A New Class of Autopoietic and Cognitive Machines

Rao Mikkilineni

doi:10.3390/info13010024

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 of replicating even the basic autopoietic and cognitive behaviors found in primitive biological systems. According to Charles Darwin, the difference in mind between humans and 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 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 fulfillment. 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 show how to use the structural machine to build a cognitive reasoning system that integrates the knowledge from various digital symbolic and sub-symbolic computations. This approach is analogous to how the neocortex repurposed the reptilian brain and paves the path for digital machines to mimic living organisms using an integrated knowledge representation from different sources.

Highlights

To make computing machines mimic living organisms, first, we must understand the unique features of the living organisms that make them sentient, resilient, and intelligent
According to Signorelli [35], any attempt to build conscious machines and try to introduce human capabilities should start with the definitions of autonomy, reproduction, and consciousness
Signorelli provides a thoughtful discussion about the current state of the art of our understanding from biology, neuroscience, and studies of cognition

Summary

Introduction

To make computing machines mimic living organisms, first, we must understand the unique features of the living organisms that make them sentient, resilient, and intelligent. Computing (the ability to transform information obtained through the senses, create and process knowledge structures capturing the dynamics), communication (the ability to pass information within its components and with external systems) and cognition (the ability to create and execute processes that sense and react to changing circumstances) are essential ingredients of intelligence that provide sentience and resilience (the ability to know and adapt appropriately to changing circumstances). Current information-processing structures with symbolic computing (based on John von Neumann’s stored program implementation of the Turing machine) and deep learning (based on algorithms that mimic neural networks) fall short [16,17,18,19] of mimicking the autopoietic and cognitive behaviors of living organisms. Biological networks enable molecules within cells, and even cells themselves, to communicate with each other and their environment. [20] p. 1.”

Phase 1

Phase 2

Infusing Autopoietic and Cognitive Behaviors into Digital Automata

Findings

Conclusions