In a wide-ranging keynote address that included aspirational statements, updates, and announcements, Ginni Rometty, the chairwoman, president, and CEO of the Armonk, N.Y.-based IBM, on Tuesday morning shared her vision of cognitive computing with attendees at the World Health Care Congress, being held this week at the Marriott Wardman Park Hotel in Washington, D.C. Telling her audience that "Healthcare has been central to us for a long time," Rometty framed the broad work that IBM is doing in cognitive computing in terms of what she sees as its potential to change the healthcare industry in three fundamental ways: with regard to "how to reinvent discovery," how to "help change how delivery happens," and how to "transform wellness." Indeed, she said, "Cognitive computing is the future of healthcare," and said that IBM's work in that area, embodied in its development of IBM Watson, its cognitive computing entity, which IBM data scientists and technologists are using to transform knowledge in a broad range of areas. Framing IBM's broad strategic thrust around cognitive computing, Rometty told her audience, "Analytics, cloud, mobile--those are all very important to be a part of the digital society and economy. I always think of digital as foundational; I believe it is disruptive… It is the dawn of a new era. Think of digital business and business intelligence put together, and that will give you cognitive," she said.

Have trouble achieving a work-life balance? Well, Google Calendar might be able to help. On Wednesday, Google released Goals, a new feature for Google Calendar that uses machine intelligence to help users find time in their busy schedule to accomplish personal goals, such as exercising, reading more, or learning a new skill. Goals is on the Google Calendar mobile app, and can be used to tackle specific, categorized goals, or to create and pursue a custom personal goal. The new feature is now available everywhere that Calendar is. For those of us who live and die by what's on our calendars, stating our goals is a good start to achieving them.

I understand that ANNs are extremely useful in searching for patterns, but I've also heard that ANNs aren't too good at approximating convoluted mathematics. The argument was suggested it is difficult to express such relationships in a graph structure. I'm not sure if it's true, but I ask generally.

All of this firepower combines to give the Tesla P100 12 times the neural network training performance of its Maxwell-based predecessor. "Deep learning is not just research anymore it's going to be in every industry", said Huang. "It will open up markets to benefit everyone". Today, Chief Executive Jen-Hsun Huang signaled that he's aiming even higher in a bid to reinvent the data center and cloud computing. By our calculations, just over 300x DGX-1 systems would be able to out-process the current number-one supercomputer in the world, with only a fraction of the power (not to mention real-estate and ancillary cooling too).

I understand that ANNs are extremely useful in searching for patterns, but I've also heard that ANNs aren't too good at approximating convoluted mathematics. The argument was suggested it is difficult to express such relationships in a graph structure. I'm not sure if it's true, but I ask generally.

Chris Bishop, of Microsoft Research in Cambridge, has joined a growing list of experts, including Elon Musk and Professor Stephen Hawking, who have expressed their concerns about the threat of AI to the human race. The computer boffin painted a vision of a Terminator-style wasteland to illustrate the dangers of ultra-intelligent machines, but added that if managed correctly they could bring huge societal benefits.

In previous articles, we've defined some of the terms being thrown around lately like "machine learning" and "artificial intelligence". These disruptive technologies will soon change the world as we know it. While some pundits predicted that we were years away from a computer that could beat a human expert at "Go", this achievement was recently announced. If a "deep learning" program can now beat a game that has more possible moves than atoms in the known universe, then what's stopping us from unleashing it upon the stock market and making millions? The idea of using computers to trade stocks is hardly new.

As the co-founder of App Developer Magazine, Richard has several industry recognitions and endorsements from tech companies such as Microsoft, Apple and Google for accomplishments in the mobile market. He was part of the early Google AFMA program, and also involved in the foundation of Google TV. He has been developing for mobile since 2003 and serves as CEO of Moonbeam Development, a mobile app company with 200 published titles in various markets throughout the world. He has been a featured presenter at trade-shows and conferences, and stays active with new projects relating to mobile development.

Mark Zuckerberg at F8. "I've never met anyone who likes calling businesses." ANALYSIS: Love it or hate it, Messenger is Facebook's most important product. Already, Facebook the social network feels like the clunky backend of the real product, an app where you actually talk to your friends, who barely post anything personal on the site itself any more. It's not as splashy as Snapchat, but there's a reason it consistently beats those apps in engagement and app downloads. Sixty billion messages are sent a day on Messenger.

While handwriting recognition might be considered deep-learning 101, Japanese is a whole other ballgame. That's because the language includes symbolic characters such as kanji, which is composed of elements that can be read independently, making it difficult to know where one ends and another begins. There are also more than 2,000 common pictograms made up of dozens of strokes. The trick is to tackle one squiggle at a time. Reactive's algorithm queries the neural network for a match, adds another stroke and repeats, all the while refining the probability of an accurate hit.