In open sourcing its artificial intelligence engine--freely sharing one of its most important creations with the rest of the Internet--Google showed how the world of computer software is changing. These days, the big Internet giants frequently share the software sitting at the heart of their online operations. Open source accelerates the progress of technology. In open sourcing its TensorFlow AI engine, Google can feed all sorts of machine-learning research outside the company, and in many ways, this research will feed back into Google. But Google's AI engine also reflects how the world of computer hardware is changing.

Frances Haugen was part of the first wave of people to use Google back in 1996. Her mother, a faculty member at the University of Iowa1, showed her the search engine, which was still a research project at Stanford University. Haugen was blown away at what Larry Page and Sergey Brin had built. "The idea that you could actually peer into a giant mountain of data was amazing," she says. Haugen has been obsessed with search technology ever since.

Tonight, Tesla makes its cars autonomous. And it did it with an over-the-air update, effectively making tens of thousands of cars already sold to customers way better. There are two things to talk about here. There's the small story about the features and what the upgrade actually looks like and how it works. That's a good place to start: This is the biggest change to the visual display of the Model S and X ever.

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.

Computer vision software has a long way to go. We know it can have trouble recognizing even the simplest of images, so it's little surprise the human face, in all its complexity, is an inevitable stumbling block. But what happens when you intentionally distort a face? In a new project, two artists exploit the technology's shortcomings to produce wonky-looking portraits that test the limits of facial recognition software. Unseen Portraits is the work of Philipp Schmitt and Stephan Bogner, both designers from Germany.

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.

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.

One of the best things about videogames these days is that you can play against your friends, even if they're not on the same continent as you. With the Forza racing series, Microsoft's Turn 10 Studios has taken that a step further: Gamers can race against their friends, even when their friends are offline. Forza Horizon 2, available for Xbox One on September 30, is different from last year's Forza 5, which aims to offer an exact reproduction of real-world racing. Instead of being limited to tracks, Horizon 2 drivers have access to a huge chunk of southern France and Italy, from Nice to Castelletto (complete with stunning visuals and realistic weather). Even better, unlike the original, Colorado-based Forza Horizon, drivers aren't limited to roadways.

Mark Zuckerberg recently handpicked the longtime NYU professor to run Facebook's new artificial intelligence lab. The IEEE Computational Intelligence Society just gave him its prestigious Neural Network Pioneer Award, in honor of his work on deep learning, a form of artificial intelligence meant to more closely mimic the human brain. And, perhaps most of all, deep learning has suddenly spread across the commercial tech world, from Google to Microsoft to Baidu to Twitter, just a few years after most AI researchers openly scoffed at it. All of these tech companies are now exploring a particular type of deep learning called convolutional neural networks, aiming to build web services that can do things like automatically understand natural language and recognize images. At China's Baidu, they drive a new visual search engine.

Singapore's Changi airport is the best in the world and an awesome place to spend a long layover. So it's kind of a bummer that it's getting a real-time data system that will cut delays by rethinking its operations and streamlining how its moves planes, people, and personnel. It's easy to track people and goods as they move around these days, but that power hasn't really been put to use making airports less horrible. That's why ST Electronics, a subsidiary of Singapore's largest technology and defense contractor, ST Engineering, is partnering up with Changi on a solution called Intelligent Airport. The SimCity-esque system will collect information like precise aircraft arrival times, the location of airport assets and personnel, and crowd movement, and make it available in one place, so everything flows more smoothly and delays can be cut down.