Qualcomm just announced its latest premium mobile processor, the Snapdragon 888. This 5 nm chip, rumored to be made at a Samsung facility, provides multiple levels of improvement in central processing power, high end graphics that approach the capabilities of a gaming console. There are also camera improvements that threaten stand-alone DSLR, and AI functions that enhance and protect camera still and video images from "Deep Fakes" while also providing big improvements in AI inference workloads. And, of course, it runs on 5G networks, along with supporting faster Wi-Fi 6 and 6E capability. One feature that stands out for me seems to be buried in most coverage of the 888 processor and has the potential for dramatically changing the way mobile devices work, as well as enhancing security well beyond where we are today.

The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the outstanding predictive power of artificial intelligence, triggered the application of deep learning to human mobility. In particular, the literature is focusing on three tasks: next-location prediction, i.e., predicting an individual's future locations; crowd flow prediction, i.e., forecasting flows on a geographic region; and trajectory generation, i.e., generating realistic individual trajectories. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides: (i) basic notions on mobility and deep learning; (ii) a review of data sources and public datasets; (iii) a description of deep learning models and (iv) a discussion about relevant open challenges. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, and trajectory generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.

Although Apple's latest A14 Bionic chip enabled the iPhone 12 family and iPad Air tablets to deliver impressive performance improvements, Qualcomm is making clear that the next generation of Android devices will rely heavily on advanced AI and computer vision processors to retake the performance lead. Teased yesterday at Qualcomm's virtual Tech Summit, the Snapdragon 888 is getting a full reveal today, and the year-over-year gains are impressive, notably including the largest jump in AI performance in Snapdragon history. The Snapdragon 888's debut is significant for technical decision makers because the chip will power most if not all of 2021's flagship Android phones, which collectively represent a large share of the over two billion computers sold globally each year. Moreover, the 888's increasing reliance on AI processing demonstrates how machine learning's role is now critical in advancing all areas of computing, ranging from how devices work when they're fully on to what they're quietly doing when not in active use. From a high-level perspective, the Snapdragon 888 is a sequel to last year's flagship 865 chips, leveraging 5-nanometer process technology and tighter integration with 5G and AI chips to deliver performance and power efficiency gains.

This article looks at the unique challenges introduced by Edge computing for AI/ML workloads, which can have a negative impact on results. It applies available machine learning models to real-world Edge datasets, to show how these challenges can be overcome, while preserving accuracy in the dynamic nature of Edge environments. The field of machine learning has experienced an explosion of innovation over the past 10 years. Although its roots date back more than 70 years when Alan Turing devised the Turing Test, it has not matured significantly until recently. Two primary contributing factors are the exponential growth in both compute power and data that can be used for training. There is now enough data and compute power (some in specialized hardware like GPUs/FPGAs) that new, real-world problems are being solved every day with machine learning.

Deep Learning methods have been proven to be flexible to model complex phenomena. This has also been the case of Intelligent Transportation Systems (ITS), in which several areas such as vehicular perception and traffic analysis have widely embraced Deep Learning as a core modeling technology. Particularly in short-term traffic forecasting, the capability of Deep Learning to deliver good results has generated a prevalent inertia towards using Deep Learning models, without examining in depth their benefits and downsides. This paper focuses on critically analyzing the state of the art in what refers to the use of Deep Learning for this particular ITS research area. To this end, we elaborate on the findings distilled from a review of publications from recent years, based on two taxonomic criteria. A posterior critical analysis is held to formulate questions and trigger a necessary debate about the issues of Deep Learning for traffic forecasting. The study is completed with a benchmark of diverse short-term traffic forecasting methods over traffic datasets of different nature, aimed to cover a wide spectrum of possible scenarios. Our experimentation reveals that Deep Learning could not be the best modeling technique for every case, which unveils some caveats unconsidered to date that should be addressed by the community in prospective studies. These insights reveal new challenges and research opportunities in road traffic forecasting, which are enumerated and discussed thoroughly, with the intention of inspiring and guiding future research efforts in this field.

Customer Churn is one of the most important and challenging problems for businesses like banks, SAAS or telecommunication companies. Churn is expensive for the business since it costs more to acquire new customers than it does to retain the existing ones. Stakeholders invest a lot of time and effort in finding out how they can accurately distinguish existing customers that are about to leave. The answer to this question allows teams to take action. Most of the churn analysis approaches focus on predicting which customers are about to churn.

Stephen de Campos, CIO at Hunt Consolidated, co-authored this article. Understanding the role of IT through the eyes of organizational stakeholders is an effective technique for determining how IT may need to change. For the longest time, IT was viewed as a cost center, with a primary emphasis on performance and cost. Over the past 10 years, IT's role has been elevated in many organizations. IDG's 2020 State of the CIO survey personifies this trend: 75% of surveyed CIOs identified themselves as business strategists or transformation agents, and 67% claim revenue generation among their job responsibilities. However, in the era of digital transformation, CIOs need to work harder (and smarter) to secure or maintain the right to be viewed (and funded) as a differentiator.

Chinese tech giants Baidu Inc. and Huawei Technologies Co. Ltd. announced today that they're teaming up to build an open artificial intelligence ecosystem for mobile devices. The two companies said they want to create platform that fosters the development of mobile AI that "knows you better." The new ecosystem will combine Baidu Brain, which is the catch-all term for Baidu's various AI tools, with Huawei's HiAI mobile computing platform. The project will also take advantage of the AI-focused hardware components that Huawei installs in its latest smartphone models. "The future is all about smart devices that will actively serve us, not just respond to what we tell them to do," Richard Yu (pictured, right), chief executive of Huawei's Consumer Business Group, said in a statement.

This has put India first in terms of participation among the foreign countries registered in this year's AIJ, which is an international artificial intelligence and …

Future beyond fifth-generation (B5G) and sixth-generation (6G) mobile communications will shift from facilitating interpersonal communications to supporting Internet of Everything (IoE), where intelligent communications with full integration of big data and artificial intelligence (AI) will play an important role in improving network efficiency and providing high-quality service. As a rapid evolving paradigm, the AI-empowered mobile communications demand large amounts of data acquired from real network environment for systematic test and verification. Hence, we build the world's first true-data testbed for 5G/B5G intelligent network (TTIN), which comprises 5G/B5G on-site experimental networks, data acquisition & data warehouse, and AI engine & network optimization. In the TTIN, true network data acquisition, storage, standardization, and analysis are available, which enable system-level online verification of B5G/6G-orientated key technologies and support data-driven network optimization through the closed-loop control mechanism. This paper elaborates on the system architecture and module design of TTIN. Detailed technical specifications and some of the established use cases are also showcased.