AI Definitions: Machine learning
/Machine learning (ML) - This type of AI can spot patterns and then improve what it can do on its own. ML makes predictions or decisions based on patterns in data sets. This process evolves and adapts as it is exposed to new data, improving the output without explicit human programming. An example would be algorithms recommending ads for users, which become more tailored the longer it observes the users‘ habits (someone’s clicks, likes, time spent, etc.). A developer of a ML system creates a model and then “trains” it by providing it with many examples. Data scientists combine ML with other disciplines (like big data analytics and cloud computing) to solve real-world problems. However, the results are limited to probabilities, not absolutes. It doesn’t reveal causation. A subset of “narrow AI,” ML is an alternative approach to symbolic artificial intelligence, better at such chores as spotting faces and recognizing voices. There are four types of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learning. A clever computer program that simply mimics human-like behavior can be considered AI, but the computer system itself is not machine learning unless its parameters are automatically informed by data without human intervention. Video: Introduction to Machine Learning