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by webjprgm
3792 days ago
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So we want unsupervised learning, huh. I think this line is rather important: > But there are lots of limitations of this approach. For one, to teach a person something new, you typically don't need to tell them about it thousands of times. So the state of the art in AI is still much slower than how we learn. Unsupervised learning is not necessarily the answer. The computer would have to be given plenty of spare time to learn random patterns from the universe and have enough inteligence to apply these to a problem. What would be better is if we could tell the AI once and have it figure things out. When you start a new job that's what happens, someone tells you the instructions. (They may or may not show you as well, and may or may not stick around to correct your mistakes.) This requires interpreting the instructions into rules and then attempting to apply them, learning from mistakes, and then evolving that into rules that work, then after enough examples of success, evolving into patterns recognition that makes it more automatic. Human expertise can be broken into three levels, the first is strategic planning and takes a lot of mental effort, then there are rules-based responses which are faster, then there is the muscle-memory-like automatic responses. Right now it seems we either manually program in all the rules or else we use 1000s of examples to build up the automatic level, but we don't have the strategic level for the AI to build its own rules or the level of using its rules to learn from examples over time. (Though I am not well enough versed in AI to know for sure that we don't have pieces of those solutions.) It would also be nice for the AI to be able to take patterns it has learned and articulate them as rules which someone else could learn. |
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Reinforcement Learning is an approach to learn rules by observing a positive or negative feedback from the world. Recently a single algorithm learned 40 Atari games on its own and a Go playing algo beat the European champion. They both used RL.