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Oct 20, 2023

Why AI is Harder Than We Think with Melanie Mitchell (podcast)

This discussion explores why the field of artificial intelligence has cycled several times between periods of hype and disenchantment since its emergence in the 1950s.

By Templeton Staff

Public sentiment around artificial intelligence (AI) has seen ups and downs. Since the field's emergence in the 1950s, there have been times of high hopes and investments — or "AI spring" — followed by loss of confidence and reduced funding — or "AI winter."

"Even with today's seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected," says Dr. Melanie Mitchell, an author and researcher focused on the fields of artificial intelligence, cognitive science, and complex systems. "One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself." In this episode of Many Minds podcast, she further describes this and discusses other factors that may contribute to why AI often falls short of human expectations.

Many Minds podcast host, cognitive scientist, and writer Kensy Cooperrider introduces the episode:

"I’m betting you’ve heard about the next generation of artificial intelligence, the one that’s just around the corner. It’s going to be pervasive, all-competent, maybe super-intelligent. We’ll rely on it to drive cars, write novels, diagnose diseases, and make scientific breakthroughs. It will do all these things better, faster, more safely than we bumbling humans ever could. The thing is, we’ve been promised this for years. If this next level of AI is coming, it seems to be taking its time. Might it be that AI is simply harder than we thought?

My guest today is Dr. Melanie Mitchell. She is the Davis Professor at the Santa Fe Institute and the author of a number of books, including her latest, which is titled ‘Artificial Intelligence: A Guide for Thinking Humans.’ 

In this conversation we zoom in on Melanie’s widely discussed recent essay, 'Why AI is harder than we think.’ We talk about the repeating cycle of hype and disenchantment within AI, and how it stretches back to the first years of the field. We walk through four fallacies that Mitchell identifies that lead us to think that super smart AI is closer than it actually is. We talk about self-driving cars, brittleness, adversarial perturbations, Moravec’s paradox, analogy, brains in vats, and embodied cognition, among other topics. And we discuss an all-important concept, one we can’t easily define but we can all agree AI is sorely lacking: common sense." 

Play the episode with the above player.

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Templeton World Charity Foundation's Diverse Intelligences is a multiyear, global effort to understand a world alive with brilliance in many forms. Its mission is to promote open-minded, forward-looking inquiry in animal, human, and machine intelligences. We collaborate with leading experts and emerging scholars from around the globe, developing high-caliber projects that advance our comprehension of the constellation of intelligences.

Many Minds is a project of the Diverse Intelligences Summer Institute (DISI), made possible through a grant from TWCF to the University of California, Los Angeles (UCLA). The Many Minds podcast is hosted and produced by Kensy Cooperrider, with help from Assistant Producer Urte Laukaityte. Creative support is provided by DISI Directors Erica Cartmill and Jacob Foster. Artwork featured as the podcast badge is by Ben Oldroyd.