Signup Schedule: http://apps.cs.utexas.edu/talkschedules/cgi/list_events.cgi

Talk Audience: UTCS Faculty, Grads, Undergrads, Other Interested Parties

Host: Risto Miikkulainen

Talk Abstract: Our fast, deep / recurrent neural networks have many biologically plausible, non-linear processing stages. They won eight recent international pattern recognition competitions in a row, and are the first machine learning methods to achieve human-competitive or even superhuman performance on well-known vision benchmarks. We also can evolve big NN controllers without any supervision, using "compressed" encodings of NN weight matrices represented indirectly as a set of Fourier-type coefficients. Recently, the largest, evolved, vision-based NN controller to date, with over 1 million weights, learned to drive a car around a track using raw video images from the driver's perspective in the TORCS driving game.

Speaker Bio: Prof. Jürgen Schmidhuber is with the Swiss AI Lab IDSIA & USI & SUPSI (ex-TUM CogBotLab & CU). Since age 15 or so his main scientific ambition has been to build an optimal scientist, then retire. This is driving his research on self-improving Artificial Intelligence. His team won many international competitions and awards, and pioneered the field of mathematically rigorous universal AI and optimal universal problem solvers. He also generalized the many-worlds theory of physics to a theory of all constructively computable universes - an algorithmic theory of everything. His formal theory of creativity & curiosity & fun (1990-2010) explains art, science, music, and humor.