BEIJING – Chinese authorities have begun deploying a new surveillance tool: "gait recognition" software that uses people's body shapes and how they walk to identify them, even when their faces are hidden from cameras. Already used by police on the streets of Beijing and Shanghai, "gait recognition" is part of a push across China to develop artificial-intelligence and data-driven surveillance that is raising concern about how far the technology will go. Huang Yongzhen, the CEO of Watrix, said that its system can identify people from up to 50 meters (165 feet) away, even with their back turned or their face covered. Such a capability can fill a gap in what is offered by facial recognition, which needs close-up, high-resolution images of a person's face in order to work. "You don't need people's cooperation for us to be able to recognize their identity," Huang said in an interview in his Beijing office.

Two men walk into a bar, the first one says: "robots will conquer our civilisation and make us their servants within ten years", the second one responds: "No, the principle of artificial intelligence (AI) is a far-fetched goal that will never see light". The bartender smiles, analyses their facial expressions, assigns a sentiment score to their sentences, evaluates their historical drinking trends, and decides to pour the first one a glass of gin and tonic, and the second one a glass of Scotch. Here is the spoiler: both men are lying; and the bartender is a robot. Not a funny joke, but a reality that is shadowing all conventional discussions about the future prospects of AI. In order to avoid such binary discussions about the goodness and possibilities of machine intelligence, and to eliminate the'hype' surrounding the topic, this article aims to unveil the slowly cooking, quietly simmering, unspoken truths of the inevitable global arms race of AI.

China's first artificial intelligence (AI) theme park opened to public in early November, after 10 months renovation of a municipal park in northern Beijing. Driverless shuttle buses, smart lamp posts that can record exercise data, and intelligent speakers that can respond to human instructions have been installed in Haidian Park, which covers about 34 hectares near the 4th Ring Road. The district government of Haidian and Internet company Baidu signed an agreement in January to jointly explore "smart city" building. Haidian Park, which received about 1.2 million tourists last year, was chosen to run the pilot program. A total of 10 government departments and companies participated in the renovation of the park over the past 10 months, said Che Jianguo from the district's park administration office.

Conference Series welcomes you to attend the "International Conference on Advanced Robotics, Mechatronics and Artificial Intelligence" during December 03-04, 2018, Valencia, Spain. The main theme of the conference is "Boundless implication of Automation and Control Systems in Mechatronics". We cordially invite all the participants who are interested in sharing their knowledge and research in the arena of Advanced Robotics, Mechatronics and Artificial Intelligence. Advanced Robotics 2018 anticipates more than 150 participants around the globe with thought provoking Keynote lectures, Oral and Poster presentations. Opportunity to attend the presentations delivered by eminent scientists, researchers, experts from all over the world.

Tweets featuring "MAGA" and "QAnon" are largely driven by automated behavior.Omar Marques/SOPA Images via ZUMA Wire When President Donald Trump tweeted about a caravan of immigrants heading to the US border in late October, it set off a wildfire of misinformation on social media. Posts on Facebook and Twitter spread conspiracy theories that Democratic donor George Soros was funding the migrants and the false allegation that the group included terrorists and gang members. It turns out it wasn't just Republicans latching on the story--it was also Twitter bots. Mother Jones partnered with RoBhat Labs, a non-partisan social media firm that reports bot activity, to show the scope of disinformation circulating on Twitter before the election. In order to detect automated, bot-like behavior, RoBhat collects sample tweets from Twitter's application programming interface and runs them through a machine learning model.

In the time it takes some countries to slightly extend one train station platform, China can rustle up entire high-speed railways. And this stunning drone footage shows what incredible feats of engineering they can be. The clip shows 160mph trains running along the now completed 411-mile Xi'an to Chengdu high-speed line, which was started in October 2012. The clip shows 160mph trains running along the now completed 411-mile Xi'an to Chengdu high-speed line, which was started in October 2012 To connect Xi'an with Chengdu engineers had to tackle the fearsome Qinling Mountains that divide northern and southern China It's something to behold, with the £8billion (71bn yuan) line – finished in December 2017 - passing amid towering mountains and through huge tunnels. To connect the two cities engineers had to tackle the fearsome Qinling Mountains that divide northern and southern China and thread the track underneath numerous environmentally sensitive areas.

LG's rolling robots could soon arrive at the largest supermarket chain in South Korea. The tech giant has inked a deal with grocery retailer E-Mart to develop self-driving shopping carts that can follow consumers around the store, according to Yonhap News. The device will be developed under LG's CLOi brand, which has released other robots for the home, hotels and other uses. LG has inked a deal with grocery retailer E-Mart to develop self-driving shopping carts that can follow consumers around the store, help direct them to items and keep track of shopping lists. LG's cylindrical robot features a face-like screen where it can display users' shopping list.

Artificial intelligence (AI) is at the frontier of a new techno-tsunami that is transforming the way we live and work. "Historically, an AV researcher might see 10,000 viruses in a career. Today there are over 700,000 per day," says Ryan Permeh, Chief Scientist of Cylance. Could AI be the solution to solving the big data problem, and bridging the widening workforce gap in the Cyber Security industry? Intelligent machines now have the power to make observations, understand requests, reason, draw data correlations, and derive conclusions.

Collaborative filtering (CF) is a popular technique in today's recommender systems, and matrix approximation-based CF methods have achieved great success in both rating prediction and top-N recommendation tasks. However, real-world user-item rating matrices are typically sparse, incomplete and noisy, which introduce challenges to the algorithm stability of matrix approximation, i.e., small changes in the training data may significantly change the models. As a result, existing matrix approximation solutions yield low generalization performance, exhibiting high error variance on the training data, and minimizing the training error may not guarantee error reduction on the test data. This paper investigates the algorithm stability problem of matrix approximation methods and how to achieve stable collaborative filtering via stable matrix approximation. We present a new algorithm design framework, which (1) introduces new optimization objectives to guide stable matrix approximation algorithm design, and (2) solves the optimization problem to obtain stable approximation solutions with good generalization performance. Experimental results on real-world datasets demonstrate that the proposed method can achieve better accuracy compared with state-of-the-art matrix approximation methods and ensemble methods in both rating prediction and top-N recommendation tasks.

In this paper, we propose a novel linear discriminant analysis criterion via the Bhattacharyya error bound estimation based on a novel L1-norm (L1BLDA) and L2-norm (L2BLDA). Both L1BLDA and L2BLDA maximize the between-class scatters which are measured by the weighted pairwise distances of class means and meanwhile minimize the within-class scatters under the L1-norm and L2-norm, respectively. The proposed models can avoid the small sample size (SSS) problem and have no rank limit that may encounter in LDA. It is worth mentioning that, the employment of L1-norm gives a robust performance of L1BLDA, and L1BLDA is solved through an effective non-greedy alternating direction method of multipliers (ADMM), where all the projection vectors can be obtained once for all. In addition, the weighting constants of L1BLDA and L2BLDA between the between-class and within-class terms are determined by the involved data set, which makes our L1BLDA and L2BLDA adaptive. The experimental results on both benchmark data sets as well as the handwritten digit databases demonstrate the effectiveness of the proposed methods.