Today we’re joined by Melanie Mitchell, Davis Professor at the Santa Fe Institute and author of Artificial Intelligence: A Guide for Thinking Humans.
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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!
Connect with Melanie!
- Paper: Abstraction and Analogy-Making in Artificial Intelligence
- Book: Artificial Intelligence: A Guide for Thinking Humans
- Paper: Next Wave Artificial Intelligence: Robust, Explainable, Adaptable, Ethical, and Accountable
- Book: Human Compatible: Artificial Intelligence and the Problem of Control
- Paper: On the Measure of Intelligence
- Check out our TWIML Presents: series page!
- Register for the TWIML Newsletter
- Check out the official TWIMLcon:AI Platform video packages here!
- Download our latest eBook, The Definitive Guide to AI Platforms!
“More On That Later” by Lee Rosevere licensed under CC By 4.0