Is that a core part of the definition of myopia in AI/ML?
To the best of my knowledge, the use of ‘myopia’ in the AI safety context was introduced by evhub, maybe here, and is not a term used more broadly in ML.
I understood it only to mean that models lose accuracy if the environment (the non-measured inputs to real-world outcomes) changes significantly from the training/testing set.
This is typically referred to as ‘distributional shift.’
To the best of my knowledge, the use of ‘myopia’ in the AI safety context was introduced by evhub, maybe here, and is not a term used more broadly in ML.
This is typically referred to as ‘distributional shift.’