Who doesn’t? Karpathy, and a pretty much every researcher at OpenAI/Deepmind/FAIR absolutely knows the trivial concept of fully observable versus partially observable environments, which is 101 reinforcement learning.
ie., that when you're taking data from a therometer in order to estimate the temperature of coffee, the issue isnt simply partial information
Its that the information is about the mercury, not the coffee. In order to bridge the two you need a theory (eg., about the causal reliability of heating / room temp / etc.)
So this isnt just a partial/full information problem -- these are still mathematical toys. This is a reality problem. This is a you're dealing with a causal realtionship between physical systems problem. This is not a mathematical relationship. It isnt merely partial, it is not a matter of "informaton" at all. No amount could ever make the mecurary, coffee.
Computer scientists have been trained on mathematics and deployed as social scientists, and the naiveté is incredible
