Financial status: This is independent research, now supported by a grant. I welcome further financial support.

Epistemic status: I’m 90% sure that this post faithfully relates the content of the paper that it reviews.

In a recent conversation about what it means to accumulate knowledge, I was pointed towards a paper by Johannes Rauh and collaborators entitled Coarse-graining and the Blackwell order. The abstract begins:

Suppose we have a pair of information channels, κ1, κ2, with a common input. The Blackwell order is a partial order over channels that compares κ1 and κ2 by the maximal expected utility an agent can obtain when decisions are based on the channel outputs.

This immediately caught my attention because of the connection between information and utility, which I suspect is key to understanding knowledge. In classical information theory, we study quantities such as entropy and mutual information without the need to consider whether information is useful or not with respect to a particular goal. This is not a shortcoming of these quantities, it is simply not the domain of these quantities to incorporate a goal or utility function. This paper discusses some different quantities that attempt to formalize what it means for information to be useful in service to a goal.

The reason to understand knowledge in the first place is so that we might understand what an agent does or does not know about, even when we do not trust it to answer questions honestly. If we discover that a vacuum-cleaning robot has built up some unexpectedly sophisticated knowledge of human psychology then we might choose to shut it down. If we discover that the same robot has merely recorded information from which an understanding of human psychology could in principle be derived then we may not be nearly so concerned, since almost any recording of data involving humans probably contains, in principle, a great deal of information about human psychology. Therefore the distinction between information and knowledge seems highly relevant to the deception problem in particular, and, in my opinion, to contemporary AI alignment in general. This paper provides a neat overview of one way we might formalize knowledge, and beyond that presents some interesting edge cases that point at the shortcomings of this approach.

I suspect that knowledge has to do with selectively retaining and discarding ...

Alexandre See

Alexandre See

Alexandre See

Alexandre See