Oly Sourbut

I realize I'm talking kind of various kind of abstractions.

I'm waving my hands, which is a sign that I'm maybe not being concrete enough.

You can read the blog post if you're interested.

So this is a system where you can imagine building lots of tools and processes, many of which are AI-augmented.

which enable us to build up these structures and these corporate and these metadata and annotations and so on, which then themselves can be fed into downstream AI systems.

So it's using LMs to build the data sets and structures, which can enable AIs and other systems to be kind of more epistemically virtuous downstream.

I think ideally you're not asking everyone to suddenly become an expert in best practices of using AI.

But of course people will learn and adapt and best practices will spread.

I guess a slightly different spin on your question when you say, do we need to improve?

Yes, absolutely.

The whole point of this AI for human reasoning is to enable us to improve in the ways that we might endorse if we were able to coordinate better or if we were able to communicate better.

But is it necessary for us to improve in our practices for how we're even using these systems?

I think part of the point of things like the full epistemic stack is the way we're conceiving it, at least,

is it's meant to be pretty adversarially robust.

It's meant to be pretty robust to various kinds of scales of contribution.

So whether it's a kind of offhand remark on Twitter or whether it's a full scientific paper, I'm kind of thinking like ultimately that can be, ultimately that can be incorporated into the same framework

structure.

It's all discourse and it all has some degree of evidence and sourcing and so on.

So on Twitter, maybe the evidence is usually pretty implicit.

Occasionally people provide links to articles or to papers or whatever when they're making a point.