unholiness
Joined 13 years ago
Joined 13 years ago
IMO the lesson here is: even in trivial cases, Jev's outputs are just ~reasonableness scores which do not correspond to actual probabilities. They should not be treated as actual probabilities without careful calibration and plenty of meta-uncertainty about how well that calibration extrapolates.
The problem is, most of the value proposition of Jev is that it gives you the probabilities without doing that, which it doesn't.
[0] https://kantahayashiai.github.io/posts/jev-does-not-play-dic...
Land value taxes only go up if the land value (and thus the demand) is overall going up. The taxes lower desirability, but are also proportional to desirability. So, it's more like a frictional force: inherently counter-cyclical, able to slow down a property value increase but not to reverse it.
So the theory says "nobody goes to the ballpark, it's too crowded" shouldn't happen. And even imagining weird time-delays, mismodeling, etc that could cause it, everything in your scenario is directionally the same with normal property taxes. If it happened, we'd see it today.
"Here's the argument, in plain terms", "It's not just X, it's Y", "It's worth stating precisely", "The sharper distinction here"... They're things that gesture toward the relationship between the text and the prompt, making it clear where it matched the author's expectations and where it deviated.
Not that humans are immune from coding/writing for their boss instead of their user/reader!
The HuggingFace incident was at least constrained by the fact that the agents were running on compute budgets, and failed to find ways to expand that by running themselves parasitically on other exploited hardware. I'm now finding myself asking, how long are my timelines are until an incident breaks that constraint too? How long until such an incident has an R_0>1 (where the time it takes to detect and shut it down is longer than the time for the agent to replicate itself elsewhere)?
There's no law requiring sufficiently grey beards to design these models, their finetunings, their prompts, their harnesses, their VMs, their hardware, etc (and for incidents where those were designed by six different companies, there's not even a clear culprit for a law to target!)
I'm finding myself more and more convinced that something like Plan A[0] or the Ban ASI Act[0] are necessary, and less and less convinced they are sufficient.
[0] https://ai-2040.com/ [1] https://intelligence.org/2026/09/23/miris-position-on-the-ba...
[0]https://www.google.com/search?q=video+introduction+to+surrea...
Anyway, I'm much more optimistic about the worlds with AIs that make the effort to properly explain themselves.
Seems "unfair" that hexagons have multiple line lengths to consider. I think this article's modification is a good one in that framing: Shifting every number up or down doesn't make the magic squares any easier, but it certainly helps with hexagons.
He says "every order" is solvable this way but I don't think any solution could work for an order 2 hexagon, even without his simplifying constraints (since fixing any side cell to x requires 2 cells to be set to sum-x).
This premise is assuming the Mobius strip is transparent, which the header image clearly isn't. No letter-reversals or any symmetry is needed. On normal, opaque paper, you can just make a crossword as a normal long loop. (proof: Write GGGGGGG on one side of a paper, and write it upside-down on the other side. With a twist, you'll get one long GGGGGGGGGGGGGG... looping as if it were one boring single-sided loop).
I think your nuclear scenario outlines precisely why this isn't true. Nuclear regulation has terms that are "good for the US" only in an absolute sense. The US would dominate even more overwhelmingly in the unregulated scenario, which gave them negotiation power to get those favorable terms. The same seems true so far with AI.
I don't think you can use the successful negotiation of nuclear regulations to argue there is "no alternative" involving regulation. I think it strongly suggests the opposite.
Of course the details matter a lot, but the core analogy holds in many scenarios. ( https://ai-2040.com/ at least attempts to lay out details, speculative as they may be)
Ah yes, if everyone would just properly contain every deployment of every model, just all make sure there are no zero-days in their setup, just let <undefined experts with undefined authority and undefined accountability> in the room, then it would all go okay.
I have none of that confidence and expect containment to rapidly worsen. The HuggingFace incident was at least constrained by the fact that the agents were running on compute budgets, and failed to find ways to expand that by running themselves parasitically on other exploited hardware.
I'm curious to the skeptics here: How long are your timelines are until an incident breaks that constraint too? How long until such an incident has an R_0>1 (where the average time it takes to detect and shut it down is longer than the time for the agent to replicate itself elsewhere)? What, beyond the hope that everyone will just ___, will actually prevent it?