When Shayla Haddock's doctors tested her for a rare genetic disease in 2012, they couldn't pinpoint a diagnosis. Her lifelong symptoms -- which include club feet, short stature, unusual facial features and congenital deafness -- led her doctors to suspect a disease-causing gene mutation. But for children like Shayla, finding the culprit among 3 billion base pairs of DNA can be very difficult. Each case takes 20 to 40 hours of analysis by a trained geneticist after gene sequencing has been done, and around 75 percent of patients don't get a diagnosis on the first try. As I described in a recent story, Shayla's case was eventually solved by a team of Stanford computer scientists who devised an automated way to compare patients' symptoms and mutated genes to information in existing databases of genetic diseases.

Every week I read about 150 to 200 articles regarding big data, data science and technology. In the future I am going to share with you every week my favourite articles. I am still going to continue writing my on stories, like the upcoming big data platform blueprint. Baidu's chief data scientist Andrew Ng recently gave a great talk about how to apply deep learning. The talk was at the 2016 deep learning school (http://www.bayareadlschool.org)

"SAS has morphed from a pure tools-based analytics company to solutions-based company. That is driving SAS to get more involved with other technologies and ecosystems." SAS is the latest long-time analytics firm to enter the era of'open' and'cloud-based' analytics. SAS Viya, revealed last spring and rolling out now, aims to take businesses into the new-gen of analytics offering full list of lifecycle support features and capabilities, SAS' chief customer officer Fritz Lehman told IDN. "With SAS Viya, we have a complete rewrite [of the popular SAS analytics platform] for today's new business challenges. New ways to access and build analytics apps are key for so many workers inside businesses today," Lehman said.

Today, a digital stethoscope has the ability to record and store heartbeat and respiratory sounds. Tomorrow, the stethoscope could function as an "intelligent thing" by collecting a massive amount of such data, relating the data to diagnostic and treatment information, and building an artificial intelligence (AI)-powered doctor assistance app to provide the physician with diagnostic support in real-time. AI and machine learning increasingly will be embedded into everyday things such as appliances, speakers and hospital equipment. This phenomenon is closely aligned with the emergence of conversational systems, the expansion of the IoT into a digital mesh and the trend toward digital twins. Three themes -- intelligent, digital, and mesh -- form the basis for the Top 10 strategic technology trends for 2017, announced by David Cearley, vice president and Gartner Fellow, at Gartner Symposium/ITxpo 2016 in Orlando, Florida.

Nvidia, a publicly traded company that makes graphics processing units (GPUs), has been focusing its business more and more completely on artificial intelligence (A.I.) after having managed to sell considerable quantities of GPUs for that type of computing work to big companies like Facebook and Google. Those GPUs sit in servers, rather than desktops, laptops, or mobile devices, where Nvidia sticks GPUs for gaming, image processing, and other workloads. But the use of Nvidia's GPUs for A.I., and specifically deep learning -- an approach that involves training artificial neural networks on bunches of data, such as images, and then getting the neural networks to make inferences about new data -- has gained particular traction in the technology industry. Now Nvidia wants to see government agencies adopt and expand their use of deep learning -- which today typically relies on GPUs -- particularly during the training phase. "One of the reasons why I'm going to Washington is I want to talk to a lot of government customers and find out what they're most interested in and what they want to find out about," Nvidia chief scientist Bill Dally told VentureBeat in an interview.

It used to be that even sophisticated mobile robots could be easily defeated by using (say) a table to block its way. The robot would sense the table, categorize it as an obstacle, try to plan a path around it, and then give up when its planner fails. This works because robots generally don't know what most objects are, or how they work, or what you can do with them: They just get turned into obstacles to be avoided, because in most cases, that's the easiest and safest thing to do. You can't normally use a table across a hallway to deter a human, because humans understand that tables are physical objects that can be moved, and the human will just pull the table out of the way and keep on going. Even if the table doesn't behave exactly the way we'd expect it to (like, one of the wheels is stuck), we can adapt, and figure it out.

The purpose of text classification is to give conceptual organization to large collection of documents.An interesting application of text classification is to categorize research papers by most suitable conferences. Finding and selecting a suitable academic conference has always been a challenging task especially for young researchers. We can define a'suitable academic conference' as a conference, which is aligned with the researcher's work and have a good academic ranking. Usually researcher have to consult to their supervisors and search extensively to find a suitable conference. Among many conferences, few are considered to be relevant to send a research work.

Artificial intelligence just keeps getting smarter and smarter. Now Microsoft researchers say they've developed speech recognition technology that can grasp a human conversation as well as people do. The work out of the Microsoft Artificial Intelligence and Research department was published in a scientific paper this week. It shows that when the speech recognition software "listened" to people talking, it was able to transcribe the conversation with the same or fewer errors than professional – human – transcriptionists. The technology delivered a word error rate (WER) of 5.9 percent, which is roughly the same as that of people who were asked to transcribe the same conversation.

For consumer companies, large-scale leveraging of customer and behavioral data to drive personalized customer experiences is turning into a virtual arms race. Marketing technology platforms of the last 10 years were built around campaign process that were still highly manual, requiring marketing execs to do all the testing, optimization and which makes the cycle time for learning and actually influencing marketing very slow. Now more and more marketers recognize the need to deploy advanced personalization capabilities that make the use of machine-learned optimization. And, Matt Fleckenstein, Chief Product Officer at Amplero, helps marketers achieve just that. With a track record for conceiving, building, and launching martech products and services it comes easy to him.

Next-generation cognitive computing is redefining how we live and work as more businesses are using all the data available to them to improve performance and customer service, and drive innovation and revenue. Today's business challenges have never been more complex, and the critical insights that can help address these challenges are often buried in an avalanche of data. Previously, these insights were beyond the capabilities of conventional computing solutions – programmable systems based on mathematical principles that harken back to the 1940s. But IBM Watson has changed the game. IBM Watson is built upon a new foundation called cognitive computing – a system that learns and reasons from interactions with humans, files, online interactions and its environment.