Something strange happened in the world of artificial intelligence (AI) on Wednesday. Facebook CEO Mark Zuckerberg posted on his Facebook profile that his company has created an AI system that is "getting close" to beating the best humans at Chinese board game Go. Hours later, DeepMind -- a startup based in London that was bought by Google for 400 million in 2014 -- said it had already developed an AI named AlphaGo that had just beaten the best Go player in Europe. DeepMind's breakthrough was splashed across the front cover of science journal Nature yesterday evening and covered by over 200 media titles. "This is the first time that a computer Go program has defeated a human professional player, without handicap, in the full game of Go - a feat that was previously believed to be at least a decade away," explained the DeepMind research paper -- Mastering the game of Go with deep neural networks and tree search.

Now there's a better way to cheat at crossword puzzles--with artificial intelligence. Researchers from three universities designed a system that uses a type of AI known as deep learning to help computers understand language more quickly and effectively. As a bonus, the researchers built a web-based tool that's handy when a particularly baffling crossword clue comes around. The crossword assistant is just a demo of how deep learning is improving machines' ability to understand language, said Felix Hill, a Ph.D. candidate at the University of Cambridge and lead author of a paper describing the research. Learning language is challenging for computers because it's difficult to recreate the rich and diverse information sources available to humans when they learn to speak and read, he said. Researchers focused on helping computers understand phrases.

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The manufacturing hub for the electronics industry, Kunshan, in Jiangsu province, is seeking a drastic reduction in labour costs as it undergoes a makeover after an industrial explosion killed 146 people in 2014. The county, one-seventh the size of neighbouring Shanghai and the mainland's first county to achieve US 4,000 per capita income, was adjudged the best county for its economic performance by Forbes for seven years in a row. However, the blaze, blamed on poor safety standards and haphazard industrialisation, dented Kunshan's pride. More than a year on, the county, which attracts much of its investment from Taiwan, is trying to reinvent its growth strategy. It is accelerating growth by replacing humans with robots and encouraging start-ups.

As more and more civilian drones are being flown freely, the threat to commercial airspace is increasing. Over the past two years, the Federal Aviation Administration (FAA) has received countless reports of incidents related to these objects in the skies. As a result, the FAA is expanding its research into ways to detect these unmanned aircraft, in hopes that it will be able to provide more security for airports. In fact, the FAA borrowed an FBI drone detection system and field-tested it at the John F. Kennedy International Airport (JFK) last week. The FAA's press release details 40 trial runs involving the deployment of five different rotor and fixed-wing drones.

Tech journals and TV segments are overflowing with them. But bots have been around for a very long time. Microsoft's relentlessly chirpy paperclip assistant "Clippy" seems to have morphed into the PR train wreck of Tay. The global titans of social technology are bullish about a brave new Artificial Intelligence (AI)-powered world of enhanced apps. Last month Microsoft CEO Satya Nadella proclaimed that "bots are the new apps" during the company's Build developers conference.

I love the feeling of having a new way to think about the world. I especially love when there's some vague idea that gets formalized into a concrete concept. Information theory is a prime example of this. Information theory gives us precise language for describing a lot of things. How uncertain am I? How much does knowing the answer to question A tell me about the answer to question B? How similar is one set of beliefs to another? I've had informal versions of these ideas since I was a young child, but information theory crystallizes them into precise, powerful ideas. These ideas have an enormous variety of applications, from the compression of data, to quantum physics, to machine learning, and vast fields in between. Unfortunately, information theory can seem kind of intimidating. I don't think there's any reason it should be. In fact, many core ideas can be explained completely visually! Before we dive into information theory, let's think about how we can visualize simple probability distributions. We'll need this later on, and it's convenient to address now. As a bonus, these tricks for visualizing probability are pretty useful in and of themselves! Sometimes it rains, but mostly there's sun! Let's say it's sunny 75% of the time. It's easy to make a picture of that: Most days, I wear a t-shirt, but some days I wear a coat. Let's say I wear a coat 38% of the time. It's also easy to make a picture for that! What if I want to visualize both at the same time?

At its I/O Developers Conference this week, Google announced a new product that had been an open secret for several months. The Tensor Processing Unit (TPU) is named after the TensorFlow software it uses for its machine learning programs. In announcing the chip, Google referred to the TPU as an accelerator chip, able to speed up a given task, like data analysis or voice translation. When he introduced the TPU at the I/O conference, Google CEO Sundar Pichai said it provides an order of magnitude better performance per watt than existing chips for machine learning tasks. While it's unlikely to usurp CPUs and GPUs already in use in the machine learning world, the TPU could potentially speed the machine learning process without using much more energy.

Most autonomous vehicle control software is deliberately designed for well-constrained driving that's nice, calm, and under control. Not only is this a little bit boring, it's also potentially less safe: If your car autonomous vehicle has no experience driving aggressively, it won't know how to manage itself if something goes wrong. At Georgia Tech, researchers are developing control algorithms that allow small-scale autonomous cars to power around dirt tracks at ludicrous speeds. They presented some this week at the 2016 IEEE International Conference on Robotics and Automation in Stockholm, Sweden. Using real-time onboard sensing and processing, the little cars maximize their speed while keeping themselves stable and under control.

Can artificial intelligence lay paint to paper to create meaning and evoke emotion? The answer lies in the results from the first Robot Art competition. The contest, organized by artist Andrew Conru who holds a Ph.D. in mechanical engineering design from Stanford University, aims to foster innovation in A.I. and integrate aesthetics and technology. It was open to students from universities and high schools and awarded cash prizes in two categories: fully automated execution or manually generated. Here are the results from the 10 winners, which were chosen thanks to feedback from the public as well as that from a panel of judges.