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GPT-3 Creative Fiction

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Re: GPT-3 Creative Fiction

#41

Post by qayxc »

YeGoblynQueenne wrote: post #32
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…

Well, there is no formal definition of "understanding" in the context of CS, AI, or machine learning so anyone can claim anything they like, with respect to the term. For example, I have a thermos that keeps my coffee cold in the summer and hot in the winter. It u n d e r s t a n d s .

There are a number of NLP-tasks that aim to quantify understanding, e.g. textual entailment. No currently published model is even remotely close to human-level performance on all of these tasks.

As long as there are no ways to properly query models, it's hard to qualify their level of understanding. It would help immensely if we could ask models for rules as in "why was the object labelled 'a car'" (in case of image recognition) or directly query any grammatical rules discovered during the processing of language.

Especially in classification tasks, knowledge extraction (e.g. by outputting rules) would be so much more helpful than simply having an AI looking at a CT image and spit out "yep - that's a tumour, alright", while having radiologists scratch their heads as to why...

Re: GPT-3 Creative Fiction

#43

Post by gwern »

czzr wrote: post #17

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?

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

Re: GPT-3 Creative Fiction

#44

Post by gwern »

ixvvqktiwl wrote: post #9

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…

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 comparison with artificial neural net models that they aren't really doing anything we'd call "natural intelligence"; it's advanced mimicry at best, just elaborate pattern matching.

I get that it's very easy to create these natural neural net models and be carried away by excitement, and it can even be profitable (witness the many VC-funded startups which use natural neural nets as a core technology), but we should remain skeptical of any claims by those natural neural net models, much less their promoters online, that they are 'intelligent' in the strict definition of the word.

Re: GPT-3 Creative Fiction

#45

Post by YeGoblynQueenne »

qayxc wrote: post #41
YeGoblynQueenne wrote: post #32

Earlier quoted context omitted.

Well, there is no formal definition of "understanding" in the context of CS, AI, or machine learning so anyone can claim anything they like, with respect to the term. For example, I have a thermos that keeps my coffee cold in the summer and hot in the winter. It u n d e r s t a n d s .

There are a number of NLP-tasks that aim to quantify understanding, e.g. textual entailment. No currently published model is even remotely close to human-level performance on all of these tasks. As long as there are no ways to properly query models, it's hard to qualify their level of understanding. It would help immensely if we could ask models for rules as in "why was the object labelled 'a car'" (in case of image…

I had to look up textual entailment (on wikipedia) because I wasn't sure of its formal definition. It turns out, it doesn't have one:

>> "t entails h" (t ⇒ h) if, typically, a human reading t would infer that h is most likely true"

So in other words it's down to good old eyballing. I'm not impressed, but not surprised either, it's just one of the many poorly defined tasks in machine learning, particularly NLP which has turned into a quagmire of shoddy work ever since people started firing linguists to improve their systems' performance.

Anyway, since logical entailment is central to my field of study I can tell that if textual entailment is less strictly defined than logical entailment (as per the wikipedia article), then it doesn't require anything that we could recognise as "understanding". Because logical entailment certainly doesn't require understanding and its definition is as strict, as a very strict thing [1]. I mean, I can see how loosening a requirement for precision of any justification of a decision that "A means B" can improve performance, but I can't see how it can improve understanding.

Edit: I'm not sure we disagree, btw, sorry for the grumpy tone. I fully agree with your gist about explainability etc.

______________

[1] Roughly, "A |= B iff for each model M, of A, M is a model of B", where A and B are sets of first order logic formulae and a "model" in this context is a logical interpretation under which a set of formulae is true. A "logical interpretation" is a partition of a predicate's atoms to true and false.

Re: GPT-3 Creative Fiction

#46

Post by czzr »

gwern wrote: post #43
czzr wrote: post #17

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?

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

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.

Re: GPT-3 Creative Fiction

#48

Post by [deleted] »

minimaxir wrote: post #37

After spending a lot of time working with GPT-3/the OpenAI API ( 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]

Re: GPT-3 Creative Fiction

#49

Post by nschucher »

gwern wrote: post #44
ixvvqktiwl wrote: post #9

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…

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 ask GPT-3 and see what it thinks :P

Re: GPT-3 Creative Fiction

#50

Post by gdb »

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 please get in touch — gdb@openai.com.

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