Jiřī Wiedermann

AI research is continually challenged to explain cognitive processes as being computational. Whereas existing notions of computing seem to have their limits for it, we contend that the recent, epistemic approach to computations may hold the key to understanding cognition from this perspective. In this approach, computations are seen as processes generating knowledge over a suitable knowledge domain, within the framework of a suitable knowledge theory. This, machine-independent, understanding of computation allows us to explain a variety of higher cognitive functions such as accountability, self-awareness, introspection, free will, creativity, anticipation and curiosity in computational terms. It also opens the way to understanding the self-improving mechanisms behind the development of intelligence. The argumentation does not depend on any technological analogies.

AI research is continually challenged to explain cognitive processes as being computational. Whereas existing notions of computing seem to have their limits for it, we contend that the recent, epistemic approach to computations may hold the key to understanding cognition from this perspective. In this approach, computations are seen as processes generating knowledge over a suitable knowledge domain, within the framework of a suitable knowledge theory. This, machine-independent, understanding of computation allows us to explain a variety of higher cognitive functions such as accountability, self-awareness, introspection, free will, creativity, anticipation and curiosity in computational terms. It also opens the way to understanding the self-improving mechanisms behind the development of intelligence. The argumentation does not depend on any technological analogies.

Open peer commentary on the article “Exploration of the Functional Properties of Interaction: Computer Models and Pointers for Theory” by Etienne B. Roesch, Matthew Spencer, Slawomir J. Nasuto, Thomas Tanay & J. Mark Bishop. Upshot: We challenge the authors’ claim in the target article that “departing from cognitivism requires the development of a new functional framework that will support causal, powerful and goal-directed behavior in the context of the interaction between the organism and the environment.” We argue that rather than a departure from cognitivism, the indicated goal is a natural complement or extension of the classical understanding of cognitivism. In order to reach such a goal, no new functional framework has to be developed right from scratch: there are many insights in related areas of research that can serve such a purpose well and can become an integral part of constructive cognitivism. We welcome the idea to build constructive foundations of cognitivism

Cognitive processes are often modelled in computational terms. Can this still be done if only minimal assumptions are made about any sort of representation of reality? Is there a purely knowledge-based theory of computation that explains the key phenomena which are deemed to be computational in both living and artificial systems as understood today? We argue that this can be done by means of techniques inspired by the modelling of dynamical systems. In this setting, computations are defined as curves in suitable metaspaces and knowledge is generated by virtue of the operation of the underlying mechanism, whatever it is. Desirable properties such as compositionality will be shown to fit naturally. The framework also enables one to formally characterize the computational behaviour of both knowledge generation and knowledge recognition. The approach may be used in identifying when processes or systems can be viewed as being computational in general. Several further questions pertaining to the philosophy of computing are considered.