Molecular Cybernetics: Challenges toward Cellular Chemical Artificial Intelligence

Research on so‐called “chemical artificial intelligence” (CAI) is an emerging field with the aim of constructing information‐processing systems with learning capabilities based on chemical methodologies. This can be regarded as an attempt to reconstruct Cybernetics using molecular based systems. Man...

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Bibliographic Details
Published in:Advanced functional materials 2022-09, Vol.32 (37), p.n/a
Main Authors: Murata, Satoshi, Toyota, Taro, Nomura, Shin‐ichiro M., Nakakuki, Takashi, Kuzuya, Akinori
Format: Article
Language:English
Subjects:
Artificial intelligence
cellular chemical artificial intelligence
Chemical reactions
Cybernetics
DNA circuits
Learning
Liposomes
Materials science
molecular cybernetics
Network topologies
plasticity
reconfigurability
recursiveness
scalability
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Summary:Research on so‐called “chemical artificial intelligence” (CAI) is an emerging field with the aim of constructing information‐processing systems with learning capabilities based on chemical methodologies. This can be regarded as an attempt to reconstruct Cybernetics using molecular based systems. Many chemical reaction systems with computational abilities are proposed, but most are fixed functions that deliver molecular output for a given molecular input. On the other hand, chemical AI is a system with learning capability; namely, the output should be variable and gradually change upon repeated molecular inputs. In this paper, a compartmentalization approach for implementing cellular chemical AI using liposomes is discussed. The existing studies in terms of the methods used for assembling systems consisting of many liposomes with different functions, methods for achieving recursiveness and plasticity in chemical reaction systems, and methods for reconfiguring the network topology by liposome deformation are reviewed. Issues that must be addressed in order to realize chemical AI are also identified. Chemical AI is a system with learning capability. A compartmentalization approach for implementing chemical AI is discussed in this paper. The existing studies on the methods for assembling liposomes with different functions, achieving recursiveness and plasticity in computational reaction circuits, and reconfiguring the network topology by liposome deformation are reviewed.
ISSN:1616-301X
1616-3028
DOI:10.1002/adfm.202201866