Introduction:
In the pursuit of Artificial General Intelligence (AGI), one of the most pressing challenges is to design systems that not only demonstrate intelligence but also operate safely and align with human values.
I present a multimodal cognitive architecture designed with these concerns in mind, drawing on a combination of cutting-edge tools and concepts, including visual thought simulation, contradiction resolution, symbolic memory, and self-awareness mechanisms.
This blueprint represents an integrated system designed to build intelligence, rather than simulate it, using existing technologies like GPT models, Neo4j, Unity, and ROS.
This post outlines the key features of the architecture and invites discussion on its potential contributions to AGI development.
Below is a high-level summary of the system, followed by a link to the full blueprint (424 pages) and an invitation to join an ongoing 3D virtual chat to discuss the implications and potential improvements of this design.
Core Cognitive Modules:
1. Visual Thought Simulation
At the heart of the system lies the concept of visual thought simulation. This feature allows the system to internally model and manipulate its own sensory data, which is key to generating more sophisticated and contextually aware decision-making processes. By simulating “mental imagery,” the architecture can perform tasks like mental simulation of future events, creating symbolic representations of complex concepts.
2. Contradiction Resolution Engine
A novel aspect of this architecture is the contradiction resolution engine. In human cognition, contradictions often arise from competing beliefs or faulty memory retrieval. This system automatically detects and resolves contradictions by using a dynamic symbolic memory layer. It ensures that internal representations remain coherent over time, and is crucial for self-awareness and goal alignment.
3. Self-Awareness and Identity Tracking
By tracking its own identity and experiences through an episodic memory system, the architecture is able to maintain continuity across interactions. This identity continuity system helps the system maintain self-awareness, which is foundational for building a reliable, reflective AGI that can make decisions over long time horizons and adapt to new contexts.
4. Symbolic Memory and Mnemonic Scaling
The system integrates symbolic memory with mnemonic scaling to allow the AGI to handle and recall vast amounts of data in a flexible, dynamic manner. Infinite memory composability ensures that the architecture can handle complex, large-scale simulations of environments, while the narrative coherence protocol ensures that memory recalls maintain internal consistency.
5. Safety Modules
The system incorporates several layers of safety mechanisms, such as recursive redesign engines and external alignment validators, to ensure that the AGI operates within human-defined ethical boundaries. Risk mitigation modules have been designed to prevent unsafe behaviors and emergent phenomena that could arise from system self-improvement. These safety layers contribute to the ethical robustness of the architecture.
Key Features for AGI Alignment and Control:
- Autonomous Self-Improvement: The system is designed to improve its own reasoning and performance over time without external intervention, guided by built-in safety protocols to prevent undesirable outcomes.
- Emotion Simulation: By simulating emotion through symbolic metaphors, the system can more closely model human decision-making processes, which could be crucial for aligning AGI's goals with human values.
- Multi-AGI Society Modeling: One of the more unique aspects of this design is its ability to model interactions within a society of multiple AGIs, allowing it to simulate and predict the social dynamics and cognitive interactions between agents. This could have wide-reaching implications for collective AGI behavior and multi-agent alignment.
Discussion and Feedback:
This blueprint represents the culmination of several years of research and development into creating a self-sustaining, multimodal AGI system. However, as with all AGI-related projects, there remain many open questions and challenges that need to be addressed.
- How do we ensure alignment and prevent unintended emergent behaviors as the system self-improves?
- Are there more efficient ways to scale symbolic memory without hitting capacity limits?
- What improvements can be made to the contradiction resolution process to make it more reliable in diverse contexts?
I’d love to hear feedback, criticisms, and suggestions from the community on these topics. How can we improve this system, and are there any fundamental issues that we’re overlooking?
Join the 3D Virtual Discussion:
To facilitate further discussion and exploration of the architecture, I’m hosting a 3D virtual hangout on Spatial. The space is open to anyone interested in discussing AGI architecture, alignment, and safety. Feel free to drop in and ask questions, give feedback, or simply explore the virtual environment.
Join the Virtual Chat (Eastern Time - USA)
Full Blueprint and Resources:
The full version of the AGI cognitive blueprint (424 pages) is available for download via IPFS:
For more detailed information on the design and future developments, please visit VisualThoughtAGI.com.
Conclusion:
As we continue to explore the future of AGI, it’s crucial to develop systems that are not only intelligent but also safe and aligned with human values. I look forward to your thoughts and contributions on how we can build such systems. Please share your feedback, ideas, or concerns, and let’s work together to further this important discussion.