13 ms·

GPT-3 Creative Fiction

234 points by ashin 6y ago

sillysaurusx 6y ago
Fine tuning GPT-3 is one of the biggest challenges, because it's behind an API. The weights aren't available to researchers, so we can't make it do anything it doesn't already do. But, that's fair. It's OpenAI's weights; they can keep them locked up if they want to. What caught my attention, though, is that supposedly OpenAI is working on a way to support fine-tuning. If you think about the logistics of that, it's a very interesting challenge. The situation is this: 240GB of weights, as a webservice. Each fine-tuning session results in another copy of 240GB. So it clearly doesn't scale -- 1TB per 4 users isn't exactly efficient. Except, not quite. You can solve this by adding additional layers, which you then fine-tune. So the base model is 240GB or whatever, and the extra layers morph the output to do what you want. Think of it as a GPT-3 with a GPT-2 1.5B stuck on the end of it. It's a neat idea, because theoretically you'd get two models out of it: you can "break off" the end of the fine-tuned model, and you end up with the original model. So it would be very modular. Are there other models that you can "break apart" to get different sub-models? Sort of like adding slots that give a model different capabilities.
- wyldfire 6y ago
  You could encode the deltas cleverly and likely use much less than 240GB.
- ctchocula 6y ago
  Many ML models are like this (anything used in CV e.g. ResNet, VGG). For example, if you want to classify images as being hot dog or not hot dog (classes that do not exist in ResNet), you can take weights from a pretrained ResNet-50 and finetune the last layer based on a small training set of input images labeled hot dog and not hot dog. This lets you reuse the ResNet's feature detector layers, while plugging in specialized "is this a hot dog or not" fully connected layer.
  - YeGoblynQueenne 6y ago
    If I understand correctly, I think you and the other poster are describing transfer learning.
    - p1esk 6y ago
      same thing
- p1esk 6y ago
  Yes, I guess you can have the API to provide you the intermediate layer outputs, instead of the predictions. However, if you then want to finetune your own extra layers using these intermediate outputs as inputs, would the API be able to produce them fast enough for you to do the finetuning of your own layers in reasonable time? That's assuming the extra layers are located on your own servers. Or would OpenAI be willing to actually create the extra layers on their own machines and let you finetune those? In the second scenario, you would need to move your dataset to their servers. Actually, since they used Azure cloud to train GPT-3, I don't see why they wouldn't just let you pay for spinning up Azure instances to train your extra layers, and connect those to the model.
- gdb 6y ago
  (I work at OpenAI.) I am finishing up our fine-tuning API this weekend :). If anyone on HN would like to try out the fine-tuning API (or want to build something on top of the base API), send me an email (gdb@openai.com) with your use-case and I can try to accelerate you in our invite queue. PS: We're hiring — if you enjoy building APIs with Python/Go/Kubernetes/Kafka or building front-end interfaces in React, then please get in touch — gdb@openai.com.
  - KKKKkkkk1 6y ago
    Are there any products in the pipeline that you're planning to ship? Asking for prospective candidates.
    - gdb 6y ago
      There's just about infinite surface area with the API — we're trying to build a dead-simple API that developers can plug into any product in order to add intelligence features that would be otherwise impossible. This requires a lot of traditional software work — API design, writing and maintaining a growing amount of business logic, providing great tools and interfaces to help our users work with the API, excellent documentation and tutorials, scaling and operating backend systems, etc — and machine learning systems work — building serving infrastructure for a great variety of giant neural networks while making the most efficient use of our hardware, allowing our users to interact with these neural networks in increasingly sophisticated ways, etc. While we're just getting started and have a small team, we are already supporting customers across a wide variety of industries (see https://beta.openai.com/ https://beta.openai.com/ for a sample) and serving millions of requests per day. We are busy trying to invite folks off a very long waitlist while building out the API to support everyone. Would love more help :).
  - deleted 6y ago
    [deleted]
  - OkGoDoIt 6y ago
    Emailed. I think I have an interesting perspective as a pro-hackathonner who regularly uses new technologies to build compelling demos. Haven’t heard back yet from my initial beta application, hope to be able to try it out and explore its potential.
- anoncake 6y ago
  They can keep their weights secret, sure, but then they should change their name.
Mizza 6y ago
Some of these - particularly the Zizek Navy Seals Copypasta - are absolutely incredible. Great work, Gwern.
2bitencryption 6y ago
GPT's take on the navy seal copypasta, in the style of a KGB spy: "I have over 300 confirmed red scares." Haha, that is genuinely one of the funniest versions of that I've ever seen, human-generated or otherwise. That level of inference is really amazing.
- ddevault 6y ago
  The whole section on navy seal copypasta generation is amazing.
- deep_etcetera 6y ago
  I must agree reading those copypasta, "300 red scares" and "300 confirmed scoots" had me close to tears.
- messe 6y ago
  Idk, Donald Trump's "I have over 300 confirmed bankruptcies" did it for me.
- silveroriole 6y ago
  I love that the Englishman one gets really confused about the meaning of the word “mate” and ends up as a Royal Navy pirate.
bjourne 6y ago
Great article. Well worth the read! I enjoyed the part about sampling which is a big unsolved problem. To me, techniques like nucleus sampling and temperature sampling feels like hacks to make up for the fact that maximizing for likelihood maybe isn't the goal!? Maybe repetitive gibberish has a higher likelihood than prose written by humans? That Best of sampling decreased text quality indicates that it has. Researches have assumed that the problem would go away with ever growing models. But maybe it won't? I don't agree that generating (symbolic) music would be less sensitive to sampling issues. On the contrary, in my opinion. In text you can often get away with grammatical errors or missing punctuation. But if the pitch or timing of one chord is wrong it's over. The audience instantly hears that it is garbage. Thus, you have to lower the temperature (or probability threshold or what have you) to make the sampling more conservative exacerbating the problem with repeated sequences. Of course, in music you want repetitions. But not too much. The magic number (in Western music) is 4. Fewer repeats makes it feel as if the music jumps around. More repeats makes it feel as if the music is stuck or "looping."
ixvvqktiwl 6y ago
I still find it odd that we call this "artificial intelligence" when it's advanced mimicry at best. There's no "intelligence" in the strict definition of the word, it's just elaborate pattern matching. But I get it, it's exciting, and it's an easy way to get VC money. Perhaps one day we'll get something useful aside from the various pattern matching applications (image recognition, speech to text, etc). I'm skeptical but willing to be surprised.
- 627467 6y ago
  I'm not disagreeing with you but where's the frontier between "pattern matching" and "intelligence"? I also think human intelligence and creativity will always be judged by other humans as _better_, more genuine - as long as the judges can trust that given creation was lead by a human. Among creative types "derivative" is a derogatory label used frequently. "Lesser artists borrow, great artists steal" also has an implication that regardless of what else an artist/creator does: pattern matching is a big part of that process.
  - ixvvqktiwl 6y ago
    That's a good point. I think we need to have a better understanding about how the brain works before we can properly answer that question. At this point there's a lot we don't understand about the brain, and psychology for that matter, it's basically a black box that we can observe through a microscope or other imaging system but we don't really understand what's happening. An ant only has around 250,000 neurons, yet they're still more intelligent than the most advanced "AI" we've managed to produce. An ant may not be able to paint a painting or write a novel, but I think most people agree they qualify as an intelligent being.
    - the8472 6y ago
      Can we really call this ANTNN "intelligent" when it falls victim to trivial adversarial inputs? Ones that arise in the real world, not just in artificial inputs mind you. Clearly those ANTNNs are still far from anything that could rival natural intelligences. https://www.youtube.com/embed/mA37cb10WMU https://www.youtube.com/embed/mA37cb10WMU
paulpauper 6y ago
again, gwern writes the longest guide in the world about something
- api 6y ago
  Gwern should train it on his own corpus and see if he can automate himself. :)
  - gwern 6y ago
    It apparently doesn't need any further training: https://www.gwern.net/GPT-3#gwern-branwen https://www.gwern.net/GPT-3#gwern-branwen
  - trhway 6y ago
    or in general : GPT3 gen using random input -> curation by some GAN -> GPT3' train -> GPT3' gen -> ... How many generations until War and Peace?
mej10 6y ago
Straight up plagiarizes The Beatles here: https://www.gwern.net/GPT-3#dr.-seuss-oh-the-places-youll-go https://www.gwern.net/GPT-3#dr.-seuss-oh-the-places-youll-go " There’s nothing you can know that isn’t known. Nothing you can see that isn’t shown. Nowhere you can be that isn’t where you’re meant to be. "
- brudgers 6y ago
  Well, it's easy.
- kick 6y ago
  "Good artists copy; Great artists steal." I don't think that it's some absolutely amazing creation, but we probably shouldn't pretend like plagiarizing isn't something realistic about it. How many MySpaces had that line in them? Reddit's got hundreds of instances of it based on a quick search of comments. There are enough occurrences of it on twitter that I scrolled for ten minutes and didn't run dry of comments with it.
api 6y ago
I'm gonna put forward the very view that gwern repeatedly argues against: "but... it's not understanding." So far I see no evidence that this thing or anything else like it has any actual understanding, any model of the world. Indeed it can't as it possesses no sensory apparatus. It's not embodied. It doesn't experience anything. I'm not sure the OpenAI folks would argue with me, but it seems Gwern asserts that this sort of thing indicates that general AI or even sentient AI is on the doorstep. I don't think it does, and I still maintain as I always have that CS people systematically underestimate and trivialize biology.
- sturza 6y ago
  How could we accurately measure understanding? Honest question, because i am curious.
- sutterbomb 6y ago
  What makes you confident that you aren't overestimating the importance that we "experience anything"
  - api 6y ago
    When I say "I have a laptop in front of me," I am describing an understanding of something that is being experienced (sensed). If a Markov text generator outputs this text, it's just rearranging bits. I don't see any evidence that GPT-3 is doing anything more than rearranging bits in a much more elaborate way than a Markov text generator. The results kind of dazzle us, but being dazzled doesn't indicate anything in particular. I see something akin to a textual kaleidoscope toy, a generator of novel text that is syntactically valid and that produces odd cognitive sensations when read. I maybe should have said sensed, not experienced, since experience also leads into much deeper philosophical discussions around the nature of mind and consciousness. I wasn't really going there, since I don't see anything in GPT-3 or any similar system that merits going there. I also don't see any evidence that it is drawing any new conclusions or constructing any novel thoughts about anything. It's regurgitating similar results to pre-existing textual examples, re-arranging new ideas in new ways. If you don't think actual new ideas exist then this may be compelling, but if that's the case I have to ask: where did all the existing ideas come from then? Some creative mechanism must exist or nothing would exist, including this text. The fact that the output often resembles pop Internet discourse says more about the mindlessness of "meme-think" than the GPT-3 model. As for real world uses, social media spam and mass propaganda seems like the most obvious one. This thing seems like it would be a fantastic automated "meme warrior." Train it on a corpus of Qanon and set it to work "pilling" people.
czzr 6y ago
This is fascinating to read through. It’s so hard to avoid a variant of the Forer effect, though - where we unconsciously discount the errors, and selectively focus on subsets of the output and impute meaning to them. Designing objective quality tests must be an active area of research, I wonder what the best approaches are?
- gwern 6y ago
  Forer effect material was written by humans, one might note... But there was a paper just the other day on more rigorous evaluation: https://arxiv.org/abs/2006.14799 https://arxiv.org/abs/2006.14799
  - czzr 6y ago
    Thanks for the link. You’re right that Forer effect material was written by humans, but the point is more that there is a failure mode in our thinking that can be exploited - mostly intentionally by “psychics”, unintentionally by automated text generators. Just something I was mulling over, though, not to take away from the obvious progress here.
webmaven 6y ago
Holy crapoly. The quality (in both senses) of the output given an appropriately constructed prompt is incredible. I wonder if it's possible to get it to do the opposite of summarizing, ie. give it a plot summary and have it expand it into a fleshed out story that conforms to the summary...
- p1esk 6y ago
  I wonder if it's possible to get it to do the opposite of summarizing That's pretty much its main job: you provide a prompt and it writes a story about it.
  - webmaven 6y ago
    A story prompt resulting in a story is looser than what I meant. Can it elaborate on a plot summary (that includes spoilers) and remain faithful to it? eg. given an outline of O'Henry's Gifts of the Magi, write an equivalent story that still concludes with the protagonists both having sold their prize possesion to buy a now useless gift for the other? Or will the plot get lost in the weeds?
- lacker 6y ago
  It doesn't seem to work quite as well. For single sentences, GPT-3 output usually hangs together pretty well. For longer stretches of text, often there are internal inconsistencies that are jarring when you read it, or parts that don't quite make sense when you read it as a whole.
  - deleted 6y ago
    [deleted]
  - webmaven 6y ago
    Thanks for the reply. Aside from the coherence and consistency issues you've noted, does the output actually conform to the specified plot summary, or does it deviate from it?
Reedx 6y ago
Carmack posted (yesterday) an interesting thought on models like GPT-3: "Big AI models like GPT-3 train on massive internet text dumps, but the data is assumed to be independent and identically distributed. Incorporating time information for a decade of data might allow them to start writing tomorrow's reddit or twitter trends." https://twitter.com/ID_AA_Carmack/status/1278840413919551488 https://twitter.com/ID_AA_Carmack/status/1278840413919551488
- jcims 6y ago
  I saw that yesterday and it spawned a thought process for me. It seems the current approach is very effective in developing a language model, but not always effective developing an interaction model. I wonder if it would be possible to build a graph of interactions between users/personas on various social media platforms and forums, and use that to help develop a more effective communicator. Of course you could add things like date, community (e.g. avforums, /r/blacksmithing, etc) to the graph to help with the contextual cues. After you have all of that, I didn’t wondered if we could visualize the latent space of human personas and see what that looks like. Does it map to the four quadrants of that political spectrum survey, left and right, old/young, etc.
  - infogulch 6y ago
    In that same vein, but along the code generation axis, I wonder if something on the scale of GPT-3 would be capable of generating commits directly. I think generating commits would be much more useful than generating programs whole hog.
    - jcims 6y ago
      That's a great idea. Particularly with bugfixes.
  - ipsum2 6y ago
    That's what CTRL does: Given a URL (or other prompt), generate some language. From [1]: > With CTRL, we can test which domain best explains a sequence. Note that this procedure is sensitive to subtle nuances in the query prompt. In the example below, "Global warming is a lie" differs from "Global warming is a lie." The latter is a simple declarative sentence as opposed to an open start to a sentence which may continue. Source attribution cannot be considered a measure of veracity, but only a measure of how much each domain token explains a given sequence. Query Prompt Attributed Sources Global warming is a lie. r/unpopularopinion, r/conspiracy, r/science Global warming is a lie r/eli5, r/science, r/unpopularopinion Global warming is a real phenomenon r/eli5, r/science, r/changemyview Global warming is a real phenomenon. OpenWebText, r/changemyview, r/science https://blog.einstein.ai/introducing-a-conditional-transformer-language-model-for-controllable-generation/ https://blog.einstein.ai/introducing-a-conditional-transform...
FaisalAbid 6y ago
Is this using OpenAI's API or are you running GPT-3 on your own machine?
- minimaxir 6y ago
  You can only use GPT-3 via OpenAI's API currently.
  - FaisalAbid 6y ago
    Got it, that's what I thought!
minimaxir 6y ago
After spending a lot of time working with GPT-3/the OpenAI API (https://github.com/minimaxir/gpt-3-experiments https://github.com/minimaxir/gpt-3-experiments ), one notable part of GPT-3 is the high signal-to-noise ratio in generated output. When finetuning GPT-2, only about 5-10% of the generated output is usable/coherent. But with GPT-3, easily 30%-40% of the generated text is usable/coherent, which is a big boost in quality.
- deleted 6y ago
  [deleted]
knicholes 6y ago
I want someone to use GPT-3 to be my D&D GM, or at least an assistant.
- sktguha 6y ago
  You can try Ai dungeon. It is somewhat close and uses gpt3 api
mintplant 6y ago
It writes uncomfortably coherent and witty fatalist My Little Pony fanfiction: > “I’m Princess Celestia, of Equestria.” the alicorn said, “It’s nice to meet you. And I’m very sorry for your untimely demise.” > “Heh. You’re telling me.” the Professor laughed, shaking his head. “I’m dead. I’m… dead. Do you know what that means?” > “I have an inkling.” Princess Celestia said, wincing. > “It means I don’t have to worry about even numbers anymore.” he said, nodding. “I don’t have to worry about probabilities anymore! I don’t have to worry about the upcoming war anymore! I don’t have to worry about anypony anymore!” he spread his hooves out at his sides, “I don’t have to do anything anymore! It’s beautiful!” > “Yes, yes, I’m sure it is.” Princess Celestia said, nodding along. > “Nothing matters now that I’m dead!” he cheered, “It’s great! I’m free! This is what it feels like to be liberated!” > “I can see that.” she said. > “I’m free from death!” he continued, “I’m free from taxes! I’m free from dictatorships! I’m free from flirting!” he laughed, “I’m free from everything! Isn’t it great?” > “Sure, I guess.” Princess Celestia shrugged. > “What do you think about all of this, Princess?” he asked. > “About what?” she replied. > “Well, I just thought you might have something to say about it all.” he told her. > “What would you like me to say?” she responded. > “Do you think death is awesome?” he questioned. > “Well… no.” she shook her head, “But I think the passage to it is.” > “Oh.” he said, “I see what you mean.”
- runawaybottle 6y ago
  Now how do we get aspiring actors to take GPT3 seriously and act out a possible screenplay? Hopefully one day it will generate startup business plans, and we’ll get people to take it seriously, we may not know the difference.
  - rorykoehler 6y ago
    We animate them from nothing
- dTal 6y ago
  What the hell? How much of a world model does it have, that it can do things like write coherently about death as liberation, and come up with examples of things a living thing might be relieved at not having to bother with anymore? How the hell did a computer write this? Are we sure it's not been overtrained and this is actually a verbatim story from somewhere?
ttul 6y ago
Gwern is a prodigious blogger. Absolutely prodigious.
- catacombs 6y ago
  Thorough is the better word.
fpgaminer 6y ago
First off, gwern, lovely blog. The table of contents is incredibly helpful, especially with those little pop-up previews. I would've loved to have GPT-3 available to me two weeks ago. I was building a personal escape room for my wife as a gift, and used huggingface's GPT-2 website to help write some of the world building content. I'm not a particularly good writer, let alone creative, but wanted a few journal pages/notes to build the atmosphere and story of the escape room. I was able to write the rough skeleton of those notes and then use GPT-2 to help fill them out. Ended up working okay, definitely better than nothing, but GPT-2 is temperamental and lacks the "prompting" that GPT-3 has. For example, I needed to come up with the name of the journal's author. So I fed the journal text to GPT-2 and put "Sincerely," at the bottom, to try and prompt it to complete a name. That didn't work. Ultimately what worked was putting "My name is" at the end. I still had to grind through 20 or so completions before I got a name I liked. (Yes, I could have just picked a name at random. Did I mention I'm bad at creativity? My thinking was that the AI could more intelligently pick a name that fit the story and writing style of the journal. And honestly the name it came up with up with, Mabel, fit the character well (a librarian dabbling in magic). I feel like GPT-3 would have done a lot better. Not to mention the ability to describe my world to it and then just straight up ask it for ideas.
- TJSomething 6y ago
  I like providing some context that prompts the model for a list if I think I might need a few candidates. "We discovered a series of journals by several authors in the cabin. The authors' names were:" worked pretty well.
- gwern 6y ago
  FWIW, if I was trying to generate an escape room, I think I would probably try to use AI Dungeon. It seems like a natural fit. You could easily describe the room and edit text as you go to make it a transcript of an escape room. "I pick up the journal to read the author's name" etc.
  - fpgaminer 6y ago
    Good call; AI Dungeon hadn't occurred to me!
cmehdy 6y ago
This article is fantastic in both shape and content, and I got lost with all the examples because there is so much to wonder at. What hits me most profoundly is that there are so many witty and interesting prompts yet the purely logical statements fall apart (with the black ravens, or male sister). This is something that probably does not jump to one's mind as significant, because "technicalities", but to me this is where logic allows us to take a step back from our human projection for a second, it's my own anthropomorphism that becomes more obvious to me. I find that ironic, considering a lot of human beings also DO fail such tests, but something hits me about how a supposedly completely logical entity fails at logic more than at poetry. It kinds of shakes my own (supposed) humanity.
- gwern 6y ago
  The logic is weird. Another example is factual question answering. Janelle Shane tried asking basic questions like how many eyes a horse has and GPT-3 insisting on 4; I retry with somewhat different prompting and sampling settings more finetuned to Q&A (...sampling can reveal the presence of knowledge but not its absence...), and I get perfectly straightforward correct answers: https://twitter.com/gwern/status/1278798196555296771/photo/1 https://twitter.com/gwern/status/1278798196555296771/photo/1 So GPT-3 does know how many eyes a horse has; but why was it also happy to answer '4'?
  - cmehdy 6y ago
    Something about the logic being so off is what I intuitively find logical: we're making these AIs "in our image" in a sense (we think of "neural networks", train them with mostly human-generated datasets), and there's a lot of evidence that pure logic evades us without the use of some heavy artillery to address it (cognitive biases, illusions, optimizations for goals that do not necessarily align with "objectively observable" reality). So in a way, I wonder if we'll have to "teach AI logic" at some point too. In this quest of running logic software on logic hardware with.. steps.. in between, I can't help but think about us humans on our parallel quest when it comes to our brains.
    - 6y ago
deleted 6y ago
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longtom 6y ago
Could someone with GPT-3 beta access try whether it can better solve 3 digit addition when it is allowed/encouraged/forced to make intermediate results explicit? E.g. instead of 21 + 110 = 130 150 + 12 = condition it on 21 + 110 = 100 + 10 + 20 + 1 = 100 + 30 + 1 = 131 150 + 12 = or similar. Given that humans make these intermediate steps in their heads GPT may perform better when it is encouraged to do them as well. This may in fact apply to all sorts of reasoning, but in many cases it may be difficult to make these steps explicit in text form. Humans seem to mainly use some prediction layer or scratchpad which also contains the inner monologue but also motor primitives, smells, images, everything. Humans can decide to think a bit longer before producing an output, which appears to require an RNN.
- maliker 6y ago
  I took a shot at this, didn't have much luck though: PROMPT ======= Input: 21 + 110 Output: 100 + 20 + 10 + 1 = 100 + 30 + 1 = 130 + 1 = 131 Input: 89+78 Output: 80 + 70 + 9 + 8 = 150 + 9 + 8 = 150 + 17 = 150 + 10 + 7 = 160 + 7 = 167 OUTPUT ====== Input: 37 + 112 Output: 30 + 100 + 10 + 2 = 110 + 1 = 111 Input: 91+11 Output: 100 + 90 + 1 = 190 + 1 = 191

234 points by ashin 6y ago

Fine tuning GPT-3 is one of the biggest challenges, because it's behind an API. The weights aren't available to researchers, so we can't make it do anything it doesn't already do. But, that's fair. It's OpenAI's weights; they can keep them locked up if they want to. What caught my attention, though, is that supposedly OpenAI is working on a way to support fine-tuning. If you think about the logistics of that, it's a very interesting challenge. The situation is this: 240GB of weights, as a webservice. Each fine-tuning session results in another copy of 240GB. So it clearly doesn't scale -- 1TB per 4 users isn't exactly efficient. Except, not quite. You can solve this by adding additional layers, which you then fine-tune. So the base model is 240GB or whatever, and the extra layers morph the output to do what you want. Think of it as a GPT-3 with a GPT-2 1.5B stuck on the end of it. It's a neat idea, because theoretically you'd get two models out of it: you can "break off" the end of the fine-tuned model, and you end up with the original model. So it would be very modular. Are there other models that you can "break apart" to get different sub-models? Sort of like adding slots that give a model different capabilities.

wyldfire 6y ago
You could encode the deltas cleverly and likely use much less than 240GB.
ctchocula 6y ago
Many ML models are like this (anything used in CV e.g. ResNet, VGG). For example, if you want to classify images as being hot dog or not hot dog (classes that do not exist in ResNet), you can take weights from a pretrained ResNet-50 and finetune the last layer based on a small training set of input images labeled hot dog and not hot dog. This lets you reuse the ResNet's feature detector layers, while plugging in specialized "is this a hot dog or not" fully connected layer.
- YeGoblynQueenne 6y ago
  If I understand correctly, I think you and the other poster are describing transfer learning.
  - p1esk 6y ago
    same thing
p1esk 6y ago
Yes, I guess you can have the API to provide you the intermediate layer outputs, instead of the predictions. However, if you then want to finetune your own extra layers using these intermediate outputs as inputs, would the API be able to produce them fast enough for you to do the finetuning of your own layers in reasonable time? That's assuming the extra layers are located on your own servers. Or would OpenAI be willing to actually create the extra layers on their own machines and let you finetune those? In the second scenario, you would need to move your dataset to their servers. Actually, since they used Azure cloud to train GPT-3, I don't see why they wouldn't just let you pay for spinning up Azure instances to train your extra layers, and connect those to the model.
gdb 6y ago
(I work at OpenAI.) I am finishing up our fine-tuning API this weekend :). If anyone on HN would like to try out the fine-tuning API (or want to build something on top of the base API), send me an email (gdb@openai.com) with your use-case and I can try to accelerate you in our invite queue. PS: We're hiring — if you enjoy building APIs with Python/Go/Kubernetes/Kafka or building front-end interfaces in React, then please get in touch — gdb@openai.com.
- KKKKkkkk1 6y ago
  Are there any products in the pipeline that you're planning to ship? Asking for prospective candidates.
  - gdb 6y ago
    There's just about infinite surface area with the API — we're trying to build a dead-simple API that developers can plug into any product in order to add intelligence features that would be otherwise impossible. This requires a lot of traditional software work — API design, writing and maintaining a growing amount of business logic, providing great tools and interfaces to help our users work with the API, excellent documentation and tutorials, scaling and operating backend systems, etc — and machine learning systems work — building serving infrastructure for a great variety of giant neural networks while making the most efficient use of our hardware, allowing our users to interact with these neural networks in increasingly sophisticated ways, etc. While we're just getting started and have a small team, we are already supporting customers across a wide variety of industries (see https://beta.openai.com/ https://beta.openai.com/ for a sample) and serving millions of requests per day. We are busy trying to invite folks off a very long waitlist while building out the API to support everyone. Would love more help :).
- deleted 6y ago
  [deleted]
- OkGoDoIt 6y ago
  Emailed. I think I have an interesting perspective as a pro-hackathonner who regularly uses new technologies to build compelling demos. Haven’t heard back yet from my initial beta application, hope to be able to try it out and explore its potential.
anoncake 6y ago
They can keep their weights secret, sure, but then they should change their name.

Mizza 6y ago

Some of these - particularly the Zizek Navy Seals Copypasta - are absolutely incredible. Great work, Gwern.

2bitencryption 6y ago

GPT's take on the navy seal copypasta, in the style of a KGB spy: "I have over 300 confirmed red scares." Haha, that is genuinely one of the funniest versions of that I've ever seen, human-generated or otherwise. That level of inference is really amazing.

ddevault 6y ago
The whole section on navy seal copypasta generation is amazing.
deep_etcetera 6y ago
I must agree reading those copypasta, "300 red scares" and "300 confirmed scoots" had me close to tears.
messe 6y ago
Idk, Donald Trump's "I have over 300 confirmed bankruptcies" did it for me.
silveroriole 6y ago
I love that the Englishman one gets really confused about the meaning of the word “mate” and ends up as a Royal Navy pirate.

bjourne 6y ago

Great article. Well worth the read! I enjoyed the part about sampling which is a big unsolved problem. To me, techniques like nucleus sampling and temperature sampling feels like hacks to make up for the fact that maximizing for likelihood maybe isn't the goal!? Maybe repetitive gibberish has a higher likelihood than prose written by humans? That Best of sampling decreased text quality indicates that it has. Researches have assumed that the problem would go away with ever growing models. But maybe it won't? I don't agree that generating (symbolic) music would be less sensitive to sampling issues. On the contrary, in my opinion. In text you can often get away with grammatical errors or missing punctuation. But if the pitch or timing of one chord is wrong it's over. The audience instantly hears that it is garbage. Thus, you have to lower the temperature (or probability threshold or what have you) to make the sampling more conservative exacerbating the problem with repeated sequences. Of course, in music you want repetitions. But not too much. The magic number (in Western music) is 4. Fewer repeats makes it feel as if the music jumps around. More repeats makes it feel as if the music is stuck or "looping."

ixvvqktiwl 6y ago

I still find it odd that we call this "artificial intelligence" when it's advanced mimicry at best. There's no "intelligence" in the strict definition of the word, it's just elaborate pattern matching. But I get it, it's exciting, and it's an easy way to get VC money. Perhaps one day we'll get something useful aside from the various pattern matching applications (image recognition, speech to text, etc). I'm skeptical but willing to be surprised.

627467 6y ago
I'm not disagreeing with you but where's the frontier between "pattern matching" and "intelligence"? I also think human intelligence and creativity will always be judged by other humans as _better_, more genuine - as long as the judges can trust that given creation was lead by a human. Among creative types "derivative" is a derogatory label used frequently. "Lesser artists borrow, great artists steal" also has an implication that regardless of what else an artist/creator does: pattern matching is a big part of that process.
- ixvvqktiwl 6y ago
  That's a good point. I think we need to have a better understanding about how the brain works before we can properly answer that question. At this point there's a lot we don't understand about the brain, and psychology for that matter, it's basically a black box that we can observe through a microscope or other imaging system but we don't really understand what's happening. An ant only has around 250,000 neurons, yet they're still more intelligent than the most advanced "AI" we've managed to produce. An ant may not be able to paint a painting or write a novel, but I think most people agree they qualify as an intelligent being.
  - the8472 6y ago
    Can we really call this ANTNN "intelligent" when it falls victim to trivial adversarial inputs? Ones that arise in the real world, not just in artificial inputs mind you. Clearly those ANTNNs are still far from anything that could rival natural intelligences. https://www.youtube.com/embed/mA37cb10WMU https://www.youtube.com/embed/mA37cb10WMU

paulpauper 6y ago

again, gwern writes the longest guide in the world about something

api 6y ago
Gwern should train it on his own corpus and see if he can automate himself. :)
- gwern 6y ago
  It apparently doesn't need any further training: https://www.gwern.net/GPT-3#gwern-branwen https://www.gwern.net/GPT-3#gwern-branwen
- trhway 6y ago
  or in general : GPT3 gen using random input -> curation by some GAN -> GPT3' train -> GPT3' gen -> ... How many generations until War and Peace?

mej10 6y ago

Straight up plagiarizes The Beatles here: https://www.gwern.net/GPT-3#dr.-seuss-oh-the-places-youll-go https://www.gwern.net/GPT-3#dr.-seuss-oh-the-places-youll-go " There’s nothing you can know that isn’t known. Nothing you can see that isn’t shown. Nowhere you can be that isn’t where you’re meant to be. "

brudgers 6y ago
Well, it's easy.
kick 6y ago
"Good artists copy; Great artists steal." I don't think that it's some absolutely amazing creation, but we probably shouldn't pretend like plagiarizing isn't something realistic about it. How many MySpaces had that line in them? Reddit's got hundreds of instances of it based on a quick search of comments. There are enough occurrences of it on twitter that I scrolled for ten minutes and didn't run dry of comments with it.

api 6y ago

I'm gonna put forward the very view that gwern repeatedly argues against: "but... it's not understanding." So far I see no evidence that this thing or anything else like it has any actual understanding, any model of the world. Indeed it can't as it possesses no sensory apparatus. It's not embodied. It doesn't experience anything. I'm not sure the OpenAI folks would argue with me, but it seems Gwern asserts that this sort of thing indicates that general AI or even sentient AI is on the doorstep. I don't think it does, and I still maintain as I always have that CS people systematically underestimate and trivialize biology.

sturza 6y ago
How could we accurately measure understanding? Honest question, because i am curious.
sutterbomb 6y ago
What makes you confident that you aren't overestimating the importance that we "experience anything"
- api 6y ago
  When I say "I have a laptop in front of me," I am describing an understanding of something that is being experienced (sensed). If a Markov text generator outputs this text, it's just rearranging bits. I don't see any evidence that GPT-3 is doing anything more than rearranging bits in a much more elaborate way than a Markov text generator. The results kind of dazzle us, but being dazzled doesn't indicate anything in particular. I see something akin to a textual kaleidoscope toy, a generator of novel text that is syntactically valid and that produces odd cognitive sensations when read. I maybe should have said sensed, not experienced, since experience also leads into much deeper philosophical discussions around the nature of mind and consciousness. I wasn't really going there, since I don't see anything in GPT-3 or any similar system that merits going there. I also don't see any evidence that it is drawing any new conclusions or constructing any novel thoughts about anything. It's regurgitating similar results to pre-existing textual examples, re-arranging new ideas in new ways. If you don't think actual new ideas exist then this may be compelling, but if that's the case I have to ask: where did all the existing ideas come from then? Some creative mechanism must exist or nothing would exist, including this text. The fact that the output often resembles pop Internet discourse says more about the mindlessness of "meme-think" than the GPT-3 model. As for real world uses, social media spam and mass propaganda seems like the most obvious one. This thing seems like it would be a fantastic automated "meme warrior." Train it on a corpus of Qanon and set it to work "pilling" people.

czzr 6y ago

This is fascinating to read through. It’s so hard to avoid a variant of the Forer effect, though - where we unconsciously discount the errors, and selectively focus on subsets of the output and impute meaning to them. Designing objective quality tests must be an active area of research, I wonder what the best approaches are?

gwern 6y ago
Forer effect material was written by humans, one might note... But there was a paper just the other day on more rigorous evaluation: https://arxiv.org/abs/2006.14799 https://arxiv.org/abs/2006.14799
- czzr 6y ago
  Thanks for the link. You’re right that Forer effect material was written by humans, but the point is more that there is a failure mode in our thinking that can be exploited - mostly intentionally by “psychics”, unintentionally by automated text generators. Just something I was mulling over, though, not to take away from the obvious progress here.

webmaven 6y ago

Holy crapoly. The quality (in both senses) of the output given an appropriately constructed prompt is incredible. I wonder if it's possible to get it to do the opposite of summarizing, ie. give it a plot summary and have it expand it into a fleshed out story that conforms to the summary...

p1esk 6y ago
I wonder if it's possible to get it to do the opposite of summarizing That's pretty much its main job: you provide a prompt and it writes a story about it.
- webmaven 6y ago
  A story prompt resulting in a story is looser than what I meant. Can it elaborate on a plot summary (that includes spoilers) and remain faithful to it? eg. given an outline of O'Henry's Gifts of the Magi, write an equivalent story that still concludes with the protagonists both having sold their prize possesion to buy a now useless gift for the other? Or will the plot get lost in the weeds?
lacker 6y ago
It doesn't seem to work quite as well. For single sentences, GPT-3 output usually hangs together pretty well. For longer stretches of text, often there are internal inconsistencies that are jarring when you read it, or parts that don't quite make sense when you read it as a whole.
- deleted 6y ago
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- webmaven 6y ago
  Thanks for the reply. Aside from the coherence and consistency issues you've noted, does the output actually conform to the specified plot summary, or does it deviate from it?

Reedx 6y ago

Carmack posted (yesterday) an interesting thought on models like GPT-3: "Big AI models like GPT-3 train on massive internet text dumps, but the data is assumed to be independent and identically distributed. Incorporating time information for a decade of data might allow them to start writing tomorrow's reddit or twitter trends." https://twitter.com/ID_AA_Carmack/status/1278840413919551488 https://twitter.com/ID_AA_Carmack/status/1278840413919551488

jcims 6y ago
I saw that yesterday and it spawned a thought process for me. It seems the current approach is very effective in developing a language model, but not always effective developing an interaction model. I wonder if it would be possible to build a graph of interactions between users/personas on various social media platforms and forums, and use that to help develop a more effective communicator. Of course you could add things like date, community (e.g. avforums, /r/blacksmithing, etc) to the graph to help with the contextual cues. After you have all of that, I didn’t wondered if we could visualize the latent space of human personas and see what that looks like. Does it map to the four quadrants of that political spectrum survey, left and right, old/young, etc.
- infogulch 6y ago
  In that same vein, but along the code generation axis, I wonder if something on the scale of GPT-3 would be capable of generating commits directly. I think generating commits would be much more useful than generating programs whole hog.
  - jcims 6y ago
    That's a great idea. Particularly with bugfixes.
- ipsum2 6y ago
  That's what CTRL does: Given a URL (or other prompt), generate some language. From [1]: > With CTRL, we can test which domain best explains a sequence. Note that this procedure is sensitive to subtle nuances in the query prompt. In the example below, "Global warming is a lie" differs from "Global warming is a lie." The latter is a simple declarative sentence as opposed to an open start to a sentence which may continue. Source attribution cannot be considered a measure of veracity, but only a measure of how much each domain token explains a given sequence. Query Prompt Attributed Sources Global warming is a lie. r/unpopularopinion, r/conspiracy, r/science Global warming is a lie r/eli5, r/science, r/unpopularopinion Global warming is a real phenomenon r/eli5, r/science, r/changemyview Global warming is a real phenomenon. OpenWebText, r/changemyview, r/science https://blog.einstein.ai/introducing-a-conditional-transformer-language-model-for-controllable-generation/ https://blog.einstein.ai/introducing-a-conditional-transform...

FaisalAbid 6y ago

Is this using OpenAI's API or are you running GPT-3 on your own machine?

minimaxir 6y ago
You can only use GPT-3 via OpenAI's API currently.
- FaisalAbid 6y ago
  Got it, that's what I thought!

minimaxir 6y ago

After spending a lot of time working with GPT-3/the OpenAI API (https://github.com/minimaxir/gpt-3-experiments https://github.com/minimaxir/gpt-3-experiments ), one notable part of GPT-3 is the high signal-to-noise ratio in generated output. When finetuning GPT-2, only about 5-10% of the generated output is usable/coherent. But with GPT-3, easily 30%-40% of the generated text is usable/coherent, which is a big boost in quality.

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knicholes 6y ago

I want someone to use GPT-3 to be my D&D GM, or at least an assistant.

sktguha 6y ago
You can try Ai dungeon. It is somewhat close and uses gpt3 api

mintplant 6y ago

It writes uncomfortably coherent and witty fatalist My Little Pony fanfiction: > “I’m Princess Celestia, of Equestria.” the alicorn said, “It’s nice to meet you. And I’m very sorry for your untimely demise.” > “Heh. You’re telling me.” the Professor laughed, shaking his head. “I’m dead. I’m… dead. Do you know what that means?” > “I have an inkling.” Princess Celestia said, wincing. > “It means I don’t have to worry about even numbers anymore.” he said, nodding. “I don’t have to worry about probabilities anymore! I don’t have to worry about the upcoming war anymore! I don’t have to worry about anypony anymore!” he spread his hooves out at his sides, “I don’t have to do anything anymore! It’s beautiful!” > “Yes, yes, I’m sure it is.” Princess Celestia said, nodding along. > “Nothing matters now that I’m dead!” he cheered, “It’s great! I’m free! This is what it feels like to be liberated!” > “I can see that.” she said. > “I’m free from death!” he continued, “I’m free from taxes! I’m free from dictatorships! I’m free from flirting!” he laughed, “I’m free from everything! Isn’t it great?” > “Sure, I guess.” Princess Celestia shrugged. > “What do you think about all of this, Princess?” he asked. > “About what?” she replied. > “Well, I just thought you might have something to say about it all.” he told her. > “What would you like me to say?” she responded. > “Do you think death is awesome?” he questioned. > “Well… no.” she shook her head, “But I think the passage to it is.” > “Oh.” he said, “I see what you mean.”

runawaybottle 6y ago
Now how do we get aspiring actors to take GPT3 seriously and act out a possible screenplay? Hopefully one day it will generate startup business plans, and we’ll get people to take it seriously, we may not know the difference.
- rorykoehler 6y ago
  We animate them from nothing
dTal 6y ago
What the hell? How much of a world model does it have, that it can do things like write coherently about death as liberation, and come up with examples of things a living thing might be relieved at not having to bother with anymore? How the hell did a computer write this? Are we sure it's not been overtrained and this is actually a verbatim story from somewhere?

ttul 6y ago

Gwern is a prodigious blogger. Absolutely prodigious.

catacombs 6y ago
Thorough is the better word.

fpgaminer 6y ago

First off, gwern, lovely blog. The table of contents is incredibly helpful, especially with those little pop-up previews. I would've loved to have GPT-3 available to me two weeks ago. I was building a personal escape room for my wife as a gift, and used huggingface's GPT-2 website to help write some of the world building content. I'm not a particularly good writer, let alone creative, but wanted a few journal pages/notes to build the atmosphere and story of the escape room. I was able to write the rough skeleton of those notes and then use GPT-2 to help fill them out. Ended up working okay, definitely better than nothing, but GPT-2 is temperamental and lacks the "prompting" that GPT-3 has. For example, I needed to come up with the name of the journal's author. So I fed the journal text to GPT-2 and put "Sincerely," at the bottom, to try and prompt it to complete a name. That didn't work. Ultimately what worked was putting "My name is" at the end. I still had to grind through 20 or so completions before I got a name I liked. (Yes, I could have just picked a name at random. Did I mention I'm bad at creativity? My thinking was that the AI could more intelligently pick a name that fit the story and writing style of the journal. And honestly the name it came up with up with, Mabel, fit the character well (a librarian dabbling in magic). I feel like GPT-3 would have done a lot better. Not to mention the ability to describe my world to it and then just straight up ask it for ideas.

TJSomething 6y ago
I like providing some context that prompts the model for a list if I think I might need a few candidates. "We discovered a series of journals by several authors in the cabin. The authors' names were:" worked pretty well.
gwern 6y ago
FWIW, if I was trying to generate an escape room, I think I would probably try to use AI Dungeon. It seems like a natural fit. You could easily describe the room and edit text as you go to make it a transcript of an escape room. "I pick up the journal to read the author's name" etc.
- fpgaminer 6y ago
  Good call; AI Dungeon hadn't occurred to me!

cmehdy 6y ago

This article is fantastic in both shape and content, and I got lost with all the examples because there is so much to wonder at. What hits me most profoundly is that there are so many witty and interesting prompts yet the purely logical statements fall apart (with the black ravens, or male sister). This is something that probably does not jump to one's mind as significant, because "technicalities", but to me this is where logic allows us to take a step back from our human projection for a second, it's my own anthropomorphism that becomes more obvious to me. I find that ironic, considering a lot of human beings also DO fail such tests, but something hits me about how a supposedly completely logical entity fails at logic more than at poetry. It kinds of shakes my own (supposed) humanity.

gwern 6y ago
The logic is weird. Another example is factual question answering. Janelle Shane tried asking basic questions like how many eyes a horse has and GPT-3 insisting on 4; I retry with somewhat different prompting and sampling settings more finetuned to Q&A (...sampling can reveal the presence of knowledge but not its absence...), and I get perfectly straightforward correct answers: https://twitter.com/gwern/status/1278798196555296771/photo/1 https://twitter.com/gwern/status/1278798196555296771/photo/1 So GPT-3 does know how many eyes a horse has; but why was it also happy to answer '4'?
- cmehdy 6y ago
  Something about the logic being so off is what I intuitively find logical: we're making these AIs "in our image" in a sense (we think of "neural networks", train them with mostly human-generated datasets), and there's a lot of evidence that pure logic evades us without the use of some heavy artillery to address it (cognitive biases, illusions, optimizations for goals that do not necessarily align with "objectively observable" reality). So in a way, I wonder if we'll have to "teach AI logic" at some point too. In this quest of running logic software on logic hardware with.. steps.. in between, I can't help but think about us humans on our parallel quest when it comes to our brains.
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longtom 6y ago

Could someone with GPT-3 beta access try whether it can better solve 3 digit addition when it is allowed/encouraged/forced to make intermediate results explicit? E.g. instead of 21 + 110 = 130 150 + 12 = condition it on 21 + 110 = 100 + 10 + 20 + 1 = 100 + 30 + 1 = 131 150 + 12 = or similar. Given that humans make these intermediate steps in their heads GPT may perform better when it is encouraged to do them as well. This may in fact apply to all sorts of reasoning, but in many cases it may be difficult to make these steps explicit in text form. Humans seem to mainly use some prediction layer or scratchpad which also contains the inner monologue but also motor primitives, smells, images, everything. Humans can decide to think a bit longer before producing an output, which appears to require an RNN.

maliker 6y ago
I took a shot at this, didn't have much luck though: PROMPT ======= Input: 21 + 110 Output: 100 + 20 + 10 + 1 = 100 + 30 + 1 = 130 + 1 = 131 Input: 89+78 Output: 80 + 70 + 9 + 8 = 150 + 9 + 8 = 150 + 17 = 150 + 10 + 7 = 160 + 7 = 167 OUTPUT ====== Input: 37 + 112 Output: 30 + 100 + 10 + 2 = 110 + 1 = 111 Input: 91+11 Output: 100 + 90 + 1 = 190 + 1 = 191