Machine Learning

Machine learning (ML) is a type of artificial intelligence (AI) focused on building computer systems that learn from data. The broad range of techniques ML encompasses enables software applications to improve their performance over time.

Machine learning algorithms are trained to find relationships and patterns in data. They use historical data as input to make predictions, classify information, cluster data points, reduce dimensionality and even help generate new content, as demonstrated by new ML-fueled applications such as ChatGPT, Dall-E 2 and GitHub Copilot.

Machine learning has played a progressively central role in human society since its beginnings in the mid-20th century, when AI pioneers like Walter Pitts, Warren McCulloch, Alan Turing and John von Neumann laid the groundwork for computation. The training of machines to learn from data and improve over time has enabled organizations to automate routine tasks that were previously done by humans -- in principle, freeing us up for more creative and strategic work.

Machine learning also performs manual tasks that are beyond our ability to execute at scale -- for example, processing the huge quantities of data generated today by digital devices. Machine learning's ability to extract patterns and insights from vast data sets has become a competitive differentiator in fields ranging from finance and retail to healthcare and scientific discovery. Many of today's leading companies, including Facebook, Google and Uber, make machine learning a central part of their operations.

Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic types of machine learning: supervised learning, unsupervised learning, semisupervised learning and reinforcement learning.