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I have no discomfort with the notion that our bodies, which grow in response to direct causal contact with our environment, contain in-their-structure the gener

I have no discomfort with the notion that our bodies, which grow in response to direct causal contact with our environment, contain in-their-structure the generative capbaility for knoweldge, imagination, skill, growth -- and so on. I have no discomfort with the basically schiozphrenic notion that the shapes of words have something to do with the nature of the world. I just think its a kind of insantity which absolutely destroys our ability to reason carefully about the use of these systems. That "tr" occurs before "ee" says as much about "trees" as "leaves are green" says -- it is only that *we* have the relevant semantics that the latter is meaningful when interpreted in the light of our "environmental history" recorded in our bodies, and given weight and utility by our imaginations. The structure of text is not the structure of the world. This thesis is mad. Its a scientific thesis. It is trivial to test it. It is trivial to wholey discred it. It's pseudoscience. No one here is a scientist and no one treats any of this as science. Where's the criteria for the emprical adequecy of NLP systems as models of language? Specifying any, conducting actual hypothesis tests, and establishing a theory of how NLP systems model language -- this would immediately reveal the smoke-and-mirros. The work to reveal the statistical tricks underneath them takes years, and no one has much motivation to do it. The money lies in this sales pitch, and this is no science. This is no scientific method.

rafaelero 5y ago
Ok, boomer.
whimsicalism 5y ago
Agree to disagree. I think you are opining about things that you are lacking fundamental knowledge on. > The structure of text is not the structure of the world. This thesis is mad. Its a scientific thesis. It is trivial to test it. It is trivial to wholey discred it. It's pseudoscience. It's unclear what you even mean by that. Are the electrical impulses coming to our brain the "structure of the world"?
- yldedly 5y ago
  The structure of having X apples in Y buckets is the same as the structure in the expression "X * Y", as long as the expression exists in a context that can parse it using the rules of arithmetic, such as a human, or a calculator. These language models lack context, not just for arithmetic, but for everything. They can't parse "X * Y" for any X and Y, they've just associated the expression with the right answer for so many values of X and Y, that we get fooled into thinking they know the rules. We get fooled into thinking they've learned the structure of the world. But they've only learned the structure of text.
  - whimsicalism 5y ago
    It would be trivial for a network of this size to code general rules for multiplication. At a certain point, when you have enough data, finding the actual rule is actually the easier solution than memorizing each data point. This is the key insight of deep learning.
    - yldedly 5y ago
      Really? Better inform all the researchers working on this that they're wasting their time then: https://arxiv.org/abs/2001.05016 https://arxiv.org/abs/2001.05016 More fundamentally, any finite neural net is either constant or linear outside the training sample,depending on the activation function. Unless you design special neurons like in the paper above, which solves this specific problem for arithmetic, but not the general problem of extrapolation.
      - FeepingCreature 5y ago
        Isn't that per-layer?
        yldedly 5y ago
        No, no matter how many piecewise linear functions you compose, the result is still piecewise linear.
nl 5y ago
> No one here is a scientist and no one treats any of this as science. Where's the criteria for the emprical adequecy of NLP systems as models of language? Specifying any, conducting actual hypothesis tests, and establishing a theory of how NLP systems model language -- this would immediately reveal the smoke-and-mirros. What do you mean? I'm not a scientist but I play one sometimes, and I managed a whole team of them working in this field. The theory of language models is well established. > Where's the criteria for the emprical adequecy of NLP systems as models of language? There are lots(!?) I think the Winograd schema challenge[1] is an easy one to understand, and meets a lot of your objections because it is grounded in physical reality. Statement: The city councilmen refused the demonstrators a permit because they [feared/advocated] violence. Question: Does "they" refer to the councilmen or the demonstrators? The human baseline for this challenge is 92%[1]. PaLM (this Google language model) scored 90% (4% higher than the previous best)[3]. [1] https://en.wikipedia.org/wiki/Winograd_schema_challenge https://en.wikipedia.org/wiki/Winograd_schema_challenge [2] http://ceur-ws.org/Vol-1353/paper_30.pdf http://ceur-ws.org/Vol-1353/paper_30.pdf [3] https://storage.googleapis.com/pathways-language-model/PaLM-paper.pdf https://storage.googleapis.com/pathways-language-model/PaLM-... pg 12
- mjburgess 5y ago
  Indeed, all these test are not of empirical adequacy which really evidences the point. The whole field is in this insular pseudoscientific mould of "its true if it passes an automated test to x%". A theory with empirical adequecy would require you to do some actual research into language use in humans; all of its features; how it works; various theories of its mechanisms etc. And after a comprehensive, experimental and detailed theoretical work -- show that NLP models even *any* of it. Ie., that any NLP model is a model of language. All you do above is design your own win condition, and say you've won. This precludes actually knowing anything about how language works, and is profoundly pseudoscientific. If you set-up tests for toys, and they pass -- good, you've made a nice toy. You may only claim is models some target after actually doing some science.
  - nl 5y ago
    A theory with empirical adequecy would require you to do some actual research into language use in humans; all of its features; how it works; various theories of its mechanisms etc. And after a comprehensive, experimental and detailed theoretical work -- show that NLP models even any* of it.* What - specifically - do you mean? There's an entire field adjacent to NLP called Computational Linguistics. Most people in the field work across them both, and there is significant cross pollination. It's unclear if think there is some process in the brain that you think NLP models should be similar to. If this is the case you should look at studies similar to [1] where they do MRI imaging and can see similar responses in semantically similar words. This is very similar to how word vectors put similar concept closely together (and of course how more complex models put concept close together). Or perhaps you think that NLP models do not understand syntactic concepts like nouns, verbs etc. This is incorrect too[2]. [1] https://www.tandfonline.com/doi/full/10.1080/23273798.2017.1323109 https://www.tandfonline.com/doi/full/10.1080/23273798.2017.1... [2] https://explosion.ai/demos/displacy https://explosion.ai/demos/displacy

rafaelero 5y ago

Ok, boomer.

whimsicalism 5y ago

Agree to disagree. I think you are opining about things that you are lacking fundamental knowledge on. > The structure of text is not the structure of the world. This thesis is mad. Its a scientific thesis. It is trivial to test it. It is trivial to wholey discred it. It's pseudoscience. It's unclear what you even mean by that. Are the electrical impulses coming to our brain the "structure of the world"?

yldedly 5y ago
The structure of having X apples in Y buckets is the same as the structure in the expression "X * Y", as long as the expression exists in a context that can parse it using the rules of arithmetic, such as a human, or a calculator. These language models lack context, not just for arithmetic, but for everything. They can't parse "X * Y" for any X and Y, they've just associated the expression with the right answer for so many values of X and Y, that we get fooled into thinking they know the rules. We get fooled into thinking they've learned the structure of the world. But they've only learned the structure of text.
- whimsicalism 5y ago
  It would be trivial for a network of this size to code general rules for multiplication. At a certain point, when you have enough data, finding the actual rule is actually the easier solution than memorizing each data point. This is the key insight of deep learning.
  - yldedly 5y ago
    Really? Better inform all the researchers working on this that they're wasting their time then: https://arxiv.org/abs/2001.05016 https://arxiv.org/abs/2001.05016 More fundamentally, any finite neural net is either constant or linear outside the training sample,depending on the activation function. Unless you design special neurons like in the paper above, which solves this specific problem for arithmetic, but not the general problem of extrapolation.
    - FeepingCreature 5y ago
      Isn't that per-layer?
      - yldedly 5y ago
        No, no matter how many piecewise linear functions you compose, the result is still piecewise linear.

nl 5y ago

> No one here is a scientist and no one treats any of this as science. Where's the criteria for the emprical adequecy of NLP systems as models of language? Specifying any, conducting actual hypothesis tests, and establishing a theory of how NLP systems model language -- this would immediately reveal the smoke-and-mirros. What do you mean? I'm not a scientist but I play one sometimes, and I managed a whole team of them working in this field. The theory of language models is well established. > Where's the criteria for the emprical adequecy of NLP systems as models of language? There are lots(!?) I think the Winograd schema challenge[1] is an easy one to understand, and meets a lot of your objections because it is grounded in physical reality. Statement: The city councilmen refused the demonstrators a permit because they [feared/advocated] violence. Question: Does "they" refer to the councilmen or the demonstrators? The human baseline for this challenge is 92%[1]. PaLM (this Google language model) scored 90% (4% higher than the previous best)[3]. [1] https://en.wikipedia.org/wiki/Winograd_schema_challenge https://en.wikipedia.org/wiki/Winograd_schema_challenge [2] http://ceur-ws.org/Vol-1353/paper_30.pdf http://ceur-ws.org/Vol-1353/paper_30.pdf [3] https://storage.googleapis.com/pathways-language-model/PaLM-paper.pdf https://storage.googleapis.com/pathways-language-model/PaLM-... pg 12

mjburgess 5y ago
Indeed, all these test are not of empirical adequacy which really evidences the point. The whole field is in this insular pseudoscientific mould of "its true if it passes an automated test to x%". A theory with empirical adequecy would require you to do some actual research into language use in humans; all of its features; how it works; various theories of its mechanisms etc. And after a comprehensive, experimental and detailed theoretical work -- show that NLP models even *any* of it. Ie., that any NLP model is a model of language. All you do above is design your own win condition, and say you've won. This precludes actually knowing anything about how language works, and is profoundly pseudoscientific. If you set-up tests for toys, and they pass -- good, you've made a nice toy. You may only claim is models some target after actually doing some science.
- nl 5y ago
  A theory with empirical adequecy would require you to do some actual research into language use in humans; all of its features; how it works; various theories of its mechanisms etc. And after a comprehensive, experimental and detailed theoretical work -- show that NLP models even any* of it.* What - specifically - do you mean? There's an entire field adjacent to NLP called Computational Linguistics. Most people in the field work across them both, and there is significant cross pollination. It's unclear if think there is some process in the brain that you think NLP models should be similar to. If this is the case you should look at studies similar to [1] where they do MRI imaging and can see similar responses in semantically similar words. This is very similar to how word vectors put similar concept closely together (and of course how more complex models put concept close together). Or perhaps you think that NLP models do not understand syntactic concepts like nouns, verbs etc. This is incorrect too[2]. [1] https://www.tandfonline.com/doi/full/10.1080/23273798.2017.1323109 https://www.tandfonline.com/doi/full/10.1080/23273798.2017.1... [2] https://explosion.ai/demos/displacy https://explosion.ai/demos/displacy