Tractica's recently published Artificial Intelligence for Telecommunications Applications report is a comprehensive examination of market and technology issues surrounding key telecom use cases. One of the cases in which telecom service providers are aggressively investing is intelligent customer relationship management (CRM). CRM systems help organizations track and make sense of customer sales, marketing, and support interactions. What was born primarily as a sales tracking tool has expanded, with the advent of digital and social media, into robust platforms designed to unify insights around broader customer interactions and transactions, beyond just sales. The goal of these systems is to facilitate a 360º view of individual customers, improving customer share and retention, reducing churn, and increasing revenue.

LG's S9-rival, the G7 ThinQ, has arrived in the US and is now available at various retailers and major carriers. The G7 ThinQ is one of the Korean phonemaker's latest premium devices that comes infused with AI features -- there's also the V30S ThinQ and the V35 ThinQ, which shares many of the G7's characteristics and could make LG's lineup a bit confusing. It boasts the first dedicated Google Assistant button found on an Android phone, so you don't need to say "OK, Google" to summon the voice assistant. That button can also conjure up Google Lens, which can detect text, landmarks, media and other real-life objects, giving you a way to quickly translate foreign signs or to look for info on various objects. The device's star features, however, are its AI-powered cameras: it has one wide-angle and one standard 16-megapixel cameras on the back, as well as a 5-megapixel front cam.

A fresh set of rumors about Google's upcoming Pixel 3 claim that the phone could look a lot like the iPhone X. Images of purported screen protectors of the Pixel 3 were shared, showing both a base model and what seems to be an XL model. The regular Pixel 3 is expected to look like the Pixel 2, while the XL will get some features that resemble the iPhone X, according to Bloomberg. It's also believed that the Pixel 3 will launch in October with Verizon as its exclusive carrier. The Pixel 3 XL is expected to feature a'nearly edge-to-edge screen', aside from thicker bezel, or'chin', at the bottom of the display, Bloomberg noted. It will also feature a notch at the top of its display that's'taller' than the one included on Apple's iPhone X. Google is reportedly hoping to remove the bezels entirely in the future, but has kept them as part of the phone's design this year to make room for stereo speakers on the front of the device.

Even though SoftBank has spread billions across multiple ride-hailing and autonomous vehicle companies, it has no plans to integrate them, said Michael Ronen, a managing partner. "Each company will make its own decisions about what's in the best interest of the company."

Black swans are the ultimate outliers. They have the ability to surprise and disrupt the status quo. I was recently in Shenzhen and was permitted access to Huawei's campus. I know I didn't see it all, but I saw enough to get me thinking. I had heard lots of stories, but reality was even more interesting. Seeing and talking to the people gave me new insights. I'd heard that in China, tech employees worked 10 hours straight a day. The offices, campus and the university (yes, a University where all employees study) are perhaps even more modern and inviting than many I've seen in the United States.

Theoretical understanding of deep learning is one of the most important tasks facing the statistics and machine learning communities. While deep neural networks (DNNs) originated as engineering methods and models of biological networks in neuroscience and psychology, they have quickly become a centerpiece of the machine learning toolbox. Unfortunately, DNN adoption powered by recent successes combined with the open-source nature of the machine learning community, has outpaced our theoretical understanding. We cannot reliably identify when and why DNNs will make mistakes. In some applications like text translation these mistakes may be comical and provide for fun fodder in research talks, a single error can be very costly in tasks like medical imaging. As we utilize DNNs in increasingly sensitive applications, a better understanding of their properties is thus imperative. Recent advances in DNN theory are numerous and include many different sources of intuition, such as learning theory, sparse signal analysis, physics, chemistry, and psychology. An interesting pattern begins to emerge in the breadth of possible interpretations. The seemingly limitless approaches are mostly constrained by the lens with which the mathematical operations are viewed. Ultimately, the interpretation of DNNs appears to mimic a type of Rorschach test --- a psychological test wherein subjects interpret a series of seemingly ambiguous ink-blots. Validation for DNN theory requires a convergence of the literature. We must distinguish between universal results that are invariant to the analysis perspective and those that are specific to a particular network configuration. Simultaneously we must deal with the fact that many standard statistical tools for quantifying generalization or empirically assessing important network features are difficult to apply to DNNs.

Communication Service Providers will now be able to leverage the power of artificial intelligence and machine learning to turn vast amounts of telecommunications network data into proactive resolutions for pressing assurance challenges. Hewlett Packard Enterprise unveiled its HPE Intelligent Assurance Suite at Digital Transformation World this week, describing it as an AI platform that can automate a dynamic world and enable zero-touch operations. According to HPE's VP and GM of communications and media solutions, David Sliter, HPE intelligent Assurance is a new and'major' step in the achievement of the company's vision. "It combines machine learning based intelligence with AI-driven automation to predict problems and proactively resolve them, 24/7." Sliter also says communication service providers (CSPs) now have an opportunity to transform into digital service providers.

Future 5G wireless networks will rely on agile and automated network management, where the usage of diverse resources must be jointly optimized with surgical accuracy. A number of key wireless network functionalities (e.g., traffic steering, energy savings) give rise to hard optimization problems. What is more, high spatio-temporal traffic variability coupled with the need to satisfy strict per slice/service SLAs in modern networks, suggest that these problems must be constantly (re-)solved, to maintain close-to-optimal performance. To this end, in this paper we propose the framework of Online Network Optimization (ONO), which seeks to maintain both agile and efficient control over time, using an arsenal of data-driven, adaptive, and AI-based techniques. Since the mathematical tools and the studied regimes vary widely among these methodologies, a theoretical comparison is often out of reach. Therefore, the important question "what is the right ONO technique?" remains open to date. In this paper, we discuss the pros and cons of each technique and further attempt a direct quantitative comparison for a specific use case, using real data. Our results suggest that carefully combining the insights of problem modeling with state-of-the-art AI techniques provides significant advantages at reasonable complexity.

Seventy two years after launching the iconic Vespa scooter, Italian motor vehicle company Piaggio has unveiled its newest creation: A robot designed to help you get around without a car at all. Piaggio Fast Forward, Piaggio's American sibling established in 2015, has been testing the Gita, a two-foot-high, two-wheeled mobile carrying robot, out of its Boston offices for a while now. The company has not yet disclosed a price, but it could start popping up in businesses and construction sites as soon as early 2019. The company's hope with them is to encourage walking, by eliminating the need for people to need their cars to lug stuff around. The company's motto is "autonomy for humans" -- in other words, creating autonomous products in the service of humans, not replacing them.

The broad reality, thankfully, is far less terrifying. Businesses across the country use AI to increase efficiencies, reduce errors and better serve customers. According to a study from Boston Consulting Group and MIT Sloan Management Review, 72% of those in the technology, media and telecommunications industries expect AI to have a major impact on product offerings over the next five years. Sixty-one percent of organizations across industries believe developing an AI strategy is urgent.