China is building its next-generation cruise missiles using "high-level artificial intelligence" to make them suitable for specific combat situations. A senior missile designer has claimed that Chinese engineers have been studying the use of artificial intelligence in missiles for years and are now pushing ahead to turn it into reality. Wang Changqing, director of the general design department of the Third Academy of the China Aerospace Science and Industry Corp, told the state-run China Daily that flexible and modular design for future weapons was the need of the hour. A modular missile system is flexible and multifunctional, and enables manufacturers to cut down on the development and storage costs, a senior researcher at the Beijing Hiwing Scientific and Technological Information Institute, told the paper on condition of anonymity. Terming the plans to develop the modular cruise missiles a "promising approach", he added that such weapons also prolong the operational range and duration of a mission.

Microsoft's Rajesh Jha, Corporate Vice President of Outlook and Office 365, said on Monday that the company has acquired scheduling service Genee. Powered by artificial intelligence, this service is expected to make Office 365 more streamlined and intelligent when it comes to scheduling and rescheduling meetings. This is due to Genee's algorithms that are fine-tuned for decision-making and natural language processing. Co-founders Ben Cheung and Charles Lee launched Genee in 2014. The service allows users to simply describe what kind of meeting they want, when it should take place, and who should attend.

M erely twenty years ago, artificial intelligence (AI) was the plot line in movies, books, and short stories. While it loomed on the horizon, it is only now getting the attention of world, and business leaders. A few days into 2016, Mark Zuckerberg of Facebook announced that he plans to spend 2016 developing an AI system to help run his life. He stated in his Facebook post: "My personal challenge for 2016 is to build a simple AI to run my home and help me with my work. You can think of it kind of like Jarvis in Iron Man."

A user demonstrates the look and feel of Windows 10 operating system for smartphones and at the Microsoft stall at the CeBIT technology fair in Hanover, Germany. SAN FRANCISCO -- Microsoft has made yet another acquisition designed to retool the former software king into an enterprise solutions leader. The Redmond, Wash-based company announced Monday that it was buying virtual-assistant start-up Genee for an undisclosed sum, according to blog posts by Microsoft and Genee executives. The move comes on the heels of Microsoft's 26 billion purchase of professional networking site LinkedIn, a bold chess move by CEO Satya Nadella as he aims to make his company's cloud-based Office 365 platform ubiquitous in the business environment. Genee makes software that uses artificial intelligence to schedule appointments.

A machine learning template demonstrates the standard industry practices and common building blocks in building a machine learning solution for a specific domain, starting from **data preparation, data processing, feature engineering, model training** to **model deployment**. The goal of the templates is to enable data scientists to quickly build and deploy custom machine learning solutions with Azure Machine Learning platform, and increase their productivity with a higher starting point. The template includes a collection of pre-configured Azure ML modules, as well as custom R scripts in the Execute R Script modules, to enable an end-to-end solution. Each template includes the following: * A data schema (with sample dataset) applicable to the specific domain * Domain specific data processing and feature engineering steps * Models training algorithms fit to the specific domain * Domain specific evaluation metric (if applicable) * Model deployment as a web service Iterative in nature, and for ease of understanding and experimentation to the user, each template is put into multiple steps (and multiple experiments) and have detailed documentation and instructions on how to use the template in the Azure ML Gallery. Predict when a customer churn happens.

Computers can be trained to be more accurate than pathologists in assessing slides of lung cancer tissues, according to a new study by researchers at the Stanford University School of Medicine. The researchers found that a machine-learning approach to identifying critical disease-related features accurately differentiated between two types of lung cancers and predicted patient survival times better than the standard approach of pathologists classifying tumors by grade and stage. "Pathology as it is practiced now is very subjective," said Michael Snyder, PhD, professor and chair of genetics. "Two highly skilled pathologists assessing the same slide will agree only about 60 percent of the time. This approach replaces this subjectivity with sophisticated, quantitative measurements that we feel are likely to improve patient outcomes."

Cancer, the single most feared diagnosis imaginable, is being tackled by some of the biggest companies in the world using the most formidable weapon: à la artificial intelligence. In fact, the milestones we have hit in past two years in diagnosing rare forms of cancer using clinical data, has stumped the scientific community. At the University of Tokyo's Institute of Medical Science, IBM's Watson matched a patients' symptoms against 20 million clinical oncology studied. In the western world, Google and Amazon are helping scientists analyze genetic data. Both, Google Genomics and Amazon Web Services, are offering analytical functions of the cloud to help scientists make sense of genomics data.

IT security is a dangerous and expensive hellhole. Vast amounts of money are spent protecting company data and networks. Hordes of bad guys are motivated to break in, and the consequences for failure are more painful than the cost of protection. Worse, the current ways that Chief Security Officers (CSOs) deal with security are intrusive. While core security tools such as managed endpoint protection will always be necessary, every one of us has bemoaned the difficulty of managing passwords, cussed about access rights to the software we need, and complained about the barriers between us and the work we need to do.

Part of being the training data arm of today's (and tomorrow's) biggest AI and machine learning initiatives means every day we're talking to people building the coolest dang stuff. Hearing about what our customers are up to and what's on their roadmaps is so interesting--these are the technologies of the future! Many of these peeps are just like our customers were before signing on: building mind-blowing things, but facing significant challenges, especially around acquiring and annotating training data. And okay, of course we're interested in their projects and problems--we aim to boost and solve them (respectively). We all like to have a peek into the experiences of our peers, to know what sort of developments they're cookin' up, where they're finding success, and what roadblocks they're hitting.

Spare5 makes gathering accurate, labeled training data easy and scalable. Our tooling for computer vision and natural language is fully customizable and we do all the heavy lifting for you. You can easily integrate the process into your workflow by hooking into our API.