The Future of Human Augmentation | Maurice Conti | TEDxPortland
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0:11 - 0:14All right, Portland, wow.
Thank you so much for having me. -
0:14 - 0:15I love visiting this city.
-
0:16 - 0:19I mean, where else in the world
can I have breakfast like this. -
0:19 - 0:20(Laughter)
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0:20 - 0:24And clearly, this is
a very creative place. -
0:24 - 0:25(Laughter)
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0:25 - 0:27How many of you are creatives,
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0:27 - 0:31designers, engineers,
entrepreneurs, artists, -
0:31 - 0:33or maybe you just have
a really big imagination? -
0:34 - 0:35Show of hands? (Cheers)
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0:35 - 0:37That's most of you.
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0:38 - 0:40I have some news for us creatives.
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0:41 - 0:44Over the course of the next 20 years,
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0:46 - 0:49more will change around
the way we do our work -
0:50 - 0:52than has happened in the last 2,000.
-
0:53 - 0:57In fact, I think we're at the dawn
of a new age in human history. -
0:58 - 1:03Now, there have been four major historical
eras defined by the way we work. -
1:04 - 1:07The Hunter-Gatherer Age
lasted several million years. -
1:08 - 1:11And then the Agricultural Age
lasted several thousand years. -
1:11 - 1:15The Industrial Age
lasted a couple of centuries. -
1:15 - 1:19And now the Information Age
has lasted just a few decades. -
1:19 - 1:24And now today, we're on the cusp
of our next great era as a species. -
1:26 - 1:28Welcome to the Augmented Age.
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1:28 - 1:32In this new era, your natural human
capabilities are going to be augmented -
1:32 - 1:35by computational systems
that help you think, -
1:35 - 1:37robotic systems that help you make,
-
1:37 - 1:39and a digital nervous system
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1:39 - 1:43that connects you to the world
far beyond your natural senses. -
1:45 - 1:47Let's start with cognitive augmentation.
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1:47 - 1:49How many of you are augmented cyborgs?
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1:49 - 1:52(Laughter)
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1:52 - 1:53There's three or four of you,
-
1:53 - 1:55that's just because it's Portland.
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1:55 - 1:58(Laughter)
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1:58 - 1:59Yeah, keep it weird.
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1:59 - 2:02(Laughter)
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2:02 - 2:04I would actually argue
that we're already augmented. -
2:05 - 2:07Imagine you're at a party,
-
2:07 - 2:10and somebody asks you a question
that you don't know the answer to. -
2:10 - 2:14If you have one of these,
in a few seconds, you can know the answer. -
2:15 - 2:17But this is just a primitive beginning.
-
2:18 - 2:21Even Siri is just a passive tool.
-
2:21 - 2:25In fact, for the last
three-and-a-half million years, -
2:25 - 2:28the tools that we've had
have been completely passive. -
2:30 - 2:33They do exactly what we tell them
and nothing more. -
2:33 - 2:36Our very first tool
only cut where we struck it. -
2:37 - 2:40The chisel only carves
where the artist points it. -
2:41 - 2:47And even our most advanced tools
do nothing without our explicit direction. -
2:48 - 2:51In fact, to date, and this
is something that frustrates me, -
2:51 - 2:52we've always been limited
-
2:52 - 2:56by this need to manually
push our wills into our tools -- -
2:56 - 2:58like, manual, literally using our hands,
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2:58 - 3:00even with computers.
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3:01 - 3:03But I'm more like Scotty in "Star Trek."
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3:03 - 3:05(Laughter)
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3:05 - 3:07I want to have a conversation
with a computer. -
3:07 - 3:10I want to say, "Computer,
let's design a car," -
3:10 - 3:12and the computer shows me a car.
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3:12 - 3:14And I say, "No, more fast-looking,
and less German," -
3:14 - 3:17and bang, the computer shows me an option.
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3:17 - 3:18(Laughter)
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3:19 - 3:21That conversation might be
a little ways off, -
3:21 - 3:24probably less than many of us think,
-
3:24 - 3:26but right now,
-
3:26 - 3:27we're working on it.
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3:27 - 3:31Tools are making this leap
from being passive to being generative. -
3:31 - 3:36Generative design tools
use a computer and algorithms -
3:36 - 3:39to synthesize geometry
-
3:39 - 3:42to come up with new designs
all by themselves. -
3:42 - 3:45All it needs are your goals
and your constraints. -
3:45 - 3:46I'll give you an example.
-
3:46 - 3:49In the case of this aerial drone chassis,
-
3:49 - 3:52all you would need to do
is tell it something like, -
3:52 - 3:53it has four propellers,
-
3:53 - 3:55you want it to be
as lightweight as possible, -
3:55 - 3:58and you need it to be
aerodynamically efficient. -
3:58 - 4:02Then what the computer does
is it explores the entire solution space: -
4:02 - 4:06every single possibility that solves
and meets your criteria -- -
4:06 - 4:08millions of them.
-
4:08 - 4:10It takes big computers to do this.
-
4:10 - 4:12But it comes back to us with designs
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4:12 - 4:15that we, by ourselves,
never could've imagined. -
4:15 - 4:18And the computer's coming up
with this stuff all by itself -- -
4:18 - 4:20no one ever drew anything,
-
4:20 - 4:22and it started completely from scratch.
-
4:23 - 4:26And by the way, it's no accident
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4:26 - 4:29that the drone body looks just like
the pelvis of a flying squirrel. -
4:29 - 4:32(Laughter)
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4:32 - 4:34It's because the algorithms
are designed to work -
4:34 - 4:36the same way evolution does.
-
4:37 - 4:40What's exciting is we're starting
to see this technology -
4:40 - 4:41out in the real world.
-
4:41 - 4:43We've been working with Airbus
for a couple of years -
4:43 - 4:45on this concept plane for the future.
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4:45 - 4:47It's a ways out still.
-
4:47 - 4:51But just recently we used
a generative-design AI -
4:51 - 4:53to come up with this.
-
4:54 - 4:59This is a 3D-printed cabin partition
that's been designed by a computer. -
4:59 - 5:02It's stronger than the original
yet half the weight, -
5:02 - 5:05and it will be flying
in the Airbus A320 later this year. -
5:07 - 5:09So computers can now generate;
-
5:09 - 5:14they can come up with their own solutions
to our well-defined problems. -
5:15 - 5:16But they're not intuitive.
-
5:16 - 5:19They still have to start from scratch
every single time, -
5:19 - 5:22and that's because they never learn.
-
5:23 - 5:25Unlike Maggie.
-
5:25 - 5:26(Laughter)
-
5:26 - 5:29Maggie's actually smarter
than our most advanced design tools. -
5:30 - 5:31What do I mean by that?
-
5:31 - 5:33If her owner picks up that leash,
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5:33 - 5:35Maggie knows with a fair
degree of certainty -
5:35 - 5:37it's time to go for a walk.
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5:37 - 5:38And how did she learn?
-
5:38 - 5:41Well, every time the owner picked up
the leash, they went for a walk. -
5:41 - 5:43And Maggie did three things:
-
5:43 - 5:45she had to pay attention,
-
5:45 - 5:47she had to remember what happened
-
5:47 - 5:51and she had to retain and create
a pattern in her mind. -
5:52 - 5:54Interestingly, that's exactly what
-
5:54 - 5:57computer scientists
have been trying to get AIs to do -
5:57 - 5:58for the last 60 or so years.
-
6:00 - 6:01Back in 1952,
-
6:01 - 6:05they built this computer
that could play tic-tac-toe. -
6:06 - 6:07Big deal.
-
6:08 - 6:11Then 45 years later, in 1997,
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6:11 - 6:14Deep Blue beats Kasparov at chess.
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6:15 - 6:202011, Watson beats these two
humans at Jeopardy, -
6:20 - 6:23which is much harder for a computer
to play than chess is. -
6:23 - 6:27In fact, rather than working
from predefined recipes, -
6:27 - 6:30Watson had to use reasoning
to overcome his human opponents. -
6:31 - 6:34And then a couple of weeks ago,
-
6:34 - 6:38DeepMind's AlphaGo beats
the world's best human at Go, -
6:38 - 6:40which is the most difficult
game that we have. -
6:40 - 6:44In fact, in Go, there are more
possible moves -
6:44 - 6:46than there are atoms in the universe.
-
6:48 - 6:50So in order to win,
-
6:50 - 6:53what AlphaGo had to do
was develop intuition. -
6:53 - 6:57And in fact, at some points,
AlphaGo's programmers didn't understand -
6:57 - 6:59why AlphaGo was doing what it was doing.
-
7:01 - 7:03And things are moving really fast.
-
7:03 - 7:06I mean, consider --
in the space of a human lifetime, -
7:06 - 7:08computers have gone from a child's game
-
7:10 - 7:13to what's recognized as the pinnacle
of strategic thought. -
7:14 - 7:16What's basically happening
-
7:16 - 7:19is computers are going
from being like Spock ... -
7:20 - 7:22to being a lot more like Kirk.
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7:22 - 7:26(Laughter)
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7:26 - 7:30Right? From pure logic to intuition.
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7:32 - 7:33Would you cross this bridge?
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7:34 - 7:36Most of you are saying, "Oh, hell no!"
-
7:36 - 7:38(Laughter)
-
7:38 - 7:40And you arrived at that decision
in a split second. -
7:40 - 7:43You just sort of knew
that bridge was unsafe. -
7:43 - 7:45And that's exactly the kind of intuition
-
7:45 - 7:48that our deep-learning systems
are starting to develop right now. -
7:49 - 7:51Very soon, you'll literally be able
-
7:51 - 7:54to show something you've made,
you've designed, to a computer, -
7:54 - 7:55and it will look at it and say,
-
7:55 - 7:58"Sorry, homie, that'll never work.
You have to try again." -
7:59 - 8:02Or you could ask it if people
are going to like your next song, -
8:03 - 8:05or your next flavor of ice cream.
-
8:07 - 8:10Or, much more importantly,
-
8:10 - 8:12you could work with a computer
to solve a problem -
8:12 - 8:14that we've never faced before.
-
8:14 - 8:15For instance, climate change.
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8:15 - 8:17We're not doing
a very good job on our own, -
8:17 - 8:19we could certainly use
all the help we can get. -
8:19 - 8:21That's what I'm talking about,
-
8:21 - 8:23technology amplifying
our cognitive abilities -
8:23 - 8:27so we can imagine and design things
that were simply out of our reach -
8:27 - 8:30as plain old un-augmented humans.
-
8:31 - 8:34So what about making
all of this crazy new stuff -
8:34 - 8:37that we're going to invent and design?
-
8:37 - 8:41I think the era of human augmentation
is as much about the physical world -
8:41 - 8:45as it is about the virtual,
intellectual realm. -
8:45 - 8:47How will technology augment us?
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8:48 - 8:50In the physical world, robotic systems.
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8:52 - 8:53OK, there's certainly a fear
-
8:53 - 8:56that robots are going to take
jobs away from humans, -
8:56 - 8:58and that is true in certain sectors.
-
8:58 - 9:01But I'm much more interested in this idea
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9:01 - 9:06that humans and robots working together
are going to augment each other, -
9:06 - 9:08and start to inhabit a new space.
-
9:08 - 9:11This is our applied research lab
in San Francisco, -
9:11 - 9:14where one of our areas of focus
is advanced robotics, -
9:14 - 9:16specifically, human-robot collaboration.
-
9:17 - 9:20And this is Bishop, one of our robots.
-
9:20 - 9:22As an experiment, we set it up
-
9:22 - 9:25to help a person working in construction
doing repetitive tasks -- -
9:26 - 9:30tasks like cutting out holes for outlets
or light switches in drywall. -
9:30 - 9:33(Laughter)
-
9:34 - 9:37So, Bishop's human partner
can tell what to do in plain English -
9:37 - 9:38and with simple gestures,
-
9:38 - 9:40kind of like talking to a dog,
-
9:40 - 9:42and then Bishop executes
on those instructions -
9:42 - 9:44with perfect precision.
-
9:44 - 9:47We're using the human
for what the human is good at: -
9:47 - 9:49awareness, perception and decision making.
-
9:49 - 9:52And we're using the robot
for what it's good at: -
9:52 - 9:53precision and repetitiveness.
-
9:54 - 9:57Here's another cool project
that Bishop worked on. -
9:57 - 10:00The goal of this project,
which we called the HIVE, -
10:00 - 10:04was to prototype the experience
of humans, computers and robots -
10:04 - 10:07all working together to solve
a highly complex design problem. -
10:08 - 10:09The humans acted as labor.
-
10:09 - 10:13They cruised around the construction site,
they manipulated the bamboo -- -
10:13 - 10:16which, by the way,
because it's a non-isomorphic material, -
10:16 - 10:17is super hard for robots to deal with.
-
10:18 - 10:20But then the robots
did this fiber winding, -
10:20 - 10:22which was almost impossible
for a human to do. -
10:22 - 10:26And then we had an AI
that was controlling everything. -
10:26 - 10:29It was telling the humans what to do,
telling the robots what to do -
10:29 - 10:32and keeping track of thousands
of individual components. -
10:32 - 10:34What's interesting is,
-
10:34 - 10:37building this pavilion
was simply not possible -
10:37 - 10:41without human, robot and AI
augmenting each other. -
10:42 - 10:46OK, I'll share one more project.
This one's a little bit crazy. -
10:48 - 10:52We're working with Amsterdam-based artist
Joris Laarman and his team at MX3D -
10:54 - 10:56to generatively design
and robotically print -
10:56 - 10:59the world's first autonomously
manufactured bridge. -
11:01 - 11:05So, Joris and an AI are designing
this thing right now, as we speak, -
11:05 - 11:06in Amsterdam.
-
11:07 - 11:09And when they're done,
we're going to hit "Go," -
11:09 - 11:13and robots will start 3D printing
in stainless steel, -
11:13 - 11:16and then they're going to keep printing,
without human intervention, -
11:16 - 11:17until the bridge is finished.
-
11:19 - 11:22So, as computers are going
to augment our ability -
11:22 - 11:24to imagine and design new stuff,
-
11:24 - 11:27robotic systems are going to help us
build and make things -
11:27 - 11:29that we've never been able to make before.
-
11:30 - 11:34But what about our ability
to sense and control these things? -
11:34 - 11:38What about a nervous system
for the things that we make? -
11:40 - 11:43Our nervous system,
the human nervous system, -
11:43 - 11:45tells us everything
that's going on around us. -
11:46 - 11:49But the nervous system of the things
we make is rudimentary at best. -
11:49 - 11:52I would say it's rather
shitty, at this point. -
11:52 - 11:53(Laughter)
-
11:53 - 11:57For instance, a car doesn't tell
the city's public works department -
11:57 - 12:00that it just hit a pothole at the corner
of Broadway and Morrison. -
12:01 - 12:03A building doesn't tell its designers
-
12:03 - 12:06whether or not the people inside
like being there, -
12:08 - 12:11and the toy manufacturer doesn't know
-
12:11 - 12:13if a toy is actually being played with --
-
12:13 - 12:15how and where and whether
or not it's any fun. -
12:16 - 12:20Look, I'm sure that the designers
imagined this lifestyle for Barbie -
12:20 - 12:21when they designed her.
-
12:21 - 12:22(Laughter)
-
12:23 - 12:26But what if it turns out that Barbie's
actually really lonely? -
12:26 - 12:30(Laughter)
-
12:30 - 12:32If the designers had known
-
12:32 - 12:34what was really happening
in the real world -
12:34 - 12:36with their designs --
the road, the building, Barbie -- -
12:36 - 12:39they could've used that knowledge
to create an experience -
12:39 - 12:40that was better for the user.
-
12:41 - 12:42What's missing is a nervous system
-
12:42 - 12:46connecting us to all of the things
that we design, make and use. -
12:47 - 12:51What if all of you had that kind
of information flowing to you -
12:51 - 12:53from the things you create
in the real world? -
12:54 - 12:56With all of the stuff we make,
-
12:56 - 12:58we spend a tremendous amount
of money and energy -- -
12:58 - 13:01in fact, last year,
about two trillion dollars -- -
13:01 - 13:04convincing people to buy
the things we've made. -
13:04 - 13:07But if you had this connection
to the things that you design and create -
13:07 - 13:09after they're out in the real world,
-
13:09 - 13:12after they've been sold
or launched or whatever, -
13:13 - 13:14we could actually change that,
-
13:14 - 13:18and go from making people want our stuff,
-
13:18 - 13:21to just making stuff that people
want in the first place. -
13:24 - 13:27The good news is, we're working
on digital nervous systems -
13:27 - 13:30that connect us to the things we design.
-
13:31 - 13:33We're working on one project
-
13:33 - 13:38with a couple of guys down in Los Angeles
called the Bandito Brothers -
13:38 - 13:39and their team.
-
13:39 - 13:43And one of the things these guys do
is build insane cars -
13:43 - 13:46that do absolutely insane things.
-
13:47 - 13:49These guys are crazy --
-
13:49 - 13:50(Laughter)
-
13:50 - 13:51in the best way.
-
13:55 - 13:57And what we're doing with them
-
13:57 - 13:59is taking a traditional race-car chassis
-
13:59 - 14:01and giving it a nervous system.
-
14:01 - 14:04So we instrumented it
with dozens of sensors, -
14:04 - 14:06put a world-class driver behind the wheel,
-
14:06 - 14:10took it out to the desert
and drove the hell out of it for a week. -
14:10 - 14:12And the car's nervous system
captured everything -
14:12 - 14:14that was happening to the car.
-
14:14 - 14:16We captured four billion data points;
-
14:17 - 14:19all of the forces
that it was subjected to. -
14:19 - 14:20And then we did something crazy.
-
14:21 - 14:23We took all of that data,
-
14:23 - 14:26and plugged it into a generative-design AI
we call "Dreamcatcher." -
14:27 - 14:31So what do get when you give
a design tool a nervous system, -
14:31 - 14:34and you ask it to build you
the ultimate car chassis? -
14:35 - 14:37You get this.
-
14:38 - 14:42This is something that a human
could never have designed. -
14:43 - 14:45Except a human did design this,
-
14:45 - 14:49but it was a human that was augmented
by a generative-design AI, -
14:49 - 14:50a digital nervous system
-
14:50 - 14:53and robots that can actually
fabricate something like this. -
14:54 - 14:58So if this is the future,
the Augmented Age, -
14:58 - 15:02and we're going to be augmented
cognitively, physically and perceptually, -
15:02 - 15:03what will that look like?
-
15:04 - 15:07What is this wonderland going to be like?
-
15:08 - 15:11I think we're going to see a world
-
15:11 - 15:14where we're moving
from things that are fabricated -
15:15 - 15:17to things that are farmed.
-
15:18 - 15:22Where we're moving from things
that are constructed -
15:22 - 15:24to that which is grown.
-
15:27 - 15:29We're going to move from being isolated
-
15:30 - 15:31to being connected.
-
15:33 - 15:36And we'll move away from extraction
-
15:36 - 15:38to embrace aggregation.
-
15:40 - 15:44I also think we'll shift
from craving obedience from our things -
15:45 - 15:47to valuing autonomy.
-
15:49 - 15:51Thanks to our augmented capabilities,
-
15:51 - 15:54our world is going to change dramatically.
-
15:55 - 15:58I think a good analogy is the incredible
microcosm of a coral reef. -
15:59 - 16:02We're going to have a world
with more variety, more connectedness, -
16:02 - 16:04more dynamism, more complexity,
-
16:04 - 16:07more adaptability and, of course,
-
16:07 - 16:08more beauty.
-
16:09 - 16:10The shape of things to come
-
16:10 - 16:13will be unlike anything
we've ever seen before. -
16:13 - 16:14Why?
-
16:14 - 16:18Because what will be shaping those things
is this new partnership -
16:18 - 16:21between technology, nature and humanity.
-
16:23 - 16:27That, to me, is a future
well worth looking forward to. -
16:27 - 16:28Thank you all so much.
-
16:28 - 16:35(Applause)
- Title:
- The Future of Human Augmentation | Maurice Conti | TEDxPortland
- Description:
-
What do you get when you give a design tool a digital nervous system? Maurice Conti explores a new partnership between technology, nature and humanity - the future of human augmentation.
Maurice is a designer, futurist and innovator. He is the Director of Strategic Innovation at Autodesk and has worked with startups, government agencies, renowned artists and corporations to explore what the future might
hold for them, and design solutions to get them there. As leader of Autodesk’s Applied Research Lab, he focuses on advanced robotics, applied machine learning and sea
level rise. He has circled the globe and was honored for his bravery by the US, New Zealand and the United Nations for saving the lives of three shipwrecked sailors. Maurice lives
in Muir Beach, CA, where he serves as a volunteer firefighter.This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
- Video Language:
- English
- Team:
closed TED
- Project:
- TEDxTalks
- Duration:
- 16:42
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Krystian Aparta edited English subtitles for The Future of Human Augmentation | Maurice Conti | TEDxPortland | |
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Krystian Aparta edited English subtitles for The Future of Human Augmentation | Maurice Conti | TEDxPortland | |
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Krystian Aparta edited English subtitles for The Future of Human Augmentation | Maurice Conti | TEDxPortland |