I did a simple draft translation of general complex inference to
   machine learning (introspectively):

   3 models (or prompts):

   1. a generative or simulation model for a context

   2. a classifier identifying the context

   3. a trainee that generates missing system representations

   Models 1 and 2 perform forward inference from the output of 3 to make a
   loss function that compares with known data, and bam you have a system
   that can depict plausible underlying causes in whatever mode you train
   it on. It’s cool because similar to human thought. 3 ends up making a
   decision tree since multiple outputs compared. (guessing)

   For example you could use this to predict the source code for a binary
   without looking inside it (easier with an RL env or just user
   handholding of it):

   1. prompt language model to execute source code

   2. prompt language model to identify it is correct code and any other
   known data

   3. soft tune a prompt to produce source

   (oops)