Machine learning, one of the key building blocks of AI, has been a part of the technological world since the 1950s, when the earliest programmers asked computers to make sense of large sets of data. Programmers have increasingly refined the ability of machines to study data in order to detect patterns that allow computers to then organize information, identify relationships, make predictions and detect anomalies. Today, modern applications of AI have already given us self-driving cars and virtual assistants and have helped us detect fraud and manage resources like electricity more efficiently.
“Deep learning is only going to be used when it really makes sense—where it can quickly find intricate, variable relationships hidden in large volumes of data that we haven’t been able to pull out in any other way yet,” explains Mary Beth Ainsworth, global product marketing manager of artificial intelligence and text analytics at SAS. “But deep learning means a machine can look at a problem through a completely different analytic lens than its human counterpart. It could be used to tackle all sorts of issues. The potential in all the data we collect every day is yet to be realised.”