A subsidiary of Google has entered a two-year partnership with Fudan University, the leading university in China's eastern Shanghai municipality, with a focus on emerging technologies such as artificial intelligence. Google China's Education Cooperation Division support Fudan's curriculum related to emerging science and technology, online news outlet The Paper reported, adding that the pair will jointly build a laboratory as well as an exchange center to boost interaction between students in China and the US. Google's China-based education unit has been working with schools in the country since 2006, covering undergraduate, higher vocational education and secondary schools. The projects supported include joint scientific research, curriculum construction, teacher training and information technology education for middle school students. AI is a key focus of Google's development in China.

In the 2011 CBS show Person of Interest, reclusive computer scientist Harold Finch builds an artificial intelligence system called "the Machine" that compiles and analyzes troves of data to predict murders. Finch and his henchman, John, then chase down the perpetrator and prevent the crime. Public security officials in the Chinese autonomous region of Xinjiang, it turns out, are turning that fiction into fact. An enormous data-driven program that pulls from health records, financials, vehicle checkpoints and police reports to identify individuals likely to commit crimes in China's politically sensitive northwest region has just been revealed. Launched in 2016, the "big data" policing initiative in Xinjiang is one of many law enforcement initiatives the country is launching, drawing on its growing capabilities in the fields of AI and robotics. While a number of countries … boast the technological capabilities China is integrating into law enforcement, political considerations make it difficult for these countries to follow suit.

TL;DR, we released the largest and most diverse driving video dataset with richannotations called BDD100K. You can access the data for research now at http://bdd-data.berkeley.edu. We haverecently released an arXivreport on it. And there is still time to participate in our CVPR 2018 challenges! Autonomous driving is poised to change the life in every community.

Japanese businesses will share industrial data with members of other sectors and even direct rivals in hopes of boosting productivity overall, through a private initiative led by retailer Seven & i Holdings and a separate government platform. Advances in artificial intelligence enable companies to use so-called big data in ways they could not before, to seek deeper understanding of customer behavior. For instance, pooling information from Seven & i's 23 million daily customer interactions and mobile provider NTT Docomo's network of 76 million mobile subscribers could help pinpoint areas where everyday shopping is inconvenient, which would help with planning the expansion of online supermarkets. Combining knowledge of people's movements and tastes could also help with building attractive towns and planning store locations. From June, Seven & i -- the department store operator and parent of convenience store giant Seven-Eleven Japan -- will link up with nine other companies, including Docomo, railway operator Tokyu, trading company Mitsui & Co. and megabank Sumitomo Mitsui Financial Group.

The use of drones is already growing across various sectors but an Ajman-based start-up -- Deep Aero -- is fuelling an autonomous drone economy driven by artificial intelligence and blockchain. Gurmeet Singh Anand, CEO of Deep Aero, told Gulf News that the use of unmanned aerial vehicles is increasing exponentially. "In the coming future, we will see millions or billions of commercial drones flying in the air. When that happens, there has to be an autonomous system to manage the drone traffic. Otherwise, it will be a nightmare for regulators," he said.

Whereas artificial intelligence is being used in some countries to mark multiple choice questions, China is experimenting with machine intelligence to mark essays. According to the South China Morning Post, technology has been developed to interpret the general logic and meaning of the text. The platform can then undertake human-like judgment into an essay's overall quality. The platform can then assign a grade to the essay and also provide recommended for improvement, selecting categories such as writing style, sentence structure and overall theme. At present the application of artificial intelligence in Chinese schools is assisting an assessment by a teacher and not removing the teacher from the equation.

On 25 April 2018, a new Communication was published that sets out the European Commission's (EC's) new strategy to boost Europe's artificial intelligence (AI) capabilities and related industries, while at the same time preparing for socioeconomic changes emanating from these emerging technologies. The Communication also poses questions as to whether – and, if so, where and how – the European legal and ethical framework needs to be adapted due to the advent of AI. The EC refers to AI as "systems that show intelligent behaviour by analysing their environment, and performing various tasks with some degree of autonomy to achieve specific goals."1 European leaders are considering AI as a top priority. On 10 April, 24 member states2 and Norway co-signed a Declaration which commits them to working together on AI.

Neural machine translation (NMT) is a deep learning based approach for machine translation, which yields the state-of-the-art translation performance in scenarios where large-scale parallel corpora are available. Although the high-quality and domain-specific translation is crucial in the real world, domain-specific corpora are usually scarce or nonexistent, and thus vanilla NMT performs poorly in such scenarios. Domain adaptation that leverages both out-of-domain parallel corpora as well as monolingual corpora for in-domain translation, is very important for domain-specific translation. In this paper, we give a comprehensive survey of the state-of-the-art domain adaptation techniques for NMT.

We investigate the use of attentional neural network layers in order to learn a `behavior characterization' which can be used to drive novelty search and curiosity-based policies. The space is structured towards answering a particular distribution of questions, which are used in a supervised way to train the attentional neural network. We find that in a 2d exploration task, the structure of the space successfully encodes local sensory-motor contingencies such that even a greedy local `do the most novel action' policy with no reinforcement learning or evolution can explore the space quickly. We also apply this to a high/low number guessing game task, and find that guessing according to the learned attention profile performs active inference and can discover the correct number more quickly than an exact but passive approach.

Behavior Analytics Market size is set to exceed USD 3.5 billion by 2024; according to a new research report by Global Market Insights, Inc. Technology advancement has bolstered the demand for behavior analytics market solutions among organizations to detect threats even before they occur and to mitigate their impact. Integration of advanced analytics and machine learning algorithms for analyzing user behavior allows automatic analysis enables organizations to link user and entity activity to support security analyst in threat detection and remediation. Besides these, advanced behavior analytics market systems also offer certain benefits over conventional security enterprise systems, such as end to end protection, automated response and access control. The healthcare sector has emerged as one of the major end-users of the behavior analytics market and is anticipated to register substantial growth during the forecast timeline. The growing demand for these solutions among healthcare organizations is attributed to the growing threat of insider attacks and data breaches among healthcare institutes, which poses a financial risk. Besides, healthcare institutes are more exposed to the risk of insider attacks owing to the general lack of cybersecurity infrastructure.