Lee Sedol is rather surprised that Google has fashioned an artificially intelligent system that so skillfully plays the ancient game of Go. But after losing to this Google creation Wednesday in the opening contest of a five-game match here in South Korea--a match that will test the progress of modern AI--the young Go grandmaster believes he can recover lost ground. I can admit that," Lee Sedol said through an interpreter during the post-game press conference at Seoul's Four Seasons hotel. "But what's done is done." Prior to Wednesday's game, he was quite confident he would beat Google's system, known as AlphaGo, and afterwards, he indicated that such confidence may have contributed to his loss. "The failure I made at the very beginning of the game lasted until the the very end," he said.

Last week, Ben Goertzel and his company, Aidyia, turned on a hedge fund that makes all stock trades using artificial intelligence--no human intervention required. "If we all die," says Goertzel, a longtime AI guru and the company's chief scientist, "it would keep trading." Goertzel and other humans built the system, of course, and they'll continue to modify it as needed. But their creation identifies and executes trades entirely on its own, drawing on multiple forms of AI, including one inspired by genetic evolution and another based on probabilistic logic. Each day, after analyzing everything from market prices and volumes to macroeconomic data and corporate accounting documents, these AI engines make their own market predictions and then "vote" on the best course of action. If we all die, it would keep trading.

The eyes of a self-driving car are called LIDAR sensors. LIDAR is a portmanteau of "light" and "radar." In essence, these sensors monitor their surroundings by shining a light on an object and measuring the time needed for it to bounce back. They work well enough, but they aren't without their drawbacks. Today's self-driving cars typically use LIDARs that are quite large and expensive.

When Jimoh Ovbiagele was ten years old, his parents decided to get a divorce. But as the couple got deeper into the process, the legal fees grew more and more expensive, until they ended up abandoning the whole plan. "It had a negative impact on my family," Ovbiagele says. In high school and beyond, when Ovbiagele was looking into various career options, he discovered that most of a lawyer's time is actually spent researching cases. Ovbiagele ended up studying computer science rather than law, but when he had the opportunity to pursue an artificial intelligence project at the University of Toronto, he had a pretty good idea of what he wanted to work on.

Dharmendra Modha walks me to the front of the room so I can see it up close. About the size of a bathroom medicine cabinet, it rests on a table against the wall, and thanks to the translucent plastic on the outside, I can see the computer chips and the circuit boards and the multi-colored lights on the inside. It looks like a prop from a '70s sci-fi movie, but Modha describes it differently. "You're looking at a small rodent," he says. He means the brain of a small rodent--or, at least, the digital equivalent. The chips on the inside are designed to behave like neurons--the basic building blocks of biological brains.

Yann LeCun is among those bringing a new level of artificial intelligence to popular internet services from the likes of Facebook, Google, and Microsoft. As the head of AI research at Facebook, LeCun oversees the creation of vast "neural networks" that can recognize photos and respond to everyday human language. And similar work is driving speech recognition on Google's Android phones, instant language translation on Microsoft's Skype service, and so many other online tools that can "learn" over time. Using vast networks of computer processors, these systems approximate the networks of neurons inside the human brain, and in some ways, they can outperform humans themselves. This week in the scientific journal Nature, LeCun--also a professor of computer science at New York University--details the current state of this "deep learning" technology in a paper penned alongside the two other academics most responsible for this movement: University of Toronto professor Geoff Hinton, who's now at Google, and the University of Montreal's Yoshua Bengio. The paper details the widespread progress of deep learning in recent years, showing the wider scientific community how this technology is reshaping our internet services--and how it will continue to reshape them in the years to come.

Computer vision is quickly advancing, but it tends to trickle into the world in scattered, specific applications. We encounter it when Facebook automatically tags a friend in a photo, or when Google suggests images similar to one we're searching for. But the real promise is much more exciting. A camera, properly trained, could answer simple, human questions like: "Are my kids home from school?" or "Is there a parking spot open at work?" or "How many people are in line at Shake Shack?" In other words, computer vision could make our homes and our cities smart.

Old-school financial institutions are typically slow-moving giants. And that's a shame, because banks also tend to accumulate deep troves of data on their customers that goes mostly untapped. Your usual recourse would probably involve a lot of digging through bank statements and bank website pages, or endless hours on the phone with a customer service rep. But as of late, a swell of banking startups are seeking to change this. They take all that undifferentiated data tucked into your bank statements, and then, harnessing artificial intelligence, transform and organize it into helpful information that people can actually understand--and act on.

Shortly after appearing on Israeli television with a new computer game you control merely by moving your head, Oded Ben Dov got a phone call. It came from a complete stranger who just happened to see this TV appearance, and he had a question. "I wasn't sure if it was a prank call or not, but then he started to say some serious stuff," Ben Dov remembers. The man on the other end of the line was Giora Livne, a former Israeli navy commander and electrical power engineer. He'd been quadriplegic for seven years, he explained, which made it impossible to use a smartphone without help.

When in 2012 a computer learned to recognize cats in YouTube videos and just last month another correctly captioned a photo of "a group of young people playing a game of Frisbee," artificial intelligence researchers hailed yet more triumphs in "deep learning," the wildly successful set of algorithms loosely modeled on the way brains grow sensitive to features of the real world simply through exposure. Using the latest deep-learning protocols, computer models consisting of networks of artificial neurons are becoming increasingly adept at image, speech and pattern recognition -- core technologies in robotic personal assistants, complex data analysis and self-driving cars. But for all their progress training computers to pick out salient features from other, irrelevant bits of data, researchers have never fully understood why the algorithms or biological learning work. Now, two physicists have shown that one form of deep learning works exactly like one of the most important and ubiquitous mathematical techniques in physics, a procedure for calculating the large-scale behavior of physical systems such as elementary particles, fluids and the cosmos. The new work, completed by Pankaj Mehta of Boston University and David Schwab of Northwestern University, demonstrates that a statistical technique called "renormalization," which allows physicists to accurately describe systems without knowing the exact state of all their component parts, also enables the artificial neural networks to categorize data as, say, "a cat" regardless of its color, size or posture in a given video.