[HN Gopher] Machine learning won't solve natural language unders... ___________________________________________________________________ Machine learning won't solve natural language understanding Author : andreyk Score : 36 points Date : 2021-08-09 21:01 UTC (1 hours ago) (HTM) web link (thegradient.pub) (TXT) w3m dump (thegradient.pub) | visarga wrote: | 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 wrote: | 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 wrote: | 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 wrote: | > 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... | ppod wrote: | That's not a particularly impressive publication record. | mnky9800n wrote: | My h index is twice as high and that's a terrible h index | still lol | lalaithion wrote: | > 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. | emodendroket wrote: | Well, that's true, but I don't think we can pretend that | computers are anywhere near as good at humans at extracting the | intended meaning from human utterances. | burnte wrote: | > 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. | emodendroket wrote: | That's a different problem, isn't it? That's more about | transcription -- getting the speech into words -- than about | what he's talking about, making sense of the words once you | have them. | drdeca wrote: | The two tasks are interconnected. The reasoning flows both | ways. | amirkdv wrote: | > But, can humans do this? I think not; | | > Even humans are only probably approximately correct. | | Fair point. But how about this: | | It's true that what John would do with the sentence is | technically an "approximation" of what Alice would do because | they have slightly different understandings of correct | behavior. However, for humans to do what they do, they still do | build an absolutely correct model of meaning in their mind wrt | their (subjective) notion of correctness. | | This may sound like an obtuse play with words but the point is | that to even attempt to do the right _kind_ of reasoning in | NLU, you need a different framework than PAC. You can 't for | example _approximate_ whether "during the Cold War" qualifies | "was based in" or qualifies "an Eastern European country". You | just need to decide. And once you decide, you have an absolute | correct interpretation, not an approximate one. | | EDIT: wording. | 2muchcoffeeman wrote: | Maybe your computer science brain kicked in took a CA | interpretation of the question once they mentioned a query. Eg: | | Q: Are you a man or a woman? A: Yes | | Despite your interpretations of the article's question. I bet | you know which one is the more likely answer. | bopbeepboop wrote: | 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. | hamilyon2 wrote: | 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 wrote: | 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. | exporectomy wrote: | 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 wrote: | > 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. | criticaltinker wrote: | > 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 | fungiblecog wrote: | "Who was based" surely? | Eliezer wrote: | 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? | drdeca wrote: | 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 was 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 wrote: | > 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. | | No, it wouldn't. 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. | | I'd also be curious to know how you would propose to represent | intentions in neural networks. ___________________________________________________________________ (page generated 2021-08-09 23:00 UTC)