The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Complexity and Intelligence with Melanie Mitchell - #464

Mar 15, 2021

In this engaging discussion, Melanie Mitchell, a Davis Professor at the Santa Fe Institute and author, dives into the complexities of intelligence and AI. She highlights the challenges of getting AI to make analogies, drawing parallels with social learning observed in humans. The conversation explores alternative learning paradigms and their implications for machine intelligence. Mitchell also addresses the limitations of current AI systems, emphasizing the need for responsible application and a focus on interdisciplinary research to advance the field.

32:48

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Episode guests

Melanie Mitchell

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Episode notes

Intro

2min

Exploring the Depths of AI and Intelligence

5min

Exploring Learning Paradigms: From Supervision to Social Interaction

2min

The Complexity of Analogies in AI

15min

Limits and Lessons of AI Intelligence

6min

Exploring the Intersection of AI, Neuroscience, and Child Development

3min

Today we’re joined by Melanie Mitchell, Davis Professor at the Santa Fe Institute and author of Artificial Intelligence: A Guide for Thinking Humans.

While Melanie has had a long career with a myriad of research interests, we focus on a few, complex systems and the understanding of intelligence, complexity, and her recent work on getting AI systems to make analogies. We explore examples of social learning, and how it applies to AI contextually, and defining intelligence.

We discuss potential frameworks that would help machines understand analogies, established benchmarks for analogy, and if there is a social learning solution to help machines figure out analogy. Finally we talk through the overall state of AI systems, the progress we’ve made amid the limited concept of social learning, if we’re able to achieve intelligence with current approaches to AI, and much more!

The complete show notes for this episode can be found at twimlai.com/go/464.

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