LessWrong 2.0 Reader

Looking for specific tips and tricks to break AI out of formal/corporate writing patterns. Tried style mimicry ('write like Hemingway') and direct requests ('be more creative') - both fell flat. What works?

Should I be using different AI models ( I am using GPT and Claude)? The base models output an enormous creative storm, but somehow the RLHF has partially lobotomized LLMs such that they always seem to output either cheesy stereotypes or overly verbose academise/corporatespeak. 

I'm not sure where I'm proposing bureaucracy? The value is in making sure a conversation efficiently adds value for both parties, by not having to spend time rehashing things that are much faster absorbed in advance. This avoids the friction of needing to spend much of the time rehashing 101-level prerequisites. A very modest amount of groundwork beforehand maximizes the rate of insight in discussion.

I'm drawing in large part from personal experience. A significant part of my job is interviewing researchers, startup founders, investors, government officials, and assorted business people. Before I get on a call with these people, I look them (and their current and past employers, as needed) up on LinkedIn and Google Scholar and their own webpages. I briefly familiarize myself with what they've worked on and what they know and care about and how they think, as best I can anticipate, even if it's only for 15 minutes. And then when I get into a conversation, I adapt. I'm picking their brain to try and learn, so I try to adapt to their communication style and translate between their worldview and my own. If I go in with an idea of what questions I want answered, and those turn out to not be the important questions, or this turns out to be the wrong person to discuss it with, I change direction. Not doing this often leaves everyone involved frustrated at having wasted their time.

Also, should I be thinking of this as a debate? Because that's very different than a podcast or interview or discussion. These all have different goals. A podcast or interview is where I think the standard I am thinking of is most appropriate. If you want to have a deep discussion, it's insufficient, and you need to do more prep work or you'll never get into the meatiest parts of where you want to go. I do agree that if you're having a (public-facing) debate where the goal is to win, then sure, this is not strictly necessary. The history of e.g. "debates" in politics, or between creationists and biologists, shows that clearly. I'm not sure I'd consider that "meaningful" debate, though. Meaningful debates happen by seriously engaging with the other side's ideas, which requires understanding those ideas.

Interesting preliminary results! 

Do you expect abductive reasoning to be significantly different from deductive reasoning? If not, (and I put quite high weight on this,) then it seems like (Berglund, 2023) already tells us a lot about the cross-context abductive reasoning capabilities of LLMs. I.e. replicating their methodology wouldn't be very exciting. 

One difference that I note here is that abductive reasoning is uncertain / ambiguous; maybe you could test whether the model also reduces its belief of competing hypotheses (c.f. 'explaining away'). 

Wow, I didn't realize bluesky already supports user-created feeds, which can seemingly use any algorithm? So if you don't like "no algorithm" or "discover" you can create a new ranking method and also share it with other people? 

Anyone want to create a lesswrong starter pack? Are there enough people on bluesky for that to be viable?

I wonder how domination and submission relate to these concepts.

Note that d/s doesn't necessarily need to have a sexual connotation, although it nearly always does.

My understanding of the appeal of submission is that the ideal submissive state is one where the dominant partner is anticipating the needs and desires of the submissive partner, supplies these needs and desires, and reassures or otherwise convinces the submissive that they are capable of doing so, and will actively do so for the duration of the scene.

After reading your series, I'd assume what is happening here is a number of things all related to the belief in the homunculus and the constant valence calculations that the brain performs in order to survive and thrive in society.

• You have no need to try to fight for dominance or be 'liked' or 'admired'. The dominant partner is your superior, and the dominant partner likes and admires you completely.

• You have no need to plan things and determine their valence -- the dominant will anticipate any needs, desires and responsibilities, and take care of them for you.

• You have no need to maintain a belief in your own 'willpower', 'identity', 'ego', etc... for the duration of the scene, you wear the mask of 'the obedient submissive'.

All things considered, it's absolutely no surprise that 'subspace' is an appealing place to be, it's sort of a shortcut to the truth you're describing. I wouldn't be surprised if some people even have an experience bordering on nirodha samapatti during a particularly deep, extensive scene, where they have little memory of the experience afterwards. I'm also not surprised that hypnodomination, a combination of d/s and trance, is so common, given that the two states are so similar.

Is true Novelty a Mirage?

One view on novelty is that it's a mirage. Novelty is 'just synthesis of existing work, plus some randomness.'

I don't think that's correct. I think true novelty is more subtle than that. Yes sometimes novel artforms or scientific ideas are about noisily mixing existing ideas. Does it describe all forms of novelty?

A reductio ad absurdum of the novelty-as-mirage point of view is that all artforms that have appeared since the dawn of time are simply noised versions of cavepaintings. This seems absurd.

Consider AlphaGO. Does AlphaGO just noisily mix human experts? No, alphaGO works on a different principle and I would venture strictly outcompetes anything based on averaging or smoothing over human experts. 

AlphaGO is based on a different principle than averaging over existing data. Instead, AlphaGO starts with an initial guess on what good play looks like, perhaps imitated from previous plays. It then plays out to a long horizons and prunes those strategies that did poorly and upscales those strategies that did well. It iteratively amplifies, refines and distilles. I strongly suspect that approximately this modus operandi underlies much of human creativity as well. 

True novelty is based on both the synthesis and refinement of existing work. 

That's still an algorithm, it's just a very simple one.

Personally, I prefer to have the posts I see be the product of a sophisticated algorithm (ex: there are some people I follow who post a lot, and for those people I would like to only see their best posts) but I want it to be one that is in my interest.

It's completely valid. And we can simplify it further to:

not Consistent(ZFC) -> not Consistent(ZFC + not Consistent(ZFC))

because if a set of axioms is already inconsistent, then it's inconsistent with anything added. But you still won't be able to actually derive a contradiction from this.

Edit: I think the right thing to do here is look at models for PA + not consistent(PA). I can't find a nice treatment of this at the moment, but here's a possibly wrong one by someone who was learning the subject at the time: https://angyansheng.github.io/blog/a-theory-that-proves-its-own-inconsistency

Okay, I kinda understood where I am wrong spiritually-intuitively, but now I don't understand where I'm wrong formally. Like which inference in chain

not Consistent(ZFC) -> some subsets of ZFC don't have a model -> some subsets of ZFC + not Consistent(ZFC) don't have a model -> not Consistent(ZFC + not Consistent(ZFC))

is actually invalid?

why/how are you so sure that openai made things much worse (in the long run)?