Since the term was coined in 1956, artificial intelligence has been a kind of mirror that tells us more about our theories of intelligence, and our hopes and fears about technology, than about whether we can make computers think. AI requires us to formulate and specify: what do we mean by computation and cognition, intelligence and thought? It is a topic rife with hype and strong opinions, driven more by funding and commercial goals than almost any other field of science...with the curious effect of making massive, world-changing technological advancements even as we lack a unifying theoretical framework to explain and guide the change. So-called machine intelligences are more and more a part of everyday human life, but we still don’t know if it is possible to make computers think, because we have no universal, satisfying definition of what thinking is. Meanwhile, we deploy technologies that we don’t fully understand to make decisions for us, sometimes with tragic consequences. To build machines with common sense, we have to answer fundamental questions such as, “How do humans learn?” “What is innate and what is taught?” “How much do sociality and evolution play a part in our intelligence, and are they necessary for AI?”
This week’s guest is computer scientist Melanie Mitchell, Davis Professor of Complexity at SFI, Professor of Computer Science at Portland State University, founder of ComplexityExplorer.org, and author or editor of six books, including the acclaimed Complexity: A Guided Tour and her latest, Artificial Intelligence: A Guide for Thinking Humans. In this episode, we discuss how much left there is to learn about artificial intelligence, and how research in evolution, neuroscience, childhood development, and other disciplines might help shed light on what AI still lacks: the ability to truly think.
Visit Melanie Mitchell’s Website for research papers and to buy her book, Artificial Intelligence: A Guide for Thinking Humans.
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Podcast Theme Music by Mitch Mignano.
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