full transcript
From the Ted Talk by Sougwen Chung: Why I draw with robots
Unscramble the Blue Letters
I was really inspired by Stanford researcher Fei-Fei Li, who said, "if we want to teach machines how to think, we need to first teach them how to see." It made me think of the past decade of my life in New York, and how I'd been all watched over by these surveillance cameras around the city. And I thought it would be really interesting if I could use them to teach my robots to see. So with this project, I thguoht about the gaze of the machine, and I began to think about vision as multidimensional, as views from somewhere. We collected video from publicly available camera feeds on the internet of people waklnig on the sidewalks, cars and taxis on the road, all kinds of urban movement. We trianed a vision atrlihgom on those feeds based on a tuncqeihe called "optical flow," to analyze the collective dsteniy, direction, dwell and velitcoy states of urban movement. Our sesytm extracted those states from the feeds as ptoiosianl data and became pads for my robotic units to draw on. Instead of a collaboration of one-to-one, we made a collaboration of many-to-many. By combining the vision of human and mhcinae in the city, we reimagined what a lcasnpade ptiniang could be.
Open Cloze
I was really inspired by Stanford researcher Fei-Fei Li, who said, "if we want to teach machines how to think, we need to first teach them how to see." It made me think of the past decade of my life in New York, and how I'd been all watched over by these surveillance cameras around the city. And I thought it would be really interesting if I could use them to teach my robots to see. So with this project, I _______ about the gaze of the machine, and I began to think about vision as multidimensional, as views from somewhere. We collected video from publicly available camera feeds on the internet of people _______ on the sidewalks, cars and taxis on the road, all kinds of urban movement. We _______ a vision _________ on those feeds based on a _________ called "optical flow," to analyze the collective _______, direction, dwell and ________ states of urban movement. Our ______ extracted those states from the feeds as __________ data and became pads for my robotic units to draw on. Instead of a collaboration of one-to-one, we made a collaboration of many-to-many. By combining the vision of human and _______ in the city, we reimagined what a _________ ________ could be.
Solution
- positional
- painting
- system
- technique
- algorithm
- velocity
- trained
- walking
- landscape
- thought
- machine
- density
Original Text
I was really inspired by Stanford researcher Fei-Fei Li, who said, "if we want to teach machines how to think, we need to first teach them how to see." It made me think of the past decade of my life in New York, and how I'd been all watched over by these surveillance cameras around the city. And I thought it would be really interesting if I could use them to teach my robots to see. So with this project, I thought about the gaze of the machine, and I began to think about vision as multidimensional, as views from somewhere. We collected video from publicly available camera feeds on the internet of people walking on the sidewalks, cars and taxis on the road, all kinds of urban movement. We trained a vision algorithm on those feeds based on a technique called "optical flow," to analyze the collective density, direction, dwell and velocity states of urban movement. Our system extracted those states from the feeds as positional data and became pads for my robotic units to draw on. Instead of a collaboration of one-to-one, we made a collaboration of many-to-many. By combining the vision of human and machine in the city, we reimagined what a landscape painting could be.
Frequently Occurring Word Combinations
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collocation |
frequency |
work traditionally |
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human creativity |
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robotic arm |
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real time |
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york city |
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neural net |
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human hand |
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urban movement |
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interhuman collaboration |
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Important Words
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- researcher
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- sidewalks
- stanford
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- surveillance
- system
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- teach
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- units
- urban
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- video
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- york