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You certainly can, and that is the biggest problem with this article: a completely lack of interest in understanding what deep learning understands (ironically)

by gwern 7y ago

You certainly can, and that is the biggest problem with this article: a completely lack of interest in understanding what deep learning understands (ironically). Hofstadter, having completely missed the boat on connectionism, committed himself to a failed paradigm, and been wrong about AI time after time (remember when Hofstadter claimed beating humans at chess would require computers to have emotions and be able to write poems?), totally fails to grapple with what NNs do do. For starters, Google Translate is a free service and not necessarily SOTA, even when he wrote this. For example, if you were going to ask 'what do multi-lingual translation seq2seq RNNs (or Transformers) understand?', the immediate obvious thing to discuss would be questions about the 'interlingua' which their embedding yields, things that word embeddings learn (including surprising things like relative locations of cities or sizes of objects), or about transfer learning to tasks that require grammar or reasoning or common-sense like Winograd schemas like in GLUE or SuperGLUE, and how they improve massively over earlier approaches and how they are scaling (one of the most important trends in AI). There is a great deal which could be said here, demonstrating why it works so well and why it doesn't work sometimes, and an AI researcher is just the sort of person to ask. And unfortunately, Hofstadter does none of this and instead bangs on about some errors he found, like it's a total blackbox and understanding is a binary where you either like the same poetry Hofstadter does or you are a cretin, and takes the attitude that any error shows that the approach has failed, is failing, and always will fail because it is 'deeply lacking' (what, exactly? And to think, he complains about 'deep' being abused for rhetorical purposes before and then goes and produces a shallow exercise like this). I thought this was a lousy neo-luddite essay in 2018, and it's only gotten worse since then. In another 10 years, it'll look like Cliff Stoll.

deleted 7y ago
[deleted]
ozim 7y ago
I am not a specialist on AI, just had course on neural networks at university. Feel free to discard my opinion and do not reply if you find my way of thinking silly. From what I see and understand, even if you have multiple layers of perceptrons and back propagation. It is still really "deeply lacking" because you are not able to train networks with all context there is. You can still overtrain network, so more training is making it actually worse. You can train NN only so much, just like you cannot teach dog to speak, it can perfectly fine understand your commands but you will never communicate with the dog on a level of "this painting is beautiful, you agree?". I also do not believe in possibility of creating AGI.
- gwern 7y ago
  > From what I see and understand, even if you have multiple layers of perceptrons and back propagation. It is still really "deeply lacking" because you are not able to train networks with all context there is. I don't know what you're trying to say here. NNs can have plenty of context. Let's take just the Transformer architecture for concreteness, ignoring all the others. Something like GPT-2 uses a fixed window, but it can be expanded arbitrarily widely if you're willing to pay the compute price; the scaling can be improved within that window by not computing attention over the full window, per Sparse Transformers; recurrency can be added to make it more RNN-like, as does TransformerXL; compressed versions of the memory can be stored and fed back in, allowing lookback arbitrarily far, per Compressive Transformers; multi-modal input, like images + embeddings + text, are of course completely possible and can represent arbitrary metadata and context; there are many different ways to add in external memory stores (DeepMind regularly experiments with these, like the DNC); and so on. What exactly do you think is impossible in principle in the connectionist paradigm to make NNs do (and why do you think your brain is exempt from this)?
  - ozim 7y ago
    Ok you just skipped the second part where I say about overtraining NN's. You can put all the context you want from other sources and expand arbitrarily, but still artificial NN's are a lot more constrained than biological ones. It is because artificial NN's are still only approximation of biological NN's. We still don't know a lot about functioning of brain and other stuff. Maybe some parts of image recognition require spinal cord neurons and muscle tissue to interact? So is it just count of inputs to neuron, or there are ways where inputs to neuron are modified by other chemicals in a body? Are we making weights on inputs stronger because of good reasons, are making some other inputs weaker because of good reasons?
YeGoblynQueenne 7y ago
Regarding the "failed paradigm" (i.e: of logic- and knowledge-based AI) and how neural nets scale, specifically on language modelling tasks. I study a class of machine learning algorithms that learn logic programs (as in Prolog, or ASP) from examples. The field is Inductive Logic Programming. ILP algorithms are characterised by their ability to incorporate background knowledge and impose strong inductive biases on the hypothesis space to learn accurately from a handful of examples. My favourite example of this is learning a grammar of the aⁿbⁿ language. My implementation of one of the algorithms that my research group studies (I'm a Phd research student) is Thelma: https://github.com/stassa/thelma https://github.com/stassa/thelma In Thelma's README on github you will find an example of Thelma learning a grammar of the aⁿbⁿ language from three positive examples and no negative examples. The learned grammar generalises to any n. In typical CFG notation it's this grammar: S → AB S → AS₁ S₁ → B A → a B → b (The nonterminal S₁ is invented, i.e. it was not given in the original learning problem defintion. The preterminals A and B are background knowledge.) By contrast, neural networks can only learn fragments of this grammar up to some limited n and that only if they're given tens of thousands of training examples. For instance, a classic paper by Gers and Schmidhüber [1] claims LSTM "generalisation" from training sets of 22 to 42 thousand aⁿbⁿ strings, but their average generalisaion is, e.g. from n in [1,50] (i.e. that's the value of n in training strings) to n in [1,430] (n in testing strings) and their "best" generalisation is at most to n in [1,1000]. On Thelma's README on github I have a small testing query that tests how the aⁿbⁿ grammar it learned from 3 positive examples with n in [1,3] generalises to an aⁿbⁿ string of e.g. n = 100,000: ?- _N = 100_000, findall(a, between(1,_N,_), _As), findall(b, between(1,_N,_),_Bs), append(_As,_Bs,_AsBs), anbn:'S'(_AsBs,[]). true . Well, it's a correct grammar so it generalises perfectly. You can bind _N to any number your computer memory will allow and it will still parse. So, besides the plug of my work (sorry) I would say that the earlier paradigm that you say has "failed" with respect to recent connectionist success is alive and well and it still gets many things right that the connectionist paradigm struggles with mightily, in particular robust generalisation from very few examples (without expensive pre-training etc), and of course any task that requires reasoning [2]. ______________ [1] "LSTM recurrent networks learn simple context free and context sensitive languages": https://ieeexplore.ieee.org/document/963769 https://ieeexplore.ieee.org/document/963769 See table 2 on page 7 for the results I quote above. [2] The examples of good performance on reasoning tasks you bring up have been strongly criticised, e.g. in https://arxiv.org/abs/1907.07355 https://arxiv.org/abs/1907.07355 (Probing Neural Network Comprehension of Natural Language Arguments) or Gary Marcus' recent paper on a critical appraisal of deep learning etc. P.S. I'm very sorry to see you're being downvoted.

by gwern 7y ago

deleted 7y ago

[deleted]

ozim 7y ago

I am not a specialist on AI, just had course on neural networks at university. Feel free to discard my opinion and do not reply if you find my way of thinking silly. From what I see and understand, even if you have multiple layers of perceptrons and back propagation. It is still really "deeply lacking" because you are not able to train networks with all context there is. You can still overtrain network, so more training is making it actually worse. You can train NN only so much, just like you cannot teach dog to speak, it can perfectly fine understand your commands but you will never communicate with the dog on a level of "this painting is beautiful, you agree?". I also do not believe in possibility of creating AGI.

gwern 7y ago
> From what I see and understand, even if you have multiple layers of perceptrons and back propagation. It is still really "deeply lacking" because you are not able to train networks with all context there is. I don't know what you're trying to say here. NNs can have plenty of context. Let's take just the Transformer architecture for concreteness, ignoring all the others. Something like GPT-2 uses a fixed window, but it can be expanded arbitrarily widely if you're willing to pay the compute price; the scaling can be improved within that window by not computing attention over the full window, per Sparse Transformers; recurrency can be added to make it more RNN-like, as does TransformerXL; compressed versions of the memory can be stored and fed back in, allowing lookback arbitrarily far, per Compressive Transformers; multi-modal input, like images + embeddings + text, are of course completely possible and can represent arbitrary metadata and context; there are many different ways to add in external memory stores (DeepMind regularly experiments with these, like the DNC); and so on. What exactly do you think is impossible in principle in the connectionist paradigm to make NNs do (and why do you think your brain is exempt from this)?
- ozim 7y ago
  Ok you just skipped the second part where I say about overtraining NN's. You can put all the context you want from other sources and expand arbitrarily, but still artificial NN's are a lot more constrained than biological ones. It is because artificial NN's are still only approximation of biological NN's. We still don't know a lot about functioning of brain and other stuff. Maybe some parts of image recognition require spinal cord neurons and muscle tissue to interact? So is it just count of inputs to neuron, or there are ways where inputs to neuron are modified by other chemicals in a body? Are we making weights on inputs stronger because of good reasons, are making some other inputs weaker because of good reasons?

YeGoblynQueenne 7y ago

Regarding the "failed paradigm" (i.e: of logic- and knowledge-based AI) and how neural nets scale, specifically on language modelling tasks. I study a class of machine learning algorithms that learn logic programs (as in Prolog, or ASP) from examples. The field is Inductive Logic Programming. ILP algorithms are characterised by their ability to incorporate background knowledge and impose strong inductive biases on the hypothesis space to learn accurately from a handful of examples. My favourite example of this is learning a grammar of the aⁿbⁿ language. My implementation of one of the algorithms that my research group studies (I'm a Phd research student) is Thelma: https://github.com/stassa/thelma https://github.com/stassa/thelma In Thelma's README on github you will find an example of Thelma learning a grammar of the aⁿbⁿ language from three positive examples and no negative examples. The learned grammar generalises to any n. In typical CFG notation it's this grammar: S → AB S → AS₁ S₁ → B A → a B → b (The nonterminal S₁ is invented, i.e. it was not given in the original learning problem defintion. The preterminals A and B are background knowledge.) By contrast, neural networks can only learn fragments of this grammar up to some limited n and that only if they're given tens of thousands of training examples. For instance, a classic paper by Gers and Schmidhüber [1] claims LSTM "generalisation" from training sets of 22 to 42 thousand aⁿbⁿ strings, but their average generalisaion is, e.g. from n in [1,50] (i.e. that's the value of n in training strings) to n in [1,430] (n in testing strings) and their "best" generalisation is at most to n in [1,1000]. On Thelma's README on github I have a small testing query that tests how the aⁿbⁿ grammar it learned from 3 positive examples with n in [1,3] generalises to an aⁿbⁿ string of e.g. n = 100,000: ?- _N = 100_000, findall(a, between(1,_N,_), _As), findall(b, between(1,_N,_),_Bs), append(_As,_Bs,_AsBs), anbn:'S'(_AsBs,[]). true . Well, it's a correct grammar so it generalises perfectly. You can bind _N to any number your computer memory will allow and it will still parse. So, besides the plug of my work (sorry) I would say that the earlier paradigm that you say has "failed" with respect to recent connectionist success is alive and well and it still gets many things right that the connectionist paradigm struggles with mightily, in particular robust generalisation from very few examples (without expensive pre-training etc), and of course any task that requires reasoning [2]. ______________ [1] "LSTM recurrent networks learn simple context free and context sensitive languages": https://ieeexplore.ieee.org/document/963769 https://ieeexplore.ieee.org/document/963769 See table 2 on page 7 for the results I quote above. [2] The examples of good performance on reasoning tasks you bring up have been strongly criticised, e.g. in https://arxiv.org/abs/1907.07355 https://arxiv.org/abs/1907.07355 (Probing Neural Network Comprehension of Natural Language Arguments) or Gary Marcus' recent paper on a critical appraisal of deep learning etc. P.S. I'm very sorry to see you're being downvoted.