With large volumes of health care data comes the research area of computational phenotyping, making use of techniques such as machine learning to describe illnesses and other clinical concepts from the data itself. The "traditional" approach of using supervised learning relies on a domain expert, and has two main limitations: requiring skilled humans to supply correct labels limits its scalability and accuracy, and relying on existing clinical descriptions limits the sorts of patterns that can be found. For instance, it may fail to acknowledge that a disease treated as a single condition may really have several subtypes with different phenotypes, as seems to be the case with asthma and heart disease. Some recent papers cite successes instead using unsupervised learning. This shows great potential for finding patterns in Electronic Health Records that would otherwise be hidden and that can lead to greater understanding of conditions and treatments. This work implements a method derived strongly from Lasko et al., but implements it in Apache Spark and Python and generalizes it to laboratory time-series data in MIMIC-III. It is released as an open-source tool for exploration, analysis, and visualization, available at https://github.com/Hodapp87/mimic3_phenotyping

Aircraft fuel is without a doubt the biggest cost for any airline and often receives widespread attention, especially when airlines hedge their bets the wrong way. Cathay Pacific reported a HK$4.49 billion fuel-hedging loss in the first half of 2016, which has hurt the airline's profitability. The second biggest expense for an airline is human capital, and researchers from Hong Kong Polytechnic University and University of Nottingham Ningbo China Business School may have found a solution to ease some of Cathays financial woes through an unlikely source – Machine Learning and Data Science. The researchers say that a "poorly designed airline crew schedule can result in unreliable flight schedules, significantly jeopardizing airline operations and profitability if insufficient crew members are available or other glitches occur. For that reason, managing airline crew scheduling and costs are one of the most crucial topics for airlines because it yields enormous economic benefits and ranks as the second highest expenditure after fuel costs."

A year ago, a researcher tested Samsung's S Voice digital assistant by telling it he was depressed. "Maybe it's time for you to take a break and get a change of scenery." Researchers found Apple's Siri and Microsoft's Cortana couldn't understand queries involving abuse or sexual assault, according to a study published in March in JAMA Internal Medicine. Next week's Consumer Electronics Show will show off digital assistants' abilities to make our lives a little easier by adding more voice-powered smarts into our lights, appliances and door locks. While these smart-home ideas are likely to gain plenty of attention at CES, the JAMA study highlights the need to improve digital helpers' responses to more critical health and wellness issues, as well.

Samsung is in need of a show-stopper phone after the fiasco of its Galaxy Note 7's exploding. Now, new rumours suggest that its upcoming Galaxy S8 will use its built-in AI assistant in all of the phone's pre-installed apps. The voice-guided assistant, reportedly named'Bixby', could be used for a wide variety of functions in a similar way to Apple's Siri. The Samsung Galaxy S8 will use its built-in AI assistant in all of the phone's included Samsung-made apps, reports suggest. For instance, the assistant could be used in the Gallery app to show photos of the beach.

Ford has shown the first images of its new self-driving Fusion Hybrid with a more powerful computer and improved, better-integrated sensors. It uses an upgraded version of the Fusion Hybrid platform, bolstered by self-driving hardware, a large new computer and electrical controls that "are close to production-ready," the company said in a press release. It also packs lower-profile LIDAR units that appear to be the "Puck" models from Velodyne, a company in which it recently invested $150 million. Cameras and other bits are smoothly built into the roof, making the hybrid less "hey, look at me, I'm a self-driving car" than other models. By contrast, the last autonomous Fusion model used since 2013 featured no less than four bulky LIDAR units.

Expedia is the most recognisable brand in the world of online travel and owns several others, including Hotels.com and Trivago. Its companies operate more than 100 branded points of sale in over 60 countries. As a parent company, Expedia has made a steady flow of acquisitions over the past 15 years, and last year stepped up its M&A strategy with the takeovers of online travel agencies Travelocity and Orbitz, and holiday rental website HomeAway. A major goal in this M&A activity is to control and maintain Expedia's market-leading position in an increasingly competitive market for online travel booking, and is reflective of a general industry trend towards consolidation. Expedia was the first online travel giant and has been at the forefront of the transition in the way people book holidays, but that counts for little in the disruptive world of digital.

Clinical trials, the largest of which may enroll a few thousand patients with hematology or oncology diagnoses, represent the gold standard of clinical research. But what if clinical decisions could be made, or research questions answered, using data from tens of thousands or even a million patients? Initiatives are springing up across the country to examine the power and promise of big data – massive amounts of information that can be analyzed to provide an overview of trends or patterns – to revolutionize health care and transform how patients are diagnosed, treated, and even involved in their own care. For instance, in 2012, the National Institutes of Health (NIH) established the Big Data to Knowledge (BD2K) initiative, an effort to promote research and development of tools and approaches that would accelerate the use of big data in biomedical research.1 This spring, IBM launched IBM Watson Health and the Watson Health Cloud platform, a new unit of the IBM Watson cognitive computing system that will analyze and extract large volumes of health data from structured and unstructured medical systems.2

Today, big data, that is generated exponentially every hour, becomes a treasurable asset to many organizations. To be competitive in a dynamic data-driven and Web-centric market, most of the high-tech companies such as Google, Apple, Intel, IBM, Microsoft, etc. employ big data analytics (BDA) to mine insights within their big data to drive productivity, effectiveness, and efficiency. On the other hand, artificial intelligence (AI) has grown rapidly from a nascent stage to intermediate one in the last decade. AI is a youthful field in Computer Science in building an autonomous machine that intends to replace humans with daily activities. Robots mimic human behavior and frequently perform dangerous tasks.

Artificial intelligence is changing and improving the ways we manage our money. Research suggests Australians are ready to embrace fintech banking solutions, and the launch of three new London-based chatbot startups may be a sign the rest of the world is gearing up for a revolution too. Artificial intelligence (AI) has been rapidly progressing over the past two decades, with machines reaching and exceeding human performance on an increasing number of tasks. Just this week, the White House released a report entitled Preparing for the future of Artificial Intelligence, which describes the ways in which AI has and continues to yield new opportunities for progress in critical areas such as health, education, energy, and the environment. Another important area of business, ripe for disruption, is finance and banking.

We present a model of a basic recurrent neural network (or bRNN) that includes a separate linear term with a slightly "stable" fixed matrix to guarantee bounded solutions and fast dynamic response. We formulate a state space viewpoint and adapt the constrained optimization Lagrange Multiplier (CLM) technique and the vector Calculus of Variations (CoV) to derive the (stochastic) gradient descent. In this process, one avoids the commonly used re-application of the circular chain-rule and identifies the error back-propagation with the co-state backward dynamic equations. We assert that this bRNN can successfully perform regression tracking of time-series. Moreover, the "vanishing and exploding" gradients are explicitly quantified and explained through the co-state dynamics and the update laws. The adapted CoV framework, in addition, can correctly and principally integrate new loss functions in the network on any variable and for varied goals, e.g., for supervised learning on the outputs and unsupervised learning on the internal (hidden) states.