I'd be eager to hear about anything similar to this which is already discussed somewhere, if you know of anything.

I wrote a post in 2023 about somewhat similar ideas: https://www.lesswrong.com/posts/uDXRxF9tGqGX5bGT4/logical-share-splitting The market mechanism there is based on the formula P(A)+P(B)=P(A∧B)+P(A∨B) from probability theory.

In that post, shares aren't expected to have continuous-valued payoffs, but merely continuous-valued prices representing binary payoffs with some chance of happening. So that's a relevant difference that means the interpretation of $G\vee H$ is not quite the same (in particular, it doesn't involve a max function, since there are no continuous values to take a max of), though there's still a sense in which it's as good as having your choice between $G$ and $H$.

The conventional physics way of explaining this is as follows:

One way of asking "what is the current state of the universe?" is to pick a Cauchy surface. This is just a "slice" of the entire universe at a given time. There is a lot of freedom in the choice of slice: In Minkowsky space for example, there are slices corresponding to every choice of rest frame, and many more besides those. We just need to make sure that no points on the surface lie within each-other's light cones ($\Delta s^2<0$).

The information (about field values & derivatives) lying on any particular Cauchy surface is enough to predict the future and past from that surface. Pick any two Cauchy surfaces, and there's a unitary operator mapping one to the other. This is the relativistic version of a time-evolution operator.

Some Cauchy surfaces are entirely later in time than other Cauchy surfaces. (Though some pairs of Cauchy surfaces are partially later and partially earlier than each other.) We'll say that for Cauchy surfaces $A,B$, that $A<B$ exactly when for all points $a\in A,b\in B$, either $a$ is spacelike separated from $b$ or $b$ is in the future lightcone of $a$.

Let $S\left(\right)$ be a function that measures the entropy on a given Cauchy surface. The second law of thermodynamics then says that if $A<B$ then $S\left(A\right)\le S\left(B\right)$.

Thing likely being subtweeted: https://www.lesswrong.com/posts/dHLdf8SB8oW5L27gg/on-fleshling-safety-a-debate-by-klurl-and-trapaucius

1a3orn can correct me if I'm wrong. You're welcome, confused future readers.

I have read that some sequencing methods (nanopore) have a high error rate (comparing multiple reads can help correct this). Did you also spot-check some other genes that you have no reason to believe contain mutations to see if they look ok? Seeing a mutation in exactly the gene you expect is only damn strong evidence if there isn't a sequencing error in every third gene.

EDIT: Looks like this was checked, nice: https://www.lesswrong.com/posts/Hds7xkLgYtm6qDGPS/how-i-learned-that-i-don-t-feel-companionate-love

Inspired partially by this post and partially by trying to think of simple test cases for a machine learning project I'm working on, here is a (not too hard, you should try answering it yourself) question: Let's say we've observed $n$ trials of a Bernoulli random variable, and $k$ had a 1 outcome (so $n-k$ were 0). Laplace's rule of succession (uniform prior over success probability) says that we should estimate a probability of $(k+1)/(n+2)$ for the next trial being 1. The question is: What is the prior over bitstrings $s$ of length $n+1$ implied by Laplace's rule of succession? In other words, can we convert the rule of succession formula into a probability distribution $p\left(s\right)$ over bitstrings s that record outcomes of $n+1$ trials?

Additional clarification of the problem:

Answer:

Also tried this, and basically ended up with the same answer as commenter One.

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.

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.

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