Machine learning has taken some massive strides forward in the past few years, even emerging to assist and enhance Google’s core search engine algorithm. But again, we’ve only seen it in a limited range of applications. Throughout 2017, I expect to see machine learning updates emerge across the board, entering almost any type of consumer application you can think of, from offering better recommended products based on prior purchase history to gradually improving the user experience of an analytics app. It won’t be long before machine learning becomes a kind of “new normal,” with people expecting this type of artificial intelligence as a component of every form of technology.
Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed."[1]Evolved from the study of pattern recognitionand computational learning theory in artificial intelligence,[2] machine learning explores the study and construction of algorithms that can learn from and make predictions on data[3] – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,[4]:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach,[5] optical character recognition (OCR),[6] learning to rank, and computer vision.
Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining,[7]where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.[4]:vii[8] Machine learning can also be unsupervised[9] and be used to learn and establish baseline behavioral profiles for various entities[10] and then used to find meaningful anomalies.
Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.[11]
As of 2016, machine learning is a buzzword, and according to the Gartner hype cycle of 2016, at its peak of inflated expectations.[12]Effective machine learning is difficult because finding patterns is hard and often not enough training data is available; as a result, machine-learning programs often fail to deliver.[13][14]
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