3 ms·
Well what I mean by "hard" is that I can train a neural network to do a reasonably good job of recognizing say handwritten characters given some amount of train
by tgflynn 14y ago
Well what I mean by "hard" is that I can train a neural network to do a reasonably good job of recognizing say handwritten characters given some amount of training data.
My attempts to train similar networks to learn the rules of chess (just the rules, I'm not even talking about trying to win) have not led to good or steadily improving performance, despite the fact that there's effectively no limit to the amount of training data that can be generated for chess.
There are a number of possible reasons for this of which I think 2 likely ones are that:
1) The networks I'm using have insufficient computational/storage capacity to learn the rules of chess.
or
2) Even if (1) is not the case gradient descent is unlikely to find a sufficiently good near optimum because the response surface is too complex.
- Breakthrough 14y agoGreat response, I must say. As a conjecture, do you think splitting the logic up into several smaller neural networks with different objectives (i.e. for chess, one evaluates defensive maneuvers, one for attack, and so on) would alleviate the problem? Or at least improve the performance of the machine as a whole...
- treePhase 14y agoYou should also consider the possibility that the type of neural network you chose is not capable of learning the rules of chess, but may be sufficient for OCR.
- simonster 14y agoI would bet heavily on (1). Non-human primates, with ~10 billion neurons (~3 billion neocortical), also cannot learn the rules of chess. Nearly all neural networks have far fewer.