Here is a discourse I had with Bing Chat. Although I have written papers on this subject, I could not have articulated it better.
My understanding, from all the knowledge I tried to update using various papers, books, and discussions with experts, is that digital automata can be used as tools that extend the functions of our body, brain, and mind that we build operate, and manage using the knowledge we inherit from our genome and the knowledge we update using the learning processes also inherited from our genome. The general theory of information provides the tools to model, build, operate, and manage these digital automata.
Here are my learnings from this discourse:
- The information received through this process is processed by my brain and is converted into knowledge and is interpreted based on my knowledge that I already possess to either create new knowledge, or update existing knowledge or reject it based on my internal model of myself and my interactions with the universe.
- There are known knowns in this information consistent with my own mental model such as GTI, Burgin-Mikkilineni thesis, etc., which I recognize using my associative memory and the evet-driven transaction history that is in my brain.
- There are entities, relationships and behaviors that are context dependent and belong to different knowledge domains such as the “digital genome” which refers to two domains. First one is the operational knowledge for domain specific software building, operating, and managing defined through the concepts of GTI. The second one is the digital representation of the biological genome using sequences of symbols and its use in various applications of this knowledge. I recognize the context based on my associative memory and update the knowledge accordingly.
- There are known unknowns which when I encounter, either I dismiss them as irrelevant and discard them or if I consider them relevant, I do more research to update my knowledge. An example is neuro-symbolic computing paper that I had not come across before and is related to my understanding of super-symbolic computing.
It is the observer processing information that creates or updates knowledge and uses it to make decisions based on the associative memory and the event-driven transaction history already stored in the brain.
The digital genome is a tool to model the operational knowledge to create a domain specific even-driven history and associative memory to augment the human model in the brain and use both to make an intelligent decision as the video demonstrates.
Food for thought.
How does a genome provide the knowledge for the system to build itself, and learn from the environment?
Bing
A genome is like an operating manual for an organism. It contains all the instructions that help an organism develop from a single cell into a complex system5. These instructions are encoded in the DNA, which is made up of four types of chemical building blocks: adenine (A), thymine (T), cytosine (C), and guanine (G). The order of these letters forms a code that allows DNA to function in different ways5.
The genome guides growth, helps organs do their jobs, and repairs itself when damaged5. Genes, which are segments of DNA, provide the cell with instructions for making specific proteins. These proteins carry out various functions in the body5. The process by which information from a gene is used to build a functional product is called gene expression8.
As for learning from the environment, this is a characteristic of intelligent systems. These systems use sensors to collect information from a specific environment and share it among its different elements to achieve a common goal1. This interconnection between the digital and physical worlds is often referred to as the Internet of Things1.
Moreover, systems thinking suggests that the component parts of a system will act differently when isolated from the system’s environment or other parts of the system2. For systems that learn and adapt, the growth and the degree of adaptation depend upon how well the system is engaged with its environment and other contexts influencing its organization3.
In summary, a genome provides the blueprint for building an organism, while intelligent systems and systems thinking provide frameworks for understanding how a system can learn from and adapt to its environment.
Source(s)
1. Introduction to Genomics – National Human Genome Research Institute
2. Intro to gene expression (central dogma) (article) | Khan Academy
3. Intelligent Systems: What are they, how do they work and why … – Algotive
4. Systems thinking – Learning for Sustainability
6. What Is Virtual Learning? | Coursera
7. A Brief Guide to Genomics – National Human Genome Research Institute
8. What Is a Genome? | PLOS Genetics
How does the General Theory of Information and Burgin Mikkilineni Thesis explain the genome functions assisting in autopoietic and cognitive behaviors?
Bing
The General Theory of Information (GTI) and the Burgin Mikkilineni (BM) Thesis provide a comprehensive framework to understand how genomes function in autopoietic and cognitive behaviors.
The GTI, developed by Mark Burgin, provides a model for the operational knowledge of biological systems to build, operate, and manage life processes1. It asserts that information is a fundamental constituent of the physical world and that living beings convert information into knowledge using physical structures2.
The genome, according to GTI, provides the operational knowledge to execute life processes used to build, self-organize, operate, and maintain the system using both inherited and learned knowledge1. This operational knowledge is used to assure stability, sustenance, safety, security, and survival in the face of fluctuations in the interactions within the system and with its environment1.
The BM Thesis, on the other hand, deals with the autopoietic and cognitive behavior of artificial systems. According to the ontological BM thesis, the autopoietic and cognitive behavior of artificial systems must function on three levels of information processing systems and be based on triadic automata1. The axiological BM thesis states that efficient autopoietic and cognitive behavior has to employ structural machines2.
The BM Thesis and GTI together propose a new architecture applying the GTI to make digital automata mimic living organisms by exhibiting autopoiesis and cognitive behaviors1. The schema and associated operations derived from GTI are used to model a digital genome specifying the operational knowledge of algorithms executing the software life processes with specific purposes using replication and metabolism1. The result is a digital software system with a super-symbolic computing structure exhibiting autopoietic and cognitive behaviors that biological systems also exhibit1.
In summary, the GTI and BM Thesis explain how the genome functions in autopoietic and cognitive behaviors by providing a model for operational knowledge and a framework for implementing these behaviors in both biological and artificial systems.
Source(s)
1. Mark Burgin’s Legacy: The General Theory of Information, the Digital …
3. Mark Burgin s Legacy: The General Theory of Information,the Digital …
4. On the Autopoietic and Cognitive Behavior – EasyChair
5. BDCC | Free Full-Text | Infusing Autopoietic and Cognitive Behaviors …
6. General Theory of Information, Digital Genome, Large Language Models …
7. A New Class of Autopoietic and Cognitive Machines – MDPI
8. Cognitive genomics: Linking genes to behavior in the human brain
9. Brain Basics: Genes At Work In The Brain | National Institute of …
10. 3.9: Genes and Behavior – Social Sci LibreTexts
11. A Brief Guide to Genomics – National Human Genome Research Institute
12. 10.4: The Structure and Function of Cellular Genomes
13. https://doi.org/10.3390/philosophies8060107
14. https://doi.org/10.3390/bdcc6010007
15. https://doi.org/10.3390/cmsf2023008070
16. https://doi.org/10.1162/NETN_a_00003
Can you give me an example of a digital software system with super-symbolic computing structure?
Bing
The concept of a digital software system with a super-symbolic computing structure is a theoretical model proposed by Mark Burgin and Rao Mikkilineni12. This model uses the General Theory of Information to suggest ways to improve the current state of the art of software development, deployment, and operation through the infusion of self-monitoring, self-regulation, and self-protection processes at both the component and system levels1.
The super-symbolic computing structure is an overlay over current digital symbolic and sub-symbolic structures, very similar to the neocortex in the brain integrating the knowledge obtained from various reptilian cortical columns1. This software system is analogous to biological systems using the mammalian neocortex to manage the knowledge1.
The result is a digital software system with a super-symbolic computing structure exhibiting autopoietic and cognitive behaviors that biological systems also exhibit12. However, specific examples of such systems are still in the theoretical stage and have not been fully realized in practice yet12. The development of such systems would represent a significant advancement in the field of artificial intelligence and machine learning2.
Source(s)
1. Information Theoretical Principles of Software Development
2. Mark Burgin’s Legacy: The General Theory of Information, the Digital …
3. From Symbolic Computation to Super-Symbolic Computation
4. Mark Burgin s Legacy: The General Theory of Information,the Digital …
5. https://doi.org/10.3390/philosophies8060107
6. https://www.researchgate.net/publication/375622741_Mark_Burgin
How does a super-symbolic computing structure differ from other computing structures?
Bing
Super-symbolic computing structures differ from other computing structures in their ability to overlay and integrate knowledge from both symbolic and sub-symbolic computing structures12.
Here are the key differences:
Symbolic and Sub-symbolic Structures: Traditional computing structures can be broadly categorized into symbolic and sub-symbolic. Symbolic computing involves the manipulation of symbols or discrete units of information, such as in traditional programming languages. Sub-symbolic computing, on the other hand, involves continuous values or distributed representations, such as in neural networks12.
Super-symbolic Structure: The super-symbolic computing structure is an overlay over the current digital symbolic and sub-symbolic structures12. It integrates the knowledge derived from information gathered from symbolic and sub-symbolic computing structures2. This is very similar to the neocortex in the brain integrating the knowledge received from various cortical columns12.
Autopoietic and Cognitive Behaviors: Super-symbolic computing structures are designed to exhibit autopoietic and cognitive behaviors that biological systems also exhibit1. They provide an autopoietic and cognitive process overlay over the current symbolic and sub-symbolic computing structures without having to change them2.
Knowledge Management: Super-symbolic computing structures are analogous to biological systems using the mammalian neocortex to manage the knowledge obtained from various cortical columns2.
In summary, super-symbolic computing structures represent a significant advancement in the field of artificial intelligence and machine learning, providing a more holistic and integrated approach to information processing12.
Source(s)
1. Philosophies | Free Full-Text | Mark Burgin’s Legacy: The General …
2. Information Theoretical Principles of Software Development
3. Symbiotic Information Processing and Technological Progress – MDPI
4. https://doi.org/10.3390/philosophies8060107
How does a super-symbolic computing structure integrate knowledge from symbolic and sub-symbolic structures?
Bing
A super-symbolic computing structure integrates knowledge from symbolic and sub-symbolic structures by functioning as an overlay over these structures134. This is very similar to the neocortex in the brain integrating the knowledge obtained from various cortical columns1.
In the context of artificial intelligence and machine learning, this integration is often referred to as Neural-Symbolic Computing (NSC)25. NSC combines the robust learning capabilities of neural networks (sub-symbolic) with the reasoning and interpretability of symbolic representation (symbolic)25.
For example, the processing of images by operation with holistic shapes is an example of super-symbolic computing34. The advantage of the super-symbolic (transcendent) computation is its ability to operate big formal and informal systems of data and knowledge34.
In summary, a super-symbolic computing structure integrates knowledge from symbolic and sub-symbolic structures by functioning as an overlay over these structures, combining the strengths of both symbolic and sub-symbolic computing12345.
Source(s)
1. Philosophies | Free Full-Text | Mark Burgin’s Legacy: The General …
2. Symbiotic Information Processing and Technological Progress – MDPI
3. From Symbolic Computation to Super-Symbolic Computation
4. arXiv:1905.06088v1 [cs.AI] 15 May 2019
5. Bridging the gap: Neuro-Symbolic Computing for advanced AI … – Springer
6. https://doi.org/10.3390/philosophies8060107
How does the supersymbolic computing provide a common knowledge representation that is transparent integrating symbolic and sub-symbolic computing structures and how does the digital genome specify it using the knowledge network?
Bing
Super-symbolic computing provides a common knowledge representation by functioning as an overlay over symbolic and sub-symbolic computing structures910. It performs computations on the combined knowledge from both symbolic and sub-symbolic computations to derive higher-order autopoietic and cognitive behaviors910. This approach is analogous to how the neocortex in the brain integrates the knowledge obtained from various cortical columns56.
In the context of artificial intelligence and machine learning, this integration is often referred to as Neural-Symbolic Computing (NSC)1112. NSC combines the robust learning capabilities of neural networks (sub-symbolic) with the reasoning and interpretability of symbolic representation (symbolic)1112.
The digital genome specifies this common knowledge representation using a knowledge network124. The genome provides the operational knowledge to execute life processes used to build, self-organize, operate, and maintain the system using both inherited and learned knowledge2. The schema and associated operations derived from the General Theory of Information (GTI) are used to model a digital genome specifying the operational knowledge of algorithms executing the software life processes with specific purposes using replication and metabolism12. The result is a digital software system with a super-symbolic computing structure exhibiting autopoietic and cognitive behaviors that biological systems also exhibit12.
In summary, super-symbolic computing integrates knowledge from symbolic and sub-symbolic structures, and the digital genome specifies this common knowledge representation using a knowledge network. This results in a digital software system that exhibits autopoietic and cognitive behaviors similar to biological systems12910.
Source(s)
1. From Symbolic Computation to Super-Symbolic Computation – EasyChair
2. From Symbolic Computation to Super-Symbolic Computation
3. A New Class of Autopoietic and Cognitive Machines – MDPI
4. Infusing Autopoietic and Cognitive Behaviors into Digital Automata to …
5. Combining Sub-symbolic and Symbolic Methods for Explainability – Springer
6. On the integration of symbolic and sub-symbolic techniques for XAI: A …
7. General Theory of Information, Digital Genome, Large Language Models …
8. Mark Burgin’s Legacy: The General Theory of Information, the Digital …
9. General Theory of Information, Digital Genome, Large Language Models …
10. A New Class of Autopoietic and Cognitive Machines – MDPI
Is there any implementation to demonstrate the feasibility of defining a digital genome and create an autopoietic and cognitive software system?
Bing
Yes, there are ongoing efforts to implement digital genomes and create autopoietic and cognitive software systems. For instance, a medical-knowledge-based digital assistant has been implemented that uses medical knowledge derived from various sources including large language models4. The digital genome in this system specifies the knowledge to execute various tasks that implement functional requirements, non-functional requirements, and best practices to assure that the process objectives are achieved4.
Another example is the field of Digital Genome Engineering, which uses computational approaches to overcome challenges through modeling and optimization of genome engineering applications5. This approach has been used to develop models for predicting the overall activity of gene editing systems5.
Moreover, there are attempts to make modern computers mimic the resilience and intelligence of living beings using symbolic and sub-symbolic computing12. These efforts discuss the limitations of classical computer science for implementing autopoietic and cognitive behaviors in digital machines and propose a new architecture applying the General Theory of Information (GTI) to make digital automata mimic living organisms by exhibiting autopoiesis and cognitive behaviors12.
However, it’s important to note that these are still areas of active research and development, and the full realization of these concepts in practice is a work in progress1245.
Source(s)
1. General Theory of Information, Digital Genome, Large Language Models …
2. What is Digital Genome Engineering? – Transformational Bioinformatics
3. BDCC | Free Full-Text | Infusing Autopoietic and Cognitive Behaviors …
4. A New Class of Autopoietic and Cognitive Machines – MDPI
5. A New Class of Autopoietic and Cognitive Machines
6. Emerging Applications of Digital Genomics in Next-Generation … – BioSpace
7. https://doi.org/10.3390/cmsf2023008070
Theoretical and Foundational Problems (TFP) in Information Studies 