The bright side is that it should eventually be technically feasible to create much more powerful and effective guardrails around neural nets. At the end of the day, we have full access to the machine running the code, whereas we can't exactly go around sticking electrodes into everyone's brains, and even "just" constant monitoring is prohibitively expensive for most human work. The bad news is that we might be decades away from an understanding of how to create useful guardrails around AI, and AI is doing stuff now.
I get the feeling that you're winging the specific numbers because they're spectacularly incoherent.
But anyway, the United States is extremely rich and has essentially no big problems that can be solved by a small amount (say, a few billion) of money. The problems are either so big that it would take trillions to solve (supporting aging population etc), or blocked by something other than money (politics, regulations, etc). The big problems that can be solved just by throwing a few billion at them are solved quite easily by either the government or by private entities like the Gates Foundation.
Even the US median household income is "only" $83k. Looking at stuff like this + the rest of the blog I'm not convinced the author is any less out of touch than the people this post is criticizing.
It's frustrating how far you can go out of your way to avoid being associated with such superficially similar tropes and still fail miserably. Yudkowsky in particular hated that he couldn't get a discussion without being typecast as the guy worried about Terminator. He hated it to the point he wrote a whole article on why he thought Terminator tropes were bad (https://www.lesswrong.com/posts/rHBdcHGLJ7KvLJQPk/the-logica...).
As a side note:
> any serious attempt at discussion gets bogged down by [...] without taking a single shower in the same span of time.
This is unnecessary and (somewhat ironically) undermines your own point. I would like to see less of this on HN.
> I'd argue that the biggest reason machines are black boxes are because no one is bothering to look inside of them.
People do look, but it's extremely hard. Take a look at how hard the mechanistic interpretability people have to work for even small insights. Neel Nanda[1] has some very nice writeups if you haven't already seen them.
Error correction is possible even if the error correction is itself noisy. The error does not need to accumulate, it can be made as small as you like at the cost of some efficiency. This is not a new problem, the relevant theorems are incredibly robust and have been known for decades.
> Math fans tend to discount and dismiss applied statistics as being not math, in a way that they don't do for physics, for some reason I don't fully grasp.
It's because we're secretly afraid that the physicists are smarter than us.
Less facetiously, physicists keep discovering things that lead to new mathematics we would never have dreamed of ourselves, so we have a healthy respect for how insightful they can be.
I like good handwriting, but good, fast handwriting takes thousands of hours of practice. We used to spend a good chunk of school drilling it into kids, but now it's really hard to justify everyone spend that kind of time when we can technology our way around it and there are other valuable skills to learn.
We really have no way to know this. But I would be very surprised if modern chess engines didn't regularly blunder into losing (from the perspective of a hypothetical 32-piece tablebase) positions, and very very surprised if modern chess engines perfectly converted tablebase-winning positions.
Practicality of reproducing training runs that cost tens of millions aside, it's hopeless. Determinism is hard enough with a single GPU, fixing a seed isn't going to be much help when training is distributed across hundreds of GPUs.
Claude 3.5 just... does the multiplication correctly by independently deciding to go step-by-step (don't see a convenient way to share conversations, but the prompt was just "What is 1682671* 168363?").
Or you're using a log scale because your effects span many orders of magnitude, or whatever. The specifics are not particularly central to my point beyond that it isn't too hard to wrangle up some scenario where the equation says something completely different.
If at the end of the day you have to compare wildly different things, you have to compare wildly different things. Usually this will involve making a judgment call about how to weigh these things, or in other words, selecting units for the purpose of adding quantities of wildly different units.
Only if they are measured on a scale allowing negative values for outcomes and sufficiently high values for service. It would be unsurprising to have outcomes measured from zero and service topping out at 100%. The equation doesn't say anything remotely precise, it's just a bundle of vibes.