Google’s doing it. Uber’s doing it. Apple just put it in the latest SoC and Amazon is offering it as a service.

Machine learning is hot — and it’s moving on from pattern recognition and traditional algorithms into more elusive “deep learning.” What does it mean for IT pros to operate in a world where their computers don’t always need to be pointed in a particular direction, they only need data to figure it out on their own?

What is Machine Learning?

Machine learning draws on the idea that computers can learn from data alone, through an iterative process by which models are exposed to new data sets and evolve independent adaptations without reprogramming.

With the expansion of computing power — not to mention the exponential increase in available data and affordable, scalable storage — it’s now possible to automatically produce models to analyze ever larger quantities of information and deliver increasingly accurate predictive results in faster and faster cycles. This is a welcome development for businesses, because with more precise modeling comes the ability to identify profitable opportunities and risks before they happen.

Types of Machine Learning

Supervised learning involves data that has been categorized. If data showing apartment rents and floor space was fed into a supervised learning scenario, it would graph the relationship between the two factors. The resulting algorithm could later predict the rent of any apartment based on the floor space, “without writing a specific program to perform the same task.” The process learns and adjusts as it returns “correct” and “incorrect” results based on the data provided, increasing accuracy and self-updating as the relationship changes. The method is useful to process historical data to develop future predictions.

Unsupervised learning uses data where no “right answer” is known. The goal is more exploratory, to find as yet undiscovered relationships or structure within the data. A common example is using retail transactions to find audiences that could be treated similarly for marketing purposes.

Semisupervised learning uses labeled and unlabeled data. It can save money by using a small amount of expensive, labeled data to “train” the system and apply the results to the more inexpensive, unlabeled data.

Reinforcement learning is a trial-and-error process in which the learner aims to maximize rewards, resulting in a policy for optimizing results.

What’s New Here?

Although data mining is similar, not all of the methods used in data mining are machine learning. The field also draws on statistics, text analysis, time series analysis, and other means. The key attribute of machine learning is its ability to “discover” structure within data using an iterative approach, without needing humans to begin with any theories or assumptions to test.

Deep learning has become the latest buzzword, because it takes machine learning’s greatest asset to the extreme. Relying on neural networks, deep learning identifies more complicated patterns in larger amounts of data than previously possible. It holds great promise for language translation, medical diagnosis, and much more.