Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

SYS-CON Events announced today that Ocean9will exhibit at SYS-CON's 20th International Cloud Expo, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Ocean9 provides cloud services for Backup, Disaster Recovery (DRaaS) and instant Innovation, and redefines enterprise infrastructure with its cloud native subscription offerings for mission critical SAP workloads. Available on AWS and Azure, Ocean9 solutions de-risk and accelerate digital transformation initiatives with instant provisioning, full elasticity, strict security, right sized systems and high availability of up to 99.99%. Ocean9 is also first to market with heavy state container technology: Our customers can therefore deploy the heavy state SAP HANA in-memory database in a production grade container environment with the ability to seamlessly fall back to a virtualized environment within minutes. Learn more about how Ocean9 redefines cloud enterprise infrastructure at www.ocean9.io.

Any article on the web that starts with terms like "Machine Learning" and "Artificial Intelligence," more or less drops us hints about the future. Although it all seems like preparing a foundation for the time ahead, things have started picking up pace already. It's difficult to deny that even though machine learning and A.I. sound more like future technologies, we all have been using it already. Some of the notable examples include Google Home, Amazon Echo, Google Photos etc. What's more, smartphone manufacturers are trying their luck nowadays with these technologies. Care to think of Apple Siri, Google Assistant, Microsoft Cortana, and newborn Samsung Bixby? It often reminds me of what Elon Musk once said, "Great companies are built on great products."

From phone support and mobile apps to ATMs and chatbots -- when it comes to customer service, banking automation has come a long way. However, the one missing piece that these automated banking features lack is a real human being to interact with during a transaction. That's something that Soul Machines, a New Zealand-based startup, is hoping to change. The company's new technological development is intended to bring back the human component to customer service without relying on actual people. To meet this goal, Soul Machines is producing artificial intelligence-powered avatars that are designed to emotionally engage with banking customers.

"What to do When Machines Do Everything" book authors (l-r) Malcolm Frank, Paul Roehrig, Ben Pring My interview with Malcolm Frank and Paul Roehrig, two of the three authors of What to Do When Machines Do Everything, published by Wiley. Malcolm Frank is the Executive Vice President of Strategy and Chief Marketing, Cognizant. Paul Roehrig is Chief Strategy Officer for Cognizant's Digital Business. Ben Pring is the Global Managing Director of Cognizant's Center for the Future of Work. Bruce Rogers: What has changed since you wrote Code Halos and why the need for the new book What To Do When Machines Do Everything: How To Get Ahead In A World Of AI, Algorithms, Bots and Big Data? Malcolm Frank: In Code Halos, we looked at why some digital businesses like the FAANG companies (Facebook, Apple Amazon, Netflix, Google) were succeeding and others weren't. We kept probing again and found that every one of those businesses that was a digital leader has now turned into an AI (artificial intelligence) company.

You're more likely to have your mother's cheekbones than her eyes, new research suggests. Researchers studied the facial features of 1,000 female twins to find parts of the face that are likely to be controlled by genetics. They used their results to create interactive face maps that reveal the features you're most likely to inherit from your parents. Biological traits such as facial features are influenced by genes and'environmental' factors including the socioeconomic conditions a person grew up in. Professor Giovanni Montana, from King's College London, said: 'The notion that our genes control our face is self-evident.

Researchers studying a deal in which Google's artificial intelligence subsidiary, DeepMind, acquired access to millions of sensitive NHS patient records have warned that more must be done to regulate data transfers from public bodies to private firms. The academic study says that "inexcusable" mistakes were made when, in 2015, the Royal Free NHS Foundation Trust in London signed an agreement with Google DeepMind. This allowed the British AI firm to analyse sensitive information about 1.6 million patients who use the Trust's hospitals each year. The access was used for monitoring software for mobile devices, called Streams, which promises to improve clinicians' ability to support patients with Acute Kidney Injury (AKI). But according to the study's authors, the purposes stated in the agreement were far less specific, and made more open-ended references to using data to improve services.

Google's Cloud Vision API is easily blinded by the addition of a little noise to the images it analyses, say a trio of researchers from the Network Security Lab at the University of Washington, Seattle. Authors Hossein Hosseini, Baicen Xiao and Radha Poovendran have hit arXiv with a pre-press paper titled Google's Cloud Vision API Is Not Robust To Noise (PDF) that says "In essence, we found that by adding noise, we can always force the API to output wrong labels or to fail to detect any face or text within the image." The authors explain that if one can add different types of noise to an image, the Cloud Vision API will always incorrectly analyse the pictures presented to it. The image at the top of this story (or here for m.reg readers) shows the false results the API returned. It doesn;t need to be a lot of noise: the authors found an average of 14.25 per cent "impulse noise" got the job done.

In a first for deep learning, an Oak Ridge National Laboratory-led team is bringing together quantum, high-performance and neuromorphic computing architectures to address complex issues that, if resolved, could clear the way for more flexible, efficient technologies in intelligent computing. Deep learning refers to nature-inspired, computer-based technologies that push beyond the conventional binary code, advancing emerging fields such as facial and speech recognition. "Our proposed approach can optimize and manage complexity in a low-power environment, resolving specific challenges when exploring complicated scientific data." The team's tri-fold experiment demonstrates the feasibility of using the three architectures in tandem to overcome limitations and represents a new capability not currently available. Details of the team's experiment are available online.

New Finnish technology is tapping into machine learning and artificial intelligence to support healthcare professionals in early diagnosis of neurological disorders. When a person is suspected of having a memory disorder, they undergo extensive tests ranging from brain scans to neurological examinations. It is then up to their physician to analyse the results. However, even an experienced doctor may struggle to arrive at an accurate diagnosis during the early stages of a disease. Finnish healthtech company Combinostics has a solution for this in mind: its cloud-based tools for the comprehensive analysis of patient data.