Thoughtdump on why I'm interested in computational mechanics:

• one concrete application to natural abstractions from here: tl;dr, belief structures generally seem to be fractal shaped. one major part of natural abstractions is trying to find the correspondence between structures in the environment and concepts used by the mind. so if we can do the inverse of what adam and paul did, i.e. 'discover' fractal structures from activations and figure out what stochastic process they might correspond to in the environment, that would be cool
  • ... but i was initially interested in reading compmech stuff not with a particular alignment relevant thread in mind but rather because it seemed broadly similar in directions to natural abstractions.
• re: how my focus would differ from my impression of current compmech work done in academia: academia seems faaaaaar less focused on actually trying out epsilon reconstruction in real world noisy data. CSSR is an example of a reconstruction algorithm. apparently people did compmech stuff on real-world data, don't know how good, but effort-wise far too less invested compared to theory work
  • would be interested in these reconstruction algorithms, eg what are the bottlenecks to scaling them up, etc.
• tangent: epsilon transducers seem cool. if the reconstruction algorithm is good, a prototypical example i'm thinking of is something like: pick some input-output region within a model, and literally try to discover the hmm model reconstructing it? of course it's gonna be unwieldly large. but, to shift the thread in the direction of bright-eyed theorizing ...
• the foundational Calculi of Emergence paper talked about the possibility of hierarchical epsilon machines, where you do epsilon machines on top of epsilon machines and for simple examples where you can analytically do this, you get wild things like coming up with more and more compact representations of stochastic processes (eg data stream -> tree -> markov model -> stack automata -> ... ?)
  • this ... sounds like natural abstractions in its wildest dreams? literally point at some raw datastream and automatically build hierarchical abstractions that get more compact as you go up
  • haha but alas, (almost) no development afaik since the original paper. seems cool
• and also more tangentially, compmech seemed to have a lot to talk about providing interesting semantics to various information measures aka True Names, so another angle i was interested in was to learn about them.
  • eg crutchfield talks a lot about developing a right notion of information flow - obvious usefulness in eg formalizing boundaries?
  • many other information measures from compmech with suggestive semantics—cryptic order? gauge information? synchronization order? check ruro1 and ruro2 for more.

re: second diagram in the "Bayesian Belief States For A Hidden Markov Model" section, shouldn't the transition probabilities for the top left model be 85/7.5/7.5 instead of 90/5/5?

What is the shape predicted by compmech under a generation setting, and do you expect it instead of the fractal shape to show up under, say, a GAN loss? If so, and if their shapes are sufficiently distinct from the controls that are run to make sure the fractals aren't just a visualization artifact, that would be further evidence in favor of the applicability of compmech in this setup.

If after all that it still sounds completely wack, check the date. Anything from before like 2003 or so is me as a kid, where "kid" is defined as "didn't find out about heuristics and biases yet", and sure at that age I was young enough to proclaim AI timelines or whatevs.

https://twitter.com/ESYudkowsky/status/1650180666951352320

although I've practiced opening those emotional channels a bit, so this is a less uncommon experience for me than for most

i'm curious, what did you do to open those emotional channels?

Out of the set of all possible variables one might use to describe a system, most of them cannot be used on their own to reliably predict forward time evolution because they depend on the many other variables in a non-Markovian way. But hydro variables have closed equations of motion, which can be deterministic or stochastic but at the least are Markovian.

This idea sounds very similar to this—it definitely seems extendable beyond the context of physics:

We argue that they are both; more specifically, that the set of macrostates forms the unique maximal partition of phase space which 1) is consistent with our observations (a subjective fact about our ability to observe the system) and 2) obeys a Markov process (an objective fact about the system's dynamics).

I don't see any feasible way that gene editing or 'mind uploading' could work within the next few decades. Gene editing for intelligence seems unfeasible because human intelligence is a massively polygenic trait, influenced by thousands to tens of thousands of quantitative trait loci.

I think the authors in the post referenced above agree with this premise and still consider human intelligence augmentation via polygenic editing to be feasible within the next few decades! I think their technical claims hold up, so personally I'd be very excited to see MIRI pivot towards supporting their general direction. I'd be interested to hear your opinions on their post.

I am curious as to how often the asymptotic results proven using features of the problem that seem basically practically-irrelevant become relevant in practice.

Like, I understand that there are many asymptotic results (e.g., free energy principle in SLT) that are useful in practice, but i feel like there's something sus about similar results from information theory or complexity theory where the way in which they prove certain bounds (or inclusion relationship, for complexity theory) seem totally detached from practicality?

• joint source coding theorem is often stated as why we can consider the problem of compression and redundancy separately, but when you actually look at the proof it only talks about possibility (which is proven in terms of insanely long codes) and thus not-at-all trivial that this equivalence is something that holds in the context of practical code-engineering
• complexity theory talks about stuff like quantifying some property over all possible boolean circuits of a given size which seems to me considering a feature of the problem just so utterly irrelevant to real programs that I'm suspicious it can say meaningful things about stuff we see in practice
  • as an aside, does the P vs NP distinction even matter in practice? we just ... seem to have very good approximation to NP problems by algorithms that take into account the structures specific to the problem and domains where we want things to be fast; and as long as complexity methods doesn't take into account those fine structures that are specific to a problem, i don't see how it would characterize such well-approximated problems using complexity classes.
  • Wigderson's book had a short section on average complexity which I hoped would be this kind of a result, and I'm unimpressed (the problem doesn't sound easier - now how do you specify the natural distribution??)

Found an example in the wild with Mutual information! These equivalent definitions of Mutual Information undergo concept splintering as you go beyond just 2 variables:

• $I[X;Y]=H\left[X\right]+H\left[Y\right]-H[X,Y]$
  • interpretation: common information
  • ... become co-information, the central atom of your I-diagram
    • 
• $I[X;Y]=D\left(\mathrm{Pr}\right(x,y)\parallel \mathrm{Pr}(x\left)\mathrm{Pr}\right(y\left)\right)$
  • interpretation: relative entropy b/w joint and product of margin
    • ... become total-correlation
    • 
• $I[X;Y]=H[X,Y]-H[X\mid Y]-H[Y\mid X]$
  • interpretation: joint entropy minus all unshared info
    • ... become bound information
    • 

... each with different properties (eg co-information is a bit too sensitive because just a single pair being independent reduces the whole thing to 0, total-correlation seems to overcount a bit, etc) and so with different uses (eg bound information is interesting for time-series).