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.

Generation Z, the leading edge of young people born after 1997, are now 21 years old. Many of them are graduating from college and listening to the well wishes and advice of graduation speakers. After the microphones are silenced and the last diploma is awarded, Gen Z will enter the workforce. Today's workplace is undergoing an unprecedented rate of change placing new demands on workers of all ages. A new high velocity workplace is emerging – a world of work characterized by the rapid development of new knowledge, an accelerating rate of industry disruption and advancing technology.

Qualcomm and Baidu are deepening their partnership on Artificial Intelligence, announcing that they will essentially combine their Qualcomm Artificial Intelligence and Baidu PaddlePaddle platforms. The latter was first launched as a deep learning platform for internal use at Baidu in 2013, with the company subsequently making it open source in August of 2016. And with Baidu's announcement in February of this year that it would support the Qualcomm AI Engine, bringing Baidu PaddlePaddle into the mix appears to be a logical next step. The companies will combine their technologies through the Open Neural Network Exchange ("ONNX"), an open source platform aimed at allowing developers to easily choose between a number of different tools and models as they build AI technologies. Other partners of the ONNX include Microsoft, Facebook, NVIDIA, and Amazon Web Services.

Qualcomm announced a new division that would unify all its fundamental artificial intelligence research. Qualcomm A.I. Research gives form to what was largely an amorphous effort inside the company, which is focused on moving inference out of the cloud and into devices installed with its mobile chips, such as smartphones, warehouse robots, cars and security cameras. The reorganization reflects Qualcomm's doubling down on embedded artificial intelligence, which it argues can improve privacy for applications like voice-controlled speakers and save energy wasted sending information to the cloud. Taking artificial intelligence – a blanket term that includes machine learning – out of the cloud would also lower latency, which is important in mission-critical devices that require fast reaction times, like driverless cars. Accordingly, the company is focused on model compression and efficient hardware to squeeze as much processing as possible from embedded devices constrained by power and heat. Qualcomm A.I. Research is also targeting more data efficient models in machine learning, as well as system architecture problems – like sensor fusion and multimodal learning – and device personalization.