Placeholder Image

Subtitles section Play video

  • [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.

  • Drivers, 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.

  • [chuckling]

[man] Steve Austin, astronaut, a man barely alive.

Subtitles and vocabulary

Click the word to look it up Click the word to find further inforamtion about it