Deep learning, a subset of machine learning, has emerged as the driving force behind the potential of artificial intelligence (AI) to revolutionize various industries. At its core, deep learning algorithms use massive amounts of data from a specific domain to make decisions that optimize a desired outcome. These algorithms train themselves to recognize deeply buried patterns and correlations connecting numerous data points to the desired outcome, often using complex mathematical relationships that are indecipherable to the human brain.

One of the most natural applications of deep learning is in fields like insurance and loan-making, where relevant data on borrowers is abundant (credit score, income, recent credit-card usage) and the goal to optimize is clear (minimize default rates). Another example is self-driving cars, where deep learning helps vehicles "see" the world around them by recognizing patterns in the camera's pixels (red octagons), figuring out what they correlate to (stop signs), and using that information to make decisions (apply pressure to the brake to slowly stop) that optimize for the desired outcome (deliver passengers safely home in minimal time).

The excitement around deep learning stems from its ability to be applied to a wide range of real-world problems, such as deciphering human speech, translating documents, recognizing images, predicting consumer behavior, identifying fraud, making lending decisions, helping robots "see," and even driving a car. This has led to companies like Google and Facebook acquiring deep-learning experts and startups, paying millions of dollars for ambitious research projects that have continued to awe observers and grab headlines.

In the age of implementation, data is the core component for deep learning's success. The more examples of a given phenomenon a network is exposed to, the more accurately it can pick out patterns and identify things in the real world. As a result, an algorithm designed by a handful of mid-level AI engineers with access to large amounts of data usually outperforms one designed by a world-class deep-learning researcher. This has led to the burgeoning availability of data driving deep learning's disruption of countless industries around the world.