How we can teach computers to make sense of our emotions | Raphael Arar

64,666 views ・ 2018-04-24

TED


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Translator: Ivana Korom Reviewer: Joanna Pietrulewicz
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I consider myself one part artist and one part designer.
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And I work at an artificial intelligence research lab.
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We're trying to create technology
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that you'll want to interact with in the far future.
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Not just six months from now, but try years and decades from now.
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And we're taking a moonshot
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that we'll want to be interacting with computers
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in deeply emotional ways.
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So in order to do that,
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the technology has to be just as much human as it is artificial.
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It has to get you.
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You know, like that inside joke that'll have you and your best friend
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on the floor, cracking up.
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Or that look of disappointment that you can just smell from miles away.
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I view art as the gateway to help us bridge this gap between human and machine:
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to figure out what it means to get each other
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so that we can train AI to get us.
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See, to me, art is a way to put tangible experiences
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to intangible ideas, feelings and emotions.
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And I think it's one of the most human things about us.
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See, we're a complicated and complex bunch.
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We have what feels like an infinite range of emotions,
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and to top it off, we're all different.
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We have different family backgrounds,
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different experiences and different psychologies.
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And this is what makes life really interesting.
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But this is also what makes working on intelligent technology
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extremely difficult.
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And right now, AI research, well,
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it's a bit lopsided on the tech side.
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And that makes a lot of sense.
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See, for every qualitative thing about us --
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you know, those parts of us that are emotional, dynamic and subjective --
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we have to convert it to a quantitative metric:
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something that can be represented with facts, figures and computer code.
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The issue is, there are many qualitative things
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that we just can't put our finger on.
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So, think about hearing your favorite song for the first time.
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What were you doing?
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How did you feel?
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Did you get goosebumps?
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Or did you get fired up?
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Hard to describe, right?
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See, parts of us feel so simple,
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but under the surface, there's really a ton of complexity.
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And translating that complexity to machines
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is what makes them modern-day moonshots.
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And I'm not convinced that we can answer these deeper questions
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with just ones and zeros alone.
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So, in the lab, I've been creating art
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as a way to help me design better experiences
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for bleeding-edge technology.
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And it's been serving as a catalyst
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to beef up the more human ways that computers can relate to us.
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Through art, we're tacking some of the hardest questions,
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like what does it really mean to feel?
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Or how do we engage and know how to be present with each other?
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And how does intuition affect the way that we interact?
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So, take for example human emotion.
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Right now, computers can make sense of our most basic ones,
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like joy, sadness, anger, fear and disgust,
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by converting those characteristics to math.
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But what about the more complex emotions?
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You know, those emotions
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that we have a hard time describing to each other?
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Like nostalgia.
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So, to explore this, I created a piece of art, an experience,
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that asked people to share a memory,
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and I teamed up with some data scientists
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to figure out how to take an emotion that's so highly subjective
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and convert it into something mathematically precise.
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So, we created what we call a nostalgia score
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and it's the heart of this installation.
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To do that, the installation asks you to share a story,
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the computer then analyzes it for its simpler emotions,
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it checks for your tendency to use past-tense wording
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and also looks for words that we tend to associate with nostalgia,
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like "home," "childhood" and "the past."
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It then creates a nostalgia score
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to indicate how nostalgic your story is.
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And that score is the driving force behind these light-based sculptures
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that serve as physical embodiments of your contribution.
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And the higher the score, the rosier the hue.
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You know, like looking at the world through rose-colored glasses.
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So, when you see your score
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and the physical representation of it,
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sometimes you'd agree and sometimes you wouldn't.
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It's as if it really understood how that experience made you feel.
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But other times it gets tripped up
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and has you thinking it doesn't understand you at all.
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But the piece really serves to show
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that if we have a hard time explaining the emotions that we have to each other,
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how can we teach a computer to make sense of them?
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So, even the more objective parts about being human are hard to describe.
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Like, conversation.
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Have you ever really tried to break down the steps?
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So think about sitting with your friend at a coffee shop
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and just having small talk.
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How do you know when to take a turn?
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How do you know when to shift topics?
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And how do you even know what topics to discuss?
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See, most of us don't really think about it,
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because it's almost second nature.
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And when we get to know someone, we learn more about what makes them tick,
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and then we learn what topics we can discuss.
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But when it comes to teaching AI systems how to interact with people,
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we have to teach them step by step what to do.
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And right now, it feels clunky.
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If you've ever tried to talk with Alexa, Siri or Google Assistant,
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you can tell that it or they can still sound cold.
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And have you ever gotten annoyed
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when they didn't understand what you were saying
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and you had to rephrase what you wanted 20 times just to play a song?
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Alright, to the credit of the designers, realistic communication is really hard.
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And there's a whole branch of sociology,
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called conversation analysis,
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that tries to make blueprints for different types of conversation.
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Types like customer service or counseling, teaching and others.
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I've been collaborating with a conversation analyst at the lab
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to try to help our AI systems hold more human-sounding conversations.
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This way, when you have an interaction with a chatbot on your phone
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or a voice-based system in the car,
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it sounds a little more human and less cold and disjointed.
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So I created a piece of art
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that tries to highlight the robotic, clunky interaction
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to help us understand, as designers,
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why it doesn't sound human yet and, well, what we can do about it.
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The piece is called Bot to Bot
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and it puts one conversational system against another
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and then exposes it to the general public.
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And what ends up happening is that you get something
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that tries to mimic human conversation,
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but falls short.
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Sometimes it works and sometimes it gets into these, well,
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loops of misunderstanding.
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So even though the machine-to-machine conversation can make sense,
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grammatically and colloquially,
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it can still end up feeling cold and robotic.
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And despite checking all the boxes, the dialogue lacks soul
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and those one-off quirks that make each of us who we are.
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So while it might be grammatically correct
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and uses all the right hashtags and emojis,
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it can end up sounding mechanical and, well, a little creepy.
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And we call this the uncanny valley.
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You know, that creepiness factor of tech
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where it's close to human but just slightly off.
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And the piece will start being
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one way that we test for the humanness of a conversation
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and the parts that get lost in translation.
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So there are other things that get lost in translation, too,
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like human intuition.
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Right now, computers are gaining more autonomy.
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They can take care of things for us,
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like change the temperature of our houses based on our preferences
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and even help us drive on the freeway.
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But there are things that you and I do in person
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that are really difficult to translate to AI.
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So think about the last time that you saw an old classmate or coworker.
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Did you give them a hug or go in for a handshake?
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You probably didn't think twice
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because you've had so many built up experiences
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that had you do one or the other.
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And as an artist, I feel that access to one's intuition,
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your unconscious knowing,
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is what helps us create amazing things.
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Big ideas, from that abstract, nonlinear place in our consciousness
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that is the culmination of all of our experiences.
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And if we want computers to relate to us and help amplify our creative abilities,
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I feel that we'll need to start thinking about how to make computers be intuitive.
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So I wanted to explore how something like human intuition
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could be directly translated to artificial intelligence.
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And I created a piece that explores computer-based intuition
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in a physical space.
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The piece is called Wayfinding,
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and it's set up as a symbolic compass that has four kinetic sculptures.
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Each one represents a direction,
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north, east, south and west.
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And there are sensors set up on the top of each sculpture
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that capture how far away you are from them.
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And the data that gets collected
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ends up changing the way that sculptures move
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and the direction of the compass.
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The thing is, the piece doesn't work like the automatic door sensor
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that just opens when you walk in front of it.
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See, your contribution is only a part of its collection of lived experiences.
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And all of those experiences affect the way that it moves.
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So when you walk in front of it,
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it starts to use all of the data
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that it's captured throughout its exhibition history --
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or its intuition --
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to mechanically respond to you based on what it's learned from others.
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And what ends up happening is that as participants
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we start to learn the level of detail that we need
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in order to manage expectations
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from both humans and machines.
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We can almost see our intuition being played out on the computer,
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picturing all of that data being processed in our mind's eye.
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My hope is that this type of art
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will help us think differently about intuition
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and how to apply that to AI in the future.
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So these are just a few examples of how I'm using art to feed into my work
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as a designer and researcher of artificial intelligence.
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And I see it as a crucial way to move innovation forward.
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Because right now, there are a lot of extremes when it comes to AI.
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Popular movies show it as this destructive force
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while commercials are showing it as a savior
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to solve some of the world's most complex problems.
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But regardless of where you stand,
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it's hard to deny that we're living in a world
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that's becoming more and more digital by the second.
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Our lives revolve around our devices, smart appliances and more.
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And I don't think this will let up any time soon.
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So, I'm trying to embed more humanness from the start.
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And I have a hunch that bringing art into an AI research process
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is a way to do just that.
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Thank you.
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(Applause)
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