DER SPIEGEL: Mr. Schönbohm, the German government has decided not to exclude Huawei from building networks for the next generation of mobile communications. Do you not see any risk in allowing this controversial Chinese company from participating in the construction of the 5G network in Germany? Schönbohm: I think the risk is manageable. There are essentially two fears: First, espionage -- i.e. that data will be siphoned off involuntarily. But we can counter that with improved encryption.

Many approaches have been proposed to discover clusters within networks. Community finding field encompasses approaches which try to discover clusters where nodes are tightly related within them but loosely related with nodes of other clusters. However, a community network configuration is not the only possible latent structure in a graph. Core-periphery and hierarchical network configurations are valid structures to discover in a relational dataset. On the other hand, a network is not completely explained by only knowing the membership of each node. A high level view of the inter-cluster relationships is needed. Blockmodelling techniques deal with these two issues. Firstly, blockmodelling allows finding any network configuration besides to the well-known community structure. Secondly, blockmodelling is a summary representation of a network which regards not only membership of nodes but also relations between clusters. Finally, a unique summary representation of a network is unlikely. Networks might hide more than one blockmodel. Therefore, our proposed problem aims to discover a secondary blockmodel representation of a network that is of good quality and dissimilar with respect to a given blockmodel. Our methodology is presented through two approaches, (a) inclusion of cannot-link constraints and (b) dissimilarity between image matrices. Both approaches are based on non-negative matrix factorisation NMF which fits the blockmodelling representation. The evaluation of these two approaches regards quality and dissimilarity of the discovered alternative blockmodel as these are the requirements of the problem.

At Mobile World Congress this year, 93% of those surveyed said that machine learning (ML) and artificial intelligence (AI) will be a game-changer and that, within three years or less, 76% of telco operators will have incorporated these technologies into their businesses. With recent innovations speeding up ML to analyze bigger, more complex data and deliver faster, more accurate results, it is becoming an indispensable tool for network automation -- and for improving operating efficiency. Machine learning can also be a valuable technology in improving the security and quality of the overall user experience. This is especially important considering the strains high definition streaming video is putting on mobile networks, the amount of m-banking, payments and other mission-critical applications that are vulnerable to cyberattacks and the amount of potentially harmful content on the internet that is easy for children to access. Here are five ways that artificial intelligence can be used to improve the user experience and security.

Vehicle-to-vehicle (V2V) communications have distinct challenges that need to be taken into account when scheduling the radio resources. Although centralized schedulers (e.g., located on base stations) could be utilized to deliver high scheduling performance, they cannot be employed in case of coverage gaps. To address the issue of reliable scheduling of V2V transmissions out of coverage, we propose Vehicular Reinforcement Learning Scheduler (VRLS), a centralized scheduler that predictively assigns the resources for V2V communication while the vehicle is still in cellular network coverage. VRLS is a unified reinforcement learning (RL) solution, wherein the learning agent, the state representation, and the reward provided to the agent are applicable to different vehicular environments of interest (in terms of vehicular density, resource configuration, and wireless channel conditions). Such a unified solution eliminates the necessity of redesigning the RL components for a different environment, and facilitates transfer learning from one to another similar environment. We evaluate the performance of VRLS and show its ability to avoid collisions and half-duplex errors, and to reuse the resources better than the state of the art scheduling algorithms. We also show that pre-trained VRLS agent can adapt to different V2V environments with limited retraining, thus enabling real-world deployment in different scenarios.

Multi-parameter cognition in a cognitive radio network (CRN) provides a more thorough understanding of the radio environments, and could potentially lead to far more intelligent and efficient spectrum usage for a secondary user. In this paper, we investigate the multi-parameter cognition problem for a CRN where the primary transmitter (PT) radiates multiple transmit power levels, and propose a learning-based two-stage spectrum sharing strategy. We first propose a data-driven/machine learning based multi-level spectrum sensing scheme, including the spectrum learning (Stage I) and prediction (the first part in Stage II). This fully blind sensing scheme does not require any prior knowledge of the PT power characteristics. Then, based on a novel normalized power level alignment metric, we propose two prediction-transmission structures, namely periodic and non-periodic, for spectrum access (the second part in Stage II), which enable the secondary transmitter (ST) to closely follow the PT power level variation. The periodic structure features a fixed prediction interval, while the non-periodic one dynamically determines the interval with a proposed reinforcement learning algorithm to further improve the alignment metric. Finally, we extend the prediction-transmission structure to an online scenario, where the number of PT power levels might change as a consequence of PT adapting to the environment fluctuation or quality of service variation. The simulation results demonstrate the effectiveness of the proposed strategy in various scenarios.

Lia's creator Soul Machines is developing digital humans, complete with digital brains, who are portrayed by actual humans. Verizon's Labs showcases innovators like Soul Machines to explore how 5G networks support cutting edge technology that contributes to the betterment of society. Having the speed and bandwidth of a 5G connection is critical to ensuring that digital interactions feel humanized. In human-to-human engagement, the brain rapidly identifies and processes data points such as tone and non-verbal cues. In digital-to-human engagement, mimicking human-like interactions requires 5G's bandwidth and speed.

We have the domain expertise in driving strategic decision-making with our data science and AI technologies. Our advanced data science accelerators uncover hidden patterns in the data and provide intelligent and predictive insights. Our deep knowledge of machine learning, natural language processing, advanced statistical, and mathematical decision science helps significantly improve the business bottom line, reduce risk, boost customer satisfaction, and win in a globally competitive marketplace. We take pride in delivering successful experiences in a broad range of industries such as manufacturing, FMCG, telecommunications, banking, hospitality, healthcare, and others.

According to Master Lu's AI chip rankings for the first half of 2019, Qualcomm Snapdragon 855 remains the best. In addition to the SD855, Apple A12, and MediaTek P90 is second and third respectively on the list. This is the first time that a MediaTek chip is on Master Lu top 10 AI chips. Master Lu AI performance runs on the new AImark2.0. With the new systems and algorithms, ARM, Qualcomm, HiSilicon, MediaTek, Samsung, and other AI core SoC can be comprehensively evaluated.

Chinese benchmarking outfit Master Lu has now released the list of best mobile chipsets in terms of AI performance for the first half of 2019. The list of combined processors for Q1 2019 was topped by the Qualcomm Snapdragon 855 chipset, followed by the Snapdragon Apple A12 and the Kirin 980 chipset from Huawei. As is expected, we've got the Snapdragon 855 in the lead, followed by Apple's excellent A12 chip. Yes, you heard that right, MediaTek has managed to get the Helio P90 up there, and it's apparently a pretty great performer when it comes to AI performance. According to Master Lu, while the Helio P90 isn't exactly the most powerful chip around, the AI performance on it is above most other competitors.

BQE Software, Inc, a global leader in accounting and project management software for professional services firms, launched Core Intelligence, an Artificial Intelligence tool that allows users to have human-like conversations with their business software to gain actionable insights about their company and perform work-related tasks. Also read: British Telecom's EE keeps Huawei in for 5G launch BQE is the first company in the project management and accounting software industry to release an AI that goes beyond being a voice input and functions as an intelligent business analyst. Instead of just providing simple answers to a user's questions, Core Intelligence looks deeply into a company's data to make contextual connections that provide additional insights related to the question asked. For example, while asking for your To-Dos for today, it will also remind you of the past-due To-Dos that need your attention. "Core Intelligence will change the way we manage our business and make our business decisions," said Shafat Qazi, founder and CEO of BQE Software.