On Implementing Technomorph Biology for Inefficient Computing

It is commonly accepted that 'The brain computes' and that it served as a model for establishing principles of technical (first of all, electronic) computing. Even today, some biological implementation details inspire the implementation of more performant electronic implementations. However, grasping out details without context often leads to decreasing operating efficiency. In the cases of those major implementations, the notion of 'computing' has an entirely different meaning. We provide the notion of generalized computing from which we derive technical and biological computing, and by showing how the functionalities are implemented, we also highlight what performance losses lead the solution. Both implementations have been developed using a success-failure method, keeping the successful part-solutions (and building on top of them) and replacing a less successful one with another. Both developments proceed from a local minimum of their goal functions to another, but some principles differ fundamentally. Moreover, they apply entirely different principles, and the part-solutions must cooperate with others, so grasping some biological solution without understanding its context and implementing it in the technical solution usually leads to a loss of efficiency. Today, technical systems' absolute performance seems to be saturated while their computing and energetic inefficiency are growing.