Recent Advances in Natural Language Processing
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All of these results are very interesting, but I'm not really feeling like we've been proved wrong yet. There is a big question of scalability here, at least as far as the goal of AGI goes, which the author also admits:
> Of course everyday language stands in a woolier relation to sheep, pine cones, desire and quarks than the formal language of chess moves stands in relation to chess moves, and the patterns are far more complex. Modality, uncertainty, vagueness and other complexities enter but the isomorphism between world and language is there, even if inexact.
This woolly relation between language and reality is well-known. It has been studied in various ways in linguistics and the philosophy of language, for instance by Frege and not least Foucault and everything after. I also think many modern linguistic schools take a very different view of "uncertainty and vagueness" than I sense in the author here, but they are obviously writing for non-specialist audience and trying not to dwell on this subject here.
My point is, when making and evaluating these NLP methods and the tools they are used to construct, it is extremely important to understand that language models social realities rather than any single physical one. It seems to me all too easy, coming from formal grammar or pure stats or computer science, to rush into these things with naive assumptions about what words are or how they mean things to people. I dread to think what will happen if we base our future society on tools made in that way.
Unfortunately, many people want to believe that they are being understood. But, on the bright side, this stuff seems artistically useful? Entertainment is a big business.
Not only that, they also want to understand. I read some computer poetry a few years ago that worked. The computer did not intend any meaning, but I found it anyway.
And of course I did. This is how language works. We assume that people (and by extension, the texts they write) follow a set of rules which structures how we engage with them in a successful manner. Paul Grice formulated this as the cooperative principle in his study of everyday language, but I believe it is the exact thing that is at play when people meet NLP:
I'd claim actual involves language both a general shared reality and quite concrete and specific discussions of single physical and logic facts/models. Some portion of language certainl looks mostly like a stream of associations. But within it is also are references to physical reality and world model and the two are complexly intertwined (making logical sense is akin to a "parity check" - you can for a while without it but then you have to look at the whole to get it). I believe one can see this in a GPT paragraph, where the first two sentences seem intelligence, well written but the third sentence contradicts the first two sentences sufficiently one's mind isn't sure "what's being said" (and even here, our "need for logic" might be loose enough that we only notice the "senselessness" after the third logical error).
Yes, the point is you can neglect physical (and logic) reality for a while in a stream of sentences. But not forever and that's where current NLP's output has it's limits. Just a simple level, a stream of glittering adjectives can be added to a thing and just add up to "desirable" unless those adjectives "go over threshold" and contradict each other and then the description can get tagged by the brain as a bit senseless.
It seems like at this point, there's no way to distinguish the coherent comments of a bot from a person. The bot could have written sentence X for just about any X. It's just that bots can't sustain stream of logical claims that are consistent with each other.
So it's easier to demonstrate a paragraph is done by a bot, ie, that a paragraph makes no sense, than it is demonstrate that paragraph is not done by a bot - since both humans and bots write some sensible paragraphs.
Still, I'm egotistic enough to think a bot couldn't come up with that argument though I could be wrong.
The best way I can describe the feeling is that it reminds me of conversations and friendships I've had with schizophrenics, people in the process of having a psychotic breakdown, and people with alzheimers.
There's a feeling that what they're saying is not entirely non-sensical, a feeling of 'catching up' to what they're trying to say (akin to translating in a language one isn't too proficient in). But reflecting on the conversation, I find myself wondering how much I managed to understand what they were trying to convey, and how much it was just my brain trying to make sense of something that ultimately doesn't.
'Understanding' or 'communication' aside, I've often valued these kinds of conversations because they tickle the more free-associative side of my own thinking, and the results, however I/we got there, were useful to me.
As a result, I'm much more interested in how these developments in 'AI' might augment this creative process than I am in how they might convincingly appear human. Not that the latter isn't interesting too, though.
Oh gosh yes. Gorge the ML tool with the right streams of text and you'd have a glib anti-vax, white supremacist, flat-earth generator pouring out endless logorrheic twaddle.
Oh wait... could that already... it would explain so much...
It's a model of reality as filtered through the human brain, with all of its neuroses, most of which we have only begun to model.
Aren't we stuck on things like sarcasm? How are you going to model everything from confirmation bias to undisclosed PTSD, in order to have a prayer of filtering the noise from actionable information?
"The condition of the air outdoors at a certain time ofday is known as (A) friction (B) light (C) force (D)weather[correct](Q) joule (R) gradient[selected](S)trench (T) add heat"
I assume this might be characteristic for other questions as well, although I don't know anything about the Regents Science Exam and whether there are multiple questions about closely related topics.
Jeopardy is fun and games and it was great for the blooper reel, but they're trying to sell this stuff to diagnose cancer and guide police efforts. Failure modes are kind of important.
https://en.wikipedia.org/wiki/Digital_root#Properties
Example from ASVAB practice math test:
(x+4)(x+4) =
A. x^2+16x+8
B. x^2+16x+16
C. x^2+8x+16
D. x^2+8x+8
Since we don't have to solve for X in this problem, we can just assume x is 1, which would make the digital root of the problem expression 7. Assuming x = 1, the digital roots of A, B, C and D are 7, 6, 7 and 8 respectively. C is the answer because its last term is the square of 4.
You can do the same thing without the digital root. substitute 1 into all the possible answers gives: A: 25 B: 33 C: 25 D: 17
Substitute 1 into the question gives 25. Therefore only A and C are possible answers and it must be C because 4*4 = 16
This is the same process as your answer but without the digital root!
For example, 25 * 26 = 650, digital root 2. Or take digital root of 25 and 26 first, get 7 * 8 = 56, same digital root 2.
(x + 14284) (x + 14284)=
A: x^2 + 204032656x + 28568
B: x^2 + 204032656x + 204032656
C: x^2 + 28568x + 204032656
D: x^2 + 204032656x + 204032656
The digital root of the problem expression is 4. The digital roots of A, B, C and D are 4, 6, 4 and 3 respectively. The digital root of the square of 14284 is 1, and the digital roots of the last term of A and C are 2 and 1, respectively. The answer is C.
When a problem of this form uses big enough numbers to warrant a shortcut, digital root is almost always a suboptimal shortcut.
Of course there's a tiny chance that one of the other equations would be equal at your arbitrary x value. If so and you get two 'right' answers just try again with a different x.
If two polynomials evaluate to equal numbers with random variables values, the polynomials are almost certainly equal themselves
Ofc, x = 0 and x = 1 are hardly random
A dog can learn to turn left or to turn right for treats. But they don’t understand the concept of “direction”, their brain isn’t wired that way.
Machine learning models perform tricks for treats. The tricks they do get more impressive by the day. But don’t be deceived, they aren’t wired to gain knowledge.
Do you honestly believe statistical inference completely explains a human’s ability to learn?
Induction isn’t probabilistic. It’s the origin of all discovery and based in sussing out what’s important in patterns, selectively proposing causal mechanisms and testing those hypotheses by following priors and implications — an essential basis for original and creative thought that no purely probabilistic engine can employ.
Stating that we are no more than statistical inference is no different to saying that we are no more math - really, you can build the deepest mathematical truths on statistical inference.
The thing is that the math/s.i. that ML models do is too trivial in comparison to the math that would be required to describe a human identity. It can detect faces very well, it can calculate fifths roots very quickly, it can solve discrete problems quickly, yes, but its achievements are, in comparison to actual human understanding (and purpose), more closer to those of another tool, like a hammer, than those of humans.
edit: typos/grammar
"The thing is that the math/s.i. that ML models do is too trivial in comparison to the math that would be required to describe a human identity" why do you think this? How do you know the expressive power of neural networks? How have you guaged the expressive power of biological neural networks. How have you made any of these conclusions?
I am exasperated.
I never assumed we are more than math. Just that there is a lot to still learn about the structure in which those buildings blocks are organized in order to fully tame what is understood as human intelligence. If we could simulate all the neurons of a real adult living brain accurately enough it would be intelligent from a human perspective, and still, both the model and us would be equally far, very far, from understanding how to arrive to that point of neural organization to achieve that kind of intelligence.
But why think reason isn't a kind of statistical inference? GPT-3 demonstrates a rudimentary capacity for reasoning and its "merely" a statistical inference engine.
Dogs haven’t been to the moon, however. There’s more to the brain we don’t understand.
Choosing the best from 8 answers where some of them were adversarially derived should be equivalent to choosing the best from all possible answers, of which there could be tens of thousands. How would a human do in that situation?
Although the kinds of mistakes the network makes seem like a mistake you would never do (i.e. you wouldn't call the condition of air outdoors gradient), the opposite could also be true, that it would easily answer questions you would have a problem with.
For one thing, I can imagine a knowledgeable human working through ten thousand alternatives and picking the best, (if, objectively, there is one) - it would just take a long time. On the other hand, It does not seem obvious to me that current NLP systems would do better than humans on such a task, and nor is it obvious to me that one can conclude that from the fact that the machine sometimes ignores adversarial examples (if the assumption is that most of the ten thousand would be de-facto adversarial, that is a lot to not make one mistake on; that’s not so much a problem for a human with an understanding of the issue, and who might well come up with the right answer unprompted.)
This is probably all moot, however, as the most obvious way to compare the machine results to those of humans is to give both the actual tests in question, rather than substitute a dubious “equivalent” test.
Doesn't mean that they are not "actually" learning any more than we are.
This right here is a great way of putting the success of GPT-3 into context. We think GPT is smart, because when it says something eerily human-like we apply our model of the world onto what it is saying. A conversation like this:
> Me: So, what happened when you fell off the balance beam?
> GPT: It hurt.
> Me: Why'd it hurt so bad?
> GPT: The beam was high up and I feel awkwardly.
> Me: Wow, that sounds awful.
In this conversation, one of us is thinking far harder than the other. GPT can have conversations like this now, which is impressive. But only I can model the beam, the fall, and the physical reality. When I say "that sounds awful," I actually do a miniature physics simulation in my head, imagining losing my balance and falling off a high beam, landing, the physical pain, etc. GPT does none of that. In either case, when it asks the question or when it answers it, it is entirely ignorant of this sort of "shadow" model that's being constructed.
Generalizing a bit, our "shadow" model of reality in every single domain is far more powerful than language's approximation. That's why we won't be able to use GPT to do a medical diagnosis or create a piece of architecture or whatever else people are saying it's going to do now.
More broadly there is a growing group of researchers who are working on trying to achieve better world modeling (although of course there have been many for decades, it just seems that the number who are coming from deep learning towards AGI with more emphasis on world modeling is increasing).
Such as Ferran Alet. Sort of the Tenenbaum school I guess. Or Melanie Mitchell has been saying similar for quite awhile. They have recent talks on Good AI's YouTube channel.
Filling in "GPT" for x, "text" for y, and "world" for z, the author is stating "if GPT models text, and text models the world, then GPT models the world."
In particular, the author directly addresses your point that "Generalizing a bit, our 'shadow' model of reality in every single domain is far more powerful than language's approximation."
> Modality, uncertainty, vagueness and other complexities enter but the isomorphism between world and language is there, even if inexact.
My own personal view tends to lean towards the author's. GPT-3 , despite its many astonishing achievements, has many many limitations. Yet I don't see any indication as of yet that any of those limitations are fundamental ones inherent to the approach behind the GPT series, which is why GPT-3 both excites and frightens me.
There are many other things that go into modelling the world, like drawing pictures, visualising things, communicating in non-text ways essentially. GPT can only model the world in the ways that text can model the world, which is limited.
The rise of computing and its myriad encoders is an existence proof that the world we humans know can be, to an arbitrary degree of precision, modeled by 0s and 1s and what are 0s and 1s but the purest essence of text?
Although fraught with many large implementation difficulties, a GPT trained on binary data does not represent a fundamental difficulty vs one trained on text.
Models of all kinds are always poor surrogates for reality, and models that cannot employ logic or causality CLEARLY cannot model a world in which mechanisms cause all change.
Statistical models can indeed describe many observations, but can never break out from the echo chamber of copy-catting patterns it has already seen. If a probabilistic engine like a deep net hasn’t been exposed to a concept during training, it will never induce its existence. Imagination requires initiative, the proposal of an unknown and unfamiliar outcome via logical inference or causal induction. Until deep nets can employ both of these skills, they will never master many skills humans use routinely to explain the world or extend our understanding of how it works.
GPT-3 could presumably write a paragraph like that one. You can claim to have a working physics model in your head, but why should I believe that unless it becomes evident from the things that you communicate to me? I've certainly met humans who could have a superficially legitimate conversation about objects in motion while harbouring enormous misconceptions about the physics involved.
Maybe the biggest takeaway from GPT-3 should be that we should raise our standards for human conversation, demanding more precise language and giving less credit to flourishes that make the meaning ambiguous.
The physical mechanics of being an embodied thing isn't the only example of this. Any experiential knowledge, any knowledge with feedback suffers from this. And any knowledge that is difficult to put into words, like emotional knowledge, interpersonal knowledge, etc., will be off-limits.
That's my point.
Relevant article for this problem: https://news.ycombinator.com/item?id=24026511
Standard Chinese can be spoken in Cantonese, but while understandable, it is never done, in contrast to Mandarin.
In recent years written online conversations (like blogs, Facebook posts, instant messages) by Cantonese speakers are increasingly written in Cantonese instead, and this is slowly spreading to more "formal" media such as books.
Source: I'm a native Cantonese speaker.
They're still a lot closer than English and Spanish or even French and Spanish (https://www.ezglot.com/most-similar-languages.php).
E.g. I bought this book: ngóh(S) maáihjó(V) nìbún syù(O).
Contrast with Japanese: watashi wa(S) kono hon o(O) kaimashita(V).
Differences from Mandarin Grammar are listed here: https://en.wikipedia.org/wiki/Cantonese_grammar#Differences_...
Classical Chinese is also SVO: https://en.wikipedia.org/wiki/Classical_Chinese_grammar
I'm taking bets.
This applies to all languages BTW. There are only very, very few NLP systems that actually work, and they are all in a setting which tolerates a fairly high error rate (e.g. ranking).
This has a lot more to do with smaller Russian corpora than anything to do with peculiarities of the Russian language.
> This applies to all languages BTW. There are only very, very few NLP systems that actually work, and they are all in a setting which tolerates a fairly high error rate (e.g. ranking).
I've trained NLP models used on over 15 different languages used in production
data --> system --> model
It would be unremarkable if the same system would have to be retrained from scratch to work with a different language, but I take it that you mean more than that. One guess is that the training data is annotated with grammatical information, in which case I would wonder if this is just a shortcut, or whether it solves a fundamental problem for such systems. Another guess is that the training set includes disambiguation, but that would seem to render meaningless the results on Winograd schema. (Update: I withdraw this last point, as presumably the Winograd-schema questions themselves are not disambiguated. Disambiguation of the training corpus would be a quite significant language-specific accommodation, if that is what is happening.)
It really is that unremarkable, despite the GPs insinuations to the contrary.
In any case, I don't remember anyone ever naming their n-gram models or HMMs etc. The reason for this of course is that training a language model with a deep neural network architecture is anything but unremarkable, not least because the costs in data, human-hours and compute are beyond the reach of most entities other than large corporate teams. If training a new model from scratch were truly "unremarkable" we'd all have our own.
If, on the other hand, a given architecture could not be retrained to perform well on a different language, or only on a group of related languages, that might be indicative of a fundamental limitation in how far it could be developed, depending on what sort of accommodations have to be made for new languages.
Basically retraining the same system on a different dataset is a bit like entering the same river in two points in time. Yes, in theory it's "the same" system and only the dataset chnges. In practice, it takes so much architectural tweaking and fine-tuning that it's a different system and you've done almost all the work from scratch. This also is part and parcel of why so much fanfare surrounds the publication of a new model. Because it's really, really hard to train a new model on a new dataset (assuming one wants to get state of the art results).
Actual NLP practitioners are not having a hype-fest every time someone spends a shitton to train a transformer LM on a new dataset. It's definitely cool, but I wouldn't cal it remarkable. What was cool about GPT-3 was the demonstration of the effectiveness of the "few-shot"/"conditioning" approach. I didn't find it surprising, but it was cool to push it to the next level.
> In any case, I don't remember anyone ever naming their n-gram models or HMMs etc
That's because they are literally different model architectures. The 3-gram model is the same everywhere, it doesn't depend on dataset. A 3-gram "trained" on Russian would still be a 3-gram. Similarly, BERT trained on a German corpus would still be BERT, it wouldn't need a "new catchy name." That's my point.
As a side remark, you are vastly overestimating the amount of dataset specific tuning that you need - you can often just use the exact same hyperparams and run it on a new dataset - there's been some recent literature showing that a lot of these architectures are actually quite resilient to different datasets and hyperparameter choices and arrive at similar local minima. Sure, you might be off by 1-2% from SOTA accuracy (let's say on a classification task) but in production systems you're generally not shooting for the SOTA that was released in the last 2 months but rather something close-ish to those results.
Honestly, quite confused this is the hill you want to die on.
>> That's because they are literally different model architectures. The 3-gram model is the same everywhere, it doesn't depend on dataset. A 3-gram "trained" on Russian would still be a 3-gram. Similarly, BERT trained on a German corpus would still be BERT, it wouldn't need a "new catchy name." That's my point.
N-gram models are not "the same everywhere". An n-gram model is a set of probabilities assigned to, well, n-grams. Different sets of n-grams, or probabilities trained from different datasets would be- different. The n-grams would be different and the probabilities would be different and the structure of the model would be different. A Russian n-gram model would represent the probabilities of Russian n-grams, an English one would reprsent the probabilities of English n-grams and so on. The type of model would be the same, an n-gram model; but the model itself would be different. Otherwise, why bother training more models?
Same goes for a model trained with a transformer architecture. BERT is an English language model trained with a transformer architecture and representing the probabilities of tokens in an English corpus. A model trained with a transformer architecture and representing the probabilities of tokens in a German corpus would be, again, a different model. You could also call it "BERT" but that would eventually become very confusing.
>> Honestly, quite confused this is the hill you want to die on.
I'm not dying on any hill. Could you please not do that?
I don't really think it's super worth quibbling over, since I think we're generally on the same page. It's worth noting that BERT trained on different corpora is generally still called BERT (you can look at the huggingface/transformers library, for instance).
I'm sorry that my message came across as more adversarial than intended.
Thanks. I enjoyed our conversation.
Lots of people are having fun playing with GPT-3 or AI Dungeon, myself included, but it seems like there is other interesting research going on like the REALM paper [1], [2]. What should I be reading? Why aren't people talking about REALM more? I'm no expert, but it seems like keeping the knowledge base outside the language model has a lot going for it?
[1] https://ai.googleblog.com/2020/08/realm-integrating-retrieva... [2] https://arxiv.org/abs/2002.08909
Basically the NLP groups from Google and FB[2][3] are always worth watching.
[1] https://ai.googleblog.com/2020/06/pegasus-state-of-art-model...
[2] https://ai.facebook.com/blog/covost-v2-expanding-the-largest...
[3] https://ai.facebook.com/blog/introducing-a-new-large-scale-d...
realm is definitely cool, and i agree that techniques like this are the way forward.
To my mind, all this benchmark-beating is just pointless faffing about and not a little frustrating to see that this is where the current state of the art is at. NLP systems keep getting better and better at beating benchmarks and yet we still don't have artificial agents that can hold an intelligent conversation, even in a restricted context.
Or rather, we had such programs since the late 1960's, with no one else's than Terry Winograd's own doctoral thesis system, SHRDLU [1], but it seems that in the race to build powerful classifiers, we forgot how to build systems that can interact intelligently with a human.
Here's a small excerpt from SHRDLU (taken from the wikipedia page on SHRDLU, originally in an appendix in Terry Winograd's thesis):
Person: Is there a large block behind a pyramid?
Computer: YES, THREE OF THEM: A LARGE RED ONE, A LARGE GREEN CUBE, AND THE BLUE ONE.
Person: Put a small one onto the green cube which supports a pyramid.
Computer: OK.
Person: Put the littlest pyramid on top of it.
Computer: OK.
Person: Does the shortest thing the tallest pyramid's support supports support anything green?
Computer: YES, THE GREEN PYRAMID.
Person: What color is it?
Computer: BY "IT", I ASSUME YOU MEAN THE SHORTEST THING THE TALLEST PYRAMID'S SUPPORT SUPPORTS.
Computer: RED
SHRDLU was a traditional program hand-crafted by a single PhD student- no machine learning, no statistical techniques. It included, among other things, a context-free grammar (!) of natural English and a planner (to control the robot's hand) all written in Lisp and PLANNER. In its limited domain, it was smarter than anything ever created with statisical NLP methods.
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"Colorless green ideas sleep furiously" is a famous example of a sentence that is grammatical, but meaningless. The goal of SHRDLU was far more ambitious than approximating English grammar.
Statistical methods traded that. With data, some labelled, some unlabelled and some weakly-labelled, we can generate these models with much more efficient human involvement (refine the statistical models and labelling data).
I honestly don't see the frustration. Yes, current NLP model may not be the "intelligent agent" everyone looking for yet to any extent. But claiming it is all faffing and no better than 1960s is quite a stretch.
And "limited domain" is relative. A program that gets many aspects of language right talking about a small world might be said to have a larger domain than a program that outputs stream of semi-plausible, semi-gibberish involving the whole of English language. Which again isn't saying modern NLP is nothing but rather we should a somewhat better to talk of it (and machine learning generally) than "hitting benchmarks".
Yes, we did. So- where are the natural language interfaces by which we can communicate with artificial agents in such limited domains? Where are the applications, today, that exhibit behaviour as seemingly intelligent as SHRDLU in the '60s? I mean, have you personally seen and interacted with one? Can you show me an example of such a modern system? Edit: Note again that SHRDLU was created by a single PhD student with all the resources of ... a single PhD student. It's no stretch to imagine that an entity of the size of Google or Facebook could achieve something considerably more useful, still in a limited domain. But this has never even been attempted.
Yes, it is faffing about. Basically NLP gave up on figuring out how language works and switched to a massive attempt to model large datasets evaluated by contrived benchmarks that serve no other purpose than to show how well modern techniques can model large datasets.
There was the whole AI winter thing, of course, but that was as much a result of things not living up to the hype as a cause.
So, in the UK at least the AI winter was precipitated by the Lighthill Report, a report published in 1973, compiled by a Sir James Lighthill and commissioned by the British Research Council, i.e. the people who held all the research money at the time in the UK. The report was furiously damning of AI research of the time, mostly because of grave misunderstandings e.g. with respect to combinatorial explosion and basically accused researchers of, well, faffing about and not doing anything useful with their grant money. The only exception to this was SHRDLU, that Lighthill praised as an example of how AI should be done.
Anyway, if you have time, you can watch the televised debate between Lighthill and three luminaries of AI, John McCarthy (the man who named the field, created Lisp and did a few other notable things), Donald Michie (known for his MENACE reinforcement-learning program running on... matchboxes, and basically setting up AI research in the UK) and Richard Gregorie (a cognitive scientist from the US for whom I confess I don't know much). The (short) wikipedia article on the Lighthill Report has links to all the youtube videos:
https://en.wikipedia.org/wiki/Lighthill_report
It's interesting to see in the videos the demonstration of the Freddy robot from Edinburgh, that was capable of constructing objects by detecting their components with early machine vision techniques. In the 1960's. Incidentally:
Even with today's knowledge, methodology, software tools, and so on, getting a robot to do this kind of thing would be a fairly complex and ambitious project.
http://www.aiai.ed.ac.uk/project/freddy/
The above was written sometime in the '90s, I reckon but it is still true today. Unfortunately, Lighthill's report killed the budding robotics research sector in the UK and it has literally never recovered since. This is typical of the AI winter of the '80s. Promising avenues of research were abandoned not because of any scientific reasons, as is sometimes assumed ("expert systems didn't scale" etc) but, rather, because pencil pushers in charge of disbursing public money didn't get the science.
Edit: A couple more pointers. John McCarthy's review of the Lighthill Report:
http://www-formal.stanford.edu/jmc/reviews/lighthill/lighthi...
An article on the AI winter of the '80s by the editor of IEEE Intelligent Systems:
https://www.computer.org/csdl/magazine/ex/2008/02/mex2008020...
They are also completely incapable of reasoning about their environment, not least because they don't have any concept of an "environment" - which was represented by the planner and the PROGRAMMAR language in SHRDLU.
And of course, systems like Siri and Alexa can't do anything even remotely like correctly disambiguating the "support support supports" show-off sentence in the excerpt above. Not even close.
Edit: Sorry, there's a misunderstanding about "limited domain" in your comment. Alexa and Siri don't operate in a limited domain. A "limited domain" would be something like being in charge of your music collection and nothing else. Alexa annd Siri etc are supposed to be general-use agents. I mean, they are, it's just that they suck at it... and would still suck in a limited domain also.
Time and causality and even basic probability were all absent from SHRDLU’s model. Not surprisingly the work was a dead end that even Winograd was quick to abandon, as he subsequently exited the field of experimental AI for the more conceptual models of cognitive science and HCI.
If that were true then SHRDLU would have operated in a one-dimensional world. Instead, it moved blocks around a 3-dimensional world. It could understand more spatial relations than "above" and "below", as in the example I quote above where it is asked "Is there a large block behind a pyramid?". It stacked blocks, but in doing so it also had to move others out of the way etc. That is no big mystery, like I say SHRDLU used a planner and even in the 1960s' there were planners capabe of solving block stacking problems in 3d environments.
Time and causality were certainly not "absent" from SHRDLU! Like I say, it had a memory that allowed it to keep track of what it had done previously and it could explain the reasoning behind its actions, like in this excerpt from the demonstration quoted in wikipedia:
Person: Had you touched any pyramid before you put the green one on the little cube?
Computer: YES, THE GREEN ONE.
Person: When did you pick it up?
Computer: WHILE I WAS STACKING UP THE RED CUBE, A LARGE RED BLOCK AND A LARGE GREEN CUBE.
Person: Why?
Computer: TO GET RID OF IT.
Person: Why did you do that?
Computer: TO CLEAN OFF THE RED CUBE.
Person: Why did you clear off that cube?
Computer: TO PUT IT ON A LARGE GREEN CUBE.
Person: Why did you do that?
Computer: TO STACK UP THE RED CUBE , A LARGE RED BLOCK AND A LARGE GREEN CUBE.
Person: Why did you do that?
Computer: BECAUSE YOU ASKED ME TO.
Person: How did you do it?
Computer: BY PUTTING A LARGE RED BLOCK ON THE TABLE ; THEN LETTING GO OF IT ; THEN PUTTING A LARGE GREEN CUBE ON IT ; THEN LETTING GO OF THAT CUBE ; THEN PUTTING THE RED CUBE ON THAT CUBE; THEN LETTING GO OF THAT CUBE.
>> Not surprisingly the work was a dead end that even Winograd was quick to abandon, as he subsequently exited the field of experimental AI for the more conceptual models of cognitive science and HCI.
Regarding Winograd's subsequent work direction, this is what he had to say about it himself:
How would you say SHRDLU influenced your subsequent work and/or philosophy in AI?
Having insight into the limitations I encountered in trying to extend SHRDLU beyond micro-worlds was the key opening to the philosophical views that I developed in the work with Flores. The closest thing I have online is the paper Thinking machines: Can there be? Are we?
How would you characterize AI since SHRDLU? Why do you think no one took SHRDLU or SHRDLU-like applications to the next level?
There are fundamental gulfs between the way that SHRDLU and its kin operate, and whatever it is that goes on in our brains. I don't think that current research has made much progress in crossing that gulf, and the relevant science may take decades or more to get to the point where the initial ambitions become realistic. In the meantime AI took on much more doable goals of working in less ambitious niches, or accepting less-than-human results (as in translation).
What future do you see for natural language computing and/or general AI?
Continued progress in limited domain and approximate approaches (including with speech). Very long term research is needed to get a handle on human-level natural language.
http://maf.directory/misc/shrdlu.html
My reading of this is he realised that natural language understanding is not an easy thing. I don't disagree one bit and I don't think for a moment that SHRDLU could "understand" anything at all. But it was certainly capable of much more intelligent-looking behaviour than modern statistical machine-learning based systems. Winograd's reply above says that it's hard to extend SHRDLU outside of its limited domain, but my point is that a program that can operate this well in a limited domain is still useful and much more useful than a program that can operate in arbitrary domains but is dumb as bricks, like modern conversational agents that have nothing like a context of their world that they can refer to, to choose appropriate contributions to a conversation. He also hints to the shift of AI research targets from figuring out how natural language works to "less ambitious niches" and "less than human results", which I also point out in my comments in this thread. This is condensed - I'm happy to elaborate if you wish.
I have to say I was very surprised by your comment, particularly the certainty with which you asserted SHRDLU's limitations. May I ask- where did you come across information that SHRDLU could only understand "above and below" relations and that time and causality were absent from its "model"?
As you say SHRDLU was the best program of that time.
I've read Winnograd's book and looked at the SHRDLU source code (not that I'm much of a lisp hacker but I got some time). It's built-on a parser and planner (logic program, pre-prolog). And it's built the old-fashioned-way, rewriting the source code with the parser rewriting input and then re-rerunning and other harry things. I think this achieves the parsing of idiomatic constructions on a high level. I believe the "raw lisp" of the day was both incredibly powerful since you could do anything and incredibly hard to scale ... you could do anything.
Winnograd wrote it himself but I think that's because he had to write it himself. In a sense, a programmer is always most productive when they are writing something by themselves because they don't have to explain anything they are doing (until the fix-ups and complexity overwhelm them but the base fact remains). And in the case of SHRDLU, Winnograd would have had an especially hard time explaining what he was doing. I mean, there was a theory behind - I've read Winnograd's book. But there was lots and lots of brilliant spaghetti/glue-code to actually make it work, code that jumped module and function boundaries. And the final had a reputation of being very buggy, sometimes it worked and sometimes it didn't. And Winnograd was a brilliant programmer and widely read in linguistics and other fields.
The software industry is an industry. No company wants to depend on the brilliance of it's workers. A company needs to produce based on some sort of average and a person working with just average skills isn't going to do SHRDLU.
So, yeah, I think that's why actual commercial programs never reached the level of SHRDLU
Perhaps one could create a different kind of enterprise for this but it's kind of an open problem.
There's some information about all that here:
http://maf.directory/misc/shrdlu.html
[Dave McDonald] (davidmcdonald@alum.mit.edu) was Terry Winograd's first research student at MIT. Dave reports rewriting "a lot" of SHRDLU ("a combination of clean up and a couple of new ideas") along with Andee Rubin, Stu Card, and Jeff Hill. Some of Dave's interesting recollections are: "In the rush to get [SHRDLU] ready for his thesis defense [Terry] made some direct patches to the Lisp assembly code and never back propagated them to his Lisp source... We kept around the very program image that Terry constructed and used it whenever we could. As an image, [SHRDLU] couldn't keep up with the periodic changes to the ITS, and gradually more and more bit rot set in. One of the last times we used it we only got it to display a couple of lines. In the early days... that original image ran like a top and never broke. Our rewrite was equally so... The version we assembled circa 1972/1973 was utterly robust... Certainly a couple of dozen [copies of SHRDLU were distributed]. Somewhere in my basement is a file with all the request letters... I've got hard copy of all of the original that was Lisp source and of all our rewrites... SHRDLU was a special program. Even today its parser would be competitive as an architecture. For a recursive descent algorithm it had some clever means of jumping to anticipated alternative analyses rather than doing a standard backup. It defined the whole notion of procedural semantics (though Bill Woods tends to get the credit), and its grammar was the first instance of Systemic Functional Linguistics applied to language understanding and quite well done." Dave believes the hardest part of getting a complete SHRDLU to run again will be to fix the code in MicroPlanner since "the original MicroPlanner could not be maintained because it had hardwired some direct pointers into the state of ITS (as actual numbers!) and these 'magic numbers' were impossible to recreate circa 1977 when we approached Gerry Sussman about rewriting MicroPlanner in Conniver."
Regarding the advantage of a lone programmer- that's real, but large teams have built successful software projects before, very often. I guess you don't even need a big team, just a dozen people who all know what they're doing. That shouldn't be hard to put together given FANG-level resources. Hell, that shouldn't be hard to do given a pool of doctoral students from a top university... but nowadays even AI PhD students would have no idea how to recreate something like SHRDLU.
Edit: I got interested in SHRDLU recently (hence the comments in this thread) and I had a look at Winograd's thesis to see if there was any chance to recreate it. The article above includes a link to a bunch of flocharts of SHRDLU'S CFG but even deciphering those hand-drawn and occasionally vague plans would take a month or two of doing nothing else, something for which I absolutely do not have the time. And that's only the grammar - the rest of the program woudl have to be reverse-engineered from Winograd's thesis, examples of output from the original code or later clones, etc. That's a project for a team of digital archeologists, not software developers.
I can believe this but I think your details overall reinforce my points above.
For a recursive descent algorithm it had some clever means of jumping to anticipated alternative analyses rather than doing a standard backup.
Yeah, fabulous but extremely hard to extend or reproduce. The aim of companies was to scale something like this. It seems like the fundamental problem was only a few really smart people could programs to this level and no one could take them beyond it (the saw that a person half to be twice as smart to debug a program as to write comes in, etc).
How do you know if no one has attempted it, or if all attempts so far have failed to achieve their goals? One of the claimed downsides of "hand-crafted systems in limited domains" (i.e., something like the SHRDLU approach) is that they would take too much effort to create when the domain is expanded to something even slightly bigger than SHRDLU's domain, so a lack of successful systems could be evidence of no one trying, or it could be evidence that the claimed downside is indeed true.
The fact that a working system for the limited domains of e.g. customer support or medical diagnosis would be worth a lot of money suggests to me that they must have been tried, but that nothing useful could be built, and we didn't hear about the failed attempts, meaning that those domains (at least) are too big for hand-crafted systems to work.
> Yes, it is faffing about. Basically NLP gave up on figuring out how language works and switched to a massive attempt to model large datasets evaluated by contrived benchmarks that serve no other purpose than to show how well modern techniques can model large datasets.
It is inaccurate to say that all benchmarks are useless. For instance, language translation (as in Google Translate) is a benchmark NLP task, but it is also something I personally use at least every week, and deep learning based solutions beat handcrafted systems by a lot for this particular task (speaking as an end user who has used systems based on both approaches). The same comments apply to audio transcription (e.g. generating subtitles on YouTube) as well.
That's actually a good point and one I've made myself a few times in the past- negative results never see the light of day. So, yes, I agree we can't know for sure that such systems haven't been attempted, despite the certainty with which I assert this in my comment above.
On the other hand, there are some strong hints. First of all, there was an AI winter in the 1980's after which most of the field turned very hard towards statistical techniques and away from the kind of work in SHRDLU. This kind of work became radioactive for many years and it would have been very hard to justify putting a PhD student, or ten, to work trying to even reproduce it, let alone extend it. That's in academia. In the industry, it's clear that nowadays at least companies like the FANGs strongly champion statistical machine learning and anyone proposing spending actual money on such a research program ("Hey, let's go back to the 1960's and start all over again!") would be laughed out of the building. That is, I believe there are strong hints that the political climate in academia and the culture in large companies, has suppressed any attempt to do work of this kind. But that's only my conjecture, so there you have it.
>> It is inaccurate to say that all benchmarks are useless.
Of course. My point is that current benchmarks are useless.
The fact that you find Google translate useful is unrelated to how well Google translate scores in benchmarks, which are not designed to measure user satisfaction but instead are supposed to tell us something about the formal properties of the system. In any case, for translation in particular, it's not controversial that there are no good benchmarks and metrics and many people in NLP will tell you that is the case. In fact, I'm saying this myself because I was told during my Master's, by our NLP tutor who is a researcher in the field. Also see the following article which indcludes a discussion of commonly used metrics in machine translation and the difficulty of evaluating machine translation systems:
On the subject of benchmarks, how about speech transcription? I was under the impression that those benchmarks are pretty reliable indicators of "real-world accuracy" (or about as reliable as benchmarks are in general)
How do you mean that machine translation outperforms non deep learning based approaches at the real world task? How is this evaluated?
There were two ways that performance can be evaluated that I had in mind:
1. Commercial success - what approach does popular sites like Google Translate use? 2. Human evaluation - in a research setting, ask humans to score translations - this is mentioned in your link
More to the point, you still have yet to show that NMT "serves no other purpose than to show how well modern techniques can model large datasets", given that they do well on human evaluations and they're actually producing value by serving actual production traffic (you know, things humans actually want to translate) in Google Translate. If serving production traffic like this is not "serving a purpose", what is?
Regarding whether human evaluations are a good target to optimise for, no, I certainly don't think so. That's not very different than calculating BLEU scores, except that instead of comparing a machine generated translation with one reference text, it's compared with peoples' subjective criteria, which actually serves to muddle the waters even more -because who knows why different people thought the same translation was good or bad? Are they all using the same criteria? Doubtful! But if they're not, then what have we learned? That a bunch of humans agreed that some translation was good, or bad, each for their own reasons. So what? It doesn't make any difference when the human evaluators are blinded, you can make the same experiment with human translations only and you'll still have not learned anything about the quality of the translation- just the subjective opinions of a particular group of humans about it.
See, the problem is not just with machine translation. Evaluating human translaion results is also very hard to do because translation itself is a very poorly characterised task. The question "what is a good translation is very difficult to answer. We don't have, say, a science of translation to tell us how a text should be translated between two languages. So in machine translation people try to approximate not only the task of translation, but also its evaluation- without understanding either. That's a very bad spot to be in.
In fact, a "science of translation" would be a useful goal for AI research, but it's the kind of thing that I complain is not done anymore, having been replaced with beating meaningless benchmarks.
Regarding the fact that neural machine translation "generates value", you mean that it's useful because it's deployed in production and people use it? Well, "even a broken clock is right twice a day" so that's really not a good criterion of quality at all. In fact, as a criterion for an AI approach it's very disappointing. Look at the promise of AI: "machines that think like humans!". Look at the reality: "We're generating value!". OK. Or we could make an app that adds bunny ears and cat noses to peoples' selfies (another application of AI). People buy the app- so it's "generating value". Or we can generate value by selling canned sardines. Or selling footballs. Or selling foot massages. Or in a myriad other ways. So why do we need AI? It's just another useless trinket that is sold and bought while remaining completely useless. And that for me is a big shame.
OK, I hadn't realised we had such different implicit views on what the "goal" of AI / AI research was. Of course, I agree that the goal of "having machines think like humans" is a valid goal, and "generates value by serving production traffic" is not a good subgoal for that. However, this is not the only goal of AI research, nor is it clear to me that for e.g. public funding bodies see it as the only goal.
I use MT (at least) every week for my job and for my hobbies, mostly translating stuff I want to read in another language. I love learning languages but I could not learn all the languages I need to a high enough level to read the stuff I want to read. The old non-NMT approaches produced translations that were often useless, whereas the NMT-based translations I use now (mostly deepl.com), while not perfect, are often quite good, and definitely enough for my needs. Without NMT, realistically speaking, there is no alternative for me (i.e., I can't afford to pay a human translator, and I can't afford to wait until I'd learned the language well enough). So how can you say that AI "remains completely useless"?
Basically, you have implicitly assumed that "make machines that think like humans" is the only valid goal of AI research. And, from that point of view, it is understandable that evaluating NMT systems for how well they approach that goal using human evaluations, has many downsides. However, while some people working on NMT do have that goal, many of them also have the goal of "help people (like zodiac) translate stuff", and in the context of that goal, human evaluation is a much better benchmark target.
Personally, I came into AI (I'm a PhD research student) because I got (really, really) interested in logic programming languages and well, to be frank, there's no other place than in academia that I can work on them. On the other hand, my interest in logic programming is very much an interest in ways to make computers not be so infuriatingly dumb as they are right now.
This explains why I dislike neural machine translation and similar statistical NLP approaches: because while they can model the structure of language well, they do nothing for the meaning carried by those structures which they completely ignore by design. My favourite example is treating sentences as a "bag of words", as if order doesn't make a difference- and yet this is a popular technique... because it improves performance on benchmarks (by approximately 1.5 fired linguists).
The same goes with google translate. While I'd have to be more stubborn than I am to realise that people use it and like it, I find it depends on the use case -and on the willingness of users to accept its dumbness. For me, it's good where I don't need it and bad where I do. For example, it translates well enough between languages I know and can translate to some extent myself, say English and French. But if I want to translate between a language that is very far from the ones I know - say I want to translate from Hungarian to my native Greek- that's just not going to work, not least because the translation goes through English (because of a dearth of parallel texts and despite the fact that Google laughably claims its model actually has an automatically learned "interlingua") and the result is mangled twice and I get gibberish on the other end.
I could talk at length on why and how this happens, but the gist of it is that Google translate stubbornly refuses to use any information to decide what translation to choose, among many possible translations of an expression, by looking at the frequencies of token collocations- and nothing else. So for example, if I ask it to translate a single word, "χελιδόνι", meaning the bird swallow, from Greek to French, I get back "avaler" which is the word for the verb to swallow- because translation goes through English where "swallow" has two meanings and the verb happens to be more common than the bird. The information that "χελιδόνι" is a noun and "avaler" is a verb exists, but Google translate will just not use it. Why? Well, because the current trend in AI is to learn everything end-to-end from raw data and without prior knowledge. And that's because prior knowledge doesn't help to beat benchmarks, which are not designed to test world-knowledge in the first place. It's a vicious circle.
So, yes, to some extent it's what you say- I don't quite expect "machines that think like humans", but I do want machines that can interact with a human user in a slightly more intelligent manner than now. I gave the example of SHRDLU above because it was such a system. I'm sad the effort to reproduce such results, even in a very limited domain, has been abandoned.
P.S. Sorry, this got a bit too long, especially for the late stages of an HN conversation :)
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[1] That was in 1956: https://en.wikipedia.org/wiki/Dartmouth_workshop
I don't think that "NMT and similar techniques" ignore meaning by design though. What they do do by design, compared to expert systems etc, is avoid having explicitly encoded knowledge (of the kind SHRDLU had). Take word2vec for instance, it's not NMT but fits into the "statistical NLP" description - its purpose is to find encodings of words that carry some semantic content. Now, of course it's very little semantic content compared to what an expert could plausibly encode, but it is some semantic content, and this content improves the (subjective human) evaluation of NMT systems that do use word2vec or something similar.
Also, we should carefully distinguish "prior knowledge" as in "prior common-sense knowledge" and "prior linguistic knowledge". The end-to-end trend eschews "prior linguistic knowledge", while current NLP systems tend to lack "common-sense" knowledge, for rather different reasons.
End-to-end training tends to eschew prior linguistic knowledge because it improves (subjectively evaluated) performance in real-world tasks - I believe this is true for MT as well, but an easier example if you want to look into it is in audio transcription. I don't think there's a consensus about why this happens, but I think it is something like - the previously way people were encoding linguistic knowledge was too fragile / simplified (think about how complicated traditional linguistic grammars are), and if that information can somehow be learned in the end-to-end process, that performs better.
Lacking "common-sense" knowledge - that's more in the realm of AGI, so there's a valid debate about to what extent neural networks can learn such knowledge, but the other side of that debate is that expressing common-sense knowledge in today's formal systems is really hard and expensive, and AIUI this is also something that attempts to generalize SHRDLU run into. But it is definitely incorrect to say that it's ignored by anyone by design...
BTW, the biggest improvements (as subjectively evaluated by me) I've seen in MT on "dissimilar languages" have come from black box neural nets and throwing massive amounts of (monolingual or bilingual) data at it, rather than anything from formal systems. I use deepl.com for Japanese-English translation of some technical CS material, and that language pair used to be really horrible in the pre-deep-learning days (and it's still not that good on google translate for some reason).
I agree about word2vec and embeddings in general- they're meant to represent meaning or capture something of it anyway. I'm just not convinced that they work that well in that respect. Maybe I can say how king and queen are analogous to man and woman etc, but that doesn't help me if I don't know what king, queen, man or woman mean. I don't think it's possible to represent the meaning of words by looking at their collocation with other words- whose meaning is also supposedly represented by their collocation with other words etc.
I confess I haven't used any machine translation systems other than google translate. For instance, I've never used deepl.com. I'll give it a try since you recommend it although my use case would be to translate technical terms that I only know in English to my native Greek and I don't think anything can handle that use case very well at all. Not even humans!
Out of curiousity, you say neural machine translation is better than earlier techniques, which I think is not controversial. But, have you tried such earlier systems? I've never had the chance.
(I don't assert that it has hasn't. I know just enough about the topic to see how little I know. But it seems, from the outside, a valid criticism, and not one unique to the field.)
The "Winograd schema" in Glue/SuperGlue refers to the Winograd-NLI benchmark which is simplified with respect to the original Winograd Schema Challenge [1], on which the state-of-the-art still significantly lags human performance:
The Winograd Schema Challenge is a dataset for common sense reasoning. It employs Winograd Schema questions that require the resolution of anaphora: the system must identify the antecedent of an ambiguous pronoun in a statement. Models are evaluated based on accuracy.
WNLI is a relaxation of the Winograd Schema Challenge proposed as part of the GLUE benchmark and a conversion to the natural language inference (NLI) format. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. While the training set is balanced between two classes (entailment and not entailment), the test set is imbalanced between them (35% entailment, 65% not entailment). The majority baseline is thus 65%, while for the Winograd Schema Challenge it is 50% (Liu et al., 2017). The latter is more challenging.
https://nlpprogress.com/english/common_sense.html
There is also a more recent adversarial version of the Winograd Schema Challenge called Winogrande. I can't say I'm on top of the various results and so I don't know the state of the art, but it's not yet "near human", not without caveats (for example, wikipedia reports 70% accuracy on 70 problems manually selected from the originoal WSC).
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[1] https://www.aaai.org/ocs/index.php/KR/KR12/paper/view/4492
What this article is about is more (question/prompt) -> (answer/continuation of prompt)
Does anyone know if there is progress in the (timeline of events) -> (narrative about said events) space?
Some define intelligence (entirely separately from consciousness) precisely as the ability to develop an internal model. Coupled to a regulatory feedback the system can then modify itself in response to some set of internal and/or external conditions (Joscha Bach for instance suggests consciousness is a consequence of extremely complex self-models)
(Tasks here referring to questions in the New York Regent’s science exam)
Same for me.
But it makes sense of course that learning from text only is entirely possible. I certainly have not directly observed the answer to eg. 'Which process in an apple tree primarily results from cell division? (1) growth (2) photosynthesis (3) gas exchange (4) waste removal', I have been taught, from text books, what the answer should be.
I do have a much better grounding of what growth is, what apples and apple trees are though.
> This is to say the patterns in language use mirror the patterns of how things are(1).
> (1)- Strictly of course only the patterns in true sentences mirror, or are isomorphic to, the arrangement of the world, but most sentences people utter are at least approximately true.
Presumably this should really say something like "...but most sentences people utter are at least approximately true of their mental representation of the world."
All of the examples in the article work on English text, not spoken English. I would consider spoken English to be a much better "Gold standard" of natural language.
I'm really looking forward to machine translation operating purely on a speech in/speech out basis, instead of converting to text as an intermediate step.
for example, you can finetune gpt-2 to have an idea of sexual biology by having it read erotica. just like how you can have a model learn the same by watching porn. but it is much more efficient to read the text, since there is much less information that is "useless"
What if they did? Do you see where the big deal is now?
A lot of support requests aren't actually intricate and special: there's almost always loads of simple requests that come in again and again.
When you ask a request like that and get the answer instantly, that really is ML delivering value.
You mightn't think it, but a lot of people work in customer support, and spend a lot of time answering rote questions again and again.
People talk about how much hype there is, or the chance of another AI winter. Yes there's a ton of hype - but I think they aren't considering the real value already being delivered this time around.
Everyone is excited about GPT3, but there's already been amazing progress in practical NLP already, over the last few years.
A: Of course not, let's do something else. B: What? You put text in the maths and stuff comes out.
That's the whole point of the experiment called GPT-3.
I use NLP and associates s2s techniques every day. I struggle to see how so many people don't see the obvious benefits deep inference is bringing to stuff all around them.
https://github.com/Bergvca/string_grouper
Saved me hours of work because all the Levenshtein implementations are pretty slow and I’m going to need to rerun the analysis as the dataset grows.
I don’t know about consumer-facing tools, but NLP stuff has helped me solve all kinds of tedious data problems at work.
https://en.wikipedia.org/wiki/Galileo's_Leaning_Tower_of_Pis...
An equivalent experiment was famously carried out for real in 1971 on the surface of the Moon.
Rumination, deep thinking, etc is simply actor-critic learning of these mental models for story-telling.
"The car is red" is not a lie just because I didn't phrase it internally as "The car reflects photons of a frequency of around 700nm".
We have to be able to simplify and internalize simplified models in order to make any sense of anything. It's the same reason your eyes only have a focal point in the dead center: attention requires vast amounts of processing power.
To reiterate, a simplification is not a lie. Especially not a flat-out lie.
That wouldn't be the case if they were built based on "flat-out lies".
Edit: the sticking point here is the use of the word "lie", I think.
A lie is a falsehood told with intent to deceive.
Thus, novels are not "lies", despite being works of the imagination. Everyone knows that the people in Moby-Dick or Wuthering Heights were made up.
Neither is a simplified model a "lie" if it is close enough.
All engineers know that the steel and concrete components of a bridge have quantum and relativistic effects going on inside them, so ignoring those effects isn't a "lie" in any meaningful sense.
It just doesn't matter for the purpose at hand.
And if the researcher asked it to explain how it could know anything about what is fiction and what is not, it might be able to use the contents of these comments to figure out how to converse intelligently about its own limitations.
Isn't that self-contradictory?
I don't know if that question is rhetorical or not, but GPT-3 can do basic math for problems it has not been directly trained on, and there's been a fair amount of debate, including right here at hn, about what the takeaway is supposed to be.
tl;dr, GPT-3 can't do basic math for problems it has not been directly trained on.
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[1] https://arxiv.org/abs/2005.14165
See section 3.9.1 and Figure 3.10. There is an additional category of problems of combinations of addition, subtraction and multiplication between three single-digit numbers. Performance is poor.
Which grade of school it corresponds to? It would be astonishing to have an AI system of this capability level mere few years ago.
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[1] From the paper linked above:
In addition, inspection of incorrect answers reveals that the model often makes mistakes such as not carrying a “1”, suggesting it is actually attempting to perform the relevant computation rather than memorizing a table.
Now that I re-read this I'm struck by the extent to which the authors are willing to pull their results this way and that to force their preferred interpretation on them. Their model answers two-digit addition problems correctly? It's learned addition! Their model is making mistakes? It's because it's actually trying to compute the answer and failing! The much simpler explanation, that their model has memorised solutions to a few problems but there are many more it hasn't even seen, seems to be assigned a very, very low prior. Who cares about such a boring interpration? Language models are few shot learners! (once trained with a few billion examples that is).
That can look like a surface-level linguistic understanding, but it's not, it's a surface-level abstraction. It's not arbitrary, it has structure, and when you flesh it out you're fleshing it out with actual abstract structure, not just painting over the gaps with arbitrary language.