Original subtitles

Freeman: We are in the midst of a revolution so insidious,

we can't even see it.

Robots live and work beside us.

And now we're designing them to think for themselves.

We're giving them the power to learn to move on their own.

Will these new life-forms evolve

to be smarter and more capable than us?

Or will we choose to merge with the machines?

Are robots the future of human evolution?

Through The Wormhole

Space, time, life itself.

The secrets of the cosmos lie through the wormhole.

-- Captions by vitac -- www.Vitac.Com

Captions Paid For By Discovery Communications

Sync & Corrected: iscol.

We humans like to think of ourselves

as the pinnacle of evolution.

We are the smartest, most adaptable form of life on earth.

We have reshaped the world to suit our needs.

But just as homo sapiens replaced homo erectus,

it's inevitable something will replace us.

What if we're building our own successors?

Just as we learn to move, think, and feel for ourselves,

we're now giving robots those same powers.

Where will this lead?

Is this what humanity will become?

When I was a teenager, I built a bicycle from spare parts.

My bicycle was so well-balanced,

i could jog alongside it without holding onto it.

Nobody was that impressed, but it made me think.

Would we one day have machines

that truly could move on their own?

Would they even need us anymore?

Daniel wolpert of the university of Cambridge

believes that if robots

are to be the future of human evolution,

they're going to have to learn to move as well as we do...

Because movement is the supreme achievement

of our powerful intellect.

The most fundamental question I think we can ever ask is,

why and when have animals ever evolved a brain?

Now, when I ask my students this question,

they'll tell me,

we have ones to think or to perceive the world,

and that's completely wrong.

We have a brain for one reason and one reason only,

and that's to produce adaptable and complex movement,

because movement is the only way we have

of affecting the world around us.

Freeman: All of our brains' intellectual capacity

grew from one primal motivation --

to learn how to move better.

It was our ability to walk on two legs,

to speak and emote with complex facial movements,

and to manipulate our dexterous limbs

that put humans on top of the food chain.

There can be no value

to perception or emotions or thinking

without the ability to act.

All those other features,

like memory, cognition, love, fear play into movement,

which is the final output of the brain.

Freeman: No machine could handle

the huge variety of complex movements we perform every day.

Just imagine a robot

trying to play one of england's most famous pastimes.

So, although that shot looks simple

and it felt effortless to me,

the complexity of what's going on in my brain

is really quite remarkable.

I have to follow the ball as the bowler bowls it

and predict where it's going to bounce

and how it's gonna rise from the ground.

I then have to make a decision

as to what type of shot I'm going to make.

And finally, I have to contract my 600 muscles

in a particular sequence to execute the shot.

Now, each of those components

has real mathematical complexity,

which is currently beyond the ability of any robotic device.

Freeman: One of the greatest challenges

in getting robots to move like we do

is teaching them to deal with uncertainty --

something our brains do intrinsically.

A ball will never come at you the same way twice.

You must instantly adjust your swing each time.

The question is...

How does the human brain deal with all this uncertainty?

Daniel thinks it uses a theory of probability estimation

called bayesian inference

to figure it out.

Wolpert: So, a critical thing the batsman now has to do

is decide where this ball is going to bounce,

so as they can prepare the correct shot,

and for that, they need bayesian inference.

What bayesian inference is all about

is deciding, optimally, the bounce location of the ball

from two different sources of information.

Freeman: One source of information is obvious.

You look at the ball.

Wolpert: So, you can use vision of the trajectory of the ball

as it comes in

to try and estimate where it's going to bounce.

But vision is not perfect,

in that we have variability in our visual processors,

so at least where distribution's shown in red here

as the probable bounce locations.

But bayes' rule says there's another source of information.

It's the prior knowledge about possible bounce locations.

If you're a good batter,

then you can effectively look at the bowler

and maybe know by his particular bowling style or small cues --

and that's shown by the blue shading --

which is a different area.

So, bayesian inference is a way of combining

this red distribution with the blue distribution,

and you do that by multiplying the numbers together in each

to generate this yellow distribution,

which is termed the belief.

And using that information,

the batsman can now prepare his shot.

Okay, I should probably get out of the way.

Freeman: The batsman's brain, like all of ours,

is doing this math automatically.

We are a species that's honed for movement prediction.

It's what has made us

the planet's best hunters and toolmakers.

We already have robots that are faster and more accurate

than we are.

But we have to program their every move.

For robots to walk down the evolutionary road

we've already traveled,

they're going to have to learn to move on their own.

What happens then?

Will they evolve complex brains like ours?

Robot builders Josh bongard of the university of Vermont

and hod lipson of Cornell university

are trying to answer that question.

Increasingly, we see that interaction with the world,

with the physical world, is important for intelligence.

You can't just build a brain in a jar.

Freeman: Hod and Josh's goal is to build a machine

that's smart enough to learn how to move around all by itself.

They've created a menagerie of strange robotic forms

along the way.

But their work starts with the computer program

designed to evolve robot bodies.

It simulates various body plans

and then tries various strategies to get them to move.

Okay, so, let's walk our way through --

no pun intended -- an actual evolutionary simulation.

So, in this case,

we've told the computer that we want a robot that has two legs,

but we want the computer to figure out

how to get the robot

to orchestrate the movement of the robot's legs.

And here, we see something a little bit surprising,

that evolution hasn't discovered the solution that we use.

Sometimes when we run this evolutionary process,

it produces something familiar like walking,

and in other cases, it produces something that's not familiar,

something we wouldn't have come up with on our own.

Freeman: It's survival of the fittest

or perhaps the least awkward.

Just as mother nature selects generations

based on their ability to survive,

so does the simulation.

The computer deletes the robots

that aren't doing a very good job,

and the computer then takes the robots

that are doing a slightly better job

and makes modified copies of them

and repeats this process over and over again.

And after a while, the computer starts to discover robots

that, in this case, are able to walk

from the left side of the screen to the right side of the screen.

Freeman: This is evolution on steroids.

What took mother nature millions of years

takes the computer just a few hours.

Overnight, the computer tests thousands of generations,

and eventually it produces a robot that meets the goal.

When the simulation makes something

that looks particularly interesting,

hod and Josh take that body plan and build it.

Now they can test whether

the strategies for moving learned in simulation

work as well in the real world.

So, this robot is called the quadratot,

and it's basically a robot that learns how to walk

using evolutionary robotic techniques.

And so, what we can see here

is a particular example of how this robot learns.

This is one of the earliest gaits that it did,

and we can see that it's not moving very far or very fast.

It's kind of like a child

during its very early behaviors of crawling.

It's trying out different things.

Some things work better. Some things work less well.

And it's taking the experiences and learning from them

and gradually improving its gait.

Freeman: There are many robots that can move well

while executing a specific predesigned task.

But hod and Josh's robots

must start to learn by themselves from scratch

in an unknown environment.

It can sense its own progress,

and like a baby learning to crawl,

it becomes more aware of its body

with every step and every tumble.

Hod and Josh believe this self-awareness

gradually builds into a basic form of consciousness.

Often, we've raised the question,

is something conscious, or is it not?

But it's really not a black-and-white thing.

It's more about

to what degree an entity's able to conceive of itself,

to simulate itself, to think about itself,

to be self-aware.

As robots learn to move in more complex ways,

it's possible they will develop levels of consciousness

equal to ours and maybe beyond.

But according to one scientist,

for a robot to become truly conscious...

...it must develop feelings.

What is consciousness?

The answer depends on who you talk to.

A doctor's definition would be different from a priest's.

But we all agree that our high-level consciousness

is what separates us from other organisms

and, of course, from robots.

What would it take for robots to become conscious?

Can they get there on logic alone?

Or must they also learn to feel?

Professor pentti haikonen from the university of Illinois

believes machines will only become conscious

when they can experience emotions.

It's a belief he has held since he was very young

when he first contemplated what it meant to be conscious.

When I was 4 or 5 years old, I was standing in our kitchen,

and suddenly I was struck by the mystery of existence...

...how and why I was me

and why I was not my sister or my brother.

How did I get inside myself?

Freeman: As he got older,

pentti realized that what made him feel conscious

of being inside his head

were his emotional reactions to the people and objects

in the world around him.

The neural processes that are behind our consciousness

take place inside our brain,

but we don't see things that way.

For instance, when you cut your finger,

the pain is in the finger or so it appears,

but, actually, the pain is in here.

To feel is to be conscious.

Freeman: Our brain's raw experience of the world around us

is just a series of electrical impulses

generated by our senses.

However, we translate these impulses into mental images

by making emotional associations with them.

A sound is pleasing.

A view is peaceful.

Consciousness, according to pentti,

is just a rich mosaic of emotionally laden mental images.

He believes that to have a truly conscious machine,

you must give it the power

to associate sensory data with emotions.

And in this robot, he has begun that process.

This is the first robot

that utilizes associative neural networks.

It is the same kind of learning that we humans use.

When we see and hear something,

we make a connection between those things,

and later on when we see or hear the other thing,

the other thing comes to our mind.

Freeman: The xcr-1 experiences the world

directly through its senses like we do.

On board are the basics of touch, sight, and sound.

Pentti has begun the process of giving it emotional associations

to specific sensory data,

like the color green.

Pentti places a green object in front of the robot,

which it recognizes.

Green.

Then he gives green a bad association --

a smack on the backside.

The associative learning is similar to little children.

Hurt.

And you say that this is not good or this is good,

or you may also smack the little child.

Hurt.

I don't recommend that.

Green bad.

Freeman: The robot's mental image of the green object

is now associated with the emotion bad.

And from now on, it will avoid the green bottle.

But it's not all pain for the xcr-1.

Just like we teach the robot

to associate pain with the green object,

we can teach the robot

to associate, also, pleasure with objects,

in this case with the blue object, like this.

Blue.

Freeman: To give blue a good association,

pentti gently strokes the top of the robot.

Blue good.

This simple experiment demonstrates

that this robot has mental images of objects

and mental content.

Freeman: It's still early in its development,

but the xcr-1 has learned the basics of emotional reaction

from fear...

Green bad.

Green bad.

...to desire.

Blue good.

♪ Now's my time for love ♪

♪ lonely moments seem to... ♪

As a more advanced version of the xcr-1

fills its memory with mental images...

Dentist bad.

...it will start to be able

to react to new situations on its own

and eventually experience the world

much like any emotionally-driven being.

It is my great dream

to build robot that is one day able to ask,

"How did I get inside myself?"

Freeman: Once robots reach this point,

what's to stop them from moving on

and becoming conscious of things we're not?

This man thinks robots will become the future of humanity

because they'll have something we lack.

Their brains will have the capacity for genius

long after the last human ever says "Eureka."

for Archimedes, Eureka happened in the bathtub.

Einstein was riding a streetcar when relativity dawned on him.

These brilliant minds had a flash of inspiration

and drove all of humanity forward.

But the scientific questions of today,

probing shoals of subatomic particles

and our vast genetic code,

have become so complex

that they take teams of thousands of researchers

to solve.

Is the age of the single scientific genius over?

Not if machines have their way.

Data scientist Michael schmidt

sees the world filled with intricate beauty --

the flowering of a rose,

the veins branching on a leaf,

the flight of a Bumblebee.

But below the surface of nature's wonders,

Michael also sees a treasure trove

of uncharted mathematical complexity.

Schmidt: Well, I love coming out here. Nature is beautiful.

There are equations hidden in every plant and every bee

and the ecosystems involved in this garden.

And part of science is figuring out

what causes those things to happen.

Freeman: Science is our effort to make sense of nature,

and this quest has given us some very famous discoveries.

In Newton's time,

he was able to figure out a very important rule in physics,

which is the law of gravity.

It predicts how this apple falls

and the forces that act upon this apple.

Today in science, we're interested in similar problems

but not just about how the apple falls

but the massive complexity that follows

from this very simple dynamic to the world around us.

For example, when I drop this apple,

the apple stirs up dust.

This dust could hit a flower,

and a bee may be less likely to pollinate that flower.

And the entire ecosystem in this garden

could change dramatically from that single event.

Freeman: Scientists understand the basic forces of nature,

but making precise predictions

about what will happen in the real world

with its staggering complexity

is overwhelming to the human mind.

So, one of the reasons why it's extremely difficult

for humans to understand and figure out

the equations and the laws of nature

is literally the number of variables that are at play.

There could be thousands of variables

that influence a system

that we're only just beginning to tease apart.

In fact, there are so many of these equations,

we'll never be able to finish analyzing them

if we do it by hand.

Freeman: In 2006,

Michael began developing intelligent computer software

that could observe complex natural systems

and derive meaning from what seems like chaos.

So, what I have here is a double pendulum.

If you look at it, it consists of two arms.

One arm swings along the top axis,

and the second arm is attached to the bottom of the first arm,

and it's two pendulums that are hooked together,

one pendulum at the end of the other.

Now, the pendulum is a great example of complexity

because it exhibits some of the most complex behavior

that we're aware of, which is called chaos.

So, when you collect data from this sort of device,

it looks almost completely random,

and there doesn't appear to be any sort of pattern.

But because this is a physical deterministic system,

a pattern does exist.

Freeman: Finding a pattern amidst the chaos of the double pendulum

has stumped scientists for decades.

But then Michael had a flash of inspiration.

Why not grow new ideas the same way nature created us,

using evolution?

He called his program Eureka.

Eureka starts with a primordial soup of random equations

and checks how closely they fit

the behavior of the double pendulum.

If they don't fit, the computer kills them.

If they do, the computer moves them into the next generation,

where they mutate and try to get an even closer fit.

Eventually, a winning equation emerges,

one that Archimedes would be proud of.

Eureka!

Schmidt: And I'm running our algorithm now.

On the left pane are the lists of the equations

that Eureka has thought up for this double pendulum.

Walking up, we can see we increase the complexity,

and we're also increasing the agreement with the data.

And eventually, as you go up,

you start to get an extremely close agreement with the data,

and eventually you snap on to a truth

where you get a large improvement in the accuracy.

And we can actually look in here and see exactly what pops out.

For example here, you might notice we have a 9.8,

and if you remember from physics courses,

that is the coefficient of gravity on earth.

What's very important is the difference

between the two angles of the double pendulum.

This pops out.

Essentially, we've used this software

and the data we've collected to model chaos,

and we've teased out the solution directly from the data.

Freeman: Eureka has not only discovered

a single equation to explain how a double pendulum moves.

It has found meaning in what looks like chaos --

something no human or machine has done before.

Schmidt: So, we could collect an entirely new data set,

run this process again,

and even though the data is completely different --

we could have different observations --

we can still identify the underlying truth,

the underlying pattern, which is this equation.

Freeman: To Michael, the future of scientific exploration

isn't inside our heads.

It's inside machines.

Whether they're looking at patterns of data

from genetics, particle physics, or meteorology,

programs like Eureka can evolve inspiration on demand,

finding basic truths about nature

that no human ever could.

We're gonna reach a point

where we decide what we want to discover

and we let the machines figure this out for us.

Eureka can find these relationships

without human bias and without human limitations.

We created robots to serve us.

As the machines learn their own ways to move, feel, and think,

they will eventually grow out of that role.

What if they start working together?

Could they build their own society,

one made by the robots for the robots?

There's no species on earth more successful than us.

We owe that success

to the powerful computer inside our heads.

But it takes more than one brain to conquer a planet.

Homo sapiens thrive because we have learned

to make those computers work together as a society.

What will happen when robots put their heads together?

Roboticist by day and gourmet chef by night,

Professor Dennis Hong of Virginia tech

is a specialist in building cooperative robots.

But he also sees cooperation outside the lab.

So, we don't really think about it,

but everything in our daily lives involves cooperation.

For example, cooking oftentimes is thought of as a solo act,

but if you think about it, a lot of people are involved

and a lot of careful coordination is required

to make it happen.

Oh, thank you, charli.

Take this tomato as an example.

This tomato most likely started its life as a seed,

where a group of breeders

need to choose the right sequence of genes

for a plump, juicy, tasty tomato.

The seeds needed to be planted, grown, harvested,

then the tomatoes needed to get to the market.

Freeman: Food production is a complex web of coordination.

But as good as it is,

human cooperation has its limits.

Hong: Oops.

Freeman: Every day, like most of us,

Dennis has to contend with the prime example

of human cooperation gone wrong --

traffic.

The problem is, us being human,

we all need to, want to get to our destination

as quick as possible, thus we have traffic jams.

Freeman: If it wasn't for traffic lights,

which are, in reality, very simple robots,

it will be almost impossible to get anywhere.

These traffic lights, they talk to each other.

They communicate with other traffic lights

at other intersections.

And they have cameras,

so they actually see the traffic patterns

and make decisions for us, for humans.

Oh, there you go.

Thank you, traffic light.

Freeman: Traffic is a nuisance.

But other failures of human cooperation

are much more serious...

And often deadly.

[ Machine-gun fire ]

Dennis believes a society of robots

can be much better collaborators than we are.

So, in collaboration with Daniel Lee

at the university of Pennsylvania,

he designed a group of robots to compete in the robocup,

an international robotic soccer championship.

Robocup is an autonomous robot soccer competition,

which means that you have a team of robots,

you press "Start," And then nobody touches anything.

And the robots need to look around, see where the ball is,

need to coordinate and actually play a game of soccer.

Freeman: Dennis' soccer robots, called Darwin-op,

are fully autonomous.

They use complex sensors and software

to navigate the playing field.

And they have a serious competitive edge

over their human counterparts.

Teammates can read each other's minds.

So, if you look at human soccer players,

obviously they're great at what they do.

They communicate sometimes by shouting,

sometimes by a subtle gesture,

but, again, it's not really accurate,

and they cannot share all the information together

at the same time in real time,

but robots can do that.

Freeman: Each robot knows the exact location and destination

of the other robots at all times.

They can adjust their strategy

and even their roles as necessary.

Hong: Depending on where the ball is, where the opponents are,

they didactically switch their roles.

So the goalie becomes a striker,

a striker becomes a goalie or defense.

Freeman: They may not be as agile as pelé or bend it like Beckham,

but they are able to dribble past their opponents,

pass the ball, score a goal...

And even celebrate.

Dennis believes robocup

is just the beginning of robot societies.

Dennis imagines a connected community of thinking machines

that would be far more sophisticated

than human communities.

He calls it cloud robotics.

Hong: Cloud robotics is a shared network of intelligence.

It's similar to what we call common sense in humans.

So, just like those smaller robots

that play soccer for robocup,

they share a common data, team data, to achieve the goal,

in this case, winning the soccer game.

For cloud robotics,

robots from the furthest corners in the world,

they can all connect to the cloud

and share information and intelligence to do their job.

Freeman: Humans spend a lifetime mastering knowledge,

but future robots could learn it all in microseconds.

They could create their own hyper-connected network

using the same spirit of cooperation

that built human society

without the selfishness and greed that hold us back.

Robots operate by a very well-defined set of rules.

The human impetus to break them is just not there.

Freeman: Robots already know how to talk to one another.

But now a scientist in Berlin

has taken robotic communication a step further.

His machines are speaking a language he doesn't understand.

Motakay tokima.

Did you understand what I just said?

Of course you didn't

because I wasn't speaking any known human language.

But it wasn't nonsense.

It was a robot language.

We humans took tens of thousands of years

to develop our complex means of communication.

Now robots are following our lead,

and they're doing it at light speed.

Someday soon,

robots may decide to exclude us from their conversation.

Robot: Tokima.

Lucabo.

Miyoto.

Motakay.

Tokima, kymamu.

Tokima.

Simeta.

Tokima.

Motakay.

Steels: Without language,

our species would never be where it is today.

It's the most magnificent thing

that has ever been created by humanity.

If you look at ourselves,

then it's pretty clear that without language,

we would not be able

to do the kinds of things that we're doing.

Freeman: Luc steels, a Professor of artificial intelligence,

sees language as the key

to developing true robot intelligence.

Steels: What I'm trying to understand is,

how can we synthesize this process

so that we can start up a kind of evolution in a robot

or in a population of robots

that will also lead to the growth

of a rich communication system like we have.

Freeman: Machines already communicate with each other,

but these are based

on predetermined, human-coded languages.

Luc wants to know

how future robot societies might communicate

given the chance to make a language on their own.

Luc gives his robots the basic ingredients of language,

like potential sounds to use,

and possible ways to join them together.

But what the robots say is up to them.

We put in learning mechanisms,

we put in invention mechanisms,

mechanisms so that they can coordinate their language.

They can kind of negotiate how they're gonna speak,

but we don't put in our language or our concepts.

Freeman: It's not enough for the robots to know how to speak.

They need to have something to speak about.

Luc's next step

is to teach the robots how to recognize their own bodies.

Steels: In order to learn language,

you actually have to learn about your own body

and the movements of your own body.

So, what you see here is an internal model

that the robot is building of itself.

This robot is trying to learn here,

is the relationship

between all these different sensory channels

and its own motor commands.

Freeman: As a robot watches itself move in the mirror,

it forms a 3-d model of its limbs and joints.

It stores this information in sense memory

and is now ready to talk to another robot about movement.

Tokima.

So, now, this robot is talking.

He's asking an action.

This robot is doing -- you know, stretching the arm.

No, this is not what was requested,

and he's showing again what the right action is.

Freeman: She's unsuccessful in her first attempt,

but eventually the robot learns

that "Tokima" Means "Raise two arms."

after repeating this process with different words,

they try again.

Homakey.

Another request.

He's doing the action.

Yes, this is the right kind of action.

So, in other words,

this robot has learned the word from the other one

and vice versa.

They now have a way to talk about actions.

Freeman: The robots' language is already so well-developed,

they can teach it to Luc.

Let's see what, you know, what he asks me to do.

Motakay.

Okay, motakay.

No, this is not right, so he's showing it to me.

Okay, I'm learning this gesture now.

Motakay.

Motakay.

Motakay is this.

Okay, I got it right.

So, now I'm going to use the gesture with him.

Motakay.

Okay, yes, you're doing the right thing.

Thank you.

As the robots repeat this process,

they generate words and actions

that have real meanings for one another.

And so the robots' vocabulary grows.

Every new word they create

is one more that we can't understand.

Is it only a matter of time

before they lock us out of the conversation completely?

Steels: And I think it's actually totally possible,

but society will kind of have to find the balance

between what it is that we want robots for

and how much autonomy are we willing to give them.

Freeman: If we're giving robots autonomy to move,

to feel, to make their own language,

could that be enough for them to surpass us?

After all, what's robot for "Exterminate"?

But one Japanese scientist

doesn't see the future as robots versus humans.

In fact, he is purposefully engineering their intersection.

We know that homo sapiens cannot be the end of evolution.

But will our descendents be biological or mechanical?

Some believe that intelligent machines

will eventually become the dominant creatures on earth.

But the next evolutionary step

may not be robot replacing human.

It could be a life-form that fuses man and machine.

This is yoshiyuki sankai.

Inspired by authors like Isaac Asimov,

he has always dreamed of fusing human and robotic life-forms

into something he calls the hybrid assistive limb system...

Or h.A.L.

Sankai: That one is one of my dreams.

We could develop such kind of devices,

like a robot suit h.A.L. System,

for supporting the humans and human's physical movements.

Freeman: And now, after 20 years of research,

he has succeeded.

H.A.L. Assists the human body by reading the brain's intentions

and providing assistive power to support the wearer's movement.

If she wish to or try to move as her brain generates intentions,

and the robot detects these intention signals

and wants to assist her movements.

Freeman: When the brain signals a muscle to move,

it transmits a pulse through the spinal cord

and into the area of movement.

This bioelectric signal

is detectable on the surface of the skin.

Yoshiyuki designed the h.A.L. Suit to pick up these impulses

and then activate the appropriate motors

in order to assist the body in its movement.

The human brain is directly controlling the robotic suit.

It's not just a technological breakthrough.

Yoshiyuki already has h.A.L. Suits at work

in rehabilitation clinics in Japan.

So, if some of the body has some problems, like a paralyzed...

So this would help patients, who are such kind,

and a handicapped person can use it.

Freeman: People who haven't walked in years

are now on the move again

thanks to these brain-powered robot legs.

Yoshiyuki has also developed a model for the torso and arm

that can provide

up to 200 kilograms of extra lifting power,

turning regular humans into strongmen.

But it's not all about strength.

He believes

the merging of robotic machinery and human biology

will allow us to preserve great achievements in movement.

Athletes like Tiger Woods or Roger federer

bring unique skill and artistry to their sports.

However, when they die, so does their movement.

But since the h.A.L. Suit

can detect and memorize the movements of its wearer,

that knowledge doesn't have to disappear.

If some of these athletes like Tiger Woods,

if they wear it and they swing it,

every data -- motion data and physiological data --

also gather in the computers.

Freeman: We once built great libraries to preserve knowledge

expressed through writing for future generations.

Yoshiyuki wants to create a great library of movement.

By merging our bodies with robotic exoskeletons,

we will not only be stronger.

We will all move as well

as the most talented athletes and artists.

The last century of popular culture

has focused on apocalyptic scenarios of robotic mutiny.

But the h.A.L. Suit opens up a different future.

We tend to think about robotics as an alternative life-form

that may someday replace or compete with humans.

But I think the reality of the matter is

that, increasingly, we'll see humans and robots cooperate

and actually become one kind of species

both physically and mentally.

Schmidt: Absolutely, I think robots are the future.

I think we need to rely on them.

Otherwise, we will stagnate and make no more progress.

Eventually, life on the earth will come to an end.

What is our legacy?

We will leave nothing unless we leave consciousness.

We need conscious robots everywhere.

That will be our legacy.

That will be the legacy of mankind.

Robots are rapidly becoming smarter, more agile,

and are developing human traits

like consciousness, emotions, and inspiration.

Will they leave us behind on the evolutionary highway,

or will humans join the machines in a new age?

Evolution is unpredictable...

And is bound to surprise us.