AI content for specialists
There is a lot of AI content recently, and it is sometimes of the kind that requires specialized technical knowledge, which I (an ordinary software developer) do not have. Similarly, articles on decision theories are often written in a way that assumes a lot of background knowledge that I don't have. As a result there are many articles I don't even click at, and if I accidentally do, I just sigh and close them.
This is not necessarily a bad thing. As something develops, inferential distances increase. So maybe, as a community we are developing a new science, and I simply cannot keep up with it. -- Or maybe it is all crackpottery; I wouldn't know. (Would you? Are some of us upvoting content they are not sure about, just because they assume that it must be important? This could go horribly wrong.) Which is a bit of a problem for me, because now I can no longer recommend Less Wrong in good faith as a source of rational thinking. Not because I see obviously wrong things, but because there are many things where I have no idea whether they are right or wrong.
We had some AI content and decision theory here since the beginning. But those articles written back then by Eliezer were quite easy to understand, at least for me. For example, "How An Algorithm Feels From Inside" doesn't require anything beyond high-school knowledge. Compare it to "Hypothesis: gradient descent prefers general circuits". Probably something important, but I simply do not understand it.
Just like historically MIRI and CFAR split into two organizations, maybe Less Wrong should too.
Feeling of losing momentum
I miss the feeling that something important is happening right now (and I can be a part of it). Perhaps it was just an illusion, but at the first years of Less Wrong it felt like we were doing something important -- building the rationalist community, inventing the art of everyday rationality, with the perspective to raise the general sanity waterline.
It seems to me that we gave up on the sanity waterline first. The AI is near, we need to focus on the people who will make a difference (whom we could recruit for an AI research), there is no time to care about the general population.
Although recently, this baton was taken over by the Rational Animations team!
Is the rationalist community still growing? Offline, I guess it depends on the country. In Bratislava, where I live, it seems that ~ no one cares about rationality. Or effective altruism. Or Astral Codex Ten. Having five people at a meetup is a big success. Nearby Vienna is doing better, but it is merely climbing back to pre-COVID levels, not growing. Perhaps it is better at some other parts of the world.
Online, new people are still coming. Good.
Also, big thanks to all people who keep this website running.
But still it no longer feels to me anymore like I am here to change the world. It is just another form of procrastination, albeit a very pleasant one. (Maybe because I do not understand the latest AI and decision theory articles; maybe all the exciting things are there.)
Etc.
Some dialogs were interesting, but most are meh.
My greatest personal pet peeve was solved: people no longer talk uncritically about Buddhism and meditation. (Instead of talking more critically they just stopped talking about it at all. Works for me, although I hoped for some rational conclusion.)
It is difficult for me to disentangle what happens in the rationalist community from what happens in my personal life. Since I have kids, I have less free time. If I had more free time, I would probably be recruiting for the local rationality (+adjacent) community, spend more time with other rationalists, maybe even write some articles... so it is possible that my overall impression would be quite different.
(Probably forgot something; I may add some points later.)
Two points:
Anyway, let me explain the first point.
I interpret Abram to be saying that we should focus on solutions that scale to superintelligence, rather than solutions that only work on sub-superintelligent systems but break down at superintelligence. This was in response to Alex's claim that "whitelisting contributes meaningfully to short- to mid-term AI safety, although I remain skeptical of its robustness to scale."
In other words, Alex said (roughly): "This solution seems to work for sub-superintelligent AI, but might not work for superintelligent AI." Abram said in response that we should push against such solutions, since we want solutions that scale all the way to superintelligence. This is not the same thing as saying that any solution to the house-cleaning robot provides negligible evidence of progress, because some solutions might scale.
It's definitely arguable, but I think it's likely that any realistic solution to the human-level house cleaning robot problem -- in the strong sense of getting a robot to genuinely follow all relevant moral constraints, allow you to shut it down, and perform its job reliably in a wide variety of environments -- will be a solution that scales reasonably well above human intelligence (maybe not all the way to radical superintelligence, but at the very least I don't think it's negligible evidence of progress).
If you merely disagree that any such solutions will scale, and you've been consistent on this point for the last five years, then I guess I'm not really addressing you in my original comment, but I still think what I wrote applies to many other researchers.
I think it's actually 2 points,
I'm not sure how much to be compelled by this piece of evidence. I'll point out that naming the same problem multiple times might have gotten repetitive, and there was also no explicit ranking of the problems from most important to least important (or from hardest to easiest). If the order you wrote them in can be (perhaps uncharitably) interpreted as the order of importance, then I'll note that it was listed as problem #3, which I think supports my original thesis adequately.