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GPT-3 Creative Fiction

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Re: GPT-3 Creative Fiction

#91

Post by tripzilch »

api wrote: post #16

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…

yesterday I watched a youtube video about GPT3 (https://www.youtube.com/watch?v=_8yVOC4ciXc), and it showed two poems. One was human made, the other was from AI trained on that human's poems.

Both poems were pretty good. But one of them had a metaphor about the moon reflecting in ocean waves, being distorted and taking on monstrous forms.

I figured this had to be the human one, it was a novel description (because metaphor) of a very real experience (how the moon appears in reflection on the ocean).

Re: GPT-3 Creative Fiction

#92

Post by [deleted] »

gdb wrote: post #50
sillysaurusx wrote: post #2

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…

(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 pl…

[deleted]

Re: GPT-3 Creative Fiction

#93

Post by cmehdy »

waltpad wrote: post #89
cmehdy wrote: post #69

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…

> fails at logic more than at poetry This is purely subjective. Your expectations in Poetry might be different from those of other people or even specialists. I am not particularly good in that domain, but I don't really like the results shown sometimes.

I agree with you that it's subjective. Testing logic vs. art is going to bring in this kind of problem to the surface (how do you test art in a comparable way to how you test logic?). This is why I wrote that noticing the thoughts made me take a step back from my own projections (my subjectivity). That's the whole point.

Re: GPT-3 Creative Fiction

#94

Post by Udik »

nschucher wrote: post #49
gwern wrote: post #44

Earlier quoted context omitted.

I'm sorry, your comment explaining why deep learning & GPT-3 do not truly understand anything is more poorly reasoned and explained than GPT-3's explanation why GPT-3 does not truly understand anything: https://www.gwern.net/GPT-3#why-deep-learning-will-never-tru... While it's true that recent natural neural net models like ixvvqktiwl may sound superficially coherent and like they 'understand' things, we can see by c…

I'm as impressed as anyone with GPT-3 samples, but you're sort of ignoring the symbol grounding elephant in the room regarding language models ( https://openreview.net/pdf?id=GKTvAcb12b ). Language models are not grounded learners. The language produced does not really correspond meaningfully to our world except in superficial (albeit complex) ways. Do you have thoughts on how to move forward on this problem? Maybe a…

The problem, if I understand correctly, is that we're feeding enormous amounts of text to language models hoping that they might contain, hidden in their patterns, enough information about the real world to allow prodigiously complex NNs to extract it and create their own representation of reality.

And while this is possible, it feels there should be more effective ways to impart a knowledge of reality- if only we had huge databases of usable data to feed to these NNs instead of dumps of text. At the moment it feels like we're trying to teach advanced physics to a subject with no previous knowledge of physics or math by just feeding it with everything on arXiv and physics textbooks in random order. What you get is someone who can produce text that mimics the superficial style of scientific articles, but with an extremely confused understanding of the subject, if any at all.

Re: GPT-3 Creative Fiction

#95

Post by gwern »

cmehdy wrote: post #86
gwern wrote: post #82

Earlier quoted context omitted.

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/127879…

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 ob…

I tend to write it off as less any kind of deep truth about humans (well, maybe the "bachelors can be married" one given that 9/10 students agreed with GPT-3 that bachelors can be married) than just the current weaknesses of how we train NNs like GPT-3 (small, unidirectional, unimodal, not to convergence, missing most of science in PDFs, etc).

In particular, I bet the "how many eyes does a horse have" example would be much less likely with a multimodal model which has actually seen photographs or videos of what the word "horse" describes and can see that, like most mammals, they only have 2 eyes. Think of it as like layers of Swiss cheese: every modality's datasets has its own weird idiosyncrasies and holes where the data is silent & the model learns little, but another modality will have different ones, and the final model trained on them all simultaneously will avoid the flaws of each one in favor of a more correct universal understanding.

I'm very keen to see how much multimodal models can improve over current unimodal models over the next few years.

Re: GPT-3 Creative Fiction

#96

Post by maliker »

longtom wrote: post #84

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.…

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

Re: GPT-3 Creative Fiction

#97

Post by OkGoDoIt »

gdb wrote: post #50
sillysaurusx wrote: post #2

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…

(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 pl…

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.

Re: GPT-3 Creative Fiction

#98

Post by ipsum2 »

jcims wrote: post #68
Reedx wrote: post #30

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

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 t…

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-transform...

Re: GPT-3 Creative Fiction

#99

Post by jcims »

ipsum2 wrote: post #98
jcims wrote: post #68

Earlier quoted context omitted.

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 t…

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 continu…

Whoa, cool, will check this out. Thanks!

Re: GPT-3 Creative Fiction

#100

Post by gwern »

nschucher wrote: post #49
gwern wrote: post #44

Earlier quoted context omitted.

I'm sorry, your comment explaining why deep learning & GPT-3 do not truly understand anything is more poorly reasoned and explained than GPT-3's explanation why GPT-3 does not truly understand anything: https://www.gwern.net/GPT-3#why-deep-learning-will-never-tru... While it's true that recent natural neural net models like ixvvqktiwl may sound superficially coherent and like they 'understand' things, we can see by c…

I'm as impressed as anyone with GPT-3 samples, but you're sort of ignoring the symbol grounding elephant in the room regarding language models ( https://openreview.net/pdf?id=GKTvAcb12b ). Language models are not grounded learners. The language produced does not really correspond meaningfully to our world except in superficial (albeit complex) ways. Do you have thoughts on how to move forward on this problem? Maybe a…

I would be more impressed by that paper if they didn't make trivially falsifiable claims: https://twitter.com/gwern/status/1280204127876808705

I am happy to take them at their word that their theory about symbol grounding proves that no LM will ever be able to solve "Three plus five equals" (appendix B); and thus, by modus tollens, GPT-3's ability to (already) solve "Three plus five equals" means their theory is wrong and I need not consider it any further.

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