AI research paper advocates for enactive perception and embodied interaction

This paper proposes integrating enactive approaches into artificial intelligence, viewing perception as an active, embodied engagement with the environment rather than passive input processing. It highlights four key enactive concepts: experience, action-perception inseparability, autonomy, and embodiment, arguing that mainstream AI, including large language models, has overlooked these. While reinforcement learning shares some enactive principles through its focus on action and interaction, the paper suggests a broader incorporation of enactive ideas is needed for more robust AI. AI

IMPACT Proposes a new theoretical framework for AI, emphasizing embodied interaction and active perception over passive data processing.

RANK_REASON Academic paper proposing a new theoretical framework for AI. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Artificial Intelligence
• Enactive AI
• Large Language Models
• Reinforcement Learning

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