{"id":790,"date":"2017-01-26T23:34:03","date_gmt":"2017-01-27T07:34:03","guid":{"rendered":"http:\/\/gsds.mrl.ucsb.edu\/?p=790"},"modified":"2017-02-06T09:09:50","modified_gmt":"2017-02-06T17:09:50","slug":"philip-nelson-machine-learning-for-bio-research","status":"publish","type":"post","link":"http:\/\/gsds.mrl.ucsb.edu\/?p=790","title":{"rendered":"Philip Nelson: Machine Learning for Bio Research"},"content":{"rendered":"
\"Philip<\/a>

Philip Nelson, PhD
Google Director, Software Engineering<\/p><\/div>\n

In March of last year, the AlphaGo computer program beat world champion (and human) Lee Sedol at the board game Go. The program\u2019s success reflected the significant progress that machine learning research has made in recent years. However, AlphaGo was just one example of what can be achieved with machine learning. On January 12th, Google director Philip Nelson discussed some recent and ongoing work by his team to advance the applications of machine learning in the field, particularly its role in biomedical research.<\/p>\n

The general approach taken in machine learning programs is developing a deep neural network from a large training data set that subsequently can be used to describe new data. Deep neural networks are composed of thousands of interconnected nodes whose weights are updated as the network \u2018learns\u2019 from the inputs.<\/p>\n

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Philip provided examples of several Google products that apply machine learning techniques by taking advantage of the massive amounts of data they process. These include the following, which you can click to learn more about:<\/p>\n