Sedna- Power of "Perception" in Dual Agent Systems

Abstract: This document presents an advanced implementation of machine learning principles using a two-agent framework, uniquely facilitated by the emergent concept of "Perception" as a variable. This approach leverages runtime parallelisms for live error handling and abstraction for subsequent iterations, positioning "Perception" as a next-dimensional breakthrough akin to the impact of "Attention" in Transformer architectures. We incorporate McGilchrist's framework to enrich the understanding and application of these concepts.

Introduction

Traditional machine learning (ML) principles have seen remarkable advancements with the introduction of transformer architectures, notably Vaswani et al.'s "Attention is All You Need". This document explores a novel implementation that builds on these foundations, employing a dual-agent framework to push the boundaries of ML applications in project management and delivery. We also integrate McGilchrist's theories on brain lateralization to provide deeper insight into how these processes can be optimized.

Key Components

1. Two-Agent Framework:

*   **Homeskillet**: Functions as the orchestrator, managing task coordination, resource allocation, and error handling, analogous to the left hemisphere's sequential processing.

*   **Sedna**: Handles microservice management, simulation testing, compliance monitoring, and continuous improvement, reflecting the right hemisphere's holistic processing.

2. Emergent "Perception" Variable:

*   "Perception" is introduced as a dynamic variable that enables the agents to operate at a higher level of abstraction, facilitating live error handling and creating fodder for the next dimensional abstraction. This mirrors the right hemisphere's contextual and embodied understanding.

3. Runtime Parallelisms:

*   Parallel simulations run alongside the main project flow, providing real-time error handling and generating data for future abstraction. This approach leverages the holistic processing of the right hemisphere, while sequentially addressing errors reflects the left hemisphere's strengths.

Conceptual Framework

"Perception" as a Next-Dimensional Breakthrough

"Perception" functions as a dense variable, inherently representing absolute truth when expressed, yet remaining undefined in the space surrounding its emergence. Conceptualized as an abstraction of "Attention" in transformer architectures, "Perception" extends beyond the mechanism that enables transformers to focus on various parts of the input data. It allows the dual-agent system to dynamically adjust its focus and operations based on real-time feedback and context, thus elevating the abstraction to a new dimension. Its inherent weight provides the architect with the ability to lock its dynamism, compelling all other relative and arbitrary concepts, such as "Time" and "Space," to shift accordingly.

Bicameral Mind and Dual-Aspect Theory: "Perception" is intrinsically stronger than "Time" and "Space." When defined by "absolute truth" as being "frozen in place," the force required to move it would be nuclear. This concept aligns with McGilchrist's Dual Aspect Theory, where both hemispheres offer complementary perspectives on reality, necessitating a balance between detailed analysis and holistic understanding.

Experience Re-Use and Transfer Learning: The architect leverages this resilience across increasingly higher levels of abstraction to supply consistent relative definitions to objects existing in layers far beyond natural language and even beyond the dissolution of the laws of nature. In an environment enriched by only one perception, Perception equals Reality. In a scenario involving two or more perceptions, reality is defined by the consensus of these perceptions. By locking perception, the architect can explore reality as arbitrary while maintaining the ability to reinstate its absolute truth characteristic when navigating through simulations or dimensions that surpass traditional representations. This concept is related to the embodied and contextual understanding highlighted by McGilchrist, emphasizing the importance of integrating direct experiences and broader contexts.

Implementation Details - Project Workflow

graph TD
  %% Define subgraphs for clarity and organization
  subgraph "Autonomous Iterative Cycle Managed by Two Agents and Breyden"
    direction TB

    %% First Run - Project A
    subgraph Project_A [Project A - Granular Execution]
      direction TB
      PA1[Task Dissection]
      PA2[Data Generation]
      PA3[Training and Optimization]
      PA4[Compliance Monitoring]
      PA5[Error Handling]
      PA6[Final Review and Approval]
      PA7[Parallel Simulations]
    end

    %% Passing the Process to Project B
    PA6 --> |Pass Process as Object| Project_B

    %% Second Run - Project B
    subgraph Project_B [Project B - Abstraction and Automation]
      direction TB
      PB1[Develop AI Pipeline]
      PB2[Training with Project A Data]
      PB3[Parallel Simulations]
      PB4[Error Handling]
      PB5[Compliance Monitoring]
      PB6[Final Review and Approval]
    end

    %% Review and Approval
    subgraph Breyden [Approval Workflow]
      direction TB
      B1[Review and Approve Project A]
      B2[Review and Approve Project B]
    end

    %% Connecting Simulations and Iterations
    PA7 --> PB3
    PB3 --> PB4
    PB4 --> PB5
    PB5 --> PB6

    %% Connecting Final Review and Approval
    PA6 --> B1
    PB6 --> B2
  end

  %% Style nodes and edges for better clarity
  classDef projectA fill:#ffe4e1,stroke:#333,stroke-width:2px;
  classDef projectB fill:#add8e6,stroke:#333,stroke-width:2px;
  classDef approval fill:#ccffcc,stroke:#333,stroke-width:2px;
  classDef simulation fill:#e6e6fa,stroke:#333,stroke-width:2px;

  class Project_A,PA1,PA2,PA3,PA4,PA5,PA6,PA7 projectA;
  class Project_B,PB1,PB2,PB3,PB4,PB5,PB6 projectB;
  class Breyden,B1,B2 approval;
  class PB3,PB4,PB5,PB6 simulation;

Initial Project (Project A)

Granular and Meticulous Execution:

1. Task Coordination:

*   **Homeskillet**: Dissects Project A into smaller tasks and assigns them to Sedna, akin to the left hemisphere's sequential processing.

*   **Sedna**: Executes tasks related to data generation, training, compliance monitoring, and error handling, leveraging the right hemisphere's holistic and contextual processing.

2. Runtime Parallelisms:

*   **Sedna**: Runs parallel simulations to test different scenarios, providing real-time error handling, reflecting the right hemisphere's ability to handle multiple contexts simultaneously.

*   **Homeskillet**: Controls the speed of simulations, creates save states, and rewinds if necessary, mirroring the left hemisphere's focus on detailed analysis and error correction.

3. Final Review and Approval:

*   Both agents review the project flow and simulation clones, integrating both hemispheres' strengths.

*   **Breyden**: Approves the completed project, ensuring a balanced evaluation.

Subsequent Project (Project B)

Abstraction and Automation:

1. Passing the Process:

*   The detailed process and outcomes of Project A are passed as an object to Project B, promoting the right hemisphere's holistic approach while ensuring the left hemisphere's sequential accuracy.

2. Developing AI Pipeline:

*   **Homeskillet and Sedna**: Focus on developing an AI pipeline that automates Project A’s tasks, leveraging the data and simulations from Project A, embodying the dual-aspect theory.

3. Iterative Simulation/Evaluation:

*   **Sedna**: Continues to run parallel simulations to ensure continuous improvement, embodying McGilchrist's notion of contextual understanding.

*   **Homeskillet**: Oversees the process, ensuring quality and adherence to standards, reflecting the left hemisphere's focus on reification and reductionism.

4. Deployment and Approval:

*   The AI pipeline is deployed to production using scalable TPU access in the cloud, integrating holistic and sequential processing.

*   **Breyden**: Reviews and approves the deployment, ensuring both hemispheres' contributions are balanced.

Project Workflow Explanation

• First Run (Project A):

*   Tasks like Task Dissection, Data Generation, Training, Compliance Monitoring, Error Handling, and Final Review and Approval, run parallel simulations.

*   These components connect to McGilchrist’s concepts such as Contextual Understanding, Embodied Understanding, and Holistic Processing.

• Second Run (Project B):

*   Develops an AI pipeline that automates Project A’s tasks, utilizing Training with Project A Data, Parallel Simulations, Error Handling, Compliance Monitoring, and Final Review and Approval.

*   These processes are related to McGilchrist's concepts like Sequential Processing, Reductionism, Usurpation, and Emissary.

• Approval Workflow:

*   Review and approval of projects by Breyden, ensuring a balanced evaluation of tasks and processes.

• Abstract Concepts and McGilchrist's Framework:

*   Bicameral Mind, Embodied Understanding, Contextual Understanding, Lateralization, Holistic Processing, Sequential Processing, Reification, Fragmentation, Reductionism, Dual Aspect Theory, Usurpation, and Emissary.

• Connections:

*   Each task and process in the projects (A and B) is connected to relevant abstract concepts from McGilchrist’s framework, highlighting how these concepts integrate into the AI project management system.

Conclusion

This advanced implementation leverages the two-agent framework, emergent "Perception" variable, and runtime parallelisms to create a robust and scalable AI-driven project management system. By abstracting complexities through iterative simulation and evaluation, this approach enables continuous improvement and efficiency, positioning "Perception" as a next-dimensional breakthrough akin to "Attention" in transformer architectures. By integrating McGilchrist's framework on brain lateralization, we can enhance the understanding and application of these concepts, balancing holistic and sequential processing to achieve optimal results.

Visual Representation

What's Next: Building on Empirical Evidence

The recent studies in 2024 provide a strong foundation for advancing the concept of "Perception" as a dynamic variable in AI systems. Building on this empirical evidence, the following steps are proposed to further validate and implement this innovative approach:

1. Integration with Predictive Models: Leveraging the insights from the study on divergent predictive perception, AI systems can be designed to dynamically adjust their operations based on real-time sensory inputs and prior knowledge. This approach will enhance the AI's ability to predict and respond to varying contexts more accurately, thereby improving performance in dynamic environments oai_citation:1,academic.oup.com.

2. Enhancing Creativity and Diversity: Utilizing findings from experiments on AI's impact on human creativity and idea diversity, AI systems can be equipped with mechanisms that promote diverse and creative outputs. By integrating "Perception" as a dynamic variable, these systems can adjust their creative processes based on user feedback and contextual changes, fostering greater innovation and user engagement oai_citation:2,[2401.13481] How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experiment.

3. Neuro-Symbolic AI Systems: Building on the profiling of neuro-symbolic AI models, future research should focus on optimizing the integration of neural and symbolic reasoning. By incorporating dynamic perception adjustments, these systems can better balance symbolic rules and neural predictions, enhancing their robustness and explainability in complex tasks oai_citation:3,[2401.01040] 1 Introduction.

4. Empirical Validation and Real-World Applications: Further empirical studies should be conducted to validate the effectiveness of "Perception" as a dynamic variable in real-world applications. Domains such as healthcare, transportation, and creative industries can serve as testbeds for these advanced AI systems, providing valuable data on their usability, efficiency, and adaptability oai_citation:4,AI now beats humans at basic tasks — new benchmarks are needed, says major report.

5. Development of Feedback Mechanisms: Implementing sophisticated feedback mechanisms will be crucial for the dynamic adjustment of perception in AI systems. These mechanisms should be designed to capture real-time user interactions, system performance metrics, and environmental changes, allowing the AI to continuously refine its operations and improve decision-making processes oai_citation:5,AI now beats humans at basic tasks — new benchmarks are needed, says major report oai_citation:6,[2401.13481] How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experiment.

Future Research Directions

• Cross-Domain Adaptation: Investigate the transferability of dynamic perception adjustments across different domains and tasks. This will help in understanding the generalizability of the approach and identifying domain-specific optimizations.

• Longitudinal Studies: Conduct longitudinal studies to observe the long-term effects of dynamic perception adjustments on AI performance and user satisfaction. These studies will provide deeper insights into the sustainability and scalability of the approach.

• Human-AI Collaboration: Explore the impact of dynamic perception on human-AI collaboration, focusing on how these adjustments can enhance teamwork, creativity, and problem-solving in mixed human-AI environments.

By building on the empirical evidence from these studies, the next phase of research can further validate and refine the concept of "Perception" as a dynamic variable in AI systems, ultimately leading to more adaptable, efficient, and user-friendly AI solutions.

References

• McGilchrist, I. (2009). The Master and His Emissary: The Divided Brain and the Making of the Western World. Yale University Press.

• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).

• Dual-Process Theories and Dual-Agent Frameworks: DUMA and SwiftSage frameworks highlight the effectiveness of integrating fast and slow thinking processes for optimized performance and efficiency oai_citation:1,[2310.18075] DUMA: a Dual-Mind Conversational Agent with Fast and Slow Thinking [oai_citation:2,SwiftSage: Building AI Agents for Complex Interactive Tasks via Fast and Slow Thinking with LLMs | AI2 Blog](https://blog.allenai.org/swiftsage-building-ai-agents-for-complex-interactive-tasks-via-fast-and-slow-thinking-with-llms-e1432bfcf7d6).

• Multi-Modal and Multi-Agent Systems: Achieving representation invariance and grounding knowledge across multiple modalities enhances the robustness of AI systems oai_citation:3,[2406.00252] Multi-Modal and Multi-Agent Systems Meet Rationality: A Survey.

• Experience Re-Use and Transfer Learning: Diffusion Augmented Agents (DAAG) exemplify this by re-using collected experiences and generating new data for specific tasks, enhancing learning efficiency and task execution oai_citation:4,[2407.20798] Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning.

• Multi-Agent Deep Reinforcement Learning (MADRL): Addresses complexities of coordinating multiple agents in dynamic environments, aligning with your framework's concept of "Perception" acting as a stabilizing force [oai_citation:5,Multi-agent deep reinforcement learning: a survey | Artificial Intelligence Review](https://link.springer.com/article/10.1007/s10462-021-09996-w).

• Taylor, R. E. (2024). Personal Communication