For many, the expression “artificial intelligence” (AI) conjures up images of a dystopian future in which humans are ruled by malevolent computers or androids. In our real-world, not-quite-dystopic lives, AI is responsible for driving autonomous vehicles, powering intelligent assistants such as Alexa and Siri, and placing annoying advertisements on the web pages we frequently view. Yet, AI is also improving many aspects of pediatric medicine, and in the not-too-distant future AI will dramatically change the way we practice.
AI, machine learning, and deep learning
Simply stated by Merriam-Webster, artificial intelligence is the “capability of a machine to imitate intelligent human behavior.” It is a generic term, and it is important to understand that computers—machines—can be programmed with a series of “if-then” statements that give the appearance of “intelligence.” A good example would be web- or software-based programs used to prepare taxes. There is nothing natively “intelligent” about these programs, but they accomplish something that a human—eg, an accountant—does routinely.
Machine learning (ML) is a subset of AI, with its programs utilizing algorithms to modify themselves by responding to inputted data (Figure 1). Such ML programs can be presented with labeled data and perform “supervised learning,” or be taught to extract data from unlabeled data, which is to perform “unsupervised” learning. Supervised ML can detect faces, identify objects in images, transcribe speech to text, and classify text as spam. Unsupervised ML can compare documents for keywords, detect anomalies in images, and predict changes in health status. Whereas ML programs are capable of some autonomy, human programmers need to modify code when errors occur.
Deep learning (DL) is a subset of ML that is dependent on the development of neural networks. These networks consist of layered sets of algorithms, modeled after the human brain, to recognize patterns within data. Thus, DL systems can modify their algorithms independent of human programming. The layers are made of computational nodes that determine which information should be passed on to subsequent nodes (Figure 2). The more data provided to DL systems, the better they become at doing what they were designed to do. Over the last 10 to 20 years, DL systems have evolved significantly. In the past they beat humans at chess, on the game show “Jeopardy,” and most recently at the Chinese game of Go, which requires many magnitudes of calculations more than chess.
IBM, creator of Watson, the AI system that communicates with users via human-like speech, has coined the alliterative term “cognitive computing” to encompass AI, ML, and DL. The term was adopted to give a human spin on the use of AI systems, representing IBM’s belief that Watson and its offspring will complement human judgement and experience rather than replace them. Other AI experts suggest replacing the term “artificial intelligence” with “augmented intelligence” to convey a similar message.
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