LessWrong 2.0 Reader

I learned about lucid dreaming at 13 and decided to try it. I practiced self-hynosis trance states while falling asleep. Took a couple weeks of practice before getting it to work. After that, it was easy.

Too easy.

Couldn't turn it off. Every dream became a lucid dream, except no, they were lucid dreams within dreams. I'd wake up and go to school then wake up and realize I was still dreaming. Real life and dreams began to blur together. My dreams were so vivid I couldn't tell them apart reliably. My dreams would alternate between lucid and not, nightmares crept in. It was thoroughly unnerving. So, I did the same self-hypnosis practice in reverse. A week of so of demanding normalcy again, and my dreams went back to normal.

Eventually, in grad school, I went through a similar process to stop myself from remembering my dreams, because I was so stressed out that all my dreams were nightmares.

After grad school I went back to allowing myself to occasionally remember dreams, at least fuzzily while waking or recalling the previous night's dream while drifting to sleep.

Seeing the distribution calibration you point out does update my opinion a bit.

I feel like there’s still a significant distinction though between adding one calculation step to the question versus asking it to model multiple responses. It would have to model its own distribution in a single pass rather than having the distributions measured over multiple passes align (which I’d expect to happen if the fine-tuning teaches it the hypothetical is just like adding a calculation to the end).

As an analogy, suppose I have a pseudorandom black box function that returns an integer. In order to approximate the distribution of its outputs mod 10, I don’t have to know anything about the function; I just can just sample the function and apply mod 10 post hoc. If I want to say something about this distribution without multiple samples, then I actually have to know something about the function.

They were likely using inferior techniques to RLHF to implement ~Google corporate standards; not sure what you mean by "ethics-based," presumably they have different ethics than you (or LW) do,es but intent alignment has always been about doing what the user/operator wants, not about solving ethics. 

I don't find any use for the concept of fuzzy truth, primarily because I don't believe that such a thing meaningfully exists. The fact that I can communicate poorly does not imply that the environment itself is not a very specific way. To better grasp the specific way that things actually are, I should communicate less poorly. Everything is the way that it is, without a moment of regard for what tools (including language) we may use to grasp at it.

(In the case of quantum fluctuations, the very specific way that things are involves precise probabilistic states. The reality of superposition does not negate the above.)

How about "AI scam"? You know, something people will actually understand. 

Unlike "gas lighting", for example, which is an obscure reference whose meaning cannot be determined if you don't know the reference.

I am not well-read on this topic (or at-all read, really), but it struck me as bizarre that a post about epistemology would begin by discussing natural language. This seems to me like trying to grasp the most fundamental laws of physics by first observing the immune systems of birds and the turbulence around their wings.

The relationship between natural language and epistemology is more anthropological* that it is information-theoretical. It is possible to construct models that accurately represent features of the cosmos without making use of any language at all, and as you encounter in the "fuzzy logic" concept, human dependence on natural language is often an impediment to gaining accurate information.

Of course, natural language grants us many efficiencies that make it extremely useful in ancestral human contexts (as well as most modern ones). And given that we are humans, to perform error correction on our models, we have to model our own minds and the process of examination and modelling itself as part of the overall system we are examining and modelling. But the goal of that recursive modelling is to reduce the noise and error caused by the fuzziness of natural language and other human-specific* limitations, so that we can make accurate and specific predictions about stuff.

*The rise of AI language models means natural language is no longer a purely human phenomenon. It also had the side-effect of solving the symbol grounding problem by constructing accurate representations of natural language using giant vectors that map inputs to abstract concepts, map abstract concepts to each other, and map all of that to testable outputs. This seems to be congruent with what humans do, as well. Here again, formalization and precise measurement in order to discover the actual binary truth values that really do exist in the environment is significantly more useful than accepting the limitations of fuzziness.

"English" keeps ending up as a catch-all in K-12 for basically all language skills and verbal reasoning skills that don't obviously fit somewhere else.  Read and summarize fiction - English, Write a persuasive essay - English, grammar pedantry - English, etc.

On the bonus objective:

But there is no possible world with a perfect predictor, unless it has a perfect track record by chance.  More obviously, there is no possible world in which we can deduce, from a finite number of observations, that a predictor is perfect.  The Newcomb paradox requires the decider to know, with certainty, that Omega is a perfect predictor.  That hypothesis is impossible, and thus inadmissible; so any argument in which something is deduced from that fact is invalid.