This approach is supervised because the model infers an algorithm from feature-target pairs and is informed, by the target, whether it has predicted correctly. 8, 9 Supervised learning uses patterns in the training dataset to map features to the target so that an algorithm can make housing price predictions on future datasets. 8, 9, 11 Datasets are generally split into training, validation, and testing datasets (models will always perform optimally on the data they are trained on).
8, 9, 11 The target is the feature to be predicted, in this case the housing price. Features are the recorded properties of a house that might be useful for predicting prices (e.g., total square-footage, number of floors, the presence of a yard). 8, 9, 11 Each instance represents a singular observation of a house and associated features.
To begin, the company would first gather a dataset that contains many instances. Suppose the real estate company would like to predict the price of a house based on specific features of the house. This review summarizes machine learning and deep learning methodology for the audience without an extensive technical computer programming background. These more complicated tasks are where ML and DL methods perform well. Although symbolic AI is proficient at solving clearly defined logical problems, it often fails for tasks that require higher-level pattern recognition, such as speech recognition or image classification. For example, if one were to program an algorithm to modulate room temperature of an office, he or she likely already know what temperatures are comfortable for humans to work in and would program the room to cool if temperatures rise above a specific threshold and heat if they drop below a lower threshold. These rules, written by humans, come from a priori knowledge of the particular subject and task to be completed. However, AI includes approaches that do not involve any form of “learning.” For instance, the subfield known as symbolic AI focuses on hardcoding (i.e., explicitly writing) rules for every possible scenario in a particular domain of interest. That is, they are within the realm of AI ( Fig. 1). In 1956, a group of computer scientists proposed that computers could be programmed to think and reason, “that every aspect of learning or any other feature of intelligence, in principle, be so precisely described that a machine be made to simulate it.” 7 They described this principle as “artificial intelligence.” 7 Simply put, AI is a field focused on automating intellectual tasks normally performed by humans, and ML and DL are specific methods of achieving this goal.
In this review, we (attempt to) forgo technical jargon to better explain these concepts to a clinical audience. The terms are highly associated, but are not interchangeable. 1 – 6 Yet there still remains confusion around AI, ML, and DL. Over the past decade, artificial intelligence (AI) has become a popular subject both within and outside of the scientific community an abundance of articles in technology and non-technology-based journals have covered the topics of machine learning (ML), deep learning (DL), and AI.
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