- Machine learning is a branch of computer science that combs through data sets to make predictions about the future.
- It is used to identify economic trends, personalize recommendations and build computers that appear to think.
- Although machine learning has become incredibly popular, it only works on problems with large data sets.
- Practitioners of machine learning must be careful to avoid having machines identify patterns that do not really exist.
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A couple of years ago the directors of a women's clothing company asked me to help them develop better fashion recommendations for their clients. No one in their right mind would seek my personal advice in an area I know so little about—I am, after all, a male computer scientist—but they were not asking for my personal advice. They were asking for my machine-learning advice, and I obliged. Based purely on sales figures and client surveys, I was able to recommend to women whom I have never met fashion items I have never seen. My recommendations beat the performance of professional stylists. Mind you, I still know very little about women's fashion.
Machine learning is a branch of computer science that enables computers to learn from experience, and it is everywhere. It makes Web searches more relevant, blood tests more accurate and dating services more likely to find you a potential mate. At its simplest, machine-learning algorithms take an existing data set, comb through it for patterns, then use these patterns to generate predictions about the future. Yet advances in machine learning over the past decade have transformed the field. Indeed, machine-learning techniques are responsible for making computers “smarter” than humans at so many of the tasks we wish to pursue. Witness Watson, the IBM computer system that used machine learning to beat the best Jeopardy players in the world.