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Michael Shermer

scientist · 3 mentions across 2 readings

In this course

Shermer is a science communicator best known for his concept of "patternicity"—the human tendency to find meaningful connections in random data—which the course draws on to theorize how AI systems and humans alike construct false coherence from noise. His work appears here to ground discussions of how both algorithmic pattern-recognition and human cognition can generate spurious meaning-making, a critical concern when examining the feedback loops between machine learning and cultural belief systems.

Mentioned in 2 readings

Appears alongside

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Pandaemonium Architecture 6.0 — ATEK-639/439 — Fall 2025