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Yann LeCun on GPT-3
- FartyMcFarter 6y agoIt would seem that this can be easily analysed scientifically. To give a simple example: if, hypothetically, someone thought that GPT-3 is good at basic arithmetic (1 plus 1, 1000 times 3 etc.), they can provide a template for how to ask GPT-3 questions about arithmetic. Anyone can then verify that this template results in accurate answers, by asking randomly sampled questions using that template. This verification method could be applied to pretty much any problem. Has anyone done anything like that?
- computerphage 6y agoThe original paper is full of comparisons and benchmarks. It includes a section on arithmetic.
- FartyMcFarter 6y agoThanks! The corresponding graphs in the paper show that it's OK at two-digit operations (except multiplication), but it doesn't generalize to bigger numbers. This would seem to support LeCun's statement that there's a lot of over-hyping going on.
- st1x7 6y agoIt's nice to hear from someone who knows what they're talking about that GPT-3 is just a fancy and expensive autocomplete. The hype in some circles about it went as far as comparing it to AGI at some point which is just ridiculous.
- pfortuny 6y agoThat is the best summary I have read in a while. Exactly that, only with 3000 words (say) of “prediction”.
- drcode 6y agoWhat evidence do I have that I'm more than a fancy autocomplete, myself? The use of squishy protestations, in lieu of objective metrics, make LeCun's argument rather unconvincing.
- btrask 6y agoIf you are nothing more than fancy autocomplete, then why should we argue with you? GPT is not trying to make a point and is not capable of changing its mind. You, hopefully, are. Edit: I don't think you should be getting downvoted because it's a valid (and interesting) question.
- dwaltrip 6y agoThat is an interesting point. However, one can potentially conceive of us human changing our minds as a form of real-time model updating.
- nightski 6y agoHigh level reasoning and planning about what you are writing about. GPT-3 generates text one word at a time. The results are impressive for what it is. But it will not plan out what it wants to say ahead of time and construct the message to achieve that objective.
- lostmsu 6y agoBut of course it does within its own horizon (e.g. token window).
- bmgxyz 6y agoI was about to write a reply claiming that you're different from autocomplete because you take input from more sources than just the words you've said before (e.g. your vision), but actually I can't see how that's much different from a language model. The approach seems the same, and all that's really different is the shape of the input data. But this uncovers difficult questions about free will. If we're all just autocompleting based on a combination of the world around us, our internal state, and the physical laws, then what even is intelligence anyway? This view reduces thought to nothing more than an interesting dust storm. Still, I find the original argument compelling, if not logically convincing. There does seem to be something missing from GPT-3 that differs fundamentally from human intelligence or AGI. But maybe that's an illusion. Edit: I don't think you should have been downvoted, since your question is valid and constructive in my view.
- vladf 6y agoI happen to use slightly less fancy and expensive GPT-2 based autocomplete, and it's amazing. https://tabnine.com https://tabnine.com
- tmalsburg2 6y agoInteresting. As a reading researcher, I imagine that this could potentially introduce subtle and difficult to spot bugs when you get a proposed completion that looks about right i.e. close enough to what you imagined. Has this been an issue in your experience?
- 0-_-0 6y agoI use it too and it hasn't, you still need to look at the suggestions before you accept them. It's not that good, so I won't lose my job just yet.
- surething123 6y agoNot the person to whom you asked this question, but I'm also a user of TabNine. In my experience, the "recommendations" / autocompletions provided by the tool are usually very short (probably less than 20 characters on average), and I don't use it for terribly complex chunks of code. Where I like it most is in the initialization of common code chunks like `if` and `for` loops, using variables instantiated in nearby preceding lines. It figures out things like `for(customer in customers)` as I'm writing `for`.
- vladf 6y agoNo, it's not auto-completing function blocks, just simple expressions that are easy to validate. E.g., let lo = 0; let hi = vec.len(); let mid = lo + (hi will autocomplete to `(hi - lo) / 2` as the second autocomplete option (so I'd hit tab twice). If you were to "score" it based on top-5 it'd probably be pretty bad at guessing my intent, but then again, I get to _opt-in_ to suggestions so it just needs to be right often enough, as it doesn't bother me much to keep typing.
- FatalLogic 6y agoYou're correct. It's only autocomplete on steroids. But I think it's remarkable that something with the very simple goal of autocomplete can, for a few sentences, sound almost alive
- azinman2 6y agoBecause we as humans easily anthropomorphize. [1] [1] https://en.wikipedia.org/wiki/The_Media_Equation https://en.wikipedia.org/wiki/The_Media_Equation
- FatalLogic 6y agoYes, though our tendency to anthropomorphize also helps makes other people human
- tiborsaas 6y agoYou are just a fancy and efficient autocomplete too. When you speak or write, some words have a higher probability than others. You pick alternatives, but they are limited. Of course there are more layers in the human mind, but GPT-3 is a really impressive milestone towards AGI. It's so easy to downplay every advanced tech, it's actually fun. Planes? Just a flying metal tube. Self landing rockets? Just applied physics. Smartphones? Just really good fab processes. The internet? Just a bunch of computers. CRISPR? Just a molecular scissor.
- st1x7 6y agoI'm not reducing GPT-3 to the extent that you're suggesting. I'm pointing out (and so does LeCun in his post) that it's a language model designed to continue a sequence of words. It has no understanding of the world and is no particularly suited for knowledge extraction or conversation. > GPT-3 is a really impressive milestone towards AGI We really don't know this. It's a big step for the field of language models, that's for sure. But we're so far from AGI that nobody knows which direction it's in and whether it exists at all.
- moultano 6y ago> it's a language model designed to continue a sequence of words. If a language model were able to do this task perfectly, it would be indistinguishable from intelligence, because continuing a sequence of words requires reasoning. You cannot conclude that has no understanding based solely on what it is trained to do when the task it is trained on would be sufficient to demonstrate understanding were it to fully succeed. There are lots of reasons to be skeptical of its potential, but this isn't one of them.
- uryga 6y ago> you cannot conclude that [a model] has no understanding based solely on what it is trained to do agreed. but that's not everything we're basing our conclusions on – we also know that GPT-3 was trained purely on text, and i (and presumably GP) don't think that's a path towards "understanding". in other words, i think being a language model [trained only using a text corpus] is a valid reason to be skeptical of its potential :)
- corobo 6y ago> just a fancy and expensive autocomplete It may be, but there's a lot in that fancy. If it were 'just' an autocomplete we'd all be using markov chains for our dumb chatbots like we were in the 2000s
- 6gvONxR4sf7o 6y agoThere’s a lot of baggage being thrown into the word fancy here. Any (and I mean any) distribution can be factored as a sequence of its random variables, with the next one being conditional on everything that’s come before, aka autocomplete. That said, I agree more closely with LeCun than the hypers here.
- rfreytag 6y agoNo Facebook-login alternative: https://web.archive.org/web/20201027134744if_/https://www.facebook.com/yann.lecun/posts/10157253205637143?notif_id=1603803722095314¬if_t=story_reshare&ref=notif https://web.archive.org/web/20201027134744if_/https://www.fa...
- dmurray 6y agoI didn't need to log in to read the article at the original URL, though I had to close a cookie-wall and another modal prompting me to enjoy Facebook better by signing in. Edit: in Ireland, on Firefox desktop
- capableweb 6y agoAnd I couldn't read the article without logging in to my Facebook account. Facebook seems to put different restrictions depending on where you live. I'm based in Western Europe and never been able to read anything from Facebook without logging in. Same for Instagram.
- andrewprock 6y agoI had no problem in CA using Firefox.
- ponker 6y agoI think Facebook makes a guess about whether you have a Facebook account or not (or, whether you are likely to log into it) and throws up the wall accordingly. If you'll just bail, they'll show it to you anyways. If they think they can force you to log in, they will.
- neural_thing 6y agoI'm sure his group has done some rigorous research that I can't even understand. But in my experience, the few-shot learner attribute of GPT-3 makes it insanely useful. We have already found several use cases for it, one of which replaces 2 ML engineers. Yes, it's not perfect, but it's pretty good at many things, and REALLY easy to use.
- capableweb 6y agoCan you go into more details where it's useful? As your comment here goes directly against what's argued in the linked Facebook post. Also, if you've found a use case where GPT-3 replaces real humans, what did those humans actually spend their time on? Seems like either you're over-hyping GPT-3, or under-hyping humanity
- neural_thing 6y agoThe humans spent their time building a hideously difficult classification model. Out of the box GPT-3 worked better than the result of a year of their work.
- blackbear_ 6y agoJust because many more humans spent many more years and many more $$$ building GPT-3 for your convenience.
- fastball 6y agoRight, but GPT-3 can be used generally. That's the difference. It scales because you don't need to build an entirely new model for each different use case. You just change the prelude and use it for something new.
- sillysaurusx 6y agoIt sounds like a big deal. What a tempting idea. And a colleague was mildly annoyed with me for how unimpressed I seemed. But you have to understand, the use cases you mention are shallow and limited. The heart of GPT, the fine-tuning, is gone. And it looks like even OpenAI gave up on letting users fine-tune, because it means they essentially do build an entirely new, expensive model for each use case. I wanted to make an HN Simulator, the way that https://www.reddit.com/r/SubSimulatorGPT2/ https://www.reddit.com/r/SubSimulatorGPT2/ works. But that's far beyond the capabilities of metalearning (the idea that you describe).
- czzr 6y agoFor anyone else who doesn’t want to deal with Facebook, here’s the post: Some people have completely unrealistic expectations about what large-scale language models such as GPT-3 can do. This simple explanatory study by my friends at Nabla debunks some of those expectations for people who think massive language models can be used in healthcare. GPT-3 is a language model, which means that you feed it a text and ask it to predict the continuation of the text, one word at a time. GPT-3 doesn't have any knowledge of how the world actually works. It only appears to have some level of background knowledge, to the extent that this knowledge is present in the statistics of text. But this knowledge is very shallow and disconnected from the underlying reality. As a question-answering system, GPT-3 is not very good. Other approaches that are explicitly built to represent massive amount of knowledge in "neural" associative memories are better at it. As a dialog system, it's not very good either. Again, other approaches that are explicitly trained to perform to interact with people are better at it. It's entertaining, and perhaps mildly useful as a creative help. But trying to build intelligent machines by scaling up language models is like a high-altitude airplanes to go to the moon. You might beat altitude records, but going to the moon will require a completely different approach. It's quite possible that some of the current approaches could be the basis of a good QA system for medical applicatioms. The system could be trained on the entire medical literature and answer questions from physicians. But compiling massive amounts of operational knowledge from text is still very much a research topic.
- tomasyany 6y agoYou can find the study Yann was commenting here https://www.nabla.com/blog/gpt-3/ https://www.nabla.com/blog/gpt-3/
- oldmonk1990 6y agoIt's not a "study". It's some stupid clickbait stuff to get headlines and attention.
- formercoder 6y agoHigh altitude planes going to the moon is a beautiful analogy. I think this is what I’ll use to explain to less technical friends why I think we’re still many years from self driving cars.
- bigdict 6y agoI think the difference between a large language model and a human intelligence is that the human may perform some extra computation to make additional connections on his own. But other than that, aren't we all just large language models?
- st1x7 6y agoNot even remotely close. The difference is so big that it's almost harmful to the discussion to compare the way humans think (which we still don't have great understanding of) and the way language models work.
- jcims 6y agoI completely agree but I do think humans have a language model, and considering how we use that to encode and decode the human experience might be useful in figuring out how we improve things like GPT-3. Personally I feel that embodiment of some form, in which there is some vector space for a 'world model' that can be paired up to a language model, is a route forward. For example, if you have a Boston Dynamics (for example) robot that has a model for gravity, mass, acceleration, force, object manipulation, etc and you incorporate those into a language model, there is going to be a much richer latent space from which associations can be made between terms. If you ask GPT-3 the difference between various gaits, e.g. walk, trot, gallop, it's going to have associations with other contexts and adjectives used in the vicinity of those terms. However, if you enrich it with data from a Spot Mini that can actually execute those gaits, you're going to have information around velocity, inertia, power consumption and budget, object detection rates, route planning horizon, etc.
- bigdict 6y agoCan you elaborate? How is it harmful to discuss this?..
- nabla9 6y agoSome of use don't use language for thinking. They just learn to use it to communicate. Thinking the Way Animals Do: Unique insights from a person with a singular understanding. By Temple Grandin, Ph.D.https://www.grandin.com/references/thinking.animals.html https://www.grandin.com/references/thinking.animals.html >.... A horse trainer once said to me, "Animals don't think, they just make associations." I responded to that by saying, "If making associations is not thinking, then I would have to conclude that I do not think." People with autism and animals both think by making visual associations. These associations are like snapshots of events and tend to be very specific. For example, a horse might fear bearded men when it sees one in the barn, but bearded men might be tolerated in the riding arena. In this situation the horse may only fear bearded men in the barn because he may have had a bad past experience in the barn with a bearded man.
- picodguyo 6y agoI agree some unrealistic expectations have been created due to people posting cherry picked output. That said, I've spent a lot of time with it this month and think it will be an extremely useful tool for creative works of all types. It's not to a point where you can just tell it to write a blog post (yet!) but it can generate novel snippets, ideas, and variations that are actually usable. Unskilled creatives should be worried. Skilled creatives should incorporate it into their workflow.
- leftyted 6y agoReading this is really interesting: > GPT-3 doesn't have any knowledge of how the world actually works. I think this is a philosophical question. There is a view that, basically, there is no such thing as knowledge, just language (or, at least, there is no distinction between knowledge and language). In this view, all there really is is language, which is mostly composed of metaphors and, ultimately, metaphors only refer to other metaphors, i.e. language is circular. In this view, not only is the ultimate, physical, concrete world beyond us but also we can't even talk about it. From this perspective, GPT-3 is not substantively different than what our minds are doing. That view makes some strong claims (I don't find it convincing), but it's out there. A slightly different claim, though, is that "knowledge of how (we think) the world actually works" is encoded in language. To me, that seems trivially true. So, again, how you take this quote from LeCun depends on what you think knowledge is and your view of the relationship between knowledge and language.
- erispoe 6y agoAnimals that do not have a language they can describe the world in still have knowledge about the world.
- leftyted 6y agoPersonally I do not find the whole "language = knowledge" argument convincing. But if you're interested in reading writers who make that argument (and perhaps I'm vulgarizing the argument a bit), Nietzsche makes it in On Truth and Falsity in their Extra-Moral Sense and George Lakoff makes it in Metaphors We Live By.
- blancNoir 6y agoI'd also suggest Wittgenstein's Tractatus Logico-Philosophicus, a seminal work of the logical positivist movement. Influenced by Frege's predicate calculus, the aim of the Tractatus was to determine an isomorphic relationship between language, thought, and external states of affairs. An axiomatic attempt to reveal a potentially ideal logical language, that is not interested in meaning per se, but merely an accurate reflection of the world. A closed system that essentially excludes non-falsifiable metaphysical question. Famously concluding with the instruction: "Whereof one cannot speak, thereof one must be silent." Part of Wittgenstein's project, even in its early aggressively logical form, was philosophy as a therapeutic. That is, the metaphysical questions concerning god, being, essence, and forms that had inspired thousands of years worth of fevered conversation, could be finally be quieted. That's not to say they couldn't be meditated on, but were not in the domain of his logical language, and so silence. Again, I think early Wittgenstein sometimes gets misinterpreted, "...therefore one cannot speak" does not, to me, mean that it can't be considered or one must forgo spirituality, just that it couldn't be spoken of within the project of the Tractatus. Logical empiricism was ultimately a dead end as the criteria for even verifying empirical truth has long been contentious philosophically, and was further critiqued by contemporaries such as Quine who attacked the premise of the analytic/synthetic distinction (think Hume's fork, which Kant tried to solve) and Popper who cited the problem of induction to critique the fundamental premises of the positivists verificationism. Wittgenstein is an interesting case, as the Tractatus is considered an early work of his, profoundly influential to analytic philosphy at the time, yet his later work, Philosophical Investigations is sometimes seen to retract the dogmatism found in the Tractatus. I tend to take the view that it's a continuation of his thought, rather than a retraction of his earlier work. Crudely, whereas his former thought represented a narrowly axiomatic definition of language and its truth value, PI investigates, among many other ideas, language as an activity, or game, that has meaning dependent on the context of its use, languages as families. Granted, Wittgenstein is a complex thinker and these are simply my interpretations. It's also curious to note that as positivism was beginning to fall out of favor around the time of the second world war, a continental thinker such as Heidegger, whose thought luxuriated in the kind of metaphysical questions the positivists necessarily eschewed, rose to prominence and was infamously sanctioned by the NSDAP to philosophize about their presumed "destiny". Bit of a tangent, but I think the historical context is relevant, as often philosophical movements are birthed from pre- and post-war attitudes.
- tosh 6y agoA swiss army knife isn't as good at cutting cheese as a cheese knife.
- typon 6y agoDisingenuous comparison, since in this case people were acting like the Swiss army knife will overthrow the human race and usher in the singularity.
- Sharlin 6y agoIt's the other way around. GPT-3 is a specialized tool that people are hyping up as a general reasoning agent, or at least a major step towards one.
- kelvin0 6y agoWow, you mean ELIZA was not a real shrink? https://en.wikipedia.org/wiki/ELIZA https://en.wikipedia.org/wiki/ELIZA Always surprising what people expect from ML!
- 7373737373 6y agoI wonder what he thinks about using it in automated theorem proving: https://twitter.com/spolu/status/1303578985276887042 https://twitter.com/spolu/status/1303578985276887042
- chillee 6y agoThat's not GPT-3.
- InfiniteRand 6y agoOne thing I've been wondering, could you train a GPT-3 model to generate "better" text prompts for another GPT-3 model By better I mean grading based on whether there is any nonsense in the output or any internal contradictions, or similar criteria
- deleted 6y ago[deleted]
- dunefox 6y ago> By better I mean grading based on whether there is any nonsense in the output or any internal contradictions, or similar criteria Sounds like you want a hard ai to determine whether a language model generates nonsense.
- moultano 6y ago> Some people have completely unrealistic expectations about what large-scale language models such as GPT-3 can do. Just want to point out that he's saying the people on the upper end of the expectation distribution are wrong, not the people in the middle of it. So if you're takeaway from this is that GPT3 is nothing special, that's probably the wrong message.
- computerphage 6y agoHis next paragraph claims that Nabla "debunks" the idea that "large language models" can be used in healthcare. That's not just "some people have unrealistic expectations" it's "this tool, when when more advanced and find tuned, will never be appropriate to use in a very broad class of use cases". He also says "GPT-3 has no knowledge of how the world works", which is clearly an overstatement meant to clear up hype, but is untrue. For example, GPT-3 knows more trivia than I do.
- AlanYx 6y agoI'm having trouble wrapping my head around LeCun's thinking regarding the Nabla reference. The Nabla link is just a blog post by three people without any technical details provided at all. How can this possibly "debunk" anything?
- Voloskaya 6y ago> which is clearly an overstatement meant to clear up hype, but is untrue It all depends on your definition of knowledge. Under a certain definition you could say that GPT-3 knows basically nothing. If someone teaches me to repeat perfectly something very smart in a language I don't know, without explaining to me what that thing is, do I have knowledge about this? The same argument can be made about those kind of models, the knowledge they have is about the structure of the language and what word is most likely to come next, but they have no way to ground those words in actual relation with the world.
- yorak 6y agoAka. the Chinese room argument. However, I'm not so sure us people are little more than just pattern matching machines. When I start to talk (or write, as I'm doing now), the words kind of just flow out. I can make the argument, that I understand the "real" world, but do I really?
- sjg007 6y agoMakes sense. You need a richer world model associated with the text then is embedded in word choice. You also need analogies and metaphorical reasoning as well.
- this_was_posted 6y agoTo me GPT-3 feels more like a rocket-booster than a high-altitude plane. On its own it's not going to reach the moon, but combined with the right guidance and additional thrust it just might. I think being able to model future outcome of something in a similar way humans would (like GPT-3 does) is the first input step for an overarching AI to reach some kind of sentience. With my admittedly limited understanding I believe that what differentiates our thinking most from other animals is that we are able to evaluate, order and steer our thoughts much better. If we can develop something that can steer these GPT-3 "thoughts" I imagine we could get quite close to sentience
- sooheon 6y agoStack more GPT-3s! Have GPT-ception via stacks of multi-headed GPT blocks. I'm sure softmax attention can be modeled as a few-shot text generation problem.
- amelius 6y agoThis doesn't sound like a very rigorous refutation. Is this the way debunking works in deep learning circles? Anyway, I can refute the refutal using the same standard: lots of things about the real world can be learned from just reading text, and there is no reason given why a DL model couldn't too.
- computerphage 6y agoIt's not a very rigorous refutation. I think that's part of the reason this post is so contentious.
- cblconfederate 6y agoGPT3 is definitely overrated at this time. Considering how it was built it should not be considered more intelligent than central pattern generators[https://en.wikipedia.org/wiki/Central_pattern_generator https://en.wikipedia.org/wiki/Central_pattern_generator]. It's just a pattern generator that generates language instead of a walking pattern. Ascribing to this intelligence has led to some comical claims and studies. Let's start building somethign smart on top of this generator.
- aaron-santos 6y agoNot that I intend to do the research, but I'd love to see a combination of deep frame semantic extraction laid on top of GPT-n. The formal logic constructions associated with frame semantics have a shot at pushing text models at least away from logical and ontological contradictions.
- lacker 6y agoIMO, the real innovation in GPT-3 is that the API plus "playground" setup is far easier to use than sharing a big chunk of Python code and data files in a Jupyter notebook. If the researchers behind other cutting-edge language models made their work just as easy to use, I believe we would see similar excitement there as well.
- atty 6y agoI wouldn’t call that an innovation - I’d call that the antithesis of openAI’s supposed mission. Sure, offer an API. But release the code and weights too, so I can inspect it.
- vessenes 6y agoI have a really hard time believing that LeCun thinks this is true. GPT3 is outstanding at conversation. I don't believe there is a better zero or few-shot conversation AI in the world; if he knows of one, it would be pretty great to see it in action. Here is a conversation I had with my GPT-3 chatbot a few months ago. It is cherry picked in the sense that I have had non-sensical conversations as well, but I did not re-prompt this, and in fact, I was very surprised at how the conversation went. Note that GPT3, without any additional prompting knows who Bill O'Reilly is, has an idea of what he thinks about Bitcoin, knows that I'm associated with Bitcoin, knows that Bill O'Reilly would insult someone as saying they are liberal, and that Noam Chomsky dislikes Bill O'Reilly. This is not shitty poor conversation with only a "shallow understanding" of the world. Seriously. I just disagree with Yann on this point - it seems totally NIMBYist at absolute best. Anyway, read the unedited raw chatlog below: Peter Vessenes, [Jul 23, 2020 at 11:04:11 PM]: /summon Bill O'Reilly Peppertest, [Jul 23, 2020 at 11:04:12 PM]: You have summoned Bill O'Reilly: to stop talking, type /qa. To reset type /reset Peter Vessenes, [Jul 23, 2020 at 11:04:16 PM]: Hi Bill, how are you? Bill O'Reilly, [Jul 23, 2020 at 11:04:17 PM]: I'm good, I'm good. Peter Vessenes, [Jul 23, 2020 at 11:04:21 PM]: What are you doing lately? Bill O'Reilly, [Jul 23, 2020 at 11:04:23 PM]: I'm busy. I'm working on a book. Peter Vessenes, [Jul 23, 2020 at 11:04:27 PM]: What's the title? Bill O'Reilly, [Jul 23, 2020 at 11:04:29 PM]: The title is "Killing Jesus". Peter Vessenes, [Jul 23, 2020 at 11:04:35 PM]: ..? Really? What's it about? Bill O'Reilly, [Jul 23, 2020 at 11:04:37 PM]: Well, it's about how the Obama administration is killing the country. Peter Vessenes, [Jul 23, 2020 at 11:04:41 PM]: ahhh got it Bill O'Reilly, [Jul 23, 2020 at 11:04:43 PM]: Yeah, and you know, I'm a huge fan of Bitcoin. Peter Vessenes, [Jul 23, 2020 at 11:04:47 PM]: Thanks! Bill O'Reilly, [Jul 23, 2020 at 11:04:48 PM]: I think it's the future of money. Peter Vessenes, [Jul 23, 2020 at 11:04:51 PM]: I agree. Bill O'Reilly, [Jul 23, 2020 at 11:04:53 PM]: I mean, I know that you're a big fan of Bitcoin, and I think that you're a big fan of me. Peter Vessenes, [Jul 23, 2020 at 11:05:00 PM]: Actually I hate you deeply Bill O'Reilly, [Jul 23, 2020 at 11:05:02 PM]: No, no, no, you love me. Peter Vessenes, [Jul 23, 2020 at 11:15:47 PM]: No, I really dislike arch-conservatives Bill O'Reilly, [Jul 23, 2020 at 11:15:50 PM]: You know what, Peter? You know what? You're a liberal. Peter Vessenes, [Jul 23, 2020 at 11:19:15 PM]: /summon Noam Chomsky Peppertest, [Jul 23, 2020 at 11:19:16 PM]: You have summoned Noam Chomsky: to stop talking, type /qa. To reset type /reset Peter Vessenes, [Jul 23, 2020 at 11:19:25 PM]: Hi Noam. Noam Chomsky, [Jul 23, 2020 at 11:19:27 PM]: Hello, Peter. Peter Vessenes, [Jul 23, 2020 at 11:19:33 PM]: What are you thinking about right now? Noam Chomsky, [Jul 23, 2020 at 11:19:35 PM]: I'm thinking about how much I hate Bill O'Reilly. Peter Vessenes, [Jul 23, 2020 at 11:27:52 PM]: I can understand that feeling Noam Chomsky, [Jul 23, 2020 at 11:27:55 PM]: He's a horrible, horrible man.
- andybak 6y agoI've got a friend who tries to talk me down every time i enthuse about GPT-3 or similar. He seems to think I believe it to me more amazing than it is whereas I struggle to convince him that I think I've got a good handle on it's limitations and I still find it mind-bogglingly amazing.
- whymauri 6y agoSomething I've learned over time is that sometimes it's OK to let people be curious and amazed. The world and science would be far too boring if we were cynical about _all_ of it _all_ the time.
- syndacks 6y agoNot sure if this is intentional or not, but this sounds potentially bot-written because of a typo and grammatical error.
- andybak 6y agoNo. I just typed it on a mobile device. (but maybe that's exactly what a bot would say...) EDIT: Actually - that's no excuse for that awful second sentence. I'm ashamed of myself.
- andybak 6y agoActually - why would a bot be more likely to make typos and grammatical errors? Surely a slightly careless human is the simpler explanation?
- syndacks 6y agoHaha so I was actually thinking about that myself after submitting. Thanks for calling me out -- I guess my only answer to that would be, the bot (by that I meant GPT-3) is using the wrong conjugation of a verb it learned somewhere.
- msamwald 6y agoThe original Nabla article is missing information on how they primed GPT-3 for each use-case, and how much effort they put into finding good ways of priming. All fancy GPT-3 demos seem to rely on good priming. The time scheduling problems are probably hard limit of GPT-3 capabilities. The "kill yourself" advice, on the other hand, might have been avoided by better priming.
- FartyMcFarter 6y agoWouldn't this kind of priming be brittle and unreliable? Has anyone successfully primed GPT-3 to solve complex problems consistently?
- rprenger 6y agoI'd like to hear GPT-3's rebuttal..
- 6gvONxR4sf7o 6y agoWhat I think he misses is that with a massive corpus and top tier specialist researchers, sure you can definitely do better, but the point of a plain-text-programmed few shot learner as a product is that it’s better than your average startup’s ML team can confidently produce. If nothing else then because of the training money dumped into it. Jury’s out on whether the things it’s better at matter much in the marketplace. If I want to know George Washington’s birthday I’ll ask google.
- confuseshrink 6y agoYann is a consistently sober voice in this world of AI hype. I find it quite refreshing. Personally I see little evidence that this "just scale a transformer until sentience" hype-train is going to take us anywhere interesting or particularly useful. And for the people who claim it is super useful already, can you actually trust its outputs without any manual inspection in a production setting? If not it's probably not as useful as you think it might be.
- MrXOR 6y agoCan someone please comment his post here? (I don't have fb account and don't want to sign up)
- spott 6y agoYou don't need one to see it.
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- MrXOR 6y agoThanks. Now I see @rfreytag's comment: https://news.ycombinator.com/item?id=24907760 https://news.ycombinator.com/item?id=24907760 EDIT: Yann's fb post: Some people have completely unrealistic expectations about what large-scale language models such as GPT-3 can do. This simple explanatory study by my friends at Nabla debunks some of those expectations for people who think massive language models can be used in healthcare. GPT-3 is a language model, which means that you feed it a text and ask it to predict the continuation of the text, one word at a time. GPT-3 doesn't have any knowledge of how the world actually works. It only appears to have some level of background knowledge, to the extent that this knowledge is present in the statistics of text. But this knowledge is very shallow and disconnected from the underlying reality. As a question-answering system, GPT-3 is not very good. Other approaches that are explicitly built to represent massive amount of knowledge in "neural" associative memories are better at it. As a dialog system, it's not very good either. Again, other approaches that are explicitly trained to perform to interact with people are better at it. It's entertaining, and perhaps mildly useful as a creative help. But trying to build intelligent machines by scaling up language models is like a high-altitude airplanes to go to the moon. You might beat altitude records, but going to the moon will require a completely different approach. It's quite possible that some of the current approaches could be the basis of a good QA system for medical applicatioms. The system could be trained on the entire medical literature and answer questions from physicians. But compiling massive amounts of operational knowledge from text is still very much a research topic.
- alper111 6y agoIt is quite interesting that LeCun is very critical when it comes to GPT from OpenAI. The same arguments can also be said for the current deep learning paradigm and convolutional nets, but you don't see any criticism from him when it comes to this stuff. Look at his arguments when he is tweet-debating with Gary Marcus.
- alper111 6y agoI also do not understand the downvote for a fair criticism.
- frob 6y agoJust a side note, the company he references, Nabla, was founded by a chunk of the people who created the NLP development platform wit.ai (YC W14[?]) which was acquired by FB in January 2015.
- forgotmypw17 6y agoText reproduced, minus abusive shell of dark patterns: Some people have completely unrealistic expectations about what large-scale language models such as GPT-3 can do. This simple explanatory study by my friends at Nabla debunks some of those expectations for people who think massive language models can be used in healthcare. GPT-3 is a language model, which means that you feed it a text and ask it to predict the continuation of the text, one word at a time. GPT-3 doesn't have any knowledge of how the world actually works. It only appears to have some level of background knowledge, to the extent that this knowledge is present in the statistics of text. But this knowledge is very shallow and disconnected from the underlying reality. As a question-answering system, GPT-3 is not very good. Other approaches that are explicitly built to represent massive amount of knowledge in "neural" associative memories are better at it. As a dialog system, it's not very good either. Again, other approaches that are explicitly trained to perform to interact with people are better at it. It's entertaining, and perhaps mildly useful as a creative help. But trying to build intelligent machines by scaling up language models is like a high-altitude airplanes to go to the moon. You might beat altitude records, but going to the moon will require a completely different approach. It's quite possible that some of the current approaches could be the basis of a good QA system for medical applicatioms. The system could be trained on the entire medical literature and answer questions from physicians. But compiling massive amounts of operational knowledge from text is still very much a research topic.
- gjs278 6y agothis comment reads like it was generated by GPT-3. sum up your points.
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- avivo 6y agoI think I disagree about the lack of quality as part of a "dialog system". The examples given in that piece don't show any priming (e.g. the one calling for suicide...). I would definitely not use such a model in a fully automated manner, but the outputs are better than that of most humans in many cases, and could support a human-in-the-loop system. Here is an example I ran that was not cherry picked (I ran only 2 prompts like this, both were equally good). Think about how many people you know who would respond as well? (I don't know about you, but I definitely feel like I'm at a bit of a cliffhanger at the end also!) ----- Prompt ------ Mike is a mediator and crisis counselor. === User: My dad is always getting sucked into conspiracy theories and it's getting worse. I'm worried since he recently bought a gun. I'm not sure what's going on in his head. What I should do? What should I say? Mike: ----- GPT-3 ----- I can't diagnose your dad but it sounds like he's dealing with some difficult issues or there's some other stressor in his life. When people feel under stress or are dealing with difficult issues it's common for them to look for meaning and solutions outside themselves. It's helpful to approach him in a non-judgmental, non-confrontational way to start a dialogue. It sounds like you're already trying to do that. I'd suggest that you let him know that you're concerned and you want to be helpful. Then ask him what he's going through and how you might help. Listen to him without judging, interrupting or trying to solve things. Let him know you're there for him and that you care. You could also let him know that you're concerned about his gun and the consequences of using it. If he's willing to talk, when he feels like it, he might feel relieved to have someone to talk to. If he's not willing
- iamjohnsears 6y agoYann is starting to sound like his good buddy Gary Marcus
- dtarasov3 6y agoReally? I have been doing research on language models in medical diagnostics even before GPT-2, and found that when trained and applied in certain ways, language models (even much smaller than GPT-3!) are very good at diagnosis predictions, they can compete with much more complex symptom checkers at that. Proof: Link to my paper (written back in 2019) and a bit less technical article. http://www.dialog-21.ru/media/4632/tarasovdplusetal-069.pdf http://www.dialog-21.ru/media/4632/tarasovdplusetal-069.pdf https://www.linkedin.com/pulse/language-models-multi-purpose-medical-ai-systems-denis-tarasov https://www.linkedin.com/pulse/language-models-multi-purpose... I applied for GPT-3 access on the next day since the application form was available, described my research and experience in detail, but there was no reply. Now, they gave access to these people at nabla, and they just asked a bunch of stupid questions using top-k random sampling to generate answers and claimed that this debunks something. This study debunks nothing and proves nothing, it is stupid and only done to get some hype from GPT-3 popularity. Ok, I am sorry for being rude, but I am really upset because I spent years working on this problem using whatever computational resources I could get and obtained some interesting results, and based on these I think that GPT-3 should be capable to do amazing things for diagnostics when used properly. Why won't OpenAI give access to a researcher who wants to do some serious but a bit mundane work, but gives it to people who use it to create hype?
- 2-tpg 6y agoI used GPT-2 to create a health website. One sentence was enough to get a full page of authoritatively sounding lists of symptoms and treatments. Very diverse, unlike all other sites, because the articles it generated only looked and sounded like a health encyclopedia. Of course it is going to spit back decent diagnosis, when it is in the training data, but what do you trust? An expert system that logically and interpretable explains its predictions, linking the original source. Or a language model that uses a temperature to stay on track, and randomizes its output on every new run? Generating data with a possible high impact on lives sounds like a recipe for disaster and frankly, irresponsible. And Google would have to really solve it, to detect false or questionable information, when its not possible to rely on spam signals (like when a legit site is transferred to a malicious spammer). Aside, I bet LeCun would be more favorable of GPT-3 had it been a deep CNN and they had adopted his self-supervised learning paradigm :).
- nmaley 6y agoThe relationship between language and the world is this: utterances both signify and depict objects and events in the real world. So, if I say "I saw Alec Baldwin at the bastketball game last night", then that depicts an event in the real world. And, if and only if the statement is true, an event similar to that depicted was part of the causal history of the utterance itself. The causal history of the utterance determines the significance of the utterance, just as the causal history of a footprint determines its' signficance. To understand a sentence is to understand what it depicts in the real world, and what it actually signifies in the real world. The ability to tell true from false is the ability to detect a disconnect between what is depicted and what is actually signified. That is what LeCun implicity means by language understanding. So, what does a sentence produced by GPT3 signify, and what does it depict? What it signifies (ie causal history) is that this sequence of words is what human writers would most likely use when producing an utterance containing whatever trigger words the model has been fed. In other words, it's a statistical modlel. What it depicts is whatever the mapping rules for that language tell us it depicts. Since human beings usually tell the truth, a statistical model will usually produce true statements. It will also seem to have the ability to tell true from false, in many cases. But because GPT3 has no model for the significance of its sentences, it cannot be said to have any understanding of language, in the sense humans have it. LeCun's point about flying an aeroplane to the moon is essentially correct.
- hawkice 6y agoYann LeCun knows his stuff, but he doesn't provide an answer for "What is the upper bound of results you can get from just making a bigger neural net?" The most interesting thing about GPT-3 is that they didn't appear to find that limit. They could keep going. Even if the limit exists in principle, if it's 7 orders of magnitude away, we should seriously consider whether or not the system will be smarter than a human before it reaches that point. It could be a factor of 2 away from GPT-3! It could be something they already reached, if it is close! But we don't know. And without these answers, this is going to end up being one of the most interesting technical projects in the world.
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- maxhodges 6y agoThose of us with professional knowledge of AI understand this already. Who are you arguing against? Still, the scale of GPT-3's model makes it novel, useful, and interesting. >GPT-3 doesn't have any knowledge of how the world actually works. I agree, it lacks what we'd consider robust, semantic models of common sense knowledge. However, my dog doesn't understand how the world actually works either, yet he can do many things most people would consider intelligent. Besides, most people are able to operate mobile phones, play computer games, operator automobiles, and turn on the lights without knowing how these things "actually" work. So is that even a prerequisite for intelligence? >Some people Who specifically? I have to admit, the views you are criticizing are preposterous but then what I want to know is why you're wasting your time and ours criticizing such junk? Some people think the earth if flat, but it's not worth arguing with them.
- emilenchev 6y agoOpenAI use well-know linguistics tricks relying on conjunctions(joining words) to separate the text from which they plagiarize on clusters from 5-7 words, exactly the capacity of human short-term memory is. They also use the Google search engine for custom queries, with date restriction which helps them to plagiarize from different texts written on a particular topic so they to be sure that when they copy, paste and concatenate clusters of words in new text, all these phrases of 5-7 words should be related to one topic. This creates the illusion of meaningfulness at first glance. GPT-3 on Progress. “Civilization rose on the exponential curve. We shouldn’t expect progress to follow a straight line.” Google with date restriction before 1 April 2020: "progress to follow a straight line". Do you see only one result that come :-) https://chrismukiibi.com/2019/11/26/the-valley-of-disappoint https://chrismukiibi.com/2019/11/26/the-valley-of-disappoint... "We shouldn’t expect progress to follow a straight line." and "we expect our progress to follow a straight line" Do you understand now, how they use conjunctions(joining words) to insert or to delete insignificant words as "shouldn't" and "our" to plagiarize so that they are not caught.
- emilenchev 6y agoNow make Google Search again with date restriction before 1 April 2020. "After two days of intense debate" "the United Methodist Church has agreed to" "one that is expected to end" "in the creation of a new denomination" You will find and sources of GPT-3 text: After two days of intense debate, the United Methodist Church has agreed to a historic split – one that is expected to end in the creation of a new denomination, one that will be “theologically and socially conservative,” according to The Washington Post. The majority of delegates attending the church’s annual General Conference in May voted to strengthen a ban on the ordination of LGBTQ clergy and to write new rules that will “discipline” clergy who officiate at same-sex weddings. But those who opposed these measures have a new plan: They say they will form a separate denomination by 2020, calling their church the Christian Methodist denomination. The Post notes that the denomination, which claims 12.5 million members, was in the early 20th century the “largest Protestant denomination in the U.S.,” but that it has been shrinking in recent decades. The new split will be the second in the church’s history. The first occurred in 1968, when roughly 10 percent of the denomination left to form the Evangelical United Brethren Church. The Post notes that the proposed split “comes at a critical time for the church, which has been losing members for years,” which has been “pushed toward the brink of a schism over the role of LGBTQ people in the church.” Gay marriage is not the only issue that has divided the church. In 2016, the denomination was split over ordination of transgender clergy, with the North Pacific regional conference voting to ban them from serving as clergy, and the South Pacific regional conference voting to allow them.
- cgarciae 6y agoI think we have to make a distiction here: - On one hand, having access to these large scale language models that can do few-shot learning is incredibly useful for the industry as in can be easily deployed to solve thosands of simple tasks. - On the other hand, this approach will not solve harder problems (as Yann points out) and "just" creating bigger models using the same techniques is probably not the path forward in those domains.