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AI Democratization in the Era of GPT-3

thegradient.pub

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Re: AI Democratization in the Era of GPT-3

#61

Earlier quoted context omitted.

Shouldn't I own part of gpt3 because it's trained on my data and likely spitting out what I have commented somewhere on the internet? I think ml models should be public unless the data itself isn't.

Curious about the downvotes. If you remove compute and code, what remains is data that isn't owned by a single entity in the case of gpt3. You can sell both compute and code which is what you own but should you be able to sell data? You can't translate a commerical book without paying the copyright holder. You own the copyright for the translation but everything else still remains that of the original author. Why wou…

The current legal understanding is that according to the current copyright laws the ML models trained on some data are not considered derivative works of that data, so if the data was obtained legally and without other restrictions (e.g. if the researchers sign a contract with the data owner that gives them access to data but agree to certain conditions) then the authors of the data do not have any claim on some rights to the resulting model.

One aspect of this is that historically statistical models calculated from large volumes of text (which is a notion that predates computers, e.g. frequency dictionaries and the whole [sub]field of quantitative corpus linguistics) have been considered facts about that corpus of text and thus not copyrightable at all or (depending on jurisdiction) entitled to different set of protections/limitations assigned to compilations of facts, which give some rights to the people who compiled the facts but no rights to the source of these facts (since facts as such can't be copyrighted).

This also applies to many forms of analysis of audiovisual data, where the copyrights of the source works do not transfer to the results of the statistical or qualitative analysis and can't limit their creation, distribution or sale.

The appropriate analogy to a commercial book or movie is not a translation, but some analysis of it - e.g. a thorough literary review and critique of some book or movie is a separate work with its own copyright, and the original author has no claim on it despite the fact that is (obviously) based on the contents of the work and describes it in great detail. Including verbatim fragments of the work is limited (fair use allows some inclusions but not all), but all the other details are not.

Also, the whole notion of copyrightability of ML model weight files is interesting and IMHO not settled. You could argue that there is some creative expression in forming the model (which would support it being copyrightable) or you could argue that it's a mechanistic result of the application of some algorithm and settings (which have the creative part, and are copyrightable on their own), and so the output can't be copyrightable, no matter how much work (human or machine), time and cost it took - at least in USA copyright law doctrine (e.g. Feist Publications v. Rural Telephone Service) is that mere "sweat of the brow" (no matter how much) does not entitle a work to copyright protection; it requires application of human creativity to create an original work, and automated processes can't satisfy that requirement.

So from the perspective of GPT-3 authors it might be prudent to distribute the model only to people who agree to specific, explicit contractual/licensing restrictions on redistribution and usage of the model instead of simply relying on the default application of copyright laws.

And crucially, if some output is not copyrightable, it can't be considered a derived work to which the authors of a source work could have some claim.

Re: AI Democratization in the Era of GPT-3

#62

The PC was supposed to democratize computing. It did, and then Microsoft found a choke point. The internet was supposed to democratize communication. It did, and then Google found a choke point. It seems like this two steps forward, one step back pattern might be the rule rather than the exception. Even the article defines AI democratization in terms of using models rather than training models, as the costs of traini…

Neither Google nor Microsoft were or are actually choke points. The internet is plenty democratized. People simply choose to use a few big centralized services. They have the ability to use others. A democracy where everyone votes for the same candidate because they're the best candidate is still a democracy.

Re: AI Democratization in the Era of GPT-3

#63

The PC was supposed to democratize computing. It did, and then Microsoft found a choke point. The internet was supposed to democratize communication. It did, and then Google found a choke point. It seems like this two steps forward, one step back pattern might be the rule rather than the exception. Even the article defines AI democratization in terms of using models rather than training models, as the costs of traini…

“The amount of data as well requires a massive telemetry apparatus, and designing likely models seems the province of people with PhDs.”

Transfer learning takes care of your first point. The second is taken care of by the plethora of open source libraries such as fast.ai that abstract away most of the decisions one needs to make when creating a deep learning model.

Oh did I also mention Kaggle and Google Collab offer free GPU to train your models, in addition to the thousands of publicly available datasets?

Re: AI Democratization in the Era of GPT-3

#64
post #7

Earlier quoted context omitted.

Fun fact, OpenAI did not collect the data itself - they mainly used data from Common Crawl (in addition to a couple other datasets), which is compiled by a non profit that shares the dataset for free. So perhaps the license of such datasets can encourage free sharing of research outcomes. https://commoncrawl.org/

Shouldn't I own part of gpt3 because it's trained on my data and likely spitting out what I have commented somewhere on the internet? I think ml models should be public unless the data itself isn't.

It makes its own new arrangements of words. It’s not just clips of what it was trained with pasted together.

Re: AI Democratization in the Era of GPT-3

#65
post #56

Earlier quoted context omitted.

> The PC was supposed to democratize computing It was?

Yes. The name gives it away. And Microsoft's mission for many years was "a computer in every home".

"One ring to rule them all, and in the darkness bind them."

Re: AI Democratization in the Era of GPT-3

#66
post #58

The PC was supposed to democratize computing. It did, and then Microsoft found a choke point. The internet was supposed to democratize communication. It did, and then Google found a choke point. It seems like this two steps forward, one step back pattern might be the rule rather than the exception. Even the article defines AI democratization in terms of using models rather than training models, as the costs of traini…

How else would you define “AI democratization”? The fact I can even program puts me in an elite of less than 0.5% of the world population [0], and that includes all specialties not just AI. I do iPhone apps these days; even though I can follow the various tutorials for how to train an AI to recognise handwritten digits [1], I don’t actually grok the maths behind the back-propagation algorithm and why it’s better than…

What’s so difficult to understand about back propagation? All you’re doing is computing a derivative.

Re: AI Democratization in the Era of GPT-3

#67
post #48
post #41

Earlier quoted context omitted.

That's for model inference. For training OpenAI said they used around 3000 PetaFLOPS / days on the largest GPT-3 model. That translates to about 300 Nvidia A100 GPUs if you want training to finish in a month (any slower and your researchers are not going to be able to make much progress). A system like that would cost at least $5M, probably more like $10M.

Surely $10 million should be well within the spending abilities of a fair number of tech people? Many universities now have fairly large computing clusters as well.

People give more than this to get their lackluster children into the Ivy League.

Re: AI Democratization in the Era of GPT-3

#68
The models are so unwieldy that had the big companies not handled the training and infrastructure, no one would be able to explore these models. We would have had no democratization.

Are these big firms are beholden to open up their work to the public for this utopian ideal of democratization?

Re: AI Democratization in the Era of GPT-3

#69

Earlier quoted context omitted.

Shouldn't I own part of gpt3 because it's trained on my data and likely spitting out what I have commented somewhere on the internet? I think ml models should be public unless the data itself isn't.

Should you own part of my brain if I read your comment?

Good one !!!

Re: AI Democratization in the Era of GPT-3

#70
post #47
post #38

Earlier quoted context omitted.

how big do the models get? are you able to say?

As big as you want, kind of. The challenge, as in large-scale physics, is how many nodes you can stick together with sufficiently high bandwidth (low latency is less important in the ML space, because there are lots of ops per byte, unlike some CFD Simulations that have very few per update). On our Cloud TPU product page [1], we have a single TPU v3 pod with 32 TB of memory. For the most recent MLperf submission, the…

Hi, where can I learn more about Distributed Systems? I guess your job requires that knowledge. Any accessible resources for a fresh grad?
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