Improvisation, context, and challenges for AI researchers are all topics on this episode of The AI Minute. For more on Artificial Intelligence: https://voicesinai.com https://gigaom.com https://byronreese.com https://amzn.to/2vgENbn... Transcript: Two areas that AI isn’t very good at are improvising and contextualizing, both of which come very naturally for humans. Everyone, of every skill level, can improvise in a way far beyond any machine. If the door handle breaks off in your hand, you don’t just stand there frozen, unable to fathom what to do in a universe you never contemplated, a universe where door knobs don’t turn, but they literally break off in your hand. No, you try to figure out a way to get the door open. Consider, for instance, the challenge of building a robotic plumber. Every house is different, and there are countless variants of plumbing products, and there are almost limitless things that can go wrong with your plumbing. A human plumber doesn’t have to train on every variant of every product. So that when the owner of a historic home calls a plumber and says, “I need to have my downstairs bathroom made handicap-accessible, but I want as little changed as possible,†the plumber doesn’t panic and think, “Oh no! I haven’t trained on that.†With regard to contextualizing, AI also has a hard time. If you were driving through town and saw a puppy in the road, a toddler running towards it, and a grown woman darting frantically out her front door running towards the toddler, you wouldn’t have trouble piecing that scene together. But to a computer, that’s just a series of patterns and vectors. Really, it’s just a bunch of ones and zeros. Think how easy it is for a human to figure out what is going on in a photo. A human can look and say: Oh, that’s a conga line. This is people hiding for a surprise party. That’s a prom photo taken by a parent. That’s a piano recital, a school play, a christening, and so forth. Every one of those is easy for us because we have the cultural context to decipher it. These are just a couple of the many challenges that AI researchers struggle with today.
2019年5月17日星期五
Why AI Struggles with Context and Improvisation
Improvisation, context, and challenges for AI researchers are all topics on this episode of The AI Minute. For more on Artificial Intelligence: https://voicesinai.com https://gigaom.com https://byronreese.com https://amzn.to/2vgENbn... Transcript: Two areas that AI isn’t very good at are improvising and contextualizing, both of which come very naturally for humans. Everyone, of every skill level, can improvise in a way far beyond any machine. If the door handle breaks off in your hand, you don’t just stand there frozen, unable to fathom what to do in a universe you never contemplated, a universe where door knobs don’t turn, but they literally break off in your hand. No, you try to figure out a way to get the door open. Consider, for instance, the challenge of building a robotic plumber. Every house is different, and there are countless variants of plumbing products, and there are almost limitless things that can go wrong with your plumbing. A human plumber doesn’t have to train on every variant of every product. So that when the owner of a historic home calls a plumber and says, “I need to have my downstairs bathroom made handicap-accessible, but I want as little changed as possible,†the plumber doesn’t panic and think, “Oh no! I haven’t trained on that.†With regard to contextualizing, AI also has a hard time. If you were driving through town and saw a puppy in the road, a toddler running towards it, and a grown woman darting frantically out her front door running towards the toddler, you wouldn’t have trouble piecing that scene together. But to a computer, that’s just a series of patterns and vectors. Really, it’s just a bunch of ones and zeros. Think how easy it is for a human to figure out what is going on in a photo. A human can look and say: Oh, that’s a conga line. This is people hiding for a surprise party. That’s a prom photo taken by a parent. That’s a piano recital, a school play, a christening, and so forth. Every one of those is easy for us because we have the cultural context to decipher it. These are just a couple of the many challenges that AI researchers struggle with today.
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