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An AI enthusiast has proposed a series of innovative solutions to address the persistent issue of ‘hallucinations’ in Large Language Models (LLMs) and bring them closer to human-level reasoning. In a detailed Reddit post, the systems engineer outlines three key strategies for improvement.

Firstly, the author suggests incorporating ‘confidence monitoring,’ enabling AI systems to assess their own certainty levels and proactively admit when they lack sufficient information. This would allow the AI to retry its reasoning process with more rigorous checks. Secondly, the proposal includes ‘rumination,’ a systematic review process where AI cross-references past responses with external sources, identifies potential errors, and updates its knowledge base. Finally, the engineer advocates for ‘hierarchical memory systems,’ which would create dedicated short-term memory for consolidating general knowledge and long-term memory for maintaining persistent conversational context with users.

The poster claims to have collaborated with Claude AI to refine these concepts and is now actively seeking feedback from the broader AI community. The original Reddit discussion can be found at [https://old.reddit.com/r/artificial/comments/1n1060k/my_thoughts_on_ai_vs_human_intelligence_and_how/].