5 ms·

"Namely, that it uncovers latent representational structure not present in the data." Who thinks this? You're talking to straw-men. The latent structure is abs

"Namely, that it uncovers latent representational structure not present in the data." Who thinks this? You're talking to straw-men. The latent structure is absolutely present in the data, but a series of transforms makes the structure more readily available. (Where else would it come from? Forest spirits whispering to the model?) The 'availability' is made evident by training a linear regression for classification on the learned representation (keeping the base network fixed). This works decently-well for a good representation, and not at all for raw pixels. The point is that you can use 'simple' algorithms - including kNN - on this transformed view of the inputs; it's not magical at all. FWIW, I find the classification problem space to be riddled with conceptual holes, while other application areas are much less problematic. The use of neural networks for compression makes a lot of sense, and much of representation learning can be thought of as augmentation-invariant compression for some family of augmentations. Some of the recent big advances are really about getting away from the problems of classification. Meanwhile, reinforcement learning, diffusion learning, recurrent networks, and GANs all bring different kinds of 'interactivity' into the learning problem, and resolve your complaint about ML algorithms working on 'just data.' The iterative processes in these models provide space to push against the data with decisions, and react accordingly. I find your complaints to be lacking in curiosity or even knowledge about what people are actually doing in the field... Great results keep piling up and ML systems are used by billions of people every day, while you continue to call no-true-scotsman...

mjburgess 5y ago
Representations in the animal sense can be observed as that which is in common across inferential contexts inn which animals are confronted by the need to use them: My model of "pen" can be observed in action when: I write with a pen; I'm asked to pass a pen; when I'm asked "what color pen do you like to write with?" and so on. What is this structure "pen"? Is it a latent pattern within my experience? No! It is a means of regulating my body, imagination, emotion, etc. "The pen is mightier than the sword!" is something I can agree, or disagree with; I have a feeling about it. And in inferential contexts which require me to feel, I feel. This is what animals acquire when they understand the world. This is what everyone believes ML is uncovering, whether they realise it or not. We just dont have these ML-like templates, so we don't realise how thin and brittle they are. When ML is run on text, is detects character patterns which form "character templates" that other text can be compared to. I cannot ask, sincerely, what the machine thinks about whether "the pen is mightier than the sword" -- because it has no attitude towards that proposition. The character structure of the phrase "the pen is mightier than the sword" relative to a body of text, has nothing to do with any concepts -- that structure has nothing to do with the inferential contexts in which those concepts will become useful. Rather, this thin character-space text structure can be repeated to people in the form of an illusion: we the dumb apes who have the concepts are fooled into thinking that some machine assembling of these words had something to say. This is a very cheap trick, and fails pretty trivially. The relevant structure to be learnt, the representation, has nothing to do with sequences of characters. Though the industry likes to talk about "latent structure" in this data, it isnt latent. It is right there on the surface, indeed: it just is that surface. When the machine is challenged to do anyything other than report this average surface structure, it fails. ML systems dont work. They improve ROI on the margins, but automating basically broken inferential rules at scale -- it is the automation here, and speed that is useful. Not the actual quality of inference.
- sdenton4 5y ago
  "They improve ROI on the margins, but automating basically broken inferential rules at scale -- it is the automation here, and speed that is useful. Not the actual quality of inference." This is trivially incorrect, and again shows a lack of curiosity. A couple obvious counterexamples are AlphaGo (and descendants) and AlphaFold, both of which have quality of inference far out stripping previous efforts. In AlphaFold's case the results are pushing forward a whole field of science. Plenty of systems at this point in history outperform human experts, and not just on speed metrics. '"The pen is mightier than the sword!" is something I can agree, or disagree with; I have a feeling about it. And in inferential contexts which require me to feel, I feel.' What are these feelings? Are they necessary for learning? Will airplanes never fly until they learn to flap their wings like birds? 'we the dumb apes who have the concepts are fooled into thinking that some machine assembling of these words had something to say.' Still arguing with straw men here... 'The relevant structure to be learnt, the representation, has nothing to do with sequences of characters.' Of course it does. For BERT, the output is a conditional probability of words based on surrounding context.
  - goodtraveler 5y ago
    OP makes great points that are not being addressed here. If you believe that animal intelligence is reducible to abstract symbol shuffling then it should be possible to extend the existing approaches to actual robotic automatons but it is clear that these models are unable to deal with the dynamic structure of the world and there is no reasonable avenue to actually make that happen. So either intelligence is reducible to abstract symbol shuffling or there is a semantic fib happening here that substitutes "abstract symbol shuffling" for "intelligence". The fact that we imbue these abstract symbols with meaning is where the intelligence comes from, i.e. people attribute intelligent behavior to these systems because we have learned to attribute intelligent behavior to the production of abstract symbols. It's clear to me that current mathematics is insufficient for creating intelligence (let alone general intelligence). If you know of anyone that makes a coherent case for the mathematical basis of intelligence then I would like to see those references.
    - 5y ago

mjburgess 5y ago

Representations in the animal sense can be observed as that which is in common across inferential contexts inn which animals are confronted by the need to use them: My model of "pen" can be observed in action when: I write with a pen; I'm asked to pass a pen; when I'm asked "what color pen do you like to write with?" and so on. What is this structure "pen"? Is it a latent pattern within my experience? No! It is a means of regulating my body, imagination, emotion, etc. "The pen is mightier than the sword!" is something I can agree, or disagree with; I have a feeling about it. And in inferential contexts which require me to feel, I feel. This is what animals acquire when they understand the world. This is what everyone believes ML is uncovering, whether they realise it or not. We just dont have these ML-like templates, so we don't realise how thin and brittle they are. When ML is run on text, is detects character patterns which form "character templates" that other text can be compared to. I cannot ask, sincerely, what the machine thinks about whether "the pen is mightier than the sword" -- because it has no attitude towards that proposition. The character structure of the phrase "the pen is mightier than the sword" relative to a body of text, has nothing to do with any concepts -- that structure has nothing to do with the inferential contexts in which those concepts will become useful. Rather, this thin character-space text structure can be repeated to people in the form of an illusion: we the dumb apes who have the concepts are fooled into thinking that some machine assembling of these words had something to say. This is a very cheap trick, and fails pretty trivially. The relevant structure to be learnt, the representation, has nothing to do with sequences of characters. Though the industry likes to talk about "latent structure" in this data, it isnt latent. It is right there on the surface, indeed: it just is that surface. When the machine is challenged to do anyything other than report this average surface structure, it fails. ML systems dont work. They improve ROI on the margins, but automating basically broken inferential rules at scale -- it is the automation here, and speed that is useful. Not the actual quality of inference.

sdenton4 5y ago
"They improve ROI on the margins, but automating basically broken inferential rules at scale -- it is the automation here, and speed that is useful. Not the actual quality of inference." This is trivially incorrect, and again shows a lack of curiosity. A couple obvious counterexamples are AlphaGo (and descendants) and AlphaFold, both of which have quality of inference far out stripping previous efforts. In AlphaFold's case the results are pushing forward a whole field of science. Plenty of systems at this point in history outperform human experts, and not just on speed metrics. '"The pen is mightier than the sword!" is something I can agree, or disagree with; I have a feeling about it. And in inferential contexts which require me to feel, I feel.' What are these feelings? Are they necessary for learning? Will airplanes never fly until they learn to flap their wings like birds? 'we the dumb apes who have the concepts are fooled into thinking that some machine assembling of these words had something to say.' Still arguing with straw men here... 'The relevant structure to be learnt, the representation, has nothing to do with sequences of characters.' Of course it does. For BERT, the output is a conditional probability of words based on surrounding context.
- goodtraveler 5y ago
  OP makes great points that are not being addressed here. If you believe that animal intelligence is reducible to abstract symbol shuffling then it should be possible to extend the existing approaches to actual robotic automatons but it is clear that these models are unable to deal with the dynamic structure of the world and there is no reasonable avenue to actually make that happen. So either intelligence is reducible to abstract symbol shuffling or there is a semantic fib happening here that substitutes "abstract symbol shuffling" for "intelligence". The fact that we imbue these abstract symbols with meaning is where the intelligence comes from, i.e. people attribute intelligent behavior to these systems because we have learned to attribute intelligent behavior to the production of abstract symbols. It's clear to me that current mathematics is insufficient for creating intelligence (let alone general intelligence). If you know of anyone that makes a coherent case for the mathematical basis of intelligence then I would like to see those references.
  - 5y ago