I believe it is unfair (and unwise?) to throw in modifiers such as "very long-term" in the quoted sentence, or "very distant" in:
> It is hard for me to empathize with Musk’s fixation on evil super-intelligent AGI killer robots in a very distant future.
The truth is we don't know, (we can't know,) and we (on the whole) are making enormous strides. And it could take one small breakthrough to find ourselves "very" suddenly on the precipice of AGI and evil super-intelligent killer robots. Dismissing the current state of progress as trying to beat Atari is silly.
I, for one, am glad that some people with the resources are taking some efforts to get ahead of that "evil" aspect. But I don't see this as an "us" vs "them" scenario: it's also great that there are people helping make neural net algorithms more accessible.
My issue is that, while of course you're right that one can't know for sure, some people conclude that simply not being able to know justifies whipping up a frenzy and (in all likelihood) wasting part of a billion dollars that could go to, say, more productive research even within AI/ML.
Anyone could make a long list of things it's impossible to know for sure that would destroy humnaity. Many such things are vastly more likely to do so than malicious AGI.
Ok, I'll bite. Can you list several things that are vastly more likely to lead to the complete extinction of the human race (I'll assume that's what you meant by "destroy humanity") than malicious AGI?
That would be at the top of my list. And then there's nuclear attacks/disaster, asteroid hitting the earth, superbugs and biological weapons gone awry, physics experiment gone awry creating a black hole... I put superintelligent AI down towards alien invasion on my personal list of humanity-ending risks.
We rank all these scenarios very differently then :)
Climate change and nuclear war are huge issues, and we correctly invest orders of magnitude more money and time (which might still not be enough) trying to prevent or limit these than trying to prevent the birth of a malicious AGI, but they are extremely unlikely to lead to a complete extinction of the human race.
Regarding the other scenarios, if you allow me to move the goalposts from pure risk assessment:
- Preparing against an alien invasion seems futile, since given the timescales at play in the universe, the first aliens we meet will likely be millions of years behind or ahead of us.
- One way to survive an asteroid impact is to colonize another planet ahead of time, which Musk is working on.
- Physics experiments requiring specialized hardware like the LHC already have much more oversight than AI research where a breakthough could potentially happen in a garage on commodity hardware.
So it makes a lot of sense to me to invest some money in preventing an AI doomsday scenario next.
Musk says AI is the single biggest existential threat (unless I'm mistaken and someone else similarly famous said that, maybe Thiel?). From this it appears that you, along with the rest of society (as you mentioned by how much is spent on oversight), think he is wrong.
Nobody is arguing against funding AI. It's the fear-mongering we disagree with. It harms the field.
The question was about complete extinction. A population reduction by 90% is a horrible disaster, but it sets us back "just" a couple centuries, but it's hardly something that can eliminate humanity permanently; unlike quite a few other things.
> It is hard for me to empathize with Musk’s fixation on evil super-intelligent AGI killer robots in a very distant future.
The truth is we don't know, (we can't know,) and we (on the whole) are making enormous strides. And it could take one small breakthrough to find ourselves "very" suddenly on the precipice of AGI and evil super-intelligent killer robots. Dismissing the current state of progress as trying to beat Atari is silly.
I, for one, am glad that some people with the resources are taking some efforts to get ahead of that "evil" aspect. But I don't see this as an "us" vs "them" scenario: it's also great that there are people helping make neural net algorithms more accessible.