Thinking fast and slow in AI: The role of metacognition (2021)

AI agents can now choose between fast, experience-based reactions and slower, deliberate reasoning, impacting system responsiveness and problem-solving depth.

In a 2021 paper, researchers proposed a novel multi-agent AI architecture inspired by Daniel Kahneman's "Thinking Fast and Slow" theory. This architecture aims to imbue AI systems with broader human-like intelligence beyond current narrow AI capabilities. The proposed model differentiates between "fast" (System 1) agents, which rely on past experience for quick responses, and "slow" (System 2) agents, which are activated for deliberate reasoning and optimal solution-finding. Both agent types are supported by a world model containing domain knowledge and a self-model detailing past actions and solver skills. The research suggests that studying human cognitive mechanisms can guide the development of more capable AI, moving beyond AI's current reliance on massive datasets and computational power for specific tasks like image recognition or natural language processing.

This architectural approach signals a move towards more adaptable and potentially generalizable AI by explicitly modeling different modes of cognitive processing. The inclusion of a 'self-model' that tracks solver skills and past actions suggests a pathway towards AI that can learn more efficiently and strategically, not just improve performance on specific tasks. This is a significant step beyond current systems that largely operate without an internal model of their own capabilities or limitations.

For developers and researchers, this framework implies a need to design systems that can dynamically allocate computational resources between rapid, intuitive responses and more intensive, analytical processing. It also points to the importance of robust world and self-modeling for future AI development, potentially leading to more robust and explainable AI systems that can better handle novel or complex problems.