Yes, I agree. I expect abstractions, typically, to involve much more than 4-8 bits of information. On my model, any neural network, be it MLP, KAN or something new, will approximate abstractions with multiple nodes in parallel when the network is wide enough. I.e. the causal graph I mentioned is very distinct from the NN which might be running it.
Though now that you mentioned it, I wonder if low-precision NN weights are acceptable because of some network property (maybe SGD is so stochastic that higher precision doesn't help) or the environment (maybe natural latents tend to be lower-entropy)?
Anyways, thanks for engaging. It's encouraging to see someone comment.
This one was a lot of fun!
I know this post is old(ish), but still think this exercise is worth doing!
I agree that this seems like a very promising direction.
Beyond that, we of course want our class of random variables to be reasonably general and cognitively plausible as an approximation - e.g. we shouldn’t assume some specific parametric form.
Could you elaborate on this; "reasonably general" sounds to me like the redundancy axiom, so I'm unclear about whether this sentence is an intuition pump.
I think it depends on which domain you're delegating in. E.g. physical objects, especially complex systems like an AC unit, are plausibly much harder to validate than a mathematical proof.
In that vein, I wonder if requiring the AI to construct a validation proof would be feasible for alignment delegation? In that case, I'd expect us to find more use and safety from [ETA: delegation of] theoretical work than empirical.
I'm in a very similar situation, graduating next spring with a math degree in the US. I'll sketch out my personal situation (to help contextualize my advice) followed my approach for career scouting. If you haven't checked out 80k hours, I really suggest doing so, because they have much more thorough and likely wiser advice than I do.
I'm a 19-year-old undergrad in a rural part of the US. My dad's a linguistics professor and pushing me to do a PhD. I want to do AI safety research, and am currently weighing the usefulness of a PhD compared to saving money to do self-funded work. I'm also sort-of Buddhist / nihilist / absurdist, which points me towards utilitarianism.
I strongly encourage anything to do with AI safety. Specific examples here include working to donate money to Open Phil's longtermism fund, policy research, nonprofit alignment research, being a DeepMind Scalable Alignment researcher, and software development for Lightcone Infrastructure. I'd be very careful here though; are you looking for local or global goods? E.g. I've a friend working to improve ethical data collection, which I think is important in a platonic sense, but not comparable to x-risk work.
Onto processes. Writing out all of my thoughts helps me to be rigorous and honest with myself. It increases my functional working memory because my thoughts are saved on screen, freeing up cognitive capacity for introspection.
Say for example that I'm weighing how I'd research in the EU vs US. I write down how I feel initially, including possible biases (EU probably has better living conditions; US has more researchers; I should be careful not to anchor on these feelings). As I go through, I find knowledge gaps (where will I have more free time, and by how much?) and brainstorm how to fill them (my dad knows German researchers. They'd be good to ask about this). I find extelligence helps me move much faster and build a game plan.
Another thing is to discuss your plans with others. I know LessWrong is an example, but in-person discussion is probably better.
If I can make a difference to enough people or to the world and leave it a better place than I found it then at least I wasn't entirely pointless or a complete waste of space, oxygen and other natural resources. So far, I have spent my life learning and becoming a functioning adult, but now it's time to start really earning my place here.
I strongly caution you to watch out for obligation / guilt. Even if you don't feel it yet, the mindset "I owe this to the world" can push you to some dark and counterproductive places. As said here, make sure you've put your own oxygen mask on before helping others.
Feel free to message me. Best of luck.
We know that some genes are only active in the womb, or in childhood, which should make us very skeptical that editing them would have an effect.
Would these edits result in demethylated DNA? A reversion of the epigenome could allow expression of infant genes. There may also be robust epigenomic therapies developed by the time this project would be scalable.
Companies like 23&Me genotyped their 12 millionth customer two years ago and could probably get at perhaps 3 million customers to take an IQ test or submit SAT scores.
Just as you mentioned academics' aversion from this area, I think genomics companies would be reluctant at best to ask their customers for test scores. Perhaps it wouldn't be bad PR once the public is more concerned about existential AI. Governments might be more willing to provide data.
Shoot, thanks. Hopefully it's clearer now.