5 ms·

> It's not that learning anbn is not a toy problem. It is. But it's a toy problem for ILP. I don't see why the distinction you are making matters. > Neural ne

> It's not that learning anbn is not a toy problem. It is. But it's a toy problem for ILP. I don't see why the distinction you are making matters. > Neural nets can't solve it. They can, it takes additional training to do so from scratch. Also, the work you cited is also nearly two decades old, I have no idea how SOTA would perform. > solve the same old classification problems again and again I truly have no idea what you're talking about, please point me to where, for instance, [0] has been solved "again and again"? The claim I am making is not that Deep Learning can solve all problems, it cannot, the claim is that logic-based AI has failed to come up with anything of value whatsoever, whereas statistical ML has had sweeping results across many real problems -- for instance: AlphaZero solving Go, Chess, and Shogi; GPT-2 creating readable stories given a prompt; cars that can drive themselves under a wide range of circumstances. If you think a^nb^n is even remotely comparable to those feats, I don't think we can find any common ground. [0] https://www.youtube.com/watch?v=Wwwyr7cOBlU https://www.youtube.com/watch?v=Wwwyr7cOBlU

YeGoblynQueenne 7y ago
Neural nets can still not solve aⁿbⁿ. This is the latest attempt of which I'm aware: On the Practical Computational Power of Finite Precision RNNs for Language Recognition https://arxiv.org/abs/1805.04908 https://arxiv.org/abs/1805.04908 Typically, it claims generalisation but fails to demonstrate it. As we agreed, it's a simple problem. Neural nets can't solve it. Do you understand why? Re: classification, AlphaZero is a great example of what I'm talking about. The game-playing logic is provided by MCTS, a GOFAI algorithm (it's a variant of good, old minimax). The neural net component is only used to identify promising board positions, i.e. for classification. As to chess, it was already solved by DeepBlue using alpha-beta minimax and an opening book of moves- another example of GOFAI. Even AlphaGo, AlphaZero's predecessor used domain knowledge in the form of example games played by humans. But I'm getting the feeling that, while you are making this extraordinary claim that "logic-based AI has failed to come up with anything of value whatsoever", you are not very familiar with the history of AI research. As a quick reminder, logic-based AI was the dominant paradigm in research for some 60 years. The big success of course were expert systems. As an early example, look for information on MYCIN, the first system to beat human experts at medical diagnosis (of infections, in partiuclar). Even today, after the recent meteoric rise of deep learning, logic-based algorithms are state-of-the-art for many applications: in particular, planning, constraint solving, game playing (as in minimax variants) and anything that requires reasoning. In applied work, decision-tree learners, a class of machine learning algorithms learning propositional-logic models, are still widely used in data science work, more so than brittle and expensive to train deep neural nets.
- YeGoblynQueenne 7y ago
  >> But I'm getting the feeling that, while you are making this extraordinary claim that "logic-based AI has failed to come up with anything of value whatsoever", you are not very familiar with the history of AI research. Ugh. This sounds like a personal attack. I apologise unrservedly. I really did not mean to insult you or disparage your knowledge of AI research. There's much better ways to say what I wanted to say here. Apologies, again. I can't edit my comment now.

YeGoblynQueenne 7y ago

Neural nets can still not solve aⁿbⁿ. This is the latest attempt of which I'm aware: On the Practical Computational Power of Finite Precision RNNs for Language Recognition https://arxiv.org/abs/1805.04908 https://arxiv.org/abs/1805.04908 Typically, it claims generalisation but fails to demonstrate it. As we agreed, it's a simple problem. Neural nets can't solve it. Do you understand why? Re: classification, AlphaZero is a great example of what I'm talking about. The game-playing logic is provided by MCTS, a GOFAI algorithm (it's a variant of good, old minimax). The neural net component is only used to identify promising board positions, i.e. for classification. As to chess, it was already solved by DeepBlue using alpha-beta minimax and an opening book of moves- another example of GOFAI. Even AlphaGo, AlphaZero's predecessor used domain knowledge in the form of example games played by humans. But I'm getting the feeling that, while you are making this extraordinary claim that "logic-based AI has failed to come up with anything of value whatsoever", you are not very familiar with the history of AI research. As a quick reminder, logic-based AI was the dominant paradigm in research for some 60 years. The big success of course were expert systems. As an early example, look for information on MYCIN, the first system to beat human experts at medical diagnosis (of infections, in partiuclar). Even today, after the recent meteoric rise of deep learning, logic-based algorithms are state-of-the-art for many applications: in particular, planning, constraint solving, game playing (as in minimax variants) and anything that requires reasoning. In applied work, decision-tree learners, a class of machine learning algorithms learning propositional-logic models, are still widely used in data science work, more so than brittle and expensive to train deep neural nets.

YeGoblynQueenne 7y ago
>> But I'm getting the feeling that, while you are making this extraordinary claim that "logic-based AI has failed to come up with anything of value whatsoever", you are not very familiar with the history of AI research. Ugh. This sounds like a personal attack. I apologise unrservedly. I really did not mean to insult you or disparage your knowledge of AI research. There's much better ways to say what I wanted to say here. Apologies, again. I can't edit my comment now.