Prof. Pedro Domingos,

University of Washington


There are five main schools of thought in machine learning,
and each has its own master algorithm – a general-purpose learner that
can in principle be applied to any domain. The symbolists have inverse
deduction, the connectionists have backpropagation, the evolutionaries
have genetic programming, the Bayesians have probabilistic inference,
and the analogizers have support vector machines. What we really need,
however, is a single algorithm combining the key features of all of
them. In this talk I will describe my work toward this goal, including
in particular Markov logic networks, and speculate on the new
applications that such a universal learner will enable, and how
society will change as a result.


Pedro Domingos is a professor of computer science at the
University of Washington and the author of “The Master Algorithm”. He
is a winner of the SIGKDD Innovation Award, the highest honor in data
science. He is a Fellow of the Association for the Advancement of
Artificial Intelligence, and has received a Fulbright Scholarship, a
Sloan Fellowship, the National Science Foundation’s CAREER Award, and
numerous best paper awards. He received his Ph.D. from the University
of California at Irvine and is the author or co-author of over 200
technical publications. He has held visiting positions at Stanford,
Carnegie Mellon, and MIT. He co-founded the International Machine
Learning Society in 2001. His research spans a wide variety of topics
in machine learning, artificial intelligence, and data science,
including scaling learning algorithms to big data, maximizing word of
mouth in social networks, unifying logic and probability, and deep


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