EP33 Melanie Mitchell on the Elements of AI

Melanie Mitchell & Jim talk about the many approaches to creating AI, hype cycles, self-driving cars, what can be learned from human intelligence, and much more... Professor & Author Melanie Mitchell and Jim have a wide-ranging talk about her work in the field of Artificial Intelligence (AI). They explore the differences between deep learning, symbolic techniques & hybrid systems, AI springs/winters & hype cycles, self-driving cars, strong (general) vs weak (narrow) intelligence, the black-box element of human & artificial intelligence, limitations of neural nets, the potential of evolutionary approaches to AI, embodied & social cognition, whether consciousness is needed for intelligence, reinforcement learning, common sense & understanding in AI, the value of metaphors & analogies in intelligence, and much more. Episode Transcript Mentions & Recommendations Melanie’s Book, Introduction to Genetic Algorithms Complexity Explorer Courses Melanie’s Book, Artificial Intelligence: A Guide for Thinking Humans Rod Brooks’ Twitter Thread on Evolutionary AI The Feeling of What Happens by António Damásio The Cyc Platform Gödel, Escher, Bach by Douglas Hofstadter Metaphors We Live By George Lakoff Melanie's Book, Complexity: A Guided Tour Melanie’s Recommended Papers on Analogy & Metaphor Papers on Analogy and Similarity, The Copycat project, Seeing the Meaning, Deep Visual Analogy-Making, Beyond imitation, Visalogy Melanie Mitchell is Professor of Computer Science at Portland State University, and External Professor and Co-Chair of the Science Board at the Santa Fe Institute. Mitchell has also held faculty or professional positions at the University of Michigan, Los Alamos National Laboratory, and the OGI School of Science and Engineering. She is the author or editor of seven books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems, including her latest, Artificial Intelligence: A Guide for Thinking Humans.