Intelligence

Scrutinizing time awareness in biological computing, learning, and intelligence reveals [29] that they have practically nothing in common with the technical ones. As a consequence, ”biological brains are more efficient learners than modern Machine Learning (ML) algorithms due to extra ‘prior structure’” [30]. We must not forget ”There is no reason whatever for believing that our brain is the supreme ne plus ultra of an organ of thought in which the world is reflected.” [12] Furthermore, ”it is also possible that non-biological hardware and computational paradigms may permit yet other varieties of machine intelligence we have not yet conceived” [30, 19]. We start to conceive them by analyzing the fundamental processes which can be ”directly associated with consciousness” [12].

to react appropriately to stimuli from its environment, we still do not know the details and the underlying general principles how neuronal networks represent, process, and store the information they use. What makes the case worse is that, due to the lack of knowledge of the abstract way of neuronal operation, the so-called ”neural information science” relies on a flawed mathematical foundation. We understand how neurons represent, process, and transfer information [16, 28]. We introduce the appropriate interpretation of information for biology.