Other résumé screening systems currently in use may have training biases that cause them to upgrade candidates who are “like” current employees in ways that legally aren’t supposed to matter (e.g. Amazon had to withdraw its internal system because of training sample biases that caused it to downgrade all job applications from women. Amazon product ratings, which couple a comment with a numerical score) are large and easy to access.Īutomatic screening of résumés is a controversial area. Automatic sentiment analysis of social media has a reasonably good success rate, probably because the training sets (e.g. Automatic language translation has been largely successful, although some language pairs work better than others, and many automatic translations can still be improved by human translators.Īutomatic speech to text works fairly well for people with mainstream accents, but not so well for people with some strong regional or national accents performance depends on the training sets used by the vendors. Game-playing machine learning is strongly successful for checkers, chess, shogi, and Go, having beaten human world champions. Self-driving cars are a good example, where tasks range from simple and successful (parking assist and highway lane following) to complex and iffy (full vehicle control in urban settings, which has led to several deaths). We hear about applications of machine learning on a daily basis, although not all of them are unalloyed successes. Unsupervised learning is further divided into clustering (finding groups of similar objects, such as running shoes, walking shoes, and dress shoes), association (finding common sequences of objects, such as coffee and cream), and dimensionality reduction (projection, feature selection, and feature extraction).
Supervised machine learning problems are further divided into classification (predicting non-numeric answers, such as the probability of a missed mortgage payment) and regression (predicting numeric answers, such as the number of widgets that will sell next month in your Manhattan store). Machine learning algorithms are often divided into supervised (the training data are tagged with the answers) and unsupervised (any labels that may exist are not shown to the training algorithm). Whereas a rule-based system will perform a task the same way every time (for better or worse), the performance of a machine learning system can be improved through training, by exposing the algorithm to more data. Unlike a system that performs a task by following explicit rules, a machine learning system learns from experience. Machine learning is a branch of artificial intelligence that includes methods, or algorithms, for automatically creating models from data.
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