VentureBeat October 24, 2024
AI agents must solve a host of tasks that require different speeds and levels of reasoning and planning capabilities. Ideally, an agent should know when to use its direct memory and when to use more complex reasoning capabilities. However, designing agentic systems that can properly handle tasks based on their requirements remains a challenge.
In a new paper, researchers at Google DeepMind introduce Talker-Reasoner, an agentic framework inspired by the “two systems” model of human cognition. This framework enables AI agents to find the right balance between different types of reasoning and provide a more fluid user experience.
System 1, System 2 thinking in humans and AI
The two-systems theory, first introduced by Nobel laureate Daniel Kahneman, suggests that human...
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