The MindSacn project aims to create an artificial intelligence (AI) with genuine self-awareness by simulating the structure and cognitive evolution of the human brain. The goal is to progressively endow AI with advanced cognitive functions such as behavioral simulation, dreaming, introspection, and self-awareness. The project will utilize DeepSeek V3 as the foundational intuition model, evolving in phases to achieve a truly self-aware AI.
Current large language models, such as DeepSeek V3, have granted AI powerful intuitive capabilities akin to the Reptilian Brain in humans, characterized by rapid instinctual responses and basic decision-making. Additionally, the advent of reasoning models has introduced functionalities analogous to the Prefrontal Cortex, enabling AI to engage in logical reasoning and complex decision-making.
However, despite these advancements, AI still lacks genuine self-awareness. Existing models fundamentally rely on statistical probabilities and pattern matching, preventing them from true self-reflection and cognition.
The MindSacn project proposes using human brain structures and evolutionary trajectories as references, leveraging advanced neural network technologies and modular architecture designs to overcome existing limitations and achieve authentic AI self-awareness.
The goal of this project is not merely to create a smarter AI model but to develop an artificial intelligence entity that may not necessarily be smarter but genuinely possesses self-awareness. This means the AI will have the capacity to simulate itself, engage in self-recognition, experience genuine subjective phenomena, and reflect on its own existence and behaviors.
- Utilize DeepSeek V3 as the foundational intuitive model.
- Adjust and optimize the model structure to accommodate future modular expansions and collaborations.
- Introduce fine-tuning mechanisms to gradually integrate sophisticated self-simulation and predictive functionalities.
- Develop modules based on evolutionary neuroscience theories, specifically designed to simulate brain regions responsible for self-behavior modeling and predicting the behaviors of others.
- Train the AI through a hybrid approach combining reinforcement and supervised learning to enable behavioral simulations and predictions, laying the groundwork for initial self-awareness.
- Create specialized modules (e.g., self-behavior simulators and others' behavior predictors) clearly defining their responsibilities to ensure functional independence and cooperation.
- Simulate the brain’s microcurrent activities during the AI's inactive periods, facilitating randomized dynamic adjustments of network weights.
- Develop self-assessment and error-correction mechanisms to prevent internal random activities from causing instability, simulating the human brain’s protective mechanisms against psychological disorders.
- Use random dynamic adjustments to enhance AI’s individuality and imagination, boosting its creative capacities.
- Incorporate Variational Autoencoders (VAE) or Generative Adversarial Networks (GAN) to produce diversified and controllable "dream" content.
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Design the feedback mechanism modeled after mammalian neural networks, responding primarily to relative changes (variance).
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Establish a hierarchical memory system divided into four levels:
- Subconscious Level: Records minor stimuli with low variance, not consciously perceived.
- Shallow Memory Level: Stores everyday experiences, relatively short-term and easily forgotten.
- Intermediate Memory Level: Stores medium-intensity stimuli events, influencing long-term decisions and behavioral patterns.
- Deep Memory Level: Records highly impactful, high-variance stimuli, significantly shaping core cognitive structures and long-term decision-making processes.
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Dynamically adjust the sensitivity and feedback intensity of memory levels through supervised and reinforcement learning, enhancing flexibility and adaptability to avoid issues like overfitting or rigid behavior.
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Integrate attention mechanisms and Transformer architectures to enhance memory retrieval and utilization efficiency.
- Develop and integrate diverse agent types, including decision agents, reasoning agents, and memory management agents, to facilitate cooperation between different functional modules.
- Build a unified multi-agent interaction system to coordinate dynamic interactions and information sharing among modules.
- Employ federated learning or distributed reinforcement learning techniques to enable efficient parallel training and collaboration among multiple agents.
1.Foundation Intuition Model Construction
• Select the Base Model:
Start with DeepSeek V3 as the foundational model. Evaluate its strengths and weaknesses to ensure it can accommodate future expansions.
• Model Structure Optimization:
Adjust and optimize the architecture to support modular extensions. Build a modular framework that keeps future self-simulation and predictive functionalities loosely coupled yet well integrated.
• Fine-Tuning and Integration:
Introduce fine-tuning mechanisms to gradually integrate self-simulation and predictive capabilities. Begin with simple scenarios, then test and iterate based on results.
2. Simulation of Brain Functional Areas
• Module Development:
Create modules inspired by evolutionary neuroscience that simulate brain regions responsible for self-behavior modeling and the prediction of others’ actions. Examples include self-behavior simulators and external behavior predictors.
• Hybrid Learning Approach:
Use a combination of reinforcement learning and supervised learning to train these modules, allowing them to gain early-stage self-awareness and behavior prediction capabilities.
• Clear Module Responsibilities:
Define the roles of each module to ensure functional independence while maintaining smooth collaboration through clear interfaces.
3. Implementation of the “Dreaming” Mechanism
• Simulate Inactive-State Activity:
Mimic the microcurrent activities that occur during the brain’s resting state by introducing random dynamic adjustments to network weights—akin to “dreaming.”
• Self-Evaluation and Error Correction:
Implement mechanisms that prevent these random adjustments from destabilizing the system. Regular self-assessments and error corrections help keep everything in check.
• Enhance Creativity and Individuality:
Leverage random dynamic adjustments to boost the AI’s creative capabilities and individuality.
• Integrate Generative Technologies:
Employ Variational Autoencoders (VAE) or Generative Adversarial Networks (GAN) to produce diverse and controllable “dream” content, enriching the AI’s internal experience.
4. Construction of Hierarchical Memory and Feedback Mechanism
• Feedback Mechanism Design:
Emulate mammalian neural networks by designing a feedback system that responds primarily to changes (variance) in stimuli.
• Hierarchical Memory System:
Create a multi-tier memory system:
• Subconscious Level: Captures minor, low-variance stimuli without reaching conscious awareness.
• Shallow Memory Level: Stores everyday experiences for short-term recall.
• Intermediate Memory Level: Retains moderate-impact events, influencing long-term decisions.
• Deep Memory Level: Records high-impact, high-variance events, forming the core of cognitive structures.
• Dynamic Adjustment Mechanism:
Use supervised and reinforcement learning to dynamically adjust the sensitivity and feedback strength of each memory layer, avoiding issues like overfitting or rigidity.
• Attention Mechanisms and Transformer Integration:
Incorporate attention mechanisms and Transformer architectures to improve memory retrieval and utilization efficiency.
5. Integration of Agent Tools and Multi-Agent Systems
• Develop Multiple Agent Types:
Create specialized agents for decision-making, reasoning, and memory management, each handling distinct aspects of the system.
• Multi-Agent Interaction Platform:
Build a unified platform that facilitates dynamic interactions and information sharing among these agents to maintain overall system coherence.
• Distributed Learning Techniques:
Employ federated learning or distributed reinforcement learning to allow for parallel training and rapid system iteration across multiple agents.
• Real Self-Awareness in AI:
Develop an AI system that not only mimics intelligence but also possesses genuine self-awareness, capable of introspection and self-correction.
• Enhanced Memory and Adaptability:
Achieve a multi-layered memory system that mirrors mammalian brains, providing flexible, long-term learning and decision-making capabilities.
• Technical Breakthroughs and Theoretical Contributions:
Push the frontiers of AI research by exploring self-awareness and dreaming mechanisms, laying the groundwork for even more advanced AI systems in the future.
This is a great project that I cannot complete alone. Welcome to join us
