The two largest economies in the world are at the forefront of artificial intelligence development, but the jury is still out on which country is leading the race, according to a start-up with a presence in both the United States and China. "I think it's still early days. China probably leads on the retail end and consumer front, whereas the U.S. probably leads on autonomous driving for now," Daniel Tu, president and chief product and platform officer at Gen.Life, said Monday at the UBS Greater China Conference in Shanghai. Gen.Life is a start-up that uses technology such as A.I. to measure risks and improve the pricing and distribution of insurance products. The one-year-old firm is backed by major insurance firms such as AIA and American Family Insurance.

A strong declaration from a historically antagonist foe should put chills in the hearts of Americans preparing themselves for the world ahead: Russian President Vladimir Putin says the nation that leads in AI will be the ruler of the world … The ruler of the world! "The development of artificial intelligence has increasingly become a national security concern in recent years. It is China and the US (not Russia), which are seen as the two frontrunners, with China recently announcing its ambition to become the global leader in AI research by 2030. Many analysts warn that America is in danger of falling behind, especially as the [current US] administration prepares to cut funding for basic science and technology research." Elon Musk, one of America's foremost technology advocates, predicts that countries seeking leadership (and domination) from artificial intelligence will be the basis for World War III It's great when other countries just blindly buy whatever we're selling, but globalization eventually creates a level playing field.

Since entering a global alliance in the field of corporate solutions in 2002, Fujitsu and Microsoft have expanded the scope of collaboration in line with a changing market, including in such areas as the cloud and IoT for manufacturing. Since 2015, the two companies have been building a strong collaboration in the field of work-style transformation, with Fujitsu, through technical assistance from Microsoft, deploying a globally unified communications platform based on Office 365 for its approximately 160,000 employees. It then began using the knowledge and experience gained from that internal deployment to make its Global Communications Platform available to about 1.5 million customers in Japan. Now, as the movement to increase productivity using rapidly advancing digital technology gains traction globally, Fujitsu and Microsoft have agreed to further expand the framework of their collaboration, jointly building and launching solutions that further transform the ways people work by using the two companies' services and AI technologies.

Byton has revealed the first look at the high-tech electric car it claims will'perceive more than what a human being will ever do.' The firm finally unveiled its first drivable prototype at the Consumer Electronics Show in Las Vegas today, after teasing the so-called Smart Intuitive Vehicle (SIV) since September. The Nanjing-based company plans to roll out the mid-sized crossover vehicle with level 3 autonomy in China in 2019, starting at $45,000, before hitting the US and Europe in 2020. The car will respond to conditions on the road to avoid potential threats; for example, 'if the exterior lighting conditions change, your Byton will adjust to your needs automatically,' the co-founder said. The base model will be able to achieve 250 miles on a charge, while the higher end variant will go up to about 325 miles – and, the firm claims it will charge incredibly fast.

Learning with streaming data has attracted much attention during the past few years. Though most studies consider data stream with fixed features, in real practice the features may be evolvable. For example, features of data gathered by limited-lifespan sensors will change when these sensors are substituted by new ones. In this paper, we propose a novel learning paradigm: \emph{Feature Evolvable Streaming Learning} where old features would vanish and new features would occur. Rather than relying on only the current features, we attempt to recover the vanished features and exploit it to improve performance. Specifically, we learn two models from the recovered features and the current features, respectively. To benefit from the recovered features, we develop two ensemble methods. In the first method, we combine the predictions from two models and theoretically show that with the assistance of old features, the performance on new features can be improved. In the second approach, we dynamically select the best single prediction and establish a better performance guarantee when the best model switches. Experiments on both synthetic and real data validate the effectiveness of our proposal.

In data-mining applications, we are frequently faced with a large fraction of missing entries in the data matrix, which is problematic for most discriminant machine learning algorithms. A solution that we explore in this paper is the use of a generative model (a mixture of Gaussians) to compute the conditional expectation of the missing variables given the observed variables. Since training a Gaussian mixture with many different patterns of missing values can be computationally very expensive, we introduce a spanning-tree based algorithm that significantly speeds up training in these conditions. We also observe that good results can be obtained by using the generative model to fill-in the missing values for a separate discriminant learning algorithm.

IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. XX, MONTH 2016 1 Polar n -Complex and n -Bicomplex Singular V alue Decomposition and Principal Component Pursuit Tak-Shing T. Chan, Member, IEEE and Yi-Hsuan Y ang, Member, IEEE Abstract--Informed by recent work on tensor singular value decomposition and circulant algebra matrices, this paper presents a new theoretical bridge that unifies the hypercomplex and tensor-based approaches to singular value decomposition and robust principal component analysis. We begin our work by extending the principal component pursuit to Olariu's polar n - complex numbers as well as their bicomplex counterparts. In so doing, we have derived the polar n -complex and n -bicomplex proximity operators for both the 1-and trace-norm regularizers, which can be used by proximal optimization methods such as the alternating direction method of multipliers. Experimental results on two sets of audio data show that our algebraically-informed formulation outperforms tensor robust principal component analysis. We conclude with the message that an informed definition of the trace norm can bridge the gap between the hypercomplex and tensor-based approaches. Our approach can be seen as a general methodology for generating other principal component pursuit algorithms with proper algebraic structures. I NTRODUCTION T HE robust principal component analysis (RPCA) [1] has received a lot of attention lately in many application areas of signal processing [2]-[5]. Owing to the NPhardness of the above formulation, the principal component pursuit (PCP) [1] has been proposed to solve this relaxed problem instead [6]: min L, S ‖L ‖ λ‖S ‖ 1 s.t. X L S, (2) where ‖·‖ is the trace norm (sum of the singular values),‖·‖ 1 is the entrywise 1-norm, andλ can be set toc/ max(l,m) where c is a positive parameter [1], [2]. The trace norm and the 1-norm are the tightest convex relaxations of the rank and Manuscript received August 26, 2015; revised May 26, 2016 and July 16, 2016; accepted September 3, 2016. This work was supported by a grant from the Ministry of Science and Technology under the contract MOST102-2221-E-001-004-MY3 and the Academia Sinica Career Development Program. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Masahiro Y ukawa. The authors are with the Research Center for Information Technology Innovation, Academia Sinica, Taipei 11564, Taiwan (email: taksh-ingchan@citi.sinica.edu.tw;

Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the translation from head entities to tail entities, which achieve the state-of-the-art results among knowledge graph embedding methods. However, a major limitation of these methods is the time consuming training process, which may take several days or even weeks for large knowledge graphs, and result in great difficulty in practical applications. In this paper, we propose an efficient parallel framework for translating embedding methods, called ParTrans-X, which enables the methods to be paralleled without locks by utilizing the distinguished structures of knowledge graphs. Experiments on two datasets with three typical translating embedding methods, i.e., TransE [3], TransH [17], and a more efficient variant TransE- AdaGrad [10] validate that ParTrans-X can speed up the training process by more than an order of magnitude.

China aims to implement artificial intelligence technology in at least half of all its new cars by 2020 and cover 90 percent of its big cities and highways with a wireless network that can support smart vehicles, its top state planner said on Friday. These goals are part of a draft for a national smart car strategy that the National Development and Reform Commission (NDRC) wants to seek public feedback for, it said in a statement. China is aiming to become a world leader in artificial intelligence by 2025, with an aim to grow its core AI industries to over 150 billion yuan ($22.15 billion) by 2020. Smart cars are a key part of the project. The NDRC said it wanted to build a national team with officials from the State Council to guide smart car innovation and planned to encourage the launch of preferential policies for related businesses which could include tax cuts.

The year 2017 saw artificial intelligence bringing the stuff of science fiction closer to reality by not only gaining foothold in all spheres of life, but also getting the better of humans in many fields. From acquiring citizenship to outsmarting humans at complex games, from composing music to writing novels, from assisting doctors to helping fight judicial cases, artificial intelligence (AI) made its presence felt throughout the year. Artificial intelligence is a term used to describe systems or machines that mimic the cognitive functions of human minds, such as learning and problem solving. Although by no means a new concept, the technology made headlines throughout the year. Perhaps, among the most talked about AI machines this year was Sophia, a humanoid robot designed by a company in Hong Kong, that was granted citizenship in Saudi Arabia – a country where women were not allowed to drive until recently.