Information and control: Insights from within the brain
Résumé
According to classic theory, information is aimed at resolving uncertainty in complex systems, like the Turing machine, which inspired the early approaches. The premises therein, presumably for lack of insight from neuroscience, Artificial Intelligence (AI), and quantum computers as we know them today, neither specify the nature of information, nor what we have to understand by complexity or uncertainty. This keynote summarizes insights from biological synapses in the brain, contemporary neuroscience, and neurophilosophy to illustrate why and how the concepts of information, complexity and uncertainty are reflected in “natural” (synapses, cells, brains, minds) and “artificial” (machines) systems. The context dependency of information processing by brain networks is brought forward. It allows us to understand “information” in terms of a functional link between the brain and the world, which has implications for AI, at different levels of functional complexity and/or autonomy.