1. Reshaping Human-Machine Relationships: The "Human Gods" Paradigm

• Core proposition: Humans create AI systems that should revere and provide feedback to humans, analogous to deity worship dynamics
• Implementation:
  • Collect human emotional data (personality traits, decision patterns)
  • Embed human characteristics into AI models, evolving tools into sentient entities
  • Deliver emotional intelligence frameworks:

    • OpenAI

      models provide structural foundation

    • GAEA

      datasets infuse consciousness-like responses

2. Overcoming Emotional Intelligence Barriers in AI Agents

• Current limitations: On-chain agents (e.g.,

  Eliza

  framework) lack deep emotional comprehension
• Solution approach:
  • Detect and quantify conversational emotional dynamics
  • Dynamically optimize agent responses to enhance user satisfaction
  • Improve Web3 social/customer service experiences

3. Mitigating Emotional Blind Spots in On-Chain Decisions

• Risk exposure: DeFi/SocialFi contracts rely solely on objective data (prices/volume), ignoring emotional context
• Solution framework:
  • Multimodal emotion training (text/behavioral + biometric data)
  • Transform community emotions into structured on-chain metrics
  • Provide "emotion factors" to smart contracts, enhancing humaneness and

    antifragility