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Shoot, thanks. Hopefully it's clearer now.
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!
- ROS activity in some region of the body is a function of antioxidant bioavailability, heat, and oxidant bioavailability. I imagine this relationship is the inverse of some chemical rate laws, i.e. dependent on which antioxidants we're looking at. But since I expect most antioxidants to work as individual molecules, the relationship is probably , i.e. ROS activity is inverse w.r.t. some antioxidant's potency and concentration if we ignore other antioxidants. The bottom term can also be a sum across all antioxidants, given no synergistic / antagonistic interactions!
- Transistor reliability is probably a function of heat, band gap and voltage? I imagine that, in fact, reliability is hysteretic in terms of band gap and voltage! When the gap is lower, noise can cross more easily, and when it's too high there won't be enough voltage for it to pass (without overheating your circuit). And heat increases noise. I think that information transmission might be exponential or Gaussian centered around the optimum, parameterized by . Does anyone have an equation for this?
- Ant movement speed is probably an equilibrium between evolved energy-conservation priors, available calories and pheromones. Let's just focus on pheromones which make the ant move faster. Energy (perhaps as ) and pheromones (say, ) are probably each about predictors of speed, since I'm imagining material stress of movement () to be the main energy sink. Let , where . I don't know what the evolved frugality priors look like, but expect they can just map without needing the subcomponents and , at least as far as big-O notation goes.
- Sleep / wakefulness; hypnagogia seems transient and requires conscious effort to maintain. Outside stimuli and internal volition can wake people up; lack thereof can do the opposite.
- Friendships; I tend to have few, close friendships. I don't interact much with more distant friends because it's less emotionally fulfilling, so they slowly fade towards being acquaintances. I distance myself from people I don't connect with / feel safe around, and try to strengthen bonds with people I think are emotionally mature and interesting.
- Focus; I tend to either be checked out or deeply zoned-in. There's strong momentum here, especially for cognitively engaging tasks. Anything which I expect to impair my work will push me into "maintenance" mode, where I conserve energy and do less object-level work. This takes engagement with interesting stuff plus willed focus to recover from.
I know this post is old(ish), but still think this exercise is worth doing!
- Deep ocean currents; I expect changes in ocean floor topography and deep-water inertial/thermal changes to matter. I don't expect shallow-water topography to matter, nor wind (unless we have sustained 300+kph winds for weeks straight).
- Earth's magnetic pole directions; I'm not sure what causes them. I think they're generated by induction from magma movement? In that case, our knobs are those currents. I don't think anything can change the equilibrium without changing the flow patterns, minus stuff like magma composition which can eliminate magnetism.
- Tourism to, say, Tokyo; the following factors are both compared to other destinations and just Tokyo, and don't span our knob-space. Public opinion and salience, travel costs (time and money), hotel availability, and number of people who speak Japanese. I think that if we know these, most other markets become rounding errors, though I wouldn't be too sure.
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.