Subtitles section Play video
-
After 13.8 billion years of cosmic history,
-
our universe has woken up
-
and become aware of itself.
-
From a small blue planet,
-
tiny, conscious parts of our universe have begun gazing out into the cosmos
-
with telescopes,
-
discovering something humbling.
-
We've discovered that our universe is vastly grander
-
than our ancestors imagined
-
and that life seems to be an almost imperceptibly small perturbation
-
on an otherwise dead universe.
-
But we've also discovered something inspiring,
-
which is that the technology we're developing has the potential
-
to help life flourish like never before,
-
not just for centuries but for billions of years,
-
and not just on earth but throughout much of this amazing cosmos.
-
I think of the earliest life as "Life 1.0"
-
because it was really dumb,
-
like bacteria, unable to learn anything during its lifetime.
-
I think of us humans as "Life 2.0" because we can learn,
-
which we in nerdy, geek speak,
-
might think of as installing new software into our brains,
-
like languages and job skills.
-
"Life 3.0," which can design not only its software but also its hardware
-
of course doesn't exist yet.
-
But perhaps our technology has already made us "Life 2.1,"
-
with our artificial knees, pacemakers and cochlear implants.
-
So let's take a closer look at our relationship with technology, OK?
-
As an example,
-
the Apollo 11 moon mission was both successful and inspiring,
-
showing that when we humans use technology wisely,
-
we can accomplish things that our ancestors could only dream of.
-
But there's an even more inspiring journey
-
propelled by something more powerful than rocket engines,
-
where the passengers aren't just three astronauts
-
but all of humanity.
-
Let's talk about our collective journey into the future
-
with artificial intelligence.
-
My friend Jaan Tallinn likes to point out that just as with rocketry,
-
it's not enough to make our technology powerful.
-
We also have to figure out, if we're going to be really ambitious,
-
how to steer it
-
and where we want to go with it.
-
So let's talk about all three for artificial intelligence:
-
the power, the steering and the destination.
-
Let's start with the power.
-
I define intelligence very inclusively --
-
simply as our ability to accomplish complex goals,
-
because I want to include both biological and artificial intelligence.
-
And I want to avoid the silly carbon-chauvinism idea
-
that you can only be smart if you're made of meat.
-
It's really amazing how the power of AI has grown recently.
-
Just think about it.
-
Not long ago, robots couldn't walk.
-
Now, they can do backflips.
-
Not long ago,
-
we didn't have self-driving cars.
-
Now, we have self-flying rockets.
-
Not long ago,
-
AI couldn't do face recognition.
-
Now, AI can generate fake faces
-
and simulate your face saying stuff that you never said.
-
Not long ago,
-
AI couldn't beat us at the game of Go.
-
Then, Google DeepMind's AlphaZero AI took 3,000 years of human Go games
-
and Go wisdom,
-
ignored it all and became the world's best player by just playing against itself.
-
And the most impressive feat here wasn't that it crushed human gamers,
-
but that it crushed human AI researchers
-
who had spent decades handcrafting game-playing software.
-
And AlphaZero crushed human AI researchers not just in Go but even at chess,
-
which we have been working on since 1950.
-
So all this amazing recent progress in AI really begs the question:
-
How far will it go?
-
I like to think about this question
-
in terms of this abstract landscape of tasks,
-
where the elevation represents how hard it is for AI to do each task
-
at human level,
-
and the sea level represents what AI can do today.
-
The sea level is rising as AI improves,
-
so there's a kind of global warming going on here in the task landscape.
-
And the obvious takeaway is to avoid careers at the waterfront --
-
(Laughter)
-
which will soon be automated and disrupted.
-
But there's a much bigger question as well.
-
How high will the water end up rising?
-
Will it eventually rise to flood everything,
-
matching human intelligence at all tasks.
-
This is the definition of artificial general intelligence --
-
AGI,
-
which has been the holy grail of AI research since its inception.
-
By this definition, people who say,
-
"Ah, there will always be jobs that humans can do better than machines,"
-
are simply saying that we'll never get AGI.
-
Sure, we might still choose to have some human jobs
-
or to give humans income and purpose with our jobs,
-
but AGI will in any case transform life as we know it
-
with humans no longer being the most intelligent.
-
Now, if the water level does reach AGI,
-
then further AI progress will be driven mainly not by humans but by AI,
-
which means that there's a possibility
-
that further AI progress could be way faster
-
than the typical human research and development timescale of years,
-
raising the controversial possibility of an intelligence explosion
-
where recursively self-improving AI
-
rapidly leaves human intelligence far behind,
-
creating what's known as superintelligence.
-
Alright, reality check:
-
Are we going to get AGI any time soon?
-
Some famous AI researchers, like Rodney Brooks,
-
think it won't happen for hundreds of years.
-
But others, like Google DeepMind founder Demis Hassabis,
-
are more optimistic
-
and are working to try to make it happen much sooner.
-
And recent surveys have shown that most AI researchers
-
actually share Demis's optimism,
-
expecting that we will get AGI within decades,
-
so within the lifetime of many of us,
-
which begs the question -- and then what?
-
What do we want the role of humans to be
-
if machines can do everything better and cheaper than us?
-
The way I see it, we face a choice.
-
One option is to be complacent.
-
We can say, "Oh, let's just build machines that can do everything we can do
-
and not worry about the consequences.
-
Come on, if we build technology that makes all humans obsolete,
-
what could possibly go wrong?"
-
(Laughter)
-
But I think that would be embarrassingly lame.
-
I think we should be more ambitious -- in the spirit of TED.
-
Let's envision a truly inspiring high-tech future
-
and try to steer towards it.
-
This brings us to the second part of our rocket metaphor: the steering.
-
We're making AI more powerful,
-
but how can we steer towards a future
-
where AI helps humanity flourish rather than flounder?
-
To help with this,
-
I cofounded the Future of Life Institute.
-
It's a small nonprofit promoting beneficial technology use,
-
and our goal is simply for the future of life to exist
-
and to be as inspiring as possible.
-
You know, I love technology.
-
Technology is why today is better than the Stone Age.
-
And I'm optimistic that we can create a really inspiring high-tech future ...
-
if -- and this is a big if --
-
if we win the wisdom race --
-
the race between the growing power of our technology
-
and the growing wisdom with which we manage it.
-
But this is going to require a change of strategy
-
because our old strategy has been learning from mistakes.
-
We invented fire,
-
screwed up a bunch of times --
-
invented the fire extinguisher.
-
(Laughter)
-
We invented the car, screwed up a bunch of times --
-
invented the traffic light, the seat belt and the airbag,
-
but with more powerful technology like nuclear weapons and AGI,
-
learning from mistakes is a lousy strategy,
-
don't you think?
-
(Laughter)
-
It's much better to be proactive rather than reactive;
-
plan ahead and get things right the first time
-
because that might be the only time we'll get.
-
But it is funny because sometimes people tell me,
-
"Max, shhh, don't talk like that.
-
That's Luddite scaremongering."
-
But it's not scaremongering.
-
It's what we at MIT call safety engineering.
-
Think about it:
-
before NASA launched the Apollo 11 mission,
-
they systematically thought through everything that could go wrong
-
when you put people on top of explosive fuel tanks
-
and launch them somewhere where no one could help them.
-
And there was a lot that could go wrong.
-
Was that scaremongering?
-
No.
-
That's was precisely the safety engineering
-
that ensured the success of the mission,
-
and that is precisely the strategy I think we should take with AGI.
-
Think through what can go wrong to make sure it goes right.
-
So in this spirit, we've organized conferences,
-
bringing together leading AI researchers and other thinkers
-
to discuss how to grow this wisdom we need to keep AI beneficial.
-
Our last conference was in Asilomar, California last year
-
and produced this list of 23 principles
-
which have since been signed by over 1,000 AI researchers
-
and key industry leaders,
-
and I want to tell you about three of these principles.
-
One is that we should avoid an arms race and lethal autonomous weapons.
-
The idea here is that any science can be used for new ways of helping people
-
or new ways of harming people.
-
For example, biology and chemistry are much more likely to be used
-
for new medicines or new cures than for new ways of killing people,
-
because biologists and chemists pushed hard --
-
and successfully --
-
for bans on biological and chemical weapons.
-
And in the same spirit,
-
most AI researchers want to stigmatize and ban lethal autonomous weapons.
-
Another Asilomar AI principle
-
is that we should mitigate AI-fueled income inequality.
-
I think that if we can grow the economic pie dramatically with AI
-
and we still can't figure out how to divide this pie
-
so that everyone is better off,
-
then shame on us.
-
(Applause)
-
Alright, now raise your hand if your computer has ever crashed.
-
(Laughter)
-
Wow, that's a lot of hands.
-
Well, then you'll appreciate this principle
-
that we should invest much more in AI safety research,
-
because as we put AI in charge of even more decisions and infrastructure,
-
we need to figure out how to transform today's buggy and hackable computers
-
into robust AI systems that we can really trust,
-
because otherwise,
-
all this awesome new technology can malfunction and harm us,
-
or get hacked and be turned against us.
-
And this AI safety work has to include work on AI value alignment,
-
because the real threat from AGI isn't malice,
-
like in silly Hollywood movies,
-
but competence --
-
AGI accomplishing goals that just aren't aligned with ours.
-
For example, when we humans drove the West African black rhino extinct,
-
we didn't do it because we were a bunch of evil rhinoceros haters, did we?
-
We did it because we were smarter than them
-
and our goals weren't aligned with theirs.
-
But AGI is by definition smarter than us,
-
so to make sure that we don't put ourselves in the position of those rhinos
-
if we create AGI,
-
we need to figure out how to make machines understand our goals,
-
adopt our goals and retain our goals.
-
And whose goals should these be, anyway?
-
Which goals should they be?
-
This brings us to the third part of our rocket metaphor: the destination.
-
We're making AI more powerful,
-
trying to figure out how to steer it,
-
but where do we want to go with it?
-
This is the elephant in the room that almost nobody talks about --
-
not even here at TED --
-
because we're so fixated on short-term AI challenges.
-
Look, our species is trying to build AGI,
-
motivated by curiosity and economics,
-
but what sort of future society are we hoping for if we succeed?
-
We did an opinion poll on this recently,
-
and I was struck to see
-
that most people actually want us to build superintelligence:
-
AI that's vastly smarter than us in all ways.
-
What there was the greatest agreement on was that we should be ambitious
-
and help life spread into the cosmos,
-
but there was much less agreement about who or what should be in charge.
-
And I was actually quite amused
-
to see that there's some some people who want it to be just machines.
-
(Laughter)