TEDSummit
Kevin Kelly: How AI can bring on a second Industrial Revolution
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"The actual path of a raindrop as it goes down the valley is unpredictable, but the general direction is inevitable," says digital visionary Kevin Kelly -- and technology is much the same, driven by patterns that are surprising but inevitable. Over the next 20 years, he says, our penchant for making things smarter and smarter will have a profound impact on nearly everything we do. Kelly explores three trends in AI we need to understand in order to embrace it and steer its development. "The most popular AI product 20 years from now that everyone uses has not been invented yet," Kelly says. "That means that you're not late."
Kevin Kelly - Digital visionary
There may be no one better to contemplate the meaning of cultural change than Kevin Kelly, whose life story reads like a treatise on the value and impacts of technology. Full bio
There may be no one better to contemplate the meaning of cultural change than Kevin Kelly, whose life story reads like a treatise on the value and impacts of technology. Full bio
Double-click the English transcript below to play the video.
00:14
I'm going to talk a little bit
about where technology's going.
about where technology's going.
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And often technology comes to us,
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we're surprised by what it brings.
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But there's actually
a large aspect of technology
a large aspect of technology
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that's much more predictable,
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and that's because technological systems
of all sorts have leanings,
of all sorts have leanings,
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they have urgencies,
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they have tendencies.
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And those tendencies are derived
from the very nature of the physics,
from the very nature of the physics,
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chemistry of wires
and switches and electrons,
and switches and electrons,
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and they will make reoccurring
patterns again and again.
patterns again and again.
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And so those patterns produce
these tendencies, these leanings.
these tendencies, these leanings.
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You can almost think of it
as sort of like gravity.
as sort of like gravity.
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Imagine raindrops falling into a valley.
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The actual path of a raindrop
as it goes down the valley
as it goes down the valley
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is unpredictable.
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We cannot see where it's going,
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but the general direction
is very inevitable:
is very inevitable:
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it's downward.
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And so these baked-in
tendencies and urgencies
tendencies and urgencies
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in technological systems
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give us a sense of where things
are going at the large form.
are going at the large form.
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So in a large sense,
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I would say that telephones
were inevitable,
were inevitable,
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but the iPhone was not.
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The Internet was inevitable,
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but Twitter was not.
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So we have many ongoing
tendencies right now,
tendencies right now,
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and I think one of the chief among them
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is this tendency to make things
smarter and smarter.
smarter and smarter.
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I call it cognifying -- cognification --
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also known as artificial
intelligence, or AI.
intelligence, or AI.
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And I think that's going to be one
of the most influential developments
of the most influential developments
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and trends and directions and drives
in our society in the next 20 years.
in our society in the next 20 years.
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So, of course, it's already here.
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We already have AI,
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and often it works in the background,
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in the back offices of hospitals,
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where it's used to diagnose X-rays
better than a human doctor.
better than a human doctor.
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It's in legal offices,
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where it's used to go
through legal evidence
through legal evidence
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better than a human paralawyer.
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It's used to fly the plane
that you came here with.
that you came here with.
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Human pilots only flew it
seven to eight minutes,
seven to eight minutes,
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the rest of the time the AI was driving.
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And of course, in Netflix and Amazon,
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it's in the background,
making those recommendations.
making those recommendations.
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That's what we have today.
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And we have an example, of course,
in a more front-facing aspect of it,
in a more front-facing aspect of it,
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with the win of the AlphaGo, who beat
the world's greatest Go champion.
the world's greatest Go champion.
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But it's more than that.
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If you play a video game,
you're playing against an AI.
you're playing against an AI.
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But recently, Google taught their AI
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to actually learn how to play video games.
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Again, teaching video games
was already done,
was already done,
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but learning how to play
a video game is another step.
a video game is another step.
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That's artificial smartness.
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What we're doing is taking
this artificial smartness
this artificial smartness
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and we're making it smarter and smarter.
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There are three aspects
to this general trend
to this general trend
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that I think are underappreciated;
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I think we would understand
AI a lot better
AI a lot better
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if we understood these three things.
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I think these things also would
help us embrace AI,
help us embrace AI,
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because it's only by embracing it
that we actually can steer it.
that we actually can steer it.
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We can actually steer the specifics
by embracing the larger trend.
by embracing the larger trend.
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So let me talk about
those three different aspects.
those three different aspects.
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The first one is: our own intelligence
has a very poor understanding
has a very poor understanding
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of what intelligence is.
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We tend to think of intelligence
as a single dimension,
as a single dimension,
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that it's kind of like a note
that gets louder and louder.
that gets louder and louder.
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It starts like with IQ measurement.
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It starts with maybe a simple
low IQ in a rat or mouse,
low IQ in a rat or mouse,
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and maybe there's more in a chimpanzee,
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and then maybe there's more
in a stupid person,
in a stupid person,
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and then maybe an average
person like myself,
person like myself,
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and then maybe a genius.
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And this single IQ intelligence
is getting greater and greater.
is getting greater and greater.
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That's completely wrong.
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That's not what intelligence is --
not what human intelligence is, anyway.
not what human intelligence is, anyway.
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It's much more like a symphony
of different notes,
of different notes,
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and each of these notes is played
on a different instrument of cognition.
on a different instrument of cognition.
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There are many types
of intelligences in our own minds.
of intelligences in our own minds.
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We have deductive reasoning,
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we have emotional intelligence,
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we have spatial intelligence;
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we have maybe 100 different types
that are all grouped together,
that are all grouped together,
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and they vary in different strengths
with different people.
with different people.
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And of course, if we go to animals,
they also have another basket --
they also have another basket --
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another symphony of different
kinds of intelligences,
kinds of intelligences,
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and sometimes those same instruments
are the same that we have.
are the same that we have.
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They can think in the same way,
but they may have a different arrangement,
but they may have a different arrangement,
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and maybe they're higher
in some cases than humans,
in some cases than humans,
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like long-term memory in a squirrel
is actually phenomenal,
is actually phenomenal,
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so it can remember
where it buried its nuts.
where it buried its nuts.
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But in other cases they may be lower.
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When we go to make machines,
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we're going to engineer
them in the same way,
them in the same way,
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where we'll make some of those types
of smartness much greater than ours,
of smartness much greater than ours,
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and many of them won't be
anywhere near ours,
anywhere near ours,
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because they're not needed.
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So we're going to take these things,
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these artificial clusters,
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and we'll be adding more varieties
of artificial cognition to our AIs.
of artificial cognition to our AIs.
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We're going to make them
very, very specific.
very, very specific.
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So your calculator is smarter
than you are in arithmetic already;
than you are in arithmetic already;
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your GPS is smarter
than you are in spatial navigation;
than you are in spatial navigation;
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Google, Bing, are smarter
than you are in long-term memory.
than you are in long-term memory.
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And we're going to take, again,
these kinds of different types of thinking
these kinds of different types of thinking
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and we'll put them into, like, a car.
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The reason why we want to put them
in a car so the car drives,
in a car so the car drives,
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is because it's not driving like a human.
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It's not thinking like us.
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That's the whole feature of it.
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It's not being distracted,
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it's not worrying about whether
it left the stove on,
it left the stove on,
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or whether it should have
majored in finance.
majored in finance.
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It's just driving.
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(Laughter)
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Just driving, OK?
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And we actually might even
come to advertise these
come to advertise these
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as "consciousness-free."
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They're without consciousness,
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they're not concerned about those things,
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they're not distracted.
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So in general, what we're trying to do
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is make as many different
types of thinking as we can.
types of thinking as we can.
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We're going to populate the space
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of all the different possible types,
or species, of thinking.
or species, of thinking.
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And there actually may be some problems
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that are so difficult
in business and science
in business and science
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that our own type of human thinking
may not be able to solve them alone.
may not be able to solve them alone.
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We may need a two-step program,
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which is to invent new kinds of thinking
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that we can work alongside of to solve
these really large problems,
these really large problems,
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say, like dark energy or quantum gravity.
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What we're doing
is making alien intelligences.
is making alien intelligences.
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You might even think of this
as, sort of, artificial aliens
as, sort of, artificial aliens
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in some senses.
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And they're going to help
us think different,
us think different,
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because thinking different
is the engine of creation
is the engine of creation
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and wealth and new economy.
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The second aspect of this
is that we are going to use AI
is that we are going to use AI
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to basically make a second
Industrial Revolution.
Industrial Revolution.
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The first Industrial Revolution
was based on the fact
was based on the fact
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that we invented something
I would call artificial power.
I would call artificial power.
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Previous to that,
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during the Agricultural Revolution,
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everything that was made
had to be made with human muscle
had to be made with human muscle
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or animal power.
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That was the only way
to get anything done.
to get anything done.
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The great innovation during
the Industrial Revolution was,
the Industrial Revolution was,
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we harnessed steam power, fossil fuels,
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to make this artificial power
that we could use
that we could use
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to do anything we wanted to do.
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So today when you drive down the highway,
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you are, with a flick of the switch,
commanding 250 horses --
commanding 250 horses --
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250 horsepower --
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which we can use to build skyscrapers,
to build cities, to build roads,
to build cities, to build roads,
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to make factories that would churn out
lines of chairs or refrigerators
lines of chairs or refrigerators
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way beyond our own power.
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And that artificial power can also
be distributed on wires on a grid
be distributed on wires on a grid
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to every home, factory, farmstead,
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and anybody could buy
that artificial power,
that artificial power,
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just by plugging something in.
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So this was a source
of innovation as well,
of innovation as well,
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because a farmer could take
a manual hand pump,
a manual hand pump,
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and they could add this artificial
power, this electricity,
power, this electricity,
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and he'd have an electric pump.
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And you multiply that by thousands
or tens of thousands of times,
or tens of thousands of times,
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and that formula was what brought us
the Industrial Revolution.
the Industrial Revolution.
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All the things that we see,
all this progress that we now enjoy,
all this progress that we now enjoy,
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has come from the fact
that we've done that.
that we've done that.
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We're going to do
the same thing now with AI.
the same thing now with AI.
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We're going to distribute that on a grid,
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and now you can take that electric pump.
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You can add some artificial intelligence,
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and now you have a smart pump.
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And that, multiplied by a million times,
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is going to be this second
Industrial Revolution.
Industrial Revolution.
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So now the car is going down the highway,
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it's 250 horsepower,
but in addition, it's 250 minds.
but in addition, it's 250 minds.
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That's the auto-driven car.
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It's like a new commodity;
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it's a new utility.
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The AI is going to flow
across the grid -- the cloud --
across the grid -- the cloud --
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in the same way electricity did.
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So everything that we had electrified,
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we're now going to cognify.
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And I owe it to Jeff, then,
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that the formula
for the next 10,000 start-ups
for the next 10,000 start-ups
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is very, very simple,
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which is to take x and add AI.
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That is the formula,
that's what we're going to be doing.
that's what we're going to be doing.
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And that is the way
in which we're going to make
in which we're going to make
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this second Industrial Revolution.
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And by the way -- right now, this minute,
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you can log on to Google
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and you can purchase
AI for six cents, 100 hits.
AI for six cents, 100 hits.
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That's available right now.
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So the third aspect of this
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is that when we take this AI
and embody it,
and embody it,
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we get robots.
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And robots are going to be bots,
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they're going to be doing many
of the tasks that we have already done.
of the tasks that we have already done.
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A job is just a bunch of tasks,
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so they're going to redefine our jobs
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because they're going to do
some of those tasks.
some of those tasks.
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But they're also going to curate
whole new categories,
whole new categories,
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a whole new slew of tasks
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that we didn't know
we wanted to do before.
we wanted to do before.
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They're going to actually
engender new kinds of jobs,
engender new kinds of jobs,
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new kinds of tasks that we want done,
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just as automation made up
a whole bunch of new things
a whole bunch of new things
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that we didn't know we needed before,
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and now we can't live without them.
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So they're going to produce
even more jobs than they take away,
even more jobs than they take away,
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but it's important that a lot of the tasks
that we're going to give them
that we're going to give them
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are tasks that can be defined
in terms of efficiency or productivity.
in terms of efficiency or productivity.
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If you can specify a task,
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either manual or conceptual,
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that can be specified in terms
of efficiency or productivity,
of efficiency or productivity,
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that goes to the bots.
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Productivity is for robots.
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What we're really good at
is basically wasting time.
is basically wasting time.
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(Laughter)
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We're really good at things
that are inefficient.
that are inefficient.
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Science is inherently inefficient.
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It runs on that fact that you have
one failure after another.
one failure after another.
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It runs on the fact that you make tests
and experiments that don't work,
and experiments that don't work,
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otherwise you're not learning.
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It runs on the fact
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that there is not
a lot of efficiency in it.
a lot of efficiency in it.
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Innovation by definition is inefficient,
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because you make prototypes,
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because you try stuff that fails,
that doesn't work.
that doesn't work.
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Exploration is inherently inefficiency.
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Art is not efficient.
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Human relationships are not efficient.
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These are all the kinds of things
we're going to gravitate to,
we're going to gravitate to,
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because they're not efficient.
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Efficiency is for robots.
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We're also going to learn
that we're going to work with these AIs
that we're going to work with these AIs
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because they think differently than us.
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When Deep Blue beat
the world's best chess champion,
the world's best chess champion,
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people thought it was the end of chess.
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But actually, it turns out that today,
the best chess champion in the world
the best chess champion in the world
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is not an AI.
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12:14
And it's not a human.
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It's the team of a human and an AI.
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The best medical diagnostician
is not a doctor, it's not an AI,
is not a doctor, it's not an AI,
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it's the team.
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We're going to be working with these AIs,
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and I think you'll be paid in the future
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by how well you work with these bots.
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So that's the third thing,
is that they're different,
is that they're different,
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they're utility
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and they are going to be something
we work with rather than against.
we work with rather than against.
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We're working with these
rather than against them.
rather than against them.
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So, the future:
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Where does that take us?
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I think that 25 years from now,
they'll look back
they'll look back
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and look at our understanding
of AI and say,
of AI and say,
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12:52
"You didn't have AI. In fact,
you didn't even have the Internet yet,
you didn't even have the Internet yet,
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12:56
compared to what we're going
to have 25 years from now."
to have 25 years from now."
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There are no AI experts right now.
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There's a lot of money going to it,
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there are billions of dollars
being spent on it;
being spent on it;
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it's a huge business,
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but there are no experts, compared
to what we'll know 20 years from now.
to what we'll know 20 years from now.
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So we are just at the beginning
of the beginning,
of the beginning,
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we're in the first hour of all this.
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We're in the first hour of the Internet.
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13:21
We're in the first hour of what's coming.
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13:23
The most popular AI product
in 20 years from now,
in 20 years from now,
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that everybody uses,
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has not been invented yet.
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That means that you're not late.
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Thank you.
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(Laughter)
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(Applause)
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2757
ABOUT THE SPEAKER
Kevin Kelly - Digital visionaryThere may be no one better to contemplate the meaning of cultural change than Kevin Kelly, whose life story reads like a treatise on the value and impacts of technology.
Why you should listen
Kelly has been publisher of the Whole Earth Review, executive editor at Wired magazine (which he co-founded, and where he now holds the title of Senior Maverick), founder of visionary nonprofits and writer on biology, business and “cool tools.” He’s renounced all material things save his bicycle (which he then rode 3,000 miles), founded an organization (the All-Species Foundation) to catalog all life on Earth, championed projects that look 10,000 years into the future (at the Long Now Foundation), and more. He’s admired for his acute perspectives on technology and its relevance to history, biology and society. His new book, The Inevitable, just published, explores 12 technological forces that will shape our future.
Kevin Kelly | Speaker | TED.com