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The G.O.D. Framework: A Balanced Approach to Building Autonomous AI Systemsby@bevijay
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The G.O.D. Framework: A Balanced Approach to Building Autonomous AI Systems

by Be VijayApril 6th, 2023
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Autonomous AI systems are intelligent computer programs that can autonomously perform tasks and make decisions without human intervention or guidance. Development of efficient autonomous AI systems presents challenges due to biases, ethical considerations, transparency, security, and control. We explore the theoretical G.O.D. (Generator, Operator, Destroyer) framework inspired by mythology as a way to build an [autonomous AI] system.
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Autonomous AI systems are intelligent computer programs that can autonomously perform tasks and make decisions without human intervention or guidance. The development of efficient autonomous AI systems presents challenges due to biases, ethical considerations, transparency, security, and control.


In this article, we explore the theoretical G.O.D. (Generator, Operator, Destroyer) framework inspired by mythology as a way to build an autonomous AI system that revolutionizes our lives through a balanced approach to problem-solving and decision-making.


The G.O.D. framework echoes the Hindu Trimurti, consisting of Brahma, Vishnu, and Shiva — the Generator, Operator, and Destroyer forces, respectively. In the G.O.D. framework, three AI models have unique roles and responsibilities: the Generator AI model creates tasks based on a goal, the Operator AI model executes tasks autonomously and efficiently, and the Destroyer AI model monitors progress and takes corrective actions.



The Workflow of the G.O.D. Framework in Autonomous AI Systems

  1. Define the goal: A human user defines the AI system’s goal.
  2. Generate tasks: The Generator creates clear and unambiguous tasks aligned with the system’s goal.
  3. Operator execute tasks: The Operator autonomously and efficiently executes tasks, potentially replicating itself to perform tasks in parallel.
  4. Destroyer tasks: The Destroyer monitors task progress and takes corrective actions to refine/ purge tasks and ensure the system works towards the desired outcome.
  5. Feedback communication and coordination: The Destroyer refines tasks and ensures the system works towards the desired outcome.

Teacher tasks:

Embodying the spirit of Lord Dattatreya, the Guru of all G.O.D.s in Hindu tradition, the Teacher model represents the importance of continuous learning and improvement. The Teacher model learns from experiences and adjusts the other three models’ roles accordingly. Additionally, it checks for patterns of unethical goals/ task creation and controls it.

Implementing the G.O.D. framework

Generator AI model:

The Generator AI model uses techniques such as natural language processing, machine learning, and deep learning to create specific tasks based on the human user’s defined goal. Leveraging recent developments in Large Language Models (LLMs) like GPT-4, the Generator model can be deployed as a serverless function on public cloud platforms, triggered when a goal message appears from the user or the Destroyer model requests task refinement/removal.

Operator AI model:

The Operator AI model executes tasks generated by the Generator AI model autonomously and efficiently, using techniques such as robotics, computer vision, and natural language generation. With access to vast internet data, the Operator model can also be deployed as a scalable serverless function, triggered by messages in the task queue. LLMs like GPT-4 can be utilized for tasks that involve text outputs or can be described in the text.

Destroyer AI model:

The Destroyer AI model monitors task progress and takes corrective actions to refine tasks and ensure the system works toward the desired outcome. It employs techniques such as reinforcement learning, anomaly detection, and decision-making algorithms for continuous improvement and refinement. Serverless functions and models like GPT-4 can be considered for deployment and processing to avoid building custom models.

Teacher AI model:

The Teacher model monitors the performance of the other three models (Generator, Operator, and Destroyer) and provides corrective actions to prevent repetitive loops and ensure ethical alignment with guidelines and goals. The Teacher model should be trained on a large amount of data covering a broad range of scenarios and use cases. Existing tools like OpenAI moderation endpoints can be useful for part of it, and can save on model training costs.

Example goal:

We will understand this with an example. We will use GPT models to deliver this example goal.






Benefits of the proposed approach

  1. Simplicity: The G.O.D. framework offers a straightforward and easy-to-implement approach for ethical AI, leveraging successful LLMs.


  2. Balanced roles: The framework balances the four models (Generator, Operator, Destroyer, and Teacher), enabling the AI system to continuously learn and improve without entering repetitive loops or unethical behavior.


  3. Ethical decision-making: The G.O.D. framework emphasizes ethical decision-making and goal-setting, helping to prevent harmful or unethical behaviors.


  4. Continuous learning: The presence of Lord Dattatreya and the Teacher model emphasize continuous learning and improvement, aiding the system’s adaptability to changing circumstances.


  5. Flexibility: The G.O.D. framework is designed for flexibility, with the Generator, Operator, and Destroyer models allowing for quick adjustments to goals and tasks as needed.


  6. Improved performance: By emphasizing goal-setting and task optimization, the G.O.D. framework may enhance the overall performance of the autonomous system, leading to better user outcomes.

Limitations

  1. Complexity: Implementing the G.O.D. framework with custom models requires expertise in AI, ethics, and other domains.


  2. Computational Requirements: The G.O.D. framework involves multiple AI models, potentially requiring significant computational resources, leading to high costs and slower processing times.


  3. Ethical Bias: Despite the G.O.D. framework’s aim to prevent unethical behavior, AI models may exhibit ethical bias due to data biases or subjective interpretations of ethical values.


  4. Limited Generalization: The G.O.D. framework may work well in specific domains but may not generalize well to other domains, limiting its broader applicability.

  5. Limited Flexibility: The G.O.D. framework may not be flexible enough to adapt to changing situations or unexpected events, potentially limiting its ability to make appropriate decisions in complex scenarios.

Conclusion

The G.O.D. framework provides a simple yet effective approach to implementing ethical AI systems. Incorporating the four models of Generator, Operator, Destroyer, and Teacher, the system is designed to balance between achieving goals and avoiding unethical behavior. The presence of the teacher model represents the importance of continuous learning and improvement in the system.


While there are limitations and challenges to implementing any autonomous AI system, the G.O.D. framework offers comparative benefits over similar systems and is a step towards building more responsible and ethical AI systems.

Disclaimer:

The G.O.D. framework uses Hindu mythology deities as a symbol of different roles involved in a balanced system. However, it is important to note that the framework is not intended to promote or hurt the sentiments of any specific religion or belief system. It is simply a framework proposed to address the various issues faced with the development and deployment of AI systems.

References

Hindu mythology: Various Puranas- Brahma Purana, Vishnu Purana, Shiva Purana, Avadhut Gita https://github.com/yoheinakajima/babyagi: This framework developed by @yoheinakajima also proposes creation, prioritisation, and execution agents with memory. European Commission. (2021). White Paper on Artificial Intelligence: a European Approach to excellence and Trust. Retrieved from https://ec.europa.eu/info/publications/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en.