DNNs end up encoding parts equivalent to type theories in their operations — extracting an “internal language” (in the TT/CAT) sense which models the data. In particular, convolutions are a form of extracting/deducing types from your input. A full DNN deduces a model of the data in which it can type the incoming data and then reason about relations on those types to decide the challenge.
What is it that you think you’re doing, besides forming a model from data in which you reason based on an internal language, which describes the data you’ve seen?
Your post comes across as ignorant of eg, the overlap between TT and DNNs, while simultaneously full of woo about how humans operate.
DNNs end up encoding parts equivalent to type theories in their operations — extracting an “internal language” (in the TT/CAT) sense which models the data. In particular, convolutions are a form of extracting/deducing types from your input. A full DNN deduces a model of the data in which it can type the incoming data and then reason about relations on those types to decide the challenge.
What is it that you think you’re doing, besides forming a model from data in which you reason based on an internal language, which describes the data you’ve seen?
Your post comes across as ignorant of eg, the overlap between TT and DNNs, while simultaneously full of woo about how humans operate.