+1 I was really really upset safe.ai decided to use an established acronym for something very different
Yes, check eg https://www.lesswrong.com/posts/H5iGhDhQBtoDpCBZ2/announcing-the-alignment-of-complex-systems-research-group or https://ai.objectives.institute/ or also partially https://www.pibbss.ai/
You won't find much of this on LessWrong, due to LW being an unfavorable environment for this line of thinking.
It's probably worth noting you seem to be empirically wrong: I'm pretty confident I'd be able to do >half of human jobs, with maybe ~3 weeks of training, if I was able to understand all human languages (obviously not in parallel!) Many others here would be able to do the same.
The criterion is not as hard as it seems, because there are many jobs like cashiers or administratrative workers or assembly line workers which are not that hard to learn.
It's probably worth noting that I take the opposite update from the covid crisis: it was much easier to get governments listen to us and do marginally more sensible things than expected. With better preparation and larger resources, it would have been possible to cause order of magnitude more sensible things to happen. Also it's worth noting some governments were highly sensible and agentic about covid
Similary to johnswentworth: My current impression is core alignment problems are the same and manifest at all levels - often sub-human version just looks like a toy version of the scaled-up problem, and the main difference is, in the sub-human version problem, you can often solve it for practical purposes by plugging in human at some strategic spot. (While I don't think there are deep differences in the alignment problem space, I do think there are differences in the "alignment solutions" space, where you can use non-scalable solutions, or in risk space, where dangers being small due to the systems being stupid.)
I'm also unconvinced about some of practical claims about differences for... (read 357 more words →)
This text originated from a retreat in late 2018, where researchers from FHI, MIRI and CFAR did an extended double-crux on AI safety paradigms, with Eric Drexler and Scott Garrabrant in the core. In the past two years I tried to improve it in terms of understandability multiple times, but empirically it seems quite inadequate. As it seems unlikely I will have time to invest further work into improving it, I'm publishing it as it is, with the hope that someone else will maybe understand the ideas even at this form, and describe them more clearly.
The box inversion hypothesis consists of the two following propositions
Getting oriented fast in complex/messy real world situations in fields in which you are not an expert
I like the metaphor!
Just wanted to note: in my view the original LW Sequences are not functional as a stand-alone upgrade for almost any human mind, and you can empirically observe it: You can think about any LW meet-up group around the world as an experiment, and I think to a first approximation it's fair to say aspiring Rationalists running just on the Sequences do not win, and good stuff coming out of the rationalist community was critically dependent of presence of minds Eliezer & others. (This is not say Sequences are not useful in many ways)
I basically agree with Vanessa:
the correct rule is almost always: first think about the problem yourself, then go read everything about it that other people did, and then do a synthesis of everything you learned inside your mind.
Thinking about the problem myself first often helps me understand existing work as it is easier to see the motivations, and solving solved problems is good as a training.
I would argue this is the case even in physics and math. (My background is in theoretical physics and during my high-school years I took some pride in not remembering physics and re-deriving everything when needed. It stopped being a good approach for physics ca since 1940... (read more)
Epistemic status: Wild guesses based on reading del Guidice's Evolutionary psychopathology and two papers trying to explain autism in terms of predictive processing. Still maybe better than the "tower hypothesis"
0. Let's think in terms of two parametric model, where one parameter tunes something like capacity of the brain, which can be damaged due to mutations, disease, etc., and the other parameter is explained bellow.
1. Some of the genes that increase risk of autism tune some parameter of how sensory prediction is handled, specifically, making the system to expect higher precision from sensory inputs/being less adaptive about it. (lets call it parameter p)
2. Several hypothesis - Mildly increased p sounds like something... (read 442 more words →)
I think silence is a clearly sensible strategy for obvious reasons.