This paper presents a Bayesian optimization method with exponential convergence without the need of auxiliary optimization and without the delta-cover sampling. Most Bayesian optimization methods require auxiliary optimization: an additional non-convex global optimization problem, which can be time-consuming and hard to implement in practice. Also, the existing Bayesian optimization method with exponential convergence requires access to the delta-cover sampling, which was considered to be impractical. Our approach eliminates both requirements and achieves an exponential convergence rate.

Dave Perkon is technical editor for Control Design. He has engineered and managed automation projects for Fortune 500 companies in the medical, automotive, semiconductor, defense and solar industries. Put simply, the IoT provides the connection, the cloud provides online storage and convenient applications, and big data provides analysis, management and maintenance of information, which, when combined, can overwhelm the data users and decision makers. Fortunately computers and specifically machine-learning applications, although in their early stages, can help. From the industry or manufacturing side of business, machine learning can be applied to just about any control system that is smart enough to actually alter how it controls a machine in response to changing conditions, but there is much more to it than that.

Bengaluru-based Bazmi Husain heads the research & development (R&D) vertical of Swiss engineering major ABB that spends 1.5 billion annually on R&D. Bazmi took charge as the chief technology officer of the group in January 2016 after being the managing director of ABB India. Bazmi, who also heads the venture capital arm of the group, talks to Jyoti Mukul about the global technology trends and how India is uniquely placed. Edited excerpts: How important is ABB's India centre in its R&D operations? India is the largest and fastest growing R&D location for ABB, with footprints across Bengaluru, Chennai, Vadodara and Nashik.

Artificial intelligence, the science of making computers ''think,'' has long been the preserve of theoreticians who were little concerned with practical applications. ''When they said'real things,' they meant computers that can play chess,'' said Dr. Roger Schank, chairman of the computer science department at Yale University. ''They were not going to talk to Wall Street, let alone own a suit.'' Now, however, business is taking an interest in artificial intelligence, or A.I., and some professors, such as Dr. Schank, are forming or joining companies to capitalize on the expected boom. But the new move toward commercialization is disrupting the academic community and provoking fears that university research will be hurt.

The artificial intelligence (AI) market is estimated to grow from USD 419.7 million in 2014 to USD 5.05 billion by 2020, at a CAGR of 53.65% from 2015 to 2020. This growth can be attributed to the factors such as diversified application areas, improved productivity, and increased customer satisfaction. The machine learning technology is expected to account for the largest share of the overall Artificial Intelligence (AI) Market duing the forecast period. In addition, due to the increase in demand for AI from the media & advertising and finance sectors, the artificial intelligence market is expected to gain traction in the next five years. The machine learning technology market for the retail, healthcare, law, and oil & gas sectors is also expected to witness growth during the forecast period.

First the machines came for the menial jobs. Since then, they've climbed the corporate ladder and now sit on the board of directors of at least one venture capital firm in Japan. But that's Japan, you say. Well, if you thought your job was safe, you might want to think again. In the cover story of this week's MoneyWeek magazine, Matthew Partridge gets to grips with the nuts and bolts of what's driving the rise of artificial intelligence, or AI for those in the know.

At the beginning of "Welcome to the 5th Façade," a science-fiction story by architect Alan Maskin, a nameless man awakes from a cryogenic freeze. It is an unspecified date in the mid-21st century, and he's been in "big sleep," as his cryonic technician puts it, since suffering a massive heart attack decades before. He finds things drastically different. Kinetic vertical farms decorate the neo-classical brick buildings that still stand. Above them rises a new layer of the city, where farms, parks, and energy-harvesting machines form a new, high-rise urban landscape.

LIVERMORE, Calif. and ARMONK, N.Y. - 29 Mar 2016: Lawrence Livermore National Laboratory (LLNL) today announced it has purchased a first-of-a-kind brain-inspired supercomputing platform for deep learning inference developed by IBM (NYSE: IBM) Research. Based on a breakthrough neurosynaptic computer chip called IBM TrueNorth, the scalable platform will process the equivalent of 16 million neurons and 4 billion synapses and consume the energy equivalent of a tablet computer – a mere 2.5 watts of power for the 16 TrueNorth chips. The brain-like, neural network design of the IBM Neuromorphic System is able to infer complex cognitive tasks such as pattern recognition and integrated sensory processing far more efficiently than conventional chips. "Neuromorphic computing opens very exciting new possibilities and is consistent with what we see as the future of the high performance computing and simulation at the heart of our national security missions," said Jim Brase, LLNL deputy associate director for Data Science. "The potential capabilities neuromorphic computing represents and the machine intelligence that these will enable will change how we do science."

Matrix completion under interval uncertainty can be cast as matrix completion with element-wise box constraints. We present an efficient alternating-direction parallel coordinate-descent method for the problem. We show that the method outperforms any other known method on a benchmark in image in-painting in terms of signal-to-noise ratio, and that it provides high-quality solutions for an instance of collaborative filtering with 100,198,805 recommendations within 5 minutes.

According to the United Nations (UN), more than 12 million people--including 5.6 million children--have fled Syria to escape the horrors of the country's ongoing civil war and invasion by ISIS. Worldwide, the UN reports an unprecedented 59.5 million people are displaced by crisis. The flow of refugees toward Europe from Syria and other war-torn nations has caused the continent's greatest refugee crisis since World War II. Finland-based Lucify, which creates interactive data visualizations to help organizations analyze and communicate important data, recently tackled the refugee migration to Europe. Using UN data from 2012 through December 2015, its interactive map offers a time-lapse view of refugee migration and country-by-country statistics.