I've done a deep dive into SB 1047 over the last few weeks, and here's what you need to know:
*Nobody* should be supporting this bill in its current state. It will *not* actually cover the largest models, nor will it actually protect open source.
But it can be easily fixed!🧵
This is important, so don't just read this thread, instead read the 6000+ word article I just published.
In the article I explain how AI *actually* works, and why these details totally break legislation like SB 1047. Policy makers *need* to know this: answer.ai/posts/2024-06-…
SB 1047 does not cover "base models". But these are the models where >99% of compute is used. By not covering these models, the bill will probably actually not cover any models at all.
(There are also dozens of trivial workarounds for anyone wanting to train uncovered models.)
If the "influence physical or virtual environments" constraint is removed then the impact would be to make development of open source AI models larger than the covered threshold impossible.
However, the stated aims of the bill are to ensure open source developers *can* comply.
Thankfully, the issues in SB 1047 can all easily be fixed by legislating the deployment of “AI Systems” and not legislating the release of “AI Models”.
Regulating the deployment of services, instead of the release of models, would not impact big tech at all, since they rarely (if ever) release large models.
So the big tech companies would be just as covered as before, and open source would be protected.
If we can't fine-tune open sourced models, then we'll be stuck with whatever values and aims the model creators had. Chinese propaganda is a very real current example of this issue (and remember that the best current open source models are Chinese).
I don't propose that we exempt AI from regulation. However, we should be careful to regulate with an understanding of the delicate balance between control and centralization, vs transparency and access, as we've done with other technologies throughout history.
Instead of "p(doom)", let's consider "p(salvation)" too, and bring a new concept to the AI safety discussion:
“Human Existential Enhancement Factor” (HEEF): the degree to which AI enhances our ability to overcome existential threats and ensure our long-term well-being.
If you care about open source AI model development, then submit your views here, where they will be sent to the authors and appear on the public record: calegislation.lc.ca.gov/Advocates/
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And here's the docs for sqlite-utils, which is a beautifully designed library that is clear, easy to understand, and introduces minimal new concepts to learn -- it's just SQL! sqlite-utils.datasette.io/en/stable/pyth…
Do you use Starlette, FastAPI, Litestar, Quart, Uvicorn, or any other Python web thingie that's based on ASGI?
If so, do you feel like you understand the ASGI protocol reasonably well? Or do you feel like it's a bit of a mystery as to what's going on underneath the hood?
The reason I'm asking is because, until today, I didn't really understand ASGI. I've now implemented a basic ASGI server from scratch, so I get it.
Prior to doing that, I wasn't really able to use any of those ASGI frameworks and servers effectively.
For those of you using ASGI stuff, but that don't really understand the protocol well, how are you doing it exactly? Mainly by taking existing sample code and modifying it? And/or following step-by-step tutorials?
There's a new bill, SB-1047 "Safe and Secure Innovation for Frontier Artificial Intelligence Models Act".
I think it could do a great deal of harm to startups, American innovation, open source, and safety. So I've written a response to the authors: 🧵 answer.ai/posts/2024-04-…
By imposing restrictions on open-source AI, SB1047 hurts AI safety, reducing:
- Collaboration, which allows a wider range of experts to identify and address potential safety concerns
- Resilience; concentrating control creates single points of failure & increases systemic risk
Harming open source will harm developers, consumers, academics, and obstruct the development of new startups, due to:
- Overly broad definitions
- Misunderstanding of dual use
- Restrictive requirements
- Disincentivizing Openness
Today, with @Tim_Dettmers, @huggingface, & @mobius_labs, we're releasing FSDP/QLoRA, a new project that lets you efficiently train very large (70b) models on a home computer with consumer gaming GPUs. 1/🧵 answer.ai/posts/2024-03-…
"With this capability we can take huge models to new heights locally, and gigantic, hundreds of billions of parameter models are now accessible by small labs", says legendary model builder @Teknium1
As the name suggests, this project combines two key pieces: @Tim_Dettmers' QLoRA, which lets us train models ~4x smaller by using quantization, with @AIatMeta's FSDP, which shards large models across multiple GPUs.
Currying and composition in a nutshell (with APL).
(This is easier for primary school children to learn than many things they are taught. At least according to the primary school kids I've taught it to.)
Hopefully it's obvious from the context what "∘" does, but if not, look at this link (and the link there to "beside"): aplwiki.com/wiki/Bind