this post was submitted on 23 Jun 2024
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The game plan is to scrape, store and utilise as much data as possible regardless of conventions, best practice, license agreements etc until specifically regulated to stop.
At that point, a few early companies will have used vast swathes of data that any newly established company is banned from also using
And they will be “unable” to purge it.
Hoping the EU drops GDPR 2 requiring them to delete the entire model if it infringes or something.
Expecting the US to meaningfully regulate US companies is like expecting.........
You know what, even including physical impossibilities, I'm struggling to think of anything less likely
I've yet to understand how the hell they get away with "I don't know how it works". Either figure out how it works or stop using it, shithead. It's software not magic beans.
There's lots of complicated fields out there, none of them get a pass for "I don't know how my drugs work" or "I don't know how my rockets work". That's absolutely ridiculous.
Uh, we don't really know how our drugs work (especially the older ones). We have a vague understanding of their mechanisms, but we really don't know how they work. We don't even have a clear idea of what the structures of most drugs look like, and how they interact with their binding sites.
Luckily, we don't actually have to know how they work, to know that they work. Instead we use clinical trials and real world evidence to support their use.
(Fun fact: there's actually a branch of drug development called phenotypic drug discovery which actually does away with the understanding of the mechanisms altogether. )
It’s just how machine learning has been since ever.
We only know the model’s behavior by testing, hence we only know more or less the behavior in relation to the amount of testing that was done. But the model internals has always been a black box of numbers that individually mean nothing and if tracked which neurons fire here and there it’ll appear just random, because it probably is.
Remember the machine learning models aren’t carefully designed, they’re just brute-force trained for a long time and have the numbers adjusted again and again whenever the results look closer or further away from the desired output.
If the models are random then we shouldn't be trusting them to do anything, let alone serious applications. If any other type of software told us that it's based on partially random results we'd say "get that shit out of here, I want my software to work first time, every time".
"Statistically good enough" works for some applications but not for others. If a LLM finds a formula that has an 80% chance to be the cure for cancer or a new magical fuel or some amazing new material that's cool, we're not going to look the gift horse in the mouth.
But using LLM to polute the web with advertising texts that are barely inteligible, and using it as a pretext to break copyright in the process, who does that help? So far the only readily available commercial application for LLMs has been to spit out semi-nonsense so that a bunch of bottom-crawling parasitic industries can be enabled to keep on pinching pennies and shitting up everything they touch.
Which, ironically, it will help them to hit bottom all the faster, so in a strange way it's a positive return, but the problem is they're going to take down a lot of useful things with them.
I’m in the US so yeah…. Even if the current of future GDPR requires deletion I guarantee it’ll still be used in the US. I have no faith that any US company will follow rules like that. Any fines are just looked at as the cost of doing business.
Or they'll "purge" it and somehow the canaries will end up in the model anyway