Tracing a new path in the field of AI and robotics: mimicking human intelligence through chemistry. Part II: systems chemistry

Front Robot AI. 2023 Oct 17:10:1266011. doi: 10.3389/frobt.2023.1266011. eCollection 2023.

Abstract

Inspired by some traits of human intelligence, it is proposed that wetware approaches based on molecular, supramolecular, and systems chemistry can provide valuable models and tools for novel forms of robotics and AI, being constituted by soft matter and fluid states as the human nervous system and, more generally, life, is. Bottom-up mimicries of intelligence range from the molecular world to the multicellular level, i.e., from the Ångström (10-10 meters) to the micrometer scales (10-6 meters), and allows the development of unconventional chemical robotics. Whereas conventional robotics lets humans explore and colonise otherwise inaccessible environments, such as the deep oceanic abysses and other solar system planets, chemical robots will permit us to inspect and control the microscopic molecular and cellular worlds. This article suggests that systems made of properly chosen molecular compounds can implement all those modules that are the fundamental ingredients of every living being: sensory, processing, actuating, and metabolic networks. Autonomous chemical robotics will be within reach when such modules are compartmentalised and assembled. The design of a strongly intertwined web of chemical robots, with or without the involvement of living matter, will give rise to collective forms of intelligence that will probably reproduce, on a minimal scale, some sophisticated performances of the human intellect and will implement forms of "general AI." These remarkable achievements will require a productive interdisciplinary collaboration among chemists, biotechnologists, computer scientists, engineers, physicists, neuroscientists, cognitive scientists, and philosophers to be achieved. The principal purpose of this paper is to spark this revolutionary collaborative scientific endeavour.

Keywords: Bayesian inference; DNA; artificial neural networks; chemical artificial intelligence; chemical robots; fuzzy logic; proteins.

Grants and funding

The authors declare financial support was received for the research, authorship, and/or publication of this article. This work has been funded by Università degli Studi di Perugia (Fondo Ricerca di Base 2022) and the European Union—NextGenerationEU under the Italian Ministry of University and Research (MUR) National Innovation Ecosystem grant ECS00000041—VITALITY. We acknowledge Università degli Studi di Perugia and MUR for support within the project Vitality.