
01:18:18,670 --> 01:18:21,250
because they want to avoid falling behind.
913
01:18:26,030 --> 01:18:27,690
Welcome to Xinhua.
914
01:18:27,690 --> 01:18:30,260
I'm the world's first female
AI news anchor developed
915
01:18:30,260 --> 01:18:32,690
jointly by Xinhua and
search engine company Sogou.
916
01:18:33,050 --> 01:18:36,310
- A program developed by
the company OpenAI can write
917
01:18:36,310 --> 01:18:39,500
coherent and credible stories
just like human beings.
918
01:18:39,500 --> 01:18:41,520
- It's one small step for machine,
919
01:18:41,520 --> 01:18:44,330
one giant leap for machine kind.
920
01:18:44,330 --> 01:18:47,170
IBM's newest artificial
intelligence system took on
921
01:18:47,170 --> 01:18:51,570
experienced human debaters
and won a live debate.
922
01:18:51,570 --> 01:18:54,160
- Computer-generated
videos known as deep fakes
923
01:18:54,160 --> 01:18:57,980
are being used to put women's
faces on pornographic videos.
924
01:19:02,510 --> 01:19:06,450
- Artificial intelligence
evolves at a very crazy pace.
925
01:19:07,970 --> 01:19:10,180
You know, it's like progressing so fast.
926
01:19:10,180 --> 01:19:12,940
In some ways, we're only
at the beginning right now.
927
01:19:14,490 --> 01:19:17,620
You have so many potential
applications, it's a gold mine.
928
01:19:19,590 --> 01:19:23,450
Since 2012, when deep learning
became a big game changer
929
01:19:23,450 --> 01:19:25,370
in the computer vision community,
930
01:19:25,370 --> 01:19:28,730
we were one of the first to
actually adopt deep learning
931
01:19:28,730 --> 01:19:31,080
and apply it in the field
of computer graphics.
932
01:19:34,640 --> 01:19:39,800
A lot of our research is funded by
government, military intelligence agencies.
933
01:19:43,950 --> 01:19:47,840
The way we create these photoreal mappings,
934
01:19:47,840 --> 01:19:50,300
usually the way it works is
that we need two subjects,
935
01:19:50,300 --> 01:19:53,740
a source and a target, and
I can do a face replacement.
936
01:19:58,880 --> 01:20:03,700
One of the applications is, for example,
I want to manipulate someone's face
937
01:20:03,700 --> 01:20:05,450
saying things that he did not.
938
01:20:08,920 --> 01:20:12,140
It can be used for creative
things, for funny contents,
939
01:20:12,140 --> 01:20:16,110
but obviously, it can also
be used for just simply
940
01:20:16,110 --> 01:20:18,660
manipulate videos and generate fake news.
941
01:20:21,050 --> 01:20:23,340
This can be very dangerous.
942
01:20:24,790 --> 01:20:29,300
If it gets into the wrong hands,
it can get out of control very quickly.
943
01:20:33,370 --> 01:20:35,750
- We're entering an era
in which our enemies can
944
01:20:35,750 --> 01:20:39,590
make it look like anyone is
saying anything at any point in time,
945
01:20:39,590 --> 01:20:41,830
even if they would
never say those things.
946
01:20:42,450 --> 01:20:46,970
Moving forward, we need to be more
vigilant with what we trust from the Internet.
947
01:20:46,970 --> 01:20:50,710
It may sound basic, but how
we move forward
948
01:20:50,710 --> 01:20:55,180
in the age of information
is going to be the difference
949
01:20:55,180 --> 01:20:58,160
between whether we survive
or whether we become
950
01:20:58,160 --> 01:21:00,270
some kind of fucked up dystopia.
951
01:22:31,070 --> 01:22:37,337
- One criticism that is frequently raised
against my work is saying that,
952
01:22:37,337 --> 01:22:43,610
"Hey, you know there were stupid ideas
in the past like phrenology or physiognomy.-
953
01:22:45,110 --> 01:22:49,210
- "There were people claiming
that you can read a character
954
01:22:49,210 --> 01:22:52,010
"of a person just based on their face."
955
01:22:53,820 --> 01:22:56,050
People would say, "This is rubbish.
956
01:22:56,050 --> 01:23:01,050
"We know it was just thinly
veiled racism and superstition."
957
01:23:05,300 --> 01:23:09,650
But the fact that someone
made a claim in the past
958
01:23:09,650 --> 01:23:14,650
and tried to support this
claim with invalid reasoning,
959
01:23:14,960 --> 01:23:18,970
doesn't automatically
invalidate the claim.
960
01:23:23,790 --> 01:23:25,710
Of course, people should have rights
961
01:23:25,710 --> 01:23:31,020
to their privacy when it comes to
sexual orientation or political views,
962
01:23:32,310 --> 01:23:35,600
but I'm also afraid that in
the current technological environment,
963
01:23:35,600 --> 01:23:38,220
this is essentially impossible.
964
01:23:42,640 --> 01:23:44,910
People should realize
there's no going back.
965
01:23:44,910 --> 01:23:48,330
There's no running away
from the algorithms.
966
01:23:51,120 --> 01:23:55,670
The sooner we accept the
inevitable and inconvenient truth
967
01:23:56,540 --> 01:23:59,430
that privacy is gone,
968
01:24:01,770 --> 01:24:05,380
the sooner we can actually
start thinking about
969
01:24:05,380 --> 01:24:07,440
how to make
sure that our societies
970
01:24:07,440 --> 01:24:11,660
are ready for the Post-Privacy Age.
971
01:24:35,080 --> 01:24:37,610
- While speaking about facial recognition,
972
01:24:37,610 --> 01:24:43,700
in my deep thoughts, I sometimes get to
the very dark era of our history.
973
01:24:45,490 --> 01:24:49,020
When the people had to live in the system,
974
01:24:49,020 --> 01:24:52,660
where some part of the society was accepted
975
01:24:53,680 --> 01:24:56,900
and some part of the society
was accused to death.
976
01:25:01,670 --> 01:25:06,180
What would Mengele do to have
such an instrument in his hands?
977
01:25:10,740 --> 01:25:14,390
It would be very quick and
efficient for selection
978
01:25:18,390 --> 01:25:22,100
and this is the apocalyptic vision.
979
01:26:15,880 --> 01:26:19,870
- So in the near future,
the entire story of you
980
01:26:19,870 --> 01:26:25,340
will exist in a vast array of
connected databases of faces,
981
01:26:25,750 --> 01:26:28,390
genomes, behaviors and emotion.
982
01:26:30,950 --> 01:26:35,320
So, you will have a digital
avatar of yourself online,
983
01:26:35,320 --> 01:26:39,510
which records how well you
are doing as a citizen,
984
01:26:39,510 --> 01:26:41,910
what kind of a relationship do you have,
985
01:26:41,910 --> 01:26:45,890
what kind of political orientation
and sexual orientation.
986
01:26:50,020 --> 01:26:54,120
Based on all of those data,
those algorithms will be able to
987
01:26:54,120 --> 01:26:58,390
manipulate your behavior
with an extreme precision,
988
01:26:58,390 --> 01:27:03,890
changing how we think and
probably in the future, how we feel.
989
01:27:25,660 --> 01:27:30,510
- The beliefs and desires of the
first AGIs will be extremely important.
990
01:27:32,730 --> 01:27:35,180
So, it's important to
program them correctly.
991
01:27:36,200 --> 01:27:37,850
I think that if this is not done,
992
01:27:38,810 --> 01:27:44,190
then the nature of evolution
of natural selection will favor
993
01:27:44,190 --> 01:27:48,340
those systems, prioritize their
own survival above all else.
994
01:27:51,860 --> 01:27:56,660
It's not that it's going to actively
hate humans and want to harm them,
995
01:27:58,780 --> 01:28:01,100
but it's just
going to be too powerful
996
01:28:01,400 --> 01:28:05,470
and I think a good analogy would be
the way humans treat animals.
997
01:28:06,970 --> 01:28:08,200
It's not that we hate animals.
998
01:28:08,260 --> 01:28:11,930
I think humans love animals
and have a lot of affection for them,
999
01:28:12,340 --> 01:28:17,610
but when the time comes to
build a highway between two cities,
1000
01:28:17,610 --> 01:28:20,130
we are not asking
the animals for permission.
1001
01:28:20,130 --> 01:28:23,340
We just do it because
it's important for us.
1002
01:28:24,250 --> 01:28:29,010
And I think by default, that's the kind of
relationship that's going to be between us
1003
01:28:29,010 --> 01:28:34,700
and AGIs which are truly autonomous
and operating on their own behalf.
1004
01:28:47,190 --> 01:28:51,400
If you have an arms-race
dynamics between multiple kings
1005
01:28:51,400 --> 01:28:54,140
trying to build the AGI first,
1006
01:28:54,140 --> 01:28:58,000
they will have less time to make sure
that the AGI that they build
1007
01:28:58,970 --> 01:29:00,520
will care deeply for humans.
1008
01:29:03,530 --> 01:29:06,540
Because the way I imagine it
is that there is an avalanche,
1009
01:29:06,540 --> 01:29:09,220
there is an avalanche
of AGI development.
1010
01:29:09,220 --> 01:29:12,300
Imagine it's a huge unstoppable force.
1011
01:29:15,780 --> 01:29:18,740
And I think it's pretty likely
the entire surface of the
1012
01:29:18,740 --> 01:29:22,070
earth would be covered with
solar panels and data centers.
1013
01:29:26,230 --> 01:29:28,700
Given these kinds of concerns,
1014
01:29:28,700 --> 01:29:32,830
it will be important that
the AGI is somehow built
1015
01:29:32,830 --> 01:29:35,570
as a cooperation
with multiple countries.
1016
01:29:38,060 --> 01:29:41,180
The future is going to be
good for the AIs, regardless.
1017
01:29:42,130 --> 01:29:45,020
It would be nice if it would
be good for humans as well.
1018
01:30:06,750 --> 01:30:10,580
- Is there a lot of responsibility
weighing on my shoulders?
1019
01:30:10,580 --> 01:30:11,800
Not really.
1020
01:30:13,420 --> 01:30:16,740
Was there a lot of
responsibility on the shoulders
1021
01:30:16,740 --> 01:30:19,100
of the parents of Einstein?
1022
01:30:20,190 --> 01:30:21,780
The parents somehow made him,
1023
01:30:21,780 --> 01:30:25,450
but they had no way of
predicting what he would do,
1024
01:30:25,450 --> 01:30:27,300
and how he would change the world.
1025
01:30:28,580 --> 01:30:32,540
And so, you can't really hold
them responsible for that.
1026
01:30:57,640 --> 01:31:00,240
So, I'm not a very human-centric person.
1027
01:31:01,840 --> 01:31:05,500
I think I'm a little stepping
stone in the evolution
1028
01:31:05,500 --> 01:31:08,060
of the Universe towards higher complexity.
1029
01:31:10,820 --> 01:31:14,860
But it's also clear to me that
I'm not the crown of creation
1030
01:31:14,860 --> 01:31:19,320
and that humankind as a whole
is not the crown of creation,
1031
01:31:21,140 --> 01:31:26,290
but we are setting the stage for something
that is bigger than us that transcends us.
1032
01:31:28,980 --> 01:31:32,780
And then will go out there in a way
where humans cannot follow
1033
01:31:32,780 --> 01:31:37,940
and transform the entire Universe,
or at least, the reachable Universe.
1034
01:31:41,520 --> 01:31:46,520
So, I find beauty and awe in seeing myself
1035
01:31:46,640 --> 01:31:50,260
as part of this much grander theme.
1036
01:32:14,070 --> 01:32:16,030
- AI is inevitable.
1037
01:32:17,560 --> 01:32:23,370
We need to make sure we have
the necessary human regulation
1038
01:32:23,370 --> 01:32:27,940
to prevent the weaponization
of artificial intelligence.
1039
01:32:28,690 --> 01:32:32,000
We don't need any more weaponization
1040
01:32:32,000 --> 01:32:33,960
of such a powerful tool.
1041
01:32:37,100 --> 01:32:41,960
- One of the most critical things, I think,
is the need for international governance.
1042
01:32:43,920 --> 01:32:46,310
We have an imbalance of
power here because now
1043
01:32:46,310 --> 01:32:49,140
we have corporations with
more power, might and ability,
1044
01:32:49,140 --> 01:32:51,100
than entire countries.
1045
01:32:51,100 --> 01:32:54,050
How do we make sure that people's
voices are getting heard?
1046
01:32:58,030 --> 01:32:59,930
- It can't be a law-free zone.
1047
01:32:59,930 --> 01:33:02,100
It can't be a rights-free zone.
1048
01:33:02,100 --> 01:33:05,870
We can't embrace all of these
wonderful new technologies
1049
01:33:05,870 --> 01:33:10,380
for the 21st century without
trying to bring with us
1050
01:33:10,380 --> 01:33:15,380
the package of human rights
that we fought so hard
1051
01:33:15,640 --> 01:33:19,190
to achieve, and that remains so fragile.
1052
01:33:28,680 --> 01:33:32,180
- AI isn't good and it isn't evil, either.
1053
01:33:32,180 --> 01:33:36,890
It's just going to amplify the desires
and goals of whoever controls it.
1054
01:33:36,890 --> 01:33:41,240
And AI today is under the control of
a very, very small group of people.
1055
01:33:44,430 --> 01:33:49,260
The most important question that we humans
have to ask ourselves at this point in history
1056
01:33:49,260 --> 01:33:51,380
requires no technical knowledge.
1057
01:33:51,590 --> 01:33:57,540
It's the question of what sort
of future society do we want to create
1058
01:33:57,540 --> 01:33:59,740
with all this
technology we're making?
1059
01:34:01,410 --> 01:34:05,110
What do we want the role of
humans to be in this world?
88726
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