PERSON
Melanie Mitchell
American computer scientist (b. 1969) — Hofstadter's doctoral student at Michigan, principal developer of Copycat, and one of the most respected voices in contemporary AI assessment.
Mitchell joined Hofstadter's Fluid Analogies Research Group at the University of Michigan in the 1980s and made the Copycat program the subject of her doctoral dissertation. Her work on Copycat — detailed in Analogy-Making as Perception (MIT Press, 1993) — remains the canonical technical account of how fluid analogy-making could be implemented computationally. She subsequently held positions at Santa Fe Institute and Portland State University, and became Davis Professor of Complexity at the Santa Fe Institute.
In The You On AI Field Guide
Mitchell's subsequent career has developed in close dialogue with Hofstadter's framework while maintaining independent empirical and technical rigor. Her 2019 book Artificial Intelligence: A Guide for Thinking Humans brought Hofstadter-influenced critiques of deep learning to a wide audience, arguing that current systems exhibit impressive performance on narrow tasks without possessing the general understanding that allows humans to adapt across domains.
She has been one of the most visible voices in the 2023–2025 debate over whether large language models understand what they process. Her 2025