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.

Classification is one of the widely used analytical techniques in data science domain across different business to associate a pattern which contribute to the occurrence of certain event which is predicted with some likelihood. This Paper address a lacuna of creating some time window before the prediction actually happen to enable organizations some space to act on the prediction. There are some really good state of the art machine learning techniques to optimally identify the possible churners in either customer base or employee base, similarly for fault prediction too if the prediction does not come with some buffer time to act on the fault it is very difficult to provide a seamless experience to the user. New concept of reference frame creation is introduced to solve this problem in this paper

Artificial Intelligence (AI) and Machine Learning (ML) are changing everything in the electronics industry. Engineers are now evaluating how to design and train intelligence solutions in everything from sensors, to smartphones, to networks, to cloud data centers. However, just as there has never been a single processor for every application or workload, so too is there no single solution for AI. Qualcomm appears to be hedging its bets with a distributed AI solution the company calls the Artificial Intelligence Engine (AIEngine) and a dedicated AI accelerator, that was just announced at a company sponsored event in China. At the moment, most of the training of artificial neural networks is done in data centers.

Paul Steinberg, CTO of Motorola Solutions, speaks to Sam Fenwick about his company's efforts to use AI and machine learning to bring the right data to the user in the right way Paul Steinberg presides over a huge range of research and development activities, ranging from RF engineering and wireless network architectures to drones and robotics. He also manages Motorola Solutions Venture Capital's portfolio and plays a key role in managing Motorola Solutions' intellectual property. One of the things the company is moving towards is a virtual partner – a combination of AI and natural language processing, which allows someone in the field to verbally request information and give commands without talking to a human. Part of the thinking behind this is that people speak faster than they can type, and the need for field workers to stay aware of their surroundings. "The way you and I consume [mobile data] is a slab of black glass, [but the] fundamental imperative [for a police officer, etc] is eyes-up, hands-free. That slab of black glass [is] exactly the opposite: eyes-down, hands-busy. A big part of how we're navigating this problem is around ethnographics and human factors research – living a day in the life of our users and then [working] with the technologists and designers."

References to "Qualcomm"; may mean Qualcomm Incorporated, or subsidiaries or business units within the Qualcomm corporate structure, as applicable. Materials that are as of a specific date, including but not limited to press releases, presentations, blog posts and webcasts, may have been superseded by subsequent events or disclosures. Qualcomm Incorporated includes Qualcomm's licensing business, QTL, and the vast majority of its patent portfolio. Qualcomm Technologies, Inc., a wholly-owned subsidiary of Qualcomm Incorporated, operates, along with its subsidiaries, substantially all of Qualcomm's engineering, research and development functions, and substantially all of its products and services businesses. Qualcomm products referenced on this page are products of Qualcomm Technologies, Inc. and/or its subsidiaries.

Expensive flagship phones won't be the only way for you to play with advanced features like AR Emoji, Animoji and Face ID much longer. Qualcomm is making it easier for companies to create mid-range smartphones that pack those functions by launching a new mobile processor. The Snapdragon 710 will come with a multi-core AI Engine and support neural network processing, as well as image signal processors and graphics units that are typically found in higher-end chipsets. The 710 is the first of the 700-series, which was announced at MWC this year, and will sit above options like the 600- and 400-ranges but below top-tier chips like the Snapdragon 845. The Snapdragon 710 is a 10nm chipset that features a multi-core AR engine for on-device neural networking processing, as well as a Spectra 250 image signal processor that enables things like multi-frame noise reduction and AI camera features like video style transfer and active depth sensing for artificial bokeh.

In industrial environments, an increasing amount of wireless devices are used, which utilize license-free bands. As a consequence of these mutual interferences of wireless systems might decrease the state of coexistence. Therefore, a central coexistence management system is needed, which allocates conflict-free resources to wireless systems. To ensure a conflict-free resource utilization, it is useful to predict the prospective medium utilization before resources are allocated. This paper presents a self-learning concept, which is based on reinforcement learning. A simulative evaluation of reinforcement learning agents based on neural networks, called deep Q-networks and double deep Q-networks, was realized for exemplary and practically relevant coexistence scenarios. The evaluation of the double deep Q-network showed that a prediction accuracy of at least 98 % can be reached in all investigated scenarios.

The last ten years have seen radical changes in the cable television sector. Market forces, new technologies, and consumer demand have remade cable companies as telecommunications giants handling bundled services. Chances are that streaming content and upstart players will continue to remake the industry, and we'll see even more new models and technologies emerge over the next ten years. Jeanine Heck is one woman driving this dramatic shift at Comcast. If you're a Comcast customer, then chances are you're already using a product she was responsible for bringing to life: the voice remote.