niplav @niplav@schelling.pt

@wolf480pl Neural Networks are able to predict the "future"[1] better than chance: https://arxiv.org/abs/2206.15474

This scales with size, and is still far below human baseline

[1]: actually doing pastcasting

if you're german then the term "gaslighting" is slightly queasy

can you money-pump the agents simulated by large language models?

give standard vNM coherence violating scenarios to smarter language models

at higher levels of intelligence, maintaining coherence becomes more difficult since your action space widens and having high coherence might be NP-hard. so the better metric is coherence divided by size of action space

@WomanCorn sounds like a good idea! doubt it'll help in any way in the limit (also epistemic contamination is a bitch), but i'll cheer the people who try

@vriska
wildbow's fiction certainly is something

@genmaicha naturally i disagree with this a bunch :-D

do you want a more in-depth answer or nah

your mom is the optimization target

love languages as multi-armed bandits

@agdakx Doing God's work 🙏

Go nerd-snipe them, regal person 👑

onrushing tide, a mere
fifty miles away, and just
our puny channels

should i start a podcast

https://en.wikipedia.org/wiki/Returning_soldier_effect

Disappointed that [1] doesn't actually check for common vNM violations. This must be assuaged

[1]: https://sohl-dickstein.github.io/2023/03/09/coherence.html

@typeswitch I would like to relapse tho 👀