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The Sell ∀ ∃ as ∃ ∀ Scam
- _Microft 3y agoSince there are certainly people unfamiliar with the symbols, here is the Wikipedia article on them: https://en.wikipedia.org/wiki/Quantifier_(logic) https://en.wikipedia.org/wiki/Quantifier_(logic)
- nirvdrum 3y agoThe article provides abbreviated definitions that might be less confusing to follow than the comprehensive Wikipedia page. But, I agree that calling them out a bit more clearly in the article would have been helpful.
- LegitShady 3y agoI just want to know what they're called if someone were to say them out loud. Upside down A and backwards E probably aren't accurate.
- _Microft 3y agoI understand this as question on how these symbols are pronounced (the names are in the article if you are curious): actually "for all/every <...>" and "there is/exists (at least one) <...>". Example: ∀ x∈ℝ\{0} ∃y∈ℝ : x*y=2 "For all values x from the real numbers excluding zero, there is a value y from the real numbers so that x*y equals 2."
- bflesch 3y agoThis is a clever argument
- smitty1e 3y agoIt would be improved by a bit of additional editing in The Famous Article.
- simondotau 3y agoIt is a superficially clever argument. It's not actually a clever argument because it elides the existence of "but easier" or "but faster" as mechanisms for valid business models.
- adamnemecek 3y agoThis argument is disingenuous. Hyperparameter optimization is not in the same category as prompt engineering, like at all. Also no one claims like half of the things the article claims people clain.
- version_five 3y agoHe, imo correctly, puts them both in the category of extra degrees of freedom that can allow the user to overfit and get results that appear more impressive than the underlying reality about how the model has generalized.
- jameshart 3y agoBut generating new prompts for GPT is incredibly cheap! You just rephrase and ask again. That there are prompts which generate impressive results with GPT is the point. Because anyone can generate prompts - and get impressive results. Whereas hyper parameter tuning is expensive. A system that generates good results with the right tuning doesn’t tell you much about your ability to use it to generate good results, because it will be hard to try lots of different tuning approaches to discover if you can get a useful result. These things seem not remotely comparable.
- akkartik 3y agoThink about when you use GPT to generate prompts, something that seems to be growing common. Once you have a pipeline like that, changing the (meta)prompt can be expensive. You designed the pipeline thinking you only have to come up with the meta-prompt once, and it would work from now on. But you find you have to keep tuning your Kubernetes/upgrading your dependencies/tweaking your meta prompt. "Scam" is overblown, but I think OP is right to warn of a possible future issue. It's an issue endemic to all software, so not something worth calling out recent AI advances in particular for. But it seems something we should all be trying to get better about. Monitor the ongoing costs of the systems we create. Do they really pay for themselves or are we waiting for a Godot that never arrives?
- 3y ago
- sebzim4500 3y ago>To conclude: one must have different standards for developing systems than for testing, deploying, or using systems. Or: testing on your training data is a common way to cheat, but so is training on your test data. Isn't this already a solved problem? Every reasonable paper on ML separates their test data from their validation data already.
- version_five 3y agoThat in no way prevents overfitting though hyperparameter optimization / graduate student descent. All the common benchmarks, by definition of being a common benchmark, are susceptible to overfitting
- sebzim4500 3y agoThe idea is that you do all your hyperparameter optimization with the test data and then only run through the validation data once before you submit your paper.
- ummonk 3y agoThat's why you split it 3-way into train, validation, and test datasets, to ensure you didn't use too many hyperparameters and overfit to the validation data.
- version_five 3y agoIt's a good point, I'd consider it (overfitting) a pitfall or common mistake in ML rather than the only mode. I'd agree that most ML models and almost all state-of-the-art are over-fit to the point of being useless, but that's not an inevitability.
- dvt 3y agoI can walk and chew bubble gum at the same time: on one hand, yes, there's certainly a lot of Kool-Aid being drank by the AI folks. Even on HN, I constantly argue with people that genuinely think LLMs are some kind of magical black box that contain "knowledge" or "intelligence" or "meaning" when in reality, it's just a very fancy Markov chain. And on the other hand, I think that language interfaces are probably the next big leap in how we interact with our computers, but more to the point of the article: > To conclude: one must have different standards for developing systems than for testing, deploying, or using systems. In my opinion, you unfortunately will never (and, in fact could never) have reliable development and testing standards when designing purely stochastic systems like, e.g., large language models. Intuitively, the fact that these are stochastic systems is why we need things like hyper-parameters, fiddling with seeds, and prompt engineering.
- charcircuit 3y ago>contain "knowledge" or "intelligence" or "meaning" when in reality, it's just a very fancy Markov chain These are not mutually exclusive. If you have a Markov chain that 100% of the time outputs "A cat is an animal", then it has knowledge that a cat is an animal.
- version_five 3y ago$ yes "a cat is an animal" Does `yes` also have "knowledge"?
- zacgarby 3y agono, but `$ yes "a cat is an animal"` does (if you know how to interpret it)
- maxbond 3y agoIf it requires interpretation, than it is the "yes+human" system that has the knowledge. What really happened here is that a human wrote down, "a cat is an animal," and then another human read it, understood it, and believed it. And so the knowledge moved from one human to another. `yes` was only a conduit for that information to travel through.
- badloginagain 3y agoIf I understand correctly, the meat of the argument is "that is a system for every (∀) task, there exists (∃) a setting that gives the correct answer for that one task." My understanding of this (correct me if I'm wrong) is that the scam is convincing users that GPT-X can do anything with say, the correct prompts. This argument misses the mark for me. It's not that it solves all the problems, it's that the problems it does solve is economically impactful. Significantly economically impactful in some cases- obvious examples of call centers and first-line customer support.
- cglong 3y agoI ended up, ironically, asking ChatGPT for a summary of the article. That's the argument it derived too.
- jameshart 3y agoRight #1 it’s not clear that this ∃ ∀ construction is a fair representation of what is being ‘sold’ by GPT-x #2 it’s also not clear what this proposed inverted formulation (∀ ∃) that describes what the author thinks GPT actually is even means. For every setting there exists a task that it answers? Does that even make sense?
- justeleblanc 3y agoPretty sure you should read "for every task there exists a setting".
- jameshart 3y agoBut what's the inverse?
- 317070 3y agothere exists a setting which will work for all your test points. The caveat of the author is (I think) that if you have a task, you collect a set of points (questions) on which you will test this task. Then you tune your setting (prompt) to start working for your test point (questions). After that procedure, you do not know if that prompt solves the original task. You might have overfitted to your test points. And by repeatedly doing this overfitting for various tasks, you are not gathering evidence that a good setting truly exists for all tasks
- scrame 3y ago> Also, as they the hyper-parameter selection fully to the users, they become not falsifiable. If they didn’t work, it is because you didn’t pick the right hyper-parameters or training procedures. See Also: Scrum certification.
- deleted 3y ago[deleted]
- alanbernstein 3y agoMy biggest takeaway from the article is my new favorite word: cryptomorphic, meaning equivalent, but not obviously so.
- _royadar 3y agoThanks for pointing that out, I'll add it to the list of words I'm supposed to use to sound smart. However, I did find the main argument compelling, through my own waste of time (yes I learned it the hard way) I've come to acknowledge that the tradeoff for prompting GPT in order to get a valuable answer, is just not worth it. However, it does seem that most of the people are intrigued by "it can answer anything with the right prompt" promise, and they devote a lot of time in order to fulfill it. You cannot trust GPT, you cannot rely on it and it can't replace anyone, until it learns to prompt itself. I think what this article really implies is that - we the humans are doing the quality assurance for the GPT answers, if we take that out of the equation, and we don't give it quality prompts, etc. It's worth nothing.
- hanoz 3y agoI'm no linguist but it seems to me that that word doesn't really work for that definition. It sounds like it should pertain to hidden form, not hidden similar form, like say, cryptoisomorphic.
- alanbernstein 3y agoYou're right there. It's amusing to me because I suspect it is difficult or impossible to meaningfully define "obvious". In my head, it matches "cryptozoology", where the "crypto" denotation kind of includes "pseudoscience". But also "hidden shape" has a loose implication of "hidden equivalent shape".
- javajosh 3y agoThe correct term is "maybemorphism".
- mmaunder 3y ago“Or: testing on your training data is a common way to cheat, but so is training on your test data” The need to hold back training data for testing, and issues around testing using variants of training data versus real world data are well known.
- afro88 3y agoThe author says that the builders of these systems are also the ones that run the scam. And they deliberately build it in a fashion that enables the scam. Seriously? This is a gripe with sales and marketing. A tale as old as time.
- TZubiri 3y agoAre compilers a scam as well? There exists a program every problem you have, you just have to find the code.
- tialaramex 3y agoCrucially there is not a program for every problem. Many (presumably "Almost all" in a mathematical sense) problems are Undecidable and so a program can't do that.
- alextheparrot 3y agoWhy are we not OK with the program producing the undecidability result?
- cpitman 3y agoBecause it is not generally possible to determine that the result is undecidable, when maybe searching a little longer would find it.
- eternalban 3y ago> There exists a program every problem you have, you just have to find the code. That proposition is that of general programming languages. The compiler's proposition is that it will take your programs and faithfully per the language specification produce object code suited for its intended deployment platform. So > Are [general purpose programming languages] a scam as well? Well, kinda. Most languages come with a hype brigade and before you know it, darling of the day is "the only rational choice" for writing. Also, many b.s. about solving problem x whereas they only hand the problem over to something else, sometimes the programmer. So (mildly) yeah.
- frankreyes 3y agoNot at the moment. but there was a time that some thought that we should be able to produce a general purpose programming language, to solve every problem. One size fits all. It took more than 40 years to realize that was never going to be. There's a joke about that in commit strip: https://www.commitstrip.com/en/2016/08/25/a-very-comprehensive-and-precise-spec/ https://www.commitstrip.com/en/2016/08/25/a-very-comprehensi...
- bitL 3y agoThis has always been present in subfields of AI. For example, in classical computer vision, one had to figure out specific parameters working just for a single image or video scene by hand. Machine learning can in theory at least make these parameters learnable at the cost of complexity.
- primax 3y ago[flagged]
- version_five 3y agoThis isn't reddit, "butthurt" and similar low brow shit turns this into the same kind of cesspool. Make an adult argument or don't make one.
- coding123 3y agoThis is like a horrible way to describe something.
- analog31 3y ago>>> Build a system that solves problems, but with an important user-facing control. ... >>> Convince the user that it is their job to find a instantiation or setting of this control to make the system work for their tasks. By golly, you just described playing the cello.
- civilized 3y agoBut the cello sounds good.
- tobyjsullivan 3y agoAnd if it doesn’t sound good, it must be the player’s fault, not the instrument’s. For the cello certainly sounded good on another piece.
- ChainOfFools 3y agobut the cello isnt being spun by the musical instrument press (and cottage industry of self-appointed advisors to wealthy royal music patrons) as a self-playing enchanted wonder capable of performing any piece, real or imagined, with limitless creative and technical skill and requiring little more guidance than vague requests hummed out of tune by its handler.
- mardifoufs 3y agoI'm afraid to ask, but what's the lore behind the cello? I'm completely clueless about music, sadly enough.
- mcv 3y agoFretless string instruments like the cello and the violin are notoriously hard to even get a good note out of. Beginners just learning to play tend to produce a sound not unlike a cat being strangled. Playing a cello takes real skill. Even producing a single good note takes practice. Whereas on a piano, producing a good note is trivial. The skill comes in playing a complex requiring 10 fingers. So I guess the author of the article expected these new AI systems to be more like a piano, but ue got a cello instead.
- PaulHoule 3y agoChatGPT itself is in on the scam. The way i think about it is that ChatGPT is already superhuman at bullshitting, many people want to give it credit for being more capable than it really is. it is interesting to postulate if it is the “most likely word” heuristic that leads to this behavior (e.g. never says anything that startles people) or HFRL training systematically teaching it to say what people want to hear.
- PopePompus 3y agoGPT-4 is very useful to me right now, for small programming projects. Though it can't write an entire program for a nontrivial project, it is good at the things I hate doing, like figuring out regular expressions and SQL commands. I smile broadly at the fact that I may never have to write either of those things again. And GPT-4 knows of the existence of countless software libraries and modules that I've never heard of. It doesn't always use them correctly, but just alerting me to their existence is tremendously helpful. It can usually answer questions about APIs correctly. I have no idea what impact LLMs will have on the world as a whole, but they will clearly revolutionize coding.
- eska 3y agomany people want to give it credit for being more capable than it really is I believe you’re referring to the Barnum Effect
- rgbrenner 3y agoWait, how does this scam work for OpenAI? The product is free to use. Also they haven't claimed GPT is an AGI or that it can solve all your problems.
- 1023bytes 3y agoSo as long as it doesn't automagically solve any given problem it's a scam?
- LastTrain 3y agoAnd convince the user to validate the solutions to other user’s problems while they’re at it.
- simondotau 3y ago> Convince the user that it is their job to find a instantiation or setting of this control to make the system work for their tasks. As opposed to convincing the user that it is their job to brief a suitably qualified contractor or employee to make a company perform the required work? ∴ humans are a scam
- precompute 3y agoIt is difficult to conclude this is true, and it will be even more difficult in the future because tech like "AI" has the ability to almost completely saturate the amount of data any human can ingest. The relationship will soon be symbiotic, with everything showing the extent of our progress... in the same manner we can date movies by the kinds of phones they use. Many plots will become stale, many worldviews will be condensed to something that supports "AI" and the offending branches will be snipped. The only way to really forget this limitation is to be myopic enough to disregard everything else. With the way the internet is going, I'm sure one day these LLMs will be heralded as "free" and "open" media, their fuzzy recollections the only records we will have of the past, and extensive use will essentially morph civilization in their own image.
- ummonk 3y agoThe author is saying ! ∃ ∀ and selling it as ! ∀ ∃. He's the one scamming. AI researchers are rather upfront about the need to use fine-tuning and prompt engineering for current AI models. As to random forest models, wasn't the whole point of hyperparameter sweeps to remove the need for manual hyperparameter selection?
- renewiltord 3y agoUltimately, this is a tool like Google. If using a search engine doesn't work for you for some task, then it's not the tool for the job. You even get to trial it for free. You shouldn't use LLMs if you can't make them work. I can make them work for me for many things. That makes them useful for me.
- ftxbro 3y agoIn my opinion it's a weak take that only got so upvoted and commented on hacker news because it has the existential and universal logical quantifier symbols in the title and also because it uses the chiasmus rhetoric device both in the title and at the end of the article. The argument is that several technologies don't work 'out of the box' and you have to tweak their settings for each problem that you face, and that this means it's a scam. For example you have to change prompts in LLMs or change hyperparameters in other machine learning solutions. This argument is some combination of not insightful and not true.
- version_five 3y agoI'm happy to have learned about the chiasmus thing, I hadn't seen that before. In general, better writing, including the use of rhetorical devices, is what gets attention. It's better than some clickbait. Personally I though the use of symbols was a distraction (spurred some useless comments) but maybe that did attract people. My take is that the argument boils down to saying that overfitting is a thing in LLMs too, but it comes in at the prompting phase. That's almost obviously correct, but I hadn't seen it voiced explicitly before. The addition of "scam" in the title was a bit of hyperbole, which is probably responsible for a lot of the views and responses. Anyway, it seems to have triggered a lot of very reactive and defensive takes, which isn't surprising but also isn't really necessary.
- ftxbro 3y ago> "the use of rhetorical devices, is what gets attention. It's better than some clickbait." I feel like 'using rhetorical devices to get attention' is a pretty good definition of clickbait.
- version_five 3y agoIn a vacuum yes, but the rhetorical device in question isn't really a standard clickbaity one is it, it's more literary. It's also logical for good writing to attract more views.
- 3y ago
- CapsAdmin 3y agoAre general purpose tools scams? An operating system does many things. A general purpose language when contrasted with a domain specific language. A human programmer thinking that they're a wizard that can solve all problems. However I get the sentiment I think, it's a little bit like snake oil and alternative medicine. They promise to solve all your problems. What's interesting to think about is who it is to "blame". The developers of the product, the sales people of the product, the wild imagination of the buyer thinking the product can solve everything, or maybe the wild imagination of the developers behind the buyer. However if the seller can set off a wild imagination in the buyer, maybe you've successfully scammed them.
- narag 3y agoAre general purpose tools scams? No. But I'm old enough to remember how the people that bought the first IBM PCs complained that the computers were useless. Of course they needed much more than the very expensive machines: software, training, peripherals, backups... They were scammed by the vendors, even if the product was not a scam.
- CapsAdmin 3y agoIn my experience I've felt that sales people tend to act like vendors in your anecdote, just within a single company.
- version_five 3y ago> Are general purpose tools scams? No code / low code might be a good analogy here
- scandox 3y agoI think there has always been a great deal of Stone Soup in the software world. We are often promising solutions that will work as long as all the ingredients are supplied in the form of processes delivered by the customer. Strangely though the very promise of the magic stone does allow solutions (soup) to emerge.
- j-bos 3y agoSo great software is like great medicine? Half solved in product half solved in placebo
- henearkr 3y agoTo be fair, on the pure logic side (forgetting about what the link is really about, i.e. selling scams), it is true that any ∀ ∃ can be turned into a ∃ ∀. It's called skolemization.
- skybrian 3y agoI'll try to explain this better. Sometimes you hope to get a system that works without constant tweaking. Once you've adjusted it properly, it should work without adjusting the settings. So there's one setting that works for all the inputs you care about. But instead you have a system that you have to adjust all the time. For every input, you can get it to work. But you're never done tweaking. This is actually fine if you're expecting the user to do some work. Consider doing a Google search, seeing what you get, and then modifying your query when you didn't get what you want. It sucks if you're hoping to go into fully-automatic mode. You didn't get that, you got an interactive application. (Or maybe the tweaks aren't reasonable for the user to make themselves, so you got nothing?) So the scam, if there is one, is selling something as fully-automated when it's not. It's a tool that requires a person to work with a machine, and you're never going to be able to take the human out of the loop. (Let's say you're hoping for a driverless car but you never quite get there.) Often, it's not the end of the world. An interactive tool can be very valuable! But you need to set your expectations appropriately.
- 8note 3y agoThe sell for all there exists as there exists for scam? What? Better title?
- bvoq 3y agoIn the second part of the essay the writer mentions the statisticians fallacy. "Adding more hyperparameters leads to overfitting." This is not true with large neural networks anymore and is part of the magic.