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For the last four decades the digital revolution has been driven by the exponential growth in the number of transistors that can be packed onto a silicon chip. Today we are seeing a new kind of “Moore’s Law” in which the quantity of data in the world is doubling roughly every 18 months. This data deluge has the potential to transform many facets of society, from healthcare to education, and from commerce to the environment. The key to unlock this potential will be the ability to extract useful information from the data, and it is here that machine learning will play a pivotal role.
Machine learning thrives on data, and we can anticipate substantial growth in the diversity and the scale of impact of machine learning applications over the coming decade. This exciting new opportunity will also raise many challenges, and will require the development of new techniques for handling and learning from large data sets, as well as new tools to support a growing community of machine learning practitioners.
The Machine Learning Summit 2013 brought together thought leaders and researchers from a broad range of disciplines including computer science, engineering, statistics and mathematics. Together they highlighted some of the key challenges posed by this new era of machine learning, and identified the next generation of approaches, techniques and tools that will be needed to exploit the information revolution for the benefit of society.