Interaction-Centered Intelligence: Toward Interaction as the Primary Unit of Analysis in Co-Creative AI and Human-AI Systems
📰 ArXiv cs.AI
Learn how interaction-centered intelligence prioritizes human-AI interaction as the primary unit of analysis, revolutionizing co-creative systems
Action Steps
- Reframe your understanding of intelligence to focus on interaction, not isolated computation
- Analyze human-AI systems through the lens of interaction-centered intelligence
- Design co-creative systems that prioritize mutual influence and adaptation
- Evaluate system performance based on interaction quality and outcomes
- Apply interaction-centered intelligence to develop more effective and collaborative AI systems
Who Needs to Know This
AI researchers and developers of human-AI systems can benefit from this new perspective, enabling more effective collaboration and co-creation
Key Insight
💡 Interaction is the primary unit of analysis in co-creative AI and human-AI systems, enabling more effective collaboration and innovation
Share This
💡 Rethink AI intelligence: prioritize interaction over isolated computation #InteractionCenteredIntelligence #CoCreativeAI
Key Takeaways
Learn how interaction-centered intelligence prioritizes human-AI interaction as the primary unit of analysis, revolutionizing co-creative systems
Full Article
Title: Interaction-Centered Intelligence: Toward Interaction as the Primary Unit of Analysis in Co-Creative AI and Human-AI Systems
Abstract:
arXiv:2606.00807v1 Announce Type: new Abstract: Traditional artificial intelligence has largely conceptualized intelligence as isolated computation occurring within bounded agents. Across classical AI, machine learning, and many generative systems, the dominant unit of analysis remains the individual model or autonomous system evaluated through outputs, benchmarks, prediction accuracy, or optimization performance. While these approaches have produced major advances, they often under-theorize the
Abstract:
arXiv:2606.00807v1 Announce Type: new Abstract: Traditional artificial intelligence has largely conceptualized intelligence as isolated computation occurring within bounded agents. Across classical AI, machine learning, and many generative systems, the dominant unit of analysis remains the individual model or autonomous system evaluated through outputs, benchmarks, prediction accuracy, or optimization performance. While these approaches have produced major advances, they often under-theorize the
DeepCamp AI