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by tbrownaw
3489 days ago
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It's because that's just how things always work. Have you ever played one of those strategy games with a tech tree? Research A and it lets you research B, C, and D; research C and D and it lets you research E; etc? That's based on the way discoveries in the real world build on eachother. And in the real world, the research tree seems to be "lumpy". Think "agricultural revolution", "industrial revolution", etc. Something new comes available, and everyone rushes to pick off all the new low-hanging fruit. Eventually the easiest gains are all taken, and people lose interest and move to other things. And as people keep picking away more slowly at the more difficult/involved things, eventually someone will find something that -- probably combined with some completely different existing knowledge -- opens up another new field. And it repeats. Right now we're in the "low-hanging fruit" phase of (1) computers that are powerful enough to run neural networks, combined with (2) feedback algorithms that allow networks with lots of layers to learn effectively. Sooner or later the gains will get a bit tougher as we understand the field better, and then research will slow even further as many researchers find something else new and shiny -- and with better returns -- to focus on. |
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I'm not so sure about that. Places like Deepmind are not satisfied with simply having AI that does straight forward pattern matching problems (Though that's very important). They are moving into more complex problems like transfer learning, reinforcement learning and unsupervised learning for more complex, real world problems solving. They also seem to be making good progress on this as well.