"The Emergent Mind: How Intelligence Arises in People and Machines" | Jay McClelland

The AI revolution of the past few years is built on brain-inspired neural network models originally developed to study our own minds. The question is, what should we make of the fact that our own rich mental lives are built on the same foundations as the seemingly soulless chat-bots we now interact with on a daily basis?

Our guest this week is Stanford cognitive scientist Jay McClelland, who has been a leading figure in this field since the 1980s, when he developed some of the first of these artificial neural network models. Now McClelland has a new book, co-authored with SF State University computational neuroscientist Gaurav Suri, called "The Emergent Mind: How Intelligence Arises in People and Machines." 

We spoke with McClelland about the entangled history of neuroscience and AI, and whether the theory of the emergent mind described in the book can help us better understand ourselves and our relationship with the technology we've created.

Learn More 

New book sheds light on human and machine intelligence | Stanford Report

How Intelligence – Both Human and Artificial – Happens | KQED Forum 

From Brain to Machine: The Unexpected Journey of Neural Networks | Stanford HAI

Wu Tsai Neuro's Center for Mind, Brain, Computation and Technology

McClelland, J. L. & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: Part 1. An account of basic findings. Psychological Review, 88, 375-407. [PDF]

Rumelhart, D. E., McClelland, J. L., & the PDP research group. (1986). Parallel distributed processing: Explorations in the microstructure of cognition. Volumes I & II. Cambridge, MA: MIT Press.

McClelland, J. L. & Rogers, T. T. (2003). The parallel distributed processing approach to semantic cognition. Nature Reviews Neuroscience, 4, 310-322. [PDF]

McClelland, J. L., Hill, F., Rudolph, M., Baldridge, J., & Schuetze, H. (2020). Placing language in and integrated understanding system: Next steps toward human-level performance in neural language models. Proceedings of the National Academy of Sciences, 117(42), 25966-25974. [

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