Thinking Fast and Slow in AI
This is an incremental vision paper that suggests a framework for the AI research community to explore new methodologies without immediate practical impact.
The paper proposes a research direction for advancing AI by drawing inspiration from cognitive theories of human decision-making, aiming to embed causal components to address capabilities like adaptability and common sense, but it does not present specific results or concrete numbers.
This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making. The premise is that if we gain insights about the causes of some human capabilities that are still lacking in AI (for instance, adaptability, generalizability, common sense, and causal reasoning), we may obtain similar capabilities in an AI system by embedding these causal components. We hope that the high-level description of our vision included in this paper, as well as the several research questions that we propose to consider, can stimulate the AI research community to define, try and evaluate new methodologies, frameworks, and evaluation metrics, in the spirit of achieving a better understanding of both human and machine intelligence.