By Kai-Fu Lee, CEO, Sinovation Ventures
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.
Such algorithms don’t operate in a vacuum. To perform their analyses, they require huge sets of data to train on and vast computational power to process it all. Today’s A.I. also functions only in clearly defined single domains. It’s not capable of generalized intelligence or common sense—AlphaGo, for example, which beat the world’s masters in the ancient game of Go, does not play chess; algorithms trained to determine loan underwriting, likewise, cannot do asset allocation.
With deep learning and the data explosion as catalysts, A.I. has moved from the era of discovery to the era of implementation. For now, at least, the center of gravity has shifted from elite research laboratories to real-world applications. In essence, deep learning and big data have boosted A.I. onto a new plateau. Companies and governments are now exploring that plateau, looking for ways to apply present artificial intelligence capabilities to their activities, to squeeze every last drop of productivity out of this groundbreaking technology (see our next story). This is why China, with its immense market, data, and tenacious entrepreneurs, has suddenly become an A.I. superpower.
What makes the technology more powerful still is that it can be applied to a nearly infinite number of domains. The closest parallel we’ve seen up until now may well be electricity. The current era of A.I. implementation can be compared with the era in which humans learned to apply electricity to all the tasks in their life: lighting a room, cooking food, powering a train, and so on. Likewise, today we’re seeing the application of A.I. in everything from diagnosing cancer to the autonomous robots scurrying about in corporate warehouses.
This essay is adapted from Lee’s new book, AI Superpowers: China, Silicon Valley, and the New World Order (Houghton Mifflin Harcourt). He is the chairman and CEO of Sinovation Ventures and the former president of Google China.
Read the source article in Fortune.