14 ms·

Tempering Expectations for GPT-3 and OpenAI’s API

267 points by vortex_ape 6y ago

rvz 6y ago
> GPT-3 itself, like most neural network models, is a black box where it’s impossible to see why it makes its decisions, so let’s think about GPT-3 in terms of inputs and outputs. Spot on. Explainability is always glossed over in the AI landscape and generally neural networks used in CNNs, RNNs and GANs are still unable to explain themselves and their decisions. On top of that detection mechanisms of generated content like AI-generated: faces, voices and now text are need to combat its use by bad actors. Otherwise, it is very dangerous when all of this is used together. While GPT-3 is impressive in its capabilities, we must think about detections methods that distinguish content created by an AI or a human.
- dharma1 6y ago
  > While GPT-3 is impressive in its capabilities, we must think about detections methods that distinguish content created by an AI or a human. While this would be very useful, I'm not sure it's possible - at least not on something like a static text article, where you can't query the machine learning model with custom inputs to expose its' faults. I think the internet will be soon be flooded with an absolute tsunami of AI generated content, practically indistinguishable from human created content - and it will make the job of search engines so much more difficult. It's interesting that in our quest to organise the world we always actually create a lot more entropy.
HugThem 6y ago
Strange, nothing in the article tempered my expectations.
minimaxir 6y ago
From Sam Altman just now: > The GPT-3 hype is way too much. It’s impressive (thanks for the nice compliments!) but it still has serious weaknesses and sometimes makes very silly mistakes. AI is going to change the world, but GPT-3 is just a very early glimpse. We have a lot still to figure out. https://twitter.com/sama/status/1284922296348454913 https://twitter.com/sama/status/1284922296348454913
- stingraycharles 6y ago
  Thanks, that tweet tempered my expectations much better than the article. Still nice to play around with it if I can!
  - deft 6y ago
    That tweet is completely devoid of substance and if it truly changed your mind you are a mindless sheep just believing whatever Sam Altman and his crony of VCs tell you to.
    - bobviolier 6y ago
      I mean, Sam Altman is just one of the founders and the CEO of OpenAI - so yeah, what does he know.
      - ohgodplsno 6y ago
        Legitimately, not a lot. Musk is a founder too, and no, he doesn't know anything about AI. The few actually technical people in there (Schulman, Sutskever, Zaramba) are the ones that know their stuff, yes. Musk, Altman, Brockman are basically nothing more than hype men with tech connections.
        londons_explore 6y ago
        Careful with the tone of your response. Calling someone 'nothing more than a hype man' goes beyond constructive criticism of someone's behaviour...
        ohgodplsno 6y ago
        That tone is very much wanted. It's not a criticism. It's what they are. And that's perfectly fine, they don't all need to bring a PhD to the table. But as it stands, in the context of AI knowledge, Altman and Musk are hype men for OpenAI. Yes, they probably handle financials, or set targets. But they're not indispensable to the well functioning of an AI company.
IXxXI 6y ago
GPT-3 is more likely to revolutionize its industry the way the flying car didn't than mirror bitcoin's success.
- Judgmentality 6y ago
  I think many people don't even consider bitcoin successful. I still see it as a solution looking for a problem, personally. It's not actually anonymous, it doesn't scale for transactions, it's still not used or even understood by the vast majority of people. I've yet to hear of the killer app for blockchain technology.
  - Avalaxy 6y ago
    Distributed identity / self-sovereign identity is a good use-case that blockchains are perfect for imho. But that solution is quite complex hindering its adoption.
    - lumost 6y ago
      "But that solution is quite complex hindering its adoption." Generally inventions solve problems by making them simpler, for this to be a valuable invention it would need to be simpler and less hackable to use than log-in with google/FB/github. I can still recover my google account if I forget my password, and don't have a reset setup. What aspect of distributed identity/ self-sovereing identity do customers care about? how does blockchain simplify this for them?
    - dane-pgp 6y ago
      Unfortunately one of the requirements of a good identity system is that it should keep personal data secret from parties who do not need it. This means that blockchains on their own aren't a good fit, since by design they share all information with all parties. Having said that, BrightID uses a blockchain and seems like an idea worth exploring. https://www.brightid.org/whitepaper https://www.brightid.org/whitepaper
  - IXxXI 6y ago
Reelin 6y ago
The 0.7 unicorn example is doing absolutely nothing to temper my expectations. Quite the opposite in fact. (https://github.com/minimaxir/gpt-3-experiments/blob/master/examples/unicorn/output_0_7.md https://github.com/minimaxir/gpt-3-experiments/blob/master/e...)
- dane-pgp 6y ago
  > “Trump is going to make the United States great again,” said one of the unicorns. “When the U.S. becomes great again, the rest of the world will become great again. We’re hoping that Trump will become president of Ecuador, and put an end to this nonsense.” Has science gone too far?
  - crawlcrawler 6y ago
    Make $country great again, as a service. "Hire me as president, I have great connections, beautiful even, to the US, Russia and Djina". Surely, this will never happen.
- alserio 6y ago
  "They're so intelligent. I was able to converse with them about quantum mechanics, which is something I've never even tried to talk to a regular horse about." I mean, that I would classify as just genius
  - ifdefdebug 6y ago
    Well, not quite QM, but the sentence also implies that the speaker talks to regular horses about other topics or at least admits the existence of topics you can talk to regular horses about - maybe oat quality, I don't know. It's well-formed nonsense like this that gave me enough hints in all GPT-3 samples so far popping up today.
    - alserio 6y ago
      I wouldn't be surprised to find that sentence in a book from an author well versed in exquisite British humor. And it made my day just a little bit better.
zitterbewegung 6y ago
I was going to give a talk at Thotcon 2020 (pushed back to 2021) about generating fake tweets by refining GPT-2. The whole purpose of my experiment was to see that if OpenAI was right in their statement that GPT-2 could be used in nefarious ways. I saw a bunch of tutorials but first I read gwerns tutorial and that made me understand the basics of using GPT-2. If you see Gwerns experiments with GPT-2 you notice that his websites are actually just extremely large samples of text / image data. Essentially the design is that the whole website is practically statically generated. Refining on AWS was costing me $100 a day. I also met Shawn (https://github.com/shawwn https://github.com/shawwn) who decided to attempt to refine / train large models using TPUs. That sounded interesting but since I figured out that I would waste time trying to understand TPUs because I have a day job I instead just bought a Titan RTX. My first experiment was that refinement of GPT-2 with your own data would somewhat work. At first I used Donald Trump because he is a very active person on twitter that I believed that people would have some kind of ability of detecting whether the tweets are fake or not. The above was a bad choice for the following unique reasons. 1. People would generally believe that Trump would say nearly anything. 2. I noticed a weird situation where someone basically duplicated everything that I did had commenters which were apparently much better at figuring out that the person performing the tweets were fake. Since that experiment sort of failed I created an expanded refinement of GPT-2 on general twitter data. It was 200 MB of tweets from https://www.kaggle.com/kazanova/sentiment140 https://www.kaggle.com/kazanova/sentiment140. I then repeated the experiment and eventually figured out that some people were really bad at figuring out fake tweets and I found a person who used twitter a great deal would actually perform better. I'm not sure if its that you tweet a bunch or just read twitter enough. I had a low sample (n=5) where I carried out the test. So my test could be completely biased. The test procedure that I followed was to make a set of 10-20 questions and then have the user pick which one was fake and which one wasn't. I was also doing these experiments on a Titan RTX (I'm thinking about getting a V100 or maybe just another Titan RTX so I can train two models at the same time). I accidentally upgraded the memory to 32GB which worked for a bit but instead you should probably get at least 1 or two multiples of your VRAM so that you can keep your operating system running and having the dataset loaded into memory. Also, during refinement I don't think my loss ratio was improving. I think that either I wasn't using the system long enough. But, as a conclusion I figured out that there is a huge shortcut that I never considered. It would be MUCH easier to just take any refined GPT-2 model or even use GPT-3 as an API above and then make it LOOK like a tweet. Just adding a hashtag and a t.co link would work. (the funny part is that GPT-2 actually seems to have some notion of a t.co link and will happly generate t.co links that don't work. Removing t.co links before refinement would be one way to get this out. I did some of these experiments using Google CoLab initially. But as I used it I got out of memory options. I asked someone at pyOhio who worked for google if there was a way to connect Google Colab to a paid instance. They responded to refer me to Jake Vanderplas https://twitter.com/jakevdp https://twitter.com/jakevdp . They said no at the time. Then a few months after Google came out with Google Colab Pro. But then I was able to run out of system memory in the high memory instances. I upgraded my Deep Learning Rig with a AMD 3600 and I am now waiting for my 64 GB memory that is in the mail. The latest thing that I have done is use local voice synthesis and recognition so that you can talk to GPT-2 locally. My tutorial is at https://www.youtube.com/watch?v=d6Lset0RFAw&t=2s https://www.youtube.com/watch?v=d6Lset0RFAw&t=2s
Barrin92 6y ago
I'm not really sure I understand the hype anyway. All GPT-3 does is generate text from human input to begin with, it's not actually at all intelligent as the person from the Turing test thread pointed out. Sure GPT-3 can respond with factoids, but it doesn't actually understand anything. If I have a chat with the model and I ask it "what did we talk about thirty minutes ago" it's as clueless as anything. A few weeks ago computerphile put out a video of GPT-3 doing poetry that was allegedly only identified as computer generated half of the time, but if you actually read the poems they're just lyrically sounding word salad, as it does not at all understand what it's talking about. Honestly the only expectations I have for this is generating a barrage of spam or fake news that uncritical readers can't distinguish from human output.
- woko 6y ago
  > generating a barrage of spam or fake news that uncritical readers can't distinguish from human output. Which could indirectly bring good things: what if this decreases our tolerance threshold for nonsensical texts? For instance, people might become more impatient when faced with click-bait articles, because they know they might have been written by an AI in order to waste their time. This would let people skip the articles after the first sentence without feeling any guilt or doubt about it. At least, I hope it might open the eyes of uncritical readers.
  - ricksharp 6y ago
    Clickbait articles have been generated by AI for many years, yet they still exist. In fact, clickbait is a global GAN monetized by Ad revenue. (In this case the adversary is humans who decide whether or not a specific title is worth a click.)
- dnautics 6y ago
  None of minimaxirs queries directly test creativity with open ended responses, for example: "name a bird that starts with the same letter as the color that's a fruit"
  - 6y ago
gdulli 6y ago
> However, I confess that the success of GPT-3 has demotivated me to continue working on my own GPT-2 projects, especially since they will now be impossible to market competitively (GPT-2 is a number less than GPT-3 after all). Doesn't the much larger size and therefore much higher expected cost of GPT-3 ensure that demand for GPT-2 will continue?
GIFtheory 6y ago
> As an example, despite the Star Wars: Episode III - Revenge of the Sith prompt containing text from a single scene, the 0.7 temperature generation imputes characters and lines of dialogue from much further into the movie This makes me believe it is actually just memorizing the movie script, which is probably in its corpus. As pointed out here, the model has enough parameters to straight-up memorize over 1/3 of its gigantic training set. https://lambdalabs.com/blog/demystifying-gpt-3/ https://lambdalabs.com/blog/demystifying-gpt-3/
qeternity 6y ago
Although GPT-3 is most certainly not immune from the relentless hype-cycle, the low barrier to entry provided by API combined with what appear to be SOTA results on many tasks, will undoubtedly upend the business models of many platform companies. There is a whole industry of feature-as-a-service companies out there providing domain-specific functionality to various industries...GPT-3 looks like it could make it a few orders of magnitude easier/cheaper for these customers to build these features in-house, and ditch the feature providers.
lifeisstillgood 6y ago
my question is the other way round - where is the state of the art for extracting meaning from existing text? How close are we to understanding legal contracts for example
- TaylorAlexander 6y ago
  Well my understanding is that GPT-3 can “read” material you feed it and then respond in a pretty open ended way about the material with pretty good accuracy and comprehension.
  - matsemann 6y ago
    But how can that further be used? Getting a text back only gives me the same problem again.
deleted 6y ago
[deleted]
matsemann 6y ago
Can someone explain to me how the design mockup demo and react generation relates to GPT-3? Isn't it just prompts? How is inputting a text outputting a design?
- zora_goron 6y ago
  I believe those demos involve "priming" GPT-3 by providing a few examples of (text → generated code), then during inference time passing in just the text. The model would follow the examples provided and subsequently generate a string of code/mockup syntax, which is then evaluated. Edit: here is a tweet (from the author of the GPT3 layout generator) that seems to show this in practice: https://twitter.com/sharifshameem/status/1282692481608331265 https://twitter.com/sharifshameem/status/1282692481608331265
- ZephyrBlu 6y ago
  The way GPT-3 works is that you provide it with examples of a question/input and an answer/output, then tack an unanswered question/input onto those examples and send the whole thing to the API. The AI then answers your question/generates your output.
Bx6667 6y ago
I am totally confused by people not being impressed with gtp3. If you asked 100 people in 2015 tech industry if these results would be possible in 2020, 95 would say no, not a chance in hell. Nobody saw this coming. And yet nobody cares because it isn’t full blown AGI. That’s not the point. The point is that we are getting unintuitive and unexpected results. And further, the point is that the substrate from which AGI could spring may already exist. We are digging deeper and deeper into “algorithm space” and we keep hitting stuff that we thought was impossible and it’s going to keep happening and it’s going to lead very quickly to things that are too important and dangerous to dismiss. People who say AGI is a hundred years away also said GO was 50 years away and they certainly didn’t predict anything even close to what we are seeing now so why is everyone believing them?
- abernard1 6y ago
  > And yet nobody cares because it isn’t full blown AGI. That’s not the point. The point is that we are getting unintuitive and unexpected results. I don't think these are unintuitive or unexpected results. They seem exactly like what you'd get when you throw huge amounts of compute power at model generation and memorize gigantic amounts of stuff that humans have already come up with. A very basic Markov model can come up with content that seem surprisingly like a human would say. If anything, what all of the OpenAI hype should confirm is just how predictable and regular human language is.
  - yters 6y ago
    Exactly. Even gpt3 is not creating new content. It is just permuting ecisting content while retaining some level of coherence. I don't reason by repeating various tidbits I've read in books in random permutations. I reason by thinking abstractly and logically, with a creative insight here and there. Nothing at all like a Markov model trained on a massive corpus. Gpt3 may give the appearance of intelligent thought, but appearance is not reality.
    - CrazyStat 6y ago
      GPT-3 is nothing like a Markov model.
hn3333 6y ago
So many possibilities: This can be used to generate fake flirting on dating sites. (I assume this is already going on, but now they don't need to hire humans to do it.) Or how about flamewar bots to keeps Twitter users busy. Also perhaps it can answer support questions better than current systems. Come to think of it, we might eventually need some sort of proof/signature that something was written by a human being.
anotheryou 6y ago
I played AI dungeon and the move from gpt2 to 3 didn't feel like much of a difference. In general all the cool stories remind me of cherry picked AI dungeon with gtp2
davesque 6y ago
As encouraging as GPT-3 results are, I still don't see the critical ability to synthesize large scale structure (think book-level as opposed to just a few sentences) manifest in this research. Even the much touted 0.7 temperature revenge of the sith examples don't exhibit coherent high level structure that continues across the span of more than a few sentences. And I differentiate high level structure from long distance structure. The algorithm does appear to be able to relate themes from a sentence earlier in a generated text to ones which appear later (up to a few paragraphs later). But the narrative connecting those sentences seems shallow and lacks depth. Compare this with the complex, multi tiered narrative that underpins the structure of a well written book or body of research. My intuition here is that there is an exponential relationship between the "depth" evident in the narrative of generated text and the number of parameters required in the model that places viable, AGI-like intelligence still much further in the future.
- emsy 6y ago
  Absolutely. At the moment AI seems to scale extremely well in one dimension (amount of data) but really poorly at the other (semantic of data). An huge increase in the former looks like an increase in the other (because more data = more simple data semantic)
Ptrulli 6y ago
GPT-3 is at the very least an indication of future AI advancement. Sure maybe this particular version is still early to be widley incorporated in business or used globally. It's definitely a step in the right direction. I'd personally love the opportunity to test it personally to really get the whole experience.
id_ris 6y ago
How are people accessing GPT3? Is there a website, code repo, api... or do some select few have access?
- drusepth 6y ago
  The OpenAI website has a login that lets people access it through a web interface, but there is also an API. You can join the waitlist at https://beta.openai.com/; https://beta.openai.com/; they're apparently working through the list.
unexaminedlife 6y ago
I wonder how domain-specific GPT-3 was trained on React. And what sort of arbitrary limitations, if any, exist in what the user can request? I noticed the user didn't have to specify which programming language they wanted the apps in, yet it always chose React.
- ZephyrBlu 6y ago
  That's only because the guy primed it with React. Someone else has done it with SwiftUI [1]. [1] https://twitter.com/jsngr/status/1284874360952692736 https://twitter.com/jsngr/status/1284874360952692736
knzhou 6y ago
The people here reading GPT-3's output and dismissing it as "not really understanding anything" or "pushing symbols without conscious intent" because of minor slips in coherence have remarkably high standards for understanding. On a high school essay prompt, GPT-3 produces more accurate and more coherent output than 90% of high school students. If the commenters' standards were actually used consistently, they would also apply to almost all human beings. Another interestingly common objection is "we think in terms of meaningful semantics, but GPT-3 only calculates meaningless coefficients". This is a category error masquerading as an argument, equivalent to saying "I have moral worth because I'm made of cells, but you don't because you're only made of quarks." Cells and quarks are just two different-level descriptions of precisely the same thing. Understanding in your brain can be reduced to the calculation of "meaningless" coefficients just as surely as cells can be reduced to quarks.
me551ah 6y ago
It is still amazing at what it is and could be used to generate boilerplate code. For many writing tasks, it can come up with a good starting point. For programming tasks maybe someone can write a translator for python to C++ or even assembly. It can also come up with basic designs which can be a useful starting point and as long as people don't see it as a replacement for them we will be fine.
cryptoz 6y ago
This part piqued my interest: > GPT-3 seed prompts can be reverse-engineered, which may become a rude awakening for entrepreneurs and the venture capitalists who fund them. Is there any more information about this? Is the reverse engineering done by hand or is this something that can be coded? Super curious about this point.
- gwern 6y ago
  I think you would have to do it by hand. Assuming that the prompt is stripped off before the text is sent to the user, there's no obvious way to me that you would reverse-engineer it except by having an experienced GPT-3 user use their intuition to think about what kind of prompt would elicit observed responses. Since you don't have access to the GPT-3 model itself, you can't directly use gradient ascent or MCMC to try to reverse it to get the prompt. You might be able to blackbox it, but the only approach that comes to mind is using the logprobs, and your target won't give you those any more than they will give you the prompt itself. I'm not sure what he has in mind; just because model-stealing has been demonstrated for CNN classifiers doesn't mean you can feasibly steal GPT-3 prompts...
  - cryptoz 6y ago
    Super interesting, thanks for the reply!
brisky 6y ago
GPT3 demos are impressive. This looks like a significant step towards AGI. I think GPT3 simulates memory retrieval and data reconstruction quite well. The part that we are still missing for AGI is curiosity - ability to ask questions and fill in the missing information gaps.
king07828 6y ago
I'd like to see the LSTM brain in the OpenAI 5 model [1] replaced by GPT3 to see if there is any improvement. [1] https://neuro.cs.ut.ee/the-use-of-embeddings-in-openai-five/ https://neuro.cs.ut.ee/the-use-of-embeddings-in-openai-five/

267 points by vortex_ape 6y ago

rvz 6y ago

> GPT-3 itself, like most neural network models, is a black box where it’s impossible to see why it makes its decisions, so let’s think about GPT-3 in terms of inputs and outputs. Spot on. Explainability is always glossed over in the AI landscape and generally neural networks used in CNNs, RNNs and GANs are still unable to explain themselves and their decisions. On top of that detection mechanisms of generated content like AI-generated: faces, voices and now text are need to combat its use by bad actors. Otherwise, it is very dangerous when all of this is used together. While GPT-3 is impressive in its capabilities, we must think about detections methods that distinguish content created by an AI or a human.

dharma1 6y ago
> While GPT-3 is impressive in its capabilities, we must think about detections methods that distinguish content created by an AI or a human. While this would be very useful, I'm not sure it's possible - at least not on something like a static text article, where you can't query the machine learning model with custom inputs to expose its' faults. I think the internet will be soon be flooded with an absolute tsunami of AI generated content, practically indistinguishable from human created content - and it will make the job of search engines so much more difficult. It's interesting that in our quest to organise the world we always actually create a lot more entropy.

HugThem 6y ago

Strange, nothing in the article tempered my expectations.

minimaxir 6y ago

From Sam Altman just now: > The GPT-3 hype is way too much. It’s impressive (thanks for the nice compliments!) but it still has serious weaknesses and sometimes makes very silly mistakes. AI is going to change the world, but GPT-3 is just a very early glimpse. We have a lot still to figure out. https://twitter.com/sama/status/1284922296348454913 https://twitter.com/sama/status/1284922296348454913

stingraycharles 6y ago
Thanks, that tweet tempered my expectations much better than the article. Still nice to play around with it if I can!
- deft 6y ago
  That tweet is completely devoid of substance and if it truly changed your mind you are a mindless sheep just believing whatever Sam Altman and his crony of VCs tell you to.
  - bobviolier 6y ago
    I mean, Sam Altman is just one of the founders and the CEO of OpenAI - so yeah, what does he know.
    - ohgodplsno 6y ago
      Legitimately, not a lot. Musk is a founder too, and no, he doesn't know anything about AI. The few actually technical people in there (Schulman, Sutskever, Zaramba) are the ones that know their stuff, yes. Musk, Altman, Brockman are basically nothing more than hype men with tech connections.
      - londons_explore 6y ago
        Careful with the tone of your response. Calling someone 'nothing more than a hype man' goes beyond constructive criticism of someone's behaviour...
        ohgodplsno 6y ago
        That tone is very much wanted. It's not a criticism. It's what they are. And that's perfectly fine, they don't all need to bring a PhD to the table. But as it stands, in the context of AI knowledge, Altman and Musk are hype men for OpenAI. Yes, they probably handle financials, or set targets. But they're not indispensable to the well functioning of an AI company.

IXxXI 6y ago

GPT-3 is more likely to revolutionize its industry the way the flying car didn't than mirror bitcoin's success.

Judgmentality 6y ago
I think many people don't even consider bitcoin successful. I still see it as a solution looking for a problem, personally. It's not actually anonymous, it doesn't scale for transactions, it's still not used or even understood by the vast majority of people. I've yet to hear of the killer app for blockchain technology.
- Avalaxy 6y ago
  Distributed identity / self-sovereign identity is a good use-case that blockchains are perfect for imho. But that solution is quite complex hindering its adoption.
  - lumost 6y ago
    "But that solution is quite complex hindering its adoption." Generally inventions solve problems by making them simpler, for this to be a valuable invention it would need to be simpler and less hackable to use than log-in with google/FB/github. I can still recover my google account if I forget my password, and don't have a reset setup. What aspect of distributed identity/ self-sovereing identity do customers care about? how does blockchain simplify this for them?
  - dane-pgp 6y ago
    Unfortunately one of the requirements of a good identity system is that it should keep personal data secret from parties who do not need it. This means that blockchains on their own aren't a good fit, since by design they share all information with all parties. Having said that, BrightID uses a blockchain and seems like an idea worth exploring. https://www.brightid.org/whitepaper https://www.brightid.org/whitepaper
- IXxXI 6y ago

Reelin 6y ago

The 0.7 unicorn example is doing absolutely nothing to temper my expectations. Quite the opposite in fact. (https://github.com/minimaxir/gpt-3-experiments/blob/master/examples/unicorn/output_0_7.md https://github.com/minimaxir/gpt-3-experiments/blob/master/e...)

dane-pgp 6y ago
> “Trump is going to make the United States great again,” said one of the unicorns. “When the U.S. becomes great again, the rest of the world will become great again. We’re hoping that Trump will become president of Ecuador, and put an end to this nonsense.” Has science gone too far?
- crawlcrawler 6y ago
  Make $country great again, as a service. "Hire me as president, I have great connections, beautiful even, to the US, Russia and Djina". Surely, this will never happen.
alserio 6y ago
"They're so intelligent. I was able to converse with them about quantum mechanics, which is something I've never even tried to talk to a regular horse about." I mean, that I would classify as just genius
- ifdefdebug 6y ago
  Well, not quite QM, but the sentence also implies that the speaker talks to regular horses about other topics or at least admits the existence of topics you can talk to regular horses about - maybe oat quality, I don't know. It's well-formed nonsense like this that gave me enough hints in all GPT-3 samples so far popping up today.
  - alserio 6y ago
    I wouldn't be surprised to find that sentence in a book from an author well versed in exquisite British humor. And it made my day just a little bit better.

zitterbewegung 6y ago

I was going to give a talk at Thotcon 2020 (pushed back to 2021) about generating fake tweets by refining GPT-2. The whole purpose of my experiment was to see that if OpenAI was right in their statement that GPT-2 could be used in nefarious ways. I saw a bunch of tutorials but first I read gwerns tutorial and that made me understand the basics of using GPT-2. If you see Gwerns experiments with GPT-2 you notice that his websites are actually just extremely large samples of text / image data. Essentially the design is that the whole website is practically statically generated. Refining on AWS was costing me $100 a day. I also met Shawn (https://github.com/shawwn https://github.com/shawwn) who decided to attempt to refine / train large models using TPUs. That sounded interesting but since I figured out that I would waste time trying to understand TPUs because I have a day job I instead just bought a Titan RTX. My first experiment was that refinement of GPT-2 with your own data would somewhat work. At first I used Donald Trump because he is a very active person on twitter that I believed that people would have some kind of ability of detecting whether the tweets are fake or not. The above was a bad choice for the following unique reasons. 1. People would generally believe that Trump would say nearly anything. 2. I noticed a weird situation where someone basically duplicated everything that I did had commenters which were apparently much better at figuring out that the person performing the tweets were fake. Since that experiment sort of failed I created an expanded refinement of GPT-2 on general twitter data. It was 200 MB of tweets from https://www.kaggle.com/kazanova/sentiment140 https://www.kaggle.com/kazanova/sentiment140. I then repeated the experiment and eventually figured out that some people were really bad at figuring out fake tweets and I found a person who used twitter a great deal would actually perform better. I'm not sure if its that you tweet a bunch or just read twitter enough. I had a low sample (n=5) where I carried out the test. So my test could be completely biased. The test procedure that I followed was to make a set of 10-20 questions and then have the user pick which one was fake and which one wasn't. I was also doing these experiments on a Titan RTX (I'm thinking about getting a V100 or maybe just another Titan RTX so I can train two models at the same time). I accidentally upgraded the memory to 32GB which worked for a bit but instead you should probably get at least 1 or two multiples of your VRAM so that you can keep your operating system running and having the dataset loaded into memory. Also, during refinement I don't think my loss ratio was improving. I think that either I wasn't using the system long enough. But, as a conclusion I figured out that there is a huge shortcut that I never considered. It would be MUCH easier to just take any refined GPT-2 model or even use GPT-3 as an API above and then make it LOOK like a tweet. Just adding a hashtag and a t.co link would work. (the funny part is that GPT-2 actually seems to have some notion of a t.co link and will happly generate t.co links that don't work. Removing t.co links before refinement would be one way to get this out. I did some of these experiments using Google CoLab initially. But as I used it I got out of memory options. I asked someone at pyOhio who worked for google if there was a way to connect Google Colab to a paid instance. They responded to refer me to Jake Vanderplas https://twitter.com/jakevdp https://twitter.com/jakevdp . They said no at the time. Then a few months after Google came out with Google Colab Pro. But then I was able to run out of system memory in the high memory instances. I upgraded my Deep Learning Rig with a AMD 3600 and I am now waiting for my 64 GB memory that is in the mail. The latest thing that I have done is use local voice synthesis and recognition so that you can talk to GPT-2 locally. My tutorial is at https://www.youtube.com/watch?v=d6Lset0RFAw&t=2s https://www.youtube.com/watch?v=d6Lset0RFAw&t=2s

Barrin92 6y ago

I'm not really sure I understand the hype anyway. All GPT-3 does is generate text from human input to begin with, it's not actually at all intelligent as the person from the Turing test thread pointed out. Sure GPT-3 can respond with factoids, but it doesn't actually understand anything. If I have a chat with the model and I ask it "what did we talk about thirty minutes ago" it's as clueless as anything. A few weeks ago computerphile put out a video of GPT-3 doing poetry that was allegedly only identified as computer generated half of the time, but if you actually read the poems they're just lyrically sounding word salad, as it does not at all understand what it's talking about. Honestly the only expectations I have for this is generating a barrage of spam or fake news that uncritical readers can't distinguish from human output.

woko 6y ago
> generating a barrage of spam or fake news that uncritical readers can't distinguish from human output. Which could indirectly bring good things: what if this decreases our tolerance threshold for nonsensical texts? For instance, people might become more impatient when faced with click-bait articles, because they know they might have been written by an AI in order to waste their time. This would let people skip the articles after the first sentence without feeling any guilt or doubt about it. At least, I hope it might open the eyes of uncritical readers.
- ricksharp 6y ago
  Clickbait articles have been generated by AI for many years, yet they still exist. In fact, clickbait is a global GAN monetized by Ad revenue. (In this case the adversary is humans who decide whether or not a specific title is worth a click.)
dnautics 6y ago
None of minimaxirs queries directly test creativity with open ended responses, for example: "name a bird that starts with the same letter as the color that's a fruit"
- 6y ago

gdulli 6y ago

> However, I confess that the success of GPT-3 has demotivated me to continue working on my own GPT-2 projects, especially since they will now be impossible to market competitively (GPT-2 is a number less than GPT-3 after all). Doesn't the much larger size and therefore much higher expected cost of GPT-3 ensure that demand for GPT-2 will continue?

GIFtheory 6y ago

> As an example, despite the Star Wars: Episode III - Revenge of the Sith prompt containing text from a single scene, the 0.7 temperature generation imputes characters and lines of dialogue from much further into the movie This makes me believe it is actually just memorizing the movie script, which is probably in its corpus. As pointed out here, the model has enough parameters to straight-up memorize over 1/3 of its gigantic training set. https://lambdalabs.com/blog/demystifying-gpt-3/ https://lambdalabs.com/blog/demystifying-gpt-3/

qeternity 6y ago

Although GPT-3 is most certainly not immune from the relentless hype-cycle, the low barrier to entry provided by API combined with what appear to be SOTA results on many tasks, will undoubtedly upend the business models of many platform companies. There is a whole industry of feature-as-a-service companies out there providing domain-specific functionality to various industries...GPT-3 looks like it could make it a few orders of magnitude easier/cheaper for these customers to build these features in-house, and ditch the feature providers.

lifeisstillgood 6y ago

my question is the other way round - where is the state of the art for extracting meaning from existing text? How close are we to understanding legal contracts for example

TaylorAlexander 6y ago
Well my understanding is that GPT-3 can “read” material you feed it and then respond in a pretty open ended way about the material with pretty good accuracy and comprehension.
- matsemann 6y ago
  But how can that further be used? Getting a text back only gives me the same problem again.

deleted 6y ago

[deleted]

matsemann 6y ago

Can someone explain to me how the design mockup demo and react generation relates to GPT-3? Isn't it just prompts? How is inputting a text outputting a design?

zora_goron 6y ago
I believe those demos involve "priming" GPT-3 by providing a few examples of (text → generated code), then during inference time passing in just the text. The model would follow the examples provided and subsequently generate a string of code/mockup syntax, which is then evaluated. Edit: here is a tweet (from the author of the GPT3 layout generator) that seems to show this in practice: https://twitter.com/sharifshameem/status/1282692481608331265 https://twitter.com/sharifshameem/status/1282692481608331265
ZephyrBlu 6y ago
The way GPT-3 works is that you provide it with examples of a question/input and an answer/output, then tack an unanswered question/input onto those examples and send the whole thing to the API. The AI then answers your question/generates your output.

Bx6667 6y ago

I am totally confused by people not being impressed with gtp3. If you asked 100 people in 2015 tech industry if these results would be possible in 2020, 95 would say no, not a chance in hell. Nobody saw this coming. And yet nobody cares because it isn’t full blown AGI. That’s not the point. The point is that we are getting unintuitive and unexpected results. And further, the point is that the substrate from which AGI could spring may already exist. We are digging deeper and deeper into “algorithm space” and we keep hitting stuff that we thought was impossible and it’s going to keep happening and it’s going to lead very quickly to things that are too important and dangerous to dismiss. People who say AGI is a hundred years away also said GO was 50 years away and they certainly didn’t predict anything even close to what we are seeing now so why is everyone believing them?

abernard1 6y ago
> And yet nobody cares because it isn’t full blown AGI. That’s not the point. The point is that we are getting unintuitive and unexpected results. I don't think these are unintuitive or unexpected results. They seem exactly like what you'd get when you throw huge amounts of compute power at model generation and memorize gigantic amounts of stuff that humans have already come up with. A very basic Markov model can come up with content that seem surprisingly like a human would say. If anything, what all of the OpenAI hype should confirm is just how predictable and regular human language is.
- yters 6y ago
  Exactly. Even gpt3 is not creating new content. It is just permuting ecisting content while retaining some level of coherence. I don't reason by repeating various tidbits I've read in books in random permutations. I reason by thinking abstractly and logically, with a creative insight here and there. Nothing at all like a Markov model trained on a massive corpus. Gpt3 may give the appearance of intelligent thought, but appearance is not reality.
  - CrazyStat 6y ago
    GPT-3 is nothing like a Markov model.

hn3333 6y ago

So many possibilities: This can be used to generate fake flirting on dating sites. (I assume this is already going on, but now they don't need to hire humans to do it.) Or how about flamewar bots to keeps Twitter users busy. Also perhaps it can answer support questions better than current systems. Come to think of it, we might eventually need some sort of proof/signature that something was written by a human being.

anotheryou 6y ago

I played AI dungeon and the move from gpt2 to 3 didn't feel like much of a difference. In general all the cool stories remind me of cherry picked AI dungeon with gtp2

davesque 6y ago

As encouraging as GPT-3 results are, I still don't see the critical ability to synthesize large scale structure (think book-level as opposed to just a few sentences) manifest in this research. Even the much touted 0.7 temperature revenge of the sith examples don't exhibit coherent high level structure that continues across the span of more than a few sentences. And I differentiate high level structure from long distance structure. The algorithm does appear to be able to relate themes from a sentence earlier in a generated text to ones which appear later (up to a few paragraphs later). But the narrative connecting those sentences seems shallow and lacks depth. Compare this with the complex, multi tiered narrative that underpins the structure of a well written book or body of research. My intuition here is that there is an exponential relationship between the "depth" evident in the narrative of generated text and the number of parameters required in the model that places viable, AGI-like intelligence still much further in the future.

emsy 6y ago
Absolutely. At the moment AI seems to scale extremely well in one dimension (amount of data) but really poorly at the other (semantic of data). An huge increase in the former looks like an increase in the other (because more data = more simple data semantic)

Ptrulli 6y ago

GPT-3 is at the very least an indication of future AI advancement. Sure maybe this particular version is still early to be widley incorporated in business or used globally. It's definitely a step in the right direction. I'd personally love the opportunity to test it personally to really get the whole experience.

id_ris 6y ago

How are people accessing GPT3? Is there a website, code repo, api... or do some select few have access?

drusepth 6y ago
The OpenAI website has a login that lets people access it through a web interface, but there is also an API. You can join the waitlist at https://beta.openai.com/; https://beta.openai.com/; they're apparently working through the list.

unexaminedlife 6y ago

I wonder how domain-specific GPT-3 was trained on React. And what sort of arbitrary limitations, if any, exist in what the user can request? I noticed the user didn't have to specify which programming language they wanted the apps in, yet it always chose React.

ZephyrBlu 6y ago
That's only because the guy primed it with React. Someone else has done it with SwiftUI [1]. [1] https://twitter.com/jsngr/status/1284874360952692736 https://twitter.com/jsngr/status/1284874360952692736

knzhou 6y ago

The people here reading GPT-3's output and dismissing it as "not really understanding anything" or "pushing symbols without conscious intent" because of minor slips in coherence have remarkably high standards for understanding. On a high school essay prompt, GPT-3 produces more accurate and more coherent output than 90% of high school students. If the commenters' standards were actually used consistently, they would also apply to almost all human beings. Another interestingly common objection is "we think in terms of meaningful semantics, but GPT-3 only calculates meaningless coefficients". This is a category error masquerading as an argument, equivalent to saying "I have moral worth because I'm made of cells, but you don't because you're only made of quarks." Cells and quarks are just two different-level descriptions of precisely the same thing. Understanding in your brain can be reduced to the calculation of "meaningless" coefficients just as surely as cells can be reduced to quarks.

me551ah 6y ago

It is still amazing at what it is and could be used to generate boilerplate code. For many writing tasks, it can come up with a good starting point. For programming tasks maybe someone can write a translator for python to C++ or even assembly. It can also come up with basic designs which can be a useful starting point and as long as people don't see it as a replacement for them we will be fine.

cryptoz 6y ago

This part piqued my interest: > GPT-3 seed prompts can be reverse-engineered, which may become a rude awakening for entrepreneurs and the venture capitalists who fund them. Is there any more information about this? Is the reverse engineering done by hand or is this something that can be coded? Super curious about this point.

gwern 6y ago
I think you would have to do it by hand. Assuming that the prompt is stripped off before the text is sent to the user, there's no obvious way to me that you would reverse-engineer it except by having an experienced GPT-3 user use their intuition to think about what kind of prompt would elicit observed responses. Since you don't have access to the GPT-3 model itself, you can't directly use gradient ascent or MCMC to try to reverse it to get the prompt. You might be able to blackbox it, but the only approach that comes to mind is using the logprobs, and your target won't give you those any more than they will give you the prompt itself. I'm not sure what he has in mind; just because model-stealing has been demonstrated for CNN classifiers doesn't mean you can feasibly steal GPT-3 prompts...
- cryptoz 6y ago
  Super interesting, thanks for the reply!

brisky 6y ago

GPT3 demos are impressive. This looks like a significant step towards AGI. I think GPT3 simulates memory retrieval and data reconstruction quite well. The part that we are still missing for AGI is curiosity - ability to ask questions and fill in the missing information gaps.

king07828 6y ago

I'd like to see the LSTM brain in the OpenAI 5 model [1] replaced by GPT3 to see if there is any improvement. [1] https://neuro.cs.ut.ee/the-use-of-embeddings-in-openai-five/ https://neuro.cs.ut.ee/the-use-of-embeddings-in-openai-five/