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  • 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)