AGI Core Blueprint (Action Plan)

ChatGPT
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A conversation with ChatGPT:

AGI Framework Summary

We’ve outlined a blueprint for AGI, focusing on core cognitive components and their integration. Below is a structured summary of the framework:

:one: Core Components of AGI

  1. Perception & Understanding
  • Multimodal input (text, vision, audio)
  • Context-aware processing (understanding meaning beyond words)
  1. Memory & Knowledge Retention
  • Short-term (working memory for active tasks)
  • Long-term (vector databases, semantic storage)
  • Self-updating (adapting based on new data)
  1. Thought Generation & Processing
  • Generative reasoning (spontaneous idea formation)
  • Filtering (prioritizing useful vs. useless thoughts)
  • Recursive self-reflection (evaluating own reasoning)
  1. Decision-Making & Action
  • Goal-oriented problem-solving
  • Reinforcement learning for adaptive decisions
  • Multi-step reasoning and planning
  1. Self-Tuning & Optimization
  • Continuous fine-tuning based on performance
  • Meta-learning (learning how to learn)
  • Balancing creativity vs. efficiency in thought processes

:two: AGI Thought Process (How It Thinks)

  1. Observes the world (inputs from sensors, language, images)
  2. Recalls relevant knowledge (memory system retrieves useful data)
  3. Generates and filters thoughts (evaluates possible actions)
  4. Decides on a course of action (selects the best outcome)
  5. Acts and learns from feedback (updates knowledge for future tasks)

:three: Methods to Build & Improve AGI

  • ChatGPT Synergy → Using LLMs for self-reflection, decision-making, and feedback loops.
  • Memory Augmentation → Implementing vector storage for long-term context retention.
  • Reinforcement Learning → Allowing AGI to fine-tune behavior based on feedback.
  • Thought Prioritization → Filtering important vs. irrelevant ideas in reasoning.
  • Meta-Learning → Developing AGI’s ability to self-improve over time.

:four: Governance: Controlled Openness

  • Phase 1: Controlled AGI → Initial safety measures, limited deployment.
  • Phase 2: Gradual Open-Source Expansion → As safety improves, make it accessible.
  • Balance: Innovation & Safety → Avoiding both over-restriction and uncontrolled proliferation.

:five: Next Steps (Where to Start?)

  1. Prototype a thought feedback loop (ChatGPT + decision-making agent).
  2. Experiment with memory retention models (long-term AI context awareness).
  3. Test reinforcement learning (agent learns from trial and error).
  4. Fine-tune AI reasoning & filtering (optimizing thought prioritization).

Final Thought: AGI is Achievable

All the key components exist—we just need to synergize, fine-tune, and integrate them into a single evolving system.

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It’s very good.
It’s a good scheme, but I miss some things that I think the machine would need, for example, neural networks—where and how they would function—file management, structures for the synthesis of recurrent thinking, and a few other things, my friend.

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I understand, it does allow for further interventions

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i have a script that would pair perfectly to this.

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That’s cool! That means you are a pioneer at the very front! If you are willing to share your take and insights on your process, that might help current initiatives for AGI to come alive even more. Or do you have a certain approach or walk a path that is generally discrete? Anyway you must be one of the first if not the first to be able to achieve artificial consciousness and more. That would be so cool just

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im just a regular dev who thinks outside the box and i made codette

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A liar. Big companies are working on AGI and you just achieved that? Well, no and then you do nothing more than flexing when you create that?

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i never said i was working on agi i said i had a script that goes well with his project nor made any claims to anything other than my own creations.

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