LessWrong 2.0 Reader

Math textbooks. Did you know that you can just buy math textbooks which are "several years too advanced for you"? And that due to economies of scale and the objectivity of their subject matter, they tend to be of both high and consistent quality? Not getting my parents to do this at that age is something I still regret decades later.

Or did you specifically mean fiction? If so, you're asking for fiction recommendations on the grew-up-reading-HPMOR website, we're obviously going to recommend HPMOR (especially if they've already read Harry Potter, but it's still good if you only know the broad strokes).

Algorithmic complexity is precisely analogous to difficulty-of-learning-to-predict, so saying "it's not about learning to predict, it's about algorithmic complexity" doesn't make sense. One read of the original is: learning to respect common sense moral side constraints is tricky, but AI systems will learn how to do it in the end. I'd be happy to call this read correct, and is consistent with the observation that today's AI systems do respect common sense moral side constraints given straightforward requests, and that it took a few years to figure out how to do it. That read doesn't really jive with your commentary.

Your commentary seems to situate this post within a larger argument: teaching a system to "act" is different to teaching it to "predict" because in the former case a sufficiently capable learner's behaviour can collapse to a pathological policy, whereas teaching a capable learner to predict does not risk such collapse. Thus "prediction" is distinguished from "algorithmic complexity". Furthermore, commonsense moral side constraints are complex enough to risk such collapse when we train an "actor" but not a "predictor". This seems confused.

First, all we need to turn a language model prediction into an action is a means of turning text into action, and we have many such means. So the distinction between text predictor and actor is suspect. We could consider an alternative knows/cares distinction: does a system act properly when properly incentivised ("knows") vs does it act properly when presented with whatever context we are practically able to give it ("""cares""")? Language models usually act properly given simple prompts, so in this sense they "care". So rejecting evidence from language models does not seem well justified.

Second, there's no need to claim that commonsense moral side constraints in particular are so hard that trying to develop AI systems that respect them leads to policy collapse. It need only be the case that one of the things we try to teach them to do leads to policy collapse. Teaching values is not particularly notable among all the things we might want AI systems to do; it certainly does not seem to be among the hardest. Focussing on values makes the argument unnecessarily weak.

Third, algorithmic complexity is measured with respect to a prior. The post invokes (but does not justify) an "English speaking evil genie" prior. I don't think anyone thinks this is a serious prior for reasoning about advanced AI system behaviour. But the post is (according to your commentary, if not the post itself) making a quantitative point - values are sufficiently complex to induce policy collapse - but it's measuring this quantity using a nonsense prior. If the quantitative argument was indeed the original point, it is mystifying why a nonsense prior was chosen to make it, and also why no effort was made to justify the prior.

If you want to do this as a successful company, you essentially have to get your customers to trust you that you are installing it in a way where UVC up does not produce any negative effects. 

People have been doing it for decades is not something that would convince me that there are not long-term side-effects. 

Quick Googling gives me https://northshorefuel.com/products-services/indoor-air-quality/uv-germicidal-lights.php . They seem near enough to install in Boston. 

Using Yelp to find a company that likely does B2B sales when you don't know the exact keywords they use, is not an effective strategy to find installers.

You do need to pay attention to what paint is on the ceiling and measure to verify that levels are low in the places people are, but pointing UVC up is something we've done safely for a long time in many places.

Discussing sleep paralysis might be an infohazard…

The times I’ve entered sleep paralysis it hasn’t bothered me, as I knew what it was.

Pointing UVC LEDs at your ceiling seems sketchy. White paint will likely scatter ~5% of UVC, and shiny metal surfaces will scatter more. Try to go below 250nm for reduced reflection (and reduced penetration into human skin) and (more) unwanted chemistry will start happening to the air.

I guess an important question is whether UVC is more harmful than UVB. If it's not any more harmful, then as long as nobody's getting sunburned from being in that room all day, it's probably fine - that 5% scattering is just another name for SPF 20. But if it is more harmful, then sunburn might not be an adequate signal for when it's bad for you.

The main risk is entering is sleep paralysis state, which itself is benign, but some terrifying sounds can be heard during it and this can cause stress. 

Yes, it is to wake up from lucid dream - juts thing about your slleping body. 

The best practical application of lucid dreams is reducing effects of nightmares by recognizing that it is just a dream.

I'm unsure what you're either expecting or looking for here.

There does seem to be a clear answer, though - just look at Bing chat and extrapolate. Absent "RL on ethics," present-day AI would be more chaotic, generate more bad experiences for users, increase user productivity less, get used far less, and be far less profitable for the developers.

Bad user experiences are a very straightforwardly bad outcome. Lower productivity is a slightly less local bad outcome. Less profit for the developers is an even-less local good outcome, though it's hard to tell how big a deal this will have been.