I don’t know Guillaume Verdon / Beff Jezos

He is supposed to be the original instigator and public face of so-called e/acc or effective accelarationism, i.e. the rationalist (or maybe rationalist inspired idk) spin-off of people who feel whinging about alignment isn’t necessary and that it’s in fact awesome when new technology is killing people and ruining the environment because it means we’re getting to the singularity faster, and also openly rooting for fascism on main is based now.

His magic heatless AI processor startup feels like a grift to fleece investors and as far as I know has only ever produced an obviously staged video of sciency looking individuals fawning over a 3d printed wire mesh while touring what looks like a chip fab with an extremely lax contamination protocol.

I do not know if that can work because only a few percent of the company will be for sale

I think a 30% chunk will be available for non-institutional investors, which as far as these things go is supposed to be humongous and also an indication that they are very much counting on stupid money to prop their valuation.

It’s more like license to sue their pants off if they get caught propagating obviously proprietary code through the responses of their tool, and if they are doing it but you can’t tell that just means your enterprise code isn’t discernible from claudeslop so no harm done.

I’m assuming if suddenly an LLM code tool is able to do something like write a parser for an unambiguously closed source heavily copyrighted data format and the only possible leak is the devs using LLM tooling, it’s going to be a big legal deal.

Microsoft releases cost calculator for GitHub Copilot for the new token usage based billing. Previously you were being charged per request, kind of like hiring a cab and paying the same whether you went to the next corner or the next continent.

Turns out Zitron may have been seriously low balling the actual cost to subsidized billing ratio.

Building infinite compute is hard, man

As if LLMs being the last step before AGI/ASI/The Metal Messiah is a foregone conclusion. As far as I can tell even the AI 2027 thing only argues that once the bots completely nail down programming (any minute now) then the foom happens and the models will magic themselves into true AI, because apparently being good at solving coding problems is a sufficient proxy for superintelligence, hence the METR infatuation.

In other Scott of Siskind news, he just posted an entirely unnecessary amount of words to aggressively push back against the adage that “all exponentials sooner or later turn into sigmoids” as if it was by itself a load bearing claim of the side arguing against the direct imminence of the machine god.

It’s just a bunch of arguing by analogy ( “helping you build intuition” ) and you-can’t-really-knows while implying AI 2027 was very science much rigorous, but it also feels kind of desperate, like why are you bothering with this overperformative setting-the-record-straight thing, have you been feeling inadequate as an AI-curious stats fondler of note lately?

He probably paid a rationalist dating coach good money to tell him to do that.

the need to distribute sex to needy men

It always trips me up how this is about state sponsored arranged marriages (preferably to virgins), instead of like pushing to decriminalize sex work in the united states.

Isn’t this completely hypothetical though? As in having the various LLMs respond to a story prompt and calling it an experiment, AI safety research style?

Even Scott’s fantasy dream scenario for what prediction markets could be like and what questions they could answer feels… … deliberately naive? …like libertarian brainrot? …disconnected from reality?

That’s mostly because outright admitting that the point of prediction markets was to make having the prediction gene profitable so they could get on with breeding a rationailst kwisatz haderach to fight the robot god on more equal terms wouldn’t fly with the lower level thetans and other exoterics.

Well, you could maybe sort of train it not to generate “all men are cats”, but then that might also prevent it from making the more correct generalization “all cats are mortal” or even completely valid generalizations like combing “all men are mortal” and “Socrates is man” to get “Socrates is mortal”.

Just wanted to say that that ‘tal’ comes after ‘mor’ when ‘soc-rate-s’ is in the near context and in agreement with the attention mechanism is a very different type of logic than what this phrasing implies. This is also in combination with the peculiarities of word embeddings (the technique by which the tokens are translated to numeric vectors) like how it has a hard time making something useful out of numbers, it uh gets uh complicated.

The monofacts thing seems very post hoc and way too abstracted in comparison, and also the amount of text that can be categorized as strictly true or false isn’t that big all things considered.

Still if the point was to formalize the very no-duh observation that a neural net isn’t supposed to output it’s dataset verbatim at all times hence hallucinations, then fine, I guess. Their proposed sort of solution (controlled miscalibration) even amounts to forcing the model to generalize less by memorizing more, which used to be the opposite of why you would choose to use this type of topography.

The newest addition to her polycule

Isn’t this mostly a pretentious way of saying someone I recently fucked?

I feel like a lot of my writing on rationality would be a lot more popular if I could go back in time to the 1960s and present it there. “Twelve Virtues of Rationality” is what people could’ve been reading instead of Heinlein’s Stranger in a Strange Land

This is someone nakedly fantasizing about being L. Ron Hubbard.

Also an email came up where Demis Hassabis tried to convince Elon to stop insisting on open sourcing OpenAI for AI safety reasons by sending him a 2015 scott alexander blogpost.

Last summer the Web Speech API got incorporated into browser standards, it’s supposed to offer in-browser speech-to-text and the like, and full support of the API requires the browser vendor to offer the ability to download a language appropriate model for autonomous inference.

Going from this to deciding that it’s now ok to side load unspecified 4GB models without telling the user is why we should never give these people an inch.

Theoretically if the people responsible for that training and reinforcement did their jobs well then those patterns should only include true statements

That would only work if inference were some sort of massive if-the-else process. Hallucinations are downstream of neural networks’ ability to generalize from the dataset examples, they aren’t going anywhere even if you train on a corpus of perfectly correct statements.

I like Evans’ take that since there’s bound to be oodles of cult related literature and interactions and also tons of self help and guru stuff in the training datasets, it stands to reason that if you interact with a chatbot in a way that indicates vulnerability to these things there’s a considerable chance that it will decide the expected response is to prey on you.

Also Scott Aaronson jump scare near the beginning, apparently he was blurbed for something.

He absolutely does. No idea if it’s supposed to be a bit.

Is that the guy who’s always trying to use LessWrong as preemptive conversion therapy to cure him of having trans thoughts, and they’re actually having none of it?