The creators of Siri have announced a brand new creation from their brilliant minds, the artificial intelligence assistant named Viv. Apple based Siri has become both a technological powerhouse, and a cultural touchstone. Now the creators behind it have moved on to the next stage. Viv is a more advanced AI that works by connecting to multiple sources of information and drawing responses right from the pools of data themselves. No more regurgitating web search results; this assistant is smarter, sleeker, and more accurate.

A year ago the Raptors were victims of an unexpected 4-0 sweep in the first round of the NBA playoffs at the hands of the lower-ranked Washington Wizards. They're now tied with the Miami Heat 2-2 in a grueling best-of-seven series, which will see the eventual winners play LeBron James and the Cleveland Cavaliers in the Eastern Conference finals. Maple Leaf Sports & Entertainment, which owns the Raptors, announced in February that it would use cognitive analysis provided by IBM's Watson technology platform, noting that Watson would be used mostly for talent acquisition. Is IBM Watson one of the factors behind the improved performance? It's difficult to answer that question because the Raptors consider the IBM agreement to be a competitive advantage, and are therefore mum on this subject.

Thank you to Rahul Iyer from Pivotal for contributing to the software and to this article. Path functions are a powerful capability in the data science toolkit, and they are now available in the newest release of the open source Apache MADlib (incubating) library. For example, path functions can be used to reason over website, shopping cart, and customer support clickstreams to identify the golden paths to purchase, multi-channel promotion effectiveness, or customer churn. In addition, they can be used in predictive analytics use cases, like analyzing millions of sensor logs from cars or other machines to identify common patterns in part failure. These scenarios can also improve safety and substantially lower operating costs.

Scientists at Los Alamos National Laboratory and the State Key Laboratory for Mechanical Behavior of Materials in China have used a combination of machine learning, supercomputers, and experiments to speed up discovery of new materials with desired properties. The idea is to replace traditional trial-and-error materials research, which is guided only by intuition (and errors). With increasing chemical complexity, the possible combinations have become too large for those trial-and-error approaches to be practical. The scientists focused their initial research on improving nickel-titanium (nitinol) shape-memory alloys (materials that can recover their original shape at a specific temperature after being bent). But the strategy can be used for any materials class (polymers, ceramics, or nanomaterials) or target properties (e.g., dielectric response, piezoelectric coefficients, and band gaps).

Since then, making clothing has become so automated and so cheap, companies can mass produce clothing of all shapes, sizes and colors. Now, when Sirosh needs new clothes, he goes to a department store. That's how he envisions the future of machine... Read More

No one can predict the future of technology with 100 percent accuracy. But these four pillars are certainly at the forefront of innovation in the years ahead. Speaking at a machine learning and artificial intelligence event hosted by Madrona Venture Group in Seattle on Wednesday, Joseph Sirosh, corporate VP of the Data Group at Microsoft, outlined five trends to watch in a world he described as "ACID": Algorithm, Cloud, IoT, and Data. "We live in a time of great change in computing, where unreasonable effectiveness of algorithms, cloud, IoT, and data are changing how applications are built, period," he said. "Even if you are on the right track, if you don't hop on this bandwagon and actually build things and deploy them and take advantage of their strength, you won't be very effective."

This is largely due to the invaluable insights we gain through the analysis of thousands of calls received each day by the typical call centre. With speed being of the essence in making the right decision at the right time for each caller many call centres are turning to machine learning to automate their data analysis and make crucial customer experience decisions within seconds. Whether you're running an inbound or outbound contact centre, the interactions between your company representatives and your customers is a crucial area for customer success. Thanks to machine learning algorithms, businesses are able to manage those customer-facing moments more efficiently. According to techtarget.com, "Emotion analysis through text and speech analytics can paint a more complete picture when combined with the overall first call resolution (FCR) metric, indicating the level of confidence customers feel about whether the answer they received has resolved the issue at hand."

While the human brain is often seen as the ultimate model for robotic intelligence, scientists are also learning plenty from the neurobiological structures and processes of more humble creatures, from fruit flies to rodents. Take the fruit fly – or rather, the maggot that grows up to be a fruit fly. Drosophila fruit fly larvae have fewer than 10 000 neurons – compared to about 100 billion in the human brain. But they display a range of complex orientation and learning behaviours that computational theory does not adequately explain at present. By studying how the larvae change their response to stimuli such as smells when these are associated with reward or punishment, the EU-funded MINIMAL project aims to unpick the exact mechanism underlying learning processes.

Computers have slowly started to encroach on activities we previously believed only the brilliantly sophisticated human brain could handle. IBM's Deep Blue supercomputer beat Grand Master Garry Kasparov at chess in 1997, and in 2011 IBM's Watson beat former human winners at the quiz game Jeopardy. But the ancient board game Go has long been one of the major goals of artificial intelligence research. It's understood to be one of the most difficult games for computers to handle due to the sheer number of possible moves a player can make at any given point. Researchers at Google DeepMind, the Alphabet-owned artificial intelligence research company, announced today that it had created an artificial intelligence system that has beat a professional Go player at the game.

Getting stitched up by Dr. Robot may one day be reality: Scientists have created a robotic system that did just that in living animals without a real doctor pulling the strings. Much like engineers are designing self-driving cars, Wednesday's research is part of a move toward autonomous surgical robots, removing the surgeon's hands from certain tasks that a machine might perform all by itself. No, doctors wouldn't leave the bedside -- they're supposed to supervise, plus they'd handle the rest of the surgery. Nor is the device ready for operating rooms. But in small tests using pigs, the robotic arm performed at least as well, and in some cases a bit better, as some competing surgeons in stitching together intestinal tissue, researchers reported in the journal Science Translational Medicine.