Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas

68 | Melanie Mitchell on Artificial Intelligence and the Challenge of Common Sense

Oct 14, 2019

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.

01:22:16

Episode guests

Melanie Mitchell

AI Chapters

Episode notes

Intro

2min

The Duality of AI: Fears and Insights

12min

The Foundation of Neural Networks: Exploring the Perceptron

2min

Exploring the Limits of Perceptrons in AI

3min

Understanding Neural Networks vs. Perceptrons

12min

AI in Games vs. Real-World Challenges

4min

Navigating Complexity: AI Challenges and Home Fitness Solutions

3min

Understanding Common Sense in AI

8min

Navigating AI's Complexities

16min

10.

Navigating AI's Ethical Landscape

17min

11.

The Ethics of Predictive Technology

4min

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.

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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.

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