DOI: 10.1162/artl_a_00411 ISSN: 1064-5462

Explorative Synthetic Biology in AI: Criteria of Relevance and a Taxonomy for Synthetic Models of Living and Cognitive Processes

Luisa Damiano, Pasquale Stano
  • Artificial Intelligence
  • General Biochemistry, Genetics and Molecular Biology
  • Computer Science (miscellaneous)
  • Agricultural and Biological Sciences (miscellaneous)


This article tackles the topic of the special issue “Biology in AI: New Frontiers in Hardware, Software and Wetware Modeling of Cognition” in two ways. It addresses the problem of the relevance of hardware, software, and wetware models for the scientific understanding of biological cognition, and it clarifies the contributions that synthetic biology, construed as the synthetic exploration of cognition, can offer to artificial intelligence (AI). The research work proposed in this article is based on the idea that the relevance of hardware, software, and wetware models of biological and cognitive processes—that is, the concrete contribution that these models can make to the scientific understanding of life and cognition—is still unclear, mainly because of the lack of explicit criteria to assess in what ways synthetic models can support the experimental exploration of biological and cognitive phenomena. Our article draws on elements from cybernetic and autopoietic epistemology to define a framework of reference, for the synthetic study of life and cognition, capable of generating a set of assessment criteria and a classification of forms of relevance, for synthetic models, able to overcome the sterile, traditional polarization of their evaluation between mere imitation and full reproduction of the target processes. On the basis of these tools, we tentatively map the forms of relevance characterizing wetware models of living and cognitive processes that synthetic biology can produce and outline a programmatic direction for the development of “organizationally relevant approaches” applying synthetic biology techniques to the investigative field of (embodied) AI.

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