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  • I work with a bunch of mathematicians, philosophers and computer scientists,

  • and we sit around and think about the future of machine intelligence,

  • among other things.

  • Some people think that some of these things are sort of science fiction-y,

  • far out there, crazy.

  • But I like to say,

  • okay, let's look at the modern human condition.

  • (Laughter)

  • This is the normal way for things to be.

  • But if we think about it,

  • we are actually recently arrived guests on this planet,

  • the human species.

  • Think about if Earth was created one year ago,

  • the human species, then, would be 10 minutes old.

  • The industrial era started two seconds ago.

  • Another way to look at this is to think of world GDP over the last 10,000 years,

  • I've actually taken the trouble to plot this for you in a graph.

  • It looks like this.

  • (Laughter)

  • It's a curious shape for a normal condition.

  • I sure wouldn't want to sit on it.

  • (Laughter)

  • Let's ask ourselves, what is the cause of this current anomaly?

  • Some people would say it's technology.

  • Now it's true, technology has accumulated through human history,

  • and right now, technology advances extremely rapidly --

  • that is the proximate cause,

  • that's why we are currently so very productive.

  • But I like to think back further to the ultimate cause.

  • Look at these two highly distinguished gentlemen:

  • We have Kanzi --

  • he's mastered 200 lexical tokens, an incredible feat.

  • And Ed Witten unleashed the second superstring revolution.

  • If we look under the hood, this is what we find:

  • basically the same thing.

  • One is a little larger,

  • it maybe also has a few tricks in the exact way it's wired.

  • These invisible differences cannot be too complicated, however,

  • because there have only been 250,000 generations

  • since our last common ancestor.

  • We know that complicated mechanisms take a long time to evolve.

  • So a bunch of relatively minor changes

  • take us from Kanzi to Witten,

  • from broken-off tree branches to intercontinental ballistic missiles.

  • So this then seems pretty obvious that everything we've achieved,

  • and everything we care about,

  • depends crucially on some relatively minor changes that made the human mind.

  • And the corollary, of course, is that any further changes

  • that could significantly change the substrate of thinking

  • could have potentially enormous consequences.

  • Some of my colleagues think we're on the verge

  • of something that could cause a profound change in that substrate,

  • and that is machine superintelligence.

  • Artificial intelligence used to be about putting commands in a box.

  • You would have human programmers

  • that would painstakingly handcraft knowledge items.

  • You build up these expert systems,

  • and they were kind of useful for some purposes,

  • but they were very brittle, you couldn't scale them.

  • Basically, you got out only what you put in.

  • But since then,

  • a paradigm shift has taken place in the field of artificial intelligence.

  • Today, the action is really around machine learning.

  • So rather than handcrafting knowledge representations and features,

  • we create algorithms that learn, often from raw perceptual data.

  • Basically the same thing that the human infant does.

  • The result is A.I. that is not limited to one domain --

  • the same system can learn to translate between any pairs of languages,

  • or learn to play any computer game on the Atari console.

  • Now of course,

  • A.I. is still nowhere near having the same powerful, cross-domain

  • ability to learn and plan as a human being has.

  • The cortex still has some algorithmic tricks

  • that we don't yet know how to match in machines.

  • So the question is,

  • how far are we from being able to match those tricks?

  • A couple of years ago,

  • we did a survey of some of the world's leading A.I. experts,

  • to see what they think, and one of the questions we asked was,

  • "By which year do you think there is a 50 percent probability

  • that we will have achieved human-level machine intelligence?"

  • We defined human-level here as the ability to perform

  • almost any job at least as well as an adult human,

  • so real human-level, not just within some limited domain.

  • And the median answer was 2040 or 2050,

  • depending on precisely which group of experts we asked.

  • Now, it could happen much, much later, or sooner,

  • the truth is nobody really knows.

  • What we do know is that the ultimate limit to information processing

  • in a machine substrate lies far outside the limits in biological tissue.

  • This comes down to physics.

  • A biological neuron fires, maybe, at 200 hertz, 200 times a second.

  • But even a present-day transistor operates at the Gigahertz.

  • Neurons propagate slowly in axons, 100 meters per second, tops.

  • But in computers, signals can travel at the speed of light.

  • There are also size limitations,

  • like a human brain has to fit inside a cranium,

  • but a computer can be the size of a warehouse or larger.

  • So the potential for superintelligence lies dormant in matter,

  • much like the power of the atom lay dormant throughout human history,

  • patiently waiting there until 1945.

  • In this century,

  • scientists may learn to awaken the power of artificial intelligence.

  • And I think we might then see an intelligence explosion.

  • Now most people, when they think about what is smart and what is dumb,

  • I think have in mind a picture roughly like this.

  • So at one end we have the village idiot,

  • and then far over at the other side

  • we have Ed Witten, or Albert Einstein, or whoever your favorite guru is.

  • But I think that from the point of view of artificial intelligence,

  • the true picture is actually probably more like this:

  • AI starts out at this point here, at zero intelligence,

  • and then, after many, many years of really hard work,

  • maybe eventually we get to mouse-level artificial intelligence,

  • something that can navigate cluttered environments

  • as well as a mouse can.

  • And then, after many, many more years of really hard work, lots of investment,

  • maybe eventually we get to chimpanzee-level artificial intelligence.

  • And then, after even more years of really, really hard work,

  • we get to village idiot artificial intelligence.

  • And a few moments later, we are beyond Ed Witten.

  • The train doesn't stop at Humanville Station.

  • It's likely, rather, to swoosh right by.

  • Now this has profound implications,

  • particularly when it comes to questions of power.

  • For example, chimpanzees are strong --

  • pound for pound, a chimpanzee is about twice as strong as a fit human male.

  • And yet, the fate of Kanzi and his pals depends a lot more

  • on what we humans do than on what the chimpanzees do themselves.

  • Once there is superintelligence,

  • the fate of humanity may depend on what the superintelligence does.

  • Think about it:

  • Machine intelligence is the last invention that humanity will ever need to make.

  • Machines will then be better at inventing than we are,

  • and they'll be doing so on digital timescales.

  • What this means is basically a telescoping of the future.

  • Think of all the crazy technologies that you could have imagined

  • maybe humans could have developed in the fullness of time:

  • cures for aging, space colonization,

  • self-replicating nanobots or uploading of minds into computers,

  • all kinds of science fiction-y stuff

  • that's nevertheless consistent with the laws of physics.

  • All of this superintelligence could develop, and possibly quite rapidly.

  • Now, a superintelligence with such technological maturity

  • would be extremely powerful,

  • and at least in some scenarios, it would be able to get what it wants.

  • We would then have a future that would be shaped by the preferences of this A.I.

  • Now a good question is, what are those preferences?

  • Here it gets trickier.

  • To make any headway with this,

  • we must first of all avoid anthropomorphizing.

  • And this is ironic because every newspaper article

  • about the future of A.I. has a picture of this:

  • So I think what we need to do is to conceive of the issue more abstractly,

  • not in terms of vivid Hollywood scenarios.

  • We need to think of intelligence as an optimization process,

  • a process that steers the future into a particular set of configurations.

  • A superintelligence is a really strong optimization process.

  • It's extremely good at using available means to achieve a state

  • in which its goal is realized.

  • This means that there is no necessary conenction between

  • being highly intelligent in this sense,

  • and having an objective that we humans would find worthwhile or meaningful.

  • Suppose we give an A.I. the goal to make humans smile.

  • When the A.I. is weak, it performs useful or amusing actions

  • that cause its user to smile.

  • When the A.I. becomes superintelligent,

  • it realizes that there is a more effective way to achieve this goal:

  • take control of the world

  • and stick electrodes into the facial muscles of humans

  • to cause constant, beaming grins.

  • Another example,

  • suppose we give A.I. the goal to solve a difficult mathematical problem.

  • When the A.I. becomes superintelligent,

  • it realizes that the most effective way to get the solution to this problem

  • is by transforming the planet into a giant computer,

  • so as to increase its thinking capacity.

  • And notice that this gives the A.I.s an instrumental reason

  • to do things to us that we might not approve of.

  • Human beings in this model are threats,

  • we could prevent the mathematical problem from being solved.

  • Of course, perceivably things won't go wrong in these particular ways;

  • these are cartoon examples.

  • But the general point here is important:

  • if you create a really powerful optimization process

  • to maximize for objective x,

  • you better make sure that your definition of x

  • incorporates everything you care about.

  • This is a lesson that's also taught in many a myth.

  • King Midas wishes that everything he touches be turned into gold.

  • He touches his daughter, she turns into gold.

  • He touches his food, it turns into gold.

  • This could become practically relevant,

  • not just as a metaphor for greed,

  • but as an illustration of what happens

  • if you create a powerful optimization process

  • and give it misconceived or poorly specified goals.

  • Now you might say, if a computer starts sticking electrodes into people's faces,

  • we'd just shut it off.

  • A, this is not necessarily so easy to do if we've grown dependent on the system --

  • like, where is the off switch to the Internet?

  • B, why haven't the chimpanzees flicked the off switch to humanity,

  • or the Neanderthals?

  • They certainly had reasons.

  • We have an off switch, for example, right here.

  • (Choking)

  • The reason is that we are an intelligent adversary;

  • we can anticipate threats and plan around them.

  • But so could a superintelligent agent,

  • and it would be much better at that than we are.

  • The point is, we should not be confident that we have this under control here.

  • And we could try to make our job a little bit easier by, say,

  • putting the A.I. in a box,

  • like a secure software environment,

  • a virtual reality simulation from which it cannot escape.

  • But how confident can we be that the A.I. couldn't find a bug.

  • Given that merely human hackers find bugs all the time,

  • I'd say, probably not very confident.

  • So we disconnect the ethernet cable to create an air gap,

  • but again, like merely human hackers

  • routinely transgress air gaps using social engineering.

  • Right now, as I speak,

  • I'm sure there is some employee out there somewhere

  • who has been talked into handing out her account details

  • by somebody claiming to be from the I.T. department.

  • More creative scenarios are also possible,

  • like if you're the A.I.,

  • you can imagine wiggling electrodes around in your internal circuitry

  • to create radio waves that you can use to communicate.

  • Or maybe you could pretend to malfunction,

  • and then when the programmers open you up to see what went wrong with you,

  • they look at the source code -- Bam! --

  • the manipulation can take place.

  • Or it could output the blueprint to a really nifty technology,

  • and when we implement it,

  • it has some surreptitious side effect that the A.I. had planned.

  • The point here is that we should not be confident in our ability

  • to keep a superintelligent genie locked up in its bottle forever.

  • Sooner or later, it will out.

  • I believe that the answer here is to figure out

  • how to create superintelligent A.I. such that even if -- when -- it escapes,

  • it is still safe because it is fundamentally on our side

  • because it shares our values.

  • I see no way around this difficult problem.

  • Now, I'm actually fairly optimistic that this problem can be solved.

  • We wouldn't have to write down a long list of everything we care about,

  • or worse yet, spell it out in some computer language

  • like C++ or Python,

  • that would be a task beyond hopeless.

  • Instead, we would create an A.I. that uses its intelligence

  • to learn what we value,

  • and its motivation system is constructed in such a way that it is motivated

  • to pursue our values or to perform actions that it predicts we would have approved of.

  • We would thus leverage its intelligence as much as possible

  • to solve the problem of value-loading.

  • This can happen,

  • and the outcome could be very good for humanity.

  • But it doesn't happen automatically.

  • The initial conditions for the intelligence explosion

  • might need to be set up in just the right way

  • if we are to have a controlled detonation.

  • The values that the A.I. has need to match ours,

  • not just in the familiar context,

  • like where we can easily check how the A.I. behaves,

  • but also in all novel contexts that the A.I. might encounter

  • in the indefinite future.

  • And there are also some esoteric issues that would need to be solved, sorted out:

  • the exact details of its decision theory,

  • how to deal with logical uncertainty and so forth.

  • So the technical problems that need to be solved to make this work

  • look quite difficult --

  • not as difficult as making a superintelligent A.I.,

  • but fairly difficult.

  • Here is the worry:

  • Making superintelligent A.I. is a really hard challenge.

  • Making superintelligent A.I. that is safe

  • involves some additional challenge on top of that.

  • The risk is that if somebody figures out how to crack the first challenge

  • without also having cracked the additional challenge

  • of ensuring perfect safety.

  • So I think that we should work out a solution

  • to the control problem in advance,

  • so that we have it available by the time it is needed.

  • Now it might be that we cannot solve the entire control problem in advance

  • because maybe some elements can only be put in place

  • once you know the details of the architecture where it will be implemented.

  • But the more of the control problem that we solve in advance,

  • the better the odds that the transition to the machine intelligence era

  • will go well.

  • This to me looks like a thing that is well worth doing

  • and I can imagine that if things turn out okay,

  • that people a million years from now look back at this century

  • and it might well be that they say that the one thing we did that really mattered

  • was to get this thing right.

  • Thank you.

  • (Applause)

I work with a bunch of mathematicians, philosophers and computer scientists,

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