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.)
No, I don't think so. My model(s) I use for AGI risk is an outgrowth of the model I use for normal AI research, and so it makes tons of detailed predictions. That's why my I have weekly fluctuations in my beliefs about alignment difficulty.
Overall question I'm interested in: What, if any, catastrophic risks are posed by advanced AI? By what mechanisms do they arise, and by what solutions can risks be addressed?
Making different predictions. The most extreme prediction of AI x-risk is that AI presents, well, an x-risk. But theories gain and lose points not just on their most extreme predictions, but on all their relevant predictions.
I have a bunch of uncertainty about how agentic/transformative systems will look, but I put at least 50% on "They'll be some scaffolding + natural outgrowth of LLMs." I'll focus on that portion of my uncertainty in order to avoid meta-discussions on what to think of unknown future systems.
I don't know what your model of AGI risk is, but I'm going to point to a cluster of adjacent models and memes which have been popular on LW and point out a bunch of predictions they make, and why I think my views tend to do far better.
Format:
I'm happy to bet on any additional experiments related to the above.
There are probably a bunch of other things, and I might come back with more, but I'm getting tired of writing this comment. The main point is that common components of threat models regularly make meaningful mispredictions. My models often do better (though once I misread some data and strongly updated against my models, so I think I'm amenable to counterevidence here). Therefore, I'm able to refine my models of AGI risk. I certainly don't think we're in the dark and unable to find experimental evidence.
I expect you to basically disagree about future AI being a separate magisterium or something, but I don't know why that'd be true.
Often the claimed causes of future doom imply models which make pre-takeoff predictions, as shown above (e.g. fragility of value). But even if your model doesn't make pre-takeoff predictions... Since my model is unified for both present and future AI, I can keep gaining Bayes points and refining my model! This happens whether or not your model makes predictions here. This is useful insofar as the observations I'm updating on actually update me on mechanisms in my model which are relevant for AGI alignment.
If you think I just listed a bunch of irrelevant stuff, well... I guess I super disagree! But I'll keep updating anyways.
The Emulated Finetuning paper found that GPT-4 is superhuman at grading helpfulness/harmlessness. In the cases of disagreements between GPT-4 and humans, a more careful analysis revealed that 80% of the time the disagreement was caused by errors in the human judgment, rather than GPT-4’s analysis.
I recently explained more of my skepticism of the coherent-inner-goal claim.