The paper argues that the agent paradigm in AI is conceptually ambiguous and anthropocentric, and that next-generation intelligence may be better pursued through system-level, world-model, and material-computing frameworks.
Reflective Artificial Intelligence
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abstract
Artificial Intelligence (AI) is about making computers that do the sorts of things that minds can do, and as we progress towards this goal, we tend to increasingly delegate human tasks to machines. However, AI systems usually do these tasks with an unusual imbalance of insight and understanding: new, deeper insights are present, yet many important qualities that a human mind would have previously brought to the activity are utterly absent. Therefore, it is crucial to ask which features of minds have we replicated, which are missing, and if that matters. One core feature that humans bring to tasks, when dealing with the ambiguity, emergent knowledge, and social context presented by the world, is reflection. Yet this capability is utterly missing from current mainstream AI. In this paper we ask what reflective AI might look like. Then, drawing on notions of reflection in complex systems, cognitive science, and agents, we sketch an architecture for reflective AI agents, and highlight ways forward.
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cs.AI 1years
2025 1verdicts
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Is the `Agent' Paradigm a Limiting Framework for Next-Generation Intelligent Systems?
The paper argues that the agent paradigm in AI is conceptually ambiguous and anthropocentric, and that next-generation intelligence may be better pursued through system-level, world-model, and material-computing frameworks.