In the 1990s, scientists began developing computer programs that could use a large amount of data to “learn” how to draw conclusions in ways similar to the human brain. This AI fork, known as machine learning (ML), has allowed the technology to tackle increasingly complex tasks, including speech and handwriting recognition, complex gameplay, and even the ability to support medical diagnosis.
Deep learning, a subset of machine learning that gained popularity in the 2010s, took the level of complexity that AI systems can handle to a whole new level. Training, deep learning programs on multilayer neural networks simulate complex and nuanced ways in which the human brain makes decisions, allowing AI to build applications, interpret images and videos, respond to audio and text, and encourage human ways.
Today, thanks to ML and deep learning, AI automation has evolved from a simple rule-based process to a rich, sophisticated model trained on large datasets that can perform many of the same tasks as humans. This new wave of AI tools known as “intelligent automation” helps organizations improve their IT infrastructure and operations by streamlining their business operations, analyzing data, and solving complex problems that previously required human attention.
