16 ms·

Why GPT-3 Matters

223 points by teruakohatu 6y ago

ForHackernews 6y ago
> GPT-3 shows that it’s possible for a model to someday reach human levels of generalization in NLP This is a big, big claim tossed in as a throwaway line. GPT-3 shows that we haven't yet reached the limit of the "just throw more resources at it" school of AI development, but it doesn't automatically follow that it'll reach human levels of NLP if you give it enough resources. By analogy, this is claiming "New, larger steam locomotives are strictly faster than older, smaller ones, so this shows with enough coal it's possible for steam-engines to someday drive interstellar transport at 0.5c"
- teruakohatu 6y ago
  > By analogy, this is claiming "New, larger steam locomotives are strictly faster than older, smaller ones, so this shows with enough coal it's possible for steam-engines to someday drive interstellar transport at 0.5c" Making that argument about scaling up high energy fuels and engines would make interstellar travel possible would be a pretty good hypothesis. Turns out you need rocket fuel and rocket engines, not coal and steam engines. GPT-3 might not be the engine, but throwing insane amounts of electrical energy and computing power at the problem might just get us there.
- jimmySixDOF 6y ago
  >strictly faster The question is how fast is fast enough. At what point do you stop being able to distinguish an agenda determined social engineering bot from a non native speaking teenage 4chan troll ? The bar is not much higher - much less than the gain of function already seen from v2. So will it be v4? v5? For a lot of disruptive applications, just a little bit "faster" is all you need for the bad actors to act.
  - quadrifoliate 6y ago
    > At what point do you stop being able to distinguish an agenda determined social engineering bot from a non native speaking teenage 4chan troll ? I'm...not sure why you would want to? I can't see either being particularly helpful regarding discourse on the Internet or in real life.
s1t5 6y ago
Hasn't GPT-3 been out for a while now? Why are there so many articles about it on the front page over the past few days?
- gpmcadam 6y ago
  I've noticed this too. Maybe it's anecdotal, but it feels like an attempt to astroturf the dev community or drive up buzz. I can't escape the superlative-laden Tweets and articles about GPT-3.
- ZephyrBlu 6y ago
  It blew up on Twitter a few days ago, and it's still in closed beta so people are eager to see what it can do.
- lukeplato 6y ago
  They recently gave certain devs early access and a lot of demos are only now being shared by people with followings
- thecureforzits 6y ago
  Marketing campaign.
- killerstorm 6y ago
  API/Playground is in private beta, I guess more people got access to it and started showing results. And upon seeing these results people get excited and talk about implications, etc. Like, generating code and solving math is pretty damn good for a model which is not trained for generating code and solving math. Few weeks ago people didn't know it can do that.
AeP6cheo 6y ago
I really hope this tech will be miniaturized at some point. I hate to be that guy, but when I saw the article the other day that ended with "plot twist, this article was autogenerated with GPT-3!", I was not impressed, mainly because it looked like content farm's content to me, conveying no information. It basically looked like an incredibly costly spam tool. But then I thought of possible applications. Give humans a new tool, and they will amaze you, I'm sure we'll see cool applications of this tech in the future (and probably horrible ones too). One thing that I could think of where it would totally rock : games. If games were able to use such tech, their game world could be filled with casual discussions rather than the developers needing to fill everything. This means that NPCs in a game could have ever changing discussions, and even answer to the player about the subject they're discussing. Their discussion could even change based on what is happening in the world or what the players are doing, even to things with the smallest impact, or what is happening directly around them at the moment, without devs needing to script it all. That would be awesome. But yeah, this won't happen easily unless the model can be embedded and shipped on local computers.
- puranjay 6y ago
  That would be a great use. It really takes me out of a game when I talk to an NPC and they run out of dialog options. After that, every time you approach them, they repeat the last thing they said.
  - xamuel 6y ago
    I predict it wouldn't be as fun as it might initially sound. NPC dialog is part of level design just like all the rest of the level it occurs on. With few exceptions (such as roguelikes), random level generation tends to produce bland cookie-cutter levels; I don't see why dialog would be any different. The reason it's fun to talk to NPCs in a game like Chrono Trigger or FF7 is because someone put the work in to make their dialog interesting and relevant and fun. Where I could see it working better would be for e.g. newspaper headlines in a grand-strategy or SimCity-like game. When you do something crazy like have Lichtenstein conquer Western Europe, it could be funny to have some auto-generated commentary on said crazy state of affairs.
YeGoblynQueenne 6y ago
Why does GPT-3 matter? Well, the article starts with a plot that shows what looks like an exponential growth in the number of parameters, compared with previous models. So it matters because it's bigger. Further down there's a plot titled "Aggregate Performance Across Benchmarks" where we can see that performance is on average about 50%. I don't know what the baseline for this plot should be (what is the expected average if the tasks are solved by a random classifier?) but comparing this plot with the plot at the top of the article, it doesn't really look like there's a huge improvement in accuracy with a huge increase in the number of parameters. In fact, it's quite the contrary: there's a small increase and a very smooth, almost linear curve. So that's an exponential increase in the use of resources for an almost linear increase in performance? That's not that impressive. So it appears that the big thing about GPT-3 is that it's big. It should also be noted that the public interest about GPT-3 is mostly focused on its ability to generate text, for which there is no good metric. So basically, GPT-3 is big, but it's not that good in tasks for which there are formal benchmarks (such as they are, because Natural Language Understanding benchmarks are often very poorly made and don't really measure what they say they measure) and we can't really tell how good it is in the one task that interests most people the most.
- YeGoblynQueenne 6y ago
  Oh and note that this: >> GPT-3 can also finally do arithmetic, something GPT-2 was unable to do well. Is a preposterous claim that is very poorly supported by the data in the GPT-3 paper [1]. Figure 3.10 in the paper summarises the results. The authors tested addition between two to five digits, subtraction between two to five digits and multiplication between two digits drawn uniformly at random from [0,100]. There was also a composite task of addition, subtraction and multiplication with single-digit numbers (e.g. 6+(4*8), etc). On all tasks, other than two and three digit addition and subtraction, accuracy was uniformly under 20%. The other four tasks achieved high accuracy with more parameters. Of course, this doesn't show that the larger models "learned arithmetic". Two- and three-digit addition and subtraction are likely to be much better represented in a natural language dataset than other operations (and note of course the conspicuous absence of division). So it's safe to assume that the model has seen all the operations it's asked to repeat and knows their results by heart. Remember that for two and three digit addition and subtraction one only needs a dataset with the numbers up to 999, which is really tiny and easy to memorise. Edit: the authors note that they "spot checked" whether the model is simply memorising results, by searching for three-digit addition examples in their dataset. Out of 2000 three-digit addition problems they failed to find more than 17% in their dataset which "suggests" that the model had not ever seen the problems before. Or, it "suggests" the search was not capable of finding many more existing matches. In any case, why only "spot-check" three-digit addition? Who knows. The paper doesn't say. Certainly, one- and two-digit addition and subtraction should be much more common in a natural language dataset. The authors also say that the model often makes mistakes such as not carrying a one, so it must be actually performing arithmetic! Or, it's simply reproducing common arithmetic mistakes in its dataset. Overall, this sort of "testing" of arithmetic prowess simply doesn't cut the mustard. Edit 2: Also, no information about how many arithmetic problems of each type were tried. One? Ten? One hundred? Where all arithmetic tasks tested with the same number of problems? Unknown. _____________ [1] https://arxiv.org/abs/2005.14165 https://arxiv.org/abs/2005.14165
mcemilg 6y ago
We saw that the GPT-3 doesn't matter that much, right?
- andybak 6y ago
  Not personally, no.
  - mcemilg 6y ago
    Take gpt-2, bert or any other attention based language model. Apply few shot learning on any domain to model you take. You will not see so much meaningful difference that matters. Even the GPT-3 super large than the other models. There is a hype because people able to use that domain adaptation easily without serving huge ml models by themselves. OpenAI presents that serving already. That makes it easy to explore language models to developers that don't know ML that much to tune.
- evanrich 6y ago
  For me, it has kind of broken HN’s comment sections. I find myself jumping to the bottom of longer comments to look for “btw, this comment was written by gpt3”. To me it seems like we are going to be entering a perpetual April fools day where we never really know what’s real.
  - arkitaip 6y ago
    This has always been a problem with HN due to the robotic nature of its audience.
    - ss2003 6y ago
      Ouch!
simonkafan 6y ago
> that it’s possible for a model to someday reach human levels of generalization in NLP Fully disagree. There is no evidence that we are now closer to human level text understanding than before GPT-3. Yes, GPT-3 produces grammatically correct sentences but it still can't form a coherent idea or meaning and express it in sentences afterwards - that's what humans would do. GPT-3 is just better at obfuscating that the model has no clue what it's talking about. Nevertheless, compared with Eliza or other bots from 1960-2000 we made remarkable progress.
- jhrmnn 6y ago
  I wonder if 10 years ago one would believe that we’d have a model capable of generating an article that fools many given a complex prompt, yet is essentially incapable of any reasoning.
  - samastur 6y ago
    I assume I wasn't the only early reader of blogs who thought they point at this being possible, but I admit I did not expect to see it so soon.
  - newen 6y ago
    The Chinese room argument shows lots of people were thinking about it being a possibility but to have it happen so soon is something else.
  - plutonorm 6y ago
    It is capable of reasoning. Good lord what does it have to do to prove it???!
- dragonwriter 6y ago
  > Yes, GPT-3 produces grammatically correct sentences but it still can't form a coherent idea or meaning and express it in sentences afterwards - that's what humans would do. There's considerable debate over whether humans can have a coherent idea before it is reduced into symbolic language, and it's not clear how you would distinguish this sequence of events, anyway. It's pretty clear what GPT-3 does doesn't match the common rationalization of human subjective experience of cognition, but it's not at all clear, AFAICT, that what the human brain does matches that rationalization, either. Which is not to say I think GPT-3 has anything like the kind, much less the level, of understanding humans have, I just think some of the common arguments arrayed in casually dismissing it are based on suppositions about human cognition that aren't sufficiently examined.
brunoluiz 6y ago
As a programmer but without that much of data science background, could someone explain what is the whole hype/breakthrough of GPT-3? I know it generates content that makes sense, "HTML+CSS" and some other text based stuff. But in layman terms, what is the huge deal with it?
- vsskanth 6y ago
  The first two versions generated content based on a prompt that was coherent for a sentence or a paragraph. GPT-3 generates an entire article that's largely coherent (though still lacking nuanced meaning). Each version of GPT has exponentially increasing parameter count. Raw compute seems to be winning out here. Basically for this application they aren't seeming to hit diminishing returns for increasing parameters. Now people are wondering what GPT-4 can do. If you can write human-level coherent articles or if it passes the turing test or something it's gonna trigger an arms race for governments to obtain this capability.
  - brunoluiz 6y ago
    Thanks for the explanation ;) > Each version of GPT has exponentially increasing parameter count What are parameters in this context? > Now people are wondering what GPT-4 can do. If you can write human-level coherent articles or if it passes the turing test or something [...] Well, when I first heard about "text generation" last year what I first raised to my friends was "surely this will make fake news even worse". But well, any technology come with a bright and dark side. I try to not be pessimistic in these moments hehe
    - vsskanth 6y ago
      parameters here are just the size of the neural net of the model. Each unit of computation has its own parameter that needs to be fit. Bigger model = more layers or units of computations = more parameters.
  - op03 6y ago
    This is like watching parents talking about their kid develop over time and getting excited over their evolving writing ability. Doesn't mean we get Turing or Gandhi at the end of the story. Or that there is any control over what is produced. To produce those 2 nature had to iterate over 100 billion humans.
ricksharp 6y ago
For me it’s performance passes a usability threshold for human + machine collaboration. It is possible to use it to generate texts which can be quickly curated or edited by a user. Specifically this could be useful in authoring fiction (sci-fi novels, game dialog, etc.). Imagine the Star Trek holodeck characters. It’s dialog quality is nearly good enough to make that level of interaction feasible.
barbegal 6y ago
What can GPT-3 do that is useful? I can understand that it outputs text based on a prompt and some input but I don't understand how that can be leveraged to do useful things. I can ask it trivia questions but is it better than doing a Wikipedia search? Is it possible to give it a prompt of some scientific paper say and write an article about it? Or will it just generate nonsense?
- swalsh 6y ago
  Here is someone on Twitter who has been building some, albeit toys, using the tool. It seems pretty powerful: https://twitter.com/jsngr https://twitter.com/jsngr I found this to be very impressive: https://twitter.com/jsngr/status/1284874360952692736 https://twitter.com/jsngr/status/1284874360952692736 Not only did it eventually generate code based on his example for the new domain he specified, it was even able to generate new domains.
  - barbegal 6y ago
    It's somewhat impressive but it didn't do what he wanted it to. It didn't list temperatures, instead it listed weather descriptions. And to get it to do this he had to write a full template. We also don't get to see if this was the first attempt at doing this or can the model generate code like this reliably.
    - devalgo 6y ago
      You'll be looking for reasons to downplay it while dozens of startups receive funding to build automatic app generation tools.
      - actionscripted 6y ago
        You are pushing this idea so hard and blasting this thread with copy/paste comments. It's obvious you're excited about it and that's great but there is a lot that isn't shown and that's where the real work is being done. Just because it looks like magic, doesn't mean there isn't someone pulling some strings somewhere else to aid the illusion.
k__ 6y ago
Can a model like this be realized as a chip? I mean, 300GB memory is crazy. But back in the days I ran full HD videos on a netbook, because it hat special optimzied chips.
- justinmchase 6y ago
  Yeah but that chip is probably going to look a lot like a 300GB memory chip.
  - k__ 6y ago
    Like big external power bank? Doesn't sound too bad.
barrenko 6y ago
Highly distrust gwern et al. Bloggers at most.
qwerty456127 6y ago
> GPT-3 shows that it’s possible for a model to someday reach human levels of generalization in NLP—and once the impossible becomes possible, it’s only a matter of time until it becomes practical. The fact it can [occasionally] generalize and participate in a verbal conversation on a human level means it can potentially organize and direct productive (on a human level at least) action if assigned as a manager to a team of humans. Doesn't it?
justinmchase 6y ago
I don't feel like this article answered the question in its headline. I don't even know what GPT-3 is so maybe even the tiniest bit of background could have helped.
kemonocode 6y ago
GPT-3 won't matter until all of the work put into it can be replicated by someone else, and as it stands right now it's just a toy for people with far too many resources to spare.
robtigo1 6y ago
So can you feed a video to it and let 'dream' of all possible outcome? If this is achievable then the internet will eventually become a loop-back device for our senses just like the matrix movie.
nemoniac 6y ago
Speaking of unconscious bias, this quote from the original article made me raise my eyebrows: "We wanted to identify how good an average person on the internet is at detecting language model outputs, so we focused on participants drawn from the general US population."
- anchpop 6y ago
  Clearly the average USian is not the average person, but given that they need to speak English to have a shot (unless gpt-3 works for other languages?) it doesn't seem like a terrible approximation
zumachase 6y ago
We're incredibly excited about GPT-3. I think there is a fair bit hype exhaustion, especially from the likes of OpenAI ("our AI is too dangerous to release"). So this is completely understandable. However I think what's missing here is our benchmarks (a la Turing test) are about negation as opposed to affirmation. We tend to evaluate AI on whether or not we can discern the fact that it's AI. We seek to negate it as human, as opposed to affirming it as human (or close to). And this is not the right mindset when it comes to AGI because the gap between "obviously not human" and "human-like" is enormous. These are all definitely steps in the right direction, and the applications for even robotic process automation will be huge. But we're not even close to having nets that can reason about even the most basic things.
- abernard1 6y ago
  > However I think what's missing here is our benchmarks (a la Turing test) are about negation as opposed to affirmation. I would question the value of the Turing test, and maybe think that's not a great example for AI. There's always been this assumption that passing the Turing test would mean we had AI, but I think that was always predicated on the machine generating the outputs. With the GPT- models, it's not clear that this isn't a form of compression over an immense data set, and we're sending pre-existing _human_ responses back to the user. It implies to me that we can pass the Turing test with a large enough data set and no (or very little) intelligence. All of this makes me believe "These are all definitely steps in the right direction" is questionable.
jungletime 6y ago
Use it to post to Reddit and create a feedback loop. Help to hasten the AI takeover. What I find fascinating is the carful craft of framing things out of context, as a tool for politics. https://en.wikipedia.org/wiki/AI_takeover https://en.wikipedia.org/wiki/AI_takeover Narrative wins. This will be a great tool to misinform.
- thecureforzits 6y ago
  If you look at the folks who are backing this, it is clear what their intent is.
- runeb 6y ago
  There is already a pretty good one using GPT-2 https://www.reddit.com/r/SubSimulatorGPT2/ https://www.reddit.com/r/SubSimulatorGPT2/
lerchmo 6y ago
it is also a pretty good case study in the "bitter lesson" and all but ensures that the future of AI will be driven by the companies with the deepest pockets.
- devalgo 6y ago
  OpenAI is far from having the deepest pockets.
highfrequency 6y ago
There’s been a lot of discussion on HN lately about the implications of GPT-3: are we moving toward general AI or is this just a scaled up party trick? I have no idea whether scaling up transformers another 100x will lead to something resembling real intelligence, but it certainly seems possible. In particular, I find the arguments against this possibility to be fairly silly. These are the three main arguments I have seen for why GPT type models will never approach AGI, and the reasons I don’t think they are valid: 1. GPT-3 requires vast amounts of training data (hundreds of billions of words from the internet), whereas a human can become fluent in natural language after “training on” much less data. It’s not analogous to compare the GPT-3 training corpus to the education that one human receives before becoming fluent in natural language. We benefit from millions of years of evolution across billions of organisms. A massive amount of “training” is incorporated in the brain of an infant. This must be the case because even if you could somehow read all of the text on the internet to your dog, it would not approach intelligence. 2. There was no intellectual breakthrough in the development of GPT-3 just more “brute force” training on more data, therefore it or its successors can’t achieve a breakthrough in intelligence. We must remember that there was no intellectual breakthrough required for the development of human intelligence, it was just more of the same evolution. The core pattern of evolution is extremely simple: take an organism, generate random variants from it, see which ones do the best, and then create new variants from the good ones. This is perhaps the most basic scheme you could think of that might actually work. Evolution has produced amazing results in spite of its simplicity and inefficiency (random variations!) because it generalizes well to many environments and scales extremely well to millions of generations. These are exactly the strengths of gradient descent. In fact, gradient descent follows the same structure as evolution, except that at each iteration we don’t generate random variations, but instead make an educated guess about what a fruitful variation would be based on available gradient information. This improves learning efficiency tremendously; imagine being able to say: “this Neanderthal died because he stepped into a fire, let’s add some fire-avoidance to the next one” instead of waiting for this trait to be generated randomly. Speaking of brute force and amount of training, it would take 355 years to train GPT-3 on a single GPU. This strikes me as quite fast relative to evolutionary time scales. 3. Machines lack capabilities fundamental to the human experience: in particular feeling pleasure, pain, and an internal drive toward a goal. Indeed, if you turn a computer off in the middle of a computation, there is no evidence of suffering. And if the computer successfully writes a blog post of human quality, it feels no joy in the human sense. My claim is that these sensations are not core aspects of intelligence. In fact, pleasure and pain are very primitive developments that even cockroaches can claim. The most impressively human accomplishments (harnessing vast external energy sources, breaking out of bare subsistence, landing on the moon, etc.) were made in spite of the fact that we are messy bags of emotion that unpredictably feel anger, jealousy, despondence or elation. These emotional responses were selected for because they were useful as proximate goalposts orienting us toward reproduction—basically, to overcome forgetfulness in the pursuit of long-term goals. If in the future we can simply direct a computer to write a captivating novel without needing to program in lots of visceral intermediate stimuli to keep it on track, so much the better.
benlivengood 6y ago
What I would really like to see is an analysis of the weight that individual chunks of context contribute to the final probability for an output. That might allow for better prompting; when GPT-3 gets the wrong answer it would be fairly obvious what was lacking in the prompting given the context it thinks has the most influence. Also, good prompts would presumably have a higher weight.
- jadbox 6y ago
  Once discovered, you could have a kind of outline builder where you provide all the right linking outline context to have it generate appropriating paragraphs around it.
supernova87a 6y ago
Can someone answer a question for a layman: Is the number of parameters to be read as the indicator of how "advanced" the training has gotten, or the accuracy of the output? As in, this dataset/training has gotten to the point that it understands the 160 billionth small exception to the general rules of how language should be interpreted, or constructed, to be considered believable? Sometimes (as a layman) I look at this and think instead, wow, how slow these ML algorithms must be that they need 160 billion parameters to predict correctly. Is it one of these statements?
- killerstorm 6y ago
  > Is the number of parameters to be read as the indicator of how "advanced" the training has gotten, or the accuracy of the output? Accuracy, of course. > As in, this dataset/training has gotten to the point that it understands the 160 billionth small exception to the general rules of how language should be interpreted, or constructed, to be considered believable? It memorized a lot of facts, but it is also better at figuring out rules than its predecessor. > Sometimes (as a layman) I look at this and think instead, wow, how slow these ML algorithms must be that they need 160 billion parameters to predict correctly. There are more specialized models which are trained on much smaller datasets. They usually are given a specific task, such as classification. GPT-3 is trained on a very large dataset in unsupervised way. And as a result, it is able to handle a very wide variety of tasks (without re-training). If you tell it to do math, it will do math. If you tell it to translate between different languages, it will do translation. If you tell it to write JS code, it will write JS code. If you ask it to write a Harry Potter parody as if it was written by Hemingway, it will do that. So the whole point is that it can do pretty much any imaginable task involving text given only few examples, with no specific training.
  - supernova87a 6y ago
    Thanks!
TrackerFF 6y ago
Picture this grim future: 1. Companies will use models like these to generate automatic job listings 2. People will use models like these to generate automatic job applications 3. Companies from 1) will use automated tools to parse and analyse said applications We're gonna end up with abysmal SNR, as the sheer number of applicants will explode.
- ajzinsbwbs 6y ago
  It doesn’t sound like the worst way to hire model builders.
nojvek 6y ago
I can see comment and review farms wanting their hands on GPT really bad. Imagine being able to generate 1000s if Human like reviews with positive sentiment. Businesses pay real money for this. Right now AI is only available to mega tech corps. Even OpenAI is a closed research lab. So one can infer that AI will always be the divider.
anonimunos 6y ago
it seems that this is the future

223 points by teruakohatu 6y ago

> GPT-3 shows that it’s possible for a model to someday reach human levels of generalization in NLP This is a big, big claim tossed in as a throwaway line. GPT-3 shows that we haven't yet reached the limit of the "just throw more resources at it" school of AI development, but it doesn't automatically follow that it'll reach human levels of NLP if you give it enough resources. By analogy, this is claiming "New, larger steam locomotives are strictly faster than older, smaller ones, so this shows with enough coal it's possible for steam-engines to someday drive interstellar transport at 0.5c"

teruakohatu 6y ago
> By analogy, this is claiming "New, larger steam locomotives are strictly faster than older, smaller ones, so this shows with enough coal it's possible for steam-engines to someday drive interstellar transport at 0.5c" Making that argument about scaling up high energy fuels and engines would make interstellar travel possible would be a pretty good hypothesis. Turns out you need rocket fuel and rocket engines, not coal and steam engines. GPT-3 might not be the engine, but throwing insane amounts of electrical energy and computing power at the problem might just get us there.
jimmySixDOF 6y ago
>strictly faster The question is how fast is fast enough. At what point do you stop being able to distinguish an agenda determined social engineering bot from a non native speaking teenage 4chan troll ? The bar is not much higher - much less than the gain of function already seen from v2. So will it be v4? v5? For a lot of disruptive applications, just a little bit "faster" is all you need for the bad actors to act.
- quadrifoliate 6y ago
  > At what point do you stop being able to distinguish an agenda determined social engineering bot from a non native speaking teenage 4chan troll ? I'm...not sure why you would want to? I can't see either being particularly helpful regarding discourse on the Internet or in real life.

s1t5 6y ago

Hasn't GPT-3 been out for a while now? Why are there so many articles about it on the front page over the past few days?

gpmcadam 6y ago
I've noticed this too. Maybe it's anecdotal, but it feels like an attempt to astroturf the dev community or drive up buzz. I can't escape the superlative-laden Tweets and articles about GPT-3.
ZephyrBlu 6y ago
It blew up on Twitter a few days ago, and it's still in closed beta so people are eager to see what it can do.
lukeplato 6y ago
They recently gave certain devs early access and a lot of demos are only now being shared by people with followings
thecureforzits 6y ago
Marketing campaign.
killerstorm 6y ago
API/Playground is in private beta, I guess more people got access to it and started showing results. And upon seeing these results people get excited and talk about implications, etc. Like, generating code and solving math is pretty damn good for a model which is not trained for generating code and solving math. Few weeks ago people didn't know it can do that.

AeP6cheo 6y ago

I really hope this tech will be miniaturized at some point. I hate to be that guy, but when I saw the article the other day that ended with "plot twist, this article was autogenerated with GPT-3!", I was not impressed, mainly because it looked like content farm's content to me, conveying no information. It basically looked like an incredibly costly spam tool. But then I thought of possible applications. Give humans a new tool, and they will amaze you, I'm sure we'll see cool applications of this tech in the future (and probably horrible ones too). One thing that I could think of where it would totally rock : games. If games were able to use such tech, their game world could be filled with casual discussions rather than the developers needing to fill everything. This means that NPCs in a game could have ever changing discussions, and even answer to the player about the subject they're discussing. Their discussion could even change based on what is happening in the world or what the players are doing, even to things with the smallest impact, or what is happening directly around them at the moment, without devs needing to script it all. That would be awesome. But yeah, this won't happen easily unless the model can be embedded and shipped on local computers.

puranjay 6y ago
That would be a great use. It really takes me out of a game when I talk to an NPC and they run out of dialog options. After that, every time you approach them, they repeat the last thing they said.
- xamuel 6y ago
  I predict it wouldn't be as fun as it might initially sound. NPC dialog is part of level design just like all the rest of the level it occurs on. With few exceptions (such as roguelikes), random level generation tends to produce bland cookie-cutter levels; I don't see why dialog would be any different. The reason it's fun to talk to NPCs in a game like Chrono Trigger or FF7 is because someone put the work in to make their dialog interesting and relevant and fun. Where I could see it working better would be for e.g. newspaper headlines in a grand-strategy or SimCity-like game. When you do something crazy like have Lichtenstein conquer Western Europe, it could be funny to have some auto-generated commentary on said crazy state of affairs.

YeGoblynQueenne 6y ago

Why does GPT-3 matter? Well, the article starts with a plot that shows what looks like an exponential growth in the number of parameters, compared with previous models. So it matters because it's bigger. Further down there's a plot titled "Aggregate Performance Across Benchmarks" where we can see that performance is on average about 50%. I don't know what the baseline for this plot should be (what is the expected average if the tasks are solved by a random classifier?) but comparing this plot with the plot at the top of the article, it doesn't really look like there's a huge improvement in accuracy with a huge increase in the number of parameters. In fact, it's quite the contrary: there's a small increase and a very smooth, almost linear curve. So that's an exponential increase in the use of resources for an almost linear increase in performance? That's not that impressive. So it appears that the big thing about GPT-3 is that it's big. It should also be noted that the public interest about GPT-3 is mostly focused on its ability to generate text, for which there is no good metric. So basically, GPT-3 is big, but it's not that good in tasks for which there are formal benchmarks (such as they are, because Natural Language Understanding benchmarks are often very poorly made and don't really measure what they say they measure) and we can't really tell how good it is in the one task that interests most people the most.

YeGoblynQueenne 6y ago
Oh and note that this: >> GPT-3 can also finally do arithmetic, something GPT-2 was unable to do well. Is a preposterous claim that is very poorly supported by the data in the GPT-3 paper [1]. Figure 3.10 in the paper summarises the results. The authors tested addition between two to five digits, subtraction between two to five digits and multiplication between two digits drawn uniformly at random from [0,100]. There was also a composite task of addition, subtraction and multiplication with single-digit numbers (e.g. 6+(4*8), etc). On all tasks, other than two and three digit addition and subtraction, accuracy was uniformly under 20%. The other four tasks achieved high accuracy with more parameters. Of course, this doesn't show that the larger models "learned arithmetic". Two- and three-digit addition and subtraction are likely to be much better represented in a natural language dataset than other operations (and note of course the conspicuous absence of division). So it's safe to assume that the model has seen all the operations it's asked to repeat and knows their results by heart. Remember that for two and three digit addition and subtraction one only needs a dataset with the numbers up to 999, which is really tiny and easy to memorise. Edit: the authors note that they "spot checked" whether the model is simply memorising results, by searching for three-digit addition examples in their dataset. Out of 2000 three-digit addition problems they failed to find more than 17% in their dataset which "suggests" that the model had not ever seen the problems before. Or, it "suggests" the search was not capable of finding many more existing matches. In any case, why only "spot-check" three-digit addition? Who knows. The paper doesn't say. Certainly, one- and two-digit addition and subtraction should be much more common in a natural language dataset. The authors also say that the model often makes mistakes such as not carrying a one, so it must be actually performing arithmetic! Or, it's simply reproducing common arithmetic mistakes in its dataset. Overall, this sort of "testing" of arithmetic prowess simply doesn't cut the mustard. Edit 2: Also, no information about how many arithmetic problems of each type were tried. One? Ten? One hundred? Where all arithmetic tasks tested with the same number of problems? Unknown. _____________ [1] https://arxiv.org/abs/2005.14165 https://arxiv.org/abs/2005.14165

mcemilg 6y ago

We saw that the GPT-3 doesn't matter that much, right?

andybak 6y ago
Not personally, no.
- mcemilg 6y ago
  Take gpt-2, bert or any other attention based language model. Apply few shot learning on any domain to model you take. You will not see so much meaningful difference that matters. Even the GPT-3 super large than the other models. There is a hype because people able to use that domain adaptation easily without serving huge ml models by themselves. OpenAI presents that serving already. That makes it easy to explore language models to developers that don't know ML that much to tune.
evanrich 6y ago
For me, it has kind of broken HN’s comment sections. I find myself jumping to the bottom of longer comments to look for “btw, this comment was written by gpt3”. To me it seems like we are going to be entering a perpetual April fools day where we never really know what’s real.
- arkitaip 6y ago
  This has always been a problem with HN due to the robotic nature of its audience.
  - ss2003 6y ago
    Ouch!

simonkafan 6y ago

> that it’s possible for a model to someday reach human levels of generalization in NLP Fully disagree. There is no evidence that we are now closer to human level text understanding than before GPT-3. Yes, GPT-3 produces grammatically correct sentences but it still can't form a coherent idea or meaning and express it in sentences afterwards - that's what humans would do. GPT-3 is just better at obfuscating that the model has no clue what it's talking about. Nevertheless, compared with Eliza or other bots from 1960-2000 we made remarkable progress.

jhrmnn 6y ago
I wonder if 10 years ago one would believe that we’d have a model capable of generating an article that fools many given a complex prompt, yet is essentially incapable of any reasoning.
- samastur 6y ago
  I assume I wasn't the only early reader of blogs who thought they point at this being possible, but I admit I did not expect to see it so soon.
- newen 6y ago
  The Chinese room argument shows lots of people were thinking about it being a possibility but to have it happen so soon is something else.
- plutonorm 6y ago
  It is capable of reasoning. Good lord what does it have to do to prove it???!
dragonwriter 6y ago
> Yes, GPT-3 produces grammatically correct sentences but it still can't form a coherent idea or meaning and express it in sentences afterwards - that's what humans would do. There's considerable debate over whether humans can have a coherent idea before it is reduced into symbolic language, and it's not clear how you would distinguish this sequence of events, anyway. It's pretty clear what GPT-3 does doesn't match the common rationalization of human subjective experience of cognition, but it's not at all clear, AFAICT, that what the human brain does matches that rationalization, either. Which is not to say I think GPT-3 has anything like the kind, much less the level, of understanding humans have, I just think some of the common arguments arrayed in casually dismissing it are based on suppositions about human cognition that aren't sufficiently examined.

brunoluiz 6y ago

As a programmer but without that much of data science background, could someone explain what is the whole hype/breakthrough of GPT-3? I know it generates content that makes sense, "HTML+CSS" and some other text based stuff. But in layman terms, what is the huge deal with it?

vsskanth 6y ago
The first two versions generated content based on a prompt that was coherent for a sentence or a paragraph. GPT-3 generates an entire article that's largely coherent (though still lacking nuanced meaning). Each version of GPT has exponentially increasing parameter count. Raw compute seems to be winning out here. Basically for this application they aren't seeming to hit diminishing returns for increasing parameters. Now people are wondering what GPT-4 can do. If you can write human-level coherent articles or if it passes the turing test or something it's gonna trigger an arms race for governments to obtain this capability.
- brunoluiz 6y ago
  Thanks for the explanation ;) > Each version of GPT has exponentially increasing parameter count What are parameters in this context? > Now people are wondering what GPT-4 can do. If you can write human-level coherent articles or if it passes the turing test or something [...] Well, when I first heard about "text generation" last year what I first raised to my friends was "surely this will make fake news even worse". But well, any technology come with a bright and dark side. I try to not be pessimistic in these moments hehe
  - vsskanth 6y ago
    parameters here are just the size of the neural net of the model. Each unit of computation has its own parameter that needs to be fit. Bigger model = more layers or units of computations = more parameters.
- op03 6y ago
  This is like watching parents talking about their kid develop over time and getting excited over their evolving writing ability. Doesn't mean we get Turing or Gandhi at the end of the story. Or that there is any control over what is produced. To produce those 2 nature had to iterate over 100 billion humans.

ricksharp 6y ago

For me it’s performance passes a usability threshold for human + machine collaboration. It is possible to use it to generate texts which can be quickly curated or edited by a user. Specifically this could be useful in authoring fiction (sci-fi novels, game dialog, etc.). Imagine the Star Trek holodeck characters. It’s dialog quality is nearly good enough to make that level of interaction feasible.

barbegal 6y ago

What can GPT-3 do that is useful? I can understand that it outputs text based on a prompt and some input but I don't understand how that can be leveraged to do useful things. I can ask it trivia questions but is it better than doing a Wikipedia search? Is it possible to give it a prompt of some scientific paper say and write an article about it? Or will it just generate nonsense?

swalsh 6y ago
Here is someone on Twitter who has been building some, albeit toys, using the tool. It seems pretty powerful: https://twitter.com/jsngr https://twitter.com/jsngr I found this to be very impressive: https://twitter.com/jsngr/status/1284874360952692736 https://twitter.com/jsngr/status/1284874360952692736 Not only did it eventually generate code based on his example for the new domain he specified, it was even able to generate new domains.
- barbegal 6y ago
  It's somewhat impressive but it didn't do what he wanted it to. It didn't list temperatures, instead it listed weather descriptions. And to get it to do this he had to write a full template. We also don't get to see if this was the first attempt at doing this or can the model generate code like this reliably.
  - devalgo 6y ago
    You'll be looking for reasons to downplay it while dozens of startups receive funding to build automatic app generation tools.
    - actionscripted 6y ago
      You are pushing this idea so hard and blasting this thread with copy/paste comments. It's obvious you're excited about it and that's great but there is a lot that isn't shown and that's where the real work is being done. Just because it looks like magic, doesn't mean there isn't someone pulling some strings somewhere else to aid the illusion.

k__ 6y ago

Can a model like this be realized as a chip? I mean, 300GB memory is crazy. But back in the days I ran full HD videos on a netbook, because it hat special optimzied chips.

justinmchase 6y ago
Yeah but that chip is probably going to look a lot like a 300GB memory chip.
- k__ 6y ago
  Like big external power bank? Doesn't sound too bad.

barrenko 6y ago

Highly distrust gwern et al. Bloggers at most.

qwerty456127 6y ago

> GPT-3 shows that it’s possible for a model to someday reach human levels of generalization in NLP—and once the impossible becomes possible, it’s only a matter of time until it becomes practical. The fact it can [occasionally] generalize and participate in a verbal conversation on a human level means it can potentially organize and direct productive (on a human level at least) action if assigned as a manager to a team of humans. Doesn't it?

justinmchase 6y ago

I don't feel like this article answered the question in its headline. I don't even know what GPT-3 is so maybe even the tiniest bit of background could have helped.

kemonocode 6y ago

GPT-3 won't matter until all of the work put into it can be replicated by someone else, and as it stands right now it's just a toy for people with far too many resources to spare.

robtigo1 6y ago

So can you feed a video to it and let 'dream' of all possible outcome? If this is achievable then the internet will eventually become a loop-back device for our senses just like the matrix movie.

nemoniac 6y ago

Speaking of unconscious bias, this quote from the original article made me raise my eyebrows: "We wanted to identify how good an average person on the internet is at detecting language model outputs, so we focused on participants drawn from the general US population."

anchpop 6y ago
Clearly the average USian is not the average person, but given that they need to speak English to have a shot (unless gpt-3 works for other languages?) it doesn't seem like a terrible approximation

zumachase 6y ago

We're incredibly excited about GPT-3. I think there is a fair bit hype exhaustion, especially from the likes of OpenAI ("our AI is too dangerous to release"). So this is completely understandable. However I think what's missing here is our benchmarks (a la Turing test) are about negation as opposed to affirmation. We tend to evaluate AI on whether or not we can discern the fact that it's AI. We seek to negate it as human, as opposed to affirming it as human (or close to). And this is not the right mindset when it comes to AGI because the gap between "obviously not human" and "human-like" is enormous. These are all definitely steps in the right direction, and the applications for even robotic process automation will be huge. But we're not even close to having nets that can reason about even the most basic things.

abernard1 6y ago
> However I think what's missing here is our benchmarks (a la Turing test) are about negation as opposed to affirmation. I would question the value of the Turing test, and maybe think that's not a great example for AI. There's always been this assumption that passing the Turing test would mean we had AI, but I think that was always predicated on the machine generating the outputs. With the GPT- models, it's not clear that this isn't a form of compression over an immense data set, and we're sending pre-existing _human_ responses back to the user. It implies to me that we can pass the Turing test with a large enough data set and no (or very little) intelligence. All of this makes me believe "These are all definitely steps in the right direction" is questionable.

jungletime 6y ago

Use it to post to Reddit and create a feedback loop. Help to hasten the AI takeover. What I find fascinating is the carful craft of framing things out of context, as a tool for politics. https://en.wikipedia.org/wiki/AI_takeover https://en.wikipedia.org/wiki/AI_takeover Narrative wins. This will be a great tool to misinform.

thecureforzits 6y ago
If you look at the folks who are backing this, it is clear what their intent is.
runeb 6y ago
There is already a pretty good one using GPT-2 https://www.reddit.com/r/SubSimulatorGPT2/ https://www.reddit.com/r/SubSimulatorGPT2/

lerchmo 6y ago

it is also a pretty good case study in the "bitter lesson" and all but ensures that the future of AI will be driven by the companies with the deepest pockets.

devalgo 6y ago
OpenAI is far from having the deepest pockets.

highfrequency 6y ago

There’s been a lot of discussion on HN lately about the implications of GPT-3: are we moving toward general AI or is this just a scaled up party trick? I have no idea whether scaling up transformers another 100x will lead to something resembling real intelligence, but it certainly seems possible. In particular, I find the arguments against this possibility to be fairly silly. These are the three main arguments I have seen for why GPT type models will never approach AGI, and the reasons I don’t think they are valid: 1. GPT-3 requires vast amounts of training data (hundreds of billions of words from the internet), whereas a human can become fluent in natural language after “training on” much less data. It’s not analogous to compare the GPT-3 training corpus to the education that one human receives before becoming fluent in natural language. We benefit from millions of years of evolution across billions of organisms. A massive amount of “training” is incorporated in the brain of an infant. This must be the case because even if you could somehow read all of the text on the internet to your dog, it would not approach intelligence. 2. There was no intellectual breakthrough in the development of GPT-3 just more “brute force” training on more data, therefore it or its successors can’t achieve a breakthrough in intelligence. We must remember that there was no intellectual breakthrough required for the development of human intelligence, it was just more of the same evolution. The core pattern of evolution is extremely simple: take an organism, generate random variants from it, see which ones do the best, and then create new variants from the good ones. This is perhaps the most basic scheme you could think of that might actually work. Evolution has produced amazing results in spite of its simplicity and inefficiency (random variations!) because it generalizes well to many environments and scales extremely well to millions of generations. These are exactly the strengths of gradient descent. In fact, gradient descent follows the same structure as evolution, except that at each iteration we don’t generate random variations, but instead make an educated guess about what a fruitful variation would be based on available gradient information. This improves learning efficiency tremendously; imagine being able to say: “this Neanderthal died because he stepped into a fire, let’s add some fire-avoidance to the next one” instead of waiting for this trait to be generated randomly. Speaking of brute force and amount of training, it would take 355 years to train GPT-3 on a single GPU. This strikes me as quite fast relative to evolutionary time scales. 3. Machines lack capabilities fundamental to the human experience: in particular feeling pleasure, pain, and an internal drive toward a goal. Indeed, if you turn a computer off in the middle of a computation, there is no evidence of suffering. And if the computer successfully writes a blog post of human quality, it feels no joy in the human sense. My claim is that these sensations are not core aspects of intelligence. In fact, pleasure and pain are very primitive developments that even cockroaches can claim. The most impressively human accomplishments (harnessing vast external energy sources, breaking out of bare subsistence, landing on the moon, etc.) were made in spite of the fact that we are messy bags of emotion that unpredictably feel anger, jealousy, despondence or elation. These emotional responses were selected for because they were useful as proximate goalposts orienting us toward reproduction—basically, to overcome forgetfulness in the pursuit of long-term goals. If in the future we can simply direct a computer to write a captivating novel without needing to program in lots of visceral intermediate stimuli to keep it on track, so much the better.

benlivengood 6y ago

What I would really like to see is an analysis of the weight that individual chunks of context contribute to the final probability for an output. That might allow for better prompting; when GPT-3 gets the wrong answer it would be fairly obvious what was lacking in the prompting given the context it thinks has the most influence. Also, good prompts would presumably have a higher weight.

jadbox 6y ago
Once discovered, you could have a kind of outline builder where you provide all the right linking outline context to have it generate appropriating paragraphs around it.

supernova87a 6y ago

Can someone answer a question for a layman: Is the number of parameters to be read as the indicator of how "advanced" the training has gotten, or the accuracy of the output? As in, this dataset/training has gotten to the point that it understands the 160 billionth small exception to the general rules of how language should be interpreted, or constructed, to be considered believable? Sometimes (as a layman) I look at this and think instead, wow, how slow these ML algorithms must be that they need 160 billion parameters to predict correctly. Is it one of these statements?

killerstorm 6y ago
> Is the number of parameters to be read as the indicator of how "advanced" the training has gotten, or the accuracy of the output? Accuracy, of course. > As in, this dataset/training has gotten to the point that it understands the 160 billionth small exception to the general rules of how language should be interpreted, or constructed, to be considered believable? It memorized a lot of facts, but it is also better at figuring out rules than its predecessor. > Sometimes (as a layman) I look at this and think instead, wow, how slow these ML algorithms must be that they need 160 billion parameters to predict correctly. There are more specialized models which are trained on much smaller datasets. They usually are given a specific task, such as classification. GPT-3 is trained on a very large dataset in unsupervised way. And as a result, it is able to handle a very wide variety of tasks (without re-training). If you tell it to do math, it will do math. If you tell it to translate between different languages, it will do translation. If you tell it to write JS code, it will write JS code. If you ask it to write a Harry Potter parody as if it was written by Hemingway, it will do that. So the whole point is that it can do pretty much any imaginable task involving text given only few examples, with no specific training.
- supernova87a 6y ago
  Thanks!

TrackerFF 6y ago

Picture this grim future: 1. Companies will use models like these to generate automatic job listings 2. People will use models like these to generate automatic job applications 3. Companies from 1) will use automated tools to parse and analyse said applications We're gonna end up with abysmal SNR, as the sheer number of applicants will explode.

ajzinsbwbs 6y ago
It doesn’t sound like the worst way to hire model builders.

nojvek 6y ago

I can see comment and review farms wanting their hands on GPT really bad. Imagine being able to generate 1000s if Human like reviews with positive sentiment. Businesses pay real money for this. Right now AI is only available to mega tech corps. Even OpenAI is a closed research lab. So one can infer that AI will always be the divider.

anonimunos 6y ago

it seems that this is the future