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.

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.

Artificial Intelligence is the science and engineering of making computer machines able to perform tasks which normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. It is a branch of the Computer Science that aims to develop intelligent computer machines. Scope of Artificial Intelligence: The ultimate effort is to make computer programs that can solve problems and achieve goals in the world, as well as humans. There is a scope in developing the machines in game playing, speech recognition machine, language detection machine, computer vision, expert systems, robotics and many more. What should you study before or while learning AI? Study mathematics, especially mathematical logic.

For Robert Welborn, head of data science for the insurer and finance company USAA, 2015 was the year machine learning started to make commercial sense. Access to improved machine-learning tools, cheaper processing technology, and a sharp decline in the cost of storing data were key. When those developments were combined with USAA's abundance of data, a technology studied for decades suddenly seemed practical. Insurance, finance, manufacturing, oil and gas, auto manufacturing, health care: these may not be the industries that first spring to mind when you think of artificial intelligence. But as technology companies like Google and Baidu build labs and pioneer advances in the field, a broader group of industries are beginning to investigate how AI can work for them, too. How will AI develop as it is commercialized, and how will the technology change these diverse industries?