The idea that the cortex or cerebellum, for example, can be described as "general purpose re-programmable hardware" is lacking in both clarity and support.

"General purpose learning hardware" is perhaps better. I used "re-programmable" as an analogy to an FPGA.

However, in a literal sense the brain can learn to use simpe paper + pencil tools as an extended memory, and can learn to emulate a turing machine. Given huge amounts of time, the brain could literally run windows.

And more to the point, programmers ultimately rely on the ability of our brain to simulate/run little sections of code. So in a more practical literal sense, all of the code of windows first ran on human brains.

You seem to be saying that the cortex is a universal reinforcement learning machine

You seem to be hung up reinforcement learning. I use some of that terminology to define a ULM because it is just the most general framework - utility/value functions, etc. Also, there is some pretty strong evidence for RL in the brain, but the brain's learning mechanisms are complex - moreso than any current ML system. I hope I conveyed that in the article.

Learning in the lower sensory cortices in particular can also be modeled well by unsupervised learning, and I linked to some articles showing how UL models can reproduce sensory cortex features. UL can be viewed as a potentially reasonable way to approximate the ideal target update, especially for lower sensory cortex that is far (in a network depth sense) from any top down signals from the reward system. The papers I linked to about approximate bayesian learning and target propagation in particular can help put it all into perspective.

clear evidence that we have found evidence for a reinforcement learning machine in the brain already.

Well, the article summarizes the considerable evidence that the brain is some sort of approximate universal learning machine. I suspect that you have a particular idea of RL that is less than fully general.