Zong Qinghou, the CEO of one of China's largest companies, announced plans to set up a research center at the University of Haifa focusing on artificial intelligence. The Chinese Academy of Sciences will also be a research partner.

The fact that the UK punches well above its weight in AI should be a source of national pride. After all, this country is home to the largest number of AI companies after America and China, and £6.21 billion was invested in UK technology last year . Our rich history in AI builds on the work of computing pioneers such Ada Lovelace, publisher of the first algorithm; Charles Babbage, originator of the digital programmable computer; and Alan Turing who helped crack the Enigma code. We are fortunate to house some of the world's finest academic institutions; the talent pool from Cambridge, Oxford and Imperial is exceptional.

Around a decade ago at the Indian Institute of Technology Bombay (IIT-B), classmates Sneh Rajkumar Vaswani, Chintan Subhash Raikar, and Prashant Iyengar were involved in a project to build intelligent underwater vehicles for the India Navy and the oil and gas industry. In the process, they developed a knack for artificial intelligence (AI) and robotics. That culminated in their setting up of emotix in 2015, and this startup has now developed Miko, India's first companion robot. Miko, weighing around 750 grams and standing a little over a foot, engages, educates, and entertains children above the age of five. Besides talking to and playing games with the kids, Miko is also equipped to answer basic questions related to general knowledge and academics.

Baidu Inc. has raised more than US$1.9 billion for its newly spun-off financial services division from TPG, Carlyle Group and other investors, creating one of China's best-funded fintech companies. Taikang Group and other backers also joined the funding round for the newly formed company, which will be called "Du Xiaoman Financial" and use artificial intelligence to provide short-term loans and investment services. Baidu Senior Vice President Zhu Guang will run the business as an independent entity, according to a statement Saturday. The news comes a day after Baidu said it planned to sell a majority stake in the business, without providing a timetable. Baidu turns to AI to police online content, but is the technology reliable?

The growing race for military superiority between Washington and Beijing is entering a new phase, with both world powers preparing to square off in the cutting-edge realm of artificial intelligence. A cadre of tech gurus at the Defense Department and in the intelligence community are working to develop an interagency center designed to position the United States as the dominant force in the emerging technology subsector. Michael Griffin, the Pentagon's chief of research and engineering, has been making the rounds on Capitol Hill and in national security circles in Washington to extol the necessity and opportunity posed by the organization, dubbed the Joint Artificial Intelligence Center. Artificial intelligence technologies, which leverage various binary computations and algorithms to replicate human decision-making and risk assessments, has revolutionized the commercial and defense sectors. On the military side, automation fueled by artificial intelligence has assisted the U.S. and allied forces in areas such as combat logistics, resupply and analysis of raw intelligence collected by the Pentagon, the CIA and other agencies.

Google cofounder Sergey Brin speaks during a press conference after the third game of the Google DeepMind Challenge Match against Google-developed supercomputer AlphaGo at a hotel in Seoul on March 12, 2016. A new artificial intelligence ethics (AI) board was announced this week by Axon -- the US company behind the taser weapon -- but the AI ethics board many people still want to know about remains shrouded in mystery. Google quietly set up an AI ethics board in 2014 following the £400 million acquisition of a London AI lab called DeepMind, which hopes to one day build machines with human-level intelligence that will have a profound impact on the society we live in. Who sits on that board, how often that board meets, or what that board discusses, has remained a closely guarded company secret, despite DeepMind cofounder Mustafa Suleyman (who lobbied for the creation of the board) saying in 2016 that Google will publicise the names of those on it. This week, Axon, a US company that develops body cameras for police officers and weapons for the law enforcement market, demonstrated the kind of transparency that Google should aspire towards when it announced an AI ethics board to "help guide the development of Axon's AI-powered devices and services".

Journal of Parallel and Distributed Computing 104, 19-35.

For past few days, I developed a strong interesting in building a basic implementation of Vehical routing in R. The problem can be approach through many ways as there is no single handed solution to it. Vehical Routing is extension of famous algorithmic problem called TSP or Traveling Salesman Problem. It is to optimize the route for a traveling salesman to reach maximum locations in less time. I have worked on extension of TSP for Vehical Routing with addition to selection of Vendor/Truck Depot for deliveries closest to the Depot. Consider there are mulitple depots and multiple customers.

Declarative modeling uses symbolic expressions to represent models. With such expressions one can formalize high-level mathematical computations on models that would be difficult or impossible to perform directly on a lower-level simulation program, in a general-purpose programming language. Examples of such computations on models include model analysis, relatively general-purpose model-reduction maps, and the initial phases of model implementation, all of which should preserve or approximate the mathematical semantics of a complex biological model. Multiscale modeling benefits from both the expressive power of declarative modeling languages and the application of model reduction methods to link models across scale. Based on previous work, here we define declarative modeling of complex biological systems by defining the semantics of an increasingly powerful series of declarative modeling languages including reaction-like dynamics of parameterized and extended objects, we define semantics-preserving implementation and semantics-approximating model reduction transformations, and we outline a "meta-hierarchy" for organizing declarative models and the mathematical methods that can fruitfully manipulate them.

In the last years, deep learning algorithms have highly revolutionized several areas including speech, image and natural language processing. The specific field of Machine Translation (MT) has not remained invariant. Integration of deep learning in MT varies from re-modeling existing features into standard statistical systems to the development of a new architecture. Among the different neural networks, research works use feedforward neural networks, recurrent neural networks and the encoder-decoder schema. These architectures are able to tackle challenges as having low-resources or morphology variations. This manuscript focuses on describing how these neural networks have been integrated to enhance different aspects and models from statistical MT, including language modeling, word alignment, translation, reordering, and rescoring. Then, we report the new neural MT approach together with a description of the foundational related works and recent approaches on using subword, characters and training with multilingual languages, among others. Finally, we include an analysis of the corresponding challenges and future work in using deep learning in MT.