Looks like someone already made a market for it:
https://manifold.markets/DanMan314/will-claims-of-a-retrieved-craft-of
There's two types of energy associated with a current we should distinguish. Firstly there's the power flowing through the circuit, then there's energy associated with having current flowing in a wire at all. So if we're looking at a piece of extension cord that's powering a lightbulb, the power flowing through the circuit is what's making the lightbulb shine. This is governed by the equation . But there's also some energy associated with having current flowing in a wire at all. For example, you can work out what the magnetic field should be around a wire with a given amount of current flowing through it and calculate the energy stored in the magnetic field. (This energy is associated with the inductance of the wire.) Similarly, the kinetic energy associated with the electron drift velocity is also there just because the wire has current flowing through it. (This is typically a very small amount of energy.)
To see that these types have to be distinct, think about what happens when we double the voltage going into the extension cord and also double the resistance of the lightbulb it's powering. Current stays the same, but with twice the voltage we now have twice the power flowing to the light bulb. Because current hasn't changed, neither has the magnetic field around the wire, nor the drift velocity. So the energy associated with having a current flowing in this wire is unchanged, even though the power provided to the light bulb has doubled. The important thing about the drift velocity in the context of is that it moves charge. We can calculate the potential energy associated with a charge in a wire as , and then taking the time derivative gives the power equation. It's true that drift velocity is also a velocity, and thus the charge carriers have kinetic energy too, but this is not the energy that powers the light bulb.
In terms of exponential attenuation, even DC through resistors gives exponential attenuation if you have a "transmission line" configuration of resistors that look like this:
So exponential attenuation doesn't seem too unusual or surprising to me.
Do you have a name/link for that conference? I'd be interested in reading those molecular dynamics papers.
I give a lot of weight to Yudkowsky and adjacent people whereas it sounds like you don't.
To be clear, I do give a lot of weight to Yudkowsky in the sense that I think his arguments make sense and I mostly believe them. Similarly, I don't give much weight to Yann LeCun on this topic. But that's because I can read what Yudkowsky has said and what LeCun has said and think about whether it made sense. If I didn't speak English, so that their words appeared as meaningless noise to me, then I'd be much more uncertain about who to trust, and would probably defer to an average of the opinions of the top ML names, eg. Sutskever, Goodfellow, Hinton, LeCun, Karpathy, Benigo, etc. The thing about closely studying a specific aspect of AI (namely alignment) would probably get Yudkowsky and Christiano's names onto that list, but it wouldn't necessarily give Yudkowsky more weight than everyone else combined. (I'm guessing, for hypothetical non-English-speaking me, who somehow has translations for what everyone's bottom line position is on the topic, but not what their arguments are. Basically the intuition here is that difficult technical achievements like Alexnet, GANs, etc. are some of the easiest things to verify from the outside. It's hard to tell which philosopher is right, but easy to tell which scientist can build a thing for you that will automatically generate amusing new animal pictures.)
This is very different than sending continuous power through the wire where the electrons have a steady state drift velocity and the only energy required is that to maintain the drift velocity against resistance. For wave propagation the electrons are instead accelerated up from a drift velocity of zero for each bit sent. It's the difference between the energy required to accelerate a car up to cruising speed and the power required to maintain that speed against friction.
Electrons are very light so the kinetic energy required to get them moving should not be significant in any non-contrived situation I think? The energy of the magnetic field produced by the current would tend to be much more of an important effect.
As for the rest of your comment, I'm not confident enough I understand the details of your argument be able to comment on it in detail. But from a high level view, any effect you're talking about should be baked into the attenuation chart I linked in this comment. This is the advantage of empirically measured data. For example, the skin-effect (where high frequency AC current is conducted mostly in the surface of a conductor, so the effective resistance increases the higher the frequency of the signal) is already baked in. This effect is (one of the reasons) why there's a positive slope in the attenuation chart. If your proposed effect is real, it might be contributing to that positive slope, but I don't see how it could change the "1 kT per foot" calculation.
I think there's a real sense in which the band gap problem is genuinely more quantum-mechanical in nature than the protein folding problem. It's very common that people will model proteins with a classical approximation, where you assume that eg. each bond has a specific level of stiffness, etc. (Often these values themselves are calculated using density functional theory.) But even given this classical approximation, many proteins take so long to settle into a folded configuration that simulating them is very expensive.
Also, last time I looked in any detail, the current version of Alpha Fold did use multiple sequence alignment, which means that some of its utility comes from the fact that it's predicting evolved sequences, and so generalization to synthetic sequences might be iffy.
Replace "computable in practical time" with "computable on a classical computer in practical time" and it makes sense.
It seems like hypothetical-you is making a reasonable decision in all those situations. I guess my point was that us people who worry about AI ruin don't necessarily get to be seen as the group of experts who are calling these sports matches. Maybe the person most analogous to the tennis expert predicting Joe will win the match is Yann LeCun, not Eliezer Yudkowsky.
Consider two tennis pundits, one of whom has won prestigious awards for tennis punditry, and is currently employed by a large TV network to comment on matches. The other is a somewhat-popular tennis youtuber. If these two disagree about who's going to win the match, with the famous TV commentator favouring Joe and the youtuber favouring Bill, a total tennis outsider would probably do best by going with the opinion of the famous guy. In order to figure out that you should instead go with the opinion of the youtuber, you'd need to know at least a little bit about tennis in order to determine that the youtuber is actually more knowledgeable.
Alice: So... you're saying that the reasoning doesn't actually make sense to you?
Bob: I guess so.
Alice: Wait, so then why would you believe in this sea monster stuff?
I kind of think Alice is actually correct here? If you have time to evaluate the actual arguments then you pick the ones that make the most sense, taking into account all the empirical evidence you have. If you don't have time, pick the one that's made the best predictions so far. And if there's insufficient data for even that, you go with the "default mainstream consensus" position. If the reasoning about AI ruin didn't make sense to me, I wouldn't believe in it anyways, I'd believe something like "predicting the outcome of a future technology is nearly impossible. Though we can't rule out human extinction from AI, it's merely one of a large number of possibilities."
To steelman what Bob's saying here, maybe he can't understand the detailed mechanisms that cause machine learning systems to be difficult to point at subtle goals, but he can empirically verify it by observing how they sometimes misbehave. And he can empirically observe ML systems getting progressively more powerful over time. And so he can understand the argument that if the systems continue to become more powerful, as they have done in the past, eventually the fact that we can't point them at subtle things like "not ruining everything humans care about" becomes a problem. That would be fine, you don't have to understand every last detail if you can check the predictions of the parts you don't understand. But if Bob's just like, "I dunno, these Less Wrong guys seem smart", that seems like a bad way to form beliefs.
To put it another way:
If you break an outcome up into 6 or more stages and multiply out all the probabilities to get a tiny number, then there's at least a 90% chance that you've severely underestimated the true odds. Why?
Well, estimating probabilities is hard, but let's say you're really good at it. So for the first probability in your sequence, you have a full 90% chance of not underestimating. The next probability is conditional on the first one. This is harder to reason about, so your chance of not underestimating drops to 80%. Estimating probabilities that are conditional on more events gets harder and harder the more events there are, so the subsequent probabilities go: 70%, 60%, 55%, 50%. If we multiply all these out, that's only an 8% chance that you managed to build a correct model!