Why AI systems don't learn – On autonomous learning from cognitive science
Researchers examine the limitations of current AI in autonomous learning and propose a new architecture inspired by biological cognition, integrating observation and active behavior.
Computer Science > Artificial Intelligence
Title:Why AI systems don't learn and what to do about it: Lessons on autonomous learning from cognitive science
View PDF HTML (experimental)Abstract:We critically examine the limitations of current AI models in achieving autonomous learning and propose a learning architecture inspired by human and animal cognition. The proposed framework integrates learning from observation (System A) and learning from active behavior (System B) while flexibly switching between these learning modes as a function of internally generated meta-control signals (System M). We discuss how this could be built by taking inspiration on how organisms adapt to real-world, dynamic environments across evolutionary and developmental timescales.
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