16:32
TED2015

Nick Bostrom: What happens when our computers get smarter than we are?

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Artificial intelligence is getting smarter by leaps and bounds -- within this century, research suggests, a computer AI could be as "smart" as a human being. And then, says Nick Bostrom, it will overtake us: "Machine intelligence is the last invention that humanity will ever need to make." A philosopher and technologist, Bostrom asks us to think hard about the world we're building right now, driven by thinking machines. Will our smart machines help to preserve humanity and our values -- or will they have values of their own?

- Philosopher
Nick Bostrom asks big questions: What should we do, as individuals and as a species, to optimize our long-term prospects? Will humanity’s technological advancements ultimately destroy us? Full bio

I work with a bunch of mathematicians,
philosophers and computer scientists,
00:12
and we sit around and think about
the future of machine intelligence,
00:16
among other things.
00:21
Some people think that some of these
things are sort of science fiction-y,
00:24
far out there, crazy.
00:28
But I like to say,
00:31
okay, let's look at the modern
human condition.
00:33
(Laughter)
00:36
This is the normal way for things to be.
00:38
But if we think about it,
00:41
we are actually recently arrived
guests on this planet,
00:43
the human species.
00:46
Think about if Earth
was created one year ago,
00:48
the human species, then,
would be 10 minutes old.
00:53
The industrial era started
two seconds ago.
00:56
Another way to look at this is to think of
world GDP over the last 10,000 years,
01:01
I've actually taken the trouble
to plot this for you in a graph.
01:06
It looks like this.
01:09
(Laughter)
01:11
It's a curious shape
for a normal condition.
01:12
I sure wouldn't want to sit on it.
01:14
(Laughter)
01:16
Let's ask ourselves, what is the cause
of this current anomaly?
01:19
Some people would say it's technology.
01:23
Now it's true, technology has accumulated
through human history,
01:26
and right now, technology
advances extremely rapidly --
01:31
that is the proximate cause,
01:35
that's why we are currently
so very productive.
01:37
But I like to think back further
to the ultimate cause.
01:40
Look at these two highly
distinguished gentlemen:
01:45
We have Kanzi --
01:48
he's mastered 200 lexical
tokens, an incredible feat.
01:50
And Ed Witten unleashed the second
superstring revolution.
01:55
If we look under the hood,
this is what we find:
01:58
basically the same thing.
02:01
One is a little larger,
02:02
it maybe also has a few tricks
in the exact way it's wired.
02:04
These invisible differences cannot
be too complicated, however,
02:07
because there have only
been 250,000 generations
02:11
since our last common ancestor.
02:15
We know that complicated mechanisms
take a long time to evolve.
02:17
So a bunch of relatively minor changes
02:22
take us from Kanzi to Witten,
02:24
from broken-off tree branches
to intercontinental ballistic missiles.
02:27
So this then seems pretty obvious
that everything we've achieved,
02:32
and everything we care about,
02:36
depends crucially on some relatively minor
changes that made the human mind.
02:38
And the corollary, of course,
is that any further changes
02:44
that could significantly change
the substrate of thinking
02:48
could have potentially
enormous consequences.
02:51
Some of my colleagues
think we're on the verge
02:56
of something that could cause
a profound change in that substrate,
02:59
and that is machine superintelligence.
03:03
Artificial intelligence used to be
about putting commands in a box.
03:06
You would have human programmers
03:11
that would painstakingly
handcraft knowledge items.
03:12
You build up these expert systems,
03:15
and they were kind of useful
for some purposes,
03:17
but they were very brittle,
you couldn't scale them.
03:20
Basically, you got out only
what you put in.
03:22
But since then,
03:26
a paradigm shift has taken place
in the field of artificial intelligence.
03:27
Today, the action is really
around machine learning.
03:30
So rather than handcrafting knowledge
representations and features,
03:34
we create algorithms that learn,
often from raw perceptual data.
03:40
Basically the same thing
that the human infant does.
03:46
The result is A.I. that is not
limited to one domain --
03:51
the same system can learn to translate
between any pairs of languages,
03:55
or learn to play any computer game
on the Atari console.
03:59
Now of course,
04:05
A.I. is still nowhere near having
the same powerful, cross-domain
04:07
ability to learn and plan
as a human being has.
04:11
The cortex still has some
algorithmic tricks
04:14
that we don't yet know
how to match in machines.
04:16
So the question is,
04:19
how far are we from being able
to match those tricks?
04:21
A couple of years ago,
04:26
we did a survey of some of the world's
leading A.I. experts,
04:27
to see what they think,
and one of the questions we asked was,
04:30
"By which year do you think
there is a 50 percent probability
04:33
that we will have achieved
human-level machine intelligence?"
04:36
We defined human-level here
as the ability to perform
04:40
almost any job at least as well
as an adult human,
04:44
so real human-level, not just
within some limited domain.
04:47
And the median answer was 2040 or 2050,
04:51
depending on precisely which
group of experts we asked.
04:55
Now, it could happen much,
much later, or sooner,
04:58
the truth is nobody really knows.
05:02
What we do know is that the ultimate
limit to information processing
05:05
in a machine substrate lies far outside
the limits in biological tissue.
05:09
This comes down to physics.
05:15
A biological neuron fires, maybe,
at 200 hertz, 200 times a second.
05:17
But even a present-day transistor
operates at the Gigahertz.
05:22
Neurons propagate slowly in axons,
100 meters per second, tops.
05:25
But in computers, signals can travel
at the speed of light.
05:31
There are also size limitations,
05:35
like a human brain has
to fit inside a cranium,
05:36
but a computer can be the size
of a warehouse or larger.
05:39
So the potential for superintelligence
lies dormant in matter,
05:44
much like the power of the atom
lay dormant throughout human history,
05:50
patiently waiting there until 1945.
05:56
In this century,
06:00
scientists may learn to awaken
the power of artificial intelligence.
06:01
And I think we might then see
an intelligence explosion.
06:05
Now most people, when they think
about what is smart and what is dumb,
06:10
I think have in mind a picture
roughly like this.
06:14
So at one end we have the village idiot,
06:17
and then far over at the other side
06:19
we have Ed Witten, or Albert Einstein,
or whoever your favorite guru is.
06:22
But I think that from the point of view
of artificial intelligence,
06:27
the true picture is actually
probably more like this:
06:31
AI starts out at this point here,
at zero intelligence,
06:35
and then, after many, many
years of really hard work,
06:38
maybe eventually we get to
mouse-level artificial intelligence,
06:41
something that can navigate
cluttered environments
06:45
as well as a mouse can.
06:47
And then, after many, many more years
of really hard work, lots of investment,
06:49
maybe eventually we get to
chimpanzee-level artificial intelligence.
06:54
And then, after even more years
of really, really hard work,
06:58
we get to village idiot
artificial intelligence.
07:02
And a few moments later,
we are beyond Ed Witten.
07:04
The train doesn't stop
at Humanville Station.
07:08
It's likely, rather, to swoosh right by.
07:11
Now this has profound implications,
07:14
particularly when it comes
to questions of power.
07:16
For example, chimpanzees are strong --
07:20
pound for pound, a chimpanzee is about
twice as strong as a fit human male.
07:21
And yet, the fate of Kanzi
and his pals depends a lot more
07:27
on what we humans do than on
what the chimpanzees do themselves.
07:31
Once there is superintelligence,
07:37
the fate of humanity may depend
on what the superintelligence does.
07:39
Think about it:
07:44
Machine intelligence is the last invention
that humanity will ever need to make.
07:45
Machines will then be better
at inventing than we are,
07:50
and they'll be doing so
on digital timescales.
07:53
What this means is basically
a telescoping of the future.
07:56
Think of all the crazy technologies
that you could have imagined
08:00
maybe humans could have developed
in the fullness of time:
08:04
cures for aging, space colonization,
08:07
self-replicating nanobots or uploading
of minds into computers,
08:10
all kinds of science fiction-y stuff
08:14
that's nevertheless consistent
with the laws of physics.
08:16
All of this superintelligence could
develop, and possibly quite rapidly.
08:19
Now, a superintelligence with such
technological maturity
08:24
would be extremely powerful,
08:28
and at least in some scenarios,
it would be able to get what it wants.
08:30
We would then have a future that would
be shaped by the preferences of this A.I.
08:34
Now a good question is,
what are those preferences?
08:41
Here it gets trickier.
08:46
To make any headway with this,
08:48
we must first of all
avoid anthropomorphizing.
08:49
And this is ironic because
every newspaper article
08:53
about the future of A.I.
has a picture of this:
08:57
So I think what we need to do is
to conceive of the issue more abstractly,
09:02
not in terms of vivid Hollywood scenarios.
09:06
We need to think of intelligence
as an optimization process,
09:09
a process that steers the future
into a particular set of configurations.
09:12
A superintelligence is
a really strong optimization process.
09:18
It's extremely good at using
available means to achieve a state
09:21
in which its goal is realized.
09:26
This means that there is no necessary
conenction between
09:28
being highly intelligent in this sense,
09:31
and having an objective that we humans
would find worthwhile or meaningful.
09:33
Suppose we give an A.I. the goal
to make humans smile.
09:39
When the A.I. is weak, it performs useful
or amusing actions
09:43
that cause its user to smile.
09:46
When the A.I. becomes superintelligent,
09:48
it realizes that there is a more
effective way to achieve this goal:
09:51
take control of the world
09:54
and stick electrodes into the facial
muscles of humans
09:56
to cause constant, beaming grins.
09:59
Another example,
10:02
suppose we give A.I. the goal to solve
a difficult mathematical problem.
10:03
When the A.I. becomes superintelligent,
10:06
it realizes that the most effective way
to get the solution to this problem
10:08
is by transforming the planet
into a giant computer,
10:13
so as to increase its thinking capacity.
10:16
And notice that this gives the A.I.s
an instrumental reason
10:18
to do things to us that we
might not approve of.
10:21
Human beings in this model are threats,
10:23
we could prevent the mathematical
problem from being solved.
10:25
Of course, perceivably things won't
go wrong in these particular ways;
10:29
these are cartoon examples.
10:32
But the general point here is important:
10:34
if you create a really powerful
optimization process
10:36
to maximize for objective x,
10:39
you better make sure
that your definition of x
10:41
incorporates everything you care about.
10:43
This is a lesson that's also taught
in many a myth.
10:46
King Midas wishes that everything
he touches be turned into gold.
10:51
He touches his daughter,
she turns into gold.
10:56
He touches his food, it turns into gold.
10:59
This could become practically relevant,
11:01
not just as a metaphor for greed,
11:04
but as an illustration of what happens
11:06
if you create a powerful
optimization process
11:08
and give it misconceived
or poorly specified goals.
11:11
Now you might say, if a computer starts
sticking electrodes into people's faces,
11:16
we'd just shut it off.
11:21
A, this is not necessarily so easy to do
if we've grown dependent on the system --
11:24
like, where is the off switch
to the Internet?
11:29
B, why haven't the chimpanzees
flicked the off switch to humanity,
11:32
or the Neanderthals?
11:37
They certainly had reasons.
11:39
We have an off switch,
for example, right here.
11:41
(Choking)
11:44
The reason is that we are
an intelligent adversary;
11:46
we can anticipate threats
and plan around them.
11:49
But so could a superintelligent agent,
11:51
and it would be much better
at that than we are.
11:54
The point is, we should not be confident
that we have this under control here.
11:57
And we could try to make our job
a little bit easier by, say,
12:04
putting the A.I. in a box,
12:08
like a secure software environment,
12:09
a virtual reality simulation
from which it cannot escape.
12:11
But how confident can we be that
the A.I. couldn't find a bug.
12:14
Given that merely human hackers
find bugs all the time,
12:18
I'd say, probably not very confident.
12:22
So we disconnect the ethernet cable
to create an air gap,
12:26
but again, like merely human hackers
12:30
routinely transgress air gaps
using social engineering.
12:33
Right now, as I speak,
12:36
I'm sure there is some employee
out there somewhere
12:38
who has been talked into handing out
her account details
12:40
by somebody claiming to be
from the I.T. department.
12:43
More creative scenarios are also possible,
12:46
like if you're the A.I.,
12:48
you can imagine wiggling electrodes
around in your internal circuitry
12:50
to create radio waves that you
can use to communicate.
12:53
Or maybe you could pretend to malfunction,
12:57
and then when the programmers open
you up to see what went wrong with you,
12:59
they look at the source code -- Bam! --
13:02
the manipulation can take place.
13:04
Or it could output the blueprint
to a really nifty technology,
13:07
and when we implement it,
13:10
it has some surreptitious side effect
that the A.I. had planned.
13:12
The point here is that we should
not be confident in our ability
13:16
to keep a superintelligent genie
locked up in its bottle forever.
13:20
Sooner or later, it will out.
13:23
I believe that the answer here
is to figure out
13:27
how to create superintelligent A.I.
such that even if -- when -- it escapes,
13:30
it is still safe because it is
fundamentally on our side
13:35
because it shares our values.
13:38
I see no way around
this difficult problem.
13:40
Now, I'm actually fairly optimistic
that this problem can be solved.
13:44
We wouldn't have to write down
a long list of everything we care about,
13:48
or worse yet, spell it out
in some computer language
13:52
like C++ or Python,
13:55
that would be a task beyond hopeless.
13:57
Instead, we would create an A.I.
that uses its intelligence
14:00
to learn what we value,
14:04
and its motivation system is constructed
in such a way that it is motivated
14:07
to pursue our values or to perform actions
that it predicts we would approve of.
14:12
We would thus leverage
its intelligence as much as possible
14:17
to solve the problem of value-loading.
14:21
This can happen,
14:24
and the outcome could be
very good for humanity.
14:26
But it doesn't happen automatically.
14:29
The initial conditions
for the intelligence explosion
14:33
might need to be set up
in just the right way
14:36
if we are to have a controlled detonation.
14:39
The values that the A.I. has
need to match ours,
14:43
not just in the familiar context,
14:45
like where we can easily check
how the A.I. behaves,
14:47
but also in all novel contexts
that the A.I. might encounter
14:49
in the indefinite future.
14:53
And there are also some esoteric issues
that would need to be solved, sorted out:
14:54
the exact details of its decision theory,
14:59
how to deal with logical
uncertainty and so forth.
15:01
So the technical problems that need
to be solved to make this work
15:05
look quite difficult --
15:08
not as difficult as making
a superintelligent A.I.,
15:09
but fairly difficult.
15:12
Here is the worry:
15:15
Making superintelligent A.I.
is a really hard challenge.
15:17
Making superintelligent A.I. that is safe
15:22
involves some additional
challenge on top of that.
15:24
The risk is that if somebody figures out
how to crack the first challenge
15:28
without also having cracked
the additional challenge
15:31
of ensuring perfect safety.
15:34
So I think that we should
work out a solution
15:37
to the control problem in advance,
15:40
so that we have it available
by the time it is needed.
15:43
Now it might be that we cannot solve
the entire control problem in advance
15:46
because maybe some elements
can only be put in place
15:50
once you know the details of the
architecture where it will be implemented.
15:53
But the more of the control problem
that we solve in advance,
15:57
the better the odds that the transition
to the machine intelligence era
16:00
will go well.
16:04
This to me looks like a thing
that is well worth doing
16:06
and I can imagine that if
things turn out okay,
16:10
that people a million years from now
look back at this century
16:14
and it might well be that they say that
the one thing we did that really mattered
16:18
was to get this thing right.
16:22
Thank you.
16:24
(Applause)
16:26

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About the Speaker:

Nick Bostrom - Philosopher
Nick Bostrom asks big questions: What should we do, as individuals and as a species, to optimize our long-term prospects? Will humanity’s technological advancements ultimately destroy us?

Why you should listen

Philosopher Nick Bostrom envisioned a future full of human enhancement, nanotechnology and machine intelligence long before they became mainstream concerns. From his famous simulation argument -- which identified some striking implications of rejecting the Matrix-like idea that humans are living in a computer simulation -- to his work on existential risk, Bostrom approaches both the inevitable and the speculative using the tools of philosophy, probability theory, and scientific analysis.

Since 2005, Bostrom has led the Future of Humanity Institute, a research group of mathematicians, philosophers and scientists at Oxford University tasked with investigating the big picture for the human condition and its future. He has been referred to as one of the most important thinkers of our age.

Nick was honored as one of Foreign Policy's 2015 Global Thinkers .

His recent book Superintelligence advances the ominous idea that “the first ultraintelligent machine is the last invention that man need ever make.”

More profile about the speaker
Nick Bostrom | Speaker | TED.com