Using A.I. to build a better human | The Age of A.I.

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Through life changing accidents, and data minded through NASCAR, human beings are finding ways to re...

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[man] Steve Austin, astronaut, a man barely alive. We can rebuild him. We have the technology.

We can make him better than he was. Better. .

. stronger. .

. faster. [Downey] Man, when I was a kid, the Six Million Dollar Man was all the rage.

It's a show about a guy who gets rebuilt with robotic machine parts. God, I loved it. And then 35 years later, I got to play a similar role, a character who enhances himself via technology and engineering.

Now, the term "bionics" goes back to the 1950s, but the idea of enhancement actually dates back much further, to Greek mythology, Aztec gods, and even ancient Hinduism. So these next stories are about augmenting our human abilities, everything from a bionic limb that behaves naturally and understands intent, to data that improves performance, and vision enhancement that saves people in actual life-threatening situations. It seems with A.

I. . .

anything's possible. So it raises the question, do we even want to be superhuman, or is imperfection what makes life interesting in the first place? Whatevs.

I gotta get back to the gym. Normally I'd jog, but I got a wonky knee. I should probably switch it out.

We can rebuild me. Better. Faster.

Stronger. Seven miles an hour. .

. full out! [Hugh Herr] Designers within the field of bionics, they don't view the human body itself as designable media.

We now have sophistication in Artificial Intelligence, in motor technology, in material science, in how to talk to the nervous system, setting the foundation. . .

[all laughing] . . .

for the end of human disability. -[Cathy King] Do you wanna do an omelette? -[Jim Ewing] Sure.

You probably don't want too much onion, because you'll have bad breath all day. Oh, thanks, yeah. [chuckles] Anyway, what were we talking about?

[Ewing] 26 years, 29 years-- Twenty six years, almost 29 years together, yup. Our first date was, uh, uh. .

. 2000, wasn't it? -[King] 1990.

-1990, oh my God. [King] I could tell when I first met Jim that he's highly intelligent, and he said, "Would you like to go rock climbing sometime? " and I said, "Sure!

" [Ewing] I started rock climbing in my very early teens, and I consider climbing to be. . .

it's more than a passion for me. It's my lifestyle. In 2014, my family and I traveled to the Cayman Islands.

We went rock climbing. I set up the ropes for the day, and we'd done a few climbs with no problems. I started up the final section, and.

. . shifted my feet and slipped off.

. . .

. . 50 feet to the ground.

[hospital monitors beep] [King] When I saw him at the hospital, I have never felt so helpless in my entire life. It was horrible. [Ewing] The front and back of my pelvis were completely shattered.

My left wrist was shattered. My left ankle was broken into two or three chunks, but the rest of it was kind of pulverized. It slowly started to dawn on me that this was something that was going to be life-changing.

[King] You're at the hospital. [Jim murmurs] [King] We're not looking at the photos right now. I'm looking at 'em.

[King chuckles] [kissing him] You look all you want, baby. [Ewing] After a year, everything else seemed to heal well, but the ankle continued to be a problem. The bone was mostly dead, and the main fracture was still there.

[King] He was in severe pain all the time, and he just became so depressed. [Ewing] I couldn't do the things that I love. I could barely walk down the street without pain, never mind go rock climbing.

[King] Rock climbing is his passion. I mean, I just see him withering if he could not climb. [Downey] Jim didn't know what to do, and then, in a truly incredible stroke of luck, his past came back to help decide his future.

I began mountain climbing at the tender age of seven. At the age of 17, I was in a mountain-climbing accident, and I suffered severe frostbite, and my legs were amputated. I really dedicated myself to redesigning first my own legs, and then the legs of many, many people around the world.

[Downey] A few decades, a couple of M. I. T.

degrees, and a single-minded focus to innovate later, Hugh launched the prosthetics industry into the bionic future. [Herr] I'm getting a tremendous amount of energy, power from the ankles, which enables me to walk uphill with a perfectly erect posture. [Herr] My legs, they have the brain.

It's a small computer the size of your thumbnail, and that brain receives sensory information from sensors on the bionic limb, and then it runs algorithms and makes decisions on how to actuate itself. And machine learning is used as part of those algorithms. [Dr Ayanna Howard] So, machine learning is what's called a subset field of Artificial Intelligence.

We learn from experience. Machine learning is basically learning from the experience, where the experience is the data. It takes input from the world, and the input could be text in books, it can be camera images from a car, it applies a very complex mathematical function, and then has an output, which is a decision.

[Downey] The bionic legs allow Hugh to walk, run, even climb, but for him, there was still something missing. [Herr] Because I can't feel my legs, they. .

. they remain tool-like to me, and I believe if I could feel my limbs, they would become part of me, part of self, and fundamentally change my relationship to the synthetic part of my body. [Ewing] I would describe it as just this amazing, lucky coincidence that Hugh and I were roommates 34 years ago.

[Herr] We were teenagers rock-climbing together and living like dirtbags, living like bums, climbing every day. [Ewing] So I decided I was going to look up Hugh and talk to him about what my options might be. [Herr] Jim was in excruciating pain, and he asked me if I or my colleagues could help him.

[Ewing] What I really was hoping was that he could put me in touch with a reconstructive surgeon that could rebuild my ankle. Right, so this is your X-ray. .

. [Herr] Jim was evaluated, and was provided, uh, options of either maintaining the biological limb and doing certain procedures to try to improve its function and to reduce the pain, or. .

. to amputate the limb. [Ewing] The thought of amputation was just so big.

What's life gonna be like for me if I choose to amputate this foot? But it hurt so bad. I spoke with Cathy and Maxine about it.

They were behind me 100%. Whatever I needed to do. [Downey] How do you make a decision like that?

A few months later, he agreed to have his leg amputated and be the first person to try his friend's bold, but experimental procedure. [Ewing] It's gonna be good. -Yeah.

-Gonna be good. Love you. You too.

Bye, honey, love you. Love you, too. [Herr] The way in which limbs are amputated has not fundamentally changed since the U.

S. Civil War. .

. [soldiers shouting] . .

. but here at M. I.

T. , we were developing a novel way of amputating limbs. We actually create little biological joints by linking muscles together in pairs, so when a person thinks and moves the limb that's been amputated away, these muscles move and send sensations that we can directly link to a bionic limb in a bi-directional way.

So not only can the person think and actuate the synthetic limb, but they can actually feel those synthetic movements within their own nervous system. [Downey] Until recently, creating a bionic limb that a person can actually feel has been more science fiction than reality. Now machine learning is revolutionizing the way we think about medicine.

If anything can solve the hard problems in medicine, it's A. I. Let's take an example, heart disease.

No single human being can have in their head all the knowledge that it takes to understand heart disease, but a computer can. Things like radiology, pathology. You have an X-ray, and you wanna see, like, is there cancer in this lung, and can you pinpoint where the tumor is or not?

A. I. s can, actually, at this point, do this better than highly trained humans.

[surgeon] Can we get the, uh, Esmarch, please? It's so light! It's weird.

[surgeon] It really looks good. I'm super happy with this, and you've got a nice degree of padding here. [Ewing] Right after the surgery, the incredible, deep, in-the-bone pain that I had been experiencing for the past year was gone.

So to me, that was. . .

that was a success right there. Never mind whether or not the experimental part of the amputation was a success, I was glad to be free of the painful ankle. He was happy.

It was done. He was ready to move on. [Herr] Hey, guys.

How's it looking? How's progress? [M.

I. T. tech Eric] Things look good.

I'm gonna go ahead and get you wired up. [Ewing] The first time I went to Hugh's lab, Hugh started talking about "What do you think about a climbing robot ankle? Would you want us to make one of those?

" [Eric] I'm gonna go ahead and get us started here. . .

[Herr] It's a specifically designed limb that Jim can control with his mind, and actually feel the movements within his nervous system. [Ewing] I still need the evert. [Eric] Yup, that's the one we're missing.

All right, so we have you wired to the leg now. You're driving. [servos whirring] [Ewing] Cool.

[Eric] Yeah. Can you give me a fast up-down? And slow, controlled?

This freakin' blows me away every time you do it. It's so good. [chuckling] When we link Jim's nerves in that bi-directional way, we're able to create natural dynamics, so even though the limb is made of synthetic materials, it moves as if it's made of flesh and bone.

There. Now it's neurally and mechanically connected. How does it feel different?

Now it feels like it's my natural foot, somewhat. Like, I don't have the skin sensation, but all the motions make sense to my brain. [Herr] In the algorithm, we make a virtual model of his missing biological limb, so when he fires his muscles with his brain, we use an electrode to measure that signal, and then that drives the virtual muscle and sends sensations to the brain about the position and dynamics.

It kind of instantly felt part of me, almost as good as having a natural foot. [Downey] Cathedral Ledge, New Hampshire. Seven hundred feet of awe-inspiring granite and climbing routes with names like "Thin Air," "Nutcracker," and "They Died Laughing" make it one hell of a challenge for even the most serious climbers on a good day.

For Jim, it's a test to see if what works at M. I. T.

can work out on the mountain. [Ewing] I've been climbing here for 40 years, and I've probably spent more time on Cathedral Ledge than any place else on the planet. This climb is actually at the upper limit of my ability at the moment.

I'm not worried at all. What could possibly go wrong? [Downey] The mountain has no mercy, and no margin for error, and Jim's about to find out if his bionic leg can help him overcome and scale heights most people wouldn't dare try in the first place.

[Ewing] That's me. [Downey] Can machine learning take us even further? Replace not just what was lost, but enhance what we already have?

[firefighter] Okay, stay close. I'll lead. This is insane!

[Downey] Augment performance beyond the limits of our natural human ability? Make strong, smart, fast people. .

. stronger, smarter, and faster? [crowd cheering] In many ways, sports has been on the leading edge of prediction systems, and now, every serious sports contender uses sports analytics.

. . but the big opportunity going forward is embedding devices that can collect real-time data to update strategies to take advantage of that learning.

There's a revolution going on in sports, and machine learning is at the core of it. [Interviewer] The first night race of the season, I'm sure you're ready to finally get behind the wheel. For sure.

Just fired up to get going. Triple-A car's been pretty good this weekend, and we're pumped to get this thing started. Drvers, start your engines!

[crowd screaming] [Eric Warren] The race track's a fairly hostile environment. The way I describe racing, and the way I live it, it's like war. [announcer] Folks, get on your feet.

Let's send these guys off! Boogity boogity boogity! Let's go racing, boys!

[Warren] You're trying to take your race car, your team, your driver, and beat the other drivers at all costs. [race team radio chatter] [announcer] Austin Dillon, stuck in the middle of a three-wide. [Andy Petree] This kind of race produces a lot of strategy, and that's where we have to use all of our tools to help us make those strategic decisions.

[Downey] When it comes to superhuman ability, you may think of people like LeBron James, Michael Phelps, or Serena Williams. . .

but it's not just the body that can be enhanced. Sometimes it's something less tangible, like human intuition. [announcer] What a battle going on here.

You gotta be real careful here in the early stages making contact with somebody. [Warren] Information is the next battleground. [race team radio chatter] Every decision you make can have a big impact.

[Downey] Back in the day, intuition used to play a big part in sports. Athletes and coaches relied on their gut to make decisions. Now some competitors are leaning more and more on machine learning, looking to gain whatever extra edge they can.

[Warren] We use the A. I. tools to predict what the future not only is, but what it should be.

[announcer] We'll go behind the 20. You just start finish line. .

. [Rana el Kaliouby] The strength of these A. I.

systems come in having access to a ton of data and being able to find patterns in that data, generating insights and inferences that maybe people may not be aware of, and then augmenting people's abilities to make decisions based on that data. [Downey] Machine learning is transforming many industries and applications, especially in areas where there's a lot of data, and predicting outcomes can have a big payoff. Finance, sports, or medicine come to mind.

Using an emerging technology like machine learning in a classic old-school sport like stock car racing doesn't necessarily sit well with everybody. . .

which may or may not explain why this guy's doing it. . .

in a nerve center 250 miles away. [man] Clear, clear, hit the marks, drive off, man. [Warren] My role there really is looking at the data.

How do you use data you can acquire at the racetrack to get these machines to be right on the limit of performance? [announcer] His front rotors are really glowing. [Warren] We get the braking, steering, throttle, all the acceleration off of every car in the field, real time.

. . [Downey] All this data is being fed into an A.

I. program called "Pit Rho. " [race team radio chatter] [Downey] Sensors in every car measure speed, throttle, braking, and steering.

Advanced GPS tracks the car's position on the track. [man] Watch your middle, watch your middle. [Downey] All this data is made available to every team.

[Warren] This is where the power of A. I. comes in.

So, our tool basically is analyzing the optimum strategy call of every car in the field, real time. Not just our car, but every car. [Petree] It's almost threatening.

I was a crew chief for Dale Earnhardt Sr. Comin' to ya. 10-4.

[Petree] I would sit up on the box and intuitively kinda figure all these things. You kinda just make that gut call, "Bring him in now. " [Downey] Until now, many key decisions, like when to pit for tires or fuel, were made by the drivers and the crew chief using experience and intuition.

[Petree] Now, we've got artificial intelligence that's making all these calculations in real time. Some of the crew chiefs that have done what I've done over the years, sometimes it's hard for us to embrace it. [tires squealing] [announcer] They're trying to get through traffic as fast as they can so they don't get a lap down, but that's gonna use up those tires.

[Warren] You can go at this track on fuel probably 120 laps, but your tires will be shot way before then. [Downey] In a NASCAR race, pit stops are the key to a winning strategy. [Petree] You're trying to decide when in that cycle is the best to make that stop, because you lose a lot of time when you come off the track and you have to stop, but then you gain a lot of speed when you put new tires on.

[Warren] This is the first time we're facing, like, a strategy call here. [Downey] The Pit Rho A. I.

interface displays one of four suggestions. . .

stay out on the track, pit for fuel only, pit for two tires, or pit for four tires. [Warren] So right now, it's telling him to take four tires. [Downey] Eric relays the message to the Childress team at the track.

The final decision on when to pit will be up to the crew chief. [crew chief] When the pits are open, it'll be four tires here, four tires. [Downey] For the first pit stop, the crew chief follows the A.

I. 's advice. Five, four, three, two, one.

Put on the brakes, wheels lift. [Downey] The crew has to change all four tires in as little time as possible. This usually takes between 12 and 14 seconds.

[engine revving] [radio chatter] All the way, all the way! That's a good stop. Really good stop.

[Warren] Sometimes, what happens is, over the course of a race, those little bit better decisions puts you in a spot, and it puts you in an opportunity at the end of the race to be able to win the race. [Warren] Every lap, it's analyzing the field, updating its models. As the race goes on, the prediction gets more and more accurate.

[Downey] They're using an A. I. technique called "reinforcement learning," which is, basically, when the computer is given the rules of the game, plays it over and over till it learns every possible move and outcome, and then through trial and error, and patience that no human could possibly have.

. . [announcer] I wonder if we have a resignation here.

[Downey] . . .

becomes amazing. [announcer] Congratulations to AlphaGo and to the entire team. [Downey] It's what Google's DeepMind did to become a world champ at Go.

[commentator] Here we go! [Downey] It's what Open A. I.

did to conquer the video game Dota 2. . .

[commentator] He's dominating. Are you scared of a bot here? [Downey] .

. . and build a robotic hand with near-human dexterity.

It's what Eric's hoping to do to get the checkered flag. [announcer] You can see he's on his way to the top 10. [team] Yeah, we got through, Andrew, focus here.

[announcer] And you go up a few cars, you'll find the 3 of Austin Dillon up in sixth place, making up time on the race leader. So the recommendation is pit on lap 327. What my fear is is that they'll pit with the leaders instead of actually running to the strategy.

[Downey] Going to the pits when the leaders do is the safe play in the end stages of a race. . .

[Warren] Sparks, you got me? [Downey] . .

. but the A. I.

tool is recommending a riskier plan that might gain them valuable seconds. [Downey] By pitting later, Austin Dillon will have faster tires for the closing laps of the race, but he risks falling further behind the leaders once they come out of the pits with their fresh tires. [Petree] A lot of times when our Pit Rho technology tells us, "This is the time to pit," or, "This is how to do it," it doesn't feel right.

Are you sure you wanna do that now? [Petree] Sometimes you might be sitting out there running laps on older tires, where everybody else is pitted, and it's like, it doesn't feel right for the driver. [Petree] He's gonna want to pit, and you gotta convince him, "Stay, make good laps.

Trust us, it's gonna work. " Some leaders are gonna pit right here, and we need to run. [commentator] Looks like the 22 is gonna choose to come down pit road.

[announcer] So, all the front four came in on the same lap with 82 laps to go. [Downey] On lap 318, the top four cars enter the pits. [team member 1 speaking] [Warren speaking] [team member 1 speaking] [Warren] Here's where the faith in the tool ends up happening.

When they all pit, it takes a lot of faith to just stay out there and run to your lap. [team member 2 speaking] [team member 3 speaking] [Downey] Austin Dillon breaks from the A. I.

strategy and follows the leaders into the pits. [team members speaking] That's not good news. [man] Three, two, one.

Put on the brakes, wheels lift. [Downey] To maintain their position, the team needs a flawless pit stop. [tires screeching] [team member 2] Son of a bitch!

[team member 1] We lost three seconds. We're not gonna be nowhere near 'em. Got killed on pit road.

It's pretty disastrous. [Warren] Prior to the pit stop, we were about 4. 6 seconds back, but when we came out, we were nine seconds back, so we lost about four and a half seconds on that-- in that exchange.

That's hard to get back. [man] Let's go to work on him. This won't be easy.

Just fight hard here. [Downey] They've dropped from 6th place to 12th. .

. and Austin Dillon has very little time left to fight his way back to the leaders. [Eric] Come on, Austin, get him.

[Downey] . . .

but the new tires give him an edge. . .

[announcer] The white flag waves, one lap to go. [team member 1 speaking] Get it, get it, get it! [announcer] Short track win number one for Martin Truex!

[race team] Sixth place is awesome. [Downey] . .

. and he ends up finishing sixth. [team member 2] Hell of a freakin' drive, Austin Dillon.

[team member 3] Hey, nice work tonight, man, way to fight hard there. [team member 4] Hell of a job, boys. Hey, good job, guys.

[Warren] Progressing through the race definitely the cars have gotten faster, so, you know, we'll see good things that we'll take back next time we go to Richmond. Hell of a job this weekend, boys. [Warren] The hardest thing as we've incorporated more A.

I. -based tools is trust. Sometimes we're the ones that get in the way, right?

There's still times when it's counterintuitive, and everybody's like, "It's the wrong call, it's the wrong call," and over time, we have these battles because most of the time, the A. I. tools is right.

Nine times out of ten, or even more, it's the right call. [Downey] Andy and Eric's team were using A. I.

, and on track for a strong finish, but they fell behind when the team ignored the machine and went with their intuition. That'll do it. [Lav Varshney] Convincing humans that machines know what they're doing is the central difficulty in deploying A.

I. out in society, whether it's the pit boss in car racing, or even astronauts flying to the moon. [Downey] Do we trust the A.

I. to make decisions for us? We already do with GPS maps.

Perhaps here, the team just didn't have enough experience with it to override their own intuition, but what about other situations? At what point do we start trusting A. I.

in more serious matters? [dawn birdsong chorus] Matters of life and death? [firefighter] It was a smoldering fire that filled the whole house with smoke, and you couldn't see your hand in front of your face.

You literally had to feel your way up the stairs. Totally blind search. Yeah.

Sometimes that's the best thing we can do. Yeah. [Kirk McKinzie] Every two hours and 45 minutes, a U.

S. citizen dies by fire in their own home. We've lost more than 3,000 a year consistently for 30 years.

[firefighter] The Worcester fire. Three guys go in. They all get disoriented and get lost.

Two more go in to find them. They get lost. Two more go in.

I mean, before you know it, they finally had to, "Okay! We're not sending any more guys in there, 'cause they're all friggin' lost. " [news broadcast] On his radio, a commanding officer heard two firefighters desperately crying out for help.

[Worcester fire chief] "Mayday, mayday. We're running out of air. Come to the door so we can see where you are," and then, we did that, and we went beyond the door, and we yelled, and we had lights, and they were.

. . they were inside somewhere that they couldn't see us.

[firefighter] All those guys who died in that. . .

[McKinzie] When we go into a structure that's dark and smoky, the biggest challenge is the visibility. The ability to navigate is a. .

. is a challenge, and often firefighters have become disoriented, and then they run out of air. With the challenge of smoke and having no vision, I knew that there was a possibility of changing that.

That's when I finally met the C-THRU team. [Sam Cossman] Okay, is the system turning on? Let's see.

I'm gonna unplug that one. I guess the best way to describe myself is I'm infinitely curious. I like to solve problems, look at things through a new lens.

All right. . .

[Cossman] I was in disbelief that firefighting in a smoked-out building involves training their personnel to revert back to feeling around the room. How's the battery level doing? That was really the inspiration behind creating C-THRU.

[Downey] Sam Cossman saw the light when he jumped into a volcano. Line! Fire!

[Downey] Part globetrotting adrenaline junkie, part computer engineer, the self-proclaimed Indiana Jones of tech envisioned a tool that would help firefighters and save lives, a kind of X-ray vision. [Cossman] The problem that C-THRU is trying to solve is really flipping the lights on for people operating in zero-visibility conditions. [Omer Haciomeroglu] The concept of C-THRU was the helmet that had enhanced audio, enhanced vision.

. . [man] I see you!

I'm on my way. [Haciomeroglu] . .

. outlines their surrounding geometry so that they can navigate faster. So is it this plane right here that.

. . that changed recently?

[Haciomeroglu] Yes, basically like a simpler design that can achieve more. [Cossman] We have a mask, and we have a thermal-imaging device that sits on the side of that mask, and we process that image through a small computer. [Downey] Sam and Omer created a mask with special glasses clipped inside which allows firefighters to see edges as green lines in an augmented reality overlay.

How's the alignment look on that one? It's not bad. We need to calibrate it a little bit more.

Omer and I have been working on refining the prototypes for the last couple of years, just trying to MacGyver some of these problems with off-the-shelf parts, you know, duct tape and bubble gum. Move your hand around a little bit. -Okay.

-Other hand, like that one. Yeah, this is definitely better. [Downey] It may look like old Tron-era night vision, but there's actually some pretty slick artificial intelligence at work here.

Thermal imaging cameras stream video from the firefighter's helmet into an A. I. processor.

Using infrared light and a powerful edge-detection algorithm, the mask detects subtle changes in brightness to predict shapes invisible to the human eye, like a wall hidden by smoke, or a kid hiding under a bed. [Cossman] There you go, take this mask. [Downey] Sam and Omer are now at a familiar point in the innovator's journey.

. . get out of the garage and into the real world to see if their invention can take the heat.

[siren wailing, horn blares] Fire Dispatch, Medic 71 arrived on scene, have report of smoke showing. Fire Dispatch, copy. [McKinzie] One of the most important things any fire department does is regular hands-on training.

There he is. How you doing, Captain? Good to see you, brother.

[McKinzie] We're gonna give the C-THRU solution a hard run. . .

[Cossman] We've got a prototype fresh off the print. . .

. and we're gonna put it in fire and smoke, and we're gonna see how it acts while crews are working with it. -Shall we get him inside the smoke?

-Let's do it! [radio chatter] . .

. cleared for dispatch. I am a Cyborg.

Okay, we're ready. [McKinzie] Crews will be doing live fire drills in our training tower. It is active, real fire with temperatures at the ceiling at 1,200 degrees.

[yelling through masks] [man] Anybody over here? [McKinzie] Firefighters are in a hurry, looking for victims. Visibility will be limited at best.

Often, firefighters will be able to see nothing. [Downey] C-THRU's maiden voyage is cut short by a malfunction. [McKinzie] In an active firefight, it's critical that things work.

It's life and death. Uh, at first, it was good. I got through, went down to the floor, and I looked, and I could see everything clear.

Yeah. Really well, everything was lined out. Once I started working.

. . -Yeah?

-I lost it. -Yeah, the signal went out. -Signal went out.

I'm not sure what that was, but we're gonna figure it out. There was a lot of interference, or maybe a cable issue. We did encounter some challenges, the biggest of them was some wi-fi interference that we've encountered where the system would just shut down.

Yeah, it's actually like over here with the connections, -like, this pin, you know? -That's what's. .

. Yeah, the pin connections here, and here, actually. We should just shield the cables as best we can and give it another go.

Battalion Ten, Fire Dispatch, uh, we got a caller on the second floor trapped in the bathroom. So, if you wanna go ahead and try it on for a fit, we'll see how it goes. -Fire Dispatch, Battalion Ten.

. . -[radio chatter continues] [on-scene dispatch] Engine 72 arrived on scene, reporting of heavy smoke showing from the first and second floor.

[radio chatter continues] We've got smoke showing from the first and second floors. [dispatch] Engine 7-1, you're gonna be taking fire attack. [dispatch] 71, who is on scene, smoke showing from the second and first floor.

Command copies, one victim coming out of the second window, you need EMS. Medic 72, you're gonna have to take patient care. As soon as I got in, I could see the outline of the room.

As I stepped in, I just kinda took a look around, I could see where the victim was and an outline of the door. -I mean, hands free, you know? -Yeah.

[chatter on radio] It is kind of like, I mean, like Iron Man, you know, being able to see through the smoke, and having everything so clear-cut, um. . .

It's. . .

it's pretty cool. [Cossman] What we're working on is really a game-changing tool that completely has the potential to transform how the work here is done. [firefighter] This is, uh, some of the videos of the C-THRU mask, okay?

[firefighter 2] That is way crisper than I've seen. That is insane. [McKinzie] Over the 30 years that I've been at this, I've seen a lot of changes.

We have mobile data computers, we have computer-aided dispatch systems. . .

-No, that's gonna. . .

-Wow. That's gonna be a game-changer. [McKinzie] .

. . and now we have the possibility with machine learning and A.

I. to progress to a place just a couple of years ago we couldn't have imagined. -Is that completely pitch dark in there?

-That recording-- -[alert sounds] -Oh, gotta go! That is actually what you see in the mask. [firefighter] We're gonna go on another call, gentlemen.

[Downey] It's impossible to know if this technology could have saved those six firefighters in Worcester, but it's hard to believe it wouldn't have helped. Back on Cathedral Ledge, Jim is about to see if his new bionic leg will help him scale a 700-foot sheer rock face. [Ewing] I'm just gonna kinda bring everything.

[M. I. T.

tech Emily] All right. [Ewing] My own personal M. I.

T. pit crew. -[Emily] Got the socket.

-[assistant] The socket. . .

[Ewing] What we're gonna do today is climb on Cathedral Ledge with a new robot foot designed specifically for climbing. We can set up camp here. [Emily] All right, we should be ready to start calibrating.

[M. I. T.

tech Joe] Counterflex. Rest. -[Emily] You're driving now.

-[Ewing] That's me. [Emily] How's it feel? [Ewing] Pretty accurate.

It's going everywhere that I'm telling it to go. [Ewing] This climb is gonna be very challenging, because there's a variety of holds at different angles, different heights. I'd say there's a high probability of there being some falling action here and there.

I was really afraid, very. . .

worried whether or not I could make it all the way up a climb. We good there, Joe? Harness is on.

I got plenty of gear. All right, we're climbing. I think I'm at a crux section here.

[wincing] Well, first fall. I'm not sticking very well. It's hard.

Hard business. [grunting with effort] Slack! [cracking] Oh!

We have failure. The whole mechanism broke. [Ewing] I remember looking down at it, seeing the foot at a strange angle, and, "Holy crap, that is gonna hurt.

"That--" Like, I was bracing for pain. I mean, how much more of a part of you does it need to be? [Emily] Oh, my God.

[man] I would call that a catastrophic failure. [Emily] Pretty catastrophic. But it was.

. . it was a strange sensation, though, because all of a sudden, my ankle was broken, and you feel like you're losing your limb all over again.

[Eric] How're you feeling? You feel like you wanna go down again? We.

. . we did bring a spare.

Uh, sure. Okay, we'll swap it over to this one. [Emily] In engineering, we're kinda used to things not exactly going right the first time, so that's why we have contingency plans.

[Eric] So, this is the last climbing robot leg in the world, Jim. [laughter] We're good to go again. I'm a little nervous about trusting this foot now.

Watch me here. If the left-- if the robot breaks. .

. I'm going for a ride. Actually, it did that move.

[Eric] Well done. [Ewing] We're rock climbing, dude. [Ewing] With the robotic leg, I found that I could move more naturally.

Life on the edge, man. I was pain free, and it was, I don't know, it was just kind of fun and satisfying. [Herr] We have always hypothesized that if we can link the nerves of a human being to a bionic limb, the limb would become part of the person, part of identity.

Toppin' out. Remarkably, it's happened. Cyborg power!

[laughing] [team clapping] [Downey] It's a tall peak, but pales in comparison to the one Hugh is ultimately trying to climb. [Herr] We also have the goal of extending human capability beyond physiological function, jump higher, or run faster. .

. So bionics not only seeks to achieve normative function in humans, but also to extend human expression beyond what people were born with. [Downey] Human enhancement and augmentation have been around through human history and mythology, from Prometheus stealing fire to the Civil War.

Using tools to improve our abilities is a fundamental human development, whether it's stone spears to protect our families or airplanes to transport us farther. . .

. and that's really what we're seeing, the transformation of society, and not just racing, not just sports, is really using these A. I.

tools, and they'll become commonplace, won't even be thought about otherwise. [Downey] A. I.

and machine learning. . .

they're just tools, ones that makes us stronger, smarter, faster. [Herr] A. I.

will play an increasingly dominant role across all the many dimensions of what it means to be human. [Downey] There's a good chance A. I.

will continue to enhance us in ways both known and unknown, eventually becoming as invisible as the air we breathe. [Herr] That narrative will play out across all types of human conditions. That will enhance human capability, fundamentally change who we are as a human race.

[Downey] The question then becomes, if it does, what do we do with our newfound superpowers? [dog panting] [Ewing] This one's meant to be kind of an all-around athletic foot. I can run with it, hiking, biking, whatever I want.

I even use it for surfing, 'cause it has a good bit of flex to surf with. I actually liked the fit so much that it's the only one I use now. As good as this fit is, it still.

. . like I said, high activity days, I get some pressure sores.

Every night, you have to look over the skin, make sure you've got nothing nasty going on, nothing growing where it shouldn't be. This guy is a gel liner. It doesn't breathe, but this is what keeps the leg on.

A lot of, um, amputees talk about forgetting that they don't have a leg in the middle of the night, and they get up in the middle of the night to go to the bathroom, and then instantly fall on their faces. That's only happened to me once, but I managed to catch myself before I hit the ground.