All language subtitles for through.the.wormhole.s04e07.are.robots.the.future.of.human.evolution

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.