Mind Foundry, a technology spin-out from the University of Oxford's Machine Learning Research Group (MLRG), today announced the commercial launch of a revolutionary humanised machine learning platform. For the first time the new cloud-based platform allows anyone, of any technical ability and in any size of organisation, to swiftly unlock the full value of ever increasing volumes of data to make decisions on complex business issues without the need for data scientists. The platform was developed through work with some of the world's largest investment firms, telecommunications providers, manufacturers and heavy industry companies. Organisations can proactively solve business problems by easily leveraging the predictive power of their existing data. The platform automatically builds appropriate machine learning solutions for business problems in minutes or hours, rather than weeks or months, and provides full transparency and auditability of solutions.

Over the last few decades, the telecom industry has rapidly shifted from basic phone and internet services to a far more evolved space featuring mobile, wearables and automation, making it one of the biggest businesses in the world currently and always upgrading to the cutting edge technology. According to IDC, 63.5% of telecommunications organizations are making new technology investments for AI systems. While having to be on the bleeding edge of technology is a good thing for customers and the competition. The industry itself is a great candidate for adopting AI driven solutions which offer the hope of reduced costs and increased efficiencies through automation. Needless to say, frontrunners have already started playing with AI solutions and deploying them across various business areas including customer-facing and internal processes.

The Apple Watch Series 4 is the best smartwatch you can buy. There's no single killer feature that makes the Apple Watch Series 4 (as tested: 40mm with GPS and GPS/LTE), our pick for best smartwatch. It's the fact that it does almost everything better than every other smartwatch we've come across. That it can be used as a minimalist device, for keeping abreast of your smartphone notifications, or as an all-in wearable that will let you take or make phone calls, send text messages, navigate through a crowded city and listen to music without bringing your cellphone with you (provided you spring for the GPS/LTE version) is the icing on the cake. Setting up the Apple Watch to work with your iPhone is almost effortless. Using this watch, with its responsive OLED touchscreen display and rotating Digital Crown (Apple's marketing mumbo jumbo for the knob on the side of the watch) is just as easy. You can use your finger to navigate apps and menus, scroll through text with the Digital Crown or ask Siri to do some hands-free heavy lifting for you.

By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing. However, due to the resource constraint of IoT devices and wireless network, both the communications and computation resources need to be allocated and scheduled efficiently for better system performance. In this paper, we propose a joint computation offloading and multi-user scheduling algorithm for IoT edge computing system to minimize the long-term average weighted sum of delay and power consumption under stochastic traffic arrival. We formulate the dynamic optimization problem as an infinite-horizon average-reward continuous-time Markov decision process (CTMDP) model. One critical challenge in solving this MDP problem for the multi-user resource control is the curse-of-dimensionality problem, where the state space of the MDP model and the computation complexity increase exponentially with the growing number of users or IoT devices. In order to overcome this challenge, we use the deep reinforcement learning (RL) techniques and propose a neural network architecture to approximate the value functions for the post-decision system states. The designed algorithm to solve the CTMDP problem supports semi-distributed auction-based implementation, where the IoT devices submit bids to the BS to make the resource control decisions centrally. Simulation results show that the proposed algorithm provides significant performance improvement over the baseline algorithms, and also outperforms the RL algorithms based on other neural network architectures.

Cable company Comcast will add'eye control' to its suite of accessibility features, allowing physically disabled viewers to operate TV's using only their gaze. By partnering with popular makers of eye-gaze hardware, Comcast said its feature will allow users to do just about anything that can be done with a physical remote, including using the guide, scheduling recordings, and navigating other menus. Popular systems like those made by Tobii Assistive Technology use special cameras to track the movement of people's eyes and then translate those movements onto a screen. Using eye-reading hardware and software, Comcast is allowing people with physical disabilities to control their TV's with their gaze Each time someone gazes at a button, the corresponding action is initiated in Comcast's interface. Comcast's inclusion of those technologies mark a first among large telecom providers and also further add to a host of other features rolled out by the company throughout the last several years.

As 5G networks continue to expand in cities and countries across the globe, key researchers have already started to lay the foundation for 6G deployments roughly a decade from now. This time, they say, the key selling point won't be faster phones or wireless home internet service, but rather a range of advanced industrial and scientific applications -- including wireless, real-time remote access to human brain-level AI computing. That's one of the more interesting takeaways from a new IEEE paper published by NYU Wireless's pioneering researcher Dr. Ted Rappaport and colleagues, focused on applications for 100 gigahertz (GHz) to 3 terahertz (THz) wireless spectrum. As prior cellular generations have continually expanded the use of radio spectrum from microwave frequencies up to millimeter wave frequencies, that "submillimeter wave" range is the last collection of seemingly safe, non-ionizing frequencies that can be used for communications before hitting optical, x-ray, gamma ray, and cosmic ray wavelengths. Dr. Rappaport's team says that while 5G networks should eventually be able to deliver 100Gbps speeds, signal densification technology doesn't yet exist to eclipse that rate -- even on today's millimeter wave bands, one of which offers access to bandwidth that's akin to a 500-lane highway. Consequently, opening up the terahertz frequencies will provide gigantic swaths of new bandwidth for wireless use, enabling unthinkable quantities and types of data to be transferred in only a second.

FREMONT, CA: The telecommunication industry is making the most of the ongoing technological revolution. Artificial Intelligence (AI) and Machine Learning (ML) are technologies that have high potentials to drive companies in the telecom sector. AI and ML are together going to accelerate the industry to the next level of development. The consumer base for telecoms is growing at a steady rate, and companies are well poised to adopt the two technologies to keep improving services and outreach. New technologies are going to assist the industry in overcoming some of the challenges it faces currently.

For enabling automatic deployment and management of cellular networks, the concept of self-organizing network (SON) was introduced. SON capabilities can enhance network performance, improve service quality, and reduce operational and capital expenditure (OPEX/CAPEX). As an important component in SON, self-healing is defined as a network paradigm where the faults of target networks are mitigated or recovered by automatically triggering a series of actions such as detection, diagnosis and compensation. Data-driven machine learning has been recognized as a powerful tool to bring intelligence into network and to realize self-healing. However, there are major challenges for practical applications of machine learning techniques for self-healing. In this article, we first classify these challenges into five categories: 1) data imbalance, 2) data insufficiency, 3) cost insensitivity, 4) non-real-time response, and 5) multi-source data fusion. Then we provide potential technical solutions to address these challenges. Furthermore, a case study of cost-sensitive fault detection with imbalanced data is provided to illustrate the feasibility and effectiveness of the suggested solutions.

New York: Continuing its mission to build trusted partnerships with advertisers and publishers, Verizon Media today announced an innovative new omnichannel insights tool for its industry-leading demand-side platform (DSP). Powered by machine learning, the new tool is designed to give advertisers clarity on their omnichannel programmatic ecosystem, with robust performance insights and optimization recommendations for each channel, ad format and exchange. According to a recent survey by Advertiser Perceptions, among advertisers, "transparency" is the most important word of 2019, with brands rightfully seeking more insights into campaign performance and media quality than ever before. Similarly, 60% of US advertising executives cite a lack of transparency as the biggest challenge for digital spend optimization. "At Verizon Media, trust and transparency are more than just buzzwords," said Iván Markman, Chief Business Officer at Verizon Media.

Denver: Genesys introduced new orchestration capabilities powered by Genesys AI that connect native and third-party technologies to enable the most comprehensive customer journey management available today. Currently, businesses are adopting an increasing number of artificial intelligence (AI) point solutions to solve specific challenges. However, businesses are failing to realize AI's full potential to improve customer and employee journeys because data remains fragmented across the end-to-end experience. As a result, AI's ability to impact business outcomes remains limited. New orchestration capabilities from Genesys make it possible for multiple AI applications to work together harmoniously in real-time from marketing to sales to service. By leveraging all relevant data throughout the customer's entire journey, Genesys AI can orchestrate, measure and optimize processes at every touchpoint.