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How many of your are creatives?

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Designers, engineers,
entrepreneurs, artists,

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or maybe you just have
a really big imagination.

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Show of hands?

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(Applause)

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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

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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.

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Now, there have been four major historical
eras defined by the way we work.

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The Hunter-Gatherer Age
lasted several million years.

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And then the Agricultural Age
lasted several thousand years.

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The Industrial Age lasted
a couple of centuries,

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and now the Information Age
has lasted just a few decades.

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And now today,

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we're on the cusp of our next
great era as a species.

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Welcome to the Augemented Age.

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In this new era,

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your natural human capabilities are going
to be augmented by computational systems

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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
fay 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.

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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.

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If you have one of these,

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in a few seconds,

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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,

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the tools that we've had
have been completely passive.

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They do exactly what we tell them
and nothing more.

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Our very first tool only cut
where we struck it.

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The chisel only carves
where the artist points it.

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And even our most advanced tools
do nothing without our explicit direction.

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In fact, to date --

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and this 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 --

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like manual,

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like 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.

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I want to say, "Computer,
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,"

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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,

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it's actually probably less
than any 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.

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Generative design tools
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.

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All it needs are your goals
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,

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it has four propellers,

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you want it to be
as lightweight as possible,

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and you need it to be
aerodynamically efficient.

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And then what the computer does

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is it explores the entire solution space:

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every single possibility that solves
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.

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And the computer's coming up
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,

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it's no accident

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that the drone body looks just like
the pelvis of a flying squirrel.

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(Laughter)

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It's because the algorithms are designed
to work the same way that evolution does.

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What's exciting is we're starting to see
this technology out in the real world.

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We've been working with Airbus
for a couple of years

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on this concept plane for the future.

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It's aways out still,

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but just recently we used
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.

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It's stronger than the original
yet half the weight,

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and it will be flying
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.

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But they're not inutitive.

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They still have to start from scratch
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.

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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

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that it's time to 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,

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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.

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Interestingly,

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that's exactly computer scientists
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.

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Big deal.

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Then 45 years later,

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in 1997,

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Deep Blue beats Kasparov at Chess.

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2011, Watson beats these two
humans at Jeopardy,

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which is much harder for a computer
to play than Chess is.

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In fact, rather than working
from predefined recipes,

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Watson had to use reasoning
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,

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which is the most difficult
game that we have.

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In fact in Go,

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there are more possible moves
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,

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and in fact,

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at some points,

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AlphaGo's programmers didn't understand
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 --

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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.

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What's basically happening

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is computers are going
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?

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From pure logic to intuition.

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Would you cross this bridge?

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Most of you are saying,

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"Oh, hell no."

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(Laughter)

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And you arrived at that decision
in a split second.

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You just sort of knew
that 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.

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Very soon,

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you'll literally be able
to show something you've made,

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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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"Mm, sorry homey, that will never work,

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you have to try again."

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Or you could ask it if people
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 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,

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we could certainly use
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

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so we can imagine and design things

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that were simply out of our reach
as plain old unaugmented humans.

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So, what about making
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

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as it is about the virtual,
intellectual realm.

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So how will technology augment us?

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In the physcial world,

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robotic systems.

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OK, there's certainly a fear that robots
are going to take 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,

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and start to inhabit a new space.

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This is our applied
research lab in San Francisco,

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where one of our areas of focus
is advanced robotics,

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specifically human-robot collaboration.

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And this is Bishop,

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one of our robots.

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As an experiment,

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we set it up to help a person working
in construction doing repetitive tasks.

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Tasks like cutting out holes for outlets
or light switches in dry wall.

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(Laughter)

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So, Bishop's human partner 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

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with perfect precision.

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We're using the human for what
the human is good at,

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right?

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Awareness, perception and desicion making.

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And we're using the robot
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.

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The goal of this project,

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which we called the HIVE,

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was to prototype the experience
of humans, computers and robots

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all working together to solve
a highly complex design problem.

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The humans acted as labor.

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They cruised around the construction site,

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they manipulated the bamboo,

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which by the way,

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because it's a [nonisomorphic] 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,

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which was almost impossible
for a human to do.

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And then we had an AI
that was controlling everything.

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It was telling the humans what to do,

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it was telling the robots what to do,

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and keeping track of thousands
of individual components.

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What's interesting is building
this pavilion was simply not possible

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without human, robot and AI
augmenting each other.

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OK, I'll share one more project.

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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

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to generatively design
and robotically print

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the world's first autonomously
manufactured bridge.

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So, Joris and an AI are designing
this thing right now, as we speak,

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in Amsterdam.

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And when they're done,

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we're going to hit "go,"

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and robots will start 3D printing
in stainless steel,

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and then they're going to keep printing
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

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to imagine and design new stuff,

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robotic systems are going to help us

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build and make things that we've
never been able to make before.

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But what about our ability
to sense and control these things?

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What about a nervous system
for the things that we make?

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Our nervous system --

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the human nervous system --

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tells us everything that's
going on around us.

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But the nervous system of the things
we make is rudimentary at best.

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For instance,

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a car doesn't tell the city's
Public Works department

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00:11:01,681 --> 00:11:04,835
that it just hit a pothole at the corner
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,

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and the toy manufacturer doesn't know
if a toy is actually being played with --

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how and where and whether
or not it's any fun.

255
00:11:17,820 --> 00:11:21,569
Look, I'm sure that the designers
imagined this lifestyle for Barbie

256
00:11:21,569 --> 00:11:23,591
when they designed her --

257
00:11:23,591 --> 00:11:24,589
right?

258
00:11:24,589 --> 00:11:27,489
But what if it turns out that Barbie's
actually really lonely?

259
00:11:27,489 --> 00:11:29,068
(Laughter)

260
00:11:31,263 --> 00:11:34,549
If the designers had known what was
really happening in the real world

261
00:11:34,549 --> 00:11:35,551
with their designs --

262
00:11:35,551 --> 00:11:37,275
the road, the building and Barbie --

263
00:11:37,275 --> 00:11:38,863
they could've used that knowledge

264
00:11:38,863 --> 00:11:41,337
to create an experience
that was better for the user.

265
00:11:41,337 --> 00:11:43,473
What's missing is a nervous system

266
00:11:43,473 --> 00:11:47,182
connecting us to all of the things
that we design, make and use.

267
00:11:48,115 --> 00:11:51,694
What if all of you had that kind
of information flowing to you

268
00:11:51,694 --> 00:11:54,005
from the things you create
in the real world?

269
00:11:55,507 --> 00:11:56,951
With all of the stuff we make,

270
00:11:56,951 --> 00:11:59,349
we spend a tremendous amount
of money and energy --

271
00:11:59,349 --> 00:12:01,575
in fact last year about
two trillion dollars --

272
00:12:01,575 --> 00:12:04,298
convincing people to buy
the things that we've made.

273
00:12:04,666 --> 00:12:07,992
But if you had this connection
to the things that you design and create

274
00:12:07,992 --> 00:12:09,744
after they're out in the real world,

275
00:12:09,744 --> 00:12:13,725
after they've been sold,
or launched or whatever,

276
00:12:13,725 --> 00:12:15,368
we could actually change that,

277
00:12:15,368 --> 00:12:18,360
and go from making people want our stuff,

278
00:12:18,360 --> 00:12:21,617
to just making stuff that people
want in the first place.

279
00:12:21,818 --> 00:12:24,629
The good news is we're working
on digital nervous systems

280
00:12:24,629 --> 00:12:27,430
that connect us to the things we design.

281
00:12:28,732 --> 00:12:32,659
We're working on one project
with a couple of guys down in Los Angeles

282
00:12:32,659 --> 00:12:33,993
called the Bandito Brothers,

283
00:12:33,993 --> 00:12:35,447
and their team,

284
00:12:35,447 --> 00:12:38,903
and one of the things these guys do
is build insane cars

285
00:12:38,903 --> 00:12:42,104
that do absolutely insane things.

286
00:12:42,927 --> 00:12:44,579
These guys are crazy.

287
00:12:44,579 --> 00:12:45,583
(Laughter)

288
00:12:45,583 --> 00:12:47,213
In the best way.

289
00:12:49,061 --> 00:12:50,980
And what we're doing with them

290
00:12:50,980 --> 00:12:53,444
is taking a traditional racecar chassis

291
00:12:53,444 --> 00:12:55,053
and giving it a nervous system.

292
00:12:55,053 --> 00:12:58,135
So, we instrumented it
with dozens of censors,

293
00:12:58,135 --> 00:13:00,675
and then we put a world-class driver
behind the wheel,

294
00:13:00,675 --> 00:13:01,885
took it out to the desert

295
00:13:01,885 --> 00:13:03,831
and drove the hell out of it for a week.

296
00:13:04,089 --> 00:13:07,822
And the car's nervous system captured
everything that was happening to the car.

297
00:13:07,822 --> 00:13:10,841
We captured four billion data points ...

298
00:13:10,841 --> 00:13:13,038
all of the forces
that it was subjected to.

299
00:13:13,038 --> 00:13:15,087
And then we did something crazy.

300
00:13:15,363 --> 00:13:16,791
We took all of that data,

301
00:13:16,791 --> 00:13:20,638
and plugged it into a generative
design AI that we call, Dreamcatcher.

302
00:13:21,470 --> 00:13:25,458
So, what do get when you give
a design tool a nervous system,

303
00:13:25,458 --> 00:13:28,400
and you ask it to build you
the ultimate car chassis?

304
00:13:28,923 --> 00:13:31,263
You get this.

305
00:13:32,493 --> 00:13:36,578
This is something that human
could never have designed.

306
00:13:36,907 --> 00:13:38,819
Except a human did design this,

307
00:13:38,819 --> 00:13:42,936
but it was a human that was augmented
by a generative design AI,

308
00:13:42,936 --> 00:13:44,294
a digital nervous system,

309
00:13:44,294 --> 00:13:47,496
and robots that can actually
fabricate something like this.

310
00:13:47,880 --> 00:13:49,724
So, if this is the future,

311
00:13:49,724 --> 00:13:51,499
the Augmented Age,

312
00:13:51,499 --> 00:13:55,784
and we're going to be augmented
cognitively, physically and perceptually,

313
00:13:55,784 --> 00:13:57,483
what will that look like?

314
00:13:57,895 --> 00:14:01,121
What is this wonderland going to be like?

315
00:14:01,121 --> 00:14:02,854
I think we're going to see a world

316
00:14:02,854 --> 00:14:05,946
where we're moving from
things that are fabricated

317
00:14:05,946 --> 00:14:07,608
to things that are farmed.

318
00:14:08,201 --> 00:14:11,775
Where we're moving from things
that are constructed

319
00:14:11,775 --> 00:14:13,797
to that which is grown.

320
00:14:14,187 --> 00:14:16,573
We're going to move from being isolated

321
00:14:16,573 --> 00:14:18,834
to being connected,

322
00:14:18,834 --> 00:14:21,269
and we'll move away from extraction

323
00:14:21,269 --> 00:14:23,605
to embrace aggregation.

324
00:14:24,247 --> 00:14:27,958
I also think we'll shift from craving
obedience from our things

325
00:14:27,958 --> 00:14:29,906
to valuing autonomy.

326
00:14:30,554 --> 00:14:32,639
Thanks to our augmented capabilities,

327
00:14:32,639 --> 00:14:35,253
our world is going to change dramatically.

328
00:14:35,776 --> 00:14:38,048
We're going to have a world
with more variety,

329
00:14:38,048 --> 00:14:39,046
more connectedness,

330
00:14:39,046 --> 00:14:40,131
more dynamism,

331
00:14:40,131 --> 00:14:41,357
more complexity,

332
00:14:41,357 --> 00:14:42,401
more adaptability,

333
00:14:42,401 --> 00:14:43,699
and of course,

334
00:14:43,699 --> 00:14:45,058
more beauty.

335
00:14:45,373 --> 00:14:47,019
The shape of things to come

336
00:14:47,019 --> 00:14:49,333
will be unlike anything
we've ever seen before.

337
00:14:49,333 --> 00:14:50,328
Why?

338
00:14:50,328 --> 00:14:52,376
Because what will be shaping those things

339
00:14:52,376 --> 00:14:57,812
is this new partnership between
technology, nature and humanity.

340
00:14:59,125 --> 00:15:03,022
That to me is a future
well worth looking forward to.

341
00:15:03,307 --> 00:15:04,602
Thank you all so much.

342
00:15:04,602 --> 00:15:06,318
(Applause)