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A New Class of Autopoietic and Cognitive Machines
Version 1
: Received: 18 November 2021 / Approved: 19 November 2021 / Online: 19 November 2021 (08:16:17 CET)
A peer-reviewed article of this Preprint also exists.
Mikkilineni, R. A New Class of Autopoietic and Cognitive Machines. Information 2022, 13, 24. Mikkilineni, R. A New Class of Autopoietic and Cognitive Machines. Information 2022, 13, 24.
Abstract
Making computing machines mimic living organisms has captured the imagination of many since the dawn of digital computers. However, today’s artificial intelligence technologies fall short in replicating even the basic autopoietic and cognitive behaviors found in primitive biological systems. According Charles Darwin, the difference in mind between humans and the higher animals, great as it is, certainly is one of degree and not of kind. Autopoiesis refers to the behavior of a system that replicates itself and maintains its own identity and stability while facing fluctuations caused by external influences. Cognitive behaviors model the system’s state, sense internal and external changes, analyze, predict and take action to mitigate any risk to its functional fulfilment. How did intelligence evolve? what is the relationship between the mind and body? Answers to these questions should guide us to infuse autopoietic and cognitive behaviors into digital machines. In this paper we use recent advances in our understanding of general theory of information, and the role of structures in managing the transformations between information and knowledge to pave the path to infuse autopoietic and cognitive functions into digital computing and build a new class of intelligent machines going beyond the current state of the art.
Keywords
Cognition; computing models; Deep Learning; Autopoiesis; Structural Machines; Artificial Intelligence
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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