Vivaldi has released a major update for its eponymous web browser for privacy-minded power users. Version 4.0 bring with it a translation tool, along with beta versions of Vivaldi Mail, Calendar, and Feed Reader. The update is available now on Windows, Mac and Linux and Android devices. Vivaldi built its translation feature into its browser. The tool is powered by Lingvanex, a Cyprus-based company that makes translator's for a wider range of platforms including voice calls and smartwatches. As part of its focus on privacy, Vivaldi says that all your translation activity will be kept away from third-parties on its servers in Iceland.
Hip-hop artist TheHxliday is 19 years old and determined to have true creative control of his visuals as he makes his way up the music business. He's looking to a cell-phone company to achieve that. The Baltimore native, real name Noah Malik Lee, signed a deal with Motown Records last year and released his first major-label EP last week. To commemorate the occasion, he performed in a 20-minute "virtual world" hosted by Verizon on Friday (May 14th), appearing on fans' screens from an unreal landscape. Virtual effects swam around him -- but TheHxliday didn't pop up as an avatar inside a game, the way Travis Scott did with his Astroworld concert inside Fortnite, and the show wasn't meant to reproduce a concert the way Billie Eilish staged her full-length quarantine show.
Last year Qualcomm started rolling out its first chips for Android phones that supported upgradeable GPU drivers to optimize performance, so now it's doing a similar thing for on-device AI and machine learning. Droid-Life points out that during Google I/O, Google and Qualcomm have announced updatable neural network API drivers, representing a new model that will roll out along with Android 12. While NN API drivers have usually been updated along with major OS updates, now the companies say they can roll out quickly via Google Play Services. Even better, the updates will be available for older chipsets and multiple versions of Android. In an I/O presentation about advancements in machine learning, Google developers said the NN API could boost performance as though the phone had two additional CPU cores, while using less power and creating less heat.
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-of-life. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges. In this context, a wise cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed inference, training and online learning tasks across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.
The Arab region, composed of 22 countries spanning Asia and Africa, opens ample room for communications and networking innovations and services and contributes to the critical mass of the global networking innovation. While the Arab world is considered an emerging market for communications and networking services, the rate of adoption is outpacing the global average. In fact, as of 2019, the mobile Internet penetration stands at 67.2% in the Arab world, as opposed to a global average of 56.5%.12 Furthermore, multiple countries in the region are either building new infrastructure or developing existing infrastructure at an unprecedented pace. Examples include, Neom city in Saudi Arabia, the new administrative capital in Egypt, as well as the Smart Dubai 2021 project in the United Arab Emirates (UAE), among others. This provides a unique opportunity to fuse multiple advanced networking technologies as an integral part of the infrastructure design phase and not just as an afterthought.
Six months after the Trump administration dealt a crushing blow to Huawei Technologies Co.'s smartphone business, the Chinese telecommunications giant is turning to less glamorous alternatives that may eventually offset the decline of its biggest revenue contributor. Among its newest customers is a fish farm in eastern China that's twice the size of New York's Central Park. The farm is covered with tens of thousands of solar panels outfitted with Huawei's inverters to shield its fish from excessive sunlight while generating power. About 370 miles to the west in coal-rich Shanxi province, wireless sensors and cameras deep beneath the earth monitor oxygen levels and potential machine malfunctions in mine pit -- all supplied by the tech titan. And next month, a shiny new electric car featuring its lidar sensor will debut at China's largest auto show.
Artificial intelligence (AI) is on the verge to unleash its potential impact in the smartphone industry. Isn't it amazing to find how smartphones when integrated with AI have the capability of making their own choices as per the environment? Smartphones have become smarter than ever before, and with AI on the rise, the features could be limitless. However, with AI integration, your smartphone will only get better. From offering better protection to providing lower latency and efficiency improvement, your phone can make its choice without having the need to configure them.
For anyone who has ever misplaced their iPhone, Apple's "Find My" app is a game-changer that borders on pure magic. Sign into the app, tap a button to sound an alarm on your MIA device, and, within seconds, it'll emit a loud noise -- even if your phone is set on silent mode -- that allows you to go find the missing handset. Yeah, it's usually stuck behind your sofa cushions or left facedown on a shelf somewhere. You can think of SArdo, a new drone project created by researchers at Germany's NEC Laboratories Europe GmbH, as Apple's "Find My" app on steroids. The difference is that, while finding your iPhone is usually just a matter of convenience, the technology developed by NEC investigators could be a literal lifesaver.
Sentiance is awarded as the Best Mobile User Insight Platform & Innovation in Data Privacy and Security 2020 by Wealth & Finance International. The Artificial Intelligence Awards by Wealth & Finance International have been launched to acknowledge exemplary performance and innovation to companies within this rapidly evolving AI market. Sentiance uses data science and machine learning to turn smartphone sensor data into customers' rich behavioral insights. These insights benefit our clients across insurance, mobility and commerce industries to create innovative and personalized offerings. So what kind of user insights can Sentiance provide?
Accurate smartphone localization (< 1-meter error) for indoor navigation using only RSSI received from a set of BLE beacons remains a challenging problem, due to the inherent noise of RSSI measurements. To overcome the large variance in RSSI measurements, we propose a data-driven approach that uses a deep recurrent network, DeepBLE, to localize the smartphone using RSSI measured from multiple beacons in an environment. In particular, we focus on the ability of our approach to generalize across many smartphone brands (e.g., Apple, Samsung) and models (e.g., iPhone 8, S10). Towards this end, we collect a large-scale dataset of 15 hours of smartphone data, which consists of over 50,000 BLE beacon RSSI measurements collected from 47 beacons in a single building using 15 different popular smartphone models, along with precise 2D location annotations. Our experiments show that there is a very high variability of RSSI measurements across smartphone models (especially across brand), making it very difficult to apply supervised learning using only a subset of smartphone models. To address this challenge, we propose a novel statistic similarity loss (SSL) which enables our model to generalize to unseen phones using a semi-supervised learning approach. For known phones, the iPhone XR achieves the best mean distance error of 0.84 meters. For unknown phones, the Huawei Mate20 Pro shows the greatest improvement, cutting error by over 38\% from 2.62 meters to 1.63 meters error using our semi-supervised adaptation method.