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  • Translator: Leslie Gauthier Reviewer: Camille Martínez

  • How many of you are creatives,

  • designers, engineers, entrepreneurs, artists,

  • or maybe you just have a really big imagination?

  • Show of hands? (Cheers)

  • That's most of you.

  • I have some news for us creatives.

  • Over the course of the next 20 years,

  • more will change around the way we do our work

  • than has happened in the last 2,000.

  • In fact, I think we're at the dawn of a new age in human history.

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

  • The Hunter-Gatherer Age lasted several million years.

  • And then the Agricultural Age lasted several thousand years.

  • The Industrial Age lasted a couple of centuries.

  • And now the Information Age has lasted just a few decades.

  • And now today, we're on the cusp of our next great era as a species.

  • Welcome to the Augmented Age.

  • In this new era, your natural human capabilities are going to be augmented

  • by computational systems that help you think,

  • robotic systems that help you make,

  • and a digital nervous system

  • that connects you to the world far beyond your natural senses.

  • Let's start with cognitive augmentation.

  • How many of you are augmented cyborgs?

  • (Laughter)

  • I would actually argue that we're already augmented.

  • Imagine you're at a party,

  • and somebody asks you a question that you don't know the answer to.

  • If you have one of these, in a few seconds, you can know the answer.

  • But this is just a primitive beginning.

  • Even Siri is just a passive tool.

  • In fact, for the last three-and-a-half million years,

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

  • They do exactly what we tell them and nothing more.

  • Our very first tool only cut where we struck it.

  • The chisel only carves where the artist points it.

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

  • In fact, to date, and this is something that frustrates me,

  • we've always been limited

  • by this need to manually push our wills into our tools --

  • like, manual, literally using our hands,

  • even with computers.

  • But I'm more like Scotty in "Star Trek."

  • (Laughter)

  • I want to have a conversation with a computer.

  • I want to say, "Computer, let's design a car,"

  • and the computer shows me a car.

  • And I say, "No, more fast-looking, and less German,"

  • and bang, the computer shows me an option.

  • (Laughter)

  • That conversation might be a little ways off,

  • probably less than many of us think,

  • but right now,

  • we're working on it.

  • Tools are making this leap from being passive to being generative.

  • Generative design tools use a computer and algorithms

  • to synthesize geometry

  • to come up with new designs all by themselves.

  • All it needs are your goals and your constraints.

  • I'll give you an example.

  • In the case of this aerial drone chassis,

  • all you would need to do is tell it something like,

  • it has four propellers,

  • you want it to be as lightweight as possible,

  • and you need it to be aerodynamically efficient.

  • Then what the computer does is it explores the entire solution space:

  • every single possibility that solves and meets your criteria --

  • millions of them.

  • It takes big computers to do this.

  • But it comes back to us with designs

  • that we, by ourselves, never could've imagined.

  • And the computer's coming up with this stuff all by itself --

  • no one ever drew anything,

  • and it started completely from scratch.

  • And by the way, it's no accident

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

  • (Laughter)

  • It's because the algorithms are designed to work

  • the same way evolution does.

  • What's exciting is we're starting to see this technology

  • out in the real world.

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

  • on this concept plane for the future.

  • It's a ways out still.

  • But just recently we used a generative-design AI

  • to come up with this.

  • This is a 3D-printed cabin partition that's been designed by a computer.

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

  • and it will be flying in the Airbus A320 later this year.

  • So computers can now generate;

  • they can come up with their own solutions to our well-defined problems.

  • But they're not intuitive.

  • They still have to start from scratch every single time,

  • and that's because they never learn.

  • Unlike Maggie.

  • (Laughter)

  • Maggie's actually smarter than our most advanced design tools.

  • What do I mean by that?

  • If her owner picks up that leash,

  • Maggie knows with a fair degree of certainty

  • it's time to go for a walk.

  • And how did she learn?

  • Well, every time the owner picked up the leash, they went for a walk.

  • And Maggie did three things:

  • she had to pay attention,

  • she had to remember what happened

  • and she had to retain and create a pattern in her mind.

  • Interestingly, that's exactly what

  • computer scientists have been trying to get AIs to do

  • for the last 60 or so years.

  • Back in 1952,

  • they built this computer that could play Tic-Tac-Toe.

  • Big deal.

  • Then 45 years later, in 1997,

  • Deep Blue beats Kasparov at chess.

  • 2011, Watson beats these two humans at Jeopardy,

  • which is much harder for a computer to play than chess is.

  • In fact, rather than working from predefined recipes,

  • Watson had to use reasoning to overcome his human opponents.

  • And then a couple of weeks ago,

  • DeepMind's AlphaGo beats the world's best human at Go,

  • which is the most difficult game that we have.

  • In fact, in Go, there are more possible moves

  • than there are atoms in the universe.

  • So in order to win,

  • what AlphaGo had to do was develop intuition.

  • And in fact, at some points, AlphaGo's programmers didn't understand

  • why AlphaGo was doing what it was doing.

  • And things are moving really fast.

  • I mean, consider -- in the space of a human lifetime,

  • computers have gone from a child's game

  • to what's recognized as the pinnacle of strategic thought.

  • What's basically happening

  • is computers are going from being like Spock

  • to being a lot more like Kirk.

  • (Laughter)

  • Right? From pure logic to intuition.

  • Would you cross this bridge?

  • Most of you are saying, "Oh, hell no!"

  • (Laughter)

  • And you arrived at that decision in a split second.

  • You just sort of knew that bridge was unsafe.

  • And that's exactly the kind of intuition

  • that our deep-learning systems are starting to develop right now.

  • Very soon, you'll literally be able

  • to show something you've made, you've designed,

  • to a computer,

  • and it will look at it and say,

  • "Sorry, homie, that'll never work. You have to try again."

  • Or you could ask it if people are going to like your next song,

  • or your next flavor of ice cream.

  • Or, much more importantly,

  • you could work with a computer to solve a problem

  • that we've never faced before.

  • For instance, climate change.

  • We're not doing a very good job on our own,

  • we could certainly use all the help we can get.

  • That's what I'm talking about,

  • technology amplifying our cognitive abilities

  • so we can imagine and design things that were simply out of our reach

  • as plain old un-augmented humans.

  • So what about making all of this crazy new stuff

  • that we're going to invent and design?

  • I think the era of human augmentation is as much about the physical world

  • as it is about the virtual, intellectual realm.

  • How will technology augment us?

  • In the physical world, robotic systems.

  • OK, there's certainly a fear

  • that robots are going to take jobs away from humans,

  • and that is true in certain sectors.

  • But I'm much more interested in this idea

  • that humans and robots working together are going to augment each other,

  • and start to inhabit a new space.

  • This is our applied research lab in San Francisco,

  • where one of our areas of focus is advanced robotics,

  • specifically, human-robot collaboration.

  • And this is Bishop, one of our robots.

  • As an experiment, we set it up

  • to help a person working in construction doing repetitive tasks --

  • tasks like cutting out holes for outlets or light switches in drywall.

  • (Laughter)

  • So, Bishop's human partner can tell what to do in plain English

  • and with simple gestures,

  • kind of like talking to a dog,

  • and then Bishop executes on those instructions

  • with perfect precision.

  • We're using the human for what the human is good at:

  • awareness, perception and decision making.

  • And we're using the robot for what it's good at:

  • precision and repetitiveness.

  • Here's another cool project that Bishop worked on.

  • The goal of this project, which we called the HIVE,

  • was to prototype the experience of humans, computers and robots

  • all working together to solve a highly complex design problem.

  • The humans acted as labor.

  • They cruised around the construction site, they manipulated the bamboo --

  • which, by the way, because it's a non-isomorphic material,

  • is super hard for robots to deal with.

  • But then the robots did this fiber winding,

  • which was almost impossible for a human to do.

  • And then we had an AI that was controlling everything.

  • It was telling the humans what to do, telling the robots what to do

  • and keeping track of thousands of individual components.

  • What's interesting is,

  • building this pavilion was simply not possible

  • without human, robot and AI augmenting each other.

  • OK, I'll share one more project. This one's a little bit crazy.

  • We're working with Amsterdam-based artist Joris Laarman and his team at MX3D

  • to generatively design and robotically print

  • the world's first autonomously manufactured bridge.

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

  • in Amsterdam.

  • And when they're done, we're going to hit "Go,"

  • and robots will start 3D printing in stainless steel,

  • and then they're going to keep printing, without human intervention,

  • until the bridge is finished.

  • So, as computers are going to augment our ability

  • to imagine and design new stuff,

  • robotic systems are going to help us build and make things

  • that we've never been able to make before.

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

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

  • Our nervous system, the human nervous system,

  • tells us everything that's going on around us.

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

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

  • that it just hit a pothole at the corner of Broadway and Morrison.

  • A building doesn't tell its designers

  • whether or not the people inside like being there,

  • and the toy manufacturer doesn't know

  • if a toy is actually being played with --

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

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

  • when they designed her.

  • (Laughter)

  • But what if it turns out that Barbie's actually really lonely?

  • (Laughter)

  • If the designers had known

  • what was really happening in the real world

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

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

  • that was better for the user.

  • What's missing is a nervous system

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

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

  • from the things you create in the real world?

  • With all of the stuff we make,

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

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

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