Huawei's Ascend line of semiconductors includes a chip that is installed on servers and performs complex AI tasks like programming algorithms, as well as a second chip for more routine functions on smartphones and other devices. With the AI chips, Huawei, the world's biggest maker of telecommunications equipment and a major smartphone vendor, is challenging American companies like Nvidia Corp., Intel Inc. and Qualcomm Inc. The new components align with broader efforts by China to reduce its dependence on advanced U.S. technologies and develop such products domestically. Under Beijing's Made in China 2025 development plan, semiconductors and AI have emerged as key areas that authorities want to develop at home. "Computing power is the foundation of AI," said Eric Xu, Huawei's chairman, at a conference in Shanghai on Wednesday.

China's largest telecom equipment maker and smartphone vendor Huawei Technologies Co Ltd unveiled a range of artificial intelligence chips on Wednesday, in what is arguably its biggest-ever push to expand its presence in the semiconductor sector and to compete with US companies such as Nvidia Corp. The move is also part of Huawei's broader effort in AI. The company is working to offer a full-stack technological portfolio to support all AI application scenarios. Huawei said one of the new processors, known as the Ascend 910, boasts the greatest computing density in a single chip in the world, whose performance can beat its rival product Nvidia V100. Ascend 910 is scheduled to hit the market in the second quarter of 2019.

They offer automated customer support, provide advice and information to your staff, explain processes to new users and more. At the same time, online interactions are being transformed through technologies like augmented reality and virtual reality and by the explosion in the use of mobile devices. But how can these trends be combined to create the kind of highly personalized user experience your clients and coworkers are looking for? This year, avatars are making a big comeback. The major players are using them!

Softbank Robotics today announced that its robot Pepper will now use emotion recognition AI from Affectiva to interpret and respond to human activity. Pepper is about four feet tall, gets around on wheels, and has a tablet in the center of its chest. The humanoid robot made its debut in 2015 and was designed to interact with people. Cameras and microphones are used to help Pepper recognize human emotions, like hostility or joy, and respond appropriately with a smile or indications of sadness. This type of intelligence likely comes in handy for the environments where Pepper operates, like banks, hotels, and Pizza Huts in some parts of Asia.

User intended actions are widely seen in many areas. Forecasting these actions and taking proactive measures to optimize business outcome is a crucial step towards sustaining the steady business growth. In this work, we focus on pre- dicting attrition, which is one of typical user intended actions. Conventional attrition predictive modeling strategies suffer a few inherent drawbacks. To overcome these limitations, we propose a novel end-to-end learning scheme to keep track of the evolution of attrition patterns for the predictive modeling. It integrates user activity logs, dynamic and static user profiles based on multi-path learning. It exploits historical user records by establishing a decaying multi-snapshot technique. And finally it employs the precedent user intentions via guiding them to the subsequent learning procedure. As a result, it addresses all disadvantages of conventional methods. We evaluate our methodology on two public data repositories and one private user usage dataset provided by Adobe Creative Cloud. The extensive experiments demonstrate that it can offer the appealing performance in comparison with several existing approaches as rated by different popular metrics. Furthermore, we introduce an advanced interpretation and visualization strategy to effectively characterize the periodicity of user activity logs. It can help to pinpoint important factors that are critical to user attrition and retention and thus suggests actionable improvement targets for business practice. Our work will provide useful insights into the prediction and elucidation of other user intended actions as well.

Google is giving phone users the option to spam their spammers. The new Pixel 3 smartphone, which the company unveiled on Tuesday, features a virtual assistant that can help screen out robocalls by responding to the automated calls with its own automated messages. With the tap of a button, users will be able to send suspicious incoming calls to the assistant, which will tell the caller, "Hi, the person you're calling is using a screening service from Google, and will get a copy of this conversation. Go ahead and say your name, and why you're calling." Based on this response, the user can either accept the call, send a preset text, or report it as spam.

These are just some of the headline-grabbing technologies that could be part of our lives once fifth-generation, or 5G, mobile networks roll out, telcos say. A pilot network in the one-north district is in the pipeline, announced jointly by Singtel and Ericsson in July. M1 will start South-east Asia field trials, and StarHub plans to switch on 5G base stations, both by year-end. But experts and industry players acknowledge that there may be a long road to 5G coverage in Singapore, not least because the dots that are to be connected aren't all in place yet. While the propounded benefits of 5G include higher data volumes and speeds, some raise eyebrows at holding trials and building infrastructure for a system not yet in place.

One lucrative new market that can be accessed through more innovative use of software is Industrial IoT (IIoT). For example, Statista recently found discrete manufacturing, transportation/ logistics, and utilities industries are projected to spend $40 billion each on IoT platforms, systems, and services by 2020. There are many software technologies that promise to revolutionize networks, including network slicing, ONAP and software-based architecture. However, it is that AI offers a transformative opportunity through its ability to industrialise large scale pattern recognition and automate complex processes. AI-augmented core and Radio Access Networks (RANs), for example, are set to offer carriers an unprecedented level of control and flexibility to develop higher performance services, providing a clear route to access mission critical applications such as IIoT, automotive and healthcare.

Shared edge computing platforms deployed at the radio access network are expected to significantly improve quality of service delivered by Application Service Providers (ASPs) in a flexible and economic way. However, placing edge service in every possible edge site by an ASP is practically infeasible due to the ASP's prohibitive budget requirement. In this paper, we investigate the edge service placement problem of an ASP under a limited budget, where the ASP dynamically rents computing/storage resources in edge sites to host its applications in close proximity to end users. Since the benefit of placing edge service in a specific site is usually unknown to the ASP a priori, optimal placement decisions must be made while learning this benefit. We pose this problem as a novel combinatorial contextual bandit learning problem. It is "combinatorial" because only a limited number of edge sites can be rented to provide the edge service given the ASP's budget. It is "contextual" because we utilize user context information to enable finer-grained learning and decision making. To solve this problem and optimize the edge computing performance, we propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm. Furthermore, SEEN is extended to scenarios with overlapping service coverage by incorporating a disjunctively constrained knapsack problem. In both cases, we prove that our algorithm achieves a sublinear regret bound when it is compared to an oracle algorithm that knows the exact benefit information. Simulations are carried out on a real-world dataset, whose results show that SEEN significantly outperforms benchmark solutions. Mobile cloud computing (MCC) supports mobile applications in resource-constrained mobile devices by offloading computation-demanding tasks to the resource-rich remote cloud. L. Chen and J. Xu are with Department of Electrical and Computer Engineering, University of Miami, USA. S. Ren is with Department of Electrical and Computer Engineering, University of California, Riverside, USA.