Max_Limelihood

Android is open source; MacOS and Windows aren’t. This gives me more control over my computer, especially since this means LineageOS and GrapheneOS for the desktop soon.

GNU/GNU

Ubuntu replaced their core userland utils with uutils, so the bulk of it. I’m guessing most other distros will follow suit.

Not jmp?

This is a really good explanation of why I find Julia (effectively a Lisp in terms of these features) to be indispensable. The ability to generate code on the fly makes life so much easier that I just can't live without it.

Never have I seen a title so perfectly encapsulate the very problem it's trying to solve.

If you keep trying to "protect" your research from any kind of competition, you're doomed from the start.

Answer: they don’t

(Seriously, I’ve gotten so fed up with Python package management that I just use CondaPkg.jl, which uses Julia’s package manager to take care of Python packages. It is just so much cleaner and easier to use than anything in Python.)

You’re 100% correct, I think. But it’s notable that in that case, an extra functional programming language would make things worse by dividing effort, not better.

Actual AI researchers have wildly different opinions on this; IME, going off all the AI researchers I’ve talked to, they tend to split about 50/50.

I feel like you’re misunderstanding how Julia syntax works. Julia syntax is Python syntax, for the most part. They’re not identical, but the differences are very small, and typically favor Julia—for example, compare:

x*(y.^2)

To the Python equivalent:

np.matmul(x, map(lambda x : x^2, y))

Note also that the first will be much faster because it can fuse the matrix multiplication with the exponentiation. That’s because Julia is a single language, and the whole thing is compiled at the same time. Python can’t fix the above code because NumPy is written in C, not Python.

Julia is just Python but fast.

This is a perfectly reasonable concern/criticism and one that plenty of EAs make. Going off funding, which is overwhelmingly directed towards global health and development, it’s probably one that most of them agree with. It also aligns with my experience talking to EAs, who mostly don’t work in or donate to AI.

But this is a criticism about effectiveness made within an EA framework. It assumes the thing we want to do is maximize the amount of good we do with our resources, and provides rational arguments for why AI won’t do that.

The AI folks think their cause is the one that does the most good, and they have rational arguments for that position. That’s why they’re considered part of the EA movement (despite not fitting in with the original vision).

That also means we have to listen and provide counterarguments before we reject their position. What we definitely shouldn’t do is write them off as “neckbeards” just because they’re working in tech and have unusual concerns. That’s how you end up writing off some 1930s physicist worried about the existential risk of nuclear fission weapons as some “Weird neckbeard nerd.”

Yeah, and there’s also GiveWell. Probably 80% of EA money goes to development, with the rest evenly split between the other three cause areas.

I’d be very shocked if AI research got more than 5% of EA funding.