Machine learning is the process of using computers to detect patterns in massive datasets and then make predictions based on what the computer learns from those patterns. This makes machine learning a specific and narrow type of artificial intelligence. Full artificial intelligence involves machines that can perform abilities we associate with the minds of human beings and intelligent animals, such as perceiving, learning, and problem-solving.

All machine learning is based on algorithms. In general, algorithms are sets of specific instructions that a computer uses to solve problems. In machine learning, algorithms are rules for how to analyze data using statistics. Machine learning systems use these rules to identify relationships between data inputs and desired outputs–usually predictions. To get started, scientists give machine learning systems a set of training data. The systems apply their algorithms to this data to train themselves how to analyze similar inputs they receive in the future.

One area where machine learning shows huge promise is detecting cancer in computer tomography (CT) imaging. First, researchers assemble as many CT images as possible to use as training data. Some of these images show tissue with cancerous cells, and some show healthy tissues. Researchers also assemble information on what to look for in an image to identify cancer. For example, this might include what the boundaries of cancerous tumors look like. Next, they create rules on the relationship between data in the images and what doctors know about identifying cancer. Then they give these rules and the training data to the machine learning system. The system uses the rules and the training data to teach itself how to recognize cancerous tissue. Finally, the system gets a new patient’s CT images. Using what it has learned, the system decides which images show signs of cancer, faster than any human could. Doctors could use the system’s predictions to aid in the decision about whether a patient has cancer and how to treat it.

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