Facebook is now using artificial intelligence to automatically generate captions for photos in the News Feed of people who can't see them. The tool is called Automatic Alternative Text, and it dovetails with text-to-speech engines that allow blind people to use Facebook in other ways. Using deep neural networks, the system can identify particular objects in a photo, from cars and boats to ice cream and pizza. It can pick out particular characteristics of the people in the photo, including smiles and beards and eyeglasses. And it can analyze a photo in a more general sense, determining that a photo depicts sun or ocean waves or snow.

You can find this dataset in this archive. It is not perfect but will work in a pinch.

A few days before Thanksgiving, George Hotz, a 26-year-old hacker, invites me to his house in San Francisco to check out a project he's been working on. He says it's a self-driving car that he had built in about a month. But when I turn up that morning, in his garage there's a white 2016 Acura ILX outfitted with a laser-based radar (lidar) system on the roof and a camera mounted near the rearview mirror. A tangle of electronics is attached to a wooden board where the glove compartment used to be, a joystick protrudes where you'd usually find a gearshift, and a 21.5-inch screen is attached to the center of the dash. "Tesla only has a 17-inch screen," Hotz says. He's been keeping the project to himself and is dying to show it off. Hotz fires up the vehicle's computer, which runs a version of the Linux operating system, and strings of numbers fill the screen. When he turns the wheel or puts the blinker on, a few numbers change, demonstrating that he's tapped into the Acura's internal controls. After about 20 minutes of this, and sensing my skepticism, Hotz decides there's really only one way to show what his creation can do. "Screw it," he says, turning on the engine. As a scrawny 17-year-old known online as "geohot," Hotz was the first person to hack Apple's iPhone, allowing anyone--well, anyone with a soldering iron and some software smarts--to use the phone on networks other than AT&T's.

It doesn't really have a name, and not much of a personality. But a robot created by Aviano middle and high schoolers performed well enough to qualify for the FIRST Tech Challenge World Championships. At a recent competition in Prague, the robot flipped over three times, said Julia Markel, the lead programmer on the 13-member team, which calls itself Robotica Santi Robotics Club. But it rallied enough to achieve one of the team's top goals: climb up a ramp and do a pull-up. "We were one of the only teams that were able to do that," said Dave Izzo, the club's adviser.

Horizon Robotics has a very new technology that will help foster the rise of appliances operated through artificial intelligence. Founded by Institute of Deep Learning, Mainland Chinese start up Horizon Robotics is now claiming it is on the verge of completing the most advanced technology in the market with chips having built-in artificial intelligence or AI for the consumers. "General processors are too slow for AI functions. A dedicated chip will dramatically increase the speed of these functions," Yu Kai, the founder and chief executive of Horizon Robotics according to a report by South China Morning Post. It was remembered that Horizon Robotics was founded in July in Beijing and is now already developing software and chips that mimics how human brains works and solves tasks such as image and voice recognition.

Ever since the launch of Siri in its fully-integrated form on the iPhone 4s in 2011, digital assistants have become standard features on most modern smartphones. With competition growing from Microsoft, Google, and Amazon with their Cortana, Google Now, and Echo respectively, Apple continues providing updates to Siri in an attempt to find a semblance of functional advantage. As Google recently added changes to its digital assistant – Google Now – with smart intonation and expression to its speech patterns to sound less robotic, Apple has followed with their own updates looking to target Siri towards specific audiences; in this case, sports fans. Leading up to the opening days of this year's Major League baseball season, Apple has dramatically increased Siri's knowledge of and access to in-depth baseball knowledge and statistics. Though Apple added sports scores to Siri's functionality back with iOS6, previously, when asked specific baseball-related questions, Siri would typically respond with a simple search or Google queries.

Microsoft came up with the great idea of creating an artificial intelligence bot on Twitter, in the image of a teenage girl named Tay, and making her capable of learning through interactions with real people online. Within a few hours, she was a racist, a sexist and a Holocaust denier, and she was spewing all this with gusto, humor and abandon. Microsoft had to take her down before she started winning delegates for the presidential nomination. Microsoft tried to make some adjustments to Tay and put her back online, but she quickly started boasting about smoking drugs in front of the police. Tay reportedly now wants to hang out with Siri.

Codified narrative is the product of converting human-friendly narrative into computer-friendly code. In past blogs, I discussed my own approach towards this process of codification. Here, I will be covering the idea of spatial, temporal, and contextual distribution of codified narrative. I have never suggested that narrative can or should be used in place of quantitative data. However, I have reflected on how the quantitative regime has tended to dominate discourse; this has perhaps led to data being contextually constrained or deprived. Geography is a type of context that can shape the extent to which people interact with the world. Space provides a medium to distribute resources. It can be involved in forced confinement. An office full of cubicles demonstrates control and dominance over space.

Superfast quantum computers could transform the world of finance, advocates say. In a world where how fast you can assimilate and analyse data then act on it, makes the difference between profit and loss, computing speed is key. This is why banks, insurance firms, and hedge funds invest millions on technology to give them an edge when trading, and to offset human error. Quantum computers, that owe more to nuclear quantum mechanics than electronics, promise to be exponentially more powerful than traditional computers, holding out the tantalising prospect of near-perfect trading strategies and highly accurate forecasting and risk assessments. "Financial services is a data-rich environment," says Kevin Hanley, director of design at the Royal Bank of Scotland (RBS).

The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses'value networks' to evaluate board positions and'policy networks' to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.