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When you hear the words Alzheimer's disease, what do you think of? The truth is, the picture most of us have of the disease is incomplete. Alzheimer's disease doesn't start when someone starts to lose their memory. It actually starts years – sometimes decades – earlier. The Rethinking Alzheimer's Disease Podcast is an engaging, narrative-style podcast miniseries for those curious or motivated to learn about Alzheimer’s disease. Perhaps you have a family member with Alzheimer’s disease, or ca ...
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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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