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. http://bit.ly/2Yw5vcn gigaom May 16, 2019 at 03:10PM