Sure, the feds finally made it reasonably easy to get a drone pilot's certificate, but it's clear they still see unmanned aviation as a dodgy proposition. Among the many questions that come with any new tech is a basic limitation: The radio links and Wi-Fi that control the aircraft limit range to a few thousand feet, and aren't robust enough for reliable drone control over long distances. So the new rules, which took effect last month, limit drone use to visual line-of-sight operation, hamstringing operators interested in delivery, search-and-rescue, and remote-inspection operations. The solution may lurk in your own line-of-sight--on top of water towers and rooftops, or shrouded by poorly faked roadside "trees." Qualcomm Technologies and AT&T announced today they're collaborating to make wide-ranging drone operations reliable and safe, using current 4G LTE and future 5G networks.

Today, a lot of high-profile deep machine learning projects in the cloud are powered by GPUs; specifically NVIDIA GPUs. Even Facebook uses them for its own machine learning work behind-the-scenes. GPUs are able to handle the massive amounts of computing power required to train deep neural networks that facilitate these projects. But Huawei deputy chairman and rotating CEO Eric Xu believes the future of machine learning lies in dedicated processors. Read on to find out more. In the past few years, NVIDIA has made a big push to dethrone CPUs in the deep machine learning space.

On this week's NFV/SDN Reality Check we have an interview with Argyle Data to discuss how mobile operators are using big data and machine learning technologies for real time fraud detection, prevention and profit. But first, let's take a look at some top headlines from across the space. AT&T this week announced plans to partner with Intel to work on the telecom giant's cloud network initiatives. The partnership calls for work on optimizing network functions virtualization packet processing efficiency for AT&T's Integrated Cloud platform, defining reference architecture and aligning NFV roadmaps in a move to speed AT&T's ongoing network transformation. AT&T has said its Integrated Cloud platform is where the carrier runs virtual network functions using OpenStack software at its core, with the carrier having set up 74 AIC physical locations in 2015, with plans for 105 by the end of this year and adding "hundreds more" by 2020.

So now that Samsung has issued a recall for the Galaxy Note 7, you're probably wondering what to do with that potentially-combustible smartphone you're holding. Your choice is going to depend on what carrier you've got. We have some more details on what each is doing, thanks to a series of official statements. Samsung has pledged to replace devices "over the coming weeks" once it determines the cause of the problem. To date, 35 phones worldwide have been identified by the company as suffering from the faulty battery.

Creating a network capable of automatically responding to its own issues -- congestion, equipment failure, traffic spikes -- is one of the stated goals of today's virtualization push. Artificial intelligence and machine learning are key elements of this effort today, and they become even more critical going forward. That's because the looming trends of 5G wireless and the Internet of Things will reshape networks and network traffic in even more unpredictable ways than are happening already, notes Mazin Gilbert, AVP of Intelligent Services at AT&T Labs. In this second of two stories on artificial intelligence (AI) and machine learning in telecom (you can read the first one here), we take a closer look at how AT&T Inc. (NYSE: T) and Level 3 Communications Inc. (NYSE: LVLT) are deploying technology today to be ready for future services. First, there is a massive spike in data traffic expected with the advent of 5G and IoT, as much as a 10-fold increase in the next four years, Gilbert notes.

The combination of software-defined networking and machine learning/artificial intelligence is becoming a powerful tool for making networks more reliable and secure. And while not everyone is willing to talk about their activities yet -- CenturyLink Inc. (NYSE: CTL) and Verizon Communications Inc. (NYSE: VZ) declined interview requests on this topic -- a peek inside what is happening at AT&T Inc. (NYSE: T) and Level 3 Communications Inc. (NYSE: LVLT) offers a clear view of what's possible. In this first of two stories, executives at those companies share how machine learning and AI are being built into their networks today. As Mazin Gilbert, AVP of Intelligent Services at AT&T Labs, explains, artificial intelligence and machine learning are hardly new concepts, nor is the idea of using these tools to improve network performance and security. There was talk about that as far back as the 1980s, he says.

Developing intelligent systems requires combining results from both industry and academia. In this report you find an overview of relevant research fields and industrially applicable technologies for building very large scale cyber physical systems. A concept architecture is used to illustrate how existing pieces may fit together, and the maturity of the subsystems is estimated. The goal is to structure the developments and the challenge of machine intelligence for Consumer and Industrial Internet technologists, cyber physical systems researchers and people interested in the convergence of data & Internet of Things. It can be used for planning developments of intelligent systems.

On this week's NFV/SDN Reality Check we have an interview with Argyle Data to discuss how mobile operators are using big data and machine learning technologies for real time fraud detection, prevention and profit. But first, let's take a look at some top headlines from across the space. AT&T this week announced plans to partner with Intel to work on the telecom giant's cloud network initiatives. The partnership calls for work on optimizing network functions virtualization packet processing efficiency for AT&T's Integrated Cloud platform, defining reference architecture and aligning NFV roadmaps in a move to speed AT&T's ongoing network transformation. AT&T has said its Integrated Cloud platform is where the carrier runs VNFs using OpenStack software at its core, with the carrier having set up 74 AIC physical locations in 2015, with plans for 105 by the end of this year and adding "hundreds more" by 2020.

The Boy in the Ambulance Is a Stark Reminder of Aleppo's Pain What Twitter's Head of Safety Says About Rampant Abuse

Massive MIMO is a variant of multiuser MIMO where the number of base-station antennas $M$ is very large (typically 100), and generally much larger than the number of spatially multiplexed data streams (typically 10). Unfortunately, the front-end A/D conversion necessary to drive hundreds of antennas, with a signal bandwidth of the order of 10 to 100 MHz, requires very large sampling bit-rate and power consumption. In order to reduce such implementation requirements, Hybrid Digital-Analog architectures have been proposed. In particular, our work in this paper is motivated by one of such schemes named Joint Spatial Division and Multiplexing (JSDM), where the downlink precoder (resp., uplink linear receiver) is split into the product of a baseband linear projection (digital) and an RF reconfigurable beamforming network (analog), such that only a reduced number $m \ll M$ of A/D converters and RF modulation/demodulation chains is needed. In JSDM, users are grouped according to the similarity of their channel dominant subspaces, and these groups are separated by the analog beamforming stage, where the multiplexing gain in each group is achieved using the digital precoder. Therefore, it is apparent that extracting the channel subspace information of the $M$-dim channel vectors from snapshots of $m$-dim projections, with $m \ll M$, plays a fundamental role in JSDM implementation. In this paper, we develop novel efficient algorithms that require sampling only $m = O(2\sqrt{M})$ specific array elements according to a coprime sampling scheme, and for a given $p \ll M$, return a $p$-dim beamformer that has a performance comparable with the best p-dim beamformer that can be designed from the full knowledge of the exact channel covariance matrix. We assess the performance of our proposed estimators both analytically and empirically via numerical simulations.