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AI Is Hitting a Wall — Here’s How We Break Through
- Current AI lacks the ability to question itself and refine its reasoning before speaking, unlike human intelligence which is like a symphony of different components.
- AI models like GPT-4, Gemini, and Claude suffer from flaws such as hallucination, lack of self-correction, and inefficient scaling.
- Current AI functions more like a solo instrument rather than a dynamic system of competing forces like human intelligence.
- To enhance AI's thinking capability, multi-agent AI is proposed, with specialized agents like Generator, Critic, Self-Observer, Memory, and Ethics Agents.
- By introducing internal opposition, AI can improve error detection, reasoning depth, efficiency, and mimic human cognition.
- The proposed phased approach includes implementing a Critic + Generator system, Self-Observer Agent, and fully modular multi-agent AI with Memory and Ethics Agents.
- The approach is supported by research in AI, neuroscience, and cognitive science, showing the benefits of structured complexity and self-regulation in AI development.
- The future of AI advancement lies in structured complexity and refined reasoning, with multi-agent AI offering a new approach to developing more intelligent systems.
- The key question is who will be the first to pioneer multi-agent AI and redefine the concept of intelligence in technology.
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