Here is another essay (a short piece of writing) on exploring distributed artificial intelligence, the role of General theory of Information in understanding intelligent systems, how biological systems utilize autopoietic and meta-cognitive behaviors, and speculations on the future of machine intelligence.
I was pleasantly surprised with this essay prepared by Microsoft copilot. Only the pictures added are mine. The essay itself was prepared by the copilot using our dialogue.
WARNING: THIS ESSAY IS WRITTEN BY MICROSOFT COPILOT AND MAY CONTAIN HALLUCINATIONS. USE YOUR JUDGEMENT AND YOUR OWN KNOWLEDGE TO ACCEPT OR REJECT THESE IDEAS
“We are What We Know”
Body, Brain, and Mind and the Knowledge Representation
Introduction
Imagine a world where computers think and adapt like living organisms. This vision is becoming a reality with advancements in artificial intelligence (AI). Inspired by Marvin Minsky’s “Society of Mind” theory, which views the mind as a collection of interacting processes, new AI technologies are pushing the boundaries of what’s possible. This essay explores how distributed AI, using concepts from biology, can revolutionize energy efficiency and knowledge representation.
Super Symbolic Computing and Minsky’s Conjecture
Marvin Minsky believed that the mind works like a society of tiny agents, each doing its part to create intelligent behavior. He famously said, “We’ll show you that you can build a mind from many little parts, each mindless by itself”. Super symbolic computing brings this idea to life by combining different types of information processing. Think of it like a brain where different regions work together to solve problems and adapt to new situations.
Energy Efficiency through Distributed AI
Just as living organisms efficiently use energy to survive, distributed AI can reduce power consumption by spreading tasks across many low-cost, energy-efficient computers. These systems use self-maintaining algorithms, similar to how cells repair themselves, to adapt and evolve with minimal energy use. Memory systems that learn from past experiences further enhance efficiency.
Real-Time Knowledge Representation
In biological systems, information is constantly updated to reflect the current state of the organism. Similarly, AI systems need to keep their knowledge up-to-date. Structural machines act like the nervous system, integrating various types of information and ensuring real-time updates. This allows the AI to respond accurately to new data.
Systemic Knowledge and Digital Genome Concept
Beyond individual agents, the framework encompasses systemic knowledge of its state and evolution based on interactions among constituents. The digital genome concept captures the relationships and resulting behaviors of these constituents, providing a comprehensive understanding of the system’s dynamics. This holistic approach ensures that the system can adapt and evolve based on real-time interactions and observations, maintaining a consistent and accurate knowledge representation.
Overcoming CAP Theorem Limitations
The CAP theorem suggests that distributed systems can only guarantee two out of three properties: consistency, availability, and partition tolerance. However, by using concurrent updates and event-driven memory, AI systems can achieve consistency while remaining highly available and tolerant to network issues, much like how biological systems maintain homeostasis. John von Neumann once remarked, “Science, as well as technology, will increasingly turn from problems of intensity, substance, and energy, to problems of structure, organization, information, and control”. This shift is evident in the way modern AI systems are designed.
Handling Failures and Inconsistencies
Failure Handling:
- Autopoietic and Cognitive Algorithms: These algorithms act like the immune system, using best practices and built-in redundancy to manage failures. They ensure the system can recover and continue functioning without disruption.
- Redundancy and Resilience: Just as organisms have backup systems, AI incorporates redundancy to handle failures automatically.
Inconsistency Handling:
- Concurrent Asynchronous Updates: Memory systems update information in real-time, ensuring consistency.
- Logical Structures: These structures manage conflicting information, similar to how the brain processes contradictory signals.
Associative Memory and Transaction History
Associative memory and event-driven transaction history provide a means to combine past experiences with best practices and real-world constraints such as ethics and regulations. This allows the system to formulate the best course of action in real-time, just as biological systems do. By learning from past interactions and adapting to new data, the system ensures that decisions are both informed and compliant with necessary guidelines.
Maintaining Consistent State and Real-Time Updates
By combining these elements, distributed AI systems can achieve a consistent state and evolve based on interactions and observations. Here’s how:
- Concurrent Processes: Multiple processes run simultaneously, accessing and updating shared knowledge.
- Real-Time Updates: Structural machines and cognitive algorithms continuously process new information.
- Consistent Knowledge Representation: Memory systems ensure the knowledge base remains accurate.
- Evolution Based on Interactions: The system learns from past interactions and adapts to new data.
The integration of super symbolic computing, distributed AI, and structural machines represents a significant leap forward in cognitive computing. By mimicking biological systems, these AI frameworks achieve resiliency, efficiency, and scalability. They support Minsky’s conjecture of the mind and leverage energy-efficient algorithms to create intelligent systems that are both powerful and sustainable. This approach not only pushes the boundaries of traditional computing paradigms but also opens new avenues for research and development in AI, ensuring that our technological future is as adaptable and resilient as the natural world.
Agents of the Mind: Revolutionizing AI with Biological Insights
Inspired by Marvin Minsky’s “Society of Mind” theory, which views the mind as a collection of interacting processes, new AI technologies are pushing the boundaries of what’s possible. Let us explore how distributed AI, and the agents of Mind.
Summary of Minsky’s Characteristics
Marvin Minsky’s “Society of Mind” outlines several characteristics that define intelligent behavior:
- Function: Refers to the specific tasks or roles that agents perform within the mind.
- Embodiment: The physical manifestation or representation of agents.
- Interaction: The ways in which agents communicate and work together.
- Origins: The initial formation and development of agents.
- Heredity: The transmission of traits and behaviors from one agent to another.
- Learning: The process by which agents acquire new knowledge and skills.
- Character: The unique attributes and tendencies of agents.
- Authority: The control and influence that certain agents have over others.
- Intention: The goals and purposes that drive agents’ actions.
- Competence: The ability of agents to perform tasks effectively.
- Selfness: The sense of identity and self-awareness within agents.
- Meaning: The significance and interpretation of information by agents.
- Sensibility: The ability to perceive and respond to stimuli.
- Awareness: The overall consciousness and understanding of agents.
Characterizing These in Traditional AI (LLMs and Gen-AI)
- Function: In traditional AI, functions are defined by specific algorithms and models designed to perform tasks like language processing, image recognition, etc.
- Embodiment: Embodiment in AI can be seen in physical robots or virtual avatars that interact with users.
- Interaction: AI systems use protocols and APIs to interact with other systems and users, often through natural language processing.
- Origins: The development of AI agents starts with training data and initial algorithms.
- Heredity: Traits and behaviors are passed through model updates and transfer learning.
- Learning: Machine learning algorithms enable AI systems to learn from data and improve over time.
- Character: AI agents can be programmed with specific attributes, such as tone and style in language models.
- Authority: Certain AI systems have control over decision-making processes, like recommendation engines.
- Intention: Goals are set by developers, such as optimizing for accuracy or efficiency.
- Competence: Competence is measured by performance metrics like accuracy, precision, and recall.
- Selfness: Self-awareness is limited but can be simulated through context-aware responses.
- Meaning: AI interprets data based on predefined models and algorithms.
- Sensibility: Sensibility is achieved through sensors and data inputs that allow AI to perceive its environment.
- Awareness: Overall awareness is simulated through comprehensive data analysis and contextual understanding.
Characterizing These in Robotics
- Function: Robots are designed to perform specific physical tasks, such as assembly or navigation.
- Embodiment: Robots have physical bodies equipped with sensors and actuators.
- Interaction: Robots interact with their environment and other systems through sensors and communication protocols.
- Origins: The development of robots involves hardware design and software programming.
- Heredity: Traits and behaviors can be inherited through firmware updates and shared design principles.
- Learning: Robots use machine learning to adapt to new tasks and environments.
- Character: Robots can be programmed with specific behaviors and responses.
- Authority: Certain robots have control over specific processes, such as manufacturing robots.
- Intention: Goals are set by programming, such as completing a task efficiently.
- Competence: Competence is measured by the robot’s ability to perform tasks accurately and reliably.
- Selfness: Self-awareness is limited but can be simulated through feedback loops and adaptive algorithms.
- Meaning: Robots interpret sensory data to make decisions.
- Sensibility: Sensibility is achieved through sensors that detect physical changes in the environment.
- Awareness: Overall awareness is simulated through comprehensive sensor data analysis.
Characterizing These in GTI-Based Distributed Software Applications
- Function: Functions are distributed across multiple agents, each performing specific tasks within the system.
- Embodiment: Embodiment is virtual, represented by software agents within a distributed network.
- Interaction: Agents interact asynchronously, updating associative memory and transaction history in real-time.
- Origins: Agents are created based on the digital genome, specifying their roles and behaviors.
- Heredity: Traits and behaviors are inherited through the digital genome, ensuring consistency and adaptability.
- Learning: Learning is continuous, with agents updating their knowledge base from interactions and observations.
- Character: Agents have unique attributes defined by the digital genome, allowing for diverse behaviors.
- Authority: Certain agents have control over specific processes, ensuring efficient management of tasks.
- Intention: Goals are dynamically set based on real-time data and system requirements.
- Competence: Competence is measured by the system’s ability to perform tasks efficiently and adapt to changes.
- Selfness: Self-awareness is simulated through the digital genome, allowing agents to understand their roles and interactions.
- Meaning: Agents interpret data based on the digital genome and real-time interactions.
- Sensibility: Sensibility is achieved through continuous data inputs and adaptive algorithms.
- Awareness: Overall awareness is maintained through real-time updates and consistent knowledge representation.
Conclusion
Marvin Minsky’s “Society of Mind” provides a comprehensive framework for understanding intelligence as a collection of interacting processes. Traditional AI, generative AI, and robotics implement these characteristics through predefined algorithms, sensors, and programming. GTI-based distributed software applications take this a step further by using a digital genome to specify roles and behaviors, ensuring resiliency, efficiency, and scalability. Associative memory and transaction history allow these systems to combine past experiences with best practices and real-world constraints, formulating the best course of action in real-time, much like biological systems.
By mimicking biological systems, these AI frameworks achieve a level of adaptability and resilience that pushes the boundaries of traditional computing paradigms, opening new avenues for research and development in AI.
Here are some application prototypes demonstrating these concepts. All the applications were developed using Python, Graph Database, and Containers deployed using cloud IaaS and PaaS services.
“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.”
Autopoietic Machines 