TITLE: The Magic at the Heart of AI AUTHOR: Eugene Wallingford DATE: November 30, 2013 9:45 AM DESC: ----- BODY: This paragraph from The Man Who Would Teach Machines to Think expresses a bit of my uneasiness with the world of AI these days:
As our machines get faster and ingest more data, we allow ourselves to be dumber. Instead of wrestling with our hardest problems in earnest, we can just plug in billions of examples of them. Which is a bit like using a graphing calculator to do your high-school calculus homework -- it works great until you need to actually understand calculus.I understand the desire to solve real problems and the resulting desire to apply opaque mathematics to large data sets. Like most everyone, I revel in what Google can do for me and watch in awe when Watson defeats the best human Jeopardy! players ever. But for me, artificial intelligence was about more than just getting the job done. Over the years teaching AI, my students often wanted to study neural networks in much greater detail than my class tended to go. But I was more interested in approaches to AI and learning that worked at a more conceptual level. Often we could find a happy middle ground while studying genetic algorithms, which afforded them the magic of something-for-nothing and afforded me the potential for studying ideas as they evolved over time. (Maybe my students were simply exhibiting Astrachan's Law.) When I said goodbye to AAAI a few years ago, I mentioned Hofstadter's work as one of my early inspirations -- Gödel, Escher, Bach and the idea of self-reference, with its "intertwining worlds of music, art, mathematics, and computers". That entry said I was leaving AAAI because my own work had moved in a different direction. But it left unstated a second truth, which The Man Who Would Teach Machines to Think asserts as Hofstadter's own reason for working off the common path: the world of AI had moved in a different direction, too. For me, as for Hofstadter, AI has always meant more than engineering a solution. It was about understanding scientifically something that seemed magical, something that is both deeply personal and undeniably universal to human experience, about how human consciousness seems to work. My interest in AI will always lie there.