The Four Waves of A.I. By Kai-Fu Lee, October 22, 2018
The term ‘artificial intelligence” was coined in 1956, at a historic conference at Dartmouth, but it has been only in the past 10 years, for the most part, that we’ve seen the first truly substantive glimpses of its power and application. A.I., as it’s now universally called, is the pursuit of performing tasks usually reserved for human cognition: recognizing patterns, predicting outcomes clouded by uncertainty, and making complex decisions. A.I. algorithms can perceive and interpret the world around us—and some even say they’ll soon be capable of emotion, compassion, and creativity—though the original dream of matching overall “human intelligence” is still very far away.
What changed everything a decade or so ago was an approach called “deep learning”—an architecture inspired by the human brain, with neurons and connections. As the name suggests, deep-learning networks can be thousands of layers deep and have up to billions of parameters. Unlike the human brain, however, such networks are “trained” on huge amounts of labeled data; then they use what they’ve “learned” to mathematically pick out and recognize incredibly subtle patterns within other mountains of data. A data input to the network can be anything digital—say, an image, or a sound segment, or a credit card purchase. The output, meanwhile, is a decision or prediction related to whatever question might be asked: Whose face is in the image? What words were spoken in the sound segment? Is the purchase fraudulent?
This technological breakthrough was paralleled with an explosion in data—the vast majority of it coming from the Internet—which captured human activities, intentions, and inclinations. While a human brain tends to focus on the most obvious correlations between the input data and the outcomes, a deep-learning algorithm trained on an ocean of information will discover connections between obscure features of the data that are so subtle or complex we humans cannot even describe them logically. When you combine hundreds or thousands of them together, they naturally outstrip the performance of even the most experienced humans. A.I. algorithms now beat humans in speech recognition, face recognition, the games of chess and Go, reading MRIs for certain cancers, and any quantitative field—whether it’s deciding what loans to approve or detecting credit card fraud.
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