watermark

i'm glad that you wrote about AI sentience (i don't see it talked about so often with very much depth), that it was effortful, and that you cared enough to write about it at all. i wish that kind of care was omnipresent and i'd strive to care better in that kind of direction.

and i also think continuing to write about it is very important. depending on how you look at things, we're in a world of 'art' at the moment - emergent models of superhuman novelty generation and combinatorial re-building. art moves culture, and culture curates humanity on aggregate scales

your words don't need to feel trapped in your head, and your... (read 508 more words →)

Deep learning/AI was historically bottlenecked by things like 
(1) anti-hype (when single layer MLPs couldn't do XOR and ~everyone just sort of gave up collectively)
(2) lack of huge amounts of data/ability to scale

I think complexity science is in an analogous position. In its case, the 'anti-hype' is probably from a few people (probably physicists?) saying emergence or the edge of chaos is woo and everyone rolling with it resulting in the field becoming inert. Likewise, its version of 'lack of data' is that techniques like agent based modeling were studied using tools like NetLogo which are extremely simple. But we have deep learning now, and that bottleneck is lifted. It's maybe a matter... (read more)

I'm not expecting to pull off all three, exactly - I'm hoping that as I go on, it becomes legible enough for 'nature to take care of itself' (other people start exploring the questions as well because it's become more tractable (meta note: wanting to learn how to have nature take care of itself is a very complexity scientist thing to want)) or that I find a better question to answer.

For the first one, I'm currently making a suite of long-running games/tasks to generate streams of data from LLMs (and some other kinds of algorithms too, like basic RL and genetic algorithms eventually) and am running some techniques borrowed from financial analysis... (read more)

I didn't personally go about it in the most principled way, but:
1. locate the smartest minds in the field or tangential to it (surely you know of Friston and Levin, and you mentioned Krakauer - there's a handful more. I just had a sticky note of people I collected)
2. locate a few of the seminal papers in the field, the journals (e.g. entropy) 
3. based on your tastes, skim podcasts like Santa Fe's or Sean Carroll's 
4. textbooks (e.g. that theory of cas book you mentioned (chapter 6 on info theory for cas seemed like the most important if i had to pick one), multilayer networks theory, statistical field theory (for neural networks, etc.))... (read more)

Here are some resources:
1. The journal entropy (this specifically links to a paper co-authored by D. Wolpert, the guy who helped come up with the No Free Lunch Theorem)
2. John Holland's books or papers (though probably outdated and he's just one of the first people looking into complexity as a science - you can always start at the origin and let your tastes guide you from there)
3. Introduction to the Theory of Complex Systems and Applying the Free-Energy Principle to Complex Adaptive Systems (one of the sections talks about something an awful lot like embedded agency in a lot more detail)

4. The Energetics of Computing in Life and Machines

And I'm guessing non-stationary... (read more)

I forgive the ambiguity in definitions because:
1. they're dealing with frontier scientific problems and are thus still trying to hone in on what the right questions/methods even are to study a set of intuitively similar phenomena
2. it's more productive to focus on how much optimization is going into advancing the field (money, minds, time, etc.) and where the field as a whole intends to go: understanding systems at least as difficult to model as minds, in a way that's general enough to apply to cities, the immune system, etc.

I'd be surprised if they didn't run into some of the same theoretical problems involved in solving alignment. (I wouldn't be very surprised if... (read 403 more words →)