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.

LabVIEW is a software platform made by National Instruments, used widely in industries such as semiconductors, telecommunications, aerospace, manufacturing, electronics, and automotive for test and measurement applications. Earlier this month, Enthought released the Python Integration Toolkit for LabVIEW, which is a "bridge" between the LabVIEW and Python environments. Quickly and efficiently access scientific and engineering tools for signal processing, machine learning, image and array processing, web and cloud connectivity, and much more. With only minimal coding on the Python side, this extraordinarily simple interface provides access to all of Python's capabilities.

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.

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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.

Car and tech companies continue to partner up, but Honda and SoftBank's newly-announced union is a little different from most. Rather than working on self-driving cars, or a more connected vehicle, Honda and SoftBank are working on making an artificial intelligence that can actually talk to and assist drivers in a way designed to foster more feelings of friendship between human and car. If the aim is really friendliness, rather than bad-ass stunts designed to help David Hasselhoff nail bad guys, then Herbie the Love Bug might be a better analogy to what the Honda/SoftBank partnership aims to accomplish. And the track record of robots created by both companies suggests a softer side for any future car companion. Honda's Asimo is actually celebrating its sweet sixteen this year in October, and the humanoid robot is one of the most recognizable in the world. The bot has even been a guest on quiz shows, and literally made great strides when it learned to run in 2005.

Say you're driving late at night and you start to feel lonely. What if your car, detecting your change of mood through an array of sensors and cameras, suddenly asked how you were feeling? Better yet, what if your car already knew how you were feeling, and offered to cheer you up? This possible future was sketched out by SoftBank founder Masayoshi Son at an event in Tokyo Thursday, according to Reuters. The eccentric tech executive, who recently announced his company's acquisition of chip manufacturer ARM for 34.1 billion, said he is working with Honda to produce a car that can both talk and read a driver's emotions. "Imagine if robots, with their super intelligence, devoted themselves to humans," Son said, according to Reuters.

Detailing their plans during a special event in Tokyo, Softbank and Honda discussed their ideas, expressing a desire for a future where Honda's cars could speak and interact with drives utilizing SoftBank's Pepper robot. The adorable bot is life-sized and would ideally be utilized when it comes to assessing drivers' speech and other data compiled via multiple sensors and cameras. Vehicles would be given the autonomy to offer advice to drivers as well as company after assessing situations. If that sounds bizarre, think of it as having your own personal KITT in your car. With SoftBank's push into robotics and AI, it wouldn't be too far off to see additional sensors and other equipment to be entered into the "internet of things" as far as automobiles go.