The incredible inventions of intuitive AI
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0:01 - 0:03How many of you are creatives,
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0:03 - 0:07designers, engineers,
entrepreneurs, artists, -
0:07 - 0:09or maybe you just have
a really big imagination? -
0:09 - 0:11Show of hands? (Cheers)
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0:11 - 0:12That's most of you.
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0:13 - 0:16I have some news for us creatives.
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0:17 - 0:19Over the course of the next 20 years,
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0:21 - 0:24more will change around
the way we do our work -
0:25 - 0:28than has happened in the last 2,000.
-
0:29 - 0:33In fact, I think we're at the dawn
of a new age in human history. -
0:34 - 0:38Now, there have been four major historical
eras defined by the way we work. -
0:39 - 0:43The Hunter-Gatherer Age
lasted several million years. -
0:43 - 0:47And then the Agricultural Age
lasted several thousand years. -
0:47 - 0:51The Industrial Age lasted
a couple of centuries. -
0:51 - 0:55And now the Information Age
has lasted just a few decades. -
0:55 - 1:00And now today, we're on the cusp
of our next great era as a species. -
1:01 - 1:04Welcome to the Augmented Age.
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1:04 - 1:08In this new era, your natural human
capabilities are going to be augmented -
1:08 - 1:11by computational systems
that help you think, -
1:11 - 1:13robotic systems that help you make,
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1:13 - 1:15and a digital nervous system
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1:15 - 1:18that connects you to the world
far beyond your natural senses. -
1:19 - 1:21Let's start with cognitive augmentation.
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1:21 - 1:24How many of you are augmented cyborgs?
-
1:24 - 1:27(Laughter)
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1:27 - 1:30I would actually argue
that we're already augmented. -
1:30 - 1:32Imagine you're at a party,
-
1:32 - 1:35and somebody asks you a question
that you don't know the answer to. -
1:35 - 1:39If you have one of these,
in a few seconds, you can know the answer. -
1:40 - 1:42But this is just a primitive beginning.
-
1:43 - 1:46Even Siri is just a passive tool.
-
1:47 - 1:50In fact, for the last
three-and-a-half million years, -
1:50 - 1:53the tools that we've had
have been completely passive. -
1:54 - 1:58They do exactly what we tell them
and nothing more. -
1:58 - 2:01Our very first tool only cut
where we struck it. -
2:02 - 2:05The chisel only carves
where the artist points it. -
2:05 - 2:11And even our most advanced tools
do nothing without our explicit direction. -
2:11 - 2:14In fact, to date, and this
is something that frustrates me, -
2:14 - 2:16we've always been limited
-
2:16 - 2:19by this need to manually
push our wills into our tools -- -
2:19 - 2:22like, manual,
literally using our hands, -
2:22 - 2:23even with computers.
-
2:24 - 2:27But I'm more like Scotty in "Star Trek."
-
2:27 - 2:28(Laughter)
-
2:28 - 2:31I want to have a conversation
with a computer. -
2:31 - 2:34I want to say, "Computer,
let's design a car," -
2:34 - 2:35and the computer shows me a car.
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2:35 - 2:38And I say, "No, more fast-looking,
and less German," -
2:38 - 2:40and bang, the computer shows me an option.
-
2:40 - 2:42(Laughter)
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2:42 - 2:45That conversation might be
a little ways off, -
2:45 - 2:47probably less than many of us think,
-
2:47 - 2:49but right now,
-
2:49 - 2:50we're working on it.
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2:50 - 2:54Tools are making this leap
from being passive to being generative. -
2:55 - 2:58Generative design tools
use a computer and algorithms -
2:58 - 3:01to synthesize geometry
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3:01 - 3:04to come up with new designs
all by themselves. -
3:04 - 3:07All it needs are your goals
and your constraints. -
3:07 - 3:08I'll give you an example.
-
3:08 - 3:11In the case of this aerial drone chassis,
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3:11 - 3:14all you would need to do
is tell it something like, -
3:14 - 3:15it has four propellers,
-
3:15 - 3:17you want it to be
as lightweight as possible, -
3:17 - 3:19and you need it to be
aerodynamically efficient. -
3:19 - 3:24Then what the computer does
is it explores the entire solution space: -
3:24 - 3:28every single possibility that solves
and meets your criteria -- -
3:28 - 3:30millions of them.
-
3:30 - 3:32It takes big computers to do this.
-
3:32 - 3:34But it comes back to us with designs
-
3:34 - 3:37that we, by ourselves,
never could've imagined. -
3:37 - 3:40And the computer's coming up
with this stuff all by itself -- -
3:40 - 3:42no one ever drew anything,
-
3:42 - 3:44and it started completely from scratch.
-
3:45 - 3:47And by the way, it's no accident
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3:47 - 3:51that the drone body looks just like
the pelvis of a flying squirrel. -
3:51 - 3:53(Laughter)
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3:54 - 3:56It's because the algorithms
are designed to work -
3:56 - 3:58the same way evolution does.
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3:59 - 4:01What's exciting is we're starting
to see this technology -
4:01 - 4:03out in the real world.
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4:03 - 4:05We've been working with Airbus
for a couple of years -
4:05 - 4:07on this concept plane for the future.
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4:07 - 4:09It's a ways out still.
-
4:09 - 4:13But just recently we used
a generative-design AI -
4:13 - 4:15to come up with this.
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4:16 - 4:21This is a 3D-printed cabin partition
that's been designed by a computer. -
4:21 - 4:24It's stronger than the original
yet half the weight, -
4:24 - 4:27and it will be flying
in the Airbus A320 later this year. -
4:27 - 4:29So computers can now generate;
-
4:29 - 4:34they can come up with their own solutions
to our well-defined problems. -
4:35 - 4:36But they're not intuitive.
-
4:36 - 4:39They still have to start from scratch
every single time, -
4:39 - 4:42and that's because they never learn.
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4:42 - 4:44Unlike Maggie.
-
4:44 - 4:46(Laughter)
-
4:46 - 4:49Maggie's actually smarter
than our most advanced design tools. -
4:49 - 4:51What do I mean by that?
-
4:51 - 4:53If her owner picks up that leash,
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4:53 - 4:55Maggie knows with a fair
degree of certainty -
4:55 - 4:56it's time to go for a walk.
-
4:56 - 4:57And how did she learn?
-
4:57 - 5:01Well, every time the owner picked up
the leash, they went for a walk. -
5:01 - 5:02And Maggie did three things:
-
5:03 - 5:04she had to pay attention,
-
5:04 - 5:06she had to remember what happened
-
5:07 - 5:11and she had to retain and create
a pattern in her mind. -
5:11 - 5:14Interestingly, that's exactly what
-
5:14 - 5:16computer scientists
have been trying to get AIs to do -
5:16 - 5:18for the last 60 or so years.
-
5:19 - 5:20Back in 1952,
-
5:20 - 5:24they built this computer
that could play Tic-Tac-Toe. -
5:25 - 5:26Big deal.
-
5:27 - 5:30Then 45 years later, in 1997,
-
5:30 - 5:33Deep Blue beats Kasparov at chess.
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5:34 - 5:392011, Watson beats these two
humans at Jeopardy, -
5:39 - 5:42which is much harder for a computer
to play than chess is. -
5:42 - 5:46In fact, rather than working
from predefined recipes, -
5:46 - 5:49Watson had to use reasoning
to overcome his human opponents. -
5:50 - 5:53And then a couple of weeks ago,
-
5:53 - 5:57DeepMind's AlphaGo beats
the world's best human at Go, -
5:57 - 5:59which is the most difficult
game that we have. -
5:59 - 6:02In fact, in Go, there are more
possible moves -
6:02 - 6:04than there are atoms in the universe.
-
6:06 - 6:08So in order to win,
-
6:08 - 6:11what AlphaGo had to do
was develop intuition. -
6:11 - 6:15And in fact, at some points,
AlphaGo's programmers didn't understand -
6:15 - 6:18why AlphaGo was doing what it was doing.
-
6:19 - 6:21And things are moving really fast.
-
6:21 - 6:24I mean, consider --
in the space of a human lifetime, -
6:24 - 6:27computers have gone from a child's game
-
6:28 - 6:31to what's recognized as the pinnacle
of strategic thought. -
6:32 - 6:34What's basically happening
-
6:34 - 6:38is computers are going
from being like Spock -
6:38 - 6:40to being a lot more like Kirk.
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6:40 - 6:43(Laughter)
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6:43 - 6:47Right? From pure logic to intuition.
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6:48 - 6:50Would you cross this bridge?
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6:51 - 6:53Most of you are saying, "Oh, hell no!"
-
6:53 - 6:54(Laughter)
-
6:54 - 6:57And you arrived at that decision
in a split second. -
6:57 - 6:59You just sort of knew
that bridge was unsafe. -
6:59 - 7:01And that's exactly the kind of intuition
-
7:01 - 7:05that our deep-learning systems
are starting to develop right now. -
7:06 - 7:07Very soon, you'll literally be able
-
7:07 - 7:10to show something you've made,
you've designed, -
7:10 - 7:11to a computer,
-
7:11 - 7:12and it will look at it and say,
-
7:12 - 7:15"Sorry, homie, that'll never work.
You have to try again." -
7:16 - 7:19Or you could ask it if people
are going to like your next song, -
7:20 - 7:22or your next flavor of ice cream.
-
7:24 - 7:26Or, much more importantly,
-
7:26 - 7:29you could work with a computer
to solve a problem -
7:29 - 7:30that we've never faced before.
-
7:30 - 7:32For instance, climate change.
-
7:32 - 7:34We're not doing a very
good job on our own, -
7:34 - 7:36we could certainly use
all the help we can get. -
7:36 - 7:37That's what I'm talking about,
-
7:37 - 7:40technology amplifying
our cognitive abilities -
7:40 - 7:44so we can imagine and design things
that were simply out of our reach -
7:44 - 7:46as plain old un-augmented humans.
-
7:48 - 7:51So what about making
all of this crazy new stuff -
7:51 - 7:53that we're going to invent and design?
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7:54 - 7:58I think the era of human augmentation
is as much about the physical world -
7:58 - 8:01as it is about the virtual,
intellectual realm. -
8:02 - 8:04How will technology augment us?
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8:04 - 8:07In the physical world, robotic systems.
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8:08 - 8:09OK, there's certainly a fear
-
8:09 - 8:12that robots are going to take
jobs away from humans, -
8:12 - 8:14and that is true in certain sectors.
-
8:14 - 8:17But I'm much more interested in this idea
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8:17 - 8:22that humans and robots working together
are going to augment each other, -
8:22 - 8:24and start to inhabit a new space.
-
8:24 - 8:27This is our applied research lab
in San Francisco, -
8:27 - 8:30where one of our areas of focus
is advanced robotics, -
8:30 - 8:32specifically, human-robot collaboration.
-
8:33 - 8:36And this is Bishop, one of our robots.
-
8:36 - 8:38As an experiment, we set it up
-
8:38 - 8:41to help a person working in construction
doing repetitive tasks -- -
8:42 - 8:46tasks like cutting out holes for outlets
or light switches in drywall. -
8:46 - 8:49(Laughter)
-
8:50 - 8:53So, Bishop's human partner
can tell what to do in plain English -
8:53 - 8:54and with simple gestures,
-
8:54 - 8:56kind of like talking to a dog,
-
8:56 - 8:58and then Bishop executes
on those instructions -
8:58 - 9:00with perfect precision.
-
9:00 - 9:03We're using the human
for what the human is good at: -
9:03 - 9:05awareness, perception and decision making.
-
9:05 - 9:08And we're using the robot
for what it's good at: -
9:08 - 9:09precision and repetitiveness.
-
9:10 - 9:13Here's another cool project
that Bishop worked on. -
9:13 - 9:16The goal of this project,
which we called the HIVE, -
9:16 - 9:20was to prototype the experience
of humans, computers and robots -
9:20 - 9:23all working together to solve
a highly complex design problem. -
9:24 - 9:25The humans acted as labor.
-
9:25 - 9:29They cruised around the construction site,
they manipulated the bamboo -- -
9:29 - 9:32which, by the way,
because it's a non-isomorphic material, -
9:32 - 9:33is super hard for robots to deal with.
-
9:33 - 9:35But then the robots
did this fiber winding, -
9:35 - 9:38which was almost impossible
for a human to do. -
9:38 - 9:42And then we had an AI
that was controlling everything. -
9:42 - 9:45It was telling the humans what to do,
telling the robots what to do -
9:45 - 9:48and keeping track of thousands
of individual components. -
9:48 - 9:49What's interesting is,
-
9:49 - 9:52building this pavilion
was simply not possible -
9:52 - 9:57without human, robot and AI
augmenting each other. -
9:58 - 10:01OK, I'll share one more project.
This one's a little bit crazy. -
10:01 - 10:06We're working with Amsterdam-based artist
Joris Laarman and his team at MX3D -
10:06 - 10:09to generatively design
and robotically print -
10:09 - 10:12the world's first autonomously
manufactured bridge. -
10:12 - 10:16So, Joris and an AI are designing
this thing right now, as we speak, -
10:16 - 10:17in Amsterdam.
-
10:17 - 10:20And when they're done,
we're going to hit "Go," -
10:20 - 10:23and robots will start 3D printing
in stainless steel, -
10:23 - 10:26and then they're going to keep printing,
without human intervention, -
10:26 - 10:28until the bridge is finished.
-
10:29 - 10:32So, as computers are going
to augment our ability -
10:32 - 10:34to imagine and design new stuff,
-
10:34 - 10:37robotic systems are going to help us
build and make things -
10:37 - 10:39that we've never been able to make before.
-
10:40 - 10:45But what about our ability
to sense and control these things? -
10:45 - 10:49What about a nervous system
for the things that we make? -
10:49 - 10:51Our nervous system,
the human nervous system, -
10:51 - 10:53tells us everything
that's going on around us. -
10:54 - 10:58But the nervous system of the things
we make is rudimentary at best. -
10:58 - 11:01For instance, a car doesn't tell
the city's public works department -
11:01 - 11:05that it just hit a pothole at the corner
of Broadway and Morrison. -
11:05 - 11:07A building doesn't tell its designers
-
11:07 - 11:09whether or not the people inside
like being there, -
11:09 - 11:12and the toy manufacturer doesn't know
-
11:12 - 11:14if a toy is actually being played with --
-
11:14 - 11:17how and where and whether
or not it's any fun. -
11:18 - 11:21Look, I'm sure that the designers
imagined this lifestyle for Barbie -
11:21 - 11:23when they designed her.
-
11:23 - 11:24(Laughter)
-
11:24 - 11:27But what if it turns out that Barbie's
actually really lonely? -
11:27 - 11:30(Laughter)
-
11:31 - 11:33If the designers had known
-
11:33 - 11:35what was really happening
in the real world -
11:35 - 11:37with their designs -- the road,
the building, Barbie -- -
11:37 - 11:40they could've used that knowledge
to create an experience -
11:40 - 11:41that was better for the user.
-
11:41 - 11:43What's missing is a nervous system
-
11:43 - 11:47connecting us to all of the things
that we design, make and use. -
11:48 - 11:51What if all of you had that kind
of information flowing to you -
11:51 - 11:54from the things you create
in the real world? -
11:55 - 11:57With all of the stuff we make,
-
11:57 - 11:59we spend a tremendous amount
of money and energy -- -
11:59 - 12:02in fact, last year,
about two trillion dollars -- -
12:02 - 12:05convincing people to buy
the things we've made. -
12:05 - 12:08But if you had this connection
to the things that you design and create -
12:08 - 12:10after they're out in the real world,
-
12:10 - 12:13after they've been sold
or launched or whatever, -
12:13 - 12:15we could actually change that,
-
12:15 - 12:18and go from making people want our stuff,
-
12:18 - 12:22to just making stuff that people
want in the first place. -
12:22 - 12:24The good news is, we're working
on digital nervous systems -
12:24 - 12:27that connect us to the things we design.
-
12:28 - 12:30We're working on one project
-
12:30 - 12:34with a couple of guys down in Los Angeles
called the Bandito Brothers -
12:34 - 12:35and their team.
-
12:35 - 12:39And one of the things these guys do
is build insane cars -
12:39 - 12:42that do absolutely insane things.
-
12:43 - 12:44These guys are crazy --
-
12:44 - 12:45(Laughter)
-
12:45 - 12:47in the best way.
-
12:49 - 12:51And what we're doing with them
-
12:51 - 12:53is taking a traditional race-car chassis
-
12:53 - 12:55and giving it a nervous system.
-
12:55 - 12:58So we instrumented it
with dozens of sensors, -
12:58 - 13:01put a world-class driver behind the wheel,
-
13:01 - 13:04took it out to the desert
and drove the hell out of it for a week. -
13:04 - 13:06And the car's nervous system
captured everything -
13:06 - 13:08that was happening to the car.
-
13:08 - 13:11We captured four billion data points;
-
13:11 - 13:13all of the forces
that it was subjected to. -
13:13 - 13:15And then we did something crazy.
-
13:15 - 13:17We took all of that data,
-
13:17 - 13:21and plugged it into a generative-design AI
we call "Dreamcatcher." -
13:21 - 13:25So what do get when you give
a design tool a nervous system, -
13:25 - 13:28and you ask it to build you
the ultimate car chassis? -
13:29 - 13:31You get this.
-
13:32 - 13:36This is something that a human
could never have designed. -
13:37 - 13:39Except a human did design this,
-
13:39 - 13:43but it was a human that was augmented
by a generative-design AI, -
13:43 - 13:44a digital nervous system
-
13:44 - 13:47and robots that can actually
fabricate something like this. -
13:48 - 13:51So if this is the future,
the Augmented Age, -
13:51 - 13:56and we're going to be augmented
cognitively, physically and perceptually, -
13:56 - 13:57what will that look like?
-
13:58 - 14:01What is this wonderland going to be like?
-
14:01 - 14:03I think we're going to see a world
-
14:03 - 14:06where we're moving
from things that are fabricated -
14:06 - 14:07to things that are farmed.
-
14:08 - 14:12Where we're moving from things
that are constructed -
14:12 - 14:13to that which is grown.
-
14:14 - 14:16We're going to move from being isolated
-
14:16 - 14:18to being connected.
-
14:19 - 14:21And we'll move away from extraction
-
14:21 - 14:23to embrace aggregation.
-
14:24 - 14:28I also think we'll shift
from craving obedience from our things -
14:28 - 14:29to valuing autonomy.
-
14:31 - 14:32Thanks to our augmented capabilities,
-
14:32 - 14:35our world is going to change dramatically.
-
14:36 - 14:39We're going to have a world
with more variety, more connectedness, -
14:39 - 14:41more dynamism, more complexity,
-
14:41 - 14:43more adaptability and, of course,
-
14:43 - 14:45more beauty.
-
14:45 - 14:47The shape of things to come
-
14:47 - 14:49will be unlike anything
we've ever seen before. -
14:49 - 14:50Why?
-
14:50 - 14:54Because what will be shaping those things
is this new partnership -
14:54 - 14:58between technology, nature and humanity.
-
14:59 - 15:03That, to me, is a future
well worth looking forward to. -
15:03 - 15:04Thank you all so much.
-
15:04 - 15:10(Applause)
- Title:
- The incredible inventions of intuitive AI
- Speaker:
- Maurice Conti
- Description:
-
What do you get when you give a design tool a digital nervous system? Computers that improve our ability to think and imagine, and robotic systems that can come up with (and build) radical new designs for bridges, cars, drones and much more -- all by themselves. Take a tour of the Augmented Age with futurist Maurice Conti and preview a time when robots and humans will work side-by-side to accomplish things neither could do alone.
- Video Language:
- English
- Team:
closed TED
- Project:
- TEDTalks
- Duration:
- 15:23
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Brian Greene edited English subtitles for The incredible inventions of intuitive AI | |
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Brian Greene edited English subtitles for The incredible inventions of intuitive AI | |
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Camille Martínez accepted English subtitles for The incredible inventions of intuitive AI | |
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Camille Martínez edited English subtitles for The incredible inventions of intuitive AI | |
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Camille Martínez edited English subtitles for The incredible inventions of intuitive AI | |
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Leslie Gauthier edited English subtitles for The incredible inventions of intuitive AI | |
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Leslie Gauthier edited English subtitles for The incredible inventions of intuitive AI | |
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Leslie Gauthier edited English subtitles for The incredible inventions of intuitive AI |