LessWrong 2.0 Reader

I am not sure I understand this comment. Are you saying you think there are autonomous AI systems that right now are trying to accumulate power? And that present regulation should be optimized to stop those?

The question of what kind of sun exposure leads to visible skin aging is an empiric one. Saying "We err on the cautious side and assume that an arbitrary value of 0.5 SED could be regarded as an acceptable daily erythemal exposure" does not imply that there's no visible skin aging under that arbitrary value. 

I agree, I have heard that claim many times, probably including the vague claim that it's "more dangerous" than a poorly-defined imagined alternative. A bunch of pessimistic stuff in the vein of List of Lethalities focuses on reinforcement learning, analyzing how and why that is likely to go wrong. That's what started me thinking about true alternatives.

So yes, that does clarify why you've framed it that way. And I think it's a useful question.

In fact, I would've been prone to say "RL is unsafe and shouldn't be used". Porby's answer to your question is insightful; it notes that other types of learning aren't that different in kind. It depends how the RL or other learning is done.

One reason that non-RL approaches (at least the few I know of) seem safer is that they're relying on prediction or other unsupervised learning to create good, reliable representations of the world, including goals for agents. That type of learning is typically better because you can do more of it. You don't need either a limited set of human-labeled data, which is always many orders of magnititude scarcer than data gathered from sensing the world (e.g., language input for LLMs, images for vision, etc). The other alternative is having a reward-labeling algorithm which can attach reward signals to any data, but that seems unreliable in that we don't have even good guesses on an algorithm that can identify human values or even reliable instruction-following.

Here's a puzzle I came up with in undergrad, based on this idea:

Let $f\left(x\right)$ be a function with nice derivatives and anti-derivatives (like exponentials, sine, or cosine) and $p\left(x\right)$ be a polynomial. Express the $k$th anti-derivative of $p\left(x\right)f\left(x\right)$ in terms of derivatives and anti-derivatives of $p\left(x\right)$ and $f\left(x\right)$.

Can provide link to a post on r/mathriddles with the answer in the comments upon request

I checked. It's 0.67.

This seems to come from European countries.

P v NP: https://en.wikipedia.org/wiki/Generic-case_complexity

You can go ahead and post.

I did a check and am now more confident in my answer, and I'm not going to try to come up with an entry that uses fewer soldiers.

If you can get to Seattle for your partner's career, you can likely get a job nannying during the day, which will pay $25 to $30 an hour and doesn't require a car. 

This time last summer I was an incoming intern in Seattle and I was unable to pay less than $30 an hour for childcare during working hours, hiring by combing through Facebook groups for nannys and sending many messages. At this price, one of the nannys we worked with had a car and the other did not. I do not know what the childcare market is like near your current location.

Although GDP per capita is distinct from this expanded welfare metric, the correlation between GDP per capita and this expanded welfare metric is very strong at 0.96, though there is substantial variation across countries, and welfare is more dispersed (standard deviation of 1.51 in logs) than is income (standard deviation of 1.27 in logs).^[9]

I checked the paper and it looks like they're comparing welfare by "how much more would person X from the US have to consume to move to another country i?" Which results in equations like this:

which says what the factor ${\lambda }_i^{simple}$ ,  should be in terms of differences in life expectancy, consumption, lessure and inequality. So I suppose it isn't suprising that it's quite correlated with GDP, given the individual correlations at play here, but I am suprised that it is so strongly correlated since I'd expect e.g. life expectancy vs gdp to correlate at maybe 0.8^[1]. Which is a fair bit weaker than a 0.96 correlation!

1. ^{^}

   I checked. It's 0.67. 

I've heard people suggest that they have arguments related to RL being particularly dangerous, although I have to admit that I'm struggling to find these arguments at the moment. I don't know, perhaps that helps clarify why I've framed the question the way that I've framed it?