A. There are universal non composite algorithms for predicting stimuli in the real world.
Becoming better at prediction transfers across all domains.
B. There are narrow algorithms good at predicting stimuli in distinct domains.
Becoming a good predictor in one domain doesn’t easily transfer to other domains.
Human intelligence being an ensemble makes it seem like we live in a world that looks more like B, than it does like A.
Predicting diverse stimuli involves composing many narrow algorithms. Specialising a neural circuit for predicting stimuli in one domain doesn’t easily transfer to predicting new domains.
To illustrate how this matters.
Consider two scenarios:
A. There are universal non composite algorithms for predicting stimuli in the real world. Becoming better at prediction transfers across all domains.
B. There are narrow algorithms good at predicting stimuli in distinct domains. Becoming a good predictor in one domain doesn’t easily transfer to other domains.
Human intelligence being an ensemble makes it seem like we live in a world that looks more like B, than it does like A.
Predicting diverse stimuli involves composing many narrow algorithms. Specialising a neural circuit for predicting stimuli in one domain doesn’t easily transfer to predicting new domains.