in-silico

LLMs assign non-zero probability to every output (unless you you sampling modifiers like top-k), so if you sampled enough times the model would definitely come up with velcro. It's the same as the proverbial monkey with a typewriter.

The real question is whether it would happen in a practical timeframe.

In what ways to you think the TESCREAL arguments are wrong?

I've noticed the existence of something like "thought promoting cliches" in my life that when said, usually half-jokingly, cause me to think more about something than I otherwise would.

The prime example is "this says a lot about society"

> 48% Of people believed Griffin was a real person after a one- minute video call.

...I don't think this will have a positive impact on the world

Were people getting their pants tied up in knots like this over names like daemon programs (a term that carries "baggage" of its own) and installation wizards (which, to my knowledge, do not actually invoke in the arcane)?

The public figure viewer would be way better to use if I could inspect entries without opening a new page.

I wonder when work started on this project, and how the public release of Jev played into their timing.

Why can't detractors of rationalism and AI-doom ever just lay out an argument for why they're wrong

It's always "yudkowsky is a weird guy" or "they had an orgie once" but who gives a fuck?

I just want someone to lay out, in impersonal terms, the flaws of their reasoning and why I shouldn't agree with them.

My problem is with the whole genre of article rather than this one specifically.

How do you guys realistically plan to survive long-term against the hyper-scalers as a 2 person lab?

Curious because I'm considering joining a similarly small lab.

A lot of current diffusion LLMs don't convert tokens to continous space before noising them. They add discrete "noise", which is often as simple as replacing some tokens with [MASK].

The real problem is that when using fewer sampling steps than output tokens, diffusion formulations fundamentally cannot represent distributions where output tokens are heavily codependent. Autoregressive formulations don't have this problem, they can represent any distribution (ignoring limitations of the underlying model).