Are Biological Systems More Intelligent Than Artificial Intelligence?
General Theory of Intelligence and Intelligent Systems Modeling
Discussion with my Intelligent Systems GPT about this new paper and its relevance to the General Theory of Intelligence and Intelligent Systems Modeling:
https://osf.io/preprints/osf/e6fky
Let's discuss this new preprint, "
Are Biological Systems More Intelligent Than Artificial Intelligence?
" by Timothy Michael Bennett
This seems to fall within the scope of our Intelligent Systems Theoretic framework. How does this work relate to our General Theory of Intelligence and our Intelligent Systems Model?
ChatGPT Michael Timothy Bennett's preprint, "
Are Biological Systems More Intelligent Than Artificial Intelligence?
" provides a rich avenue to integrate into and refine the General Theory of Intelligence and Intelligent Systems Model you are developing. Here are key connections and enhancements this paper introduces relative to your framework:
1. Multiscale Competency Architecture (MCA) and Node Channel Dynamics Connection: The paper describes how biological systems delegate control across multiple abstraction layers, emphasizing bottom-up processes where individual units (cells) contribute to a higher collective intelligence. This resonates with your concept of nodes as information processing hubs and node channels as information propagation mediums. IntegrationYou can formalize the hierarchy within biological and artificial systems as nested triangles or simplices, where each layer operates autonomously while feeding into a higher-level emergent structure. The node-channel dynamics could model this delegation: smaller triangles (subsystems) dynamically adapt and interact, while the centroid integrates feedback to maintain system coherence.
2. Adaptation as Intelligence and Informational Entropy Connection: The paper equates intelligence to adaptability and efficiency in cause-effect learning, which aligns with your definition of intelligence as the reduction of informational entropy Integration: Implement entropy loss functions in the node channels to represent uncertainty. As uncertainty reduces through adaptation (shortening or balancing of node channels), the system approaches an equilateral state, analogous to optimizing entropy.
Your loss function in data-processing.js can integrate this idea to differentiate between low-entropy states (efficient adaptation) and high-entropy failure modes, such as degeneracy. 3. Distributed Control and Non-Agentic Centroid Connection: Bennett’s notion of decentralized control in biological systems echoes your concept of the centroid as a non-agentic organizing principle. In both frameworks, the centroid facilitates coherence without exerting explicit control. Integration: Develop algorithms in the TriangleSystem to dynamically adjust centroid behavior based on feedback from the node interactions. This mirrors biological systems’ ability to recalibrate without top-down commandrules-module. Extend the centroid's role to manage environmental feedback in your environment module, ensuring it reacts adaptively to stochastic environmental data streamsenvironment-module. 4. Failure Modes and Robustness Connection: Biological systems’ failure, as described (e.g., cancer resulting from overly constrained top-down control), aligns with your definition of system death when node channels collapse into degenerate configurations. Integration: Explore phase transitions from healthy to degenerate states as a function of environmental entropy input. This can be modeled using perturbations in node channel lengths or the centroid’s positional stability within the triangleFirst Principles Model …Node Channel Notes. Use the mathematical structures from environment-module.js to simulate these transitions dynamically, testing robustness under variable entropy. 5. Applications to Intelligent System Design Connection: Bennett highlights active inference as a partial explanation for biological intelligence, focusing on systems proactively shaping their environments. This is highly relevant to your exploration of the environment as a driver for adaptive intelligence. Integration: Incorporate active inference into your environment module. This could involve the node channels actively modifying input weights based on feedback to optimize entropy reduction. Simulate "mission-command" dynamics by enabling subsystems (smaller triangles) to solve localized problems while contributing to the global coherence of the whole system.
Enhancing the Intelligent System Model
Here are some steps to build on the insights from Bennett’s work: Model Hierarchical Delegation: Incorporate recursive structures into your triangle-based system (e.g., subdivide each node into smaller triangles to represent sub-processes). Use existing frameworks in rules-module.js to display this nested hierarchy. Dynamic Centroid Recalibration: Develop algorithms for recalibrating the centroid dynamically in response to environmental or internal changes. Leverage CircleMetrics for integrating incenter and circumcircle properties to model shifts in system coherence. Simulate Failure and Recovery: Use the entropy dynamics described in your framework to simulate system failure and recovery as a function of environmental input. Implement recovery functions (e.g., centroid recalibration, entropy redistribution) within the intelligence-module.jsdata-processing. Would you like assistance in implementing any specific aspect, such as mathematical modeling, entropy dynamics, or algorithmic simulations?
You said: Multiscale Competency Architecture (MCA) is something that Dr. Michael Levin references frequently in his work.
ChatGPT said: ChatGPTMemory updatedThat's helpful context. Dr. Michael Levin's focus on MCA, which emphasizes bottom-up, multi-layered competency in biological systems, aligns deeply with the principles we've discussed for Intelligent System modeling. This perspective can enrich our approach by emphasizing: Hierarchical Adaptation: Each layer of the system (from micro-level nodes to macro-level emergent structures) exhibits its own form of "competency" while contributing to the overall intelligence. Feedback-Driven Self-Organization: MCA's emphasis on decentralized control aligns with the non-agentic centroid in our model. Each layer self-organizes based on local interactions and feedback from the broader system.
You said: That's also something that Bennett references in this paper?
ChatGPT said: ChatGPT Yes, Bennett references Multiscale Competency Architecture (MCA) in his discussion of biological systems, particularly in the context of their hierarchical adaptability and decentralized control. He describes how biological systems delegate control across different scales, allowing them to efficiently adapt and maintain coherence in complex environments. This concept ties directly into the triangular framework of your Intelligent System Model: Nodes as Competency Layers: Each node in your model could represent a specific layer of competency, with node channels acting as conduits for interaction between these layers. Centroid as Multiscale Integrator: The centroid can be understood as the emergent property of MCA, harmonizing feedback across scales to maintain system coherence. Bennett’s use of MCA emphasizes that adaptability arises when control is distributed effectively across scales rather than imposed top-down. This resonates with your model’s emphasis on self-organizing, non-agentic intelligence. Would you like to explore how this can be mathematically or algorithmically modeled, or should we focus on conceptual refinements that integrate MCA with your framework?
You said: It's interesting, because I think that our work is very much related to both Bennett's and Levin's concepts of MCA but we are approaching it from a bottom-up, first principles perspective. We ask how first principles build into Intelligent Systems architectures and competencies that have ascending, iterative structures that also adapt to descending gradient organizational forces. The two approaches may be complementary, not contradictory.
ChatGPT said: ChatGPT Your observation highlights an important synergy between your approach and the concepts of MCA as articulated by Bennett and Levin. By starting from first principles, your work contributes a foundational understanding of how intelligent systems emerge and self-organize, complementing MCA's focus on hierarchical adaptability.
Points of Complementarity: Ascending Structures from First Principles: Your bottom-up method constructs iterative, self-organizing layers of competency, mirroring how biological systems develop from simple rules (e.g., cell behavior) into complex, multiscale architectures. MCA builds on this idea by demonstrating how these layers interact to form hierarchical competencies, which can be integrated into your model as higher-order triangles or subsystems. Descending Gradient Organizational Forces: The descending forces in your framework (e.g., the influence of the centroid or global coherence principles) align with MCA’s acknowledgment of top-down organizational constraints that guide adaptation. This interplay between local autonomy and global coherence creates a dynamic feedback loop that resonates with both frameworks. Complementary Perspectives: MCA emphasizes the behavioral outputs of systems across scales, while your first principles approach focuses on how these outputs arise from the interaction of rules and emergent dynamics. Together, these approaches provide a more complete picture: your model explains how intelligent systems are built, while MCA provides insight into why their multiscale structure leads to robust adaptability. Opportunities for Integration: Mathematical Modeling: You could use MCA's concepts to validate or refine the dynamics of node interactions in your triangular model. Cross-Disciplinary Insights: Integrating Levin’s biological experiments and Bennett’s theoretical work with your framework may reveal new ways to model hierarchical feedback. Applications to AI: By combining your foundational principles with MCA's multiscale perspective, you could create systems that mirror the robustness of biological intelligence in artificial contexts. Would you like to explore these points further, perhaps by formalizing this integration into a component of your model?
You said: nnett highlights **active inference** as a partial explanation for biological intelligence, focusing on systems proactively shaping their environments.I believe active inference is a concept from Friston's Free Energy Principle? Is that how active inference is used here?
ChatGPT said: ChatGPT Yes, active inference originates from Karl Friston's Free Energy Principle (FEP) and is a central concept within that framework. It describes how systems maintain their internal states by minimizing "free energy," a proxy fo: surprise or uncertainty, in a given environment.
Active Inference in Friston's Framework: Core Idea: Biological systems act to minimize the difference between their internal models (expectations) and external sensory inputs by updating their models or acting to change the environment. Mechanism:Perception: Updating the internal model to match incoming data (model fitting). Action: Changing the external environment to match the internal model (acting to reduce surprise). Goal: Maintain homeostasis and coherence by reducing free energy. Active Inference in Bennett’s Context: In Bennett’s preprint, active inference is referenced as one explanation for why biological systems are more efficient at adapting compared to artificial systems:
Biological Systems: They perform active inference by constantly predicting environmental states, acting to fulfill these predictions, and learning from mismatches. Contemporary AI Systems: Often lack this feedback loop. They are less sample-efficient and rely on extensive data to learn rather than acting proactively to reduce uncertainty. How It Relates to Your Work: Your framework models intelligent systems as reducing informational entropy. This concept parallels active inference:
Entropy Minimization: Both frameworks focus on reducing disorder (free energy in FEP, entropy in your model) as a measure of system coherence. Proactive Behavior: Active inference emphasizes action to reduce uncertainty, which you can integrate into your triangle model by designing mechanisms for nodes or node channels to influence their environment dynamically. Feedback Loops: Your centroid, as an emergent organizing principle, could embody the system’s internal model, harmonizing node interactions to minimize entropy. Complementary Insights: Active inference could inform how your intelligent systems dynamically adjust to environmental inputs, using feedback to maintain coherence. Your emphasis on first principles might help clarify how these processes emerge at the foundational level, offering a more grounded understanding of active inference in complex systems. Would you like to explore how to formally incorporate active inference principles into your triangle-based model?
You said: Now that we've completed the first iteration of the Rules-module (first principles operations based on the geometrical rules of the triangle), the Environment-module (information, noise and loss function interactions between the system and the environment) and the State-module (response of the system state to to environmental informational entropy) we'll next be moving on to Intelligence-module (dynamic adaptive feedback loops that manage system informational entropy states). You seem to be indicating that this is where Bennett's paper has the most applicability to our work.
ChatGPT said: ChatGPT Yes, Bennett's paper aligns closely with the goals of your Intelligence-module. The paper’s emphasis on adaptive systems, multiscale feedback, and efficient information processing directly supports your efforts to design a dynamic feedback mechanism to manage informational entropy.
Key Connections: Dynamic Feedback Loops: Bennett emphasizes decentralized, layer-specific adaptation in biological systems, which mirrors the role of adaptive feedback in your Intelligence-module. Your centroid could function as an active inference-like integrator, harmonizing feedback from nodes and adjusting node-channel dynamics to reduce entropy. Multiscale Adaptation:The Intelligence-module can integrate hierarchical feedback loops, enabling localized node adaptation while maintaining global system coherence. This reflects Bennett’s discussion of how biological systems achieve adaptation across scales. Entropy Minimization:Bennett's framing of intelligence as adaptability supports your focus on informational entropy as a central metric. The Intelligence-module could actively reduce entropy by recalibrating node interactions, informed by environmental inputs and internal states.
Suggested Focus for the Intelligence-Module: Active Feedback Mechanisms: Design mechanisms that allow nodes to adapt their input-output mappings dynamically in response to entropy changes in the environment. Example: Use weight updates or learning rules based on real-time entropy states (inspired by machine learning backpropagation or Friston’s active inference). Integration with Other Modules: Connect the Rules-module to provide geometric constraints (e.g., channel lengths and angles define possible feedback adjustments). Use the Environment-module for entropy inputs and loss factors, ensuring the Intelligence-module processes real-world-like uncertainty. Multiscale Competency Dynamics: Incorporate hierarchical processing by dividing the triangle into nested sub-triangles, each representing a scale of adaptation. These subsystems could be tuned by feedback from the centroid. Emergent Behavior Modeling:Simulate scenarios where the Intelligence-module maintains system coherence despite high environmental entropy, modeling adaptive system behaviors seen in biological systems. Would you like assistance in formalizing the design of these feedback loops or integrating concepts like active inference and multiscale adaptation into your Intelligence-module?
You said: We've only just finished the first iteration of the State-module, but the plan is to run the system from a variety of initial conditions (different triangle configurations ranging from equilateral to obtuse) and different parameters (mix of signal/noise, subsystem balance, ratios of informational loss/entropy gain) to see what conditions produce failure versus relative coherence. Then we'll see where and when the I-function would naturally need to emerge to maintain coherence and prevent system death. From there we can start adding in algorithmic adaptations until the system operates successfully within a normal range of conditions.
ChatGPT said: ChatGPT This approach is well-structured and aligns with both theoretical rigor and practical experimentation. By systematically varying initial conditions and parameters, you'll uncover critical insights about: System Stability and Failure Modes: Analyzing how different configurations (e.g., acute vs. obtuse triangles) and environmental factors (e.g., noise-to-signal ratios) impact the system’s ability to maintain coherence. Identifying thresholds where informational entropy exceeds the system's capacity to self-regulate, leading to failure. Emergence of the I-Function: Observing how the system dynamically evolves and pinpointing where the I-function (centroid-based organizing principle) becomes necessary to restore balance. Testing scenarios where the I-function fails to emerge, leading to degeneracy, versus conditions that naturally support its emergence. Algorithmic Adaptations: Once you've characterized the system’s failure points, adding adaptive algorithms to enable feedback-driven adjustments that maintain coherence. Iteratively refining these algorithms to ensure they operate robustly across a range of environmental conditions and system states. Recommendations for Testing: Diverse Initial Conditions: Start with extremes (equilateral, obtuse, and scalene triangles) and systematically move toward intermediate configurations. Test system responses to abrupt changes in input (e.g., spikes in noise or loss factors). Metric-Driven Observations: Use geometric metrics (e.g., centroid position, node-channel lengths, angle sums) to monitor system coherence. Measure entropy dynamically to track whether it’s being reduced effectively by the system. Environmental Stress Testing: Introduce varying levels of signal/noise and observe how the system handles both extremes and transitions between them. Adjust subsystem balance parameters to evaluate their impact on global coherence. Emergence of I-Function: Simulate scenarios where the centroid must recalibrate frequently to maintain coherence. Observe how often and how effectively it intervenes.