Although machine learning has been around for decades, its practical applications are now coming into focus as it helps companies better understand their customers. As a form of artificial intelligence (AI), which allows computers to learn by way of observation and experience, machine learning uses computer programs that are capable of growth and change as they process new data. The concept of learning repeated behaviors is important. As models are presented with new data, they adapt, learning from earlier experiences to provide reliable, consistent results and responses.
The big difference between machine learning and the digital technologies that preceded it is the ability to independently make increasingly complex decisions—such as which financial products to trade, how vehicles react to obstacles, and whether a patient has a disease—and continuously adapt in response to new data.
Most organizations have little knowledge of how AI systems make decisions, and how the results are applied. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.
Melody K. Smith
Sponsored by Data Harmony, harmonizing knowledge for a better search experience.