The whole magic of Bayes is that it is computationally unconstrained (e.g., AIXI). I would go so far as to say that computationally constrained Bayes is the more fundamental theory, and probably doesn't look like Bayes anymore.

I would be interested in seeing papers on this topic though!

The supervised learning dataset is basically a simulator.

Scaling for the people, by the people! Hear, hear!

(Of course, benchmarks are always a little silly)

One contributing factor is our uncertainty about the potential capabilities of AI.

Without hard limits on AI, people's pre-existing worldviews influence whether they imagine the best or worst case scenarios.

Malazan: It's long, and a love it or hate it kind-of series. Still, worth trying.

And please include the Arxiv link on the first post in a thread, and not the last one.

Too bad the reviewers and authors can't upload "alpha-level simulations" of themselves so that the bots can sort it out on openreview instead of all this point-courting.