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A lot of the recent successes in ML haven't been based on new breakthroughs in theory. For instance deep neural networks have been studied for decades now. Same
by briga 9y ago
A lot of the recent successes in ML haven't been based on new breakthroughs in theory. For instance deep neural networks have been studied for decades now. Same with the reinforcement learning algorithms behind advances like AlphaGo. So I don't think skipping ahead five years will necessarily help you.
That said, there was a lot of AI work with hand-crafted features and expert systems that has no basically been rendered obsolete by deep learning. But advances generally don't emerge out of a vacuum, and it helps to have some background knowledge of previous work to have a good idea of what has and hasn't worked in the past.
- cscurmudgeon 9y agoWe have replaced hand-crafted features with hand-crafted architectures. While performance might be important, at least the features were easy to understand by non experts. :)
- halflings 9y agoI find it's much easier to understand why 3 CNN layers applied on the raw image can learn convolutions that are relevant to solving my task... than it is to understand what is being done by "artisanal hand-engineered authentic patches" with a couple obscure dimensionality reduction algorithms thrown in, and an SVM on top. Expert systems were also incredibly hard to build and debug, and weren't nearly as useful as ML systems are nowadays.