There's no doubt that AI is a hot topic right now. Whether it's financial services or healthcare, most industries are looking to capitalize on the technology for their benefit. Looking at the hotel industry specifically, is it another example of technology looking for a problem to solve? To answer the question, we need to consider where AI is currently in use, the heady design considerations around the technology, and articulating what it could mean moving forward. In the first place, AI is not new to the travel and hospitality industry.

London: Artificial intelligence systems could outperform humans in all tasks within the next 45 years, according to a new study which also suggests that all human jobs will be automated in the next 120 years. According to a survey of over 350 artificial intelligence (AI) researchers, machines are predicted to be better than us at translating languages by 2024, writing high-school essays by 2026, driving a truck by 2027, working in retail by 2031, writing a bestselling book by 2049 and surgery by 2053. However, there is only a five per cent chance that computers will bring about outcomes that may lead to human extinction, researchers said. The survey, by the University of Oxford in the UK and Yale University in the US, was conducted among 352 researchers who had presented their research at the Conference on Neural Information Processing Systems or the International Conference on Machine Learning--the two major conferences in the field of AI. "There is accumulating evidence that machines can overpower human intelligence in complex, though specific tasks," Eleni Vasilaki at the University of Sheffield in the UK, told the New Scientist. However, there is little evidence that AI with human-like versatility will appear any time soon, Vasilaki said. The survey results showed that researchers in Asia typically gave shorter time frames than those in North America--predicting that AI would outperform humans on all tasks within 30 years, compared with 74 years.

The life of a sheep is not as cushy as it looks. They suffer injury and infection, and can't tell their human handlers when they're in pain. Recently, veterinarians have developed a protocol for estimating the pain a sheep is in from its facial expressions, but humans apply it inconsistently, and manual ratings are time-consuming. Computer scientists at the University of Cambridge in the United Kingdom have stepped in to automate the task. They started by listing several "facial action units" (AUs) associated with different levels of pain, drawing on the Sheep Pain Facial Expression Scale.

Advancements in Artificial Intelligence -- the capability of machines to make informed decisions and perform tasks usually reserved for humans -- are moving at a rapid rate, and it's threatening workers from truck drivers to surgeons, according to a new study from the University of Oxford. Spearheaded by Katja Grace of the Future of Humanity Institute at Oxford, the report surveyed more than 350 AI experts on how long it'll take machines to master certain jobs, from remedial to advanced. Within the next decade, experts predict machines will outperform humans when it comes to translating languages, writing a quality high school essay, and driving trucks. AI proficiency in sales and retail is expected by the early 2030s. The essay data is especially intriguing.

The ever-growing geriatric population across the globe is one of the key drivers influencing the growth of healthcare assistive robot market. Rising medical care demands from the physically disabled patient base is another pivotal factor favoring the healthcare assistive robot industry trends. In recent times, technological innovations have resulted in the introduction of accurately featured assistive robots that can be customized suitably in accordance with the patient requirements. As per the report by Global Market Insights, Inc., "Healthcare assistive robot market will record an annual growth rate of 18.9% over the coming years of 2016 to 2024." Moreover, the shortage of skilled caretakers is also pushing the growth of healthcare assistive robot market significantly.

SYS-CON Events announced today that EARP Integration will 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. EARP Integration is a passionate software house. Since its inception in 2009 the company successfully delivers smart solutions for cities and factories that start their digital transformation. EARP provides bespoke solutions like, for example, advanced enterprise portals, business intelligence systems and mobile applications for international enterprises across different sectors such as Energy and Utilities, GreenTech, MedTech, FinTech, Facility Management and Housing, Automotive Manufacturing, and Sport. EARP also cooperates with international software houses by providing them with highly qualified and well-selected, multilingual teams for bigger projects.

The U.S. trade deficit rose in April to the highest level since January. The politically sensitive trade gap with China registered a sharp increase. The Commerce Department said on Friday that the U.S. trade gap in goods and services climbed 5.2 percent to $47.6 billion in April from March. Exports dropped 0.3 percent to $191 billion, pulled down by a drop in automotive exports. Imports rose 0.8 percent to $238.6 billion as Americans bought more foreign-made cellphones and other goods.

In this paper, we propose a StochAstic Recursive grAdient algoritHm (SARAH), as well as its practical variant SARAH+, as a novel approach to the finite-sum minimization problems. Different from the vanilla SGD and other modern stochastic methods such as SVRG, S2GD, SAG and SAGA, SARAH admits a simple recursive framework for updating stochastic gradient estimates; when comparing to SAG/SAGA, SARAH does not require a storage of past gradients. The linear convergence rate of SARAH is proven under strong convexity assumption. We also prove a linear convergence rate (in the strongly convex case) for an inner loop of SARAH, the property that SVRG does not possess. Numerical experiments demonstrate the efficiency of our algorithm.

Many machine learning algorithms are based on the assumption that training examples are drawn independently. However, this assumption does not hold anymore when learning from a networked sample because two or more training examples may share some common objects, and hence share the features of these shared objects. We show that the classic approach of ignoring this problem potentially can have a harmful effect on the accuracy of statistics, and then consider alternatives. One of these is to only use independent examples, discarding other information. However, this is clearly suboptimal. We analyze sample error bounds in this networked setting, providing significantly improved results. An important component of our approach is formed by efficient sample weighting schemes, which leads to novel concentration inequalities.

The robot is building a tesseract. He motions at a glowing cube floating before him, and an identical cube emerges. He drags it to the left, but the two cubes stay connected, strung together by glowing lines radiating from their corners. The robot lowers its hands, and the cubes coalesce into a single shape--with 24 square faces, 16 vertices, and eight connected cubes existing in four dimensions. And the robot is Brian Greene, a physicist at Columbia University and bestselling author of several popular science books.