I could be wrong, but from what I’ve read the domain wall should have mass, so it must travel below light speed. However, the energy difference between the two vacuums would put a large force on the wall, rapidly accelerating it to very close to light speed. Collisions with stars and gravitational effects might cause further weirdness, but ignoring that, I think after a while we basically expect constant acceleration, meaning that light cones starting inside the bubble that are at least a certain distance from the wall would never catch up with the wall. So yeah, definitely above 0.95c.
DaemonicSigil
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We probably don’t disagree that much. What “original seeing” means is just going and investigating things you’re interested in. So doing lengthy research is actually a much more central example of this than coming up with a bold new idea is.
As I say above: “There’s not any principled reason why an AI system, even a LLM in particular, couldn’t do this.”
Some experimental data: https://chatgpt.com/share/67ce164f-a7cc-8005-8ae1-98d92610f658
There’s not really anything wrong with ChatGPT’s attempt here, but it happens to have picked the same topic as a recent Numberphile video, and I think it’s instructive to compare how they present the same topic: https://www.numberphile.com/videos/a-1-58-dimensional-object
My view on this is that writing a worthwhile blog post is not only a writing task, but also an original seeing task. You first have to go and find something out in the world and learn about it before you can write about it. So the obstacle is not necessarily reasoning (“look at this weird rock I found” doesn’t involve much reasoning, but could make a good blog post), but a lack of things to say.
There’s not any principled reason why an AI system, even a LLM in particular, couldn’t do this. There is plenty going on in the world to go and find out, even if you’re stuck in the internet. (And even without an internet connection, you can try and explore the world of math.) But it seems like currently the bottleneck is that LLM’s don’t have anything to say.
Maybe novels might require less of this than blog posts, but I’d guess that writing a good novel is also a task that requires a lot of original seeing.
Thanks for the reply & link. I definitely missed that paragraph, whoops.
IMO even just simple gamete selection would be pretty great for avoiding the worst genetic diseases. I guess tracking nuclei with a microscope is way more feasible than the microwell thing, given how hard it looks to make IVS work at all.
Re the “Appendix: Cheap DNA segment sensing” section, just going to throw out a thought that occurred to me (very much a non-expert). Let’s say we’re doing IVS, and assume we can separate spermatocytes into separate microwells before they undergo meiosis. The starting cells all have a known genome. Then the cell in each microwell divides into 4 cells. If we sequence 3 of them, then we know by process of elimination what the sequence on the 4th cell is, at a very high level of detail, including crossovers, etc. So we kill 3 cells and look at their DNA, and then we know what DNA the remaining living cell has without doing anything to it.
Okay, DNA sequencing is still fairly expensive, so maybe it’s super crazy to do it 3 times to get a single cell with known DNA. But:
Maybe sequencing will get cheaper.
The same trick should work for existing cheap methods that give coarser information. Eg. one can freely decondense the sperm DNA for FISH, without worrying about damaging the cell, because it’s one of the 3 that’s going to die anyway.
If it’s too hard to separate the cells into microwells while they’re still dividing, maybe there are alternate things we could do like just watching the culture with a microscope and keeping track of who split from who and where they ended up (plus some kind of microfluidics setup to shuffle the sperms around to where we want them).
This was a fun little exercise. We get many “theory of rationality” posts on this site, so it’s very good to also have some chances to practice figuring out confusing things also mixed in. The various coins each teach good lessons about ways the world can surprise you.
Anyway, I think this was an underrated post, and we need more posts in this general category.
Running parallel to the spin axis would be fine, though.
Anthropic shadow isn’t a real thing, check this post: https://www.lesswrong.com/posts/LGHuaLiq3F5NHQXXF/anthropically-blind-the-anthropic-shadow-is-reflectively
Also, you should care about worlds proportional to the square of their amplitude.
Thanks for making the game! I also played it, just didn’t leave a comment on the original post. Scored 2751. I played each location for an entire day after building an initial food stockpile, and so figured out the timing of Tiger Forest and Dog Valley. But I also did some fairly dumb stuff, like assuming a time dependence for other biomes. And I underestimated Horse Hills, since when I foraged it for a full day, I got unlucky and only rolled a single large number. For what it’s worth, I find these applet things more accessible than a full-on D&D.Sci (though those are also great), which I often end up not playing because it feels too much like work. With applets you can play on medium-low effort (which I did) and make lots of mistakes (which I did) and learn Valuable Lessons about How Not To Science (which one might hope I did).
Have to divide by number of airships, which probably makes them less safe than planes, if not cars. I think the difficulty is mostly with having a large surface-area exposed to the wind making the ships difficult to control. (Edit: looking at the list on Wikipedia, this is maybe not totally true. A lot of the crashes seem to be caused by equipment failures too.)
Are those things that you care about working towards?
No, and I don’t work on airships and have no plans to do so. I mainly just think it’s an interesting demonstration of how weak electrostatic forces can be.
Yep, Claude sure is a pretty good coder: Wang Tile Pattern Generator
This took 1 initial write and 5 change requests to produce. The most manual effort I had to do was look at unicode ranges and see which ones had distinctive-looking glyphs in them. (Sorry if any of these aren’t in your computer’s glyph library.)
Electrostatic Airships?
I’ve begun worshipping the sun for a number of reasons. First of all, unlike some other gods I could mention, I can see the sun. It’s there for me every day. And the things it brings me are quite apparent all the time: heat, light, food, and a lovely day. There’s no mystery, no one asks for money, I don’t have to dress up, and there’s no boring pageantry. And interestingly enough, I have found that the prayers I offer to the sun and the prayers I formerly offered to ‘God’ are all answered at about the same 50% rate.
-- George Carlin
Everyone who earns money exerts some control by buying food or whatever else they buy. This directs society to work on producing those goods and services. There’s also political/military control, but it’s also (a much narrower set of) humans who have that kind of control too.
Okay, I’ll be the idiot who gives the obvious answer: Yeah, pretty much.
Very nice post, thanks for writing it.
Your options are numbered when you refer to them in the text, but are listed as bullet points originally. Probably they should also be numbered there!
Now we can get down to the actual physics discussion. I have a bag of fairly unrelated statements to make.
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The “center of mass moves at constant velocity” thing is actually just as solid as, say, conservation of angular momentum. It’s just less famous. Both are consequences of Noether’s theorem, angular momentum conservation arising from symmetry under rotations and the center of mass thing arising from symmetry under boosts. (i.e. the symmetry that says that if two people fly past each other on spaceships, there’s no fact of the matter as to which of them is moving and which is stationary)
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Even the fairly nailed down area of quantum mechanics in an electromagnetic field, we make a distinction between mechanical momentum (which appears when calculating kinetic energy) and the canonical momentum (for Heisenberg). Canonical momentum has the operator while mechanical momentum is .
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Minkowski momentum is, I’m fairly sure, the right answer for the canonical momentum in particular. An even faster proof of Minkowski is to just note that the wavelength is scaled by and so gets scaled by a factor of .
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The mirror experiments are interesting in that they raise the question of what happens when we put an airgap between the mirror and the fluid. If the airgap is large, we get the vacuum momentum, , since the index of refraction for air is nearly 1. If the airgap gets taken to 0, then we’re back to . What happens in between?
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I will say that overall, option 1 looks pretty good to me.
Edit: Removed redundant video link (turned out to already be in original post).
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Good point, the whole “model treats tokens it previously produced and tokens that are part of the input exactly the same” thing and the whole “model doesn’t learn across usages” thing are also very important.
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Registering now that my modal expectation is that the situation will mostly look the same in 2028 as it does today. (To give one example from AI 2027, scaling neuralese is going to be hard, and while I can imagine a specific set of changes that would make it possible, it would require changing some fairly fundamental things about model architecture which I can easily imagine taking 3 years to reach production. And neuralese is not the only roadblock to AGI.)
I think one of your general points is something like “slow is smooth, smooth is fast” and also “cooperative is smooth, smooth is fast”, both of which I agree with. But the whole “trauma” thing is too much like Bulverism for my taste.