Proposing what-if scenarios and asking questions of your own and using your imagination generally falls under the name 'counterfactuals' -- a big area of AI res

Proposing what-if scenarios and asking questions of your own and using your imagination generally falls under the name 'counterfactuals' -- a big area of AI research. But to date, the priorities of deep learning have focused elsewhere (mostly in perception of images and speech via discrete classification). Likewise, 99% of the computing strategy used by DL practitioners and researchers continues to use large scale supervised learning -- because it continues to make advances in many problem spaces just as it is. Is it possible to escape DL's apparent limits and use it differently to solve problems that lie in the counterfactual domain? Perhaps. But so far, there's been relatively little work or modest results along those lines. Given the architectural model used by DL (large matrix multiplications and iterations using SIMD operations on GPUs), the prospect of applying it to alternative AI objectives that are more conventionally symbolic and whose methods are typically algebraic (and unnatural to share the same computational model as DL) do not seem to be especially promising places for DL to thrive. Not so far anyway.