Columnist Ed Baig reviews Pixel, which features the high-IQ Google Assistant and a competitive, high-end smartphone camera. A. When Google introduced its Pixel and Pixel XL phones in early October, it picked a hybrid distribution strategy. Instead of selling these $649-and-up smartphones only on its own site, as it had with its earlier Nexus phones, it also signed up Verizon Wireless as a distribution partner. To judge from the ads during the World Series, only the second purchase option exists. They keep touting the Pixel -- "a winner for anyone looking for an excellent phone," USA TODAY's Ed Baig wrote -- as "only on Verizon," something Verizon's own page about the phones repeats.

Fraud is a big problem in the cellular networking market, and machine learning is one potential solution to the problem. Mathematics-based cyberdefence firm claims Antigena can teach itself to fight off new malicious intrusions -- without human involvement. Fraudulent usage of cellular networks costs the industry an estimated $38 billion a year, according to the 2015 Global Fraud Loss Survey by the Communications Fraud Control Association (CFCA), an international organization that promotes revenue assurance, loss prevention, and fraud control in the industry. The CFCA says fraudsters use methods including PBX hacking, subscription fraud, dealer fraud, service abuse, and account takeover to steal from service providers. Current fraud detection approaches in the industry rely on static rules with pre-set volume or frequency thresholds, said Ole J. Mengshoel, associate research professor in the Department of Electrical and Computer Engineering and director of the Intelligent and High-Performing Systems Lab at Carnegie Mellon University.

Before we dive into the data behind Alphabet's forward-looking strategy, we must quickly assess its most mature and most profitable (by far) business line: Google search and advertising. As a reminder, Google now consists of search, mapping, cloud & enterprise, Google-branded consumer hardware and operating systems (Chrome, Android, etc.), and YouTube. All other units, from investment vehicles (GV, Google Capital) to the X arm, are now subsidiaries reporting directly to Alphabet management. We will use the Google moniker when discussing either the company proper or historical pre-Alphabet activities under its banner. The company outperformed analyst expectations in Q2'16 with surging top- and bottom-line growth.

With plans to insert itself in the driver's seat for a slice of the self-driving car market, Qualcomm said Thursday it has agreed to acquire NXP Semiconductors for about $38 billion. Qualcomm said it would pay $110 per share for the Dutch company, which commands 14% of global automotive semiconductor sales. It expects the deal to close by the end of 2017. "With innovation and invention at our core, Qualcomm has played a critical role in driving the evolution of the mobile industry," Qualcomm CEO Steve Mollenkopf said in a statement. "The NXP acquisition accelerates our strategy to extend our leading mobile technology into robust new opportunities, where we will be well positioned to lead by delivering integrated semiconductor solutions at scale."

Qualcomm said Thursday that it had agreed to acquire NXP Semiconductors for about $38 billion, expanding its presence in the increasingly lucrative market for automotive sensors and chips. Qualcomm said it would pay $110 per share for NXP and expects the deal to close by the end of 2017. The deal gives the telecommunications giant a greater foothold in the burgeoning market for technology that will power self-driving cars -- which represents a growth space compared to the saturated market for smart phones and other mobile devices. NXP also has a desirable position in the market for technology powering the so-called Internet of Things as well as security products. The acquisition also marks the latest in a series of deals for the semiconductor business, accelerating the trend of consolidation.

Black holes are an ongoing area for research and discovery. However, many mysteries of the universe can be solved with Big Data analytics. Artificial intelligence (AI) is the killer application of Big Data analytics. Logically based on machine learning and Big Data analytics, the more data available, the more intelligent it will get, resulting in more widespread applications. In future, you may own a smart robot or even a smart dog.

Soon, police officers won't need to pull you over if you're caught texting while driving. The machine vision technology company Movidius has teamed up with the Chinese security camera maker Hikvision to create smart cameras that can catch behavior such as leaving a suspicious package in a public place, driving while distracted by mobile devices, and break ins. Advanced visual analytics will also allow cameras to identify car models and detect seat belts. The partnership will debut a new line of cameras with higher accuracy than traditional computing techniques in China this week. Both search giant Google and the world's leading drone manufacturer DJI have relied on Movidius to enhance spatial awareness in virtual reality and engineer the sense and avoid features inside drones respectively.

Futurewei Technologies Inc. (DBA Huawei R&D) is a U.S. Based branch of Huawei Technologies Co. Ltd., the leading global information and communications technology solutions provider. Established in 1988, Huawei Technologies is a private high-tech enterprise specializing in research and development, the production and marketing of communications equipment, and providing customized network solutions for telecom carriers in fixed, mobile, and data communications networks. Through our dedication to customer-centric innovation and strong partnerships, we have established end-to-end advantages in telecom networks, devices and cloud computing. We are committed to creating maximum value for telecom operators, enterprises and consumers by providing competitive solutions and services. Our products and solutions have been deployed in over 170 countries, serving more than one third of the world--s population.

With the advent of Network Function Virtualization (NFV), Network Functions (NF) will no longer be tightly coupled with the hardware they are running on, which poses new challenges in network management. Noisy neighbor is a term commonly used to describe situations in NFV infrastructure where an application experiences degradation in performance due to the fact that some of the resources it needs are occupied by other applications in the same cloud node. These situations cannot be easily identified using straightforward approaches, which calls for the use of sophisticated methods for NFV infrastructure management. In this paper we demonstrate how Machine Learning (ML) techniques can be used to identify such events. Through experiments using data collected at real NFV infrastructure, we show that standard models for automated classification can detect the noisy neighbor phenomenon with an accuracy of more than 90% in a simple scenario.

Imagine a security camera that can understand and analyze the scene it's capturing. The camera can spot someone who is leaving a piece of luggage behind at an airport and issue an alert. It can identify a car being sought in connection with a crime based on its color, shape, and dented left bumper, even if the license plate has been covered up. And it can spot drivers who are texting when they should be watching the road, potentially passing on this information to law enforcement officials that can then issue a ticket. These are some of the advanced security techniques being hyped by Movidius, a chip maker specializing in artificial intelligence and computer vision.