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All right, Portland, wow.
Thank you so much for having me.

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I love visiting this city.s

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I mean, where else in the world
can I have breakfast like this.

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(Laughter)

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And clearly, this is
a very creative place.

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(Laughter)

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How many of you 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? (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

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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, we're on the cusp
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

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by computational systems
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.

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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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There's three or four of you,

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that's just because it's Portland.

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(Laughter)

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Yeah, keep it weird.

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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,
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,

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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, 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, 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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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.

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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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Then what the computer does
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, 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

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the same way evolution does.

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What's exciting is we're starting
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

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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

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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 intuitive.

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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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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.

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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, that's exactly what

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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, 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, there are more
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.

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And in fact, at some points,
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,

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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? 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.

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You just sort of knew
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, you'll literally be able

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to show something you've made,
you've designed, 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."

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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

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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,

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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
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

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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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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,

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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, 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 --

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tasks like cutting out holes for outlets
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

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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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awareness, perception and decision 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,
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,
they manipulated the bamboo --

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which, by the way,
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,

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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,
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,

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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.
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,
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
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?

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

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Our nervous system,
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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I would say it's rather
shitty, at this point.

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(Laughter)

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For instance, a car doesn't tell
the city's public works department

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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

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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.

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Look, I'm sure that the designers
imagined this lifestyle for Barbie

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when they designed her.

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(Laughter)

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00:12:23,237 --> 00:12:26,143
But what if it turns out that Barbie's
actually really lonely?

249
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(Laughter)

250
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If the designers had known

251
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what was really happening
in the real world

252
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with their designs --
the road, the building, Barbie --

253
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they could've used that knowledge
to create an experience

254
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that was better for the user.

255
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What's missing is a nervous system

256
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connecting us to all of the things
that we design, make and use.

257
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What if all of you had that kind
of information flowing to you

258
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from the things you create
in the real world?

259
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With all of the stuff we make,

260
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we spend a tremendous amount
of money and energy --

261
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in fact, last year,
about two trillion dollars --

262
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convincing people to buy
the things we've made.

263
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But if you had this connection
to the things that you design and create

264
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after they're out in the real world,

265
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after they've been sold
or launched or whatever,

266
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we could actually change that,

267
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and go from making people want our stuff,

268
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to just making stuff that people
want in the first place.

269
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The good news is, we're working
on digital nervous systems

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that connect us to the things we design.

271
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We're working on one project

272
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with a couple of guys down in Los Angeles
called the Bandito Brothers

273
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and their team.

274
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And one of the things these guys do
is build insane cars

275
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that do absolutely insane things.

276
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These guys are crazy --

277
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(Laughter)

278
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in the best way.

279
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And what we're doing with them

280
00:13:56,580 --> 00:13:59,020
is taking a traditional race-car chassis

281
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and giving it a nervous system.

282
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So we instrumented it
with dozens of sensors,

283
00:14:03,795 --> 00:14:06,430
put a world-class driver behind the wheel,

284
00:14:06,454 --> 00:14:09,811
took it out to the desert
and drove the hell out of it for a week.

285
00:14:09,835 --> 00:14:12,326
And the car's nervous system
captured everything

286
00:14:12,350 --> 00:14:13,832
that was happening to the car.

287
00:14:13,856 --> 00:14:16,477
We captured four billion data points;

288
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all of the forces
that it was subjected to.

289
00:14:18,835 --> 00:14:20,494
And then we did something crazy.

290
00:14:21,238 --> 00:14:22,738
We took all of that data,

291
00:14:22,762 --> 00:14:26,498
and plugged it into a generative-design AI
we call "Dreamcatcher."

292
00:14:27,240 --> 00:14:31,204
So what do get when you give
a design tool a nervous system,

293
00:14:31,228 --> 00:14:34,110
and you ask it to build you
the ultimate car chassis?

294
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You get this.

295
00:14:38,263 --> 00:14:41,976
This is something that a human
could never have designed.

296
00:14:42,677 --> 00:14:44,565
Except a human did design this,

297
00:14:44,589 --> 00:14:48,898
but it was a human that was augmented
by a generative-design AI,

298
00:14:48,922 --> 00:14:50,153
a digital nervous system

299
00:14:50,177 --> 00:14:53,182
and robots that can actually
fabricate something like this.

300
00:14:54,150 --> 00:14:57,745
So if this is the future,
the Augmented Age,

301
00:14:57,769 --> 00:15:02,030
and we're going to be augmented
cognitively, physically and perceptually,

302
00:15:02,054 --> 00:15:03,462
what will that look like?

303
00:15:04,046 --> 00:15:06,712
What is this wonderland going to be like?

304
00:15:08,291 --> 00:15:10,900
I think we're going to see a world

305
00:15:10,924 --> 00:15:13,992
where we're moving
from things that are fabricated

306
00:15:15,101 --> 00:15:16,546
to things that are farmed.

307
00:15:18,429 --> 00:15:22,374
Where we're moving from things
that are constructed

308
00:15:22,398 --> 00:15:24,102
to that which is grown.

309
00:15:26,504 --> 00:15:28,692
We're going to move from being isolated

310
00:15:29,660 --> 00:15:31,270
to being connected.

311
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And we'll move away from extraction

312
00:15:36,039 --> 00:15:37,912
to embrace aggregation.

313
00:15:40,437 --> 00:15:44,204
I also think we'll shift
from craving obedience from our things

314
00:15:45,128 --> 00:15:46,769
to valuing autonomy.

315
00:15:49,480 --> 00:15:51,385
Thanks to our augmented capabilities,

316
00:15:51,409 --> 00:15:53,786
our world is going to change dramatically.

317
00:15:54,702 --> 00:15:58,305
I think a good analogy is the incredible
microcosm of a coral reef.

318
00:15:58,836 --> 00:16:02,082
We're going to have a world
with more variety, more connectedness,

319
00:16:02,106 --> 00:16:04,393
more dynamism, more complexity,

320
00:16:04,417 --> 00:16:06,735
more adaptability and, of course,

321
00:16:06,759 --> 00:16:07,976
more beauty.

322
00:16:08,741 --> 00:16:10,305
The shape of things to come

323
00:16:10,329 --> 00:16:12,619
will be unlike anything
we've ever seen before.

324
00:16:12,643 --> 00:16:13,802
Why?

325
00:16:13,826 --> 00:16:17,581
Because what will be shaping those things
is this new partnership

326
00:16:17,605 --> 00:16:21,275
between technology, nature and humanity.

327
00:16:22,789 --> 00:16:26,593
That, to me, is a future
well worth looking forward to.

328
00:16:26,617 --> 00:16:27,888
Thank you all so much.

329
00:16:27,912 --> 00:16:34,912
(Applause)