Resum
Most of the bioinspired morphological computing studies have departed from a human analysis bias: to consider cognitive morphology as encapsulated by one body, which, of course, can have enactive connections with other bodies, but that is defined by clear bodily boundaries. Such complex biological inspiration has been directing the research agenda of a huge number of labs and institutions in recent decades. Nevertheless, there are other bioinspired examples or even technical possibilities that go beyond biological capabilities (such as constant morphological updating and reshaping, which asks for remapping cognitive performances). Additionally, despite the interest of swarm cognition (which includes superorganisms of flocks, swarms, packs, schools, crowds, or societies) in such non-human-centered approaches, there is still a biological constraint: such cognitive systems have permanent bodily morphologies and only interact between similar entities. In all cases, and even considering amazing possibilities, such as the largest living organism on Earth (specifically the honey fungus Armillaria ostoyae, measuring 3.8 km across in the Blue Mountains in Oregon), it has not been put over the table the possibility of thinking about cross-morphological cognitive systems. Nests of intelligent drones as a single part of AI systems with other co-working morphologies, for example. I am, therefore, suggesting the necessity of thinking about cross-embodied cognitive morphologies, more dynamical and challenging than any other existing cognitive system already studied or created.
Keywords: reservoir computing; memristor networks; cellular automata networks; protein toxicity; protein classification
Keywords: reservoir computing; memristor networks; cellular automata networks; protein toxicity; protein classification
Idioma original | Anglès |
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Nombre de pàgines | 4 |
Volum | 81 |
Edició | 10 |
DOIs | |
Estat de la publicació | Publicada - 2022 |