The AG was in charge of the prosecution of the case but they wisely didn't handle it all by themselves. New Mexico was represented in court by DiCello Levitt LLP and McGinn Montoya Love Curry & Sievers. Each probably stand to make billions if the award is upheld.
About three times as many people speak English as a second/foreign language compared to native speakers, not to mention people with no English fluency at all. That's a lot of people who might use an AI writing assistant for legitimate reasons that go beyond poor character. Same as we might do if we needed to communicate in Farsi or whatever.
It seems better for people to simply write in their native tongue. By translating what you write before sharing it, you are robbing readers from the opportunity of using future, better translation technology. It freezes text at the current capabilities of translation software. It also drives us towards a monolonguistic world, and I think a lot would be lost in that (said as a monolonguistic English speaker).
In the abstract, sure. But in terms of practical communication, especially something like a lightweight group discussion, you want to be seen by everyone, not just by those who happen to use live translating software on the receiving end. Because English is dominant in US-centric social media, perhaps you are rarely in the situation where you will be completely left out of a conversation you want to participate in if you only use English, but there's a real friction to leaving text untranslated. Think Reddit or even HN. Would most people even bother to translate the Farsi comment to see if it's insightful, or would they just skip it? Or even scold the author that this is an English dominant space and they should try to conform to that?
My suggestion is Lookdown. Kind of a play on words reminiscent of Markup/Markdown --> Lookup/Lookdown. But also a lookdown is a distinctive looking fish with a cute concave profile so you've got an instant mascot.
If the training data is basically irrelevant, then an LLM should be able to iteratively improve the programming language it uses, resulting in a custom language optimally designed to maximize its own coding ability. The source code might not even be human readable natively, just translated into pseudocode on an as-needed basis.
> If the training data is basically irrelevant, then an LLM should be able to iteratively improve the programming language it uses, resulting in a custom language optimally designed to maximize its own coding ability.
I won't be surprised if one day they do.
At least in their current form, I don't think they can independently design a language that is so much better than other available ones that it makes sense for them to use it.
There's a very good language for almost every use case already, designing one better than the ones already available is a VERY tall order.
It's almost like these languages aren't designed by morons, but built by teams of geniuses over a decade instead.
It's taken me 6 months of heavily steering an LLM to build a language that is not yet even ready for production use.
Maybe I'm the one slowing the LLM down. But it certainly does not seem that way.
The key to a good language for them - from my experience - is maximum expression plus minimum global complexity.
Anything that makes you manage memory lifetimes & memory safety is inherently unfriendly to LLMs - that's globally complex.
All scripting languages allow spaghetti aliases that let you hack your way into oblivion - and LLMs gladly ride that gravy train to hell.
Rust excels here, because it prevents the worst and is WAY more expressive than most people think.
Go has arguably the best runtime ever built, but it's not very expressive, and it still has a lot of problems from C and scripting languages - I don't think these types of languages will be the ones people chose to write code with for LLMs in the future.
