Hajo Greif, Adam Kubiak, and Paweł Stacewicz (2024). Selection, Growth and Form. Turing’s Two Biological Paths towards Intelligent Machinery. Studies in History and Philosophy of Science 106: 126-135. Special Issue “Robots and Living Organisms: New Historical and Philosophical Perspectives”, edited by Marco Tamborini and Edoardo Datteri. DOI: https://doi.org/10.1016/j.shpsa.2024.05.017
Abstract: We inquire into the role of Turing’s biological thought in the development of his concept of intelligent machinery. We trace the possible relations between his proto- connectionist notion of ‘organising’ machines in Turing (1948) on the one hand and his mathematical theory of morphogenesis in developmental biology (1952) on the other. These works were concerned with distinct fields of inquiry and followed distinct paradigms of biological theory, respectively postulating analogues of Darwinian selection in learning and mathematical laws of form in organic pattern formation. Still, these strands of Turing’s work are related, first, in terms of being amenable in principle to his (1936) computational method of modelling. Second, they are connected by Turing’s scattered speculations about the possible bearing of learning processes on the anatomy of the brain. We argue that these two theories form an unequal couple that, from different angles and in partial fashion, point towards cognition as a biological and embodied phenomenon while, for reasons inherent to Turing’s computational approach to modelling, not being capable of directly addressing it as such. We explore ways in which these two distinct-but-related theories could be more explicitly and systematically connected, using von Neumann’s contemporaneous and related work on Cellular Automata and more recent biomimetic approaches as a foil. We conclude that the nature of ‘initiative’ and the mode of material realisation are the key issues that decide on the possibility of intelligent machinery in Turing.
The research presented in this article has been supported by National Science Centre (NCN) OPUS 19 grant no. 2020/37/B/HS1/01809. Open Access with NCN support.