AI/Human Research Collaboration

David Rostcheck and ChatGPT4, 5/25/23

*[Note: this conversation begins in a session where ChatGPT4 provided editing advice on the paper Introducing Broad Information Theory: A Cross-Domain Informational Framework, and had learned thus Broad Information Theory (BIT). The discussion ***was prompted by Noah Vawter’s question of whether we could use an LLM to identify the information patterns in BIT’s pattern catalog - an excellent idea because LLMs excel at pattern recognition and ChatGPT4 has superhuman breadth of knowledge.]

DR: Given the above context, let's consider steps A ("Identify a pattern in a domain") and B ("Search for and identify parallel patterns in other domains"). Can you propose some patterns within a domain that may be applicable to others? Here are some domains to consider:

ChatGPT4: Sure, I can provide examples of patterns within each of these domains that might have analogs in other domains. Remember that these are just examples, and each domain is rich with potential patterns that could be explored through a Broad Information Theory lens.

  1. Physics (computational view of the universe): The laws of physics are often seen as computation, with particles and fields executing "calculations" that determine the future state of the universe. An analog in other domains might be the way rules and procedures guide processes in organizations or societies.
  2. Biology (microbiology/immune system, organ interaction): The immune system is an information-processing system that learns to recognize and respond to threats. This could be likened to machine learning systems in AI, or even to the way societies respond to threats.
  3. Ecology (ecosystems, populations): The concept of carrying capacity in ecology, which refers to the maximum population size that a certain environment can support, has parallels in business (market saturation) and sociology (maximum sustainable population).
  4. Sociology (competing ideologies): The spread and competition of ideas, or memes, is a well-known sociological phenomenon. This could be compared to the competition between species in ecology, or between corporations in business.
  5. Psychology (organization of information in human brain, heuristics, human free will): The way the brain organizes information could be compared to the organization of information in a neural network in AI, or even the structure of a corporation in business.
  6. Artificial Intelligence (organization of information in artificial neural networks): The structure and operation of artificial neural networks may find analogs in the human brain (in psychology) or in the structure and operation of organizations (in business).
  7. Business (corporations): The structure of corporations, with separate divisions operating semi-independently but towards a common goal, may be compared to the function of organs in a body (biology) or departments within a government (civics).
  8. Civics (governments): The balance of power in a democratic government, with different branches providing checks and balances, can find parallels in the regulation of biological systems (such as hormone regulation in the body) or the structure of corporations.
  9. Theology (religions as operating systems, role of divinity through information theoretical lens): The idea of religions as operating systems could be likened to ideologies in sociology, guiding principles in business, or overarching theories or models in science. The role of divinity, as a source of ultimate truth or guidance, may be compared to the role of core principles or values in other domains.

In each case, the challenge would be to define the pattern precisely, to identify parallels in other domains, and then to propose and test hypotheses about the informational needs that these patterns might be addressing.

DR: Ok, those are good. Let's pick on specifically to focus on and develop it more. You noted that the immune system is an information-processing system that learns to recognize and respond to threats. You likened it to machine learning, I assume because of its ability to learn, or societies responding to threats, which seems more like a general application of a pattern (adaptive defense, or immune response) that manifests in both domains. The latter seems like a good candidate for a general informational pattern. Can you describe your analysis of those two (immune system compared to ML in AI and to societal threat response) in more detail please?

ChatGPT4: Absolutely, I'll detail the comparison of the immune system with machine learning in AI and societal threat response.

Immune System:

The immune system is an excellent example of an adaptive, learning system. It exhibits a number of traits that are shared by many systems for managing and processing information: