The ancient Chinese board game go has long been considered the holy grail for artificial intelligence. Its simple rules but near-infinite number of outcomes make it exponentially more complex than chess. Mastery of the game by computers was considered by expert players and the AI community alike to be out of reach for at least another decade. Yet in 2016, Google's DeepMind team announced that they would be taking on Lee Sedol, the world's most elite go champion. The match was set for a weeklong tournament in Seoul in early 2016, and there was more at stake than the $1 million prize. Director Greg Kohs' absorbing documentary chronicles Google's DeepMind team as it prepares to test the limits of its rapidly evolving AI technology. The film pits machine against man, and reveals as much about the workings of the human mind as it does the future of AI. (Note courtesy of Tribeca Film Festival.) DIR Greg Kohs; PROD Gary Krieg, Kevin Proudfoot, Josh Rosen. U.S., 2017, color, 90 min plus a 20-min intro. NOT RATED
Presented as part of Science on Screen®, an initiative of the Coolidge Corner Theatre, with major support from the Alfred P. Sloan Foundation.
AFI Member passes accepted.
About Dr. Benjamin Bengfort
Dr. Benjamin Bengfort is a data scientist at PingThings, Inc. in Washington, DC, where he spends his days applying deep neural learning to synchrophasor sensor data in an effort to improve the efficiency and safety of the electric grid. On nights and weekends he can be found working on Yellowbrick — an open source Python package for machine learning diagnostics and interpretation. He's also the author of several books on machine learning, including "Applied Text Analysis in Python" and "Data Analytics with Hadoop." Dr. Bengfort earned his PhD in Computer Science from the University of Maryland and is also adjunct faculty at Georgetown University, where he teaches new datascientists how to integrate machine learning into their work. If he can sneak away from his computer and the classroom for a few hours, you're likely to find him in his woodshop or manning the smoker at the neighborhood barbecue (where he'd stake his brisket against an AI any day).
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