Chinese artificial intelligence start-up SenseTime has raised $620m (£465m) in funding, a month after raising $600 million from e-commerce giant Alibaba. SenseTime, which is headquartered in Beijing, develops a facial recognition product which is used by the Chinese government for its surveillance system. Other SenseTime customers include Honda, which uses the technology in its driverless cars, and Chinese smartphone companies Huawei and Xiaomi. The latest investment in SenseTime came from Fidelity International, Hopu Capital, Silver Lake and Tiger Global. Qualcomm also invested in the latest round of funding through its Qualcomm Ventures division.

Major Companies of Norway like Robot Aviation, Telenor Svalbard and Andoya Space Center are collaborating to develop and test a specialised Norwegian drone for emergency situations. With increase in commercial shipping, tourism and cruises, the Norwegian technology companies want to use drones to strengthen the understanding of situations, communication coverage and the ability to handle events. "Large geographical distances and areas without adequate coverage for mobile communications, VHF or maritime broadband radio (MBR) in the area of Svalbard can be a challenge in accidents, natural disasters and the like," says Guttorm Albrigt Hansen, head of Telenor Svalbard. The UAS would be carrying photos, sensors as well as telecommunications equipment, and the system is intended as a solution that will provide communication coverage for major events or disasters.

In a report titled "The 5G Business Potential," networking and Telecommunications Company Ericsson has found wearable devices and artificial intelligence will improve resource efficiency and meet consumer demands for greater convenience and freedom of choice in the healthcare sector. The report analyzed the 5G business opportunity that comes from industrial digitalization in eight key global industries. Telecomm operators will have several areas to work on, such as hospital applications – virtual reality used in medical training, telemetry and online booking systems – and real-time medical data management. The area that has the most potential for operators addressing this industry with 5G, according to the report, is patient applications used outside of traditional hospital environments, such as precision medicine, online consultations and applications to monitor health and administer medication remotely to better manage chronic ailments. Forty-two percent of decision makers in the industry prefer 5G-enabled devices because they consume less power, according to the report.

We can't stop Nature when it unleashes its fury in the form of volcanoes, earthquakes, storms and avalanches, but we can use technology to warn us of imminent danger - and save lives. As the sun sets over Lake Victoria, Africa's largest lake, tens of thousands of fishermen ready themselves to head out on the water for the night, fishing mostly for tilapia and Nile perch. As they push off, they know they are risking their lives - some of them may never be seen again. Lake Victoria - a lake so big it straddles Uganda, Tanzania and Kenya - is notorious for its deadly storms. At this time of year, strong winds, rain, lightning and huge waves are a regular occurrence.

Exogenous state variables and rewards can slow down reinforcement learning by injecting uncontrolled variation into the reward signal. We formalize exogenous state variables and rewards and identify conditions under which an MDP with exogenous state can be decomposed into an exogenous Markov Reward Process involving only the exogenous state reward and an endogenous Markov Decision Process defined with respect to only the endogenous rewards. We also derive a variance-covariance condition under which Monte Carlo policy evaluation on the endogenous MDP is accelerated compared to using the full MDP. Similar speedups are likely to carry over to all RL algorithms. We develop two algorithms for discovering the exogenous variables and test them on several MDPs. Results show that the algorithms are practical and can significantly speed up reinforcement learning.

China tech is highlighted regularly in Mary Meeker's annual Internet report that underscores China's advances in mobile entertainment and payments, e-commerce, artificial intelligence and on-demand transportation -- in other words, all the latest trends that define Chinese innovation today. If you want evidence of China's climb, consider that nine of the world's top 20 Internet leaders ranked by market valuation are in China today: Alibaba, Tencent, Ant Financial, Baidu, Xiaomi, Didi Chuxing, JD.com, Meituan-Dianping and Toutiao. The remaining 11 are in the U.S. led by Apple, Amazon, Microsoft, Google (Alphabet) and Facebook. China's momentum in artificial intelligence is also highlighted with notes on Chinese government support for AI development. The report cites Eric Schmidt's prediction that China could be at the level of the U.S. within five years, considering how fast Chinese AI knowhow is improving.

YANGON, MYANMAR – A Japanese information technology venture in Myanmar has launched a gourmet information app assisted by artificial intelligence technology to cater to growing numbers of smartphone users in the country. World Street Myanmar Yathar Co., established by Aichi Prefecture-based advertising agency Nagata Co. as a major shareholder, says it recently kicked off an app named Yathar, which means taste in the Myanmar language, to offer personalized recommendations at eateries and post customer feedback. Shunsuke Ichikawa, CEO of the local venture, told NNA his firm is introducing the foodie platform in Myanmar because it is good timing to invest in the emerging economy in light of recent developments in its telecommunications infrastructure. The nation's telecom market liberalization in 2013 sharply boosted the smartphone penetration rate from 2 percent in 2011 to 95.6 percent in 2016, according to International Telecommunication Union data cited by the IT venture. Ichikawa said Yathar will provide users with fair and supportive information based on reviews of food services ranging from high-end restaurants to stall vendors.

Kaleidoscope 2018: Machine learning for a 5G future is the tenth in a series of peer-reviewed academic conferences organized by ITU to bring together a wide range of views from universities, industry and research institutions. The aim of the Kaleidoscope conferences is to identify emerging developments in information and communication technologies (ICTs) and, in particular, areas in need of international standards to aid the healthy development of the Information Society. Machine learning shows promise to assist smarter use of network-generated data, enabling ICT network operators and service providers to adapt to changes in traffic patterns, security risks and user behaviour. This will also affect ITU standardization work in fields such as coding algorithms; data collection, storage and management; and network management and orchestration. Kaleidoscope 2018 calls for original scientific papers addressing advances in research on machine learning and artificial intelligence techniques for future communication networks, covering all aspects of network design, management, implementation and optimization.

Learning vector embeddings of complex networks is a powerful approach used to predict missing or unobserved edges in network data. However, an open challenge in this area is developing techniques that can reason about $\textit{subgraphs}$ in network data, which can involve the logical conjunction of several edge relationships. Here we introduce a framework to make predictions about conjunctive logical queries---i.e., subgraph relationships---on heterogeneous network data. In our approach, we embed network nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. We prove that a small set of geometric operations are sufficient to represent conjunctive logical queries on a network, and we introduce a series of increasingly strong implementations of these operators. We demonstrate the utility of this framework in two application studies on networks with millions of edges: predicting unobserved subgraphs in a network of drug-gene-disease interactions and in a network of social interactions derived from a popular web forum. These experiments demonstrate how our framework can efficiently make logical predictions such as "what drugs are likely to target proteins involved with both diseases X and Y?" Together our results highlight how imposing logical structure can make network embeddings more useful for large-scale knowledge discovery.

It is the largest metropolis and the 2nd most populous metropolis in India, and 8th most populous metropolis in the world with 16.7 million residents in the territory as of the 2011 census. Delhi is the largest commercial center in northern India. In 2010, Delhi had a per capita income of 135,820 (US$2,709.61) Being a rich city, it is also offers host of jobs & career opportunities to its people. Key service industries that provide maximum jobs are information technology, telecommunications, hotels, banking, media and tourism.