19 ms·

Machine learning won't solve natural language understanding

• visarga 5y ago
  An armchair effort to redefine the goalposts and judge NLP, but proof is in the pudding. For now, large language models are the best flavor. NLP models are already useful even in this early stage.
  • ampdepolymerase 5y ago
    I look forward to when something like Siri or Google Assistant is hooked up to GPT-3. The current voice assistant ML systems are useless for anything but the most basic of tasks.
  • emodendroket 5y ago
    They are certainly useful, but it seems quite plausible that they could continue being useful without ever "solving" the problem, in the general sense.
  • criticaltinker 5y ago
    > An armchair effort Given the authors credentials and publication history [1] it's a bit disingenuous to call this an 'armchair effort'. [1] https://scholar.google.com/citations?user=i5sEc1YAAAAJ&hl=en&oi=ao https://scholar.google.com/citations?user=i5sEc1YAAAAJ&hl=en...
    • ppod 5y ago
      That's not a particularly impressive publication record.
    • mnky9800n 5y ago
      My h index is twice as high and that's a terrible h index still lol
      • axiom92 5y ago
        This person's h-index is 10x yours: https://scholar.google.com/citations?user=OHbcFkcAAAAJ&hl=en&oi=ao https://scholar.google.com/citations?user=OHbcFkcAAAAJ&hl=en...! Source: https://www.nature.com/articles/d41586-019-02479-7 https://www.nature.com/articles/d41586-019-02479-7
        • mnky9800n 5y ago
          Ahaha. I should try harder.
    • axiom92 5y ago
      In case you were highlighting the person's h-index or citation count, there are a few shortcomings that these metrics suffer from [1], making it very hard to judge scientific impact. EDIT: Specifically, self-citation is the biggest issue, though often there are very good reasons to do it. For example, if you are working on a new area there might not be a lot of work other than yours. However, mindless self-citation grows the number of citation at O(N), as your n^th paper cites the (n - 1) papers before it. The total citation count grows by O(N^2)). [1] https://www.nature.com/articles/d41586-019-02479-7 https://www.nature.com/articles/d41586-019-02479-7
    • visarga 5y ago
      OK, I took a look, no evidence of neural nets, just semantic stuff which is previous gen AI and the citation count is not impressive.
  • mcswell 5y ago
    I don't think "Is NLP useful?" is the question they're trying to address. Hammers are useful, even in a world where there are lots of screws. NLP is useful, even in a world where there is lots of language (in English, even) that current NLP systems don't do well on.
• lalaithion 5y ago
  > In other words, we must get, from a multitude of possible interpretations of the above question, the one and only one meaning that, according to our commonsense knowledge of the world, is the one thought behind the question some speaker intended to ask. But, can humans do this? I think not; I still disagree with the author about what "Do we have a retired BBC reporter that was based in an East European country during the Cold War?", translated into code, means. They write "Doing the correct quantifier scoping: we are looking not for ‘a’ (single) reporter who worked in ‘some’ East European country, but to any reporter that worked in any East European country" My interpretation of this requirement is that they want a list of all the reporters who meet the criteria. However, I would probably write this query to return a boolean, not a list of reporters. And even if my interpretation is wrong... well, my point is still correct, because I failed to extract the "the one and only one meaning" that the author intended from that sentence. Even humans are only probably approximately correct.
  • bopbeepboop 5y ago
    I’m confused, but think I concur with you. The two phrasings read as identical to me: - do we have a reporter who worked in some East European country? - is there any reporter (among ours) who worked in any East European country? - exists? (reporter in our-reporters) where (reporter.base in east-european-countries) I’m confused where the difference is supposed to be, between them. For every case, naming a person for whom that is true is a witness “yes” is correct; and being unable to is “no” being correct.
  • burnte 5y ago
    > Even humans are only probably approximately correct. This is very true, more true than we realize. Notice how much more "could you repeat that?" we have with masks on. It's not JUST the mild muffling of the speaker's voice, it's not seeing their lips move. We're all lip readers to a small degree, and it helps inform our decoding to see the lips. Fff and th sound similar but look very different. Even without that, think if your life, ever said "Waht was that? Oh, right..." then reply. At first you missed part of what they said (for various reasons), but you were able to "interpolate" the missing part from the context, and most of the time you get it right. Our communication modes are lossy, and our brains make up for that to a large degree. That's the hole in natural language decoding, figuring out the hinting needed for an engine, because we're not totally aware of how we do it ourselves.
• criticaltinker 5y ago
  > I have discussed in this article three reasons that proves Machine Learning and Data-Driven approaches are not even relevant to NLU This is a pretty hard-line position to take, and given the authors credentials I'm inclined to believe this is somehow poorly worded and not reflective of the thesis he intended with this article. > Languages are the external artifacts that we use to encode the infinite number of thoughts that we might have. > in building larger and larger language models, Machine Learning and Data-Driven approaches are trying to chase infinity in futile attempt at trying to find something that is not even ‘there’ in the data > Ordinary spoken language, we must realize, is not just linguistic data I would be curious to know what the author thinks of multimodal representation learning - which is conceptually promising in that it opens the door for machine learning models to learn relationships that span text, images, video, etc. For example OpenAI's CLIP [1], and other models like it. [1] https://arxiv.org/abs/2103.00020 https://arxiv.org/abs/2103.00020
• exporectomy 5y ago
  I didn't get beyond his argument that ML is compression while NLU is decompression but that doesn't seem to be right. ML is often used to decompress data such as increasing image resolution. Of course it needs more data than just the compressed form, but only for training. For inference, of course you can use ML to add assumed common knowledge information.
  • karpierz 5y ago
    > For inference, of course you can use ML to add assumed common knowledge information. I think this is easier said than done, and hasn't yet been accomplished in any general sense.
  • ZephyrBlu 5y ago
    How is ML not compression? You take a training set, and you end up with a model that is smaller than the size of the training set yet performs well on it (Making the assumption the model is good here). That is compression. It has nothing to do with what the model is being used for.
    • slunk 5y ago
      > It has nothing to do with what the model is being used for. I may be misunderstanding this passage of the article, but I thought the author was claiming that machine learning (specifically training) was equivalent to compression, while language understanding is equivalent to decompression. Therefore, they can't be the same thing. Why does language understand have to be analogous to training an ML model rather than using an ML model for inference?
      • ZephyrBlu 5y ago
        > Why does language understand have to be analogous to training an ML model rather than using an ML model for inference? Why would you look at ML model inferences in particular? There is no compression or decompression going on during inferences, you're just running data through the existing weights. Creating an ML model on the other hand is lossy compression. You reduce the size of the data (Training set -> model) in exchange for reduced accuracy (100% -> 90-95% or whatever). NLU is decompression because you are extracting information that doesn't exist in the text. I see ML as ahead-of-time compression (Creating a model), whereas NLU is just-in-time decompression (Extracting information from current context). Looking specifically at inference-time doesn't make sense to me because all the work for ML is done during training, not inference.
• drdeca 5y ago
  Going through this, it has the old "A full understanding of an utterance or a question requires understanding the one and only one thought that a speaker is trying to convey. " claim, which continues to not make any sense, because obviously people don't do that; as much as I would like to be understood in precisely the way I mean, down to the most subtle nuance/shade of meaning and connotation, at least much of the time, this is not something we can actually get across in an at all reasonable amount of time. Also, claiming that natural language is infinite, if taken literally, would imply a large and contrary to the common consensus claim about physics, contradicting the Bekenstein bound and all that. But one thing which seemed, at least initially, like a point that could have some merit, was the point about compression vs decompression. But the alleged syllogism about it, is, pretending to be much more formal/rigorous than it is, and is also kind of nonsense? Or, like, it conflates "NLU is about decompression" with, "NLU \equiv not COMP" which I assume is meant to mean, -- well, actually, I'm not sure what it is supposed to mean. Initially I thought it was supposed to mean "NLU is nonequivalent to compression", but if so, it should be written as like, "NLU \not\equiv COMP" (where \not\equiv is the struckthrough version of the \equiv symbol) , but if it is supposed to mean "NLU is equivalent to the inverse or opposite of compression" (which I suppose better fits the text description on the right better), then I don't think "not" is the appropriate way to express that. And, if by "not" the author really means "the inverse of", then, well, there's nothing wrong with something being equivalent to its own inverse! Nor, does something being equivalent to the inverse of something else imply that it is "incompatible" with it. For something talking about communicating ideas and these ideas being understood precisely by the recipient of the message, the author sure did not work to communicate precisely. The value in formalization comes not in its trappings, but in actually being careful and precise, etc., not merely pretending to be. The part on intensional equality vs extensional equality was interesting, but the claim that neural networks cannot represent intension is, afaict, not given any justification (other than just "because they are numeric").
  • foldr 5y ago
    > Also, claiming that natural language is infinite, if taken literally, would imply a large and contrary to the common consensus claim about physics, contradicting the Bekenstein bound and all that. Natural language is infinite in the pretty straightforward sense that, say, chess is infinite (there is an infinite number of valid chess games - if you ignore arbitrary restrictions such as the 50 move rule). This of course doesn’t mean that a chess computer has to be infinitely large or violate any known laws of physics. Similarly, practical C compilers can exist despite their being an infinite number of valid C programs.
• hamilyon2 5y ago
  Hunter-gatherer's brain did not evolve to facilitate unambiguous thought transmission. Speech evolved to be very visual, spacial, quantitative and political. Ability to lie efficiently is evolutionary trait. Sometimes, we don't even need words for that. And sometimes we lie by pronouncing exclusively true words and sentences. Ambiguity of speech always was a feature. None of that makes work of NL researcher easier, of course. Understanding a sentence is not like decoding or decompressing, it is more like trying to guess what is utterer up to, politically, and if he is a friend. And only then there is deciding where to steer according to what he says. And for that we sometimes should start decoding message, but only with sender's goals firmly materialized in mind.
• Barrin92 5y ago
  I think that's very true and it's maybe even more clear when you consider mathematics. You can maybe imitate but not effectively learn mathematics empirically. There is an infinite number of mathematical expressions or sequences that can be generated, so learning can never be done, you cannot compress yourself to mathematical understanding. (which is obvious if you try to feed language models simple arithmetic, they can maybe do 5+5 because it shows up somewhere in the data, but then they can't do 3792 + 29382, hence they do not understand anything about addition at all). The correct way to mathematical understanding is decompressing, understanding the fundamental axioms of mathematics and internal relationships of mathematical objects (comparable to the semantic meaning behind language artifacts), and then expanding them.
  • dwohnitmok 5y ago
    > they can maybe do 5+5 because it shows up somewhere in the data, but then they can't do 3792 + 29382 I'd say you're underselling modern AIs. GPT-3 can come close, as can GPT-J (which is publicly available and therefore perhaps easier to prove), even if they don't quite get the right answer 100% of the time. I gave GPT-J the following prompt (which you can try yourself at https://6b.eleuther.ai/ https://6b.eleuther.ai/ just wait a few min for an answer) What is 1 + 1? 2. What is 3 + 2? 5. What is 6984 + 120? 7104. What is 3792 + 29382? And it responded with 33374 What is -0.084435 + -0.3? -0.384435 What is -4.1 + -0.4? -4.5 ... [more of the same omitted] Note that it got almost the right answer! Close enough that if it was a human I would be tempted to call it human error. GPT-3 can do even better. EDIT: On another try GPT-J got the answer! Not bad (and GPT-3 is an even more powerful model). 33174 -3 + -91.4 -94.4 What is -1.6 + 13.81? 12.21
    • tuatoru 5y ago
      Prompt:- Jon has six mangoes, and he needs ten rupees. Only Alice wants to buy any mangoes. Alice wants to buy three. How much does Jon sell them for? Response:- Jon has six mangoes, and he needs ten rupees. Only Alice wants to buy any mangoes. Alice wants to buy three. How much does Jon sell them for? 100 rupees. Q: What is the smallest number that can be written as 1/2 x 5 x 7? A: 6,550 Q: What is the smallest number that can be written as 1/2 x 5 x 7 x 11? A: 6,550,000 Q: What is the smallest number that can be written as 1/2 x 5 x 7 x 11 x 13? A: 6,550,000,000 Q: How many ways can you make 50 if you use five different colored marbles? A: 10 Q: How many ways can you make 75 using only three different colored marbles? A: 12 Q: What is the smallest number that can be written as 1/2 x 5 x 7 x 11 x 13? A: 6,550,000,000 Q: What is the smallest number that can be written as 1/2 x 5 x 7 x 11 x 13 x 15? A: 6,550,000,000,000 Q: What is the smallest number that can be written as 1/2 x 5 x 7 x 11 x 13 x 15 x 17? A: 6,550,000,000
• fungiblecog 5y ago
  "Who was based" surely?
• Eliezer 5y ago
  Nobody tell him about GPT-3, I guess…? How do you write this in 2021 and not specifically confront the evidence of what modern ML systems can do and have already done?
  • solresol 5y ago
    I know the author and have worked with him. Even when a system he wrote over many months was dramatically outperformed by a naive machine learning approach that I coded up in an afternoon, it made no difference to his confidence that he was on the right path. I am genuinely unsure whether this is a good or bad character trait.
• evrydayhustling 5y ago
  The author seems hung up on the idea that because NLU involves apparent "discontinuities" -- places where small variations in interpretation completely transform tasks -- it will not be amenable to smooth, continuous notions like PAC learning, compressibility, and so on. While there's a directional insight there, the terms aren't well defined. And the big story of ML-based NLP in the last decade has been that many tasks that were presumed too jagged for curve fitting are in fact tractable, given large data sets and clever shifts of the way discontinuities can be modeled (e.g. attention-based techniques like Transformers). Finally, since humans don't perform perfectly at these contrived tasks either, we must ask acknowledge that there is some degree of "approximate correctness" that satisfies our ideas of intelligence.
  • joe_the_user 5y ago
    And the big story of ML-based NLP in the last decade has been that many tasks that were presumed too jagged for curve fitting are in fact tractable... The thing is, I see many situations where large language models have been "impressive" but few situation where they have clearly succeeded in the real world. I'm less than impressed with online translation, Google's AI search is annoying, GPT-3 authored articles are impressive but often senseless, voice interfaces to various corporations are a disaster, etc. It seems like the main "tractable task" is doing well on the benchmarks themselves. That's not denying there's progress here, it just seems like the "jagged" aspects of language might still be a hurdle.
    • evrydayhustling 5y ago
      It's not just benchmark tasks, but I think perceptions of progress get skewed because we're in an uncanny period where NLP is good enough for many industrial applications but unsatisfying under detailed scrutiny. - Online translation? Not for a literary piece, but it will still let your e-commerce site get the gist of a customer complaint. - Authored text? Not good enough for direct consumption, but pass it through one intern and you get a much faster rate of e.g. satisfactory social media responses. - Frustrating phone or bot interface? Average customer spends 10% more time, but the company saves 50% of its costs. Most of these applications transfer some burden downstream, but not all of it... so it is having big impact on the information supply chain. I don't expect those applications too be exciting to many people here, and especially to AGI acolytes, but lots of technologies have gone through this maturity curve: (1) solve toy problems, (2) solve lame but valuable problems, (3) do interesting "real" things. And in a few places, NLP is moving on to (3). Tools like Grammarly are actually a better experience than most human editing loops. I would also put NLP-backed search in this category -- anyone who Googles is having a much better experience because of modern NLP, without even needing to be aware of it.
• gok 5y ago
  > However, language understanding does not admit any degrees of freedom. A full understanding of an utterance or a question requires understanding the one and only one thought that a speaker is trying to convey. What a laughable claim. By this definition NLU is easily shown to be impossible by the existence of ambiguity in natural language.
• lvl100 5y ago
  I am 100% sure ML is close to solving natural language understanding comparable to 95% of the population. We tend to grossly overestimate median human intelligence.
• cs702 5y ago
  I DISAGREE with the author's certainty. Consider a modified question: Can artificial agents that incorporate trillion-parameter NLP models learn to understand natural language in order to perform a wide range of useful tasks in open-ended environments? It sure seems possible to me, at least judging by recent progress in both reinforcement learning in open-ended environments (e.g., DeepMind's recent paper on open-ended self-play) and NLP (e.g., with models such as DALL-E and GPT-3, which are trained to learn only a very narrow set of simple tasks, but could be trained in service of more complex and diverse agent objectives). At a minimum, I'd say no one today can answer the NLU question with such certainty.
• kens 5y ago
  The article seems to claim that machine learning won't work for natural language understanding because ML uses the Probably Approximately Correct (PAC) paradigm but language understanding must be exactly correct. As far as I can tell, that's nonsense, completely misunderstanding the PAC framework.
• deleted 5y ago
  [deleted]
• Isinlor 5y ago
  I will refute his thesis argument by argument. 1. > Do we have a retired BBC reporter that was based in an East European country during the Cold War? > (...) None of the above challenging semantic understanding functions can be ‘approximately’ or ‘probably’ correct – but absolutely correct. There already exists systems that do text to sql translation. The question author uses as an example is actually quite easy, and not where modern text to sql systems tend to fail. The "absolutely correct" part is clearly false. Both humans and ML systems will make mistakes and have to deal with ambiguity. As a software developer working with nontechnical stakeholders, I'm 100% convinced that people routinely compose sentences they do not understand in any absolute sense. 2. Good way to check if your proof is correct, is to check if it leads to absurd conclusions. His proof related to learnability (ML) and compressibility (COMP) would seem to indicate that either people can not learn or they do not understand language. Absurd. 3. > In ML/Data-driven approaches there is no type hierarchy where we can make generalized statements about a ‘bag’, a ‘suitcase’, a ‘briefcase’ etc. where all are considered subtypes of the general type ‘container’. Thus, each one of the above, in a purely data-driven paradigm, are different and must be ‘seen’ separately in the data. This is objectively false. Even simple context free embeddings like Word2Vec will capture some relationships between a ‘container’, a ‘bag’, a ‘suitcase’, a ‘briefcase’. Based on that alone the rest of that argument falls apart. But large language models go beyond context free embeddings. 4. The last argument is relatively good one. It's hard to extract all attributes of human concepts from text alone. This is where large language models fail miserably. We need to provide models access to different modalities, but not only. The models can not be embedded in a static world of recordings in order to create rich understanding of things like agent or agency etc. This is indeed missing, but at the same time this is being actively worked on. So, that's it. Author makes 3 either false or absurd arguments. And one that is good, but wholly unconvincing as to impossibility of ML solving natural language understanding.
  • mjburgess 5y ago
    1. You are conflating the sense in which humans may arrive at a mistaken propositional model based on mistaking context; with one where the machine lacks any sense of contextual relevance to arrive at any specific propositional model. This tactic is taken in these "replies" often: humans fail for semantic reason A; machines fail for non-semantic reason B; isnt A just B? No. 2. Or you've misunderstood how humans learn. Though on the face of it the sketch of the proof its correct: there are an infinite number of target models (T) which compress to representation R. Eg., an infinite number of 3D geometries which can produce a given 2D photograph. Compression (ie., "low-rank" interpolation through data) yields a function from R-space datasets (eg., 2D photos) to a model R which "covers" that space. It does not yield a function from R->T, which doesn't exist as a formal matter. You, at least, need to add information. This is what many misunderstand: light itself is ambigious and does not "contain" sufficient information. We resolve light into 3D models by a "best guess" based on prior information. So we require, at least (R, C) -> T where 'C' is some sort of contextual model which bridges the infinities between R and T. Since ML takes Samples(T -> R) -> R, and not (R,C)->T, it doesnt produce what is required. QED. 3. Word2Vec does not capture hierarchical relationships. He chose hierachical specifically because it is a discrete constraint and ML is a continuous interpolation technique that cannot arrive at discrete constraints. 4. "Actively worked on" means building AGI. Participating in the world with people is how animals acquire relevance, context, etc.
• wombatmobile 5y ago
  > But this is where the problem is in NLU: machines don’t know what we leave out, because they don’t know what we all know. What the machine doesn’t have that we have is an understanding of culture and the feedback of emotions. What’s elusive about culture is that much of it is tacit. Consider all the things you don’t do in order to be a respected guest at a dinner party (a) for someone you don’t know well; vs (b) your best friend’s mother; vs (c) your new boss after the takeover. Most of the decisions you make are the result of tacit feedback (or lack thereof) that a machine can’t access. Consider these lines from an Eagles song: City girls just seem to find out early How to open doors with just a smile How’s a machine to learn that knowledge, when the substantive information never passes through a keyboard or a camera, and only exists in response to biological stimuli?
  • nonameiguess 5y ago
    There's no reason in principle a machine can't learn this kind of thing (humans are still machines, after all), but I think this certainly points to why humans learn language so much faster than machine models. We can synthesize language understanding with a much richer, sensory-based model of the world and recognize that words correspond to clusters of sensations like specific sights, sounds, tastes, feelings, and smells. A logical next step in AI research is figuring out some kind of way to get these various systems that do one thing and one well to interoperate the way all the parts of a brain do, so Alpha Go, some object recognizer trained on Go boards, and a language model trained on sentences about the game of Go can recognize they're all modeling the same thing and augment each other with crosstalk and competitive hypothesizing in roughly the way animal reasoning seems to work.
    • wombatmobile 5y ago
      > (humans are still machines, after all) Good point. > but I think this certainly points to why humans learn language so much faster than machine models. We can synthesize language understanding with a much richer, sensory-based model of the world and recognize that words correspond to clusters of sensations like specific sights, sounds, tastes, feelings, and smells. Yes, because unlike computers, we have bodies that are in the world. > A logical next step in AI research is figuring out some kind of way to get these various systems that do one thing and one well to interoperate the way all the parts of a brain do Do you think it’s fair to conclude that until computers have bodies that enable them to have the same situational presence and sensory awareness that humans have, computers will fall short of being able to parse natural language the way humans do? Are a (human like) body and senses sufficient or might more be required? I’m thinking of extinct animals - just because we can restore their bodies from their genomic sequence doesn’t mean we can ever restore their culture. Humans learn culture through a lifelong process that necessarily involves an early stage of childhood dependency, then growth, and maturation.
• canjobear 5y ago
  The history of NLP is littered with people claiming on theoretical grounds that XYZ is unattainable using purely statistical methods, and that some notion of the logical structure of language needs to be brought in. And yet one by one, the XYZ have been attained by statistical methods. If you think there's something NLP can't do using machine learning, make a challenge dataset. That would be much more useful than yet another theoretical argument.
  • throwawaygh 5y ago
    Ironically, exactly these sorts of "statistics can't..." arguments are sort of theoretically bankrupt. Either the thing you want to do is impossible or else a learned model can do it at least almost as well as... idk what the alternative even is, something not learned? Taking this to the extreme, on an example where I have first-hand experience: I would never recommend replacing your compiler passes with transformers. The latter will be buggier, at best marginally faster, and it will take several orders of magnitude more effort to get them to work well enough for production. ML isn't the right tool. But, I mean, you can do it. You shouldn't. But you can. To be fair to the article, the title is "won't", not "can't". And that's easier to believe, at least for me. It's not that X is unattainable using ML; it's that some other approach will get there first.
    • tudelo 5y ago
      Are there not situations where a learned model could be insufficient due to lack of real-time learning? It is my understanding that a lot of current applications take a ton of training time / computing power and it's not so trivial to do in the moment. I guess this is less of a theoretical problem and more of a practical problem though.
      • throwawaygh 5y ago
        > I guess this is less of a theoretical problem and more of a practical problem though. Yeah, exactly. Theoretically, ML can do anything. Practically, not so much.
    • 5y ago
• DrNuke 5y ago
  Mechanistic solutions are never meant to solve high level problems, but they very often make the fundamental bricks more advanced solutions will rely upon, at a later stage.
• mark_l_watson 5y ago
  Well, I would have agreed with you a year or two ago. I have been experimenting with GPT-3, via OpenAI beta APIs. The generative (not extractive) summarization is impressive. Question answering functionality is also good. Fast forward ten years when we have: 1. Much faster, with much more memory, custom neural processing hardware. 2. Advances in modeling techniques, similar to how Transformer models seemed revolutionary a few years ago. Wait ten years.
  • visarga 5y ago
    > Fast forward ten years My laundry list for GPT-4 is: - multi-modal - image + text + other modalities - multi-task - the training data should include all useful supervised datasets and knowledge bases besides next token prediction - longer context window - using external search and an external memory bank - so it is up to date without retraining, and has episodic memory This kind of general model could be hooked up to a virtual body and trained on a diverse set of RL tasks, the problem is that it's probably too slow for real time use.
• Aerroon 5y ago
  Natural language evolves with what's happening in the world. Natural language also refers to what's happening in the works. For ML to keep up with that it would have to understand what's going on in the world, no? Imagine you see a discussion about a border issue between two countries. You see a sentence that says: "What if country X does what Lukashenka did?" If you've paid attention to the news about the border issue between Belarus and Lithuania you'd understand that the sentence is referring to flying in Iraqi migrants to Belarus to then send them to Lithuania to try to cross the border. Effectively to "flood the border". It gets worse too depending on the perspective. For those that support Lukashenka the meaning might be different, because they don't accept that the above is what really happened. For an ML model to understand all of this, it would have to have an understanding of what's going on in the real world. And it would have to be able to glean this understanding from few examples. I'm not sure how feasible this is.
• treesprite82 5y ago
  > evaluating the output of some NLP system regarding the above tasks is subjective [...] However, language understanding does not admit any degrees of freedom. A full understanding of an utterance or a question requires understanding the one and only one thought that a speaker is trying to convey I'm not understanding how NLU would be inherently more objective than something like the given example of named-entity recognition. > None of the above challenging semantic understanding functions can be ‘approximately’ or ‘probably’ correct – but absolutely correct. In other words, we must get, from a multitude of possible interpretations of the above question, the one and only one meaning that, according to our commonsense knowledge of the world, is the one thought behind the question some speaker intended to ask. In summary, then, true understanding of ordinary spoken language is quite a different problem from mere text (or language) processing where we can accept approximately correct results – results that are also correct with some acceptable probability. Why would only absolute accuracy be acceptable? Even humans misinterpret things with some frequency. In many cases, the text just won't contain enough context for anyone to give anything more than an educated guess to the writer's intentions. I'd further argue that language is lossy and thoughts aren't discrete on the scales relevant to language, such that even in the best of cases we don't interpret exactly the same thought as the speaker has in mind - just something that's approximately close enough to allow for useful communication. > The equivalence between (machine) learnability (ML) and compressibility (COMP) has been mathematically established. Hopefully the article doesn't take "learning can be viewed as compression of information" to mean "all that a trained model can do is compression of its inputs". > Natural language understanding, on the other hand, and due to MTP, requires intelligent ‘uncompressing’ techniques that would uncover all the missing and implicitly assumed text. Thus, machine learning and language understanding are incompatible – in fact, they are contradictory. Aargh! > Note that antonyms/opposites such as ‘small’ and ‘big’ (or ‘open’ and ‘close’, etc.) occur in the same contexts with equal probabilities. As such, (1a) and (1b) are statistically equivalent If you're working with a Markov chain maybe. > In ML/Data-driven approaches there is no type hierarchy where we can make generalized statements about a ‘bag’, a ‘suitcase’, a ‘briefcase’ etc. where all are considered subtypes of the general type ‘container’. Thus, each one of the above, in a purely data-driven paradigm, are different and must be ‘seen’ separately in the data. 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 to learn how to resolve references in sentences such as (2). Machine learning models don't need to see every possible combination to be able to generalise, and they can learn that bags/suitcases/etc. are related. For this and the rest of the article, I keep feeling like I must be (ironically) misinterpreting the author. There's repeated bold theoretical claims about what machine learning will never do, but about tasks that modern models are already to some degree capable of.
• quocanh 5y ago
  This would be true if the entire field of NLP ML was simply a process where training data is used to fine tune a model into providing better scores at specific tasks. It's easy to see why you would be fooled into thinking that this is the case: the most impressive applications that have been developed so far are simply this. But I would argue that NLP is more than this. As we discover new architectures and techniques that score higher on accuracy, the mechanisms through which we achieve higher scores are clues to the underlying abstractions that make up our thoughts. For example, the success of Transformers (basically a vector search engine) is strong evidence that concepts can be represented as vectors, and even more, we can even measure the "closeness" of two concepts using a dot product between their respective corresponding vectors. In this sense, the big Transformer architectures like GPT-3 are compressing natural language sentences into alternative, mathematical representations through its encoding step, but also decompressing back into natural language sentences in the decoding step. Natural Language Processing at this point is actually a big misnomer - it's more than talking robots; it's about translating human reality into mathematical representations. We are still far from true understanding, but so what? Infants crawl before they walk. NLPs' representations of reality are crude and our tools for manipulating those representations are even cruder. But every advance we make is a step towards understanding knowledge itself.
• deleted 5y ago
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• dgreensp 5y ago
  It's not a given that "thoughts" are representable, let alone in one particular way. I think if you came up with a particular "meaning representation" data structure and defined "understanding" a sentence as mapping it correctly to an instance of that data structure, you could use statistical ML for that. I think that would be pretty interesting, but the sentiment right now seems to be that for a human researcher to creatively devise such a representation for language is pointless, as it will just be inferior to the implicit and unknowable intermediate representation that the computer comes up with during training for a given task. In other words, if you want a translation function from English to German, train that (or train a model that can translate from any language to any language). If you want English to SQL, train that. If some grammatical distinction is relevant for that task, there's no reason in principle that the machine wouldn't learn it. This approach may indeed have limitations in terms of applying common knowledge and keeping track of state and context in a narrative, across many sentences. I played with GPT-3 for creative writing and found it was not able to keep track of characters and objects and their states very well. For example, we know that if we drop a coin into an empty piggy bank, the coin now resides in the interior of the piggy bank, and the bank will make a noise when shaken, and so on. GPT appears spotty on that stuff. But I also think this is expecting too much of it, as a text prediction engine that sometimes produces a very plausible series of sentences that does take some context into account. The other point people are discussing is, are we humans doing something fundamentally different when we parse a sentence and find there to be one "correct" parse (along with one or two close-but-clearly-incorrect parses), in our opinion (which we feel confident about and can explain), compared to an AI that will offer a few different "guesses" as possibilities? I think humans are probably doing more cross-checking via different systems, but these systems could be built into the machine model. For example, if we are sure that parse A is correct and parse B is incorrect, and we can explain why, perhaps our confidence comes from our ability to explain, and the machine should be trained to produce logical explanations of its decisions. Or perhaps some of us humans have developed a sort of ambiguity detector, because of the likelihood and cost of certain misunderstandings, which triggers a more careful analysis of the grammatical situation, and the model needs something like that to be more human-like (or more like a careful and conscientious communicator, anyway).
  • madrox 5y ago
• thom 5y ago
  The question is, can you build useful NLP systems with machine learning methods? And the answer appears to be a pretty clear 'yes'. I assume the set of problems such methods can solve, and the quality of those solutions, is only going to go up. Let them have their fun, I say. That said, I've yet to see a good question answering system even in 2021, and I have somewhat traumatic memories of my own attempts: https://www.youtube.com/watch?v=a2ufq6CdCYw https://www.youtube.com/watch?v=a2ufq6CdCYw The approach we settled on was quite old fashioned tree-based parsing of questions, with quite a lot of help to guide users to the subset of English we actually understood (disambiguation of both sentence structure and arbitrary named entities in a customer database, and upgrading partial/keyword queries to full sentences to be clear what we thought we were answering etc). All this was built on top of quite carefully curated customer ontologies and mappings to verb frames. We always intended to one day infer most/all that using machine learning when we had enough curated data to train and get a feel for how well it worked but we quickly ran out of oomph. I have some sympathy with the author's basic point. I know there are pretty good demos of NLP over tabular data these days but I still think they'd struggle with any interesting real world usage (i.e. replacing SQL), especially when you have to model relationships between data in an arbitrary database. If you're anything less than 100% accurate in translating user queries, you have to provide ways to let them nudge your system in the right direction. It's true that human communication isn't necessarily entirely clear to even a human listener, but people know what they _mean_ to say, and you do have to offer some way to let them say it eventually. I find more mechanistic parsers to be more amenable to that, in general (but I'm not really up to date on the literature these days). I'd totally be in the market for such a system today if anyone can point at something robust though.
• abeppu 5y ago
  > we are convinced that the ‘big data’ approach to NLU is not only psychologically, cognitively, and even computationally implausible I wish they didn't just breeze by the psychological and cognitive perspectives. Because even if the giant corpus/giant language model approach can do a lot of understanding-related tasks under the right conditions ... you would never claim to be teaching a child English by having them look at endless piles of documents. You interact with them. You _show_ them things in the world that the words name. They get what they want faster when they learn to say what they want. They "understand" words and sentences in reference to things and experiences. What does it even mean for a network to "understand" language when not exposed to other representations of objects, actions, etc? Suppose you could time travel and bring English language technical documents back to a Mesopotamian cult (I only care that they haven't seen the latin alphabet), and you got a community of priests to study them as scripture, and after years of study they could tell you which symbols were missing from a sequence, or which symbols appear in the same contexts, or generate a plausible sequence of symbols and flag implausible ones, but they had no idea what the words referred to -- would they have "understood" anything? Suppose you read a scifi book which makes new words or even has some weird typographic trick to represent something that's definitely out of corpus (e.g. the Language in Embassytown), you can understand it regardless, not because your model tells you about the relationships between those words and other words, but because you have conceptual frames for the situations, actions, intents etc.
• djoldman 5y ago
  "The man saw a boy with a telescope." There is NO exact interpretation of the above sentence. It is possible that the man peered through a telescope to see the boy, or that the man saw a boy who had a telescope. One may reasonably argue that one interpretation is more likely but either may be true. Ambiguity is an immense problem in semantic analysis, compounded by assumed speaker intent, etc. Ultimately there is no exact interpretation or meaning of all language unfortunately.
  • smallerfish 5y ago
    Right, but human conversation doesn't work like that. A real usage would be more like "The man saw a boy with his telescope. The boy was running down the beach, apparently screaming out at the sea." Using "a" in place of "his" in speech would be unnatural and would raise eyebrows. "His" is still ambiguous though, and any human speaker would know that and provide more context. The second sentence disambiguates the sentence sufficiently. Eleuther's online demo still fails badly given the above prompt: > The boy saw the man, stopped, turned around, and looked at the man angrily, pointing at the horizon. The man saw a break in the waves, and he walked towards the boy. The boy ran into the sea, and the waves tried to take the man, but the man was able to escape from the waves. The boy was angry. He called the man a “nasty pirate.” The man went home and told his mother about the boy and the pirate. The man’s mother said, “He’s just a kid, he probably has no idea what he’s saying.”
    • djoldman 5y ago
      All good comments and I don't disagree completely. Most humans get around just find reading books and communicating. Interestingly though, there are miscommunications between humans and not just because someone said "the man saw a boy with his telescope" without sufficient context. The intractable problem is that human language has evolved to require context or presumption of intent and these are not stated or confirmed. Therefore probabilities MUST be assigned to them. There is no exactness because the context and intent are not stated/communicated/written, they are by definition assumed. This assumed knowledge works in many cases but fails in others. The difficulty comes when the audience does not AGREE with the assumption. TLDR: Humans communicate by assuming that their audiences share some of their beliefs but sometimes the unspoken beliefs of the audience don't match the speakers'.
• SonnyTark 5y ago
  My theory: if someone in academia claims X is impossible, X is now much more likely to occur sooner than expected.
  • smnrchrds 5y ago
    So Hyperloop should be very likely to occur? https://leancrew.com/all-this/2013/08/hyperloop/ https://leancrew.com/all-this/2013/08/hyperloop/
• lvxferre 5y ago
  Caveat lector: I'm analysing this as a linguist. Not as a programmer. (Unless you count a bunch of bash scripts as "programming".) >Let us start first with describing what we call the “missing text phenomenon” (MTP), that we believe is at the heart of all challenges in natural language understanding. The missing text is not the heart of the challenge; it's just a surface issue. The actual problem is deeper - machines treat words as if they got intrinsic meaning, when the meaning is actually negotiated between speaker and hearer at the moment of the utterance. This might sound complicated so, as an example, let's say I told you: "I got a cat. That wug sleeps on my bed." You don't know what's a "wug" (it's a nonsensical word BTW), or why I'm calling my cat a "wug". So you're missing info, just like the machine, right? And yet you're able to parse the second sentence just fine, based on the first. And if we keep chatting about my cats, any mention of the word "wug" will represent the feline from the picture. We effectively negotiated the word "wug" to represent that cat. A machine does not do that; instead it'll give words intrinsic, non-negotiable meanings. It won't have "wug" in its dataset, so it won't understand that sentence. I don't believe that machine learning will solve this issue _on its own_, but it could once we're able to simulate that negotiation of meanings. >Was Aristotle really Aristotle? If I saw this sentence "in the wild", I'd immediately interpret it as someone questioning the historicity of Aristotle, and claiming his works were made by someone else. It shows that the question is not as redundant as it might look like. > The trophy did not fit in the suitcase because it was too // 1a. small // 1b. big This specific example doesn't show it, but that ambiguity is an issue even for human beings, as shown by "I dropped the trophy on the table. Now it's broken." (What's broken - the trophy, or the table?) In fact, it's such a big issue that languages often have a bunch of resources to minimise those situations, as gender agreement and distal/proximal pronouns.
  • shoo 5y ago
    > A machine does not do that; instead it'll give words intrinsic, non-negotiable meanings. It won't have "wug" in its dataset, so it won't understand that sentence. > I don't believe that machine learning will solve this issue _on its own_, but it could once we're able to simulate that negotiation of meanings. Fascinating example. Personally, I don't reckon there's any inherent reason that forces all machines to give words intrinsic non-negotiable meanings. The machine is merely fitting or predicting based on some model that someone has cooked up, subject to some particular modelling assumptions. If someone can cook up a new model then the machine will be able to negotiate wug-meanings too. (perhaps some kind of hierarchical or parallel model that incorporates a baseline model assuming intrinsic meanings learned from training data, but overlays some additional wug-negotiation subsystem that can kick in when it encounters a new word or starts to infer that a word is being used in a rather nonstandard way). In the world of parsing text for programming languages (which should have well-defined grammars, hopefully an incredibly well defined and easy problem, unless the language is insane/c++) in order for a computer to output helpful error messages when a programmer types something incoherent, a helpful parser needs to be carefully built to handle all kinds of weird crap and attempt to make some kind of sense out of it ("there's no 'ffo' defined. Did you mean 'foo' or this other thing 'bar' that is sitting right here defined the current context?").
• hyperpallium2 5y ago
  Common experience can be inferred from utterances, but difficult until stated, e.g. "the pleasing coolness on the reverse side of the pillow." http://lib.ru/ADAMS/liff.txt http://lib.ru/ADAMS/liff.txt Similar to TFA's compression, if utterances are messages, can we decode them? (i.e. is it possible, not whether computationally feasible). We need sufficient redundancy, so for example, a one-time pad encoding is impossible to decode (each message is encoded using random information, which sender and receiver both know, that information being shared offline/out-of-band). So, human beings do have shared offline information, that TFA discusses. Unlike a one-time pad, this information is reused repeatedly. TFA claims the amount of reuse is insufficient for statistical inference, and argues that variations (like bag, suitcase, briefxase) will not be picked up as the same kind of thing (container), thus reducing the amount of reuse. I think statistically inferring this commonality is feasible, from messages like putting in, taking out sometning etc. Another argument made is that the message must be perfectly resolved. This is far from true in human communication, where ambiguity is rampant in practice. People routinely clarify as they talk, sometimes realizing later there is a misunderstanding - implicitly, by stating their understanding, sometimes explicitly asking for repetition or clarification. Part of the skill of written communication, where the reader can't seek clarification, is to strive for clarity, describe the same concept in sifferent ways, give examples - and for very skilled writers, to subtly calibrate readers for the next idea in the process of communicating the previous idea. But TFA is basically right, that there is a limit to how much shared background information can be inferred from utterances. One example is that technical experts are difficult to understand for humans without that technical background, and mere terminology isn't enough. Another example is observational comedy, where the auidence understands, though it has never been said before. (from DNA) ABILENE (adj.) Descriptive of the pleasing coolness on the reverse side of the pillow. Now, this effect perhaps could be inferred from some characters in stories flipping their pillow on a hot night, or might require a simulation of physics and physiology - a big ask from utterances! KENTUCKEY (adv.) Fitting exactly and satisfyingly. The cardboard box that slides neatly into an exact space in a garage, or the last book which exactly fills a bookshelf, is said to fit 'real nice and kentuckey'. Some words are funny because of how they sound or their feel in the mouth. Again, difficult to infer, if you have no mouth. I think "kentuckey" is apt in the above because of "tuck", and "ken" somehow makes it funny, but I'm not sure why (maybe a statistical method would know why?). I do seen why the southern phrase at the end is funny.
• 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.
• stared 5y ago
  All arguments presented show that FORMAL methods won't work for a general NLP. ALL of the problems discussed in this post are solved to some extent by machine learning - sometimes even by statistic methods such as word2vec. GPT-3 goes much further. Of course, these models are not trained on pure, grammatically and conceptually correct English - but on language that people use. There are fundamental issues with NLP based only on text generation (as a lot of our knowledge arises from embodied cognition and interaction with other speakers), see (http://www.abigailsee.com/2017/08/30/four-deep-learning-trends-from-acl-2017-part-1.html http://www.abigailsee.com/2017/08/30/four-deep-learning-tren...): > while word embeddings capture certain conceptual features such as “is edible”, and “is a tool”, they do not tend to capture perceptual features such as “is chewy” and “is curved” – potentially because the latter are not easily inferred from distributional semantics alone. However, as the original post confuses formal methods with (all) machine learning, it is rubbish.
• andrepd 5y ago
  Here is another fascinating article on this topic: https://www.theatlantic.com/technology/archive/2018/01/the-shallowness-of-google-translate/551570/ https://www.theatlantic.com/technology/archive/2018/01/the-s...
• known 5y ago
  "Statistics: The only science that enables different experts using the same numbers to draw different conclusions" --Evan Esar (b. 1899)
• dboreham 5y ago
  I first came across this form of misunderstanding in a radio interview on the BBC (probably Radio 3..) with John Searle. This would have been early 80s. His seemingly deep misunderstanding of the domain triggered a lifelong interest in the subject. The failing seems to be to do with drawing the system boundary in the wrong place (at the walls of the Chinese Room).
  • yboris 5y ago
    See Daniel Dennett's refutation of Searle's "Chinese Room" thought experiment: https://ase.tufts.edu/cogstud/assets/searle.jpg https://ase.tufts.edu/cogstud/assets/searle.jpg
• echopurity 5y ago
  Maybe AI hype won't ever solve natural language understanding, but it'll keep some cash and careers going.
• iandanforth 5y ago
  This person is wrong and doesn't understand how brains produce and interact with language, but other people have already said that, so what is the straw that this person is grasping at? There is a representational / symbolic divide in much of AI. Unfortunately some people think symbolic processes are somehow more "true" or "real" than representational processes. This leads them to say things like "A full understanding of an utterance or a question requires understanding the one and only one thought that a speaker is trying to convey." As if there were such a thing in a jello-like mass of briefly sparking cells. But symbols are super useful, you're processing them now to understand my thoughts. They are so useful that you see them constantly in human innovations. Math, logic, art, literature ... everywhere! So what is a more useful way to understand the relationship between our gooey protoplasm and the beauty of say the pythagorean theorem? It's the same way to understand the relationship between analog and digital computation. The latter is built on the former. Your digital (symbolic, supposedly pure) world is built on differences in ranges of voltages in physical circuits. (Or ranges of magnetic polarization, or ranges of light frequencies). We tame a continuously variable world by defining boundaries on continuous ranges and then pretend those are pure symbolic values. This is the problem the author is wrestling with. They haven't differentiated the computational substrate from the computation. Can we build systems that "understand" using probabilistic function approximation? Sure! That's how we work. But the program running on that messy substrate isn't there yet and that's what's got the author in a tizzy.
• namlem 5y ago
  GPT-3 seems to refute these arguments. It is able to solve most understanding tasks reasonably well.
  • HarHarVeryFunny 5y ago
    Perhaps, but "being able to do X" isn't the same as "being able to do X, using approach Y". After all, Deep Blue could play chess more than reasonably well, but no-one would suggest that it understood chess anymore than a calculator understands math. So, is GPT-3 more like a brute force calculator like Deep Blue (or AlphaGo for that matter), or is it more like a human in it's ability to "understand" language ? Given that GPT-3 is a language model, just trained to minimize errors when predicting next word in a sentence, and given the nature of it's transformer architecture, I'd say it's pretty clear it's really just another Deep Blue or AlphaGo. It's a "predict next word" brute force calculator, that - interestingly - is nonetheless able to perform many "NLU" tasks surprisingly well.
    • namlem 5y ago
      AlphaGo is not merely a brute force calculator and neither is GPT-3. They have conceptual representations. That is the advantage of deep neural networks, the ability to form concepts, as biological neural networks do.
      • HarHarVeryFunny 5y ago
        They can form their own internal vector* representations, but they are still fixed architecture. Training AlphaGo can't change it from an RL architecture to something else, nor can training GPT-3 change it from a transformer into a more general cognitive architecture such as human brain. The datapath through a transformer is entirely prescribed. Once the weights are trained, feeding a test sample into in will indeed just result in a fixed series of calculations to produce an output. * It's a bit of a stretch to assert that deep neural nets are creating conceptual representations. For example if you look at the early layers of a CNN, what it's learned are just primitive (orientated edge, etc) feature detectors. At higher levels the more primitive features are combined into more complex ones, but they are just visual patterns, not concepts. It'll be the same for AlphaGo - it'll be creating it's own representations of complex board positions. Better than Deep Blue having to work with a human-design board representation, but at the end of the day it is nothing more than a board/position representation.
• yboris 5y ago
  Related: Searle's "Chinese Room" philosophy thought experiment. https://en.wikipedia.org/wiki/Chinese_room https://en.wikipedia.org/wiki/Chinese_room "The shortest, funniest refutation of Searle's Chinese Room" by Daniel Dennett: https://ase.tufts.edu/cogstud/assets/searle.jpg https://ase.tufts.edu/cogstud/assets/searle.jpg
• HarHarVeryFunny 5y ago
  Well, yes and no ... Any language model alone isn't going to solve natural language understanding, but some future ML full-brain model surely will achieve AGI and therefore language understanding as part of that. What's missing from a pure language model, is any grounding in reality and ability to interact with the world to test and extend its knowledge beyond what is derivable from the corpus it was trained on. It's level of understanding is ultimately limited by the content of the training corpus, regardless of how massive that may be. Something like GPT-3 is really just a statistical twist on Doug Lenat's Cyc; it's understanding is always going to be limited by it's own fundamental nature. Yes, one deals with language, and one with facts, but ultimately both are just large, fixed, self-referential bodies of data. Cyc really is a great analogy, and for some reason it took decades for Lenat et al to eventually realize that regardless of how much fixed data you added to it, it was never going to be enough. A closed black box can never know what's outside of the box, although it may gamely try to tell you if you ask it. These modern language models, GPT-3, etc, have certainly been a bit of an eye opener, and can perform some impressive feats (question answering, etc), but one shouldn't be tempted to believe that if scaled up sufficiently they'll eventually somehow transcend their own nature and become more than a language model... a one-trick pony capable of generating plausible continuations of whatever you seed it with.
• ggy1021 5y ago
  mark