Huawei has unveiled its new smart cities digital platform utilising artificial intelligence (AI) and Internet of Things (IoT) capabilities, which it said could be used across smart public safety, environmental protection, transportation, government, education, and agriculture. Huawei's AI Digital Platform connects what it calls the brain, or command centre; the central nervous system, or network; and the peripheral nervous system, made up of sensors across a city. "Just like an operating system, the platform is compatible with different city sensors, creates a city digital twin, and supports diverse city applications," Huawei Enterprise Business Group VP Ma Yue said. The smart cities digital platform combines AI, IoT, big data, a geographic information system, video, cloud, converged communications, and security. "Huawei has also developed a middleware platform to provide services to software application partners. This is designed to help application partners quickly develop upper-layer applications to accelerate transformation and innovation in city management, city services, and industry development," the Chinese networking giant added.

In this paper, we introduce proximal gradient temporal difference learning, which provides a principled way of designing and analyzing true stochastic gradient temporal difference learning algorithms. We show how gradient TD (GTD) reinforcement learning methods can be formally derived, not by starting from their original objective functions, as previously attempted, but rather from a primal-dual saddle-point objective function. We also conduct a saddle-point error analysis to obtain finite-sample bounds on their performance. Previous analyses of this class of algorithms use stochastic approximation techniques to prove asymptotic convergence, and do not provide any finite-sample analysis. We also propose an accelerated algorithm, called GTD2-MP, that uses proximal "mirror maps" to yield an improved convergence rate. The results of our theoretical analysis imply that the GTD family of algorithms are comparable and may indeed be preferred over existing least squares TD methods for off-policy learning, due to their linear complexity. We provide experimental results showing the improved performance of our accelerated gradient TD methods.

In logic-based approaches to reasoning tasks such as Recognizing Textual Entailment (RTE), it is important for a system to have a large amount of knowledge data. However, there is a tradeoff between adding more knowledge data for improved RTE performance and maintaining an efficient RTE system, as such a big database is problematic in terms of the memory usage and computational complexity. In this work, we show the processing time of a state-of-the-art logic-based RTE system can be significantly reduced by replacing its search-based axiom injection (abduction) mechanism by that based on Knowledge Base Completion (KBC). We integrate this mechanism in a Coq plugin that provides a proof automation tactic for natural language inference. Additionally, we show empirically that adding new knowledge data contributes to better RTE performance while not harming the processing speed in this framework.

Rough inclusion functions (RIFs) are known by many other names in formal approaches to vagueness, belief, and uncertainty. Their use is often poorly grounded in factual knowledge or involve wild statistical assumptions. The concept of contamination introduced and studied by the present author across a number of her papers, concerns mixing up of information across semantic domains (or domains of discourse). RIFs play a key role in contaminating algorithms and some solutions that seek to replace or avoid them have been proposed and investigated by the present author in some of her earlier papers. The proposals break many algorithms of rough sets in a serious way. In this research, algorithm-friendly granular generalizations of such functions that reduce contamination are proposed and investigated from a mathematically sound perspective. Interesting representation results are proved and a core algebraic strategy for generalizing Skowron-Polkowski style of rough mereology is formulated.

In machine learning, a nonparametric forecasting algorithm for time series data has been proposed, called the kernel spectral hidden Markov model (KSHMM). In this paper, we propose a technique for short-term wind-speed prediction based on KSHMM. We numerically compared the performance of our KSHMMbased forecasting technique to other techniques with machine learning, using wind-speed data offered by the National Renewable Energy Laboratory. Our results demonstrate that, compared to these methods, the proposed technique offers comparable or better performance. Keywords: Wind-Speed Prediction, Kernel Methods, Kernel Mean Embedding, Spectral Learning, Hidden Markov Models. 1. Introduction Wind energy is one of the most attractive renewable energy sources.

Privacy campaigners have raised fears for patient data following Google's takeover of an artificial intelligence health app used in NHS hospitals. London-based AI firm DeepMind said its Streams app will be subsumed by the technology giant in a move that one expert described as "totally unacceptable" and a betrayal to patient's privacy. DeepMind, which is owned by Google but has operated the app independently until now, justified the decision in a blog post that explained how Google would allow the app to scale in a way that would not be possible by itself. The app uses AI to provide doctors and nurses with an easy-access dashboard of patients' medical records. "Our vision is for Streams to now become an AI-powered assistant for nurses and doctors everywhere – combining the best algorithms with intuitive design, all backed up by rigorous evidence," the post stated.

An Amazon Echo smart speaker could provide crucial evidence in a double murder case in the US after a judge in New Hampshire ordered the tech giant to provide investigators with recordings from the device. The speaker, which features the artificial intelligence voice assistant Alexa, was seized from a home in Farmington where two women were killed in January 2017. Timothy Verrill, 36, is charged with killing Christine Sullivan and Jenna Pellegrini by stabbing each woman multiple times. Judge Steven M Houran wrote in the court order that an Echo device present in the home may have captured audio that could provide key evidence in the case. How Alexa recorded a family's conversation then sent it to someone How Alexa recorded a family's conversation then sent it to someone "The court finds there is probable cause to believe the server[s] and/or records maintained for or by Amazon.com

Amazon will soon sell Apple products internationally for the first time on its platform, meaning customers could get products faster. Macbooks, iPads and iPhones will become available from the online retailer in the UK as well as other countries in Europe, the US and Japan by Christmas. Coupled with Amazon Prime membership, customers of the site could soon enjoy next day delivery on these products. Amazon already sells Apple devices using third party merchants but pricing, return policies and customer service vary from vendor to vendor. Existing listings will be removed from Amazon and firms affected will need to apply to Apple to become authorised sellers if they want to continue stocking its hardware.

In a nondescript office tower a mile from the Las Vegas Strip, two women toil in a windowless room crammed with bikinis in every size, style, and hue. Down the hall, colleagues are working to break into China's red-hot market for surveillance software powered by artificial intelligence. Bikini.com is owned by Remark Holdings, a small public company with Hollywood producer Brett Ratner on its board and financial ties to TV's Dr. Mehmet Oz. Remark's leaders are trying to transform the unprofitable, debt-loaded website operator into a provider of corporate AI technology in Asia, particularly China. Remark's business and share price are struggling, but its peculiar AI project has made some progress.

Baidu has announced a strategic investment in Xinchao Media, a media company that specializes in elevator ads, according to the company's post on Bai Jiahao (in Chinese). While Baidu did not disclose the size of the new investment, other reports suggest Xinchao Media's latest financing round led by Baidu was totaled RMB 2.1 billion and Huaxing Capital was the exclusive financial advisor on the deal. Forming a strategic partnership with Xinchao Media is part of Baidu's offline advertising push. "In the age of AI, market environment along with the development of technology is pushing and renewing the vigor of offline advertising," the Chinese search engine giant said in the post. On one hand, the growth of mobile devices and the growth of online traffic are slowing.