Large Language Models have displayed an uncanny ability to store information at scale and use it to answer a wide range of queries in robust and general ways. However, they are not truly generative in the sense that they depend on massive amounts of externally generated data for pre-training and lesser but still significant amounts of human feedback for fine-tuning. Conversely, cognitive architectures have been developed to reproduce and explain the structure and mechanisms of human cognition that give rise to robust behavior but have often struggled to scale up to real world challenges. We review a range of approaches to combine the two frameworks in ways that reflect their distinctive strengths. Moreover, we argue that, beyond their superficial differences, they share a surprising number of common assumptions about the nature of human-like intelligence that make a deep integration possible.

 

References

Laird, J. E., Lebiere, C., & Rosenbloom, P. S. (2017). A standard model of the mind: Toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and robotics. AI Magazine, 38(4), 13-26.

Lebiere, C., Pirolli, P., Thomson, R., Paik, J., Rutledge-Taylor, M., Staszewski, J., & Anderson, J. R. (2013). A functional model of sensemaking in a neurocognitive architecture. Computational Intelligence and Neuroscience.

Lebiere, C., Gonzalez, C., & Warwick, W. (2010).  Cognitive Architectures, Model Comparison, and Artificial General Intelligence. Journal of Artificial General Intelligence 2(1), 1-19.

Jilk, D. J., Lebiere, C., O’Reilly, R. C. and Anderson, J. R. (2008). SAL: an explicitly pluralistic cognitive architecture.  Journal of Experimental & Theoretical Artificial Intelligence, 20(3), 197-218.

Anderson, J. R. & Lebiere, C. (2003). The Newell test for a theory of cognition. Behavioral & Brain Sciences 26, 587-637.

Anderson, J. R., & Lebiere, C. (1998).  The Atomic Components of Thought.  Mahwah, NJ: Lawrence Erlbaum Associates.