In our quest to build robots with artificial intelligence, we face five distinct challenges, and we’ve already gone over four of them in prior episodes. The first was a robot being able to see and recognize things. The second one would be to contextualize them. The third would be to contextualize moving things the way life moves. The fourth is applying that knowledge to other disciplines. And then there’s the fifth, and that is that robots can’t improvise.
Everyone–every human at every skill level–can improvise in a way far beyond any machine. If you try to open a door and the handle breaks off in your hand, you don’t just stand there immobilized and unable to comprehend a world that you never thought about, a world where door knobs don’t turn, they break off in your hand. No, you try to figure out a way to get the door open. You stick it back in and try to get it to engage with the mechanism and all that. If you lock yourself out of your house, you figure out how to get in. If a gust of wind blows your umbrella away, you go after it without ever having been taught to go after a blowing umbrella.
So even if the computer can see and understand what it sees and derive context from it, and do that with objects in motion, and can use it in other domains, it still isn’t creative. We don’t just passively perceive the world the way our typical AI does, we react to it in ways that transcend transfer learning.