Jeffrey Dean Wants YOU To Take A Machine Learning Class

During day two of the Heidelberg Laureate Form, the term machine learning or “ML” has been popping up during talks and in conversations with young researchers and the laureates. Machine learning uses algorithms to give computers the ability to learn without them having to be explicitly programmed. The goal is for a program to learn by itself without any human intervention.

In a discussion with Jeffrey A. Dean, the winner of ACM’s 2012 Prize in Computing and the current head Google’s AI Division, he repeatedly mentioned and stressed the importance of machine learning. Google AI currently has an open source machine learning platform called TensorFlow, which Dean said is “used for training computing vision models”, as well as a ML Kit beta which brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.

Dean emphasized that it is important for every student to understand and be exposed to machine learning, most likely because 200 students at HLF had surrounded him for two days at that point. Nonetheless, his point was clear: “All students should take a machine learning class, since it is clear that ML is going to impact many fields.” When asked what role he sees for academic research in Artificial Intelligence (AI), Dean repeated the importance of ML. “We need to educate the next generation of students, we need more ML experts so we can continue to move forward in AI.”

So next semester when you are signing up for classes, or advising students on which classes to take, think about adding a ML class. The writing is on the wall that it will probably pay off in the end.

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Helen Wright is the Computing Research Association's Computing Community Consortium (CCC) Senior Program Associate. She interacts with members of the research community and policy makers by being one of the main contributors to the CCC Blog. The goal of the CCC is to strengthen the research community, articulate compelling research visions, and align those visions with pressing national and global challenges. Helen's interests are in computer science, biology, and science communication. She holds a bachelor’s of science in biology as well as a master’s of science in ecology and evolutionary biology from the University of Virginia. You can follow her on twitter: @compcomcon

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  1. Making Machine Learning the next big thing requires to make it more independent from computer science. Currently studying machine learning means specialicing in the field of computer science and because there are not too many students of computer science there are even less specialising in machine learning.
    This would change if math and physics students or students of linguistics could take machine learning courses

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