In this engaging discussion, Melanie Mitchell, a computer scientist and complexity researcher, delves into the perplexities of artificial intelligence, highlighting its struggles with common sense. She explores why AI excels in games but falters in real-world tasks, like driving. The conversation touches on the challenges of teaching AI fundamental concepts such as causality and object permanence and debates the contrasting methods of rule-based versus deep learning systems. Additionally, ethical concerns surrounding AI, including biases and societal impacts, are thoughtfully examined.
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question_answer ANECDOTE
Hofstadter's AI Concerns
Douglas Hofstadter, Melanie Mitchell's PhD advisor, visited Google and expressed his fear of AI.
He feared that AI's "cheap tricks" would trivialize human profundity, exemplified by Chopin's music being replicated by AI.
question_answer ANECDOTE
EMI Mimicking Chopin
David Cope's program, EMI, composed music mimicking Chopin, fooling music experts.
This fueled Hofstadter's fear and Mitchell's exploration into the true state of AI.
insights INSIGHT
Exponential Curve Limitations
Exponential curves are rare in nature; many initially appear exponential but then plateau.
Don't rely solely on curve fitting for AI predictions.
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This book by Douglas Hofstadter is a comprehensive and interdisciplinary work that explores the interrelated ideas of Kurt Gödel, M.C. Escher, and Johann Sebastian Bach. It delves into concepts such as self-reference, recursion, and the limits of formal systems, particularly through Gödel's Incompleteness Theorem. The book uses dialogues between fictional characters, including Achilles and the Tortoise, to intuitively present complex ideas before they are formally explained. It covers a wide range of topics including cognitive science, artificial intelligence, number theory, and the philosophy of mind, aiming to understand how consciousness and intelligence emerge from formal systems[2][4][5].
Artificial intelligence is better than humans at playing chess or go, but still has trouble holding a conversation or driving a car. A simple way to think about the discrepancy is through the lens of “common sense” — there are features of the world, from the fact that tables are solid to the prediction that a tree won’t walk across the street, that humans take for granted but that machines have difficulty learning. Melanie Mitchell is a computer scientist and complexity researcher who has written a new book about the prospects of modern AI. We talk about deep learning and other AI strategies, why they currently fall short at equipping computers with a functional “folk physics” understanding of the world, and how we might move forward.
Melanie Mitchell received her Ph.D. in computer science from the University of Michigan. She is currently a professor of computer science at Portland State University and an external professor at the Santa Fe Institute. Her research focuses on genetic algorithms, cellular automata, and analogical reasoning. She is the author of An Introduction to Genetic Algorithms, Complexity: A Guided Tour, and most recently Artificial Intelligence: A Guide for Thinking Humans. She originated the Santa Fe Institute’s Complexity Explorer project, on online learning resource for complex systems.
Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas 
