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Maurice Conti: The incredible inventions of intuitive AI
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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 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.
Maurice Conti - Designer, futurist
Maurice Conti explores new partnerships between technology, nature and humanity. Full bio
Maurice Conti explores new partnerships between technology, nature and humanity. Full bio
Double-click the English transcript below to play the video.
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How many of you are creatives,
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designers, engineers,
entrepreneurs, artists,
entrepreneurs, artists,
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or maybe you just have
a really big imagination?
a really big imagination?
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Show of hands? (Cheers)
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That's most of you.
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I have some news for us creatives.
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Over the course of the next 20 years,
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more will change around
the way we do our work
the way we do our work
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than has happened in the last 2,000.
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In fact, I think we're at the dawn
of a new age in human history.
of a new age in human history.
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Now, there have been four major historical
eras defined by the way we work.
eras defined by the way we work.
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The Hunter-Gatherer Age
lasted several million years.
lasted several million years.
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And then the Agricultural Age
lasted several thousand years.
lasted several thousand years.
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The Industrial Age lasted
a couple of centuries.
a couple of centuries.
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And now the Information Age
has lasted just a few decades.
has lasted just a few decades.
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And now today, we're on the cusp
of our next great era as a species.
of our next great era as a species.
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Welcome to the Augmented Age.
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In this new era, your natural human
capabilities are going to be augmented
capabilities are going to be augmented
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by computational systems
that help you think,
that help you think,
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robotic systems that help you make,
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and a digital nervous system
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that connects you to the world
far beyond your natural senses.
far beyond your natural senses.
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Let's start with cognitive augmentation.
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How many of you are augmented cyborgs?
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(Laughter)
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I would actually argue
that we're already augmented.
that we're already augmented.
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Imagine you're at a party,
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and somebody asks you a question
that you don't know the answer to.
that you don't know the answer to.
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If you have one of these,
in a few seconds, you can know the answer.
in a few seconds, you can know the answer.
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But this is just a primitive beginning.
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Even Siri is just a passive tool.
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In fact, for the last
three-and-a-half million years,
three-and-a-half million years,
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the tools that we've had
have been completely passive.
have been completely passive.
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They do exactly what we tell them
and nothing more.
and nothing more.
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Our very first tool only cut
where we struck it.
where we struck it.
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The chisel only carves
where the artist points it.
where the artist points it.
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And even our most advanced tools
do nothing without our explicit direction.
do nothing without our explicit direction.
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In fact, to date, and this
is something that frustrates me,
is something that frustrates me,
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we've always been limited
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by this need to manually
push our wills into our tools --
push our wills into our tools --
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like, manual,
literally using our hands,
literally using our hands,
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even with computers.
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But I'm more like Scotty in "Star Trek."
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(Laughter)
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I want to have a conversation
with a computer.
with a computer.
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I want to say, "Computer,
let's design a car,"
let's design a car,"
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and the computer shows me a car.
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And I say, "No, more fast-looking,
and less German,"
and less German,"
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and bang, the computer shows me an option.
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(Laughter)
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That conversation might be
a little ways off,
a little ways off,
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probably less than many of us think,
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but right now,
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we're working on it.
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Tools are making this leap
from being passive to being generative.
from being passive to being generative.
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Generative design tools
use a computer and algorithms
use a computer and algorithms
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to synthesize geometry
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to come up with new designs
all by themselves.
all by themselves.
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All it needs are your goals
and your constraints.
and your constraints.
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I'll give you an example.
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In the case of this aerial drone chassis,
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all you would need to do
is tell it something like,
is tell it something like,
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it has four propellers,
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you want it to be
as lightweight as possible,
as lightweight as possible,
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and you need it to be
aerodynamically efficient.
aerodynamically efficient.
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Then what the computer does
is it explores the entire solution space:
is it explores the entire solution space:
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every single possibility that solves
and meets your criteria --
and meets your criteria --
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millions of them.
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It takes big computers to do this.
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But it comes back to us with designs
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that we, by ourselves,
never could've imagined.
never could've imagined.
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And the computer's coming up
with this stuff all by itself --
with this stuff all by itself --
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no one ever drew anything,
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and it started completely from scratch.
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And by the way, it's no accident
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that the drone body looks just like
the pelvis of a flying squirrel.
the pelvis of a flying squirrel.
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(Laughter)
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It's because the algorithms
are designed to work
are designed to work
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the same way evolution does.
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What's exciting is we're starting
to see this technology
to see this technology
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out in the real world.
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We've been working with Airbus
for a couple of years
for a couple of years
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on this concept plane for the future.
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It's a ways out still.
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But just recently we used
a generative-design AI
a generative-design AI
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to come up with this.
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This is a 3D-printed cabin partition
that's been designed by a computer.
that's been designed by a computer.
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It's stronger than the original
yet half the weight,
yet half the weight,
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and it will be flying
in the Airbus A320 later this year.
in the Airbus A320 later this year.
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So computers can now generate;
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they can come up with their own solutions
to our well-defined problems.
to our well-defined problems.
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But they're not intuitive.
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They still have to start from scratch
every single time,
every single time,
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and that's because they never learn.
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Unlike Maggie.
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(Laughter)
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Maggie's actually smarter
than our most advanced design tools.
than our most advanced design tools.
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What do I mean by that?
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If her owner picks up that leash,
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Maggie knows with a fair
degree of certainty
degree of certainty
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it's time to go for a walk.
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And how did she learn?
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Well, every time the owner picked up
the leash, they went for a walk.
the leash, they went for a walk.
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And Maggie did three things:
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she had to pay attention,
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she had to remember what happened
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and she had to retain and create
a pattern in her mind.
a pattern in her mind.
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Interestingly, that's exactly what
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computer scientists
have been trying to get AIs to do
have been trying to get AIs to do
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for the last 60 or so years.
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Back in 1952,
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they built this computer
that could play Tic-Tac-Toe.
that could play Tic-Tac-Toe.
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Big deal.
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Then 45 years later, in 1997,
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Deep Blue beats Kasparov at chess.
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2011, Watson beats these two
humans at Jeopardy,
humans at Jeopardy,
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which is much harder for a computer
to play than chess is.
to play than chess is.
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In fact, rather than working
from predefined recipes,
from predefined recipes,
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Watson had to use reasoning
to overcome his human opponents.
to overcome his human opponents.
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And then a couple of weeks ago,
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DeepMind's AlphaGo beats
the world's best human at Go,
the world's best human at Go,
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which is the most difficult
game that we have.
game that we have.
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In fact, in Go, there are more
possible moves
possible moves
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than there are atoms in the universe.
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So in order to win,
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what AlphaGo had to do
was develop intuition.
was develop intuition.
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And in fact, at some points,
AlphaGo's programmers didn't understand
AlphaGo's programmers didn't understand
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why AlphaGo was doing what it was doing.
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And things are moving really fast.
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I mean, consider --
in the space of a human lifetime,
in the space of a human lifetime,
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computers have gone from a child's game
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to what's recognized as the pinnacle
of strategic thought.
of strategic thought.
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What's basically happening
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is computers are going
from being like Spock
from being like Spock
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to being a lot more like Kirk.
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(Laughter)
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Right? From pure logic to intuition.
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Would you cross this bridge?
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Most of you are saying, "Oh, hell no!"
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(Laughter)
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And you arrived at that decision
in a split second.
in a split second.
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You just sort of knew
that bridge was unsafe.
that bridge was unsafe.
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And that's exactly the kind of intuition
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that our deep-learning systems
are starting to develop right now.
are starting to develop right now.
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Very soon, you'll literally be able
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to show something you've made,
you've designed,
you've designed,
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to a computer,
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and it will look at it and say,
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"Sorry, homie, that'll never work.
You have to try again."
You have to try again."
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Or you could ask it if people
are going to like your next song,
are going to like your next song,
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or your next flavor of ice cream.
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Or, much more importantly,
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you could work with a computer
to solve a problem
to solve a problem
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that we've never faced before.
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For instance, climate change.
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We're not doing a very
good job on our own,
good job on our own,
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we could certainly use
all the help we can get.
all the help we can get.
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That's what I'm talking about,
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technology amplifying
our cognitive abilities
our cognitive abilities
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so we can imagine and design things
that were simply out of our reach
that were simply out of our reach
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as plain old un-augmented humans.
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So what about making
all of this crazy new stuff
all of this crazy new stuff
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that we're going to invent and design?
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I think the era of human augmentation
is as much about the physical world
is as much about the physical world
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as it is about the virtual,
intellectual realm.
intellectual realm.
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How will technology augment us?
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In the physical world, robotic systems.
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OK, there's certainly a fear
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that robots are going to take
jobs away from humans,
jobs away from humans,
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and that is true in certain sectors.
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But I'm much more interested in this idea
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that humans and robots working together
are going to augment each other,
are going to augment each other,
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and start to inhabit a new space.
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This is our applied research lab
in San Francisco,
in San Francisco,
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where one of our areas of focus
is advanced robotics,
is advanced robotics,
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specifically, human-robot collaboration.
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And this is Bishop, one of our robots.
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As an experiment, we set it up
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to help a person working in construction
doing repetitive tasks --
doing repetitive tasks --
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tasks like cutting out holes for outlets
or light switches in drywall.
or light switches in drywall.
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(Laughter)
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So, Bishop's human partner
can tell what to do in plain English
can tell what to do in plain English
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and with simple gestures,
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kind of like talking to a dog,
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and then Bishop executes
on those instructions
on those instructions
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with perfect precision.
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We're using the human
for what the human is good at:
for what the human is good at:
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awareness, perception and decision making.
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And we're using the robot
for what it's good at:
for what it's good at:
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precision and repetitiveness.
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Here's another cool project
that Bishop worked on.
that Bishop worked on.
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The goal of this project,
which we called the HIVE,
which we called the HIVE,
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was to prototype the experience
of humans, computers and robots
of humans, computers and robots
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all working together to solve
a highly complex design problem.
a highly complex design problem.
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The humans acted as labor.
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They cruised around the construction site,
they manipulated the bamboo --
they manipulated the bamboo --
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which, by the way,
because it's a non-isomorphic material,
because it's a non-isomorphic material,
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is super hard for robots to deal with.
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But then the robots
did this fiber winding,
did this fiber winding,
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which was almost impossible
for a human to do.
for a human to do.
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And then we had an AI
that was controlling everything.
that was controlling everything.
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It was telling the humans what to do,
telling the robots what to do
telling the robots what to do
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and keeping track of thousands
of individual components.
of individual components.
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What's interesting is,
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building this pavilion
was simply not possible
was simply not possible
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without human, robot and AI
augmenting each other.
augmenting each other.
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OK, I'll share one more project.
This one's a little bit crazy.
This one's a little bit crazy.
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We're working with Amsterdam-based artist
Joris Laarman and his team at MX3D
Joris Laarman and his team at MX3D
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to generatively design
and robotically print
and robotically print
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the world's first autonomously
manufactured bridge.
manufactured bridge.
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So, Joris and an AI are designing
this thing right now, as we speak,
this thing right now, as we speak,
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in Amsterdam.
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And when they're done,
we're going to hit "Go,"
we're going to hit "Go,"
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and robots will start 3D printing
in stainless steel,
in stainless steel,
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and then they're going to keep printing,
without human intervention,
without human intervention,
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until the bridge is finished.
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So, as computers are going
to augment our ability
to augment our ability
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to imagine and design new stuff,
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robotic systems are going to help us
build and make things
build and make things
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that we've never been able to make before.
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But what about our ability
to sense and control these things?
to sense and control these things?
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What about a nervous system
for the things that we make?
for the things that we make?
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Our nervous system,
the human nervous system,
the human nervous system,
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tells us everything
that's going on around us.
that's going on around us.
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But the nervous system of the things
we make is rudimentary at best.
we make is rudimentary at best.
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For instance, a car doesn't tell
the city's public works department
the city's public works department
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that it just hit a pothole at the corner
of Broadway and Morrison.
of Broadway and Morrison.
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A building doesn't tell its designers
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whether or not the people inside
like being there,
like being there,
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and the toy manufacturer doesn't know
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if a toy is actually being played with --
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how and where and whether
or not it's any fun.
or not it's any fun.
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Look, I'm sure that the designers
imagined this lifestyle for Barbie
imagined this lifestyle for Barbie
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when they designed her.
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(Laughter)
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But what if it turns out that Barbie's
actually really lonely?
actually really lonely?
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(Laughter)
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If the designers had known
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what was really happening
in the real world
in the real world
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with their designs -- the road,
the building, Barbie --
the building, Barbie --
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they could've used that knowledge
to create an experience
to create an experience
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that was better for the user.
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What's missing is a nervous system
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connecting us to all of the things
that we design, make and use.
that we design, make and use.
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What if all of you had that kind
of information flowing to you
of information flowing to you
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from the things you create
in the real world?
in the real world?
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With all of the stuff we make,
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we spend a tremendous amount
of money and energy --
of money and energy --
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in fact, last year,
about two trillion dollars --
about two trillion dollars --
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convincing people to buy
the things we've made.
the things we've made.
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But if you had this connection
to the things that you design and create
to the things that you design and create
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after they're out in the real world,
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after they've been sold
or launched or whatever,
or launched or whatever,
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we could actually change that,
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and go from making people want our stuff,
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to just making stuff that people
want in the first place.
want in the first place.
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The good news is, we're working
on digital nervous systems
on digital nervous systems
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that connect us to the things we design.
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We're working on one project
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with a couple of guys down in Los Angeles
called the Bandito Brothers
called the Bandito Brothers
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and their team.
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And one of the things these guys do
is build insane cars
is build insane cars
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that do absolutely insane things.
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These guys are crazy --
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(Laughter)
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in the best way.
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And what we're doing with them
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is taking a traditional race-car chassis
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and giving it a nervous system.
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So we instrumented it
with dozens of sensors,
with dozens of sensors,
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put a world-class driver behind the wheel,
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took it out to the desert
and drove the hell out of it for a week.
and drove the hell out of it for a week.
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And the car's nervous system
captured everything
captured everything
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that was happening to the car.
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We captured four billion data points;
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all of the forces
that it was subjected to.
that it was subjected to.
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And then we did something crazy.
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We took all of that data,
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and plugged it into a generative-design AI
we call "Dreamcatcher."
we call "Dreamcatcher."
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So what do get when you give
a design tool a nervous system,
a design tool a nervous system,
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and you ask it to build you
the ultimate car chassis?
the ultimate car chassis?
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You get this.
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This is something that a human
could never have designed.
could never have designed.
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Except a human did design this,
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but it was a human that was augmented
by a generative-design AI,
by a generative-design AI,
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a digital nervous system
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and robots that can actually
fabricate something like this.
fabricate something like this.
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So if this is the future,
the Augmented Age,
the Augmented Age,
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and we're going to be augmented
cognitively, physically and perceptually,
cognitively, physically and perceptually,
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what will that look like?
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What is this wonderland going to be like?
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I think we're going to see a world
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where we're moving
from things that are fabricated
from things that are fabricated
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to things that are farmed.
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Where we're moving from things
that are constructed
that are constructed
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to that which is grown.
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We're going to move from being isolated
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to being connected.
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And we'll move away from extraction
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to embrace aggregation.
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I also think we'll shift
from craving obedience from our things
from craving obedience from our things
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to valuing autonomy.
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Thanks to our augmented capabilities,
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our world is going to change dramatically.
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We're going to have a world
with more variety, more connectedness,
with more variety, more connectedness,
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more dynamism, more complexity,
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more adaptability and, of course,
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more beauty.
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The shape of things to come
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will be unlike anything
we've ever seen before.
we've ever seen before.
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Why?
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Because what will be shaping those things
is this new partnership
is this new partnership
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between technology, nature and humanity.
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That, to me, is a future
well worth looking forward to.
well worth looking forward to.
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Thank you all so much.
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(Applause)
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ABOUT THE SPEAKER
Maurice Conti - Designer, futuristMaurice Conti explores new partnerships between technology, nature and humanity.
Why you should listen
Maurice Conti is a designer, futurist and innovator. He's worked with startups, government agencies, artists and corporations to explore the things that will matter to us in the future, and to design solutions to get us there.
Conti is currently Chief Innovation Officer at Alpha -- Europe's first moonshot factory, powered by Telefónica. Conti and his team are responsible for coming up with the ideas, prototypes and proofs of concepts that will go on to become full-blown moonshots at Alpha: projects that will affect 100 million people or more, be a force for good on the planet and grow into billion-euro businesses.
Previously, Conti was Director of Applied Research & Innovation at Autodesk where built and led Autodesk's Applied Research Lab. Conti's work focuses on applied machine learning, advanced robotics, augmented and virtual realities, and the future of work, cities and mobility.
Conti is also an explorer of geographies and cultures. He has circumnavigated the globe once and been half-way around twice. In 2009 he was awarded the Medal for Exceptional Bravery at Sea by the United Nations, the New Zealand Bravery Medal and a US Coast Guard Citation for Bravery for risking his own life to save three shipwrecked sailors.
Conti lives in Barcelona, Spain, and travels around the world speaking to groups about innovation, technology trends, the future, and high adventure.
Maurice Conti | Speaker | TED.com