What is "The Six Million Dollar Man" about?

It's a show about a man who is rebuilt with robotic parts to become better, stronger, and faster.

Hugh Herr is a prosthetics innovator who lost his legs to frostbite and now develops bionic limbs that integrate with the human nervous system.

What inspired Hugh Herr to work on prosthetics?

A personal mountain-climbing accident that resulted in the loss of his legs inspired him to innovate in the field of prosthetics.

What is machine learning?

Machine learning is a subset of artificial intelligence that learns from data experiences to make decisions.

How is AI used in NASCAR?

AI in NASCAR is used through tools like Pit Rho, which analyses racing data to make strategic decisions like when cars should pit.

How does the C-THRU helmet enhance firefighting?

The C-THRU helmet provides augmented reality vision, allowing firefighters to see structures clearly in smoke-filled environments.

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

00:44:27

https://www.youtube.com/watch?v=lrv8ga02VNg

Summary

TLDRThe video explores the concept of enhancing human abilities through bionics and artificial intelligence. Starting with a reference to "The Six Million Dollar Man," it delves into real-life advancements in prosthetics and AI, telling the story of Jim Ewing, who regains his ability to climb through a bionic limb developed by his friend Hugh Herr. Herr, whose legs were amputated due to frostbite, innovates bionic prosthetics that integrate with the human nervous system to provide natural movement and sensation. The video also discusses the role of AI in sports, like NASCAR, where AI tools optimize racing strategies, and firefighting, where an AI-powered helmet helps visualize obstacles in smoke-filled environments. The overarching question is whether becoming superhuman is desirable or if our imperfections give life its interest.

Takeaways

🦾 Bionics can significantly enhance human ability, as seen in prosthetic innovations.
🤖 AI is transforming medicine, sports, and safety practices.
🧗‍♂️ Bionic limbs can restore lost capabilities, allowing individuals to pursue their passions, like climbing.
🚗 In NASCAR, AI assists with racing strategies to optimize performance.
🔥 Firefighting is being revolutionized by AI, improving safety through enhanced vision.
👣 Bionic limbs need to integrate with the nervous system for natural movement and sensation.
🧠 Machine learning algorithms drive decisions in bionic technology and other fields.
💡 AI is a tool that can push human limits beyond natural abilities.
🔍 C-THRU helmets provide firefighters a new way to navigate smoke-filled areas safely.
🤔 The ethical and philosophical implications of becoming 'superhuman' are complex and worth exploring.

Timeline

00:00:00 - 00:05:00
Narrator discusses 'The Six Million Dollar Man' tv show about a man rebuilt with bionics, expressing intrigue about superhuman abilities through technology. It references historical contexts of human enhancement and poses questions about desired human perfection versus imperfection.
00:05:00 - 00:10:00
Jim Ewing recounts his life-altering rock-climbing accident that left him severely injured. Discusses his love for climbing, the gravity of his injuries, and the consequent lifestyle change while he and his wife Cathy discuss their lives together before the accident.
00:10:00 - 00:15:00
Hugh Herr, an expert in bionics and an amputee himself, dedicates his work to advancing prosthetics into the bionic future. He and Jim Ewing have a shared history as earlier climbing partners, and now Herr is set to aid Ewing in regaining functionality and sensation through bionic limb technology.
00:15:00 - 00:20:00
Jim Ewing grapples with the decision to amputate his injured foot or continue bearing the pain. With support from family, he opts for amputation, becoming the first to undertake a new bionic limb procedure, linking muscle pairs to communicate with his nervous system and feel sensations through the bionic limb.
00:20:00 - 00:25:00
Ewing undergoes surgery and begins adapting to his new bionic leg, which he can control and feel. Herr's team at M.I.T. works on refining the setup, allowing Ewing to experience movements naturally through neural links, with promising results as Ewing tackles Cathedral Ledge with the new limb.
00:25:00 - 00:30:00
The narrative explores A.I.'s role in predictive systems across industries, notably sports and racing, illustrating its potential in enhancing performance by processing vast data. NASCAR’s use of A.I. exemplifies both reliance and challenges in executing optimal strategies amid traditional intuition.
00:30:00 - 00:35:00
As racing continues, strategic decisions miss achieving optimal results when human intuition overrides A.I. recommendations. This highlights ongoing trust issues with A.I. accuracy, despite proven benefit, leaving a commentary on society's gradual acceptance of A.I. in decision-making processes.
00:35:00 - 00:44:27
The exploration of vision-enhancing technology, particularly in firefighting, draws parallels to bionics, underscoring A.I.'s life-saving potential despite teething technological issues. Success in these fields promises transformative impacts, changing human capabilities and perceptions as they advance.

Mind Map

Video Q&A

What is "The Six Million Dollar Man" about?
It's a show about a man who is rebuilt with robotic parts to become better, stronger, and faster.
Who is Hugh Herr?
Hugh Herr is a prosthetics innovator who lost his legs to frostbite and now develops bionic limbs that integrate with the human nervous system.
What inspired Hugh Herr to work on prosthetics?
A personal mountain-climbing accident that resulted in the loss of his legs inspired him to innovate in the field of prosthetics.
What is machine learning?
Machine learning is a subset of artificial intelligence that learns from data experiences to make decisions.
How is AI used in NASCAR?
AI in NASCAR is used through tools like Pit Rho, which analyses racing data to make strategic decisions like when cars should pit.
How does the C-THRU helmet enhance firefighting?
The C-THRU helmet provides augmented reality vision, allowing firefighters to see structures clearly in smoke-filled environments.

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Subtitles

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00:00:06
[man] Steve Austin, astronaut, a man barely alive.
00:00:12
We can rebuild him. We have the technology.
00:00:15
We can make him better than he was.
00:00:18
Better...
00:00:19
stronger...
00:00:21
faster.
00:00:22
[Downey] Man, when I was a kid,
00:00:24
the Six Million Dollar Man was all the rage.
00:00:27
It's a show about a guy who gets rebuilt with robotic machine parts.
00:00:30
God, I loved it.
00:00:32
And then 35 years later, I got to play a similar role,
00:00:35
a character who enhances himself via technology and engineering.
00:00:39
Now, the term "bionics" goes back to the 1950s,
00:00:42
but the idea of enhancement actually dates back much further,
00:00:46
to Greek mythology, Aztec gods, and even ancient Hinduism.
00:00:49
So these next stories are about augmenting our human abilities,
00:00:53
everything from a bionic limb that behaves naturally and understands intent,
00:00:57
to data that improves performance,
00:00:59
and vision enhancement
00:01:00
that saves people in actual life-threatening situations.
00:01:04
It seems with A.I...
00:01:07
anything's possible.
00:01:09
So it raises the question,
00:01:10
do we even want to be superhuman,
00:01:12
or is imperfection what makes life interesting in the first place?
00:01:15
Whatevs. I gotta get back to the gym.
00:01:18
Normally I'd jog, but I got a wonky knee.
00:01:20
I should probably switch it out.
00:01:23
We can rebuild me.
00:01:25
Better.
00:01:27
Faster.
00:01:28
Stronger.
00:01:31
Seven miles an hour...
00:01:32
full out!
00:01:37
[Hugh Herr] Designers within the field of bionics,
00:01:42
they don't view the human body itself
00:01:45
as designable media.
00:01:48
We now have sophistication in Artificial Intelligence,
00:01:52
in motor technology,
00:01:55
in material science,
00:01:57
in how to talk to the nervous system,
00:01:59
setting the foundation...
00:02:01
[all laughing]
00:02:03
...for the end of human disability.
00:02:09
-[Cathy King] Do you wanna do an omelette? -[Jim Ewing] Sure.
00:02:11
You probably don't want too much onion,
00:02:13
because you'll have bad breath all day.
00:02:15
Oh, thanks, yeah. [chuckles]
00:02:17
Anyway, what were we talking about?
00:02:19
[Ewing] 26 years, 29 years--
00:02:20
Twenty six years, almost 29 years together, yup.
00:02:24
Our first date was, uh, uh... 2000, wasn't it?
00:02:28
-[King] 1990. -1990, oh my God.
00:02:31
[King] I could tell when I first met Jim
00:02:34
that he's highly intelligent,
00:02:35
and he said, "Would you like to go rock climbing sometime?"
00:02:39
and I said, "Sure!"
00:02:41
[Ewing] I started rock climbing in my very early teens,
00:02:45
and I consider climbing to be...
00:02:49
it's more than a passion for me.
00:02:50
It's my lifestyle.
00:02:54
In 2014,
00:02:56
my family and I traveled to the Cayman Islands.
00:03:00
We went rock climbing.
00:03:01
I set up the ropes for the day,
00:03:05
and we'd done a few climbs with no problems.
00:03:10
I started up the final section,
00:03:12
and... shifted my feet and slipped off...
00:03:19
...50 feet to the ground.
00:03:22
[hospital monitors beep]
00:03:24
[King] When I saw him at the hospital,
00:03:26
I have never felt so helpless in my entire life.
00:03:30
It was horrible.
00:03:33
[Ewing] The front and back of my pelvis
00:03:35
were completely shattered.
00:03:38
My left wrist was shattered.
00:03:41
My left ankle was broken into two or three chunks,
00:03:45
but the rest of it was kind of pulverized.
00:03:49
It slowly started to dawn on me
00:03:51
that this was something that was going to be life-changing.
00:03:56
[King] You're at the hospital.
00:03:58
[Jim murmurs]
00:03:59
[King] We're not looking at the photos right now.
00:04:01
I'm looking at 'em.
00:04:02
[King chuckles]
00:04:05
[kissing him] You look all you want, baby.
00:04:09
[Ewing] After a year, everything else seemed to heal well,
00:04:12
but the ankle continued to be a problem.
00:04:15
The bone was mostly dead,
00:04:18
and the main fracture was still there.
00:04:21
[King] He was in severe pain all the time,
00:04:23
and he just became so depressed.
00:04:26
[Ewing] I couldn't do the things that I love.
00:04:30
I could barely walk down the street without pain,
00:04:33
never mind go rock climbing.
00:04:36
[King] Rock climbing is his passion.
00:04:39
I mean, I just see him withering if he could not climb.
00:04:45
[Downey] Jim didn't know what to do,
00:04:48
and then, in a truly incredible stroke of luck,
00:04:51
his past came back to help decide his future.
00:04:55
I began mountain climbing at the tender age of seven.
00:04:58
At the age of 17, I was in a mountain-climbing accident,
00:05:01
and I suffered severe frostbite,
00:05:04
and my legs were amputated.
00:05:09
I really dedicated myself
00:05:11
to redesigning first my own legs,
00:05:14
and then the legs of many, many people around the world.
00:05:18
[Downey] A few decades, a couple of M.I.T. degrees,
00:05:21
and a single-minded focus to innovate later,
00:05:23
Hugh launched the prosthetics industry
00:05:26
into the bionic future.
00:05:27
[Herr] I'm getting a tremendous amount of energy,
00:05:30
power from the ankles,
00:05:31
which enables me to walk uphill
00:05:34
with a perfectly erect posture.
00:05:37
[Herr] My legs, they have the brain.
00:05:39
It's a small computer the size of your thumbnail,
00:05:42
and that brain receives sensory information
00:05:44
from sensors on the bionic limb,
00:05:47
and then it runs algorithms
00:05:49
and makes decisions on how to actuate itself.
00:05:52
And machine learning is used as part of those algorithms.
00:05:57
[Dr. Ayanna Howard] So, machine learning
00:05:59
is what's called a subset field of Artificial Intelligence.
00:06:03
We learn from experience.
00:06:05
Machine learning is basically learning from the experience,
00:06:08
where the experience is the data.
00:06:10
It takes input from the world,
00:06:12
and the input could be text in books,
00:06:14
it can be camera images from a car,
00:06:17
it applies a very complex mathematical function,
00:06:21
and then has an output, which is a decision.
00:06:24
[Downey] The bionic legs allow Hugh to walk, run,
00:06:27
even climb,
00:06:28
but for him,
00:06:29
there was still something missing.
00:06:30
[Herr] Because I can't feel my legs,
00:06:32
they... they remain tool-like to me,
00:06:35
and I believe if I could feel my limbs,
00:06:38
they would become part of me, part of self,
00:06:41
and fundamentally change my relationship
00:06:44
to the synthetic part of my body.
00:06:48
[Ewing] I would describe it as just this amazing,
00:06:51
lucky coincidence
00:06:53
that Hugh and I were roommates 34 years ago.
00:06:58
[Herr] We were teenagers rock-climbing together
00:07:01
and living like dirtbags, living like bums,
00:07:05
climbing every day.
00:07:07
[Ewing] So I decided I was going to look up Hugh
00:07:09
and talk to him about what my options might be.
00:07:14
[Herr] Jim was in excruciating pain,
00:07:16
and he asked me if I or my colleagues could help him.
00:07:19
[Ewing] What I really was hoping
00:07:21
was that he could put me in touch
00:07:23
with a reconstructive surgeon that could rebuild my ankle.
00:07:26
Right, so this is your X-ray...
00:07:27
[Herr] Jim was evaluated, and was provided, uh, options
00:07:31
of either maintaining the biological limb
00:07:34
and doing certain procedures
00:07:36
to try to improve its function and to reduce the pain,
00:07:40
or... to amputate the limb.
00:07:44
[Ewing] The thought of amputation was just so big.
00:07:48
What's life gonna be like for me
00:07:49
if I choose to amputate this foot?
00:07:54
But it hurt so bad.
00:07:57
I spoke with Cathy and Maxine about it.
00:08:02
They were behind me 100%.
00:08:04
Whatever I needed to do.
00:08:08
[Downey] How do you make a decision like that?
00:08:11
A few months later,
00:08:12
he agreed to have his leg amputated
00:08:15
and be the first person
00:08:16
to try his friend's bold, but experimental procedure.
00:08:19
[Ewing] It's gonna be good.
00:08:20
-Yeah. -Gonna be good.
00:08:23
Love you.
00:08:24
You too.
00:08:29
Bye, honey, love you.
00:08:31
Love you, too.
00:08:32
[Herr] The way in which limbs are amputated
00:08:35
has not fundamentally changed since the U.S. Civil War...
00:08:38
[soldiers shouting]
00:08:46
...but here at M.I.T.,
00:08:48
we were developing a novel way of amputating limbs.
00:08:51
We actually create little biological joints
00:08:54
by linking muscles together in pairs,
00:08:57
so when a person thinks
00:08:58
and moves the limb that's been amputated away,
00:09:01
these muscles move and send sensations
00:09:03
that we can directly link to a bionic limb
00:09:06
in a bi-directional way.
00:09:07
So not only can the person think
00:09:09
and actuate the synthetic limb,
00:09:11
but they can actually feel those synthetic movements
00:09:14
within their own nervous system.
00:09:15
[Downey] Until recently,
00:09:17
creating a bionic limb that a person can actually feel
00:09:20
has been more science fiction than reality.
00:09:23
Now machine learning
00:09:25
is revolutionizing the way we think about medicine.
00:09:28
If anything can solve the hard problems in medicine,
00:09:30
it's A.I.
00:09:31
Let's take an example, heart disease.
00:09:34
No single human being can have in their head
00:09:36
all the knowledge that it takes to understand heart disease,
00:09:39
but a computer can.
00:09:41
Things like radiology, pathology.
00:09:43
You have an X-ray,
00:09:45
and you wanna see, like, is there cancer in this lung,
00:09:46
and can you pinpoint where the tumor is or not?
00:09:49
A.I.s can, actually, at this point,
00:09:50
do this better than highly trained humans.
00:09:52
[surgeon] Can we get the, uh, Esmarch, please?
00:10:00
It's so light!
00:10:02
It's weird.
00:10:05
[surgeon] It really looks good.
00:10:07
I'm super happy with this,
00:10:08
and you've got a nice degree of padding here.
00:10:10
[Ewing] Right after the surgery,
00:10:12
the incredible, deep, in-the-bone pain
00:10:15
that I had been experiencing for the past year
00:10:17
was gone.
00:10:18
So to me, that was...
00:10:21
that was a success right there.
00:10:25
Never mind whether or not
00:10:27
the experimental part of the amputation was a success,
00:10:31
I was glad to be free of the painful ankle.
00:10:35
He was happy. It was done.
00:10:37
He was ready to move on.
00:10:43
[Herr] Hey, guys.
00:10:44
How's it looking? How's progress?
00:10:46
[M.I.T. tech Eric] Things look good.
00:10:48
I'm gonna go ahead and get you wired up.
00:10:49
[Ewing] The first time I went to Hugh's lab,
00:10:51
Hugh started talking about
00:10:53
"What do you think about a climbing robot ankle?
00:10:57
Would you want us to make one of those?"
00:10:58
[Eric] I'm gonna go ahead and get us started here...
00:11:01
[Herr] It's a specifically designed limb
00:11:02
that Jim can control with his mind,
00:11:05
and actually feel the movements within his nervous system.
00:11:08
[Ewing] I still need the evert.
00:11:10
[Eric] Yup, that's the one we're missing.
00:11:12
All right, so we have you wired to the leg now.
00:11:15
You're driving.
00:11:22
[servos whirring]
00:11:31
[Ewing] Cool.
00:11:32
[Eric] Yeah. Can you give me a fast up-down?
00:11:37
And slow, controlled?
00:11:41
This freakin' blows me away every time you do it.
00:11:44
It's so good.
00:11:45
[chuckling]
00:11:47
When we link Jim's nerves in that bi-directional way,
00:11:51
we're able to create natural dynamics,
00:11:53
so even though the limb is made of synthetic materials,
00:11:56
it moves as if it's made of flesh and bone.
00:11:59
There.
00:12:01
Now it's neurally and mechanically connected.
00:12:04
How does it feel different?
00:12:05
Now it feels like it's my natural foot, somewhat.
00:12:09
Like, I don't have the skin sensation,
00:12:11
but all the motions make sense to my brain.
00:12:15
[Herr] In the algorithm,
00:12:16
we make a virtual model of his missing biological limb,
00:12:20
so when he fires his muscles with his brain,
00:12:23
we use an electrode to measure that signal,
00:12:25
and then that drives the virtual muscle
00:12:28
and sends sensations to the brain
00:12:30
about the position and dynamics.
00:12:32
It kind of instantly felt part of me,
00:12:34
almost as good as having a natural foot.
00:12:42
[Downey] Cathedral Ledge, New Hampshire.
00:12:45
Seven hundred feet of awe-inspiring granite
00:12:48
and climbing routes with names like "Thin Air,"
00:12:50
"Nutcracker," and "They Died Laughing"
00:12:53
make it one hell of a challenge
00:12:55
for even the most serious climbers
00:12:57
on a good day.
00:12:59
For Jim, it's a test
00:13:01
to see if what works at M.I.T. can work out on the mountain.
00:13:05
[Ewing] I've been climbing here for 40 years,
00:13:08
and I've probably spent more time on Cathedral Ledge
00:13:11
than any place else on the planet.
00:13:17
This climb is actually at the upper limit
00:13:20
of my ability at the moment.
00:13:22
I'm not worried at all.
00:13:24
What could possibly go wrong?
00:13:27
[Downey] The mountain has no mercy,
00:13:29
and no margin for error,
00:13:31
and Jim's about to find out
00:13:33
if his bionic leg can help him overcome
00:13:35
and scale heights
00:13:37
most people wouldn't dare try in the first place.
00:13:39
[Ewing] That's me.
00:13:41
[Downey] Can machine learning take us even further?
00:13:44
Replace not just what was lost,
00:13:46
but enhance what we already have?
00:13:48
[firefighter] Okay, stay close. I'll lead.
00:13:50
This is insane!
00:13:53
[Downey] Augment performance
00:13:54
beyond the limits of our natural human ability?
00:13:57
Make strong, smart, fast people...
00:14:00
stronger, smarter, and faster?
00:14:02
[crowd cheering]
00:14:04
In many ways, sports has been on the leading edge of prediction systems,
00:14:06
and now, every serious sports contender
00:14:08
uses sports analytics...
00:14:12
but the big opportunity going forward
00:14:14
is embedding devices
00:14:16
that can collect real-time data
00:14:18
to update strategies
00:14:19
to take advantage of that learning.
00:14:21
There's a revolution going on in sports,
00:14:23
and machine learning is at the core of it.
00:14:32
[Interviewer] The first night race of the season,
00:14:33
I'm sure you're ready to finally get behind the wheel.
00:14:36
For sure. Just fired up to get going.
00:14:38
Triple-A car's been pretty good this weekend,
00:14:40
and we're pumped to get this thing started.
00:14:42
Drivers, start your engines!
00:14:45
[crowd screaming]
00:14:47
[Eric Warren] The race track's a fairly hostile environment.
00:14:51
The way I describe racing, and the way I live it,
00:14:53
it's like war.
00:14:54
[announcer] Folks, get on your feet. Let's send these guys off!
00:14:57
Boogity boogity boogity! Let's go racing, boys!
00:15:01
[Warren] You're trying to take your race car,
00:15:03
your team, your driver,
00:15:05
and beat the other drivers at all costs.
00:15:07
[race team radio chatter]
00:15:08
[announcer] Austin Dillon,
00:15:09
stuck in the middle of a three-wide.
00:15:13
[Andy Petree] This kind of race
00:15:15
produces a lot of strategy,
00:15:18
and that's where we have to use all of our tools
00:15:20
to help us make those strategic decisions.
00:15:23
[Downey] When it comes to superhuman ability,
00:15:26
you may think of people like LeBron James,
00:15:28
Michael Phelps, or Serena Williams...
00:15:32
but it's not just the body that can be enhanced.
00:15:35
Sometimes it's something less tangible,
00:15:38
like human intuition.
00:15:41
[announcer] What a battle going on here.
00:15:44
You gotta be real careful here in the early stages
00:15:46
making contact with somebody.
00:15:47
[Warren] Information is the next battleground.
00:15:49
[race team radio chatter]
00:15:52
Every decision you make can have a big impact.
00:15:55
[Downey] Back in the day,
00:15:56
intuition used to play a big part in sports.
00:15:59
Athletes and coaches relied on their gut
00:16:01
to make decisions.
00:16:03
Now some competitors are leaning more and more
00:16:06
on machine learning,
00:16:07
looking to gain whatever extra edge they can.
00:16:10
[Warren] We use the A.I. tools
00:16:12
to predict what the future not only is,
00:16:15
but what it should be.
00:16:17
[announcer] We'll go behind the 20. You just start finish line...
00:16:19
[Rana el Kaliouby] The strength of these A.I. systems
00:16:20
come in having access to a ton of data
00:16:23
and being able to find patterns in that data,
00:16:25
generating insights and inferences
00:16:28
that maybe people may not be aware of,
00:16:31
and then augmenting people's abilities
00:16:33
to make decisions based on that data.
00:16:35
[Downey] Machine learning
00:16:37
is transforming many industries and applications,
00:16:40
especially in areas where there's a lot of data,
00:16:42
and predicting outcomes can have a big payoff.
00:16:45
Finance,
00:16:46
sports,
00:16:48
or medicine come to mind.
00:16:51
Using an emerging technology like machine learning
00:16:54
in a classic old-school sport like stock car racing
00:16:57
doesn't necessarily sit well with everybody...
00:17:00
which may or may not explain why this guy's doing it...
00:17:06
in a nerve center 250 miles away.
00:17:08
[man] Clear, clear, hit the marks, drive off, man.
00:17:12
[Warren] My role there really is looking at the data.
00:17:15
How do you use data you can acquire at the racetrack
00:17:18
to get these machines
00:17:19
to be right on the limit of performance?
00:17:20
[announcer] His front rotors are really glowing.
00:17:23
[Warren] We get the braking, steering, throttle,
00:17:26
all the acceleration off of every car in the field,
00:17:29
real time...
00:17:30
[Downey] All this data
00:17:31
is being fed into an A.I. program called "Pit Rho."
00:17:35
[race team radio chatter]
00:17:37
[Downey] Sensors in every car
00:17:38
measure speed, throttle, braking, and steering.
00:17:42
Advanced GPS tracks the car's position on the track.
00:17:45
[man] Watch your middle, watch your middle.
00:17:47
[Downey] All this data is made available to every team.
00:17:51
[Warren] This is where the power of A.I. comes in.
00:17:54
So, our tool basically
00:17:55
is analyzing the optimum strategy call
00:17:59
of every car in the field, real time.
00:18:01
Not just our car, but every car.
00:18:04
[Petree] It's almost threatening.
00:18:06
I was a crew chief for Dale Earnhardt Sr.
00:18:08
Comin' to ya.
00:18:09
10-4.
00:18:11
[Petree] I would sit up on the box
00:18:12
and intuitively kinda figure all these things.
00:18:15
You kinda just make that gut call,
00:18:18
"Bring him in now."
00:18:19
[Downey] Until now, many key decisions,
00:18:21
like when to pit for tires or fuel,
00:18:23
were made by the drivers and the crew chief
00:18:25
using experience and intuition.
00:18:27
[Petree] Now, we've got artificial intelligence
00:18:29
that's making all these calculations
00:18:31
in real time.
00:18:32
Some of the crew chiefs
00:18:33
that have done what I've done over the years,
00:18:35
sometimes it's hard for us to embrace it.
00:18:37
[tires squealing]
00:18:40
[announcer] They're trying to get through traffic as fast as they can
00:18:43
so they don't get a lap down,
00:18:44
but that's gonna use up those tires.
00:18:46
[Warren] You can go at this track on fuel
00:18:48
probably 120 laps, but your tires will be shot way before then.
00:18:52
[Downey] In a NASCAR race,
00:18:54
pit stops are the key to a winning strategy.
00:18:56
[Petree] You're trying to decide
00:18:57
when in that cycle is the best to make that stop,
00:19:00
because you lose a lot of time when you come off the track and you have to stop,
00:19:03
but then you gain a lot of speed when you put new tires on.
00:19:06
[Warren] This is the first time
00:19:07
we're facing, like, a strategy call here.
00:19:09
[Downey] The Pit Rho A.I. interface
00:19:11
displays one of four suggestions...
00:19:15
stay out on the track,
00:19:16
pit for fuel only,
00:19:18
pit for two tires,
00:19:19
or pit for four tires.
00:19:21
[Warren] So right now,
00:19:22
it's telling him to take four tires.
00:19:24
[Downey] Eric relays the message
00:19:26
to the Childress team at the track.
00:19:28
The final decision on when to pit
00:19:30
will be up to the crew chief.
00:19:34
[crew chief] When the pits are open,
00:19:35
it'll be four tires here, four tires.
00:19:38
[Downey] For the first pit stop,
00:19:40
the crew chief follows the A.I.'s advice.
00:19:42
Five, four, three, two, one.
00:19:45
Put on the brakes, wheels lift.
00:19:47
[Downey] The crew has to change all four tires
00:19:50
in as little time as possible.
00:19:54
This usually takes between 12 and 14 seconds.
00:20:00
[engine revving]
00:20:01
[radio chatter] All the way, all the way!
00:20:04
That's a good stop.
00:20:05
Really good stop.
00:20:06
[Warren] Sometimes, what happens is, over the course of a race,
00:20:09
those little bit better decisions
00:20:10
puts you in a spot, and it puts you in an opportunity
00:20:13
at the end of the race to be able to win the race.
00:20:22
[Warren] Every lap, it's analyzing the field,
00:20:25
updating its models.
00:20:26
As the race goes on, the prediction gets more and more accurate.
00:20:30
[Downey] They're using an A.I. technique
00:20:33
called "reinforcement learning,"
00:20:34
which is, basically,
00:20:36
when the computer is given the rules of the game,
00:20:39
plays it over and over
00:20:40
till it learns every possible move and outcome,
00:20:43
and then through trial and error,
00:20:44
and patience that no human could possibly have...
00:20:47
[announcer] I wonder if we have a resignation here.
00:20:50
[Downey] ...becomes amazing.
00:20:53
[announcer] Congratulations to AlphaGo
00:20:56
and to the entire team.
00:20:57
[Downey] It's what Google's DeepMind did
00:20:59
to become a world champ at Go.
00:21:01
[commentator] Here we go!
00:21:04
[Downey] It's what Open A.I. did
00:21:06
to conquer the video game Dota 2...
00:21:09
[commentator] He's dominating.
00:21:10
Are you scared of a bot here?
00:21:11
[Downey] ...and build a robotic hand
00:21:13
with near-human dexterity.
00:21:16
It's what Eric's hoping to do to get the checkered flag.
00:21:21
[announcer] You can see he's on his way to the top 10.
00:21:25
[team] Yeah, we got through, Andrew, focus here.
00:21:29
[announcer] And you go up a few cars,
00:21:31
you'll find the 3 of Austin Dillon
00:21:33
up in sixth place,
00:21:34
making up time on the race leader.
00:21:44
So the recommendation is pit on lap 327.
00:21:51
What my fear is is that they'll pit with the leaders
00:21:54
instead of actually running to the strategy.
00:21:58
[Downey] Going to the pits when the leaders do
00:22:00
is the safe play in the end stages of a race...
00:22:02
[Warren] Sparks, you got me?
00:22:04
[Downey] ...but the A.I. tool is recommending a riskier plan
00:22:07
that might gain them valuable seconds.
00:22:13
[Downey] By pitting later,
00:22:15
Austin Dillon will have faster tires
00:22:17
for the closing laps of the race,
00:22:19
but he risks falling further behind the leaders
00:22:21
once they come out of the pits with their fresh tires.
00:22:29
[Petree] A lot of times when our Pit Rho technology tells us,
00:22:32
"This is the time to pit," or, "This is how to do it," it doesn't feel right.
00:22:38
Are you sure you wanna do that now?
00:22:40
[Petree] Sometimes you might be sitting out there
00:22:42
running laps on older tires,
00:22:43
where everybody else is pitted,
00:22:45
and it's like, it doesn't feel right for the driver.
00:22:52
[Petree] He's gonna want to pit,
00:22:54
and you gotta convince him, "Stay, make good laps. Trust us, it's gonna work."
00:22:57
Some leaders are gonna pit right here, and we need to run.
00:23:03
[commentator] Looks like the 22
00:23:05
is gonna choose to come down pit road.
00:23:07
[announcer] So, all the front four came in on the same lap
00:23:10
with 82 laps to go.
00:23:11
[Downey] On lap 318,
00:23:13
the top four cars enter the pits.
00:23:17
[team member 1 speaking]
00:23:19
[Warren speaking]
00:23:22
[team member 1 speaking]
00:23:26
[Warren] Here's where the faith in the tool ends up happening.
00:23:29
When they all pit,
00:23:30
it takes a lot of faith to just stay out there
00:23:32
and run to your lap.
00:23:36
[team member 2 speaking]
00:23:38
[team member 3 speaking]
00:23:39
[Downey] Austin Dillon breaks from the A.I. strategy
00:23:42
and follows the leaders into the pits.
00:23:44
[team members speaking]
00:23:46
That's not good news.
00:23:47
[man] Three, two, one.
00:23:50
Put on the brakes, wheels lift.
00:23:52
[Downey] To maintain their position,
00:23:54
the team needs a flawless pit stop.
00:24:08
[tires screeching]
00:24:09
[team member 2] Son of a bitch!
00:24:13
[team member 1] We lost three seconds.
00:24:14
We're not gonna be nowhere near 'em.
00:24:17
Got killed on pit road.
00:24:18
It's pretty disastrous.
00:24:20
[Warren] Prior to the pit stop, we were about 4.6 seconds back,
00:24:24
but when we came out, we were nine seconds back,
00:24:26
so we lost about four and a half seconds
00:24:28
on that-- in that exchange. That's hard to get back.
00:24:31
[man] Let's go to work on him. This won't be easy.
00:24:34
Just fight hard here.
00:24:35
[Downey] They've dropped from 6th place to 12th...
00:24:38
and Austin Dillon has very little time left
00:24:40
to fight his way back to the leaders.
00:24:42
[Eric] Come on, Austin, get him.
00:24:46
[Downey] ...but the new tires give him an edge...
00:24:51
[announcer] The white flag waves,
00:24:53
one lap to go.
00:24:55
[team member 1 speaking]
00:25:01
Get it, get it, get it!
00:25:05
[announcer] Short track win number one for Martin Truex!
00:25:09
[race team] Sixth place is awesome.
00:25:11
[Downey] ...and he ends up finishing sixth.
00:25:13
[team member 2] Hell of a freakin' drive, Austin Dillon.
00:25:16
[team member 3] Hey, nice work tonight, man, way to fight hard there.
00:25:20
[team member 4] Hell of a job, boys.
00:25:23
Hey, good job, guys.
00:25:25
[Warren] Progressing through the race
00:25:27
definitely the cars have gotten faster,
00:25:29
so, you know, we'll see good things
00:25:30
that we'll take back next time we go to Richmond.
00:25:32
Hell of a job this weekend, boys.
00:25:34
[Warren] The hardest thing
00:25:36
as we've incorporated more A.I.-based tools
00:25:39
is trust.
00:25:41
Sometimes we're the ones that get in the way, right?
00:25:44
There's still times when it's counterintuitive,
00:25:47
and everybody's like,
00:25:48
"It's the wrong call, it's the wrong call,"
00:25:50
and over time, we have these battles
00:25:52
because most of the time, the A.I. tools is right.
00:25:55
Nine times out of ten, or even more, it's the right call.
00:25:58
[Downey] Andy and Eric's team were using A.I.,
00:26:01
and on track for a strong finish,
00:26:03
but they fell behind
00:26:05
when the team ignored the machine
00:26:06
and went with their intuition.
00:26:09
That'll do it.
00:26:11
[Lav Varshney] Convincing humans
00:26:12
that machines know what they're doing
00:26:13
is the central difficulty
00:26:14
in deploying A.I. out in society,
00:26:16
whether it's the pit boss in car racing,
00:26:19
or even astronauts flying to the moon.
00:26:22
[Downey] Do we trust the A.I. to make decisions for us?
00:26:25
We already do with GPS maps.
00:26:27
Perhaps here, the team just didn't have enough experience with it
00:26:30
to override their own intuition,
00:26:33
but what about other situations?
00:26:37
At what point do we start trusting A.I.
00:26:39
in more serious matters?
00:26:41
[dawn birdsong chorus]
00:26:42
Matters of life and death?
00:26:45
[firefighter] It was a smoldering fire that filled the whole house with smoke,
00:26:48
and you couldn't see your hand in front of your face.
00:26:51
You literally had to feel your way up the stairs.
00:26:54
Totally blind search.
00:26:56
Yeah. Sometimes that's the best thing we can do.
00:26:59
Yeah.
00:27:00
[Kirk McKinzie] Every two hours and 45 minutes,
00:27:02
a U.S. citizen dies by fire in their own home.
00:27:06
We've lost more than 3,000 a year
00:27:08
consistently for 30 years.
00:27:10
[firefighter] The Worcester fire.
00:27:13
Three guys go in. They all get disoriented and get lost.
00:27:17
Two more go in to find them.
00:27:18
They get lost. Two more go in.
00:27:20
I mean, before you know it, they finally had to, "Okay!
00:27:23
We're not sending any more guys in there,
00:27:25
'cause they're all friggin' lost."
00:27:27
[news broadcast] On his radio, a commanding officer heard two firefighters
00:27:31
desperately crying out for help.
00:27:33
[Worcester fire chief] "Mayday, mayday. We're running out of air.
00:27:36
Come to the door so we can see where you are,"
00:27:38
and then, we did that, and we went beyond the door,
00:27:41
and we yelled, and we had lights,
00:27:43
and they were...
00:27:44
they were inside somewhere that they couldn't see us.
00:27:48
[firefighter] All those guys who died in that...
00:27:56
[McKinzie] When we go into a structure that's dark and smoky,
00:28:00
the biggest challenge is the visibility.
00:28:03
The ability to navigate is a... is a challenge,
00:28:07
and often firefighters have become disoriented,
00:28:11
and then they run out of air.
00:28:17
With the challenge of smoke and having no vision,
00:28:20
I knew that there was a possibility of changing that.
00:28:26
That's when I finally met the C-THRU team.
00:28:29
[Sam Cossman] Okay, is the system turning on?
00:28:31
Let's see.
00:28:33
I'm gonna unplug that one.
00:28:35
I guess the best way to describe myself
00:28:38
is I'm infinitely curious.
00:28:41
I like to solve problems,
00:28:43
look at things through a new lens.
00:28:44
All right...
00:28:47
[Cossman] I was in disbelief that firefighting in a smoked-out building
00:28:51
involves training their personnel
00:28:52
to revert back to feeling around the room.
00:28:55
How's the battery level doing?
00:28:57
That was really the inspiration
00:28:58
behind creating C-THRU.
00:29:05
[Downey] Sam Cossman saw the light when he jumped into a volcano.
00:29:08
Line!
00:29:10
Fire!
00:29:12
[Downey] Part globetrotting adrenaline junkie,
00:29:14
part computer engineer,
00:29:16
the self-proclaimed Indiana Jones of tech
00:29:19
envisioned a tool that would help firefighters
00:29:22
and save lives,
00:29:24
a kind of X-ray vision.
00:29:28
[Cossman] The problem that C-THRU is trying to solve
00:29:31
is really flipping the lights on
00:29:33
for people operating in zero-visibility conditions.
00:29:37
[Omer Haciomeroglu] The concept of C-THRU
00:29:39
was the helmet that had enhanced audio,
00:29:42
enhanced vision...
00:29:43
[man] I see you! I'm on my way.
00:29:46
[Haciomeroglu] ...outlines their surrounding geometry
00:29:50
so that they can navigate faster.
00:29:52
So is it this plane right here that... that changed recently?
00:29:55
[Haciomeroglu] Yes, basically like a simpler design that can achieve more.
00:29:59
[Cossman] We have a mask,
00:30:01
and we have a thermal-imaging device
00:30:02
that sits on the side of that mask,
00:30:04
and we process that image through a small computer.
00:30:08
[Downey] Sam and Omer created a mask
00:30:10
with special glasses clipped inside
00:30:12
which allows firefighters to see edges as green lines
00:30:15
in an augmented reality overlay.
00:30:18
How's the alignment look on that one?
00:30:20
It's not bad.
00:30:22
We need to calibrate it a little bit more.
00:30:24
Omer and I have been working on refining the prototypes
00:30:27
for the last couple of years,
00:30:28
just trying to MacGyver some of these problems
00:30:30
with off-the-shelf parts, you know, duct tape and bubble gum.
00:30:34
Move your hand around a little bit.
00:30:36
-Okay. -Other hand, like that one.
00:30:38
Yeah, this is definitely better.
00:30:41
[Downey] It may look like old Tron-era night vision,
00:30:44
but there's actually
00:30:45
some pretty slick artificial intelligence at work here.
00:30:48
Thermal imaging cameras
00:30:50
stream video from the firefighter's helmet
00:30:52
into an A.I. processor.
00:30:54
Using infrared light
00:30:56
and a powerful edge-detection algorithm,
00:30:59
the mask detects subtle changes in brightness
00:31:02
to predict shapes invisible to the human eye,
00:31:05
like a wall hidden by smoke,
00:31:07
or a kid hiding under a bed.
00:31:12
[Cossman] There you go, take this mask.
00:31:14
[Downey] Sam and Omer
00:31:16
are now at a familiar point in the innovator's journey...
00:31:19
get out of the garage and into the real world
00:31:22
to see if their invention can take the heat.
00:31:25
[siren wailing, horn blares]
00:31:27
Fire Dispatch, Medic 71 arrived on scene,
00:31:29
have report of smoke showing.
00:31:30
Fire Dispatch, copy.
00:31:32
[McKinzie] One of the most important things any fire department does
00:31:35
is regular hands-on training.
00:31:38
There he is.
00:31:39
How you doing, Captain?
00:31:41
Good to see you, brother.
00:31:42
[McKinzie] We're gonna give the C-THRU solution a hard run...
00:31:45
[Cossman] We've got a prototype fresh off the print.
00:31:47
...and we're gonna put it in fire and smoke,
00:31:50
and we're gonna see how it acts while crews are working with it.
00:31:53
-Shall we get him inside the smoke? -Let's do it!
00:31:56
[radio chatter]
00:31:57
...cleared for dispatch.
00:31:59
I am a Cyborg.
00:32:01
Okay, we're ready.
00:32:05
[McKinzie] Crews will be doing live fire drills
00:32:07
in our training tower.
00:32:10
It is active, real fire
00:32:12
with temperatures at the ceiling at 1,200 degrees.
00:32:20
[yelling through masks]
00:32:22
[man] Anybody over here?
00:32:25
[McKinzie] Firefighters are in a hurry,
00:32:27
looking for victims.
00:32:28
Visibility will be limited at best.
00:32:35
Often, firefighters will be able to see nothing.
00:33:02
[Downey] C-THRU's maiden voyage
00:33:04
is cut short by a malfunction.
00:33:06
[McKinzie] In an active firefight,
00:33:08
it's critical that things work.
00:33:10
It's life and death.
00:33:12
Uh, at first, it was good. I got through, went down to the floor,
00:33:17
and I looked, and I could see everything clear.
00:33:19
Yeah.
00:33:20
Really well, everything was lined out. Once I started working...
00:33:23
-Yeah? -I lost it.
00:33:24
-Yeah, the signal went out. -Signal went out.
00:33:27
I'm not sure what that was, but we're gonna figure it out.
00:33:29
There was a lot of interference, or maybe a cable issue.
00:33:32
We did encounter some challenges, the biggest of them
00:33:35
was some wi-fi interference that we've encountered
00:33:37
where the system would just shut down.
00:33:39
Yeah, it's actually like over here with the connections,
00:33:42
-like, this pin, you know? -That's what's...
00:33:45
Yeah, the pin connections here, and here, actually.
00:33:49
We should just shield the cables as best we can
00:33:52
and give it another go.
00:33:56
Battalion Ten, Fire Dispatch,
00:33:58
uh, we got a caller on the second floor
00:33:59
trapped in the bathroom.
00:34:01
So, if you wanna go ahead and try it on for a fit,
00:34:04
we'll see how it goes.
00:34:05
-Fire Dispatch, Battalion Ten... -[radio chatter continues]
00:34:09
[on-scene dispatch] Engine 72 arrived on scene, reporting of heavy smoke showing
00:34:14
from the first and second floor.
00:34:16
[radio chatter continues]
00:34:20
We've got smoke showing
00:34:22
from the first and second floors.
00:34:29
[dispatch] Engine 7-1,
00:34:30
you're gonna be taking fire attack.
00:34:46
[dispatch] 71, who is on scene,
00:34:49
smoke showing from the second and first floor.
00:35:05
Command copies, one victim coming out of the second window, you need EMS.
00:35:09
Medic 72, you're gonna have to take patient care.
00:35:12
As soon as I got in, I could see the outline of the room.
00:35:16
As I stepped in, I just kinda took a look around,
00:35:18
I could see where the victim was and an outline of the door.
00:35:22
-I mean, hands free, you know? -Yeah.
00:35:25
[chatter on radio]
00:35:27
It is kind of like, I mean, like Iron Man, you know,
00:35:30
being able to see through the smoke,
00:35:31
and having everything so clear-cut, um...
00:35:35
It's... it's pretty cool.
00:35:37
[Cossman] What we're working on is really a game-changing tool
00:35:41
that completely has the potential to transform
00:35:44
how the work here is done.
00:35:45
[firefighter] This is, uh, some of the videos
00:35:47
of the C-THRU mask, okay?
00:35:49
[firefighter 2] That is way crisper than I've seen.
00:35:51
That is insane.
00:35:53
[McKinzie] Over the 30 years that I've been at this, I've seen a lot of changes.
00:35:57
We have mobile data computers,
00:35:59
we have computer-aided dispatch systems...
00:36:01
-No, that's gonna... -Wow.
00:36:03
That's gonna be a game-changer.
00:36:04
[McKinzie] ...and now we have the possibility with machine learning and A.I.
00:36:09
to progress to a place
00:36:11
just a couple of years ago we couldn't have imagined.
00:36:13
-Is that completely pitch dark in there? -That recording--
00:36:16
-[alert sounds] -Oh, gotta go!
00:36:18
That is actually what you see in the mask.
00:36:20
[firefighter] We're gonna go on another call, gentlemen.
00:36:24
[Downey] It's impossible to know
00:36:25
if this technology could have saved
00:36:27
those six firefighters in Worcester,
00:36:29
but it's hard to believe it wouldn't have helped.
00:36:37
Back on Cathedral Ledge,
00:36:38
Jim is about to see if his new bionic leg
00:36:41
will help him scale a 700-foot sheer rock face.
00:36:44
[Ewing] I'm just gonna kinda bring everything.
00:36:50
[M.I.T. tech Emily] All right.
00:36:52
[Ewing] My own personal M.I.T. pit crew.
00:36:55
-[Emily] Got the socket. -[assistant] The socket...
00:36:57
[Ewing] What we're gonna do today
00:36:59
is climb on Cathedral Ledge with a new robot foot
00:37:03
designed specifically for climbing.
00:37:10
We can set up camp here.
00:37:13
[Emily] All right,
00:37:14
we should be ready to start calibrating.
00:37:18
[M.I.T. tech Joe] Counterflex.
00:37:22
Rest.
00:37:23
-[Emily] You're driving now. -[Ewing] That's me.
00:37:26
[Emily] How's it feel?
00:37:27
[Ewing] Pretty accurate.
00:37:29
It's going everywhere that I'm telling it to go.
00:37:31
[Ewing] This climb is gonna be very challenging,
00:37:34
because there's a variety of holds at different angles,
00:37:38
different heights.
00:37:39
I'd say there's a high probability
00:37:41
of there being some falling action here and there.
00:37:47
I was really afraid,
00:37:50
very... worried
00:37:51
whether or not I could make it all the way up a climb.
00:38:05
We good there, Joe?
00:38:13
Harness is on.
00:38:16
I got plenty of gear.
00:38:22
All right, we're climbing.
00:38:30
I think I'm at a crux section here.
00:38:35
[wincing]
00:38:36
Well, first fall.
00:38:39
I'm not sticking very well.
00:38:47
It's hard. Hard business.
00:38:53
[grunting with effort]
00:38:56
Slack!
00:39:03
[cracking]
00:39:04
Oh!
00:39:08
We have failure.
00:39:10
The whole mechanism broke.
00:39:13
[Ewing] I remember looking down at it,
00:39:15
seeing the foot at a strange angle,
00:39:18
and, "Holy crap, that is gonna hurt.
00:39:20
"That--" Like, I was bracing for pain.
00:39:23
I mean, how much more of a part of you
00:39:26
does it need to be?
00:39:35
[Emily] Oh, my God.
00:39:36
[man] I would call that a catastrophic failure.
00:39:38
[Emily] Pretty catastrophic.
00:39:39
But it was... it was a strange sensation, though,
00:39:42
because all of a sudden, my ankle was broken,
00:39:45
and you feel like you're losing your limb
00:39:49
all over again.
00:39:51
[Eric] How're you feeling? You feel like you wanna go down again?
00:39:54
We... we did bring a spare.
00:39:56
Uh, sure.
00:39:58
Okay, we'll swap it over to this one.
00:40:02
[Emily] In engineering,
00:40:04
we're kinda used to things not exactly going right
00:40:07
the first time,
00:40:07
so that's why we have contingency plans.
00:40:10
[Eric] So, this is the last climbing robot leg
00:40:12
in the world, Jim.
00:40:14
[laughter]
00:40:16
We're good to go again.
00:40:23
I'm a little nervous about trusting this foot now.
00:40:28
Watch me here.
00:40:29
If the left-- if the robot breaks...
00:40:33
I'm going for a ride.
00:40:43
Actually, it did that move.
00:40:44
[Eric] Well done.
00:40:46
[Ewing] We're rock climbing, dude.
00:40:49
[Ewing] With the robotic leg, I found that I could move more naturally.
00:40:54
Life on the edge, man.
00:40:55
I was pain free, and it was, I don't know,
00:40:58
it was just kind of fun and satisfying.
00:41:00
[Herr] We have always hypothesized
00:41:03
that if we can link the nerves of a human being
00:41:07
to a bionic limb,
00:41:08
the limb would become part of the person,
00:41:11
part of identity.
00:41:14
Toppin' out.
00:41:16
Remarkably, it's happened.
00:41:18
Cyborg power!
00:41:20
[laughing]
00:41:22
[team clapping]
00:41:23
[Downey] It's a tall peak,
00:41:25
but pales in comparison
00:41:27
to the one Hugh is ultimately trying to climb.
00:41:30
[Herr] We also have the goal
00:41:32
of extending human capability beyond physiological function,
00:41:35
jump higher, or run faster...
00:41:38
So bionics not only seeks to achieve normative function in humans,
00:41:42
but also to extend human expression
00:41:45
beyond what people were born with.
00:41:48
[Downey] Human enhancement and augmentation
00:41:51
have been around through human history
00:41:53
and mythology,
00:41:54
from Prometheus stealing fire
00:41:57
to the Civil War.
00:42:00
Using tools to improve our abilities
00:42:02
is a fundamental human development,
00:42:04
whether it's stone spears to protect our families
00:42:08
or airplanes to transport us farther.
00:42:10
...and that's really what we're seeing, the transformation of society,
00:42:14
and not just racing, not just sports,
00:42:16
is really using these A.I. tools, and they'll become commonplace,
00:42:19
won't even be thought about otherwise.
00:42:21
[Downey] A.I. and machine learning...
00:42:23
they're just tools,
00:42:25
ones that makes us stronger, smarter, faster.
00:42:30
[Herr] A.I. will play an increasingly dominant role
00:42:33
across all the many dimensions of what it means to be human.
00:42:36
[Downey] There's a good chance
00:42:38
A.I. will continue to enhance us in ways both known and unknown,
00:42:42
eventually becoming as invisible as the air we breathe.
00:42:46
[Herr] That narrative will play out
00:42:49
across all types of human conditions.
00:42:51
That will enhance human capability,
00:42:54
fundamentally change who we are as a human race.
00:42:59
[Downey] The question then becomes,
00:43:02
if it does,
00:43:04
what do we do with our newfound superpowers?
00:43:14
[dog panting]
00:43:16
[Ewing] This one's meant to be
00:43:19
kind of an all-around athletic foot.
00:43:21
I can run with it, hiking, biking, whatever I want.
00:43:25
I even use it for surfing,
00:43:26
'cause it has a good bit of flex to surf with.
00:43:30
I actually liked the fit so much
00:43:32
that it's the only one I use now.
00:43:35
As good as this fit is, it still...
00:43:37
like I said, high activity days, I get some pressure sores.
00:43:40
Every night, you have to look over the skin,
00:43:42
make sure you've got nothing nasty going on,
00:43:45
nothing growing where it shouldn't be.
00:43:48
This guy is a gel liner.
00:43:51
It doesn't breathe,
00:43:52
but this is what keeps the leg on.
00:43:55
A lot of, um, amputees
00:43:57
talk about forgetting that they don't have a leg
00:43:59
in the middle of the night,
00:44:01
and they get up in the middle of the night
00:44:03
to go to the bathroom,
00:44:04
and then instantly fall on their faces.
00:44:06
That's only happened to me once,
00:44:09
but I managed to catch myself before I hit the ground.
00:44:12
[chuckling]