Startup Civil Maps has a plan to get human-piloted cars to hasten their own demise. Autonomous vehicles like those being tested by Google, Uber, and major automakers rely on 3-D maps that record the position of curbstones and traffic lights with high accuracy. The maps are usually created by driving around in vehicles outfitted with expensive sensors. Civil Maps wants to use consumer cars as a low-cost mapping workforce instead, taking advantage of the sensors being added to premium models for advanced cruise control and crash avoidance. Those cheaper sensors can't match those in a dedicated mapping vehicle.

About halfway through a particularly tense game of Go held in Seoul, South Korea, between Lee Sedol, one of the best players of all time, and AlphaGo, an artificial intelligence created by Google, the AI program made a mysterious move that demonstrated an unnerving edge over its human opponent. On move 37, AlphaGo chose to put a black stone in what seemed, at first, like a ridiculous position. It looked certain to give up substantial territory--a rookie mistake in a game that is all about controlling the space on the board. Two television commentators wondered if they had misread the move or if the machine had malfunctioned somehow. In fact, contrary to any conventional wisdom, move 37 would enable AlphaGo to build a formidable foundation in the center of the board. The Google program had effectively won the game using a move that no human would've come up with. AlphaGo's victory is particularly impressive because the ancient game of Go is often looked at as a test of intuitive intelligence. The rules are quite simple. Two players take turns putting black or white stones at the intersection of horizontal and vertical lines on a board, trying to surround their opponent's pieces and remove them from play.

Talking toys have come a long way since the original Furby. Now they're connected to the Internet, use speech recognition, and are raising a host of new questions about the online privacy and security of children. Hackers have already targeted toys. Late last year, Hong Kong-based digital toy maker Vtech admitted that cybercriminals accessed the personal information of 6.4 million children. Researchers have also shown how hackers can gain control of connected dolls.

The blockish and slightly dorky computer game Minecraft may turn out to be a great place for humans and AI to learn how to work together. An experimental new version of the game, released by Microsoft researchers this month, can be used to train an AI to perform all sorts of tasks, from crossing bridges to building complex objects. The new platform, called Project Malmo, makes it possible for a learning algorithm to control a Minecraft character that's normally operated by a human player. But it also provides ways for human players and AI agents to work together, and a chat window through which a person can talk with a nascent AI. "In the long run I want to work toward AI that can be taught by any user to help them achieve their goals," says Katja Hoffman, a researcher at Microsoft Cambridge in the U.K. who leads the project. Hoffman, who gave a demo of the software to AI researchers at an academic conference in New York last week, says that human-AI collaboration is a key goal for the project: "We've built in all the capabilities that a researcher would need in order to work toward collaborative AI." Malmo is geared toward testing reinforcement-learning algorithms, a way of training a computer to perform a task by providing simulated rewards.

Is your garage door opening right now? Is your washing machine running? A growing number of products attempt to give consumers data on the sources of their household energy use--crucial data for home efficiency efforts and utility peak-hour conservation programs. But Sense, a startup in Cambridge, Massachusetts, is the first to offer a consumer product that reads incoming household power levels a million times per second--enough to tease out telltale clues to which specific appliances, even low-wattage ones, are operating in real time. "It's at the cutting edge of what I have seen people attempting in this area," says Michael Baker, a vice president at SBW, an energy efficiency consultancy in Seattle.

Investigations by the National Highway Traffic Safety Administration and the National Transportation Safety Board into accidents involving the company's Autopilot technology might suggest as much. In an industry that can seem ridiculously old-fashioned and slow-moving, it may be hard to know just how far to push. And Tesla has forged a remarkable success story by taking risks and breaking the conventions of carmaking with new technologies. With all the excitement over electric vehicles, self-driving technologies, and Uber-like apps, it can be easy to forget that the car industry is more than 100 years old, and nearly impossible for a startup to break into. It is fiercely competitive and is built on razor-thin margins.

Everything we know about the face recognition systems the FBI and police use suggests the software has a built-in racial bias. That isn't on purpose--it's an artifact of how the systems are designed, and the data they are trained on. Law enforcement agencies are relying more and more on such tools to aid in criminal investigations, increasing the risk that something could go wrong. Law enforcement agencies haven't provided many details on how they use facial recognition systems, but in June the Government Accountability Office issued a report saying that the FBI has not properly tested the accuracy of its face matching system, nor that of the massive network of state-level face matching databases it can access. And while state-of-the-art face matching systems can be nearly 95 percent accurate on mugshot databases, those photos are taken under controlled conditions with generally coöperative subjects.

A half-dozen young, mostly male engineers sit in a bright, open office digging through a mountain of code. Multi-monitor work stations sit on desks strewn with laptops, tablets, and headphones. This is not a tech startup, however. Today these factory workers are culling immediate production inefficiencies and developing systems that they hope will, by next year, allow the factory's machines to diagnose their own problems, order replacement parts, and anticipate necessary maintenance hours or even weeks in advance. Through a bank of large windows opening from the computer room onto the shop floor, row after row of machines hum a percussive melody as they turn out small parts--fuel injection nozzles, electronic safety control systems, mechanical brake systems--destined for car companies including BMW, Volkswagen, and Tesla.

The FBI has access to nearly 412 million facial photos in its facial recognition system--perhaps including the one on your driver's license. But according to a new government watchdog report, the bureau doesn't know how error-prone the system is, or whether it enhances or hinders investigations. Since 2011, the bureau has quietly been using this system to compare new images, such as those taken from surveillance cameras, against a large set of photos to look for a match. That set of existing images is not limited to the FBI's own database, which includes some 30 million photos. The bureau also has access to face recognition systems used by law enforcement agencies in 16 different states, and it can tap into databases from the Department of State and the Department of Defense.

The headquarters of Alphabet's X labs in Mountain View, California, is easy to miss. A simple yellow "X" marks the visitors' entrance to the sprawling building that was once a large indoor shopping mall. But on a weekday in late May, the parking lot is bustling, filled with employees and visitors, as X's pod-like driverless cars buzz about. Inside, various teams of mostly young people--the company won't say just how many people are employed at the facility--work on "moon shots," which Alphabet defines as transformative technologies that could have a huge impact on the world. Besides the driverless cars, publicly identified projects at X include Loon, an effort to use high-altitude balloons to deliver the Internet to remote regions of the world; Wing, which is building self-navigating drones for delivering stuff; and Makani, which is developing odd flying wind turbines tethered to a ground station.