Yeah I generally liked that discussion, with a few nitpicks, like I dislike the word “precision”, because I think it’s confidence levels attached to predictions of boolean variables (presence or absence of a feature), rather than a variances attached to predictions of real numbers. (I think this for various reasons including trying to think through particular examples, and my vague understanding of the associated neural mechanisms.)
I would state the fog example kinda differently: There are lots of generative models trying to fit the incoming data, and the “I only see fog” model is currently active, but the “I see fog plus a patch of clear road” model is floating in the background ready to jump in and match to data as soon as there’s data that it’s good at explaining.
I mean, “I am looking at fog” is actually a very specific prediction about visual input—fog has a specific appearance—so the “I am looking at fog” model is falsified (prediction error) by a clear patch. A better example of “low confidence about visual inputs” would be whatever generative models are active when you’re very deep in thought or otherwise totally spaced out, ignoring your surroundings.
Yeah I generally liked that discussion, with a few nitpicks, like I dislike the word “precision”, because I think it’s confidence levels attached to predictions of boolean variables (presence or absence of a feature), rather than a variances attached to predictions of real numbers. (I think this for various reasons including trying to think through particular examples, and my vague understanding of the associated neural mechanisms.)
I would state the fog example kinda differently: There are lots of generative models trying to fit the incoming data, and the “I only see fog” model is currently active, but the “I see fog plus a patch of clear road” model is floating in the background ready to jump in and match to data as soon as there’s data that it’s good at explaining.
I mean, “I am looking at fog” is actually a very specific prediction about visual input—fog has a specific appearance—so the “I am looking at fog” model is falsified (prediction error) by a clear patch. A better example of “low confidence about visual inputs” would be whatever generative models are active when you’re very deep in thought or otherwise totally spaced out, ignoring your surroundings.