Frustrated by claims that "enlightenment" and similar meditative/introspective practices can't be explained and that you only understand if you experience them, Kaj set out to write his own detailed gears-level, non-mysterious, non-"woo" explanation of how meditation, etc., work in the same way you might explain the operation of an internal combustion engine.
The Löwenheim–Skolem theorem implies, among other things, that any first-order theory whose symbols are countable, and which has an infinite model, has a countably infinite model. This means that, in attempting to refer to uncountably infinite structures (such as in set theory), one "may as well" be referring to an only countably infinite structure, as far as proofs are concerned.
The main limitation I see with this theorem is that it preserves arbitrarily deep quantifier nesting. In Peano arithmetic, it is possible to form statements that correspond (under the standard interpretation) to arbitrary statements in the arithmetic hierarchy (by which I mean, the union of and for arbitrary n). Not all of these statements are computable. In general, the question of whether a given statement is...
U axiomatizes a consistent guessing oracle producing a model of T. There is no consistent guessing oracle applied to U.
In the previous post I showed that a consistent guessing oracle can produce a model of T. What I show in this post is that the theory of this oracle can be embedded in propoaitional logic so as to enable probability preserving translations.
Produced while being an affiliate at PIBBSS[1]. The work was done initially with funding from a Lightspeed Grant, and then continued while at PIBBSS. Work done in collaboration with @Paul Riechers, @Lucas Teixeira, @Alexander Gietelink Oldenziel, and Sarah Marzen. Paul was a MATS scholar during some portion of this work. Thanks to Paul, Lucas, Alexander, Sarah, and @Guillaume Corlouer for suggestions on this writeup.
What computational structure are we building into LLMs when we train them on next-token prediction? In this post we present evidence that this structure is given by the meta-dynamics of belief updating over hidden states of the data-generating process. We'll explain exactly what this means in the post. We are excited by these results because
Is there some theoretical result along the lines of "A sufficiently large transformer can learn any HMM"?
I took the Reading the Mind in the Eyes Test test today. I got 27/36. Jessica Livingston got 36/36.
Reading expressions is almost mind reading. Practicing reading expressions should be easy with the right software. All you need is software that shows a random photo from a large database, asks the user to guess what it is, and then informs the user what the correct answer is. I felt myself getting noticeably better just from the 36 images on the test.
Short standardized tests exist to test this skill, but is there good software for training it? It needs to have lots of examples, so the user learns to recognize expressions instead of overfitting on specific pictures.
Paul Ekman has a product, but I don't know how good it is.
The test scores me as 'normal' with 29/36. I remember doing a similar (maybe the same) test and scoring decidedly below average about two years ago.
I understand the attraction of having this skill trainable in its own context like flashcards but consider it a false shortcut. I think it is more about directing attention.
Setting aside a few cycles of my attention to practice in every day life worked for me and I think it should be wildly superior to treating it as a problem of categorizing features.
1. You get so much more context to infer fr...
The history of science has tons of examples of the same thing being discovered multiple time independently; wikipedia has a whole list of examples here. If your goal in studying the history of science is to extract the predictable/overdetermined component of humanity's trajectory, then it makes sense to focus on such examples.
But if your goal is to achieve high counterfactual impact in your own research, then you should probably draw inspiration from the opposite: "singular" discoveries, i.e. discoveries which nobody else was anywhere close to figuring out. After all, if someone else would have figured it out shortly after anyways, then the discovery probably wasn't very counterfactually impactful.
Alas, nobody seems to have made a list of highly counterfactual scientific discoveries, to complement wikipedia's list of multiple discoveries.
To...
Maybe Hanson et al.'s Grabby aliens model? @Anders_Sandberg said that some N years before that (I think more or less at the time of working on Dissolving the Fermi Paradox), he "had all of the components [of the model] on the table" and it just didn't occur to him that they can be composed in this way. (personal communication, so I may be misremembering some details). Although it's less than 10 years, so...
Speaking of Hanson, prediction markets seem like a more central example. I don't think the idea was [inconceivable in principle] 100 years ago.
Joe’s summary is here, these are my condensed takeaways in my own words. All links in this section are to the essays.
This summarizes a (possibly trivial) observation that I found interesting.
Story
An all-powerful god decides to play a game. They stop time, grab a random human, and ask them "What will you see next?". The human answers, then time is switched back on and the god looks at how well they performed. Most of the time the humans get it right, but occasionally they are caught by surprise and get it wrong.
To be more generous the god decides to give them access (for the game) to the entirety of all objective facts. The position and momentum of every elementary particle, every thought and memory anyone has ever had (before the time freeze) etc. However, suddenly performance in the game drops from 99% to 0%. How can this be? They...
I am having trouble following you. If little-omega is a reference frame I would expect it to be a function that takes in the "objective world" (Omega) and spits out a subjective one. But you seem to have it the other way around? Or am I misunderstanding?
Elon Musk's Hyperloop proposal had substantial public interest. With various initial Hyperloop projects now having failed, I thought some people might be interested in a high-speed transportation system that's...perhaps not "practical" per se, but at least more-practical than the Hyperloop approach.
Hydrogen has a lower molecular mass than air, so it has a higher speed of sound and lower density. The higher speed of sound means a vehicle in hydrogen can travel at 2300 mph while remaining subsonic, and the lower density reduces drag. This paper evaluated the concept and concluded that:
the vehicle can cruise at Mach 2.8 while consuming less than half the energy per passenger of a Boeing 747 at a cruise speed of Mach 0.81
In a tube, at subsonic speeds, the gas...
Possibly of interest: the fastest rocket sled track uses similar idea, they put a helium filled tube over the final section of the track:
...Just as meteors are burned up by friction in the upper atmosphere, air friction can cause a high-speed sled to burn up, even if made of the toughest steel alloys. An engineering sleight-of-hand is used to increase those "burn-up" limits by reducing the density of the atmosphere around the track. To do this, one needs a safe, non-toxic, low-density gas such as helium. Helium is only one seventh the density of air, signific
Concerns over AI safety and calls for government control over the technology are highly correlated but they should not be.
There are two major forms of AI risk: misuse and misalignment. Misuse risks come from humans using AIs as tools in dangerous ways. Misalignment risks arise if AIs take their own actions at the expense of human interests.
Governments are poor stewards for both types of risk. Misuse regulation is like the regulation of any other technology. There are reasonable rules that the government might set, but omission bias and incentives to protect small but well organized groups at the expense of everyone else will lead to lots of costly ones too. Misalignment regulation is not in the Overton window for any government. Governments do not have strong incentives...
There is a belief among some people that our current tech level will lead to totalitarianism by default. The argument is that with 1970's tech the soviet union collapsed, however with 2020 computer tech (not needing GenAI) it would not. If a democracy goes bad, unlike before there is no coming back. For example Xinjiang - Stalin would have liked to do something like that but couldn't. When you add LLM AI on everyone's phone + Video/Speech recognition, organized protest is impossible.
Not sure if Rudi C is making this exact argument. Anyway if we get mass ce...
Wow, it's worse than I thought. Maybe the housing problem is "government-complete" and resists all lower level attempts to solve it.