Subtitles section Play video
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Translator: Leslie Gauthier Reviewer: Camille Martínez
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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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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,
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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."
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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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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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But what if it turns out that Barbie's 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
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with their designs -- the road, the building, Barbie --
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they could've used that knowledge 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.
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What if all of you had that kind of information flowing to you
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from the things you create 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 --
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in fact, last year, about two trillion dollars --
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convincing people to buy the things we've made.