Let them Fight
Let them Fight
My one dark hope is AI will be enough of an impetus for somebody to update DMCA
Flat epistemological statements like this are why I feel like more STEM people need to take Philosophy.
> pay once, get access to everything everywhere
> thinks about Elsevier
OH GOD PLEASE NO
This is interesting but I’ll reserve judgement until I see comparable performance past 8 billion params.
All sub-4 billion parameter models all seem to have the same performance regardless of quantization nowadays, so 3 billion is a little hard to see potential in.
I seriously doubt the viability of this, but I’m looking forward to being proven wrong.
I would recommend instead to use the AI Horde: https://stablehorde.net/ It’s a collection of people hosting stable diffusion/text generation models
There’s also openrouter which can connect to ChatGPT with a token-based system. (They check your prompts for hornyposting though)
It helps differentiate between GNU/Linux users and the five people who use GNU/Hurd
Judging by my bank account I’m transitioning to non-profit status as well.
In my experience these open models is where the real work is being done. The large supervised models like DALL-E etc are more flashy but there’s a lot more going on behind the scenes than the model itself so it feels like it’s hard to gauge the real progress being done
You could try Guix! It’s ostensibly source based but you can use precompiled binaries as well (using the substitute system)
It’s a source-first Functional package distro like Nix but uses Scheme to define everything from the packages to the way the init system (Shepherd) works.
It’s very different from other distros but between being functional, source-first, and having shepherd, I personally love it
Only if your model has a large enough token context to contain all the documents’ info would you be able to do something like that
It’s usually not the water itself but the energy used to “systemize” water from out-of-system sources
Pumping, pressurization, filtering, purifying all take additional energy.
The problem is notably “powerful”, AIs need pretty significant hardware to run well
As an example the snapdragon NPUs I think can barely handle 7B models.
This is because all LLMs function primarily based on the token context you feed it.
The best way to use any LLM is to completely fill up it’s history with relevant context, then ask your question.
Doesn’t this just do what gets done through convolution anyway?
What’s the point of this.
Seems like the thing I’ve always considered true: you can turn a mediocre game into a masterpiece with the right application of music.
Not that I’m saying Stardew is mediocre, but good music seems to uplift a game more than any other part.
This is a good move for international open source projects, with multiple lawsuits in multiple countries around the globe currently ongoing, the intellectual property nature of code made using AI isn’t really secure enough to open yourself up to the liability.
I’ve done the same internally at our company. You’re free to use whatever tool you want but if the tool you use spits out copyrighted code, and the law eventually has decided that model users instead of model trainers are liable for model output, then that’s on you buddy.
ChatGpt already is multiple smaller models. Most guesses peg chatgpt4 as a 8x220 Billion parameter mixture of experts, or 8 220 billion parameter models squished together