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I have only briefly looked through their long paper, and it looks like what you suggest is pretty much exactly what they do. Their main contribution seems to b

I have only briefly looked through their long paper, and it looks like what you suggest is pretty much exactly what they do. Their main contribution seems to be that they have incrementally improved on a symbolic learner. They explicitly tell it to search a predefined set of programmes. They use pretrained MNIST CNN classifiers as inputs into it, which already know that e.g. MNIST has 10 classes. What I was talking about is symbolic reasoning somehow 'emerging' from a connectionist approach, without being explicitly designed. The cool thing about deep nets is that they are both kind of biologically and evolutionary plausible (except back-propagation, I suppose), and are able to achieve great performance on a bunch of traditionally difficult perception tasks (vision, hearing - again, with some caveats). But how such a system could develop symbolic reasoning is not at all clear. Are any other biological systems apart from humans capable of symbolic reasoning? Of course, planes don't flap their wings and so a practical system will probably have a symbolic reasoning engine designed top-down rather than emerging from some neural net. But it is still an interesting question that would give us more insight into how our brains work.

YeGoblynQueenne 8y ago
I see what you mean- thank you for the clarification. Well, I'm not really the right person to discuss this issue since I can't claim to understand how the brain works. However, I do understand a few things about connectionist methods and it's my understanding that they are not very good models of the way the brain works at all (am I misrepresenting your turn of phrase, of "biologically plausible"?). In that sense, I doubt it's possible that a neural net would develop symbolic reasoning by dint of being in some way similar to a biological brain. In general, my experience with neural nets and gradient optimistation techniques is that unless a great deal of effort is spent directing their learning, they are prone to learn whatever is convenient, which is very often not what the human users want it to. For instance, see the following collection of anecdotes of evolutionary algorithms lerning whatever they please, rather than what the researchers were trying to teach them: https://arxiv.org/abs/1803.03453 https://arxiv.org/abs/1803.03453 Or the descriptions of the difficulties of training Deep Reinforcement Learning models in this article: https://www.alexirpan.com/2018/02/14/rl-hard.html https://www.alexirpan.com/2018/02/14/rl-hard.html Also, while I'm a staunch symbolicist myself, I'm not 100% convinced that symbolic reasoning is "natural". I think it rather took a lot of effort to develop such systems and most people still have a great deal of trouble using them with precision. If you meant to say that symbolic reasoning should arise spontaneously in a neural network, I see that as very unlikely.

YeGoblynQueenne 8y ago

I see what you mean- thank you for the clarification. Well, I'm not really the right person to discuss this issue since I can't claim to understand how the brain works. However, I do understand a few things about connectionist methods and it's my understanding that they are not very good models of the way the brain works at all (am I misrepresenting your turn of phrase, of "biologically plausible"?). In that sense, I doubt it's possible that a neural net would develop symbolic reasoning by dint of being in some way similar to a biological brain. In general, my experience with neural nets and gradient optimistation techniques is that unless a great deal of effort is spent directing their learning, they are prone to learn whatever is convenient, which is very often not what the human users want it to. For instance, see the following collection of anecdotes of evolutionary algorithms lerning whatever they please, rather than what the researchers were trying to teach them: https://arxiv.org/abs/1803.03453 https://arxiv.org/abs/1803.03453 Or the descriptions of the difficulties of training Deep Reinforcement Learning models in this article: https://www.alexirpan.com/2018/02/14/rl-hard.html https://www.alexirpan.com/2018/02/14/rl-hard.html Also, while I'm a staunch symbolicist myself, I'm not 100% convinced that symbolic reasoning is "natural". I think it rather took a lot of effort to develop such systems and most people still have a great deal of trouble using them with precision. If you meant to say that symbolic reasoning should arise spontaneously in a neural network, I see that as very unlikely.