Machine learning is a critical technique that enables AI to solve problems. Despite common misperceptions (and misnomers in popular culture), machines do not learn. They store and compute — admittedly in increasingly complex ways.
Machine learning is a purely analytical discipline. It applies mathematical models to data to extract knowledge and find patterns that humans would likely miss. ML also recommends actions, but it does not direct systems to take action without human intervention.
More specifically, machine learning creates an algorithm or statistical formula (referred to as a “model”) that converts a series of data points into a single result. ML algorithms “learn” through “training,” in which they identify patterns and correlations in data and use them to provide new insights and predictions without being explicitly programmed to do so.
Deep learning, a variant of machine learning algorithms, uses multiple layers of algorithms to solve problems by extracting knowledge from raw data and transforming it at every level. Deep learning can outperform traditional ML (or shallow learning techniques) by working with complex and often high-dimensional data, such as images, speech and text. Still, either rule-based systems or traditional ML can effectively solve many AI problems.
In most organizations, deep learning solutions are not yet a significant part of the product roadmap (rule-based systems or traditional ML can effectively enable most AI use cases today), but their use is quickly increasing alongside advancements in data processing and breakthroughs in computational techniques.
Using ML, including deep learning, to make predictions enables an AI-driven process to automate the selection of the most favorable result, which eliminates the need for a human decision maker.