AI assistants may be called "personal" but they definitely aren't personable. Never mind their obviously fake personalities, these intelligent chatbots are really intelligent in only the factual sense. Huawei, however wants AI assistants to grow beyond that to become something more relatable, more approachable, more human-like. In other words, it wants to make its AI have some EI, emotional intelligence, as well to help identify human emotions and, if needed, console their users. Considering what Huawei is going through, it might be in need of some of that emotional support itself.

For the past few months, Huawei has been making headlines for all the wrong reasons -- the US government warned against buying the company's phones, which led to the breakdown of near-final deals with AT&T and Verizon. Then Best Buy, one of its few US retail partners, backed away too. We're not sure if the concerns hold any weight, but one thing is clear: It sucks to be Huawei right now. And in the midst of that turmoil, Huawei revealed its new P20 Pro, a remarkably well-built device with a triple camera system and loads of style. I doubt that would ever win over a Sinophobic bureaucrat though, so there's a strong chance no one in the US will ever be able to walk into a store and buy one.

The former is a new generation of mobile networks while the latter is a completely new way of doing business that can transform entire industries. They may overlap in several areas, but each has its own, individual development path. Industrial giants like Siemens and Bosch have been touting the concept for some time now, using the umbrella term'Industry 4.0' to describe what is an evolution in manufacturing, incorporating advanced communications and automation. But if communications are at the heart of Industry 4.0 then 5G's low latency, high capacity and gigabit speeds could take the IIoT to the next level. It's a huge opportunity for the entire mobile sector to provide the networks, devices and applications needed.

The mine detection in an unexplored area is an optimization problem where multiple mines, randomly distributed throughout an area, need to be discovered and disarmed in a minimum amount of time. We propose a strategy to explore an unknown area, using a stigmergy approach based on ants behavior, and a novel swarm based protocol to recruit and coordinate robots for disarming the mines cooperatively. Simulation tests are presented to show the effectiveness of our proposed Ant-based Task Robot Coordination (ATRC) with only the exploration task and with both exploration and recruiting strategies. Multiple minimization objectives have been considered: the robots' recruiting time and the overall area exploration time. We discuss, through simulation, different cases under different network and field conditions, performed by the robots. The results have shown that the proposed decentralized approaches enable the swarm of robots to perform cooperative tasks intelligently without any central control.

Qualcomm is putting its processing muscle behind a new Internet of Things (IoT) platform called Vision Intelligence. What makes this family of system-on-chips (SoC) special, according to the chipset giant, is that it uses an artificial intelligence (AI) engine (coupled with a heterogeneous compute architecture and an ARM-based CPU) to make it possible to do processing at the network edge instead of in the cloud. "This is what customers have been asking for," said Joseph Bousaba, Qualcomm's vice president of product management. "They want advanced capabilities like AI or machine learning, but they want it at the edge. Because of the low latency and the responsiveness, the device is much faster."

It's been a strong fortnight for machine intelligence fans, starting with Arm's Robert Elliot and Mark O'Conner publishing a white paper on the company's Arm NN machine learning platform and its optimisations for use on low-power embedded devices. "We expect machine learning to become a natural part of programming environments, with tiny embedded neural networks being part of program execution," the pair explain of the inspiration behind Arm NN. "To prepare for this, we've developed a low-overhead inference engine with the ability to import a file produced by a handful of machine learning frameworks. This supports a'write once, deploy many' approach to development, with the same framework able to target the Cortex-A class cores used in high-end mobile as well as the Cortex-M class cores used in processing environments with very small memories. We've spent significant effort to make sure that good performance is achieved on all of these processors It enables efficient translation of existing neural network frameworks, including TensorFlow and Caffe, allowing them to run efficiently, without modification, across Arm processing platforms. The inference engine can be distributed to different devices while taking advantage of the key optimizations of each."

The first Android phone to feature 3D facial recognition technology will reportedly be released in the third quarter of 2018. It's currently being speculated that the Xiaomi Mi 7 will be the first Android phone to arrive with the feature, which Apple's iPhone X popularized. The reason why it's taking so long for Android phones to adopt 3D-sensing technology is due to the difficulty in integrating devices' hardware and software, according to Digitimes. Apple doesn't suffer from the same issue since the iPhone X's hardware and software are closely tied to each other making it possible for Face ID to work. Three-dimensional-sensing modules developed by Qualcomm, Himax Technologies and Truly Optoelectronics are believed to be the likely candidates that are coming to Android phones.

Chinese tech company Huawei wants to change the way people talk to their artificially intelligent voice assistants. The firm plans to make those conversations more emotionally interactive, according to senior executives. Voice-powered virtual assistants currently serve a functional role, by giving information -- "What's the weather like?" -- or completing small tasks like turning on a playlist. Huawei wants to take that a step further and create a voice companion to fulfill some of its users' emotional needs. "We want to provide emotional interactions," Felix Zhang, vice president of software engineering at Huawei's consumer business group, told CNBC at the company's annual global analyst summit in Shenzhen, China.

Chinese tech company Huawei wants to change the way people talk to their artificially intelligent voice assistants. The firm plans to make those conversations more emotionally interactive, according to senior executives. Voice-powered virtual assistants currently serve a functional role, by giving information -- "What's the weather like?" -- or completing small tasks like turning on a playlist. Huawei wants to take that a step further and create a voice companion to fulfill some of its users' emotional needs. "We want to provide emotional interactions," Felix Zhang, vice president of software engineering at Huawei's consumer business group, told CNBC at the company's annual global analyst summit in Shenzhen, China.

We describe two applications of machine learning in the context of IP/Optical networks. The first one allows agile management of resources at a core IP/Optical network by using machine learning for short-term and long-term prediction of traffic flows and joint global optimization of IP and optical layers using colorless/directionless (CD) flexible ROADMs. Multilayer coordination allows for significant cost savings, flexible new services to meet dynamic capacity needs, and improved robustness by being able to proactively adapt to new traffic patterns and network conditions. The second application is important as we migrate our metro networks to Open ROADM networks, to allow physical routing without the need for detailed knowledge of optical parameters. We discuss a proof-of-concept study, where detailed performance data for wavelengths on a current flexible ROADM network is used for machine learning to predict the optical performance of each wavelength. Both applications can be efficiently implemented by using a SDN (Software Defined Network) controller.