Chinese Academy of Sciences (CAS) is pouring money into an ambitious plan to develop a pocket-size artificial intelligence (AI) computer. Amid the hoopla surrounding AlphaGo's upcoming match with a Chinese Go board game master in May, researchers at the school are attempting to fit enormous amounts of computing power onto tiny chips – a feat that so far no one in the world has ever achieved. An AI algorithm will be built into a chip called "Cambicron," which researchers hope to one day put in mobile phones and smart watches. Google's AI program AlphaGo needs huge power and large servers to operate. CAS wants the same level of performance running on just one watt of power, according to Chen Yunji of the CAS Institute of Computing Technology.

When it comes to artificial intelligence (AI) and Chinese tech companies, thoughts often begin and end with Baidu. But Tencent, Asia's second highest-valued tech company behind Alibaba, has reminded the world that it too is investing in the field. Search giant Baidu was one of the first to make a major commitment to deep learning. It spent over $2.9 billion on R&D over a 2.5 year period, according to Bloomberg, and currently has more than 1,300 specialists working on a variety of technologies that include AI and augmented reality. Baidu, however, suffered a blow when its chief scientist Andrew Ng, who heads up its U.S.-based research team, announced his departure last week.

Enjoy it while it lasts. US Treasury Secretary Steven Mnuchin may think artificial intelligence (AI) isn't going to start taking humans' jobs for 50 to 100 years, but most experts believe a revolution in automation is coming far sooner, promising massive increases in efficiency -- and job losses on a huge scale. A recent study put out by PwC estimated that as many as 30% of UK jobs could be "susceptible to automation by robots and AI" by the early 2030s -- with 38% in the US at risk, 35% in Germany, and 21% in Japan -- although it believes jobs will be created elsewhere in the economy to help offset this. Are we doing enough to prepare? Absolutely not, says Bob Moritz, global chairman of consultancy firm PwC.

The year 2016 was one dominated by disruption -- from society to politics, the economy to technology -- in India, and around the world. The year 2017 is shaping up to be one where some of the dust of disruption begins to settle, and we find a new way forward. As the consequences of a customer-led and digital-centric market start to take shape, businesses in particular have had to come to grips with the stark reality that slow, calculated change won't cut it in this new environment. In the race to disrupt or be disrupted, and as organisations face fierce competition both at home and abroad, emerging technologies like artificial intelligence (AI) are seeping into the mainstream enterprise. While self-driving cars and virtual assistants continue to dominate headlines and capture the imagination of consumers, enterprises in India are quietly grappling with how they too can effectively leverage AI as they seek the holy grail of deeper contextual insights.

China has great potential in applications of artificial intelligence (AI), a senior official said Sunday. "Chinese researchers and entrepreneurs are among the best in the world, with technological innovations and good earnings in the sector," said Liu Lihua, vice minister of industry and information technology. Researchers with Chinese companies such as iFlytek, Alibaba and Baidu participated in the study of the world's leading AI technologies, said Liu, referring to technologies of reinforcement learning, paying with your face and self-driving trucks. A couple of weeks ago, the "MIT Technology Review" listed the above three and another seven technologies as its 10 breakthrough technologies in 2017. AI research started more than 60 years ago and there have been some major ups and downs.

Newswire) Remesh, a software company that is reinventing market research through artificial intelligence (AI), today announced the closing of its $2.25 million seed investment round that brings its total funding to $3.85 million. The round is led by LionBird Ventures, a venture capital firm investing in early stage digital health and business services companies with offices in Tel Aviv and Chicago. The round also includes Reimagine Holdings Group, a holding company focused on growing consumer insights and marketing services companies, as well as individual investors, representing a mix of new and returning investors. "We believe that Remesh has shown real potential to change the way brands, consultants and agencies listen to feedback from their audiences," said Ed Michael, Managing Partner at LionBird Ventures. "Remesh has recognized a way to solve for a number of inefficiencies in market research using artificial intelligence. This new model not only replaces legacy systems, but establishes entirely new market research workflows."

New Delhi, Most customers are confused about the use of artificial intelligence (AI) and are, therefore, reluctant to embrace this new technology, a study said on Friday. Released by US-based software firm Pegasystems, it revealed that these fears are often eased once the users gain firsthand AI experience -- which ironically many enjoy without even realising it. "Our study suggests the recent hype is causing some confusion and fear among consumers, who may not really understand how it's already being used and helping them every day," said Don Schuerman, Vice President (Product Marketing) Pegasystems. The study that involved 6,000 customers in six countries found that consumers were hesitant to fully embrace AI devices and services. "Only 36 per cent are comfortable with businesses using AI to engage with them. Almost 72 per cent express some sort of fear about AI," the study found.

Stochastic variance reduction algorithms have recently become popular for minimizing the average of a large but finite number of loss functions. In this paper, we propose a novel Riemannian extension of the Euclidean stochastic variance reduced gradient algorithm (R-SVRG) to a manifold search space. The key challenges of averaging, adding, and subtracting multiple gradients are addressed with retraction and vector transport. We present a global convergence analysis of the proposed algorithm with a decay step size and a local convergence rate analysis under a fixed step size under some natural assumptions. The proposed algorithm is applied to problems on the Grassmann manifold, such as principal component analysis, low-rank matrix completion, and computation of the Karcher mean of subspaces, and outperforms the standard Riemannian stochastic gradient descent algorithm in each case.

Tel.: 86-10-5168-8514 Academic Editor: Simon X. Yang Received: 30 January 2017; Accepted: 7 April 2017; Published: date Abstract: This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and northeast transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks. Keywords: transportation network; traffic speed prediction; spatiotemporal feature; deep learning; convolutional neural network 1. Introduction Predicting the future is one of the most attractive topics for human beings, and the same is true for transportation management. Understanding traffic evolution for the entire road network rather than on a single road is of great interest and importance to help people with complete traffic information in make better route choices and to support traffic managers in managing a road network and allocate resources systematically [1,2]. However, large-scale network traffic prediction requires more challenging abilities for prediction models, such as the ability to deal with higher computational complexity incurred by the network topology, the ability to form a more intelligent and efficient prediction to solve the spatial correlation of traffic in roads expanding on a two-dimensional plane, and the ability to forecast longer-term futures to reflect congestion propagation. Thus, existing models may fail to predict largescale network traffic evolution. In the existing literature, two families of research methods have dominated studies in traffic forecasting: statistical methods and neural networks [3]. Statistical techniques are widely used in traffic prediction.

People can upload a geotagged video, photo or text to social networks like Facebook and Twitter, share their present location on Foursquare or share their travel route using GPS trajectories to GeoLife [49]. A considerable amount of this spatiotemporal data is generated by the activity of users in location-based social networks (LBSN). In a typical LBSN, like Foursquare, users share the time and geolocation of their check-ins, comment about it, or unlock badges by exploring new venues. Many techniques have been proposed for processing, managing, and mining the trajectory data in the past decade [55]. Several other studies try to leverage the spatial data in recommender systems [23]. However, a few works have attempted to model the spatiotemporal behavior of users in LBSNs [5, 6]. Given the history of users' check-ins, the goal is to predict the time and location of This work is supported by ICT Innovation Center, Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.