From the street, through Beijing's heavy smog, it can sometimes be hard to make out IBM's Chinese headquarters: a towering office building with a distinctive undulating architectural flourish and a large company logo at the top. But just a short distance away, on the northeast outskirts of the capital, IBM computer scientists are using artificial intelligence to develop what they think will be a way to manage China's notorious and chronic pollution problem more successfully. The team is using complex computer models and machine learning to calculate how pollution will spread across the city. The researchers can now produce pollution forecasts, with a resolution of a kilometer square, up to 10 days in advance. These predictions can also tell the government how it might act to avoid the worst scenarios--for instance, by shutting certain factories, or by reducing the number of cars on the road.

I had thought my job was safe from automation--a computer couldn't possibly replicate the complex creativity of human language in writing or piece together a coherent story. I may have been wrong. Authors beware, because an AI-written novel just made it past the first round of screening for a national literary prize in Japan. The novel this program co-authored is titled, The Day A Computer Writes A Novel. It was entered into a writing contest for the Hoshi Shinichi Literary Award.

Full Tilt Poker is the second largest online poker room; it belongs to Rational FT which is a computer services company that develops software, business computer maintenance and performs operations based digital marketing. Two fundamental points of running a poker room online are the integrity of the site and the trust of customers. Players must be completely sure that they are protected against fraud. Negative publicity on this subject has a direct and immediate impact on customer loyalty, the ability to attract new customers and sales. The difficulty here is the volume of data to process, the complexity of the analysis, the difficulty of detecting all forms of fraud and costs.

Episodic gaming has long been dominated by narrative-driven puzzle adventures, making it hard to imagine the format being used for anything else. Enter Hitman, a pseudo-reboot of the series that simultaneously refines its stealth-focused gameplay while also delivering bite-sized portions of assassination action. A training mission prologue delves into the origins of Agent 47, the genetically enhanced titular hitman, before turning you loose in Paris to dispatch a fashion designer with designs on exposing undercover MI6 agents. Although missions have specific goals, the game excels by offering almost total freedom in how to execute them. Infiltrate a party in disguise and poison a target, or arrange unfortunate accidents to take them out.

At SVAIL (Silicon Valley AI Lab), our mission is to create AI technology that lets us have a significant impact on hundreds of millions of people. We believe that a good way to do this is to improve the accuracy of speech recognition by scaling up deep learning algorithms on larger datasets than what has been done in the past. These algorithms are very compute intensive, so much so that the memory capacity and computational throughput of our systems limits the amount of data and the size of the neural network that we can train. So a big challenge is figuring out how to run deep learning algorithms more efficiently. Doing so would allow us to train bigger models on bigger datasets, which so far has translated into better speech recognition accuracy.

Just for clarification UTC is the time at 0' Longitude. Probably worth pointing out that the Royal Observatory at Greenwich in London is also at 0' Longitude.

Shortly after walking through the front doors of Baidu in Beijing last November, I was surprised to notice that my face had transformed into that of a cheerful- looking little dog. As I chatted with one of Baidu's AI researchers, the version of me shown on his smartphone had sprouted a very realistic-looking wet snout, fluffy ears, and a big pink tongue. The trick was performed on an app called Face You, released by Baidu last Halloween, which lets you add all sorts of spooky effects or animal characteristics to a digital image of your face. Face You makes use of an AI technique called deep learning to automatically identify key points on a person's face, so that software can then position and stretch a virtual mask with amazing accuracy. Deep learning is driving a lot more than just goofy apps at Baidu, though.

Major portions of this series of posts are excerpts from my new book Augmented: Life in the Smart Lane. I also asked my Facebook followers if there were any questions they'd like answered about AI here, and I've tried to incorporate answers to those questions into this series of posts also. Deep learning is a term we're increasingly using to describe how we teach Artificial Intelligence (AI) to absorb new information and apply it in their interactions with the real world. In an interview with the Guardian newspaper in May 2015, Professor Geoff Hinton, an expert in artificial neural networks, said Google is "on the brink of developing algorithms with the capacity for logic, natural conversation and even flirtation." Google is currently working to encode thoughts as vectors described by a sequence of numbers.

TOKYO Advances in artificial intelligence are blurring the line between humans and robots. As robots interact ever more closely with us, new ethical questions are emerging related to issues from violence to sex and privacy. In February, a video uploaded to YouTube by Boston Dynamics, an American robot developer, sparked controversy. Some viewers were apparently shocked by a scene in which a man knocks down a box that was being lifted by a two-legged humanoid robot, developed by the company, and another scene in which the man knocks the robot down from behind with a stick. "Stop bullying robots," one viewer commented below the video.

Deloitte today announced an alliance with Kira Systems to bring the power of machine learning to the workplace, an innovation that could help free workers from the tedium of reviewing contracts and other documents. The alliance will combine Deloitte's business insights in cognitive technologies with Kira Systems' advances in machine-learning in creating models that quickly "read" thousands of complex documents, extracting and structuring textual information for better analysis. This capability holds broad applications for the marketplace, said Craig Muraskin, Deloitte LLP, managing director of Deloitte's US Innovation group, as the extensive review of documents goes into many pressing business activities, including investigations, mergers, contract management, and leasing arrangements. "Wading through miles of corporate jargon hunting for key words and patterns can consume considerable time and resources," said Muraskin. "By teaming with Kira Systems we can help organizations reduce their review time while redeploying talent to higher value activities--let's save our eyes for more strategic matters." Noah Waisberg, CEO of Kira Systems, said recent innovations by his company, such as Kira Quick Study, are graduating machine learning to new levels of accomplishment.