I interpreted it as an ensemble of expert models, weighted in a Bayesian fashion based on past performance. But because of the diagnostic logs, the type signature is a little different; the models output both whatever probability distributions over queries / events and arbitrary text in some place.
Then there’s a move that I think of as the ‘intentional stance move’, where you look at a system that rewards behavior of a particular type (when updating the weights based on past success, you favor predictions that thought an event was more likely than its competitors did), and so pretend that the things in the system “want” to do the behavior that’s rewarded. [Like, even in this paragraph, ‘reward’ is this sort of mental shorthand; it’s not like any of the models have an interior preference to have high weight in the ensemble, it’s just that the ensemble’s predictions are eventually more like the predictions of the models that did things that happened to lead to having higher weight.]
I interpreted it as an ensemble of expert models, weighted in a Bayesian fashion based on past performance. But because of the diagnostic logs, the type signature is a little different; the models output both whatever probability distributions over queries / events and arbitrary text in some place.
Then there’s a move that I think of as the ‘intentional stance move’, where you look at a system that rewards behavior of a particular type (when updating the weights based on past success, you favor predictions that thought an event was more likely than its competitors did), and so pretend that the things in the system “want” to do the behavior that’s rewarded. [Like, even in this paragraph, ‘reward’ is this sort of mental shorthand; it’s not like any of the models have an interior preference to have high weight in the ensemble, it’s just that the ensemble’s predictions are eventually more like the predictions of the models that did things that happened to lead to having higher weight.]