At a Senate hearing on AI’s impact on journalism, lawmakers backed media industry calls to make OpenAI and other tech companies pay to license news articles and other data used to train algorithms.
At a Senate hearing on AI’s impact on journalism, lawmakers backed media industry calls to make OpenAI and other tech companies pay to license news articles and other data used to train algorithms.
“What would that even look like?” asks Sarah Kreps, who directs the Tech Policy Institute at Cornell University. “Requiring licensing data will be impractical, favor the big firms like OpenAI and Microsoft that have the resources to pay for these licenses, and create enormous costs for startup AI firms that could diversify the marketplace and guard against hegemonic domination and potential antitrust behavior of the big firms.”
As our economy becomes more and more driven by AI, legislation like this will guarantee Microsoft and Google get to own it.
Yes, and they’ll use legislation to pull up the ladder behind them. It’s a form of Regulatory Capture, and it will absolutely lock out small players.
But there are open source AI training datasets, but the question is whether LLMs can be trained as accurately with them.
Any foundation model is trained on a subset of common crawl.
All the data in there is, arguably, copyrighted by one individual or another. There is no equivalent open - or closed - source dataset to it.
Each single post, page, blog, site, has a copyright holder. In the last year big companies have started to change their TOS to make that they are able to use, relicense and generally sell your data hosted in their services as their own for the intent of AI training, so potentially some small parts of common crawl will be licensable in bulk - or directly obtained from the source.
This does still leave out the majority of the data directly or indirectly used today, even if you were willing to pay, because it is unfeasable to search and contract every single rights holder.
On the other side of it there have been work to use less but more heavily curated data, which could potentially generate good small, domain specific, models. But still they will not be like the ones we currently have, and the open source community will not be able to have access to the same amount and quality of data.
It’s an interesting problem that I’m personally really interested to see where it leads.
Here’s the summary for the wikipedia article you mentioned in your comment:
Common Crawl is a nonprofit 501(c)(3) organization that crawls the web and freely provides its archives and datasets to the public. Common Crawl's web archive consists of petabytes of data collected since 2008. It completes crawls generally every month.Common Crawl was founded by Gil Elbaz. Advisors to the non-profit include Peter Norvig and Joi Ito. The organization's crawlers respect nofollow and robots.txt policies. Open source code for processing Common Crawl's data set is publicly available. The Common Crawl dataset includes copyrighted work and is distributed from the US under fair use claims. Researchers in other countries have made use of techniques such as shuffling sentences or referencing the common crawl dataset to work around copyright law in other legal jurisdictions.As of March 2023, in the most recent version of the Common Crawl dataset, 46% of documents had English as their primary language (followed by German, Russian, Japanese, French, Spanish and Chinese, all below 6%).article | about
Thanks for the link to Common Crawl; I didn’t know about that project but it looks interesting.
That’s also an interesting point about heavily curated data sets. Would something like that be able to overcome some of the bias in current models? For example, if you were training a facial recognition model, access a curated, open source dataset that has representative samples of all races and genders to try and reduce the racial bias. Anyone training a facial recognition model for any purpose could have a training set that can be peer reviewed for accuracy.
Face recognition is probably dead as an open endeavor. The surveillance aspect makes it too controversial. I mean that not only will we not see open source work on this, but any work is behind closed doors.
In general, a major problem is that it is often not clear what reducing bias means. With face recognition, it is clear that we just want it to work for everyone. With genAI it is unclear. EG you type “US president” into an image generator. The historical fact is that all US presidents were male, and all but one were white. What’s the unbiased output?
One answer is that it should reflect who is eligible for the US presidency. But in the future, one would expect far more people to be of “mixed race”. So would that perhaps be biased against “interracial marriage”? In either case, one could accuse the makers of covering up historical injustice. I think in practice, people want image generators that just give them what they want with minimum fuss; wants which are probably biased by social expectations.
In any case, such curated datasets are used to fine-tune models trained on uncurated data. I don’t think that is known how such a dataset should look like exactly, to yield an unbiased model (however defined).
These open datasets are used to fine-tune LLMs for specific tasks. But first, LLMS have to learn the basics by being trained on vast amounts of text. At present, there is no chance to do that with open source.
If fair use is cut down, you can forget about it. It would arguably be unconstitutional, though.
That’s not even considering the dystopian wishes to expand copyright even further. Some people demand that the model owner should also own the output. Well, some of these open datasets are made with LLMs like ChatGPT.
It’s not a case of cutting down fair use. It’s a case 9f enforcing current fair use limits.
Can you give an example of something that is outside fair use?
Just in case, there is confusion here: Obviously there is no past precedent on exactly the new circumstances, but that does not put new technologies outside the law. EG the freedom of speech and the press apply to the internet, even though there is no printing press involved.
Well fuck all those artists and writers who made the original works then I guess. Licensing is impractical.
They’re going to get fucked either way, may as well live in the world where smaller AI companies have a chance. It’s already bad enough that openai got to slurp reddit and twitter for free and nobody else can.
They won’t be fucked. They can use the AI tools as well to make novel content, and augment their production quality and quantity.
Lmao
And what about the authors whose works were injected without compensation? What should we do for them? I don’t think that these commercial AI models should get to infringe on their copyrights for nothing. If I pay for a ChatGPT subscription and ask it to tell me about the war the Middle East and it basically regurgitates and plagiarizes information it learned from a journalist, then ChatGPT has essentially stolen the copyrighted work from that journalist and the revenue that my click would have earned them.
I don’t see a problem using publicly posted copyrighted data for non-commercial use for training local language models but don’t think its fair to allow copyright infringement for commercial use.
You’re repeating some talking points which are simply misinformation. An author who makes something “for hire”, like an employed journalist, does not own the copyright. Do you believe that construction workers benefit when rents go up?
Copyrights are called intellectual property, because they work a lot like physical property. Employees create them and employers own them. They are bought and sold. A disproportionate share of property belongs to rich people, which is how they are rich.
This is about funneling more wealth to property owners. The idea that this would benefit anyone else is simply the good old trickle-down. It will not happen.
I think it’s better be pragmatic then to give everything to the big corporations.
OpenAi isn’t going to takes its tool offline so the loss of revenue isn’t going away. Payments won’t end up in the pockets of any individual journalist. The money the few journalistic sites will receive will be used to pay for the subscription fee to the next big model while cutting off their staff since it will net them more money.
If this goes through, Google and Microsoft will spend the next few years buying data or the companies that have it. The walls will be raised and we will be fucked, legislation will only help them.
And there is simply not enough public domain data to build a competitive product. Better to tax and redistribute through UBI while keeping the field competitive and avoiding monopolies imo.