“The ongoing success of applied Artificial Intelligence and of cognitive simulation seems assured. However, strong AI, which aims to duplicate human intellectual abilities, remains controversial. The reputation of this area of research has been damaged over the years by exaggerated claims of success that have appeared both in the popular media and in the professional journals. At the present time, even an embodied system displaying the overall intelligence of a cockroach is proving elusive, let alone a system rivalling a human being.”

B. J. Copeland, May 2000, AlanTuring.NET – Reference Articles on Turing (What is Artificial Intelligence? Is Strong AI Possible?)

We have new insights into our understanding of information, its carriers, its processing, its communication, and its use in both living systems and in digital automata. We now understand how biological systems enable sentience (the ability to sense and respond), resilience (the capacity to recover quickly from non-deterministic difficulties arising from within or from outside without requiring a reboot) and intelligence (the ability to acquire and apply knowledge and skills). Our understanding stems from the lessons learned from different disciplines:

We now can model the information processing structures using the new mathematics and define their schema describing the autopoietic patterns contributing to sentience, resilience and intelligence. We argue that these patterns also allow us to design and implement a new class of digital autopoietic automata exhibiting sentience, resilience and intelligence at scale.

“While answering the question “Chicken or the egg, which came first?” it is said (Dyson, G. The Darwin Among the Machines: The evolution of Global Intelligence, Basic Books, New York, 1997, P 28.) that the chicken is an egg’s way of making another egg (or we can say replicating itself). The genes in the egg are programmed to replicate themselves using the resources available effectively. They already come with an “intent”, the workflows to execute the intent and monitoring and controlling best practices to adjust the course if deviations occur, whether they be from fluctuations in resources or the impact of its interaction with the environment. The intent of the genes, it seems, is the ability to survive and replicate. There is a symbiosis of the genes (which contain the information about the intent, workflows and also process knowledge to execute the intent) and the hardware in the form of chemicals such as amino acids and proteins that provide the means.”

Rao Mikkilineni, Giovanni Morana, and Mark Burgin. 2015. Oracles in Software Networks: A New Scientific and Technological Approach to Designing Self-Managing Distributed Computing Processes. In Proceedings of the 2015 European Conference on Software Architecture Workshops (ECSAW ’15). Association for Computing Machinery, New York, NY, USA, Article 11, 1–8. DOI:https://doi.org/10.1145/2797433.2797444

Meta-Cognition

“The most interesting dimension is what I call meta-cognition where understanding how to think about a problem in a broad sense and step back and say, “Okay, how important is this answer? How could I check my answer? What external tools would help me with this?””

This comment from Bill Gates recently has generated a flurry of discussions and comments. What is missing is the recognition that recent knowledge from genomics, neuroscience, and the general theory of information points to a way to not only understand meta-cognition in our mental structures but also how to infuse it into digital structures.

Here is some food for thought.

Digital Genome and Self-Regulating Distributed Software Applications with Associative Memory and Event-Driven History

General Theory of Information: The Bridge to Mindful Machines

Here is a recent paper that explains how new knowledge derived fromn the General Theory of Information throws new light on the evolution of Machine Intelligence.

Abstract: General Theory of Information (GTI) offers a groundbreaking framework for designing “mindful machines” that bridge the divide between biological intelligence and digital automation. GTI reimagines traditional computing by introducing cognitive, autopoietic (self-regulating) capabilities in digital systems, enabling them to perceive, adapt, and respond autonomously to changing conditions. Unlike conventional AI and AGI models that operate within predefined algorithmic constraints, GTI-based systems go beyond by incorporating self-awareness and a resilient digital “self,” capable of storing interaction histories and learning from them. This paper explores GTI’s practical applications in systems requiring resilience, cognition, and ethical safeguards. In video streaming and medical assistance, GTI-based digital genomes enable systems to function independently, actively self-correct, and conform to policy-driven ethical standards. These mindful machines embody unique traits rarely achieved with traditional AI: robust resilience through self-corrective mechanisms, adaptive learning from accumulated experiences, and alignment with ethical principles that govern system behavior. In demonstrating how GTI can cultivate resilience, autonomy, and ethical decision-making, this paper reveals GTI’s potential to empower digital automata with human-like adaptability and values. As such, GTI emerges as a bridge to a new generation of digital systems designed not merely to execute but to understand, adapt, and ethically engage within complex real-world environments.

The purpose of this website is to share knowledge about the theory and practice of autopoietic machines, without exaggerated claims.