Machine learning for the future

#Machinelearning for the future  #BigData @google

  • In a keynote talk, Dean outlined the history of machine learning (ML) and neural networks and various ways to programme models to take advantage of raw data coming through in the form of images or audio.
  • Models must be able to learn unsupervised, engage in multi-tasking and transfer learning, and take action from the world (also known as reinforcement learning).
  • Dean said researchers are beginning to look at privacy preserving techniques in machine learning and added that model structures mdash; the part of machine learning where human interaction plays a big role in managing the weights around ML – is of great import.
  • What we now want most from machine learning, said Google Senior Fellow Jeff Dean to the audience at SIGMOD 2016 keynote yesterday (Tuesday, June 28), is “understanding.”
  • “There is a lot of parallelism in these models,” Dean added, pointing to the Google Translate app that can now translate signs into a different language in real time using pixel identification.

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@bobehayes: “#Machinelearning for the future #BigData @google”


Google fellow Jeff Dean outlines the history of machine learning (ML), neural networks and various ways to programme mod…


Machine learning for the future