CHIBA – Next-generation 5G networking was the big draw at Tokyo Game Show 2019, setting pulses racing with the prospect of a radically more immersive gaming experience. Offering data transmission speeds around 100 times faster than 4G, 5G is expected to enable more seamless imagery with lower latency, more vivid images and sharper motion. Industry experts say it will dramatically improve the quality of augmented and virtual reality games. "It was very smooth, responsive and consistent," said Omar Alshiji, a 23-year-old game designer from Bahrain, after trying out the fighting game "Tekken" at the NTT Docomo Inc. booth at the four-day game show in Chiba. The major mobile carrier installed 5G base stations at its booth this year, making the high-speed network available at the show. "My country does not have 5G, only 4G so I wanted to try it.

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. M. Chen is with the Chinese University of Hong Kong, Shenzhen, 518172, China, and also with the Department of Electrical Engineering, Princeton University, Princeton, NJ, 08544, USA, Email: mingzhec@princeton.edu. Z. Y ang is with the Centre for Telecommunications Research, Department of Informatics, King's College London, WC2B 4BG, UK, Email: yang.zhaohui@kcl.ac.uk. W . Saad is with the Wireless@VT, Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, V A, 24060, USA, Email: walids@vt.edu. C. Yin is with the Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, 100876, China, Emails: ccyin@ieee.org. Poor is with the Department of Electrical Engineering, Princeton University, Princeton, NJ, 08544, USA, Email: poor@princeton.edu. S. Cui is with the Shenzhen Research Institute of Big Data and School of Science and Engineering, the Chinese University of Hong Kong, Shenzhen, 518172, China, Email: robert.cui@gmail.com This work was supported in part by the U.S. National Science Foundation under Grants CNS-1836802 and CCF-0939370. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function.

For the Vision AI Developer Kit, Microsoft and Qualcomm have partnered to simplify training and deploying computer vision-based AI models. Developers can use Microsoft's cloud-based AI and IoT services on Azure to train models while deploying them on the smart camera edge device powered by a Qualcomm's AI accelerator. Let's take a close look at Vision AI Developer Kit. The Vision AI Developer Kit not only looks stylish and sophisticated, but also boasts of an impressive configuration. The kit is powered by a Qualcomm Snapdragon 603 processor, 4GB of LDDR4X memory and 16GB of eMMC storage.

It supports mainstream deep learning frameworks such as TensorFlow, PyTorch and PaddlePaddle. Tensor Engine and its operators are Huawei's equivalent of NVIDIA cuDNN, a library that makes CUDA accessible to AI developers. MindSpore is Huawei's own unified training/inference framework architected to be design-friendly, operations-friendly that's adaptable to multiple scenarios. It includes core subsystems, such as a model library, graph compute, and tuning toolkit; a unified, distributed architecture for machine learning, deep learning, and reinforcement learning; a flexible program interface along with support for multiple languages. MindSpore is highly optimized for Ascend chips. It takes advantage of the hardware innovations that went into the design of the AI chips.

Manasi: After I graduate, I would like to work in industry in the area of machine learning, possibly for healthcare applications.

These and many other fascinating insights are from the Boston Consulting Group and MIT Sloan Management Review study published this week, Reshaping Business With Artificial Intelligence. An online summary of the report is available here. The survey is based on interviews with more than 3,000 business executives, managers, and analysts in 112 countries and 21 industries. For additional details regarding the methodology, please see page 4. The research found significant gaps between companies who have already adopted and understand Artificial Intelligence (AI) and those lagging. AI early adopters invest heavily in analytics expertise and ensuring the quality of algorithms and data can scale across their enterprise-wide information and knowledge needs.

Kryo CPU - The custom built 64-bit ARM v8-compliant Kryo 385 CPU has independent efficiency and power clusters, each designed to optimize for a unique user experience. Four performance cores up to 2.8GHz (25 percent performance uplift compared to previous generation) Four efficiency cores up to 1.8GHz 2MB shared L3 cache 3MB system cache Hexagon DSP - The Hexagon 685 DSP is designed for advanced imaging and computer vision tasks and to significantly improve performance and battery life. It includes the Qualcomm All-Ways Aware sensor hub and HVX for optimal efficiency. Developers gain access to the latest graphics APIs like OpenGL 3.0/3.2, This will enable users to detect walls and other surrounding objects while using XR.

With the rise of social media like Twitter and of software distribution platforms like app stores, users got various ways to express their opinion about software products. Popular software vendors get user feedback thousandfold per day. Research has shown that such feedback contains valuable information for software development teams such as problem reports or feature and support inquires. Since the manual analysis of user feedback is cumbersome and hard to manage many researchers and tool vendors suggested to use automated analyses based on traditional supervised machine learning approaches. In this work, we compare the results of traditional machine learning and deep learning in classifying user feedback in English and Italian into problem reports, inquiries, and irrelevant. Our results show that using traditional machine learning, we can still achieve comparable results to deep learning, although we collected thousands of labels.

Telco equipment maker Nokia, Japanese telco NTT DoCoMo, and industrial automation company Omron have agreed to conduct 5G trials at their plants and production sites. As part of the trial, the trio will look to couple 5G and artificial intelligence together to create "real-time coaching" for workers. "Machine operators will be monitored using cameras, with an AI-based system providing feedback on their performance based on an analysis of their movements," Nokia said in a statement. "This will help improve the training of technicians by detecting and analysing the differences of motion between more skilled and less skilled personnel." The trial will also test how reliable 5G is when the movement of people and background noise from machinery is involved.

For the Vision AI Developer Kit, Microsoft and Qualcomm have partnered to simplify training and deploying computer vision-based AI models. Developers can use Microsoft's cloud-based AI and IoT services on Azure to train models while deploying them on the smart camera edge device powered by a Qualcomm's AI accelerator. Let's take a close look at Vision AI Developer Kit. The Vision AI Developer Kit not only looks stylish and sophisticated, but also boasts of an impressive configuration. The kit is powered by a Qualcomm Snapdragon 603 processor, 4GB of LDDR4X memory and 16GB of eMMC storage. Images are captured by an 8-megapixel camera sensor capable of recording in 4K UHD. The device also comes with a four-microphone array and speaker that can be utilized for building voice-based user interfaces.