AI Definitions: Machine Learning

Machine Learning (ML) - This type of AI can spot patterns in data sets and then improve what it can do on its own, making predictions or decisions. This process evolves and the ML 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 then 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, and it is better at spotting faces and recognizing voices. Machine learning can be divided into four types: supervised, unsupervised, semi-supervised, and reinforcement learning. A clever computer program can be considered AI if it can mimic human-like behavior. However, the computer system is not machine learning unless its parameters are automatically informed by data without human intervention. Video: Introduction to Machine Learning