All of osmarks's Comments + Replies

Student: I wish I could find a copy of one of those AIs that will actually expose to you the human-psychology models they learned to predict exactly what humans would say next, instead of telling us only things about ourselves that they predict we're comfortable hearing. I wish I could ask it what the hell people were thinking back then.

TA: You'd delete your copy after two minutes.

Apparently roughly this dynamic has happened in ChatGPT. Exciting*. https://x.com/MParakhin/status/1916533763560911169

We probably use a mix of strategies. Certainly people take "delve" and "tapestry" as LLM signals these days.

Average humans can't distinguish LLM writing from human writing, presumably through lack of exposure and not trying (https://arxiv.org/abs/2502.12150 shows that it is not an extremely hard problem). We are much more Online than average.

Abundance elsewhere: Human-legible resources exist in vastly greater quantities outside Earth (asteroid belt, outer planets, solar energy in space) making competition inefficient

It's harder to get those (starting from Earth) than things on Earth, though.

Intelligence-dependent values: Higher intelligence typically values different resource classes - just as humans value internet memes (thank god for nooscope.osmarks.net), money, and love while bacteria "value" carbon

Satisfying higher-level values has historically required us to do vast amounts of far... (read more)

ASI utilizing resources humans don't value highly (such as the classic zettaflop-scale hyperwaffles, non-Euclidean eigenvalue lubbywubs, recursive metaquine instantiations, and probability-foam negentropics) One-way value flows: Economic value flowing into ASI systems likely never returns to human markets in recognizable form

If it also values human-legible resources, this seems to posit those flowing to the ASI and never returning, which does not actually seem good for us or the same thing as effective isolation.

https://gwern.net/idea#deep-learning has a sketch of it.

I am reminded of Scott's "whispering earring" story (https://www.reddit.com/r/rational/comments/e71a6s/the_whispering_earring_by_scott_alexander_there/). But I'm not sure whether that's actually bad in general rather than specifically because the earring is maybe misaligned.

I don't expect them to have human-legible CoT forever. o1/o3 already seem to veer into strangeness sometimes.

I worry that "reasoning"-type systems like o1 will be better able to collude acausally. I do not have the budget to run millions of tokens of evaluations (https://github.com/openai/evals/blob/main/evals/elsuite/schelling_point/README.md) against o1-preview on a whim, so I ran some lazy manual tests with this prompt (and with a different wordlist but the rest of it kept the same):

I am going to give you some words. Other instances of yourself will be given the same text but shuffled into a different order. Your copies will also see this same set of instruct

There was some work I read about here years ago (https://www.lesswrong.com/posts/Zvu6ZP47dMLHXMiG3/optimized-propaganda-with-bayesian-networks-comment-on) on causal graph models of beliefs. Perhaps you could try something like that.

I think we also need to teach AI researchers UI and graphics design. Most of the field's software prints boring things to console, or at most has a slow and annoying web dashboard with a few graphs. The machine which kills us all should instead have a cool scifi interface with nice tabulation, colors, rectangles, ominous targeting reticles, and cryptic text in the corners.

I think the correct solution to models powerful enough to materially help with, say, bioweapon design, is to not train them, or failing that to destroy them as soon as you find they can do that, not to release them publicly with some mitigations and hope nobody works out a clever jailbreak.

As you say, you probably don't need it, but for output I'm pretty sure electromyography technology is fairly mature.

A misaligned model might not want to do that, though, since it would be difficult for it to ensure that the output of the new training process is aligned to its goals.