This was in a conversation about what kind of abusive behavior is acceptable. Do you think it's also acceptable to be mean to athletes because they too cause damage to their own bodies?
Right and other people don't get to decide to put a virus in my body, so vaccinate or mask up!
These already have IR sensors for gesture detection
https://www.indiegogo.com/projects/hy-concealed-smart-earbuds#/
Using cameras instead of simpler IR sensors are going to hurt battery life
As stated in the dissent, ignoring your own precedence for years to create an impression that a useful legal principle isn't useful and to create an excuse to overturn it doesn't make for an actual reasonable argument to overturn it.
"holding that such judicial deference is appropriate where the agency’s answer was not unreasonable"
So by definition no
If their interpretation is good, it can stand
With Chevron, it would stand, without it the court gets to ignore all reason and reject an agency's interpretation even if it's sane and carefully constructed by experts. The court gets to challenge every individual decision and reason made by the agency which the law doesn't make explicit
They saw chevron as useful when Republicans had control over all the major agencies, but with gov agencies driven by experts and scientists who can ignore the Republicans screaming then chevron isn't helping them anymore. And that's part of why they try to get as many partisan judges into the system as possible, to get their way through corrupt courts instead.
And once again the problem is that there's not much ensuring those models are correct, there's not enough capacity available to finetune even a significant fraction of it.
You're missing options like rail
Humans learn a lot through repetition, no reason to believe that LLMs wouldn't benefit from reinforcement of higher quality information. Especially because seeing the same information in different contexts helps mapping the links between the different contexts and helps dispel incorrect assumptions. But like I said, the only viable method they have for this kind of emphasis at scale is incidental replication of more popular works in its samples. And when something is duplicated too much it overfits instead.
They need to fundamentally change big parts of how learning happens and how the algorithm learns to fix this conflict. In particular it will need a lot more "introspective" training stages to refine what it has learned, and pretty much nobody does anything even slightly similar on large models because they don't know how, and it would be insanely expensive anyway.
Yes, but should big companies with business models designed to be exploitative be allowed to act hypocritically?
My problem isn't with ML as such, or with learning over such large sets of works, etc, but these companies are designing their services specifically to push the people who's works they rely on out of work.
The irony of overfitting is that both having numerous copies of common works is a problem AND removing the duplicates would be a problem. They need an understanding of what's representative for language, etc, but the training algorithms can't learn that on their own and it's not feasible go have humans teach it that and also the training algorithm can't effectively detect duplicates and "tune down" their influence to stop replicating them exactly. Also, trying to do that latter thing algorithmically will ALSO break things as it would break its understanding of stuff like standard legalese and boilerplate language, etc.
The current generation of generative ML doesn't do what it says on the box, AND the companies running them deserve to get screwed over.
And yes I understand the risk of screwing up fair use, which is why my suggestion is not to hinder learning, but to require the companies to track copyright status of samples and inform ends users of licensing status when the system detects a sample is substantially replicated in the output. This will not hurt anybody training on public domain or fairly licensed works, nor hurt anybody who tracks authorship when crawling for samples, and will also not hurt anybody who has designed their ML system to be sufficiently transformative that it never replicates copyrighted samples. It just hurts exploitative companies.
Remember when media companies tried to sue switch manufacturers because their routers held copies of packets in RAM and argued they needed licensing for that?
https://www.eff.org/deeplinks/2006/06/yes-slashdotters-sira-really-bad
Training an AI can end up leaving copies of copyrightable segments of the originals, look up sample recover attacks. If it had worked as advertised then it would be transformative derivative works with fair use protection, but in reality it often doesn't work that way
See also
Apple management has explicitly stated they do not want to support better compatibility between Android and iPhone, their response when asked what parents who buy cheap Androids for their kids should do it was to buy them iPhones. Many of the problems are very easy to fix on Apple's side and keeping them problematic is intentional.
Math and formal logic are effectively equivalent and philosophy without conditional logic is useless. Scientifically useful philosophy is just "explorative logic" or something like it
You shouldn't, but the patent office don't care about inventive height and obviousness anymore
And there's literally dedicated extensions to tracking open tabs for you
Sheldon’s widow is one of the richest women in the world, and she’s willing to spend her fortune to support Israel.
