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The practical utility of Google Translate and similar technologies is undeniable, and probably it’s a good thing overall, but there is still something deeply la

by ozim 7y ago

The practical utility of Google Translate and similar technologies is undeniable, and probably it’s a good thing overall, but there is still something deeply lacking in the approach, which is conveyed by a single word: understanding. It all boils down to this paragraph, and no one can say it is not true.

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?

by ozim 7y ago

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?