Right now if you look at GTP-3's output it seems like it's approaching a convincing approximation of a bluffing college student writing a bad paper, correct sentences and stuff but very 'cocky'. It cannot tell right from wrong, and it will just make up convincing rubbish, 'hoping' to fool the reader. (I know I'm anthropomorphizing but bear with me).

Current models are being trained on a huge amount of internet text. As smarmy denizens of hackernews we know that people are very often wrong (or 'not even wrong') on the internet. It seems to me that anything trained on internet data is kinda doomed to poison itself on the high ratio of garbage floating around here?

We've seen with a lot of machine-learning stuff that biased data will create biased models, so you have to be really careful what you train it on. The dataset on which GTP-n has to be trained has to be pretty huge(?); and moderation is hard(?) and doesn't scale; it's easier to generate falsehood than truth; and the further we go along the more of internet data will be (weaponized?) output of GTP-(n-1); So won't the arrival of AGI just be sabotaged by the arrival of AGI?

Has anyone written something about the process of building AGI that deals with this?

When GTP-3 writes a scientific paper it's not trying to test a premise or critically evaluate some data, it's trying to generate text that looks like that sort of thing.

It doesn't matter how many excellent quality papers you fed it, or how stringently you excluded low quality input data, it would still only be trying to produce facsimiles. It wouldn't be actually trying to do the things a real conscientious scientist is trying to do when they write a paper. Arguably at best it might be trying to do what a deceitful scientist trying to get credit for a paper with spurious results with no actual scientific merit might be doing when writing a paper that looks plausible, but thats actually a completely different activity.

The code generation examples are really interesting. Here it's being used to generate real working code that has actual value, and it appears to work pretty well. It's code often has bugs, but who's doesn't? It only works for fairly short precisely definable coding tasks though. I don't think scaling it up to more complex coding problems is going to work. Again it doesn't understand the meaning of anything. It's trying to produce code that looks like working code, not actually solve the programming task you're giving it. It doesn't even know what a program is or what a programming task is. It doesn't know what input and output are. For example you can't ask it to modify existing code to change it's behaviour. It doesn't know code has behaviour. It has no idea what that even means and has no way to find out or any route to gaining that capability, because that's not a text transformation task and all it does is transform text.

The best, in fact the only way to generate truly convincing text output on most subjects is to understand, on some level, what you're writing about. In other words, to create a higher level abstraction than simply "statistically speaking, this word seems to follow that one". Once you start to encode that words map to concepts, you can use the resulting conceptual model to create output which is conceptually consistent, then map it backwards to words. There is what humans do with sensory data, and there is good evidence that GPT-3 is doing this too, to some degree.

Take simple arithmetic, such as adding two and three digit numbers. GPT-2 could not do this very successfully. It did indeed look like it was treating it as a "find the textual pattern" problem.

But GPT-3 is much more successful, including at giving correct answers to arithmetic problems that weren't in its training set.

So what changed? We aren't sure, but the speculation is that in the process of training, GPT-3 found that the best strategy to correctly predicting the continuation of arithmetic expressions was to figure out the rules of basic arithmetic and encode them in some portion of its neural network, then apply them whenever the prompt suggested to do so.

If this is the case, and it remains speculation at this point, would you still argue that GPT-3 doesn't "understand" arithmetic, on some level? I would argue that this abstraction, this mapping of words onto higher-level concepts, which can then be manipulated to solve more complex problems, is exactly what intelligence is, once you strip away biologically-biased assumptions.

Certainly, at this point GPT-3's conceptual understanding remains somewhat primitive and unstable, but the fact that it exhibits it at all, and sometimes in spookily impressive ways, is what has people excited and worried. We have produced AIs that can perhaps think conceptually about relatively narrow topics like playing Go, but we have never before created one that can do so one such a wide range of topics. And there is no suggestion that GPT-3's level of ability represents a maximum. GPT-4 and beyond will be more powerful, meaning that it can mine more and more powerful conceptual understanding from their training data.

I don't mean to attack you personally, but this is a perfect example of what I feel is wrong with so much neural network research. (And I understand that you are just commenting in a discussion, not conducting research.)

In a word, it's baloney. And it's a really common pattern in neural networks' recent history: "How did they perform reasonably well on this task? We aren't sure, but the speculation is that they magically solved artificial general intelligence under the hood." Usually this is followed up by "I don't know how it works, but let's see if a bigger network can make even prettier text." Meanwhile, "it's funny how our image classifiers grossly misperform if you rotate the images a little or add some noise."

A rigorous scientific approach would be aimed at actually figuring out what these models can do, why, and how they work. Rather than just assuming the most optimistic possible explanation for what's happening -- that's antithetical to science.

This is where you lost me. They included important caveats to indicate not being sure, which is important to me as an indication of healthy skepticism. And you substituted a specific example: making inferences about arithmetic, for an more expansive, uncharitable, easy-to-caricature claim of "gee we must have solved general AI!" which is much easier to attack. And, unlike your counterpart, who hedged, you just went ahead and categorically declared it to be baloney, making you the only person to take a definitive side on an unsettled question before the data is in. This is a perfect example of the anti-scientific attitude exhibited in Overconfident Pessimism [0].

I don't think it's known how GPT-3 got so much better at answering math questions it wasn't trained on, I do think the explanation that it made inferences about arithmetic is reasonable, I think the commenter added all the qualifiers you could reasonably ask them to make before suggesting the idea, and frankly I would disagree that there's some sort of obvious history of parallels that GPT-3 can be compared to.

There is an interesting conversation to be had here, and there probably is much more to learn about why GPT-3 probably isn't quite as advanced as it may immediately appear to be to those who want to believe in it. But I think a huge wrench is thrown in that whole conversation with the total lack of humility required to confidently declare it 'baloney', which is the thing that sticks out to me as antithetical to science.

0: https://www.lesswrong.com/posts/gvdYK8sEFqHqHLRqN/overconfid...

As a side note, it's worth mentioning that apparently, from other responses, it seems we have little idea how much arithmetic GPT-3 has learned, and it may not be much.

Anyway, I think the important distinction between my perspective and Overconfident Pessimism, which you attribute to me, is that I'm not talking about (im)possibility of achievement, I'm talking about scientific methodology or lack thereof.

In other words, I'm not saying (here) that some NLP achievements are impossible. I'm saying that we are not rigorously testing, measuring, and verifying what we are even achieving. Instead we throw out superficially impressive examples of results and invite, or provoke, speculation about how much achievement probably must have maybe happened somewhere in order to produce them.

We have seen several years of this pattern, so this is not a GPT-3 specific criticism; it's just that particular quote so neatly captured patterns of lack of scientific rigour that we have seen repeatedly at this point.

Probably the first example was image recognition. Everyone was amazed by how well neural nets could classify images. There was a ton of analogous speculation -- along the lines of 'we're not sure, but the speculation is the networks figured out what it really means to be a panda or a stop sign and encoded it in their weights.' The terms "near-human performance" and then "human-level performance" were thrown around a lot.

Then we found adversarial examples and realized that e.g. if you rotate the turtle image slightly, the model becomes extremely confident that it's a rifle. So, obviously it has no understand of what a turtle or a rifle is. And obviously, we as researchers don't understand what those neural nets were doing under the hood, and that speculation was extremely over-optimistic.

Engineering cool things can absolutely be a part of a scientific process. But we have seen countless repetitions of this pattern (especially since GANs): press releases and impressive-looking examples without rigorous evaluation of what the models are doing or how; invitations to speculate on the best-possible interpretation; and announcing that the next step is to make it bigger. I think this approach is both anti-science and misleading to readers.

Layperson here, but my impression is that "let's see if a bigger network can make even prettier text" has _worked_ far beyond the point most people expected it would stop working.

Also my layperson impression: most "researchers" that are on the cutting edge of cool things are more interested in seeing what cool things they can do than on doing rigorous science (which makes sense -- if you optimize for rigorous science, your stuff probably isn't as flashy as the stuff produced by people optimizing for flash).

Is this a new iteration on that zigzag quote?

> Zak phases of the bulk bands and the winding number associated with the bulk Hamiltonian, and verified it through four typical ribbon boundaries, i.e. zigzag, bearded zigzag, armchair, and bearded armchair.

From "The existence of topological edge states in honeycomb plasmonic lattices"

https://iopscience.iop.org/article/10.1088/1367-2630/18/10/1...

It's similar to computer systems research. For example, a research paper on filesystems might tell us a simple trick which leads to better performance on NVMM. The paper may go into why the trick works, but it doesn't (and shouldn't need to) generalize and try to improve our general understanding of how to design filesystems on different hardware. We've been designing filesystems to this day and well, we are always still guessing about which approaches to use and hoping for the best. In the same vein, we don't even have a widely-accepted theory of how to use data structures yet.

So, I don't think that neural nets aren't scientific enough means that it's all BS. We have gaps in understanding, but the power of the models warrants a lot of continued work on finding useful applications.

Doesn't mean I don't think AI is over-hyped/overfunded though...

For example, people once thought playing chess was hard. So they thought that if a computer could beat the world champion, then computers would probably also be able to replace every job and so on. If you sent Deep Blue back in time to the 1960s, they wouldn't understand how it works so they'd probably assume that it since it could beat Petrosian in chess, it could probably drive cars and treat disease.

But then we built Deep Blue and realized that you don't need AGI to play chess; a very specialized algorithm will do it.

So we're like people in the 70s who've been handed Deep Blue. It's irresponsible, in my opinion, to over-hype it when we have no idea how it works.

Same thing arguably happens with humans with rotation. Our eyes even rotate in the roll axis to keep gravity aligned things upright. Most people can draw faces more accurately when copying from an upside down face than a right side up one.

I saw a lot of basic arithmetic in the thousands range where it failed. If we have to keep scaling it quadratically for it to learn log n scale arithmetic then we're doing it wrong.

I'm surprised you think it learned some basic rules around arithmetic. A lot of simple rules extrapolate very well, into all number ranges. To me it seems like it's just making things up as it goes along. I'll grant you this though, it can make for a convincing illusion at times.

Oh, aren’t we all?

    Hypothesis: <AI writes this>
    Results: <human observations>
    <repeat>

The novel Manna explores where this can lead quite nicely - http://www.marshallbrain.com/manna1.htm

https://plato.stanford.edu/entries/chinese-room/

Of course if you accept the required premise of the argument, you must accept that either, one, we don't live in a universe that is a pure system of deterministic rules, or, two, nothing in the universe can have true understanding.

The Chinese Room argument, scientific materialism, or the existence of true understanding—you can have at most two of those in a consistent view of the universe.

To your point though, the more interesting case is people who would disavow the Chinese Room argument, but then end up using reflecting its views while argue against the intelligence of this or that system.

https://plato.stanford.edu/entries/qualia-knowledge/

It can be read in its entirety at the author's site: https://rifters.com/real/Blindsight.htm

I strongly disagree. GPT-3 has 100% accuracy on 2-digit addition, 80% on 3-digit addition, 25% on 4-digit addition and 9% on 5-digit addition. If it could indeed "understand arithmetic" the increase in number of digits should not affect its accuracy.

My perspective as an ML practitioner is that the cool part of GPT-3 is storing information effectively and it is able to decode queries easier than before to get the information that is required. Yet with things like arithmetic, the most efficient way would be to understand the rules of addition but the internal structure is too rigid to encode those rules atm.

It's possible to train GPT3 to produce a facsimile of these transmissions, but doing so does not let us learn anything at all about these aliens, beyond statistical correlations like ⊑⏃⟒⍀ often occurring in close proximity to ⋏⟒⍙⌇ (what do they represent - who knows?). Just having the text is not enough, because we have no understanding of the underlying processes that produced the text.

That said, this is only a limitation of language models as they currently exist. I imagine it would be possible to train a ML model that encodes more of the human experience via video/audio/proprioception data.

The intuition behind this idea is that the structure inherent in a language is dependent upon features of the world being described by that language to some degree. If we can abstract out the details of the language and get at the underlying structure the language is describing, then this latent structure should be language-independent. But then translation turns out to simply be a matter of decoding and encoding a language to this latent structure. One limitation of this idea is that it depends on there being some shared structure that underlies the languages we're attempting to model and translate. It's easy to imagine this constraint holds in the real world as human contexts are very similar regardless of language spoken. The basic units and concepts that feature in our lives are more-or-less universally shared and so this shared structure provides a meaningful pathway to translation. We might even expect the world of intelligent aliens to share enough latent structure from which to build a translation given enough source text. The laws of physics and mathematics are universal after all.

[1] https://openreview.net/pdf?id=rkYTTf-AZ

Take the sentence 'I fooed a bar with a Baz' - can you infer what I did from this?

How do you define meaning? If we can find a mapping between the sequence of words in a language and the underlying structure of the world, then we by definition know what those words mean. The question then reduces to whether there will be multiple such plausible mappings once we have completely captured the regularity of a "very large" sequence of natural language text. I strongly suspect the answer is no, there will only be one or a roughly equivalent class of mappings such that we can be confident in the discovered associations between words and concepts.

The number of relationships (think graph edge) between things-in-the-world is "very large". The set of possible relationships between entities is exponential in the size of the number of entities. But the structure in a natural language isn't arbitrary, it maps to these real world relationships in natural ways. So once we capture all the statistical regularities, there should be some "innocent" mapping between these regularities and things-in-the-world. "Innocent" here meaning a relatively insignificant amount of computation went into finding the mapping (relative to the sample space of the input/output).

>Take the sentence 'I fooed a bar with a Baz' - can you infer what I did from this?

Write me a billion pages of text while using foo bar baz and all other words consistently throughout, and I could probably tell you.

Here's a good example. Suppose I had a huge set of recipe books from a human culture - just recipes, no other record of a culture.

I might be able to get as far as XYZZY meaning "a food that can be sliced, mashed, fried and diced" but how would I really tell if XYZZY means carrot, potato, or tomato, or tuna?

This seems a bit tautological to me: if being able to make certain mappings is understanding, then does this not amount to "once we understand something, understanding it is a solved problem?"

On the other hand, the apparently simplistic mappings used by these language models have achieved way more than I would have thought, so I am somewhat primed to accept that understanding turns out to be no more mysterious than qualia.

I doubt that just any mapping will do. One aspect of human understanding that still seems to be difficult for these models is reasoning about causality and motives.

I think it is a fairly common intuition that one cannot understand something just by rote-learning a bunch of facts.

I meant it in the sense of: given any reasonable definition of "understanding", finding a mapping between the sequences of words and the structure of the world must satisfy the definition.

>I think it is a fairly common intuition that one cannot understand something just by rote-learning a bunch of facts.

I agree, but its important to understand why this is. The issue is that learning an assignment between some words and some objects misses the underlying structure that is critical to understanding. For example, one can point out the names of birds but know nothing about them. It is once you can also point out details about their anatomy, how they interact with their environment, find food, etc and then do some basic reasoning using these bird facts that we might say you understand a lot about birds.

The assumption underlying the power of these language models is that a large enough text corpus will contain all these bird facts, perhaps indirectly through being deployed in conversation. If it can learn all these details, deploy them correctly within context, and even do rudimentary reasoning using such facts (there are examples of GPT-3 doing this), then it is reasonable to say that the language model captures understanding to some degree.

I think the biggest problem with this argument is the assumption that '[the structure in a natural language] maps to these real world relationships in natural ways'. One thing we know for sure is that human language maps to internal concepts of the human mind, and that it doesn't map directly to the real world at all. This is not necessarily a barrier to translation between human languages, but I think it makes the applicability of this idea to translations between human and alien languages almost certainly null.

Perhaps the most obvious aspect of this is any word related directly to the internal world - emotions, perceptions (colors, tastes, textures etc) - there is no hope of translating these between organisms with different biologies.

However, essentially any human word, at least outside the sciences, falls in this category. At the most basic level, what you perceive as an object is a somewhat arbitrary modeling of the world specific to our biology and our size and time scale. To a being that perceived time much slower than us, many things that we see as static and solid may appear as more liquid and blurry. A significantly smaller or larger creature may see or miss many details of the human world and thus be unable to comprehend some of our concepts.

Another obstacle is that many objects are defined exclusively in terms of their uses in human culture and customs - there is no way to tell the difference between a sword, a scalpel, a knife, a machete etc unless you have an understanding of many particulars of some specific human society. Even the concept of 'cutting object' is dependent on some human-specific perceptions - for example, we perceive a knife as cutting bread, but we don't perceive a spoon as cutting the water when we take a spoonful from a soup, though it is also an object with a thin metal edge separating a mass of one substance into two separate masses (coincidentally, also doing so for consumption).

And finally, even the way we conceive mathematics may be strongly related to our biology (given that virtually all human beings are capable of learning at least arithmetic, and not a single animal is able to learn even counting), possibly also related to the structure of our language. Perhaps an alien mind has come up with a completely different approach to mathematics that we can't even fathom (though there would certainly be an isomorphism between their formulation of maths and ours, neither of our species may be capable of finding it).

And finally, there are simply so many words and concepts that are related to specific organisms in our natural environment, that you simply can't translate without some amount of firsthand experience. I could talk about the texture of silk for a long while, and you may be able to understand roughly what I'm describing, but you certainly won't be able to understand exactly what a silkworm is unless you've perceived one directly in some way that is specific to your species, even though you probably could understand I'm talking about some kind of other life form, it's rough size and some other details.

I disagree. Mental concepts have a high degree of correlation with the real world, otherwise we could not explain how we are so capable of navigating and manipulating the world to the degree that we do. So something that correlates with mental concepts necessarily correlates with things-in-the-world. Even things like emotions have real world function. Fear, for example, correlates with states in the world such that some alien species would be expected to have a corresponding concept.

>There is no way to tell the difference between a sword, a scalpel, a knife, a machete

There is some ambiguity here, but not as much as you claim. Machetes, for example, are mostly used in the context of "hacking", either vegetation or people, rather than precision cuts of a knife or a scalpel. These subtle differences in contextual usage would be picked up by a strong language model and a sufficient text corpus.

This is obviously a strong association from human mental concepts to real world objects. The question is if the opposite mapping exists as well - there could well be infinitely many non-human concepts that could map onto the physical world. They could have some level of similarity, but nertheless remain significantly different.

For a trivial example, in all likelihood an alien race that has some kind of eye would perceive different colors than we do. With enough text and shared context, we may be able to understand that worble is some kind of shade of red or green, but never understand exactly how they perceive it (just as they may understand that red is some shade or worble or murble, but never exactly). Even worse, they could have some colors like purple, which only exists in the human mind/eye (it is the perception we get when we see both high-wavlength and low- wavelength light at the same time, but with different phases).

Similarly, alien beings may have a significantly different model of the real world, perhaps one not divided into objects, but, say, currents, where they perceive moving things not as an object that changes location, but as a sort of four-dimensional flow from chair-here to chair-there, just like we perceive a river as a single object, not as water particles moving on a particular path. Thus, it may be extremely difficult if not impossible to map between our concepts and the real world back to their concepts.

> Fear, for example, correlates with states in the world such that some alien species would be expected to have a corresponding concept.

Unlikely, given that most organisms on earth have no semblance of fear. Even for more universal mental states, there is no reason to imagine completely different organisms would have similar coping mechanisms as we have evolved.

> Machetes, for example, are mostly used in the context of "hacking", either vegetation or people, rather than precision cuts of a knife or a scalpel.

Well, I would say hacking VS cutting are not significantly different concepts, they are a matter of human-specific and even culturally-specific degrees, which would be unlikely to me to be uniquely identifiable, though some vague level of understanding could probably be reached.

The more imminent question is more of engineering than philosophy - what does it take for GPT-3 to not make the mistakes it does? This would require it to have some internal model for why humans generate text (persuasion, entertainment, etc.) as well as the social context in which that human generated the text. On a lower level it also needs to know about cognitive shortcuts that humans take for granted (object permanence, gravity)

Basically, some degree of human subjective experience must be encoded and fed to the model. That's a difficult problem, but not an intractable one.

I suspect that if it did that, it would be able to write a very convincing fake paper about how it designed and tested an Alcubierre drive, and that the main clue about the paper being fake being a sentence such as “we dismantled Jupiter for use as a radiation shield against the issue raised by McMonigal et al, 2012”.

Or, to put it another way, the hardest of hard SciFi, but still SciFi, not science.

Imitating existing texts better is not conceptual understanding.

"Understanding" means you can explain why you made a decision. It means there exists a model with conceptual entities that you can access and make available to others.

What GPT-3 does is this: "I am given many answers to similar questions, and I build up a huge model that reflects these answers. If I'm given a new question, I come up with a response that's probably right, based on the previous answers, but there's no explanation possible."

Don't get me wrong - it's amazing! But it's not understanding anything yet.

Even humans have skills that we know but do not understand - like "walking" for most of us!

But on abstract question, we almost always have access to a complete set of reasons. "Why did you go back to the store?" "I left my bag there." "Why did you talk to that man?" "I know he's the manager, I'm a regular." "Why were you happy?" "I had my bag."

(Indeed, this is so common that people often "backdate" reasons for actions that didn't really have any reason at the time. But I digress.)

I suspect this is how many humans do arithmetic (especially considering how many people conflate numbers with their representation as digits). So if GPT-3 is doing that, that's pretty impressive.

http://gptprompts.wikidot.com/logic:math

That's not exactly what the GPT-3 paper [1] claims. The paper claims that a search of the training dataset for instances of, very specifically, three-digit addition, returned no matches. That doesn't mean there weren't any instances, it only means the search didn't find any. It also doesn't say anything about the existence of instances of other arithmetic operations in GPT-3's training set (and the absence of "spot checks" for such instances of other operations suggests they were, actually, found- but not reported, in time-honoured fashion of not reporting negative results). So at best we can conclude that GPT-3 gave correct answers to three-digit addition problems that weren't in its training set and then again, only the 2000 or so problems that were specifically searched for.

In general, the paper tested GPT-3's arithmetic abilities with addition and subtraction between one to five digit numbers and multiplication between two-digit numbers. They also tested a composite task of one-digit expressions, e.g. "6+(4*8)" etc. No division was attempted at all (or no results were reported).

Of the attempted tasks, all than addition and subtraction between one to three digit numbers had accuracy below 20%.

In other words, the only tasks that were at all successful were exactly those tasks that were the most likely to be found in a corpus of text, rather than a corpus of arithmetic expressions. The results indicate that GPT-3 cannot "perform arithmetic" despite the paper's claims to the contrary. They are precisely the results one should expect to see if GPT-3 was simply memorising examples of arithmetic in its training corpus.

>> So what changed? We aren't sure, but the speculation is that in the process of training, GPT-3 found that the best strategy to correctly predicting the continuation of arithmetic expressions was to figure out the rules of basic arithmetic and encode them in some portion of its neural network, then apply them whenever the prompt suggested to do so.

There is no reason why a language model should be able to "figure out the rules of basic arithmetic" so this "speculation" is tantamount to invoking magick.

Additionally, language models and neural networks in general are not capable of representing the rules of arithmetic because they are incapable of representing recursion and universally quantified variables, both of which are necessary to express the rules of arithmetic.

In any case, if GPT-3 had "figure(d) out the rules of basic arithmetic", why stop at addition, subtraction and multiplication between one to five digit numbers? Why was it not able to use those learned rules to perform the same operations with more digits? Why was it not capable of performing division (i.e. the opposite of multiplication)? A very simple asnwer is: GPT-3 did not learn the rules of arithmetic.

_________

[1] https://arxiv.org/abs/2005.14165

But I don't think I've ever spent time to learn a particular word - it's almost always enough to hear it in context once, and maybe get a chance to actually use it yourself once or twice, and you'll probably remember it for life.

If it's a word for a more complex concept (e.g. some mathematical construct), you may well need more time to actually understand the meaning, and you may also pretty easily forget the meaning in time, but you'll likely not forget the word itself.

I'd strongly bet against this. If it were true, SAT and similar vocabulary tests would be trivial to anybody who has taken high school English, and I think it is not the case that most people perceive the SAT to be trivial.

In fact, even GPT-2 gets close to that. Here's what I just got on Huggingface's Write With Transformer: Prompt: "Word dfjgasdjf means happiness. What is dfjgasdjf?" GPT-2: "dfjgasdjf is a very special word that you can use to express happiness, love or joy."

What takes time is all the learning a child needs to go through before they can be taught new words on the spot.

It's certainly impressive what GPT-3 can do, but it boggles the mind how much data went into it. By contrast a well-educated renaissance man might have read a book every month or so from age 15 to 30? That doesn't seem to be anywhere near what GPT could swallow in a few seconds.

When you look at how GPT answers things, it kinda feels like someone who has heard the keywords and can spout some things that at least obscure whether it has ever studied a given subject, and this is impressive. What I wonder is whether it can do reasoning of the causality kind: what if X hadn't happened, what evidence do we need to collect to know if theory Z is falsified, which data W is confounding?

To me it seems that sort of thing is what smart people are able to work out, with a lot of reading, but not quite the mountain that GPT reads.

You're ignoring the insane amount of sensory information a human gets in 30 years. I think that absolutely dwarfs the amount of information that GPT-3 eats in a training run.

So the two training sets are completely orthogonal, and the well educated renaissance man is somehow able to take a very small exposure to written words and do at least as well as GPT(n) in processing them and responding.

The renaissance man is very obviously not working solely based on a few years of reading books (or learning to speak/write).

Ah! you cry. Don't humans have some sort of hard-wiring for speech and language? Perhaps. But it is clearly completely incapable of enabling an untrained human to deal with written text. Does it give the human a head start in learning to deal with written text? Perhaps (maybe even probably). It demonstrably takes much less training than GPT(n) does.

But that is sort of the point of the comment at the top of this chain.

For example, it might take several paragraphs to wholly capture all the meaningful information in one image in such a way that it can be reproduced accurately. Humans, and many animals, process large amounts of data before they are even capable of speech.

The data GPT-3 was provided with pales in comparison. It is unclear whether these GPT models are capable of induction because it may be that they need more or better sanitised data to develop abstract models. Therefore they should be scaled up further until they only negligepbly improve. If even then they, still, are incapable of general induction or have inaccurate models. Then the transformer model is not enough or perhaps we need a more diverse set of data (images, audio, thermosensors, etc).

Humans get this through experience and time (recognizing patterns about which sources to trust) but there is nothing magical about it. Should be very easy to add this.

Many of those ideas are now complex enough that direct experience doesn't work. IE global warming, economics, various policies. Futhermore even direct (or near-direct experience such as video) is becoming less trustworthy due to technology like deepfakes and eventually VR and neuralink.

It seems to me that this problem of validating what is real and true might soon be an issue for both humans and AI. Are we both destined to put our future in the hands of weights provided by 'experts'?

Does anyone know if anyone has investigated these questions more seriously elsewhere?

I think logic is the easy part, we can already do that with our current technology.

The difficult part is disambiguating an input text, and transforming it into something a logic subsystem can deal with. (And then optionally transform the results back to text).

Not unlike a non-technical manager discussing tech!

It's not certain that this is always the case. In at least one case I've seen, if you give it question-and-answer prompts where you don't demonstrate that you will accept the answer "your question is nonsense", it will indeed make things up; but if you include "your question is nonsense" as an acceptable answer in the sample prompts, then it will use it correctly. See https://twitter.com/nicklovescode/status/1284050958977130497 .

It seems that we have a lot to learn about how to use GPT-3 effectively!

It doesn't know which facts about the world are true and which are fabricated except through the text it's trained on, but to a large extent neither do you. It suffices to merely have the ability to reason about it. The primary difference is that whereas you reason fairly competently from one perspective with one pseudo-coherent set of goals, GPT-3 reasons to a weak degree from all perspectives, privileging no view point in particular.

How important this ends up being depends on where the model plateaus. On one extreme, if it plateaus close to where GPT-3 already is, no harm done, it's a fun toy. On the other, if it scales until perplexity gets to far-superhuman levels, it doesn't matter at all, since you can just prompt it with Terence Tao talking about his latest discovery.

Naturally, it will land somewhere in the middle of these points. The question is ultimately then whether the landing point captures enough general reasoning that you can use it to bootstrap some more advanced reasoning agent. A sufficiently powerful GPT-N should, for example, be able to deliberate over its own generated ideas and sort the coherent reasoning from the incoherent.

That same sentiment could be equally applied to humans, and not just in the internet era, but throughout all of history. There will always be misinformation and "wrong" opinions out there. "It cannot tell right from wrong" is an accusation leveled against human beings every day. We can't even all agree on what is right and wrong, truth or untruth.

A true AI is going to have to wade through all that and make its own decisions to be viewed and judged from many different perspectives, just like the rest of us.

https://en.wikipedia.org/wiki/Kessler_syndrome

Speaking with GPT-3 also makes one realize how influenced its predictions of AI scenarios are by dystopian memes. In any conversation in which you are "speaking to the AI", the AI can go rogue.

There just aren't enough positive role models authors have written for AGI.

Ah one only needs to think back to Tay to know how these sort of things will end.

https://en.wikipedia.org/wiki/Tay_(bot)

(Imagine 4chan got a wind of this bot and retrained it, which is probably what happened...)

https://news.ycombinator.com/item?id=23896293

I guess books published will be more useful than reddit rants (depending on the application)

Lol

> GPT-3 is the first NLP system that has obvious, immediate, substantial economic value.

Text mining (relation extraction, named entity recognition, terminology mining) and sentiment analysis are billion dollar industries and are being directly applied right now in marketing, finance, law, search, automotive, basically every industry. Machine translation is another huge industry of its own. Chat bots were all the hype a few years ago. Let's not reduce the whole field of NLP to language generation.

When speaking of billion dollar investments, a billion dollar industry is not substantial. Google and Facebook's industries are advertising, at $600bn/year. Amazon's industry is retail, at $25tn/year.

What's opened up by the GPT-3 and its prompt-programming abilities is services, without qualification. That's $50tn/year, and capturing some tiny percentage of it is what's needed to make a billion-dollar investment worthwhile.

That said, I admit this isn't the mindset most people take when they read 'substantial'.

e: I changed the wording from 'substantial' to 'transformative', thanks!

The best applications are probably when error rates don't matter because a human is just going to use it for inspiration.

Prompt-programming is a standard features of all LMs. What differentiates GPT3 is not this application but the quality of the output. NLP companies such as chatbot providers and specialised search (patents, legal assistants, tenders) have been using domain-specific LMs for years.

So it's taking some slice of the $50tn pie.

AI dungeon is also powered by GPT-3, and it's quite snappy. I'm not sure why GPT-3 is seen as computationally expensive, but it seems workable.

And as mentioned elsewhere, inference for a trained model is much, much cheaper.

Every time I’ve looked at the start of the art in sentiment analysis, it seems to be suffering from the same issue that bag-of-words has with modifiers like “not”. Or is that more a theoretical problem than a practical one?

I appreciate this is a rapidly moving field, so my knowledge could easily be out of date.

"This isn't a terrible horrible restaurant that nobody should ever go to" seems like 1) it doesn't mean it's actually a good restaurant either 2) the writer might be joking and sarcastic and 3) this will be very rare in actual reviews.

Put another way, certain modifiers contextually go with certain words and sentiments, so why shouldn't state of the art systems lean on that fact, notwithstanding the strict application of grammar?

A voice from the back of the room piped up, "Yeah, right."

(https://www.ling.upenn.edu/~beatrice/humor/double-positive.h...)

However, industry typically relies on sentence- or document-level sentiment in, for instance, customer reviews with systems obtaining 80-90 F1-score which is very good. Often in e-commerce, aspect-based sentiment analysis is used in which a qualifying sentiment is attached to a target aspect, e.g. from a phone review systems extract: battery: large > positive; screen: dim > positive. You might have seen these types of reports in aggregate on review our e-commerce sites yourself.

It is however an ongoing field of research to process the scope of negation and uncertainty, but the field is making strides. State-of-the-art attention-based models obtain good scores on benchmark fine-grained sentiment analysis datasets such as the GoodFor/BadFor and MPQA2.0 of around 70% F1score [1]. This performance is nearly enough for commercial systems, depending on how you employ them.

1. https://link.springer.com/article/10.1186/s13673-019-0196-3

Don't get me wrong the hype is largely deserved because of the performance and engineering/research/funding effort required. Plus cool demos and media marketing from OpenAI helps a lot in spreading awareness.

OpenAI has definitely revolutionised the marketing for language models, no doubt. Let's wait and see if they manage to do the same for the economic valorisation.

Maybe it can be an adjuvant to human in some tasks but then so could existing technologies too, I guess?

I think most people would go: yes! How much does it cost?

But everyday people are used to getting 80% answers by search engines, I don't think many would pay for something that is "like google, but a bit better".

This seems the be current issue for many things that we used to pay for (dictionaries, encyclopaedias, newspapers, etc), and I'm not sure this would be different.

For simple queries like “who was the president of country X in YYYY” it’s probably just a bit better (if cached, of course, Google search is wicked fast).

But for more complex queries, Google is still remarkably dumb. Or downright insolent, ignoring my verbatim selection or quoted terms.

I’d pay good money for scarily smart search and a “grep for the web” service, that included JSON, CSS, JavaScript, comments, whatever. A toggle button for dumb/smart search

Just look at the "Google selling your data" being uncritically accepted when five minutes of thought would conclude it is the last thing Google would want (even a better search algorithim would find it hard to bootstrap on user base and comparable training time) or the casual John Yoo worthy torture of the definition of monopoly to include Goddamned Netflix when whining about FAANG monopolies. That level of generalization and stereotyping is like blaming the Amish for flying planes into the World Trade center because both are radical Sbraham religions.

Around Mumbai I know there is a crew that can really use UIMA, and there are other Indians I know who do intelligence and defense work.

The call for legislation neglects that there exists a global arms race to make this technology succeed. Legislation in one nation will simply handicap that nation. Against that backdrop, legislation is probably unlikely among the nations already leading in AI.

Is it though? If the goal is human-level AI, or hell, even rat-level AI, the evidence is pretty convincing that you should be able to train and deploy it without requiring enough energy to sail a loaded container ship across the Pacific Ocean. Our brains draw about 20 watts, remember. This suggests to me that no, in fact, scale will not get us "there".

https://www.forbes.com/sites/robtoews/2020/06/17/deep-learni...

This happens now.

Earth is about 4.5B years old, life is about 3.7B years old, multicellular life (including life with neural nets) is about 600 million years old. I don't think the span from microbe to multicellular organism counts in brain evolution.

Training GPT-3 cost $5m; running it costs .04c per page of output.

Yes and airplanes use much more energy to fly than a bird. What that got to do with the airline industry?

http://incompleteideas.net/IncIdeas/BitterLesson.html

[1] http://web.eece.maine.edu/~vweaver/group/green_machines.html

GPT-3 and whatever succeeds it are like late-stage ornithopters: very impressive feats of engineering, but not ultimately destined to lead us to where their creators hoped. We need the Wright brothers of AI to come and show us the way.

It's fun looking at things like GPT-3 and imagining how they could be used to build the surveillance AI at the heart of Person of Interest.

(If you haven't watched Person of Interest yet, here's my pitch for it: it's a CBS procedural where the hook is that an engineer built a secret, surveillance feed tracking AI for the government after 9/11 - but he cared about civil liberties, so he built it as an impenetrable black box. All it does is kick out the SSN of someone who is about to be either the victim or the perpetrator of a terrorist attack - which means government agents still have to investigate what's going on rather than taking the AI's word for it. "The Machine" also sees victims/perpetrators of violent crimes - but the government don't care about those. Finch, the machine's inventor, does - so he fakes his own death, hooks into a backdoor into the machine that gives him those SSNs and sets up a private vigilante squad to help stop the violent crimes from happening. So that gives you the "case of the week". Only it's actually an extremely deep piece of philosophical science fiction disguised as a case-of-the-week procedural, and as time goes on the plots become much more about AI, the machine, attempts to build rival machines, AI ethics and so on. It's the best fictional version of AI I've ever seen. The creative team later worked on Westworld.)

The AIs in the show very quickly turn into godlike characters with antropomorphic personalities and the real world issues of AI such as surveillance, economics and so on are all dealt with in very shallow fashion. I had the same issues with Westworld too. It turns from an AI premise into a classical Christian morality tale very fast. ("we need to suffer to become conscious").

Way smarter than you would expect from a CBS procedural!

-GPT-3, as is, should be the inner loop of a continuously running process which generates 1000s+ of ideas for "how to respond next" to any query, with a separate network on top of it as the filter which cherry-picks the best responses (as humans are already doing with the examples they are posting)

-Since GPT-3, as is, can already predict both sides of a conversation, it can steer a conversation toward a goal state just like AlphaGo does by evaluating 1000s+ of potential moves, lots of potential responses and counter-responses until it finds the best thing to say in order to get you to say what it "wants" you to say.

It seems ready to go as the initial attempt at the inner loop of both of these tasks (and more) without modification or retraining of the core network itself, no?

I think the jury is still out on this one. It certainly seems powerful, it's doing interesting things, and it's better in many ways than any system that has come before. But there's a different between exciting demos and transformative economic value.

It's too soon to be sure, but to me, the most interesting question is whether any valuable startups will be built on top of GPT-3. Some leading indicators before that are whether useful products are built on GPT-3, and whether early-stage startups built on GPT-3 get seed investment. I'm not aware of any of these yet but maybe latitude.io counts as one.

I'm guessing that not many people have read that book, because I'm seeing here and elsewhere even technical people talking about GPT-3 as if it's heralding the imminent advent of SkyNet. I get that transformers have a somewhat longer attention span than markov chains, but it's still a statistical language model. It can't even do the kind of planning or reasoning that early AI demos like SHRDLU could.

Human: I want to test your creativity. Please invent a new word and give its meaning.

GPT-3: Ok. Um... Tana means to hit someone with the intention to wound them.

Human: Please use the word tana in a sentence.

GPT-3: You are about to tana the man attacking you.

Human: Speak like a dwarf.

GPT-3: I ain't talkin' like a dwarf.

https://www.reddit.com/r/MachineLearning/comments/hvssqn/d_g...

Consider a language with the tokens "{[()]}" and the following grammar:

S := S S | '{' S '}' | '[' S ']' | '(' S ')' | <empty>

That is, "[()]" and "[]()" are valid sequences, but "[(])" or "))))" aren't. A child would quickly figure out the grammar if presented some valid sequences.

I generated all 73206 valid sequences with 10 tokens and used it as input to the RNN text generator code at http://karpathy.github.io/2015/05/21/rnn-effectiveness/. After 500,000 iterations I'm still getting invalid sequences.

Am I doing something stupid, or is a RNN text generator weaker than a child (or a pushdown automaton)? Is GPT fundamentally more powerful than this?

> After 500,000 iterations I'm still getting invalid sequences.

How frequently? If it's only the occasional issue it might be down to the temperature-based sampling that code uses, which means it will, with some small probability, return arbitrarily unlikely outputs.

Erm, citations needed. It's a giant, inefficient and shitty KNN model, which is capable of mimicking markov chains. Wonderful marketing achievement and not much else.

This is especially obvious on stuff like lesswrong, where AI is a big part of what they talk about. I tend to agree with the LW/SSC crowd about the negative effects of AGI, but they are being so hyperbolic about GPT-3.

This article clearly sits on the peak of inflated expectations in the hype cycle.

https://en.wikipedia.org/wiki/Hype_cycle

ML is undergoing a Cambrian explosion of use-cases (see my prior comment), almost all of this over an incredibly small time period, progress is accelerating, and many of these use-cases are incredibly high value. Scale is not proving a major stopper; Google's MoE experiments show that huge models are productizable, and small models work plenty fine too in restricted places, to the point where they're literally used to parse touch screen sense data in phones.

If you want to claim we're in for another AI winter, you need a vastly stronger argument than ‘something something hype cycle’.

I think the time for AI legislation is now - before FAAMG deploys something like the next-gen of GPT-3. Of course with the legislative lag that exists even for decade-old tech I don't have the highest confidence in this being achieved by a federal government in the state it is in now.

The knowledge databases could be used to generate what would essentially be "word problems" (in math classes), starting with simple things like "If I put three marbles in a cup, and then I take one out, and each marble weighs 20g, then the remaining marbles weigh 40g in total" and moving on to progressively more complex ones.

If that were to happen, then you'd see companies employing people to create templates which essentially convert databases into sentences/paragraphs, which can then be consumed by the GPT-like model.

It seems like this data would need to be used in a sort of pre-training step though, because you want the model to encode all the relationships, but you don't want it to learn to generate these types of concrete sentences, specifically.

Theoretical wishful thinking, I suppose, but I strongly believe that corp/govt scale ML research should be treated like advanced weaponry because it isn't a matter of if but when AI will be weaponized (whether the flavor of warfare is physical or informational).

Although of course as with weapons treaties - the major powers would likely tend to be selective in what they commit to limiting themselves in.

Maybe when we have a disaster directly attributable to AI, nations can get on-board with something like the BWC and CWC. Until then, be even more pessimistic. (If you want a fun if rather dry book to read on material technology developments that were in the pipeline a couple decades ago, some of which have come to fruition, as well as some policy recommendations for the technologies that aren't generally good, check out Jürgen Altmann's Military Nanotechnology.)

Beg pardon? Plastic guns have been banned in the US since 1988 https://en.wikipedia.org/wiki/Undetectable_Firearms_Act

I assume other countries have similar bans.

But more generally, as we all know, a ban without provisions for enforcement is useless. Compare to the CWC (Chemical Weapons Convention) which I point to as one of the best pieces of international "coming together" via treaty. It includes requirements that member countries submit to inspections from its enforcement body (OPCW) and furthermore that countries can request the OPCW inspects another member country if they suspect non-compliance. It also includes restrictions on transfer of various chemicals in order to incentivize non-member countries to become members so they can purchase chemicals for industrial purposes from other members.

¹ and bigger, if you're modeling this from assumptions where it's a problem at all -- not everyone thinks it is, "an armed society is a polite society" etc.

An AI-risk maximalist would believe AI is a near-term existential threat, with the prospect of total human extinction. In that scenario, the final backstop measure to a rogue country engaging in AI research is using nuclear weapons.

This... obviously... would be very bad. If it escalated to a full nuclear war, it would kill billions of people. But it would leave survivors, who wouldn't be interested in, or be able to, pursuing AI for decades or centuries. Better than the alternative.

The goverment is openly using autonomous systems to pilot drones, but what else are they leveraging AI for? Threat analysis? Logistics? Weapons optimization? PsyOps?

The DoE is openly a very large consumer of GPUs. What about the military?

The military wants: automated chat agents/web users that can be sent to dark web markets and hacker IRC channels and report back intelligence. Common sense inference from security and drone footage: predict who the killer is when watching a movie. Author deanonimization and cross-device tracking. Global-scale 99.9%+ accurate face detection.

The Dutch Intelligence Agency organizes a yearly competition with difficult codes to crack. [1] It is rare for someone to answer all questions correctly. The answers require logic, creativity, common sense, linguistics, causal inference, spatial reasoning, expertise, analysis, and systematic thinking. I bet the military would be mighty interested in an automated problem solver for that. And mighty scared some other country gets there first.

[1] https://www.aivd.nl/onderwerpen/aivd-kerstpuzzel

Not at all. The government can throw billions of dollars at a problem that, if solved, will never turn a profit or immediately benefit a business.

And the vision of what AI was becoming was voiced much earlier by Yudkowsky, whose Singularity Institute received funding from Thiel.

If anything, they heard what a few prophets were shouting. They saw some early demos in a startup pitch. They responded and DeepMind's work soon became as public as AI research is. That is to say, most people ignored it until the Google acquisition.

https://intelligence.org/2013/01/30/we-are-now-the-machine-i...

Thiel was an early backer of SI and attendee of the Singularity Summits that ran from 2006-2012.

https://en.wikipedia.org/wiki/Singularity_Summit

Soon after that, awareness of AI and superintelligence went mainstream, and we got FHI, FLI, etc.

I don't know if Thiel backs MIRI as he did SI. Arguably, he doesn't need to. He made his money on DeepMind and helped trigger a larger movement, and other institutions with a lot more resources, like Alphabet and MSFT, carry forward the torch.

It's probably not a state-of-the-art breakthrough at this point. Who knows what OpenAI has done in the intervening two years?

Here's an example: https://www.theverge.com/2016/6/2/11837566/elon-musk-one-ai-...

Read past the fluff and the skynet. He's telling us Google scares him and makes him concerned for democracy.

Very carefully. I mean, that's not much of an argument. Lots of stuff is successfully kept secret. The US managed to keep a lid on their surveilance for decades (iirc) before the lid got blown on that, and people used to give the same argument you are in that context, too.

What's the alternative? Do you think megacorps never keep illicit things under wraps for extended periods of time?

I guess you could use it in reverse and produce an upper limit on how many people could be involved in a conspiracy if you assume that it has been secret for five years.

That said, Elon Musk gives every impression of being a massive chatterbox who can’t keep his mouth shut even when its the SEC threatening to take Tesla away from him, so I very much doubt any conspiracy involves him.

[0] https://journals.plos.org/plosone/article?id=10.1371/journal...

Ahh... I'm not so sure; see the comment by blueyes. Had it been OpenAI's goal to engage in debate and regulation for this technology, they would have been vocal about that aspect of their work already.

"Not reading OP and spouting a non-quantitative conspiracy theory" = what you did.

but you'd have no way of enforcing any treaty so that's a moot point and they would know this.

I think making people aware of the importance of the control or value loading problems is a much better use of efforts.

As far as I can tell, this is what is going on: they do not have any such thing, because GB and DM do not believe in the scaling hypothesis the way that Sutskever, Amodei and others at OA do.

GB is entirely too practical and short-term focused to dabble in such esoteric & expensive speculation, although Quoc's group occasionally surprises you. They'll dabble in something like GShard, but mostly because they expect to be likely to be able to deploy it or something like it to production in Google Translate.

DM (particularly Hassabis, I'm not sure about Legg's current views) believes that AGI will require effectively replicating the human brain module by module, and that while these modules will be extremely large and expensive by contemporary standards, they still need to be invented and finetuned piece by piece, with little risk or surprise until the final assembly. That is how you get DM contraptions like Agent57 which are throwing the kitchen sink at the wall to see what sticks, and why they place such emphasis on neuroscience as inspiration and cross-fertilization. When someone seems to have come up with a scalable architecture for a problem, like AlphaZero or AlphaStar, they are willing to pour on the gas to make it scale, but otherwise, incremental refinement on ALE and then DMLab is the game plan. Because they have locked up so much talent and have so much proprietary code and believe all of that is a major moat to any competitor trying to replicate the complicated brain, they are fairly easygoing.

OA, lacking anything like DM's long-term funding from Google or its enormous headcount, is making a startup-like bet that they know the secret: the scaling hypothesis is true and very simple DRL algorithms like PPO on top of large simple architectures like RNNs or Transformers can emerge and meta-learn their way to powerful capabilities, enabling further funding for still more compute & scaling, in a virtuous cycle. And if OA is wrong to trust in the God of Straight Lines On Graphs, well, they never could compete with DM directly using DM's favored approach, and were always going to be an also-ran footnote.

While all of this hypothetically can be replicated relatively easily (never underestimate the amount of tweaking and special sauce it takes) by competitors if they wished (the necessary amounts of compute budgets are still trivial in terms of Big Science or other investments like AlphaGo or AlphaStar or Waymo, after all), said competitors are too hidebound and deeply philosophically wrong to ever admit fault and try to overtake OA until it's too late. This might seem absurd, but look at the repeated criticism of OA every time they release a new example of the scaling hypothesis, from GPT-1 to Dactyl to OA5 to GPT-2 to iGPT to GPT-3... (When faced with the choice between having to admit all their fancy hard work is a dead-end, swallow the bitter lesson, and start budgeting tens of millions of compute, or between writing a tweet explaining how, "actually, GPT-3 shows that scaling is a dead end and it's just imitation intelligence" - most people will get busy on the tweet!)

Explainability in AI is really overlooked and often skipped over as there is little progress in this area. GPT-3 is essentially GPT-2 + tons of data, compute and parameters and yet it still cannot explain itself as to why it can generate 'human-level' text, much like how AlphaGo can't explain why it performed move 37. Not discrediting these achievements, but explainability is just as important in these AI models.

Once you have an AI-based 'auto-pilot' in any vehicle, the importance of AI explainability will haunt manufacturers when the regulators would want them to explain why this 'AI' took this decision and they're unable to explain this.

I hope GPT-4 isn't just going to be GPT-3 + 1000x the data. Otherwise nothing would have changed here other than the parameters and data.

We already see this in superhuman stock algorithms. You can "debug" them, in the sense that for a given trade, it can tell you what signals provoked it. But they don't make any sense: it saw rainfall in the Amazon tick up, the price of beef in Russia tick down, and the UK call a snap election, so it bought more GE stock.

You could... theoretically... write a story that connected those dots, but it will either be facile or nonsensical. That's because the model of the market the algo has is bigger and more complete than anything a human can have. It's drawing a straight line through some upper-dimensional manifold that you can't comprehend.

It can't explain what it's doing to you anymore than you can explain "algorithmic stock trading" to a three year old child. You can say what the outcome was, but you can't explain it in such a way that the kid could replicate the performance.

It's definitely true that the RTX 2080 Ti would be more efficient money-wise, but the Tensor Cores are not going to get you the advertised speedup. Those speedups can only be reached in ideal circumstances.

Nevertheless, the article as a whole makes a very good point. The thing that is most scary about this is that it would become very hard for new players to enter the space. Large incumbents would be the only ones able to make the investments necessary to build competitive AI. Because of that, I really hope the author isn't right - unfortunately they probably are.

What is it’s economic value? What does it transform? I’ve been trying to figure that out since I heard about it.

Anyone have any ideas?

It's good enough to actually start replacing a lot of customer service jobs. Not just being a shitty annoyance like current bots but being useful in that it will be as flexible as a human, directing you to good help via vague terms, potentially being smart enough to refer you higher up if necessary.

Getting rid of all those screening call center employees is potentially very lucrative.

GPT-3 is taking a graph-structured object ("language" inclusive of syntax and semantics) over a variable-length discrete domain and crushing it into a high-dimensional vector in a continuous euclidean space. That's like fitting the 3-d spherical earth onto a 2-d map; any way you do it you do violence to the map.

I think systems like GPT-3 are approaching an asymptote. You could put 10x the resources in and get 10% better results, another 10x and get 1% better results, something like that.

You might do better with multi-task learning oriented towards specific useful functions (e.g. "is this period the end of a sentence?") but the training problem for GPT-3 is by no means sufficient for text understanding.

GPT-3 fascinates people for various reasons, one of them being almost good enough at language, lacking understanding, faking it, and being the butt of a joke.

If GPT-3 were a person with similar language skills and people blogged about that person, mocking it's output, the way we do with GPT-3, people would find that cringeworthy. Neurotypicals welcome it as one of their own, and aspies envy it because it can pass better than they can.

At $2 a page it can replace richmansplainers such as Graham and Thiel who never listen. It's not a solution for folks like like Phillip Greenspun who read the comments on their blogs.

For that matter, it may very well model the mindlessness of corporate America: if you accept GPT-3 you prove you will see the Emperor's clothes no matter how buck naked he is. AT&T executives had a perfectly good mobile phone business: what possessed them to buy a failing satellite TV business? Could GPT-3 replace that "thinking" at $2 a page? Such a bargain.

For instance, understanding language requires some of the capabilities of a SAT solver. This was something everybody believed in 1972, but today is denied.

Fundamentally "understanding" problems require the ability to consider multiple alternative interpretations of a situation, often choose one or work with the incomplete knowledge you have.

Back in the 1970s we had intellectually honest people like Richard Dreyfus writing books like "Things Computers Can't Do" that describe many specific ways the architecture at the time fall short. People on GPT-3 are working in a way that is academically valid (able to make results that are meaningful to a community) but from engineering it is like building a bridge with one end or a tall tower that carries no load.

GPT-3 has a structural mismatch with the domain it works in. Unlike early medical diagnosis systems like MYCIN, it is never a doctor, it just plays one on TV and it does the "passing for neurotypical" terrifyingly well.

The secret of GPT-3 is that people want to believe in it. Somebody will have it generate 100 text snippets and they will show you the three best. Your mind makes up meaning to fill up for its mindlessness. When this was going on with ELIZA in 1965 people quickly understood that ELIZA was hijacking our instinct to make meaning.

For some reason people don't seem to have that insight today, and it bothers me why that is. Back in the 1980s they had a lot of fear about compressing medical images because it could lead to a wrong diagnosis. Today you see articles in the press that are completely unquestioning that a neural network that has been trained to hallucinate healthy and cancerous tissues will always hallucinate the right thing when you are looking at a patient.

To me it seemed like the opposite. They are essentially working without any hypothesis of how their model actually works, without any model of the way it actually learns or the way it produces the results that it does, and instead placing blind trust in various metrics that are improving.

They are treating this as an engineering problem - how can we make the best human-sounding text generator - and not like a traditional research problem. GPT-3 has not taught us anything about anything except "how to generate text that seems human-like to humans". We have no firm definition of what that means, we have no idea of why it works, we have no idea of any systematic failures in its model, we know next to nothing about it, other than its results on some metrics.

Imagine the same applied to physics - if instead of inventing QM and Relativity or Mechanics, physicists got it in their head to try to feed raw data into a black box and see how well it predicts some observed movements.

In fact, this would be a pretty interesting experiment: how large would a deep learning model that could accurately predict what mechanics predicts get, given only raw data (object positions, velocities, masses, colors, surface roughness, shape, taste etc.)? Unfortunately, I don't think anyone has been interested in this type of experiment, because it is not useful from an engineering (or profit) perspective.

In fact, this would be a pretty interesting experiment: how large would a deep learning model that could accurately predict what mechanics predicts get, given only raw data (object positions, velocities, masses, colors, surface roughness, shape, taste etc.)? Unfortunately, I don't think anyone has been interested in this type of experiment, because it is not useful from an engineering (or profit) perspective.

Isn't that what googles alphafold is doing pretty much?

https://deepmind.com/blog/article/AlphaFold-Using-AI-for-sci...

and it seems GPT-3 formed concepts related words together without being asked, its not picking the next best word strictly as a matter of statistic probability. So why wouldn't that apply to physics simulations / chemistry etc?

feed it chemical formulas and balancing equations from old chem 101 textbooks and it will fill in the blanks and start teaching itself how those things relate just by being corrected enough, then you can see if it has any predictive value.

My point is that an interesting scientific question is: "is the huge size of the GPT-3 model intrinsic to the problem of NLP, or is it an artifact of our current algorithms?"

One way to answer that is to apply the same algorithms and methods to mechanics data generated from, let's say, classical mechanics; and compare the generated model size with the size of the classical mechanics description. If the model ends up needing roughly the same amount of parameters as classical mechanics, then that would be a strong suggestion that NLP may intrinsically require a huge model as well. Otherwise, it would leave open the hope that and understanding can be modeled with fewer parameters than GPT-3 requires.

Your examples are still in this realm of engineering - trying to apply the black box model to see what we can get, instead of studying the model itself to try to understand it and how it maps to the problem it's trying to solve.

On the other hand, we should acknowledge that humans are also structurally wrong for most of the domains we work in. A general-purpose neural network isn't a great tool to diagnose cancer, certainly - but it doesn't have to be great to exceed some radiologist's general-purpose light detectors. I think GPT-3 starts to edge into the territory of demonstrating Dreyfus was substantially wrong, and recognizably computer-like architectures are fully capable of doing abstract reasoning.

(That's not to knock on Dreyfus! Other voices in his era were optimistic to an absurd degree, and "come on guys our computers aren't that smart" was a very necessary response.)

Adding to this, most metrics can't be embedded well in euclidean space. Even something as simple as 4 nodes in a loop using the shortest path as your metric -- there's a minimum amount of error for any embedding into any euclidean space, and it's well above 0.

It's a bit surprising to me that we've hobbled along this far shoving square pegs into round holes wrt NLP since fundamentally that can't be fixed with more parameters and bigger coprocessors. It seems that some interesting features of natural language are actually euclidean.

Are you talking about a 2d Euclidean space, or about any number of dimensions?

For that example it suffices to show it in 3D since any euclidean embedding of n+1 points can be isometrically embedded in an n-dimensional space, so if a 3D embedding with error E doesn't exist then neither does an ND embedding for any N>3.

Finding the minimum error requires a tad more effort, but it's not too bad to show that no embedding has 0 error:

Take a cycle a->b->c->d->a where every edge has length 1. Suppose a 0-error embedding exists. Points (a), (b), and (c) must be embedded colinearly, and then the only possible location for point (d) satisfying the distance requirements |a-d|=|c-d|=1 is precisely wherever we placed point (b), but then point (d) can't possibly have distance 2 from point (b).

By itself that doesn't show that infinitely small errors are impossible, but that assertion is also true in practice.

For instance, I can go from Sydney to Sao Paulo going either East or West on the territory, but on the 2-d map you can only draw one path. You can map one point on the territory to multiple points on the map but that is itself a mismatch with the territory.

A model like WordNet, for instance, loses information about out-of-dictionary words. Words like "if", and "and" and "bit" are in the dictionary, maybe 95% of the words in your text are in the dictionary, but 50% of the meaning is in the out-of-dictionary words. There are things like FastText that do a little better (have a fighting chance of guessing at latin and greek words smushed together) but still make mistakes at an early phase of analysis which can't be recovered at later stages of analysis.

For a domain such as medical notes (say abstract of a medical case study) you might want to answer some question like "Did the patient die?" or "What code would I bill insurance for this?" and much more than half the time an embedding throws out an piece of information which is essential to computing the right answer as opposed to guessing at the answer.

Are there businesses that have a tremendous needs for the possibilities it provides?

I've seen use cases that some NLG companies provide like sports and stock summaries but what world should I imagine where this is transformative?

I'd note it's rare that cost of scaling a computing project is a linear growth function.

100x-ing an AI project could be 1100x cost.

I'm curious what people think are the next stages of AI research that companies are working on... Is it Probabilistic Graphical Models? Is it Probabilistic Programming? Is it knowledge graph extraction from text? Is it something else? Curious what people think...

There are also other efforts using different types of probabilistic programming as well as symbolic and neural net combinations.

There's another link on one of the first few HN pages right now about dreaming. I think that dreaming gives one a lucid demonstration of some of the capabilities that we need to emulate if we are going to have human-like intelligence. AI will need to be able to visualize new situations, basically like on-demand, flexible simulations of mashed-up possibilities, involving things like physics and psychology etc.

I think we almost need the AI to have something like a 3d gaming engine with physics, but also it can effortlessly conjure up AI agents in this simulation, but also, many of the physics rules and behaviors of the AI agents are automatically learned with only a few examples. This is the type of capability that allows humans (and some other animals) to adjust so readily to new situations.

I speculate that there may be some representation or type of computation that has not been invented yet which facilitates both the simulation-type data and also the abstractions over it, all the way up to language, in a more seamless way than has so far been described. I saw a paper talking about the symbol grounding problem in terms of everything being categories, but really in the end it was broken down into something kind of like Lisp + probabilistic programming, and it seemed to not really have sufficient granularity to really do justice or properly integrate sense data. Certainly not in a seamless or truly unified way in my opinion. Although I guess I don't really understand category theory.

Seriously? No other piece of machine learning has had economic value? How short sighted.

This statement is unbelievably ignorant of history. Just picking one random example out of a hat: planning and scheduling systems have had a profound impact on the manufacturing and shipping industries for many decades now.

The code is non trivial but if you wait someone reimplements it .

The dataset is also nontrivial because they probably cleaned the data which.

It’s a valuable asset but it’s not like someone couldn’t reproduce it.