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