Using Analogy-Making to Discover the Mearning of Images
Melanie Mitchell, Ph.D.
Portland State University
Abstract: Enabling computers to understand images remains one of the hardest open problems
in artificial intelligence. No machine visionsystem comes close to matching human ability at
identifying the contents of images or visual scenes or at recognizing similarity between different
scenes, even though such abilities pervade human cognition.In this talk I will describe research---
currently in early stages---on bridging the gap between low-level perception and higher-level image
understanding by integrating a cognitive model of perceptual organization and analogy-making
with a neural model of the visual cortex.
Bio: Melanie Mitchell is Professor of Computer Science at Portland State University and External
Professor at the Santa Fe Institute. She attended Brown University, where she majored in
mathematics and did research in astronomy, and the University of Michigan, where she received
a Ph.D. in computer science, working with her advisor Douglas Hofstadter on the Copycat project,
a computer program that makes analogies. She is the author or editor of five books and over 70
scholarly papers in in the fields of artificial intelligence, cognitive science, and complex systems.
Her most recent book, "Complexity: A Guided Tour", published in 2009 by Oxford University Press,
was named by Amazon.com as one of the ten best science books of 2009.