14 ms·

The Norvig – Chomsky debate (2017)

102 points by rrampage 4y ago

mkoubaa 4y ago
I don't know but I hope that Chomsky is less wrong. Because if statistical methods reach an asymptote, we will have no choice but to try to better understand the principles and foundations. If statistical methods do not reach an asymptote, I don't think we will have the incentive to reach a deeper understanding
- rfrey 4y ago
  This reminds me of something I heard Geoff Hinton say .. that it was a shame and a sorrow that neural networks as currently used worked so well.
LudwigNagasena 4y ago
I think it should be marked as being from 2017. Also, I don't see much point in this article. It just butchers Norvig's article into a bunch of quotes even though his article is quite accessible and not very long.
- bsaul 4y ago
  Not sure about the article itself, but from an epistemological point of view, i believe this debate will remain one of the most famous of the 21st century (provided NN still keep giving new fantastic results, and doesn’t stop there). Never in history did we manage to achieve so much about a given complex problem (let’s say producing meaningful text) while at the same time understand so little (chatgpt didn’t provide any single useful result to the linguistic science department). If this approach spreads to other fields, it will lead to an immense scientific crysis.
  - narag 4y ago
    Actually this explains a lot. Language might be the stuff dreams are made of, but not the stuff consciousness is made of, so a specialized form of perception, a layer on top of visuals and hearing, with just another layer of logic on top of it. We still have a coarse understanding of brain processes, but if they use parallelism, they could be probabilistic in nature, so these language models would be more similar to ours than it seems.
    - wodenokoto 4y ago
      I feel like that point was equally made with “colorless green ideas sleep furiously” Moreover, in linguistics 101, students are often introduced to case studies of people with aphasia and similar issues which illustrates how humans can produce coherent and grammatical speech without meaning (just like chatgpt) and how people can lose the understanding of certain classes of words. Lastly, NN are often seen as a way to model functions (again, students are often asked to produce different logic gates by hand, to convince themselves that NN can be Turing complete) so rather than language being inherently probabilistic chatgpt might just have reasonably inferred the rules of language.
      - narag 4y ago
        Thank you, I didn't know about that sentence. Anyway my point is not that language is inherently probabilistic, but that the way our brains implement language could be. Or more precisely, one layer of language could be this way, with other "watchdog" layer on top of it filtering when the less rigorous layer goes rogue and spits nonsense. The base layer could be the graphical one, middle layer language, top layer logic. Between layers, the jokes.
- hackandthink 4y ago
  I agree. Start with reading: https://norvig.com/chomsky.html https://norvig.com/chomsky.html
- pafje 4y ago
  Actually this seems to be from 2013. https://github.com/joshuaeckroth/cse3521-website/blob/master/norvig-chomsky.org https://github.com/joshuaeckroth/cse3521-website/blob/master...
nuc1e0n 4y ago
I think the thing to note about today's large language models is that they aren't purely statistical. The topology of the neural networks behind them has been explicitly defined by the creators of those systems. They are not 'tabula rasa' as some might suppose them to be.
- bsaul 4y ago
  Yet their structure being generic for all kind of problems, it doesn’t tell much in itself about the things it managed to « understand ». Much like studying einstein’s brain biology can’t teach you much about general relativity.
  - nuc1e0n 4y ago
    But none of them do have a generic structure. For example, GPT-3 can't produce images from text prompts, and stable diffusion cannot generate language. The possible relationships of words are written into GPT-3's code, in python, by its developers. In a way all this proves is that that written language can convey meaning to people.
    - Ologn 4y ago
      > GPT-3 can't produce images from text prompts Me: Give me the entire hexadecimal format of an example PNG. Only give me the hexadecimal format. GPT-3: 89504E470D0A1A0A0000000D49484452000000640000006408020000000065238226000000014944415478DAECFD07780D44204C60F81EADAEF777F7E7E62F1BDE7DEBDED710EC15C7AC81CEEC17069C59B99A1698BEE7A484D68FDE782A7C41A8A0E7D2A2C9B00A99F32FBCED
      - nuc1e0n 4y ago
        That's not a valid png. It's just plausible hex tokens. GPT-3 is confidently wrong yet again.
        Ologn 4y ago
        $ echo 89504E470D0A1A0A0000000D49484452000000640000006408020000000065238226000000014944415478DAECFD07780D44204C60F81EADAEF777F7E7E62F1BDE7DEBDED710EC15C7AC81CEEC17069C59B99A1698BEE7A484D68FDE782A7C41A8A0E7D2A2C9B00A99F32FBCED |xxd -r -p > output.png $ file output.png output.png: PNG image data, 100 x 100, 8-bit/color RGB, non-interlaced $ eog output.png Fatal error reading PNG image file: IHDR: CRC error It seems you are correct.
foobarqux 4y ago
Chomsky has already addressed Norvig's objections before he even made them as anyone how has actually listened to what Chomsky has said would know. > Norvig's reply: He agrees, but engineering success often facilitates scientific success. There has been virtually no progress in understanding of the human language faculty from probabilistic models or LLMs. > Norvig's reply: Science is both description and explanation; you can't have one without the other; in the history of science, the laborious accumulation of data is the usual mode of operation. Probabilistic models don't describe anything in a way that leads to understanding (as the fact that no progress in understanding has been made shows). > people actually generate and understand language in some rich statistical sense (maybe with statistical models several layers deep, like the modern AI models of speech recognition). They do not; there are studies which Chomsky cites involving trying to learning "impossible" non-structural languages that give strong evidence that this is not the case. > Norvig's reply: Certain advances in statistical learning methods provide reason to believe that such learning methods will be able to do the job. It has nothing to do with the human language faculty. > My conclusion is that 100% of these articles and awards are more about "accurately modeling the world" than they are about "providing insight," although they all have some theoretical insight component as well. If you have a black box machine and you write a paper that says the black box reproduces some natural phenomenon with a billion of its knobs to turned to these specific settings you have wasted everyone's time. > Norvig illustrates that rules about language do not always capture the right phenomena. (i before e) The fundamental character of human language has nothing to do with spelling. > [Norvig]: so any valid criticism of probabilistic models would have to be because they are too expressive, not because they are not expressive enough. Yes Chomsky has explicitly said this. Any model that accepts virtually everything is a bad model. I don't have time to go through the rest.
- morelisp 4y ago
  As a connoisseur of the Chomsky-Foucault debate summarized as one intellectual giant completely missing the other's point, it's quite funny to see Chomsky in the opposite role here. The more operationalist you are, the more depressingly wrong you get - but also it seems the more likely you are to win debate club antics.
fock 4y ago
The weirdest thing to me is that a person with a high personal, financial involvement in the subject went and took that quote from an old man (which to your and my understanding only states that these things are not linguistic "science", but they solve problems alright), then created a strawman from thin air (points A-E) to then say "oh, all my arguments are void, statistical models are great, don't you dare criticizing me, you old fool". And then he went and took "Science", aka the epitome of publish or perish academia and tried to argue that all this looks the same as the thing he does. Oh well, who would have guessed... The looks on this are weird, even more so as GPT nowadays works wonders, but still doesn't help explaining why and how language evolved (which seems to be the idea of linguistics, no?).
- hackandthink 4y ago
  Norvig has strong arguments, but this in bad faith: "Chomsky has a philosophy based on the idea that we should focus on the deep whys and that mere explanations of reality don't matter. In this, Chomsky is in complete agreement with O'Reilly." (O'Really stands for mythmaking, religion or philosophy) Chomsky is no mysticist - he is an old fashioned scientist looking for a parsimonious theory. Maybe there is no simple theory for language with great explanatory power but there should be some people looking for it.
  - foobarqux 4y ago
    > Norvig has strong arguments What was one of those arguments? I didn't see any.
    - hackandthink 4y ago
      "We all saw the limitations of the old tools, and the benefits of the new" Probabilistic models work incredibly well, much better than transformational-generative grammars.
      - foobarqux 4y ago
        > Probabilistic models work incredibly well, much better than transformational-generative grammars. You've missed everything Chomsky said even though it is repeated in the article: Probabilistic models can be useful tools but they tell you nothing about the human language faculty (i.e. they are not science).
vouwfietsman 4y ago
This is cool, especially because its already 6 years old and I think not much has changed. Can anyone here speak to the current SOTA of explaining whats going on inside a neural net? If we go: problem -> nnet solution -> explanation of nnet -> insight into problem that would still be very significant to the scientific process.
hackandthink 4y ago
The debate is mostly about: Are opaque probabilistic models scientific? David Mumford's stance: "This paper is a meant to be a polemic which argues for a very fundamental point: that stochastic models and statistical reasoning are more relevant i) to the world, ii) to science and many parts of mathematics and iii) particularly to understanding the computations in our own minds, than exact models and logical reasoning" https://www.dam.brown.edu/people/mumford/beyond/papers/2000b--DawningAgeStoch-NC.pdf https://www.dam.brown.edu/people/mumford/beyond/papers/2000b...
- foobarqux 4y ago
  > The debate is mostly about: Are opaque probabilistic models scientific? No the debate is about whether probabilistic models are scientific when applied to the human language faculty (they aren't). Probabilistic models are scientific when they tell you something about the natural world. In some cases they do and in others they don't.
  - hackandthink 4y ago
    Yes, the scope of the Norvig - Chomsky ist the human language faculty. But I think Mumford's more general discussion is relevant. And Mumford explicitly refers to speech: "This approach denies that statistical inference can have anything to do with real thought ... The new applications of Bayesian statistics to vision, speech, expert systems and neural nets have now started an explosive growth in these ideas."
    - foobarqux 4y ago
      It isn't relevant. There isn't any evidence that the human language faculty is a statistical process at its core, in fact there is evidence that it isn't. Vision, etc are different processes but in any case you have to show evidence of biology using a statistical process not simply postulate it. There is lots of talk like "these statistical models could yield information about how the mind or some other system works" but they never do. In fact no one even tries. People don't really care about doing science but because science is high prestige they want to make sure to be classified in that way.
dang 4y ago
Related. Others? Debunking Statistical AI – Noam Chomsky, Gary Marcus, Jeremy Kahn [video] - https://news.ycombinator.com/item?id=33857543 https://news.ycombinator.com/item?id=33857543 - Dec 2022 (19 comments) Noam Chomsky: Where Artificial Intelligence Went Wrong (2012) - https://news.ycombinator.com/item?id=30937760 https://news.ycombinator.com/item?id=30937760 - April 2022 (2 comments) On Chomsky and the Two Cultures of Statistical Learning (2011) - https://news.ycombinator.com/item?id=16489828 https://news.ycombinator.com/item?id=16489828 - March 2018 (12 comments) On Chomsky and the Two Cultures of Statistical Learning (2011) - https://news.ycombinator.com/item?id=11951444 https://news.ycombinator.com/item?id=11951444 - June 2016 (102 comments) Norvig vs. Chomsky and the Fight for the Future of AI - https://news.ycombinator.com/item?id=5318292 https://news.ycombinator.com/item?id=5318292 - March 2013 (2 comments) Noam Chomsky on Where Artificial Intelligence Went Wrong - https://news.ycombinator.com/item?id=4729068 https://news.ycombinator.com/item?id=4729068 - Nov 2012 (177 comments) Norvig vs. Chomsky and the Fight for the Future of AI - https://news.ycombinator.com/item?id=4290604 https://news.ycombinator.com/item?id=4290604 - July 2012 (147 comments) Norvig vs. Chomsky and the Fight for the Future of AI - https://news.ycombinator.com/item?id=2710733 https://news.ycombinator.com/item?id=2710733 - June 2011 (4 comments) On Chomsky and the Two Cultures of Statistical Learning - https://news.ycombinator.com/item?id=2591154 https://news.ycombinator.com/item?id=2591154 - May 2011 (107 comments)
ribit 4y ago
What’s really interesting is that entire Chomskian syntax worldview is fairly pseudo-scientific in nature. Most of these papers are about taking an essentially Turing-complete computation system and tweaking it until it can solve a specific riddle. Rinse and repeat. Most of the arguments (like the poverty of stimulus) are purely authoritarian as well.
qikInNdOutReply 4y ago
So, if the people rise up in rebellion against a believe system, like sovjet-socialism and the defenders of that believe system thus decide to decline those people - or even regions agency over there own destiny. Does that make those defenders of the believe system pseudo-scientific, because they fail to update there heuristic models?

102 points by rrampage 4y ago

I don't know but I hope that Chomsky is less wrong. Because if statistical methods reach an asymptote, we will have no choice but to try to better understand the principles and foundations. If statistical methods do not reach an asymptote, I don't think we will have the incentive to reach a deeper understanding

rfrey 4y ago
This reminds me of something I heard Geoff Hinton say .. that it was a shame and a sorrow that neural networks as currently used worked so well.

LudwigNagasena 4y ago

I think it should be marked as being from 2017. Also, I don't see much point in this article. It just butchers Norvig's article into a bunch of quotes even though his article is quite accessible and not very long.

bsaul 4y ago
Not sure about the article itself, but from an epistemological point of view, i believe this debate will remain one of the most famous of the 21st century (provided NN still keep giving new fantastic results, and doesn’t stop there). Never in history did we manage to achieve so much about a given complex problem (let’s say producing meaningful text) while at the same time understand so little (chatgpt didn’t provide any single useful result to the linguistic science department). If this approach spreads to other fields, it will lead to an immense scientific crysis.
- narag 4y ago
  Actually this explains a lot. Language might be the stuff dreams are made of, but not the stuff consciousness is made of, so a specialized form of perception, a layer on top of visuals and hearing, with just another layer of logic on top of it. We still have a coarse understanding of brain processes, but if they use parallelism, they could be probabilistic in nature, so these language models would be more similar to ours than it seems.
  - wodenokoto 4y ago
    I feel like that point was equally made with “colorless green ideas sleep furiously” Moreover, in linguistics 101, students are often introduced to case studies of people with aphasia and similar issues which illustrates how humans can produce coherent and grammatical speech without meaning (just like chatgpt) and how people can lose the understanding of certain classes of words. Lastly, NN are often seen as a way to model functions (again, students are often asked to produce different logic gates by hand, to convince themselves that NN can be Turing complete) so rather than language being inherently probabilistic chatgpt might just have reasonably inferred the rules of language.
    - narag 4y ago
      Thank you, I didn't know about that sentence. Anyway my point is not that language is inherently probabilistic, but that the way our brains implement language could be. Or more precisely, one layer of language could be this way, with other "watchdog" layer on top of it filtering when the less rigorous layer goes rogue and spits nonsense. The base layer could be the graphical one, middle layer language, top layer logic. Between layers, the jokes.
hackandthink 4y ago
I agree. Start with reading: https://norvig.com/chomsky.html https://norvig.com/chomsky.html
pafje 4y ago
Actually this seems to be from 2013. https://github.com/joshuaeckroth/cse3521-website/blob/master/norvig-chomsky.org https://github.com/joshuaeckroth/cse3521-website/blob/master...

nuc1e0n 4y ago

I think the thing to note about today's large language models is that they aren't purely statistical. The topology of the neural networks behind them has been explicitly defined by the creators of those systems. They are not 'tabula rasa' as some might suppose them to be.

bsaul 4y ago
Yet their structure being generic for all kind of problems, it doesn’t tell much in itself about the things it managed to « understand ». Much like studying einstein’s brain biology can’t teach you much about general relativity.
- nuc1e0n 4y ago
  But none of them do have a generic structure. For example, GPT-3 can't produce images from text prompts, and stable diffusion cannot generate language. The possible relationships of words are written into GPT-3's code, in python, by its developers. In a way all this proves is that that written language can convey meaning to people.
  - Ologn 4y ago
    > GPT-3 can't produce images from text prompts Me: Give me the entire hexadecimal format of an example PNG. Only give me the hexadecimal format. GPT-3: 89504E470D0A1A0A0000000D49484452000000640000006408020000000065238226000000014944415478DAECFD07780D44204C60F81EADAEF777F7E7E62F1BDE7DEBDED710EC15C7AC81CEEC17069C59B99A1698BEE7A484D68FDE782A7C41A8A0E7D2A2C9B00A99F32FBCED
    - nuc1e0n 4y ago
      That's not a valid png. It's just plausible hex tokens. GPT-3 is confidently wrong yet again.
      - Ologn 4y ago
        $ echo 89504E470D0A1A0A0000000D49484452000000640000006408020000000065238226000000014944415478DAECFD07780D44204C60F81EADAEF777F7E7E62F1BDE7DEBDED710EC15C7AC81CEEC17069C59B99A1698BEE7A484D68FDE782A7C41A8A0E7D2A2C9B00A99F32FBCED |xxd -r -p > output.png $ file output.png output.png: PNG image data, 100 x 100, 8-bit/color RGB, non-interlaced $ eog output.png Fatal error reading PNG image file: IHDR: CRC error It seems you are correct.

foobarqux 4y ago

Chomsky has already addressed Norvig's objections before he even made them as anyone how has actually listened to what Chomsky has said would know. > Norvig's reply: He agrees, but engineering success often facilitates scientific success. There has been virtually no progress in understanding of the human language faculty from probabilistic models or LLMs. > Norvig's reply: Science is both description and explanation; you can't have one without the other; in the history of science, the laborious accumulation of data is the usual mode of operation. Probabilistic models don't describe anything in a way that leads to understanding (as the fact that no progress in understanding has been made shows). > people actually generate and understand language in some rich statistical sense (maybe with statistical models several layers deep, like the modern AI models of speech recognition). They do not; there are studies which Chomsky cites involving trying to learning "impossible" non-structural languages that give strong evidence that this is not the case. > Norvig's reply: Certain advances in statistical learning methods provide reason to believe that such learning methods will be able to do the job. It has nothing to do with the human language faculty. > My conclusion is that 100% of these articles and awards are more about "accurately modeling the world" than they are about "providing insight," although they all have some theoretical insight component as well. If you have a black box machine and you write a paper that says the black box reproduces some natural phenomenon with a billion of its knobs to turned to these specific settings you have wasted everyone's time. > Norvig illustrates that rules about language do not always capture the right phenomena. (i before e) The fundamental character of human language has nothing to do with spelling. > [Norvig]: so any valid criticism of probabilistic models would have to be because they are too expressive, not because they are not expressive enough. Yes Chomsky has explicitly said this. Any model that accepts virtually everything is a bad model. I don't have time to go through the rest.

morelisp 4y ago
As a connoisseur of the Chomsky-Foucault debate summarized as one intellectual giant completely missing the other's point, it's quite funny to see Chomsky in the opposite role here. The more operationalist you are, the more depressingly wrong you get - but also it seems the more likely you are to win debate club antics.

fock 4y ago

The weirdest thing to me is that a person with a high personal, financial involvement in the subject went and took that quote from an old man (which to your and my understanding only states that these things are not linguistic "science", but they solve problems alright), then created a strawman from thin air (points A-E) to then say "oh, all my arguments are void, statistical models are great, don't you dare criticizing me, you old fool". And then he went and took "Science", aka the epitome of publish or perish academia and tried to argue that all this looks the same as the thing he does. Oh well, who would have guessed... The looks on this are weird, even more so as GPT nowadays works wonders, but still doesn't help explaining why and how language evolved (which seems to be the idea of linguistics, no?).

hackandthink 4y ago
Norvig has strong arguments, but this in bad faith: "Chomsky has a philosophy based on the idea that we should focus on the deep whys and that mere explanations of reality don't matter. In this, Chomsky is in complete agreement with O'Reilly." (O'Really stands for mythmaking, religion or philosophy) Chomsky is no mysticist - he is an old fashioned scientist looking for a parsimonious theory. Maybe there is no simple theory for language with great explanatory power but there should be some people looking for it.
- foobarqux 4y ago
  > Norvig has strong arguments What was one of those arguments? I didn't see any.
  - hackandthink 4y ago
    "We all saw the limitations of the old tools, and the benefits of the new" Probabilistic models work incredibly well, much better than transformational-generative grammars.
    - foobarqux 4y ago
      > Probabilistic models work incredibly well, much better than transformational-generative grammars. You've missed everything Chomsky said even though it is repeated in the article: Probabilistic models can be useful tools but they tell you nothing about the human language faculty (i.e. they are not science).

vouwfietsman 4y ago

This is cool, especially because its already 6 years old and I think not much has changed. Can anyone here speak to the current SOTA of explaining whats going on inside a neural net? If we go: problem -> nnet solution -> explanation of nnet -> insight into problem that would still be very significant to the scientific process.

hackandthink 4y ago

The debate is mostly about: Are opaque probabilistic models scientific? David Mumford's stance: "This paper is a meant to be a polemic which argues for a very fundamental point: that stochastic models and statistical reasoning are more relevant i) to the world, ii) to science and many parts of mathematics and iii) particularly to understanding the computations in our own minds, than exact models and logical reasoning" https://www.dam.brown.edu/people/mumford/beyond/papers/2000b--DawningAgeStoch-NC.pdf https://www.dam.brown.edu/people/mumford/beyond/papers/2000b...

foobarqux 4y ago
> The debate is mostly about: Are opaque probabilistic models scientific? No the debate is about whether probabilistic models are scientific when applied to the human language faculty (they aren't). Probabilistic models are scientific when they tell you something about the natural world. In some cases they do and in others they don't.
- hackandthink 4y ago
  Yes, the scope of the Norvig - Chomsky ist the human language faculty. But I think Mumford's more general discussion is relevant. And Mumford explicitly refers to speech: "This approach denies that statistical inference can have anything to do with real thought ... The new applications of Bayesian statistics to vision, speech, expert systems and neural nets have now started an explosive growth in these ideas."
  - foobarqux 4y ago
    It isn't relevant. There isn't any evidence that the human language faculty is a statistical process at its core, in fact there is evidence that it isn't. Vision, etc are different processes but in any case you have to show evidence of biology using a statistical process not simply postulate it. There is lots of talk like "these statistical models could yield information about how the mind or some other system works" but they never do. In fact no one even tries. People don't really care about doing science but because science is high prestige they want to make sure to be classified in that way.

dang 4y ago

Related. Others? Debunking Statistical AI – Noam Chomsky, Gary Marcus, Jeremy Kahn [video] - https://news.ycombinator.com/item?id=33857543 https://news.ycombinator.com/item?id=33857543 - Dec 2022 (19 comments) Noam Chomsky: Where Artificial Intelligence Went Wrong (2012) - https://news.ycombinator.com/item?id=30937760 https://news.ycombinator.com/item?id=30937760 - April 2022 (2 comments) On Chomsky and the Two Cultures of Statistical Learning (2011) - https://news.ycombinator.com/item?id=16489828 https://news.ycombinator.com/item?id=16489828 - March 2018 (12 comments) On Chomsky and the Two Cultures of Statistical Learning (2011) - https://news.ycombinator.com/item?id=11951444 https://news.ycombinator.com/item?id=11951444 - June 2016 (102 comments) Norvig vs. Chomsky and the Fight for the Future of AI - https://news.ycombinator.com/item?id=5318292 https://news.ycombinator.com/item?id=5318292 - March 2013 (2 comments) Noam Chomsky on Where Artificial Intelligence Went Wrong - https://news.ycombinator.com/item?id=4729068 https://news.ycombinator.com/item?id=4729068 - Nov 2012 (177 comments) Norvig vs. Chomsky and the Fight for the Future of AI - https://news.ycombinator.com/item?id=4290604 https://news.ycombinator.com/item?id=4290604 - July 2012 (147 comments) Norvig vs. Chomsky and the Fight for the Future of AI - https://news.ycombinator.com/item?id=2710733 https://news.ycombinator.com/item?id=2710733 - June 2011 (4 comments) On Chomsky and the Two Cultures of Statistical Learning - https://news.ycombinator.com/item?id=2591154 https://news.ycombinator.com/item?id=2591154 - May 2011 (107 comments)

ribit 4y ago

What’s really interesting is that entire Chomskian syntax worldview is fairly pseudo-scientific in nature. Most of these papers are about taking an essentially Turing-complete computation system and tweaking it until it can solve a specific riddle. Rinse and repeat. Most of the arguments (like the poverty of stimulus) are purely authoritarian as well.

qikInNdOutReply 4y ago

So, if the people rise up in rebellion against a believe system, like sovjet-socialism and the defenders of that believe system thus decide to decline those people - or even regions agency over there own destiny. Does that make those defenders of the believe system pseudo-scientific, because they fail to update there heuristic models?