A subfield of artificial intelligence (AI), machine learning concentrates on getting computers to perform tasks without requiring explicit programming. AI is the science that seeks to enable machines to think and act like human beings. Most often, data scientists or programmers “feed” computers with structured data, and eventually, the latter “learn” to become better at evaluating and acting on it.
Structured data is data that fits well into spreadsheets and is easy to work with for computers. For example, a programmer may create an Excel table with a category column “food” and multiple row entries such as “fruit,” “vegetable,” and “meat.” After they program the computer, the machine can ingest new data indefinitely and sort and act on it without requiring any further human intervention.
With time, the computer may identify “fruit” as a type of food, even if the scientist stops labeling the data. Labeled data contains the input and output parameters entirely in a machine-readable form. However, this requires significant human effort. With unlabeled data, none or only one of the parameters is in a machine-readable pattern, which minimizes human effort but requires more complex solutions.
Depending on the level of the computer’s “self-reliance” and required human help, machine learning falls under three basic types: supervised and semi-supervised, unsupervised, and reinforced.
According to its name, supervised machine learning entails the most ongoing human support. It is task-driven and involves training with accurately labeled data. Programmers feed the computer with the training data and an explicit model designed to “teach” it what to do with the data.
After placing the model, they can input more data to check how the computer responds. Programmers can confirm accurate responses or refine incorrect predictions. For example, in the case of teaching a computer to classify images, they can input multiple images and task it to classify each of them as they confirm or correct each output of the computer.
Eventually, the constant supervision allows for honing the model to the extent that computers can accurately handle new datasets, which follow the patterns they have learned. However, constantly monitoring and adjusting computers’ performance is not efficient.
With semi-supervised learning, programmers input a combination of correctly labeled and unlabeled data, and the computer must look for patterns on its own. The labeled data provides it with guidance, but no ongoing correction takes place. Unsupervised learning uses only unlabeled data. The computer can freely search for patterns and associations in line with its own “judgment,” thus generating results that even human data analysts might not have predicted.
The so-called “clustering” is commonplace in unsupervised machine learning. It involves computers grouping data into common themes and layers they have identified. This technology has widespread applications in e-commerce and online shopping websites, where it is used to decide what products to recommend to specific customers according to their past purchases.
Finally, reinforced machine learning simulates how humans learn from data in their daily lives and involves computers improving themselves and executing tasks through trial and error. While neither supervised nor unsupervised learning results in any consequences for the computer if it does not correctly understand or organize the data, with reinforced learning, they get encouraged or “rewarded” when producing favorable outputs, and discouraged or “punished” in the case of unfavorable ones.
Reinforced learning is crucial for aiding machines in mastering highly complex tasks that entail large, flexible, and unpredictable datasets. It also opens up possibilities for computers attempting to accomplish goals, such as driving cars, performing surgeries, or scanning luggage for potentially dangerous objects.