Nobody's pursuing such initiatives. Doing so would be even more expensive than net zero emissions policies.

This isn't a new thing. Soviet doctrine for dealing with carriers was to fire as many missiles as they could at them to overwhelm their defences.

The US focus on expensive, highly capable weapons isn't baseless. The US military is designed to project power, and projecting power is expensive. If you're going to ship equipment halfway around the world and support it there, it might as well be the best you can make. That is very different from Ukraine or South Korea where they are, or expect to, fight in their literal back yards.

Expensive invading armies have always been vulnerable to the defending swarm, from hoplites and knights to empires getting their asses kicked in Afghanistan, Vietnam or Iran.

The people building the datacentres had a plan. The people approving them, and offering big tax incentives, also had a plan. Part of that plan was electricity rates going up.

No, the grandmaster is doing many, many more calculations, just in parallel instead of serial. The human is also not blindly searching a tree of possible moves but spending a lot of computation on figuring out which are promising branches to prioritize. Modern chess programs are so good because they do the same thing.

A human can learn with 20 hours of driving school

No, they can't. We don't generally let humans even attempt driving for something like 16 years. They're also pretty shit at it until they have a decade or so of pretty frequent practice.

Elon doesn't actually have that much in cash. Most of his net worth is tied up in assets that would probably lose considerable value if someone killed him and confiscated his property.

For now it's a thousand here and a thousand there. Also the cited $500 million is a research grant not intended to help anyone actually losing their jobs. But $500 million would go a long way to help some laid off employees start their own businesses. Just not all of them. If the program were successful on a small scale, it would need more funding in the future.

Yeah focusing on small business startups would probably be the smart play, especially if it involves hiring a lot of other displaced workers.

Okay but people are already losing their jobs now. Also humanoid robot production is scaling up considerably while cost/unit is plummeting. It isn't just tech bros that will feel the pinch.

If workers are actually being displaced by AI, then $500 million would be a nice start towards a fund intended to help displaced workers taking lower-paying jobs and/or starting businesses of their own (where they could potentially hire other displaced workers to do something else).

At the same time, I'm sure some wage slave making $15/hr or less will be thrilled to see a laid-off tech bro working next to him getting incentive pay just to take the job. So maybe the emphasis needs to be on small business startups rather than placing people in existing industries.

In any case, handing money over to a bunch of pundits, politicians, and focus groups to study the problem is likely the second-least method for dealing with the problem of AI disruption.

Ah, right, that paper. I don't think they'd use the word "continuous" the way they did if they thought about it for a bit either. They use it as a vague throwaway in the abstract and then never again. Also "fairly discontinous" is silly. It's either is or it isn't, nothing in between. That's kind of the defining property of a discontinuity.

What they actually mean is this:

Our main result is that for deep neural networks, the smoothness assumption that underlies many kernel methods does not hold. Specifically, we show that by using a simple optimization procedure, we are able to find adversarial examples, which are obtained by imperceptibly small perturbations to a correctly classified input image, so that it is no longer classified correctly.

Deep neural networks can be "highly nonlinear" (which they note). The output of nonlinear systems can vary a lot in response to small changes in the input. In fact, one way of defining smoothness is as an upper bound on local nonlinearity, so this result shouldn't be at all surprising. However, discontinuities are only a subset of things that are nonlinear and not smooth. In fact, the optimization they use to genrate their adversarial examples depends on the neural network being continuous (down to the inherent discretization of the datatype).

Unfortunately a lot of other people only read the abstract of this paper and completely missed this (the very next paragraph after the one I quoted above):

In some sense, what we describe is a way to traverse the manifold represented by the network in an efficient way (by optimization) and finding adversarial examples in the input space. The adversarial examples represent low-probability (high-dimensional) “pockets” in the manifold, which are hard to efficiently find by simply randomly sampling the input around a given example. Already, a variety of recent state of the art computer vision models employ input deformations during training for increasing the robustness and convergence speed of the models [9, 13]. These deformations are, however, statistically inefficient, for a given example: they are highly correlated and are drawn from the same distribution throughout the entire training of the model. We propose a scheme to make this process adaptive in a way that exploits the model and its deficiencies in modeling the local space around the training data.

The concept of adversarial examples isn't about "lol, neural networks are dumb and easy to fool." They're about efficiently generating supplemental training data that makes the model more robust.

Szegedy et al also only study one specific type of model, although with some internal variations, and there is a discontinuity in that model. It has nothing to do with the neural network though. It's the very last step where you decide that, for example, a vector of probabilities like [0.3, 0.29999, 0.30001, 0.1] == [0,0,1,0]. It's called thresholding, and we sometimes do it because it's necessary to make a decision, but we also do it a lot because humans don't deal well with uncertainty.

I think you can find a similar adversarial-plus-thresholding example for the human brain in optical illusions like Rubin's Vase. You see a face or a vase, switching back and forth as you imperceptibly change your focus on different parts of the completely static image. You also don't see both at the same time, it's pretty strongly either or. "Fairly discontinuous" if you will.

That must have been a while ago. Computers are effectively unbeatable at chess. One of the best programs was written by a Norwegian nerd in his spare time and then forked by an impatient Italian. It will run on your phone and will almost certainly kick your ass, although if you want to be sure of beating every human who's ever lived you might want to give it a desktop computer.

The angstrom scale business is marketing fluff

Just like the nanometer business. Really, using a linear measurement to indicate density was not-what-you-think from the beginning, so toss micrometers in there too.

make the density increase understandable to consumers.

Consumers don't care. People contracting foundry services care.

Don't worry. The current regime has plan in place to simply not deliver mailed ballots in states that have any chance of going against your desired outcome.

To paraphrase the apocryphal Stalin: Altering one ballot is a felony. Shitcanning a million ballots is a statistic.

You can generally tell when the true operational cost, including cost of capital, significantly exceeds employee cost by looking at whether they pay people to work in the middle of the night.

There are quite a lot of places where that happens. Just using the OP's list:

Airplanes: there are fewer flights at night, but that's when a lot of required maintenance happens. The Internet tells me the average lease on a Boeing 737 is around a few hundred thousand USD per month. Bigger planes would have even bigger differentials with their crew salaries.

Machinists: there's a reason you picked this example. Really expensive machine tools can run into the multiple millions though, plus maintenance and consumables. Lease rates can easily go into double digit thousands per month. And that's not even considering exotic stuff. Lots of high end shops operate around the clock.

Radiologists: Most of the operating costs for big medical imaging equipment are going to exceed the employee operating it, though maybe not the radiologist. Getting a radiologist to work outside regular business (or banking) hours is a chore, but the techs do so routinely. Sometimes the actual operation is the expensive bit so no night shifts, like anything involving a SQUID.

Tower cranes: don't work as much at night probably due to safety, but lease rates mostly in the double digit thousands a month and up.

Trading analysts: Bloomberg terminals are a few tens of thousands per year, so no. Some of the crazy HFT stuff probably does cost ridiculous amounts though, so maybe the quants writing algorithms for it rather than the traders.

Garbage men: maybe. Probably yes if you count the salary of one guy, no if two.

Concrete truck drivers: probably. Also probably semi trucks.

MWh is a unit of capacity

The word you're looking for is "energy."

"Capacity" can have pretty much any units depending on what you're talking about. Generation capacity, for example, is usually measured in Watts. It's common to talk about battery capacity in terms of power because if it can't provide enough power it's no good at all, and then time; i.e. make it work, then make it good.