6 ms·

This article is....total nonsense. None of the challenges described are impossible to solve with statistical methods. And i'd be willing to bet nearly any amoun

by darawk 5y ago

This article is....total nonsense. None of the challenges described are impossible to solve with statistical methods. And i'd be willing to bet nearly any amount of money that they will be solved that way, long before they are solved in any other way. The problems posed here are being addressed as we speak with knowledge graphs and graph neural networks, and some of the others can be addressed with more nuanced and context-aware vectorization techniques. But more to the point, the authors here seem either completely unaware, or unable to grasp what language actually is. They claim "language is not data", but language literally is data. They claim that utterances have one and only one meaning, but that is literally never true, and is a profound misunderstanding of the fundamental nature of language. Words, utterances, and grammatical rules do not have fixed meanings or structures. They have approximate, moving structures and meanings that are unique for every person on the planet. This idea that "real" understanding is some top-down deterministic, univalent process is just completely wrong, and will always fail anywhere and everywhere that it is tried. Not because it is too hard, not because our algorithms aren't good enough yet, but because it fundamentally does not comport with reality. Top-down rules-driven approaches to "understanding" are essentially the computer science version of "conceptual analysis" from philosophy, and they are a similarly seductive waste of time that we would do well to leave behind, as philosophy has done with conceptual analysis.

thesz 5y ago
[1] https://en.wikipedia.org/wiki/Cyc#MathCraft https://en.wikipedia.org/wiki/Cyc#MathCraft Cyc, an AI engine and facts database that uses rules-based methods for AI, demonstrate such high level of understanding of math it can pretend to be slightly dumber than the user of the system. I think it's fascinating. I also think it is out of reach for contemporary statistical methods.
tikwidd 5y ago
What this discussion is missing is Chomsky's distinction between I-language and E-language. I (individual/internal/intensional) language is the knowledge of an individual native speaker. E-language represents the body of external knowledge about language such as corpus data and mass statistical models. Study of I-language looks at idealised individual speaker's internal language capacity, which can generate an infinite array of structured expressions from finite pieces. From the internalist perspective, E-language is a dead end because it represents only part of the knowledge of an individual, that which happens to be externalised. Many of the most interesting and revealing linguistic are barely reflected in corpora but can be elicited in experiments with individual speakers. "E-language" approaches, like statistical models of language, do lead to practical and useful results. But using E-language approaches to seriously study the human capacity for language is about as useful as doing physics experiments in GTA.
- thomasahle 5y ago
  When you talk about internal, non externslised knowledge, do you mean stuff like "how to ride a bike"? I can see how AI wouldn't learn that from reading/listening to humans. ot would have to learn for itself with reinforcement learning. I wouldn't call thst language though. But I couldn't think of any kind of non-external knowledge that can be described as language?
LdSGSgvupDV 5y ago
Is there anyone's GPT-3 testing alerting?
didibus 5y ago
I don't think it's total nonsense, that seems a bit hyperbolic. It makes some good points in that the amount of data it would take to approximate it all seems far too large, and if you look at a human baby, I'm not sure they are fed nearly as much data, yet they can develop language understanding. I feel we have a missing piece here. Maybe it'll be solved by combining methods based on statically learning from data with others, but exclusively from learning on data might reach a limit which is we might not have enough data or computing power to do so.
- treesprite82 5y ago
  > It makes some good points in that the amount of data it would take to approximate it all seems far too large The article's way of estimating this is absurd, seemingly relying on the idea that a model can't generalise and must see every possible variation of a sentence: > If we add to the semantic differences all the minor syntactic differences to the above pattern (say changing ‘because’ to ‘although’ — which also changes the correct referent to “it”) then a rough calculation tells us a ML/Data-driven system would need to see something like 40,000,000 variations of the above
  - didibus 5y ago
    Ya, I don't know about their approximation, but I think we've already found ourselves hitting some limits with computing power and data on big NLP ML models. The appearance of custom chips is a pretty good example. Maybe for text we won't run out of data, considering the internet has so much text available. But I still think that the "human learning" is something to consider. Maybe a baby is a statistical machine, and it has a statistical based model that's so good it needs very little data, but it's also possible it uses something more logic based. In any case, I feel at least a baby would combine multiple learnings together, it would have started to learn about the dimensions and shapes and temperatures and properties of various physical objects, while simultaneously learning about the language used to refer to those things. I think this last piece can allow a baby to connect the predictions of real things with the language used, which then helps provide semantics for the language that are taken into consideration by a human.
silent_cal 5y ago
I think you might be missing the point. It's not that an utterance/proposition has "only one meaning", but that it is meant to represent only one concept (the speaker's) at the time it's spoken. Also, I thought that the argument about the impossibility of distinguishing between equally probable meanings (in terms of frequency in a corpus) was a good one.
- darawk 5y ago
  > I think you might be missing the point. It's not that an utterance/proposition has "only one meaning", but that it is meant to represent only one concept (the speaker's) at the time it's spoken. While this is closer to a coherent point, i'm not sure it's particularly relevant to the idea of "natural language understanding". We consider humans to be understanding of natural language, but humans interpreting the words of other humans are constantly confounded by various forms of ambiguity or polysemy. The idea that you don't have NLU until you can perfectly ascertain the exact singular meaning of an utterance in the mind of the speaker implies that no human has achieved NLU either. > Also, I thought that the argument about the impossibility of distinguishing between equally probable meanings (in terms of frequency in a corpus) was a good one. It's certainly a good puzzle to pose for model building, which implies statistical models will need to be a bit more nuanced than simple word frequency correlations. But it certainly isn't impossible to assign latent attributes to words, and study the statistical properties of those attribute relations, which is exactly what would be necessary to solve their puzzle. In fact, modern word vectorization techniques (i.e. language models using lstms to predict the missing word in a sequence using adjacent context) essentially do this.
  - silent_cal 5y ago
    1. Well said, but I would press it further and say that there is at least a pathway for us (human beings) to determine what the actual meaning of the utterance is by considering the context, the speaker, by asking for clarification, and so forth. The problem for the probabilistic/empirical language model is that it can't ever recognize "one right answer", even in theory. That's a problem, because there really is one discoverable meaning to every intentional utterance, even if it's not always successfully understood in practice. 2. How would you go about assigning latent attributes to words?
jakeinspace 5y ago
Although I agree that statistical / ML methods will continue to conquer hurdle after hurdle in NLP, I wouldn't say that the article is total nonsense. The fact that NLP models trained purely on text data do not have access to the context available to a human interlocutor (years of visual, auditory, and other sense experience, along with all the higher-level mental hierarchies built from those experiences) is clearly a major handicap. I'm not positive that makes text-only NLP techniques inherently limited, but it has to be acknowledged. As far as I'm aware, we're still some years off from having NLP models capable of incorporating this sort of information. If I had to guess, I'd say that the first NLP model truly able to pass the Turing Test will be trained on a multitude of data source - textual, visual, and audio. It will have a moderately sophisticated internal representation of "the world", though I doubt that model will be very amenable to human understanding. I'm not even sure what I really mean when I say a model of the world, because I don't think this quasi-intelligent NLP actor will necessarily have an understanding of the physical world in the way humans or animals do, especially when it's been trained with the purpose of demonstrating language understanding.

by darawk 5y ago

thesz 5y ago

[1] https://en.wikipedia.org/wiki/Cyc#MathCraft https://en.wikipedia.org/wiki/Cyc#MathCraft Cyc, an AI engine and facts database that uses rules-based methods for AI, demonstrate such high level of understanding of math it can pretend to be slightly dumber than the user of the system. I think it's fascinating. I also think it is out of reach for contemporary statistical methods.

tikwidd 5y ago

What this discussion is missing is Chomsky's distinction between I-language and E-language. I (individual/internal/intensional) language is the knowledge of an individual native speaker. E-language represents the body of external knowledge about language such as corpus data and mass statistical models. Study of I-language looks at idealised individual speaker's internal language capacity, which can generate an infinite array of structured expressions from finite pieces. From the internalist perspective, E-language is a dead end because it represents only part of the knowledge of an individual, that which happens to be externalised. Many of the most interesting and revealing linguistic are barely reflected in corpora but can be elicited in experiments with individual speakers. "E-language" approaches, like statistical models of language, do lead to practical and useful results. But using E-language approaches to seriously study the human capacity for language is about as useful as doing physics experiments in GTA.

thomasahle 5y ago
When you talk about internal, non externslised knowledge, do you mean stuff like "how to ride a bike"? I can see how AI wouldn't learn that from reading/listening to humans. ot would have to learn for itself with reinforcement learning. I wouldn't call thst language though. But I couldn't think of any kind of non-external knowledge that can be described as language?

LdSGSgvupDV 5y ago

Is there anyone's GPT-3 testing alerting?

didibus 5y ago

I don't think it's total nonsense, that seems a bit hyperbolic. It makes some good points in that the amount of data it would take to approximate it all seems far too large, and if you look at a human baby, I'm not sure they are fed nearly as much data, yet they can develop language understanding. I feel we have a missing piece here. Maybe it'll be solved by combining methods based on statically learning from data with others, but exclusively from learning on data might reach a limit which is we might not have enough data or computing power to do so.

treesprite82 5y ago
> It makes some good points in that the amount of data it would take to approximate it all seems far too large The article's way of estimating this is absurd, seemingly relying on the idea that a model can't generalise and must see every possible variation of a sentence: > If we add to the semantic differences all the minor syntactic differences to the above pattern (say changing ‘because’ to ‘although’ — which also changes the correct referent to “it”) then a rough calculation tells us a ML/Data-driven system would need to see something like 40,000,000 variations of the above
- didibus 5y ago
  Ya, I don't know about their approximation, but I think we've already found ourselves hitting some limits with computing power and data on big NLP ML models. The appearance of custom chips is a pretty good example. Maybe for text we won't run out of data, considering the internet has so much text available. But I still think that the "human learning" is something to consider. Maybe a baby is a statistical machine, and it has a statistical based model that's so good it needs very little data, but it's also possible it uses something more logic based. In any case, I feel at least a baby would combine multiple learnings together, it would have started to learn about the dimensions and shapes and temperatures and properties of various physical objects, while simultaneously learning about the language used to refer to those things. I think this last piece can allow a baby to connect the predictions of real things with the language used, which then helps provide semantics for the language that are taken into consideration by a human.

silent_cal 5y ago

I think you might be missing the point. It's not that an utterance/proposition has "only one meaning", but that it is meant to represent only one concept (the speaker's) at the time it's spoken. Also, I thought that the argument about the impossibility of distinguishing between equally probable meanings (in terms of frequency in a corpus) was a good one.

darawk 5y ago
> I think you might be missing the point. It's not that an utterance/proposition has "only one meaning", but that it is meant to represent only one concept (the speaker's) at the time it's spoken. While this is closer to a coherent point, i'm not sure it's particularly relevant to the idea of "natural language understanding". We consider humans to be understanding of natural language, but humans interpreting the words of other humans are constantly confounded by various forms of ambiguity or polysemy. The idea that you don't have NLU until you can perfectly ascertain the exact singular meaning of an utterance in the mind of the speaker implies that no human has achieved NLU either. > Also, I thought that the argument about the impossibility of distinguishing between equally probable meanings (in terms of frequency in a corpus) was a good one. It's certainly a good puzzle to pose for model building, which implies statistical models will need to be a bit more nuanced than simple word frequency correlations. But it certainly isn't impossible to assign latent attributes to words, and study the statistical properties of those attribute relations, which is exactly what would be necessary to solve their puzzle. In fact, modern word vectorization techniques (i.e. language models using lstms to predict the missing word in a sequence using adjacent context) essentially do this.
- silent_cal 5y ago
  1. Well said, but I would press it further and say that there is at least a pathway for us (human beings) to determine what the actual meaning of the utterance is by considering the context, the speaker, by asking for clarification, and so forth. The problem for the probabilistic/empirical language model is that it can't ever recognize "one right answer", even in theory. That's a problem, because there really is one discoverable meaning to every intentional utterance, even if it's not always successfully understood in practice. 2. How would you go about assigning latent attributes to words?

jakeinspace 5y ago

Although I agree that statistical / ML methods will continue to conquer hurdle after hurdle in NLP, I wouldn't say that the article is total nonsense. The fact that NLP models trained purely on text data do not have access to the context available to a human interlocutor (years of visual, auditory, and other sense experience, along with all the higher-level mental hierarchies built from those experiences) is clearly a major handicap. I'm not positive that makes text-only NLP techniques inherently limited, but it has to be acknowledged. As far as I'm aware, we're still some years off from having NLP models capable of incorporating this sort of information. If I had to guess, I'd say that the first NLP model truly able to pass the Turing Test will be trained on a multitude of data source - textual, visual, and audio. It will have a moderately sophisticated internal representation of "the world", though I doubt that model will be very amenable to human understanding. I'm not even sure what I really mean when I say a model of the world, because I don't think this quasi-intelligent NLP actor will necessarily have an understanding of the physical world in the way humans or animals do, especially when it's been trained with the purpose of demonstrating language understanding.