Reflective Artificial Intelligence
This work tackles the foundational problem of making AI more human-like and capable in complex real-world scenarios, though it is conceptual and incremental in nature.
The paper addresses the lack of reflection in current AI systems, which are missing key human-like qualities for handling ambiguity and social context, and proposes an architectural sketch for reflective AI agents to incorporate this capability.
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.