- 8/3/2025
We are in the midst of a revolution so insidious we can't even see it. From our telephones to our vacuum cleaners to our cars, we have robots that live and work beside us. And now we're designing them to think for themselves, giving them the power to learn to move on their own. RoboCup is an international soccer championship.
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Thanks for watching. Follow for more videos.
#cosmosspacescience
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#season4
#episode7
#cosmology
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LearningTranscript
00:00We are enriched of a revolution so insidious, we can't even see it.
00:08Robots live and work beside us.
00:12And now we're designing them to think for themselves.
00:18Giving them the power to learn to move on their own.
00:24Will these new life forms evolve to be smarter and more capable than us?
00:29Or will we choose to merge with the machines?
00:33Are robots the future of human evolution?
00:42Space. Time. Life itself.
00:49The secrets of the cosmos lie through the wormhole.
00:59We humans like to think of ourselves as the pinnacle of evolution.
01:09We are the smartest, most adaptable form of life on Earth.
01:14We have reshaped the world to suit our needs.
01:18But just as Homo sapiens replaced Homo erectus, it's inevitable something will replace us.
01:27What if we're building our own successes?
01:30Just as we learn to move, think, and feel for ourselves, we're now giving robots those same powers.
01:37Where will this lead?
01:41Is this what humanity will become?
01:48When I was a teenager, I built a bicycle from spare parts.
01:52My bicycle was so well balanced I could jog alongside it without holding onto it.
01:59Nobody was that impressed, but it made me think, would we one day have machines that truly could move on their own?
02:12Would they even need us anymore?
02:14Daniel Warburton of the University of Cambridge believes that if robots are to be the future of human evolution,
02:30they're going to have to learn to move as well as we do.
02:35Because movement is the supreme achievement of our powerful intellect.
02:40The most fundamental question I think we can ever ask is why we and other animals ever evolved a brain.
02:46Now when I ask my students this question, they'll tell me we have ones to think or to perceive the world,
02:50and that's completely wrong.
02:52We have a brain for one reason and one reason only, and that's to produce adaptable and complex movement.
02:57Because movement is the only way we have of affecting the world around us.
03:02All of our brain's intellectual capacity grew from one primal motivation,
03:07to learn how to move better.
03:10It was our ability to walk on two legs,
03:13to speak and emote with complex facial movements,
03:16and to manipulate our dexterous limbs that put humans on top of the food chain.
03:23There can be no value to perception or emotions or thinking without the ability to act.
03:29All those other features such as memory, cognition, love, fear, play into movement,
03:34which is the final output of the brain.
03:39No machine could handle the huge variety of complex movements we perform every day.
03:47Just imagine a robot trying to play one of England's most famous pastimes.
03:52So although that shot looked simple and it felt effortless to me,
04:01the complexity of what's going on in my brain is really quite remarkable.
04:04I have to follow the ball as the bowler bowls it and predict where it's going to bounce,
04:07and how it's going to rise from the ground.
04:09I then have to make a decision as to what type of shot I'm going to make,
04:13and finally I have to contract my 600 muscles in a particular sequence to execute the shot.
04:17Now each of those components has real mathematical complexity,
04:21which is currently beyond the ability of any robotic device.
04:26One of the greatest challenges in getting robots to move like we do,
04:30is teaching them to deal with uncertainty,
04:33something our brains do intrinsically.
04:36A ball will never come at you the same way twice.
04:42You must instantly adjust your swing each time.
04:48The question is, how does the human brain deal with all this uncertainty?
04:54Daniel thinks it uses a theory of probability estimation called Bayesian inference to figure it out.
05:02So a critical thing the batsman now has to do is decide where this ball is going to bounce,
05:08so they can prepare the correct shot.
05:10And for that they need Bayesian inference.
05:11What Bayesian inference is all about is deciding optimally the bounce location of the ball from two different sources of information.
05:19One source of information is obvious.
05:22You look at the ball.
05:24So you can use vision of the trajectory of the ball as it comes in to try and estimate where it's going to bounce.
05:31But vision is not perfect in that we have variability in our visual processes,
05:35so it leads to a distribution shown in red here as to probable bounce locations.
05:41But Bayes' rule says there's another source of information.
05:45It's the prior knowledge about possible bounce locations.
05:48If you're a good batter, then you can effectively look at the bowler and maybe know by his particular bowling style or small cues,
05:55and that's shown by the blue shading, which is a different area.
05:59So if Bayesian inference is a way of combining this red distribution with the blue distribution,
06:04and you do that by multiplying the numbers together in each to generate this yellow distribution, which is termed the belief.
06:11And using that information, the batsman can now prepare a shot.
06:14Okay, I should probably get out of the way.
06:15The batsman's brain, like all of ours, is doing this math automatically.
06:25We are a species that's honed for movement prediction.
06:29It's what has made us the planet's best hunters and tool makers.
06:34We already have robots that are faster and more accurate than we are.
06:39But we have to program their every move.
06:44For robots to walk down the evolutionary road we've already traveled,
06:49they're going to have to learn to move on their own.
06:53What happens then?
06:55Will they evolve complex brains, like ours?
06:59Robot builders Josh Bongard of the University of Vermont and Hod Lipson of Cornell University are trying to answer that question.
07:14Increasingly we see that interaction with the world, with the physical world, is important for intelligence.
07:20You can't just build a brain in a jar.
07:22Han and Josh's goal is to build a machine that's smart enough to learn how to move around all by itself.
07:32They've created a menagerie of strange robotic forms along the way.
07:37But their work starts with a computer program designed to evolve robot bodies.
07:44It simulates various body plans and then tries various strategies to get them to move.
07:49Okay, so let's walk our way through, no pun intended, an actual evolutionary simulation.
07:58So in this case we've told the computer that we want a robot that has two legs,
08:04but we want the computer to figure out how to get the robot to orchestrate the movement of the robot's legs.
08:11And here we see something a little bit surprising, that evolution hasn't discovered the solution that we use.
08:16Sometimes when we run this evolutionary process, it produces something familiar, like walking.
08:23And in other cases it produces something that's not familiar, something we wouldn't have come up with on our own.
08:29It's survival of the fittest, or perhaps the least awkward.
08:35Just as Mother Nature selects generations based on their ability to survive, so does the simulation.
08:42The 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,
08:50and makes modified copies of them, and repeats this process over and over again.
08:56And 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.
09:05This is evolution on steroids.
09:12What took Mother Nature millions of years, takes the computer just a few hours.
09:19Overnight, the computer tests thousands of generations, and eventually, it produces a robot that meets the goal.
09:27When the simulation makes something that looks particularly interesting, Hod and Josh take that body plan and build it.
09:37Now they can test whether the strategies for moving learned in simulation work as well in the real world.
09:44So this robot is called the QuadraTot, and it's basically a robot that learns how to walk using evolutionary robotics techniques.
09:52And so, what we can see here is a particular example of how this robot learns.
09:59This is one of the earliest gaits that it did, and we can see that it's not moving very far, very fast.
10:05It's kind of like a child doing its very early behaviors of crawling.
10:11It's trying out different things. Some things work better. Some things work less well.
10:15And it's taking that experiences and learning from them and gradually improving its gait.
10:20There are many robots that can move well while executing a specific, pre-designed task.
10:28But Hod and Josh's robots must start to learn by themselves, from scratch, in an unknown environment.
10:36It 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.
10:45Hod and Josh believe this self-awareness gradually builds into a basic form of consciousness.
10:54Often, we phrase the question, is something conscious or is it not?
10:59But it's really not a black and white thing. It's more about to what degree an entity is able to conceive of itself, to simulate itself, to think about itself, to be self-aware.
11:09As robots learn to move in more complex ways, it's possible they will develop levels of consciousness equal to ours, and maybe beyond.
11:20But according to one scientist, for a robot to become truly conscious, it must develop feelings.
11:33What is consciousness?
11:36The answer depends on who you talk to.
11:40A doctor's definition would be different from a priest's.
11:44But we all agree that our high-level consciousness is what separates us from other organisms and, of course, from robots.
11:52What would it take for robots to become conscious?
11:57Can they get there on logic alone?
12:01Or must they also learn to feel?
12:08Professor Penty Heikkinen, from the University of Illinois, believes machines will only become conscious when they can experience emotions.
12:17It's a belief he has held since he was very young, when he first contemplated what it meant to be conscious.
12:26When I was four or five years old, I was standing in our kitchen and suddenly I was struck by the mystery of existence.
12:35How and why I was me, and why I was not my sister or my brother.
12:46How did I get inside myself?
12:51As he got older, Penty realized that what made him feel conscious of being inside his head,
12:58were his emotional reactions to the people and objects in the world around him.
13:05The neural processes that are behind our consciousness take place inside our brain.
13:12But we don't see things that way.
13:14For instance, when you cut your finger, the pain is in the finger.
13:19Or so it appears, but actually the pain is in here.
13:24To feel is to be conscious.
13:26Our brain's raw experience of the world around us is just a series of electrical impulses generated by our senses.
13:39However, we translate these impulses into mental images by making emotional associations with them.
13:46The sound is pleasing.
13:52A view is peaceful.
13:54Consciousness, according to Penty, is just a rich mosaic of emotionally-laden mental images.
14:01He believes that to have a truly conscious machine, you must give it the power to associate sensory data with emotions.
14:13And in this robot, he has begun that process.
14:17This is the first robot that utilizes associative neural networks.
14:23It is the same kind of learning that we humans use.
14:27When we see and hear something, we make a connection between those things.
14:32And later on, when we see or hear the other thing, the other thing comes to our mind.
14:37The XCR-1 experiences the world directly through its senses like we do.
14:45On board are the basics of touch, sight, and sound.
14:50Penty has begun the process of giving it emotional associations to specific sensory data.
14:58Like the color green.
15:01Penty places a green object in front of the robot, which it recognizes.
15:05Then he gives green a bad association.
15:10A smack on the backside.
15:12The associative learning is similar to little children.
15:18And you say that this is not good or this is good.
15:24Or you may also smack the little child.
15:29I don't recommend that.
15:31The robot's mental image of the green object is now associated with the emotion bad.
15:41And from now on, it will avoid the green bottle.
15:46But it's not all pain for the XCR-1.
15:49Just like we teach the robot to associate pain with the green object, we can teach the robot to associate also pleasure with objects.
16:00In this case, with the blue object like this.
16:03Blue.
16:05To give blue a good association, Penty gently strokes the top of the robot.
16:11Blue. Good.
16:12This simple experiment demonstrates that this robot has mental images of objects and mental content.
16:27It's still early in its development, but the XCR-1 has learned the basics of emotional reaction.
16:33From fear.
16:34Green. Bad.
16:35Green. Bad.
16:36Green. Bad.
16:37To desire.
16:38Blue. Good.
16:39Now's my time for love.
16:44Lonely moments since...
16:48As a more advanced version of the XCR-1 fills its memory with mental images.
17:03As a more advanced version of the XCR-1 fills its memory with metal images,
17:10it will start to be able to react to new situations on its own
17:14and eventually experience the world much like any emotionally driven being.
17:20It is my great dream to build a robot that is one day able to ask,
17:26how did I get inside my cell?
17:31Once robots reach this point, what's to stop them from moving on
17:36and becoming conscious of things we're not?
17:40This man thinks robots will become the future of humanity because they'll have something we lack.
17:48Their brains will have the capacity for genius long after the last human ever says, Eureka.
17:56For Archimedes, Eureka happened in a bathtub.
18:01Einstein was riding a streetcar when relativity dawned on him.
18:07These brilliant minds had a flash of inspiration and drove all of humanity forward.
18:13But the scientific questions of today, probing showers of subatomic particles and our vast genetic code,
18:22have become so complex that they take teams of thousands of researchers to solve.
18:29Is the age of the single scientific genius over?
18:33Data scientist Michael Schmidt sees a world filled with intricate beauty.
18:48The flowering of a rose, the veins branching on a leaf, the flight of a bumblebee.
18:57But below the surface of nature's wonders, Michael also sees a treasure trove of uncharted mathematical complexity.
19:05Well, I love coming out here. Nature is beautiful.
19:10There are equations hidden in every plant and every bee and the ecosystems involved in this garden.
19:17And part of science is figuring out what causes those things to happen.
19:20Science is our effort to make sense of nature.
19:26And this quest has given us some very famous discoveries.
19:31In Newton's time, he was able to figure out a very important rule in physics, which is the law of gravity.
19:38It predicts how this apple falls and the forces that act upon this apple.
19:42Today in science, we're interested in similar problems, but not just about how the apple falls,
19:46but the massive complexity that follows from this very simple dynamics to the world around us.
19:52For example, when I drop this apple, the apple stirs up dust,
19:55this dust could hit a flower, and a bee may be less likely to pollinate that flower.
20:01And the entire ecosystem in this garden could change dramatically from that single event.
20:08Scientists understand the basic forces of nature, but making precise predictions about what will happen
20:14in the real world, with its staggering complexity, is overwhelming to the human mind.
20:20So one of the reasons why it's extremely difficult for humans to understand and figure out the equations
20:27and the laws of nature is literally the number of variables that are at play.
20:31There could be thousands of variables that influence a system that we're only just beginning to tease apart.
20:37In fact, there are so many of these equations, we'll never be able to finish analyzing them if we do it by hand.
20:42In 2006, Michael began developing intelligent computer software that could observe complex natural systems
20:53and derive meaning from what seems like chaos.
20:58So what I have here is a double pendulum. And if you look at it, it consists of two arms.
21:03One arm swings along the top axis, and the second arm is attached to the bottom of the first arm.
21:09And it's two pendulums that are hooked together, one pendulum at the end of the other.
21:13Now the pendulum is a great example of complexity, because it exhibits some of the most complex
21:18behavior that we're aware of, which is called chaos.
21:21So when you collect data from this sort of device, it looks almost completely random.
21:25And it doesn't appear to be any sort of pattern. But because this is a physical deterministic system,
21:30a pattern does exist. Finding a pattern amidst the chaos of the double pendulum
21:35of the double pendulum has stumped scientists for decades.
21:46But then Michael had a flash of inspiration. Why not grow new ideas the same way nature created us?
21:56Using evolution. He called his program Eureka.
22:01Eureka starts with a primordial soup of random equations, and checks how closely they fit the
22:08behavior of the double pendulum. If they don't fit, the computer kills them. If they do,
22:16the computer moves them into the next generation, where they mutate and try to get an even closer thing.
22:22Eventually, a winning equation emerges. One that Archimedes would be proud of.
22:29Eureka!
22:33And I'm running our algorithm now. On the left pane are the list of the equations that Eureka has
22:38thought up for, for this double pendulum. Walking up, we can see we increase the complexity,
22:44and we're also increasing the agreement with the data. And eventually, as you go up, you start to get
22:49extremely close agreement with the data. And eventually, you snap on to a truth, where you
22:53get a large improvement in the accuracy. And we can actually look in here and see exactly what pops
23:00out. For example, here, you might notice we have a 9.8. And if you remember from physics courses, that is the
23:05coefficient of gravity on Earth. What's very important is the difference between the two angles
23:10of the double pendulum. This pops out. Essentially, we've used this software and the data we've collected
23:16to model chaos. And we've teased out the solution directly from the data. Eureka has not only
23:23discovered a single equation to explain how a double pendulum moves. It has found meaning in what looks
23:30like chaos, something no human or machine has done before. So we could collect an entirely new data set,
23:39run this process again. And even though the data is completely different, we have different
23:43observations. We can still identify the underlying truth, the underlying pattern, which is this equation.
23:53To Michael, the future of scientific exploration isn't inside our heads.
23:59It's inside machines. Whether they're looking at patterns of data from genetics, particle physics,
24:06or meteorology, programs like Eureka can evolve inspiration on demand, finding basic truths about
24:15nature that no human ever could. We're going to reach a point where we decide what we want to discover,
24:24and we let the machines figure this out for us. Eureka can find these relationships without human
24:31bias and without human limitations. We created robots to serve us. As the machines learn their own ways to
24:41move, feel, and think, they will eventually grow out of that role. What if they start working together?
24:51Could they build their own society? One made by the robots for the robots?
25:01There is no species on Earth more successful than us. We owe that success to the powerful computer
25:11inside our heads. But it takes more than one brain to conquer a planet. Homo sapiens thrive because we have
25:20learned to make those computers work together as a society. What will happen when robots put their heads
25:30together? Roboticist by day and gourmet chef by night, Professor Dennis Hong of Virginia Tech is a
25:43specialist in building cooperative robots. But he also sees cooperation outside the lab.
25:50So we don't really think about it, but everything in our daily lives involves cooperation. For example,
25:55cooking oftentimes is thought of as a solo act. But if you think about it, a lot of people are involved
26:00and a lot of careful coordination is required to make it happen. Oh, thank you, Charlie.
26:08Take this tomato as an example. This tomato most likely started his life as a seed, where a group of
26:13breeders needed to choose the right sequence of genes for a plump, juicy, tasty tomato. The seeds needed to be
26:20planted, grown, harvested, then the tomatoes need to get to the market. Food production is a complex web of
26:30coordination. But as good as it is, human cooperation has its limits.
26:43Every day, like most of us, Dennis has to contend with a prime example of human cooperation gone wrong.
26:51Traffic. Now the problem is, us being human, we all need to want to get to our destination as quick as
26:57possible. Thus, we have traffic jams. If it wasn't for traffic lights, which are in reality very simple
27:04robots, it would be almost impossible to get anywhere. These traffic lights, they talk to each other,
27:10they communicate with other traffic lights at other intersections, and they have cameras so they actually
27:15see the traffic patterns and make decisions for us, for humans. Well, there you go. Thank you,
27:22traffic light. Traffic is a nuisance. But other failures of human cooperation are much more serious
27:31and often deadly. Dennis believes a society of robots can be much better collaborators than we are.
27:41So in collaboration with Daniel Lee at the University of Pennsylvania, he designed a group
27:46of robots to compete in the RoboCup, an international robotic soccer championship.
27:53RoboCup is an autonomous robot soccer competition, which means that you have a team of robots,
27:59you press start, and then nobody touches anything. And the robot needs to look around,
28:03see where the ball is, need to coordinate and actually play a game of soccer.
28:07Dennis' soccer robots, called Darwin OP, are fully autonomous. They use complex sensors and software
28:16to navigate the playing field. And they have a serious competitive edge over their human counterparts.
28:23Teammates can read each other's minds.
28:26So if you look at human soccer players, obviously they're great at what they do. They communicate
28:31sometimes by shouting, sometimes by subtle gesture. But again, it's not really accurate and they
28:36cannot share all the information together at the same time in real time. But robots can do that.
28:45Each robot knows the exact location and destination of the other robots at all times.
28:51They can adjust their strategy and even their roles as necessary.
28:56Depending on where the ball is, where the opponents are, they dynamically switch their roles.
29:01So the goalie becomes a striker, striker becomes a goalie or defense.
29:05They may not be as agile as Pele or Bended like Beckham, but they are able to dribble past their
29:12opponents, pass the ball, score a goal, and even celebrate.
29:23Dennis believes RoboCup is just the beginning of robot societies.
29:28Dennis imagines a connected community of thinking machines that would be far more sophisticated
29:34than human communities. He calls it Cloud Robotics.
29:40Cloud Robotics is a shared network of intelligence. It's similar to what we call common sense in humans.
29:45So just like those smaller robots that play soccer for RoboCup, they share a common data,
29:51team data to achieve the goal, in this case, winning the soccer game. For Cloud Robotics, robots from the
29:57furthest corners in the world, they can all connect to the cloud and share information and intelligence to do their job.
30:02RoboCupup, humans spend a lifetime mastering knowledge, but future robots could learn it all in microseconds.
30:12They could create their own hyperconnected network using the same spirit of cooperation that built human
30:18society without the selfishness and greed that hold us back.
30:24Robots operate by their very well defined set of rules. The human impetus to break them is just not there.
30:32Robots already know how to talk to one another. But now, a scientist in Berlin has taken robotic
30:40communication a step further. His machines are speaking a language he doesn't understand.
30:49Motakei Tokima.
30:54Did you understand what I just said?
30:57Well, of course you didn't. Because I wasn't speaking any known human language.
31:02But it wasn't nonsense. It was a robot language. We humans took tens of thousands of years to develop
31:12our complex means of communication. Now, robots are following our lead, and they're doing it at light speed.
31:21Someday soon, robots may decide to exclude us from their conversation.
31:32We cannot also use our automation is to get a chance to get a catchphrase.
31:34Lonkamo.
31:36Myunto.
31:41ones.
31:41Konane.
31:43Tokima.
31:44Kimamu.
31:45Tokima.
31:47Simita.
31:50Tokima.
31:55Moname.
31:56Without language, our species would never be where it is today.
32:08It's the most magnificent thing that has ever been created by humanity.
32:14If you look at ourselves, then it's pretty clear that without language, we would not
32:20be able to do the kinds of things that we're doing.
32:24Luke Stills, a professor of artificial intelligence, sees language as the key to developing true
32:33robot intelligence.
32:34What I'm trying to understand is how can we synthesize this process so that we can start
32:42up a kind of evolution in a robot or in a population of robots that will also lead to the growth
32:50of a rich communication system like we have.
32:56Machines already communicate with each other, but these are based on predetermined, human-coded
33:01languages.
33:03Luke wants to know how future robot societies might communicate given the chance to make
33:09a language on their own.
33:11Luke gives his robots the basic ingredients of language, like potential sounds to use,
33:17and possible ways to join them together.
33:20But what the robots say is up to them.
33:24We put in learning mechanisms.
33:26We put in invention mechanisms.
33:28Mechanisms so that they can coordinate their language.
33:31They can kind of negotiate how they're going to speak.
33:34But we don't put in our language or our concepts.
33:38It's not enough for the robots to know how to speak.
33:43They need to have something to speak about.
33:46Luke's next step is to teach the robots how to recognize their own bodies.
33:53In order to learn language, you actually have to learn about your own body and the movements
34:00of your own body.
34:01So what you see here is an internal model that the robot is building of itself.
34:08This robot is trying to learn here is the relationship between all these different sensory channels
34:16and its own motor commands.
34:19As a robot watches itself move in the mirror, it forms a 3D model of its limbs and joints.
34:27It stores this information in sense memory and is now ready to talk to another robot about movement.
34:38So now this robot is talking, is asking an action.
34:42This robot is doing, you know, stretching the arm.
34:46No, this is not what was requested.
34:49And it's showing again what the right action is.
34:54She's unsuccessful in her first attempt, but eventually the robot learns that tokema means
35:01raise two arms.
35:03After repeating this process with different words, they try again.
35:07Another request, he's doing the action.
35:12Yes, this is the right kind of action.
35:18So, in other words, this robot has learned the words from the other one and vice versa.
35:24They now have a way to talk about actions.
35:27The robot's language is already so well developed, they can teach it to Luke.
35:33Let's see what, you know, what he asks me to do.
35:38Motake.
35:39Okay, Motake.
35:40No, this is not right.
35:44So, he's showing it to me.
35:46Okay, I'm learning this gesture now.
35:48Motake.
35:49Motake.
35:50Motake.
35:51Motake is this.
35:54Okay.
35:55Okay, I got it right.
35:57So, now I'm going to use the gesture with him.
36:01Motake.
36:02Okay.
36:03Yes, you're doing the right thing.
36:08As the robots repeat this process, they generate words and actions that have real meanings for
36:16one another.
36:17And so, the robot's vocabulary grows.
36:21Every new word they create is one more that we can't understand.
36:26Is it only a matter of time before they lock us out of the conversation completely?
36:33And I think it's actually totally possible, but society will kind of have to find the balance
36:41between what it is that we want robots for and how much autonomy are we willing to give them.
36:48If we're giving robots autonomy to move, to feel, to make their own language, could that
36:54be enough for them to surpass us?
36:56After all, what's robot for?
36:59Exterminate.
37:01But one Japanese scientist doesn't see the future as robots versus humans.
37:07In fact, he's purposefully engineering their intersection.
37:13We know that Homo sapiens cannot be the end of evolution.
37:20But will our descendants be biological or mechanical?
37:26Some believe that intelligent machines will eventually become the dominant creatures on
37:31Earth.
37:32But the next evolutionary step may not be robot replacing human.
37:37It could be a life form that fuses man and machine.
37:44This is Yoshiyuki Sankai.
37:47Inspired by authors like Isaac Asimov, he has always dreamed of fusing human and robotic life
37:53forms into something he calls the hybrid assistive limb system, or HAL.
38:00That one is one of my dreams.
38:03We could develop such kind of devices like a robot suit house system for supporting the
38:10humans and humans' physical movements.
38:13And now, after 20 years of research, he has succeeded.
38:19HAL assists the human body by reading the brain's intentions and providing assistive power
38:25to support the wearer's movement.
38:28If she wishes to or try to move, at the time the brain generates the intentions and the
38:35robot detects these intention signals and drugs to assist her movements.
38:41When the brain signals a muscle to move, it transmits a pulse through the spinal cord and into the area of movement.
38:51This bioelectric signal is detectable on the surface of the skin.
38:56Yoshiyuki designed the HAL suit to pick up these impulses and then activate the appropriate motors in order to assist the body in its movement.
39:06The human brain is directly controlling the robotic suit.
39:11It's not just a technological breakthrough.
39:15Yoshiyuki already has held suits at work in rehabilitation clinics in Japan.
39:20So some of the parties have some problems, like a paralyzing sauna.
39:27So these drug patients or other such kind of handicapped persons can use it.
39:32People who haven't walked in years are now on the move again, thanks to these brain-powered robot legs.
39:44Yoshiyuki 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.
39:56But it's not all about strength.
40:02He believes the merging of robotic machinery and human biology will allow us to preserve great achievements in movement.
40:11Athletes like Tiger Woods or Roger Federer bring unique skill and artistry to their sports.
40:17However, when they die, so does their movement.
40:22But since the HAL suit can detect and memorize the movements of its wearer, that knowledge doesn't have to disappear.
40:30If some of these athletes, like Tiger Woods and so on, if they wear it and they swing it,
40:39every emotion data and physiological data is also gathered in the computers.
40:49We once built great libraries to preserve knowledge expressed through writing for future generations.
40:56Yoshiyuki wants to create a great library of movement.
41:02By merging our bodies with robotic exoskeletons, we will not only be stronger,
41:09we will all move as well as the most talented athletes and artists.
41:15The last century of popular culture has focused on apocalyptic scenarios of robotic mutiny.
41:23But the HAL suit opens up a different future.
41:28We tend to think about robotics as an alternative life form that may someday replace or compete with humans.
41:37But I think the reality of the matter is that increasingly we'll see humans and robots cooperate
41:45and actually become one kind of species, both physically and mentally.
41:50Absolutely, I think robots are the future.
41:53I think we need to rely on them, otherwise we will stagnate and make no more progress.
41:57Eventually, life on the earth will come to end.
42:03What is our legacy?
42:05We will leave nothing unless we leave consciousness.
42:10We need conscious robots everywhere.
42:14That would be our legacy.
42:16That would be the legacy of mankind.
42:19Robots are rapidly becoming smarter, more agile, and are developing human traits like consciousness, emotions, and inspiration.
42:33Will they leave us behind on the evolutionary highway?
42:37Or will humans join the machines in a new age?
42:40Evolution is unpredictable and is bound to surprise us.