The difference between RL and this "want" is how the goals are constructed. Currently in computational systems, it is the designer that creates the ontology of what an agent interacts with. What that means for ML, RL and evolutionary algorithms is that the agent has a predefined goal and predefined devices to accomplish said goal (the agent's sensors and actuators must explicitly be programmed in order to utilize them).
Biological agents have are seemingly able to derive goals from both internal and external signals, and are able to use affordances (aka what is usable through their environment/body) to accomplish those goals. These affordances are not predefined and the space of possible usages for any given tool is practically infinite (a screwdriver can tighten a screw, or pry open a door [0]). Biological evolution also doesn't actually have goal persay, its more of "what works sticks". Whereas evolutionary/learning algorithms have a defined objective function.
The big differentiator seems to lie in the fact that biological agents are able to engage in a semiotic or meaning-making process where given an internal state and its affordances the agent is able to make movements that betters it's state in the future without explicitly being told.
Because of the steep learning curve and troubleshooting associated with it, I've become more hesitant in recommending Emacs. However for the curious, the video Getting Started in Org Mode [1] which is a great demonstration on Emacs and Org Mode's potential as an organizational tool. Anyone still curious and would like to smooth out the learning curve, I'd recommend using an emacs starter kit such as Doom Emacs [2] which has a lot of OOB functionality without having to immediately learn Elisp.
I became very interested in CT from a biological perspective. The work originates from Robert Rosen who describes how biological/complex systems are not (completely) reducible to formal systems (aka any model of a natural system will always be incomplete+it is impossible to explicitly list out all the rules governing the system) because they "contain a model of themselves" to which they anticipate the future and act on towards their benifit.
Many relate his work as closely tied to Gödel's incompleteness theorem but for biology. The purpose of using category theory is that it is general enough to not talk about specific parts of an organism's construction, but rather how their general functional parts relate to each other.
Biological agents have are seemingly able to derive goals from both internal and external signals, and are able to use affordances (aka what is usable through their environment/body) to accomplish those goals. These affordances are not predefined and the space of possible usages for any given tool is practically infinite (a screwdriver can tighten a screw, or pry open a door [0]). Biological evolution also doesn't actually have goal persay, its more of "what works sticks". Whereas evolutionary/learning algorithms have a defined objective function.
The big differentiator seems to lie in the fact that biological agents are able to engage in a semiotic or meaning-making process where given an internal state and its affordances the agent is able to make movements that betters it's state in the future without explicitly being told.
[0] https://www.frontiersin.org/articles/10.3389/fevo.2021.80628... (title: How Organisms Come to Know the World: Fundamental Limits on Artificial General Intelligence)