Siebel Energy Institute Spurs Use of Machine Learning to Solve Energy Issues
As the root cause of a raft of economic and geopolitical issues, the way energy gets distributed and consumed winds up having a major impact on every human being on the planet. The Siebel Energy Institute, launched this week, wants to reduce all the tension surrounding energy resource allocation by providing $10 million worth of seed funding to spur the development of machine learning applications that will make energy usage more efficient.
Tom Siebel, the founder of Siebel Systems, which was eventually acquired by Oracle, says the goal of the Institute is to drive the development of a series of machine learning applications to the point where they would attract additional funding from venture capitalists.
As a field in computer science, machine learning is about making use of pattern recognition and computational learning theories to create algorithms that can learn from and make predictions based on data. The challenge now, says Siebel, is that there are not enough developers familiar with the algorithms on which machine learning applications are built to actually develop software that could, for example, reduce the amount of energy that streetlights consume when no one is walking or driving on a particular street or road.
The $10 million provided by The Thomas and Stacey Siebel Foundation is intended to provide the incentives developers need to first come up with the use cases for a machine learning application and the initial prototype. Initially, the Siebel Energy Institute is funding 24 research grants using $1 million of that funding.
In all, eight research institutions are participating in this effort, including Carnegie Mellon University, École Polytechnique, Massachusetts Institute of Technology, Politecnico di Torino, Princeton University, University of California at Berkeley, University of Illinois at Urbana-Champaign, and University of Tokyo. The Institute also counts Pacific Gas & Electric, Honeywell and C3 Energy as members of its advisory board.
Whether anything comes of this effort remains to be seen, but as Siebel notes, given all the players involved, it’s hard to see nothing of value coming from the effort. At the very least, the experience gained building machine learning applications should prove invaluable as that knowledge over time is distributed through the larger developer ecosystem.
Via: Google Alert for ML