CF has multiple options, you can use them as just a load balancer/firewall while handling your own TLS cert. I think most let them hold the cert so they can get CF caching services though
Cryptography nerd
CF has multiple options, you can use them as just a load balancer/firewall while handling your own TLS cert. I think most let them hold the cert so they can get CF caching services though
I have a frozen license with them which they’ll reactivate once I give them the receipt information they didn’t send me when I bought it from them…
I have a lifetime license from another company that got deactivated for similar reasons, and support is useless because they demand information I wasn’t given when buying it from them directly
Blackwater will never hear the end of it
This is why I want supported ports of SteamOS to the competitor devices
Look up rules for “constructive dismissal” in your jurisdiction. Not being given work can be counted as firing
The real problem with VM setups is that the host system might have crashed too
He’ll have to handle the hardware for his parents, they’re treating him firmly
13647/F/a weird anime
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
Wine/Proton on Linux occasionally beats Windows on the same hardware in gaming, because there’s inefficiencies in the original environment which isn’t getting replicated unnecessarily.
It’s not quite the same with CPU instruction translation, but the main efficiency gain from ARM is being designed to idle everything it can idle while this hasn’t been a design goal of x86 for ages. A substantial factor to efficiency is figuring out what you don’t have to do, and ARM is better suited for that.
It’s not that uncommon in specialty hardware with CPU instructions extensions for a different architecture made available specifically for translation. Some stuff can be quite efficiently translated on a normal CPU of a different architecture, some stuff needs hardware acceleration. I think Microsoft has done this on some Surface devices.
It’s the same as IPv4 (tunnel) except as mentioned above its still hard to get an IP with the right label
And that scene where she can’t pull in the non-accelerated astronaut colleague while still being in atmosphere thin enough that he wouldn’t fall behind, so he just drifts away through magic
It doesn’t usually need to go to court if the lawyer can remind them of what laws they’re breaking
With an automated refactoring step to pretend it’s really not derivative work despite being extremely derivative