So my thinking is something like: If you just throw money at FOSS ML libraries, I expect you’d mostly shorten the time from “some researcher writes a paper” and “that model is used in a billion real-world projects”. I think you’d make whatever AI tech exists be more broadly distributed. I don’t think that would directly make stronger AI arrive faster, because I think it would mostly just give people lots of easy-to-use boxes, like when CNNs got popularized, it became quite trivial to train any sort of visual classification task you wanted.
But I can see that this would make weaker AI get deployed faster, so that gains from capabilities research could be easier to see for investors, so it could indirectly pour more effort into developing stronger capabilities.
The change of timelines that would produce would depend on to what degree you believe that we are in a “deployment overhang”, in the sense of “practical deployment of models is lagging far behind what we have capacities for”. I can see arguments for both why we are and aren’t in such an overhang. Argument for would be of the shape “have you seen the stuff GPT-3 can do? Why doesn’t everyone have a competent virtual assistant yet?” An argument against would be “look at how slow we’re going with e.g. self-driving cars or AI in medicine, capabilities research just glosses over lots of stuff that blocks practical deployment”.
I think it also depends a lot on what exactly you’d be funding. For example, “cutting-edge pre-trained deep RL for everyone” versus “explainable interpretable robust models for everyone”
Me:
Woot, thanks! Can I share that with the others, including the author of the question? My interpretation was even that it’s about libraries like cchardet – super fundamental ones. Lots of websites misdeclare their encodings, so you get a lot of messy text with encoding errors when you try to read it. Make cchardet a bit more magical, add support for a few more encodings, and you can extract a bit more training data, or a developer saves a day of hard-coding the right encodings to use for a bunch of domains. That, except with thousands and thousands of libraries like cchardet.
Rai:
Sure you can share that, even as a comment with attribution to me if you think it could be useful.
With stuff like more fundamental libraries there it becomes i think a question of generally speeding up industry/innovation. I think you’d probably want to plug all this into a general development model.
My friend Rai said in a Telegram chat:
Me:
Rai: