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Huawei spends at least 10% of annual revenue on R&D to drive innovation, but how and why do we do this? This 360-degree VR tour shows some of Huawei's key labs, such as the Noah's Ark Laboratory, which focuses on AI algorithms, including computer vision, natural language processing, searches and recommendations, inference and decision-making, human-machine interactions and basic AI theories. AI will help transform every industry in the future. Watch the video for an insight into the real Huawei and find out what we are working on in our other labs. Come and see us -- you will find the future is close at hand.

Canada has been investing in machine learning and artificial intelligence (AI) for longer than most of the industrialized world. Dr. Geoff Hinton of Google helped ignite the field of graphics processing unit (GPU) deep learning at the University of Toronto. Then he became chief scientific advisor to the Vector Institute, which in collaboration with the University, aims to produce the largest number of deep learning AI graduates and innovators globally. It's the home of computer scientist Yoshua Bengio, who is another pioneer of AI technology. Hundreds of AI researchers and doctoral students are concentrated at McGill University and the University of Montreal.

Black Friday or Cyber Monday, take your pick; it's that time of year again. If you're in the market for a smartphone -- and it's statistically likely you are, given that 403.5 million handsets shipped last holiday season -- there's no better month to seek out promotions, discounts, and limited-time deals on new devices. Samsung is hosting a sale on Galaxy phones including the Galaxy S10e, S10, S10 Plus, and S10 5G, and OnePlus recently knocked $150 off the price of the OnePlus 7 Pro. Carriers like T-Mobile, Sprint, AT&T, and Verizon are awarding up to $700 in trade-in credits, and as for retailers, there's the usual doorbusters. It's almost too much of a good thing -- particularly if you aren't committed to a brand, a model, or a manufacturer. Conventional wisdom would have you judge a device by its screen or perhaps its camera, but we took a different tack last year with our guide to the best phones for the AI enthusiast.

The sheer amount of services and network complexity will require a step up of current network capabilities. Specifically, 5G networks will need to incorporate Artificial Intelligence (AI) and its offspring Machine Learning (ML). As AI/ML continue to gain steam and the rest of the business world gets on board, current networks are suffering from the lack of capabilities needed. To be honest, today's mostly manual, static networks are not suited for these advanced technologies. And while agile, self-organizing networks will exist in the future, service providers need to address their digital transformation efforts today, focusing on near-term solutions, to build the foundation for these networks of tomorrow.

Typically, Chinese technology conglomerate Huawei makes the news because of the robust sales of its handsets or controversial telecommunications equipment. However, the firm has a host of business interests, including laptop manufacturing and smart glasses. Now, the company's taken steps to further its interests in the self-driving vehicle industry. Last week, Huawei announced it would use Chinese cartography firm NavInfo Co. Ltd.'s high-definition map data in its autonomous cars. Primarily, Huawei has taken up with NavInfo to avoid a major regulatory hurdle.

Verizon and Responder Corp. last week unveiled five more companies they intend to usher into the 5G market, constituting the third and final cohort of their 5G First Responder Lab, a startup-accelerator program in Washington, D.C. Recapping the announcement at a three-day public safety event hosted by Verizon and Nokia, a news release said the new cohort will focus on uses for artificial intelligence, including for weapon detection, geo-intelligence, autonomous security and situational awareness. By participating in the accelerator, these companies will have three months of access to the lab, its 5G network and consultants from Verizon and Responder Corp. to help with use case testing and market strategies. Verizon and Responder Corp. will then make the resulting tools available to public safety agencies across the country. Verizon announced this startup accelerator program in November 2018, promising to choose 15 companies -- three cohorts of five companies apiece -- and get them to realize and sell their ideas. The latest cohort will conclude the first year of the program, although statements in the news release suggested the program would continue in 2020.

This paper investigates the anti-jamming performance of a cognitive radar under a partially observable Markov decision process (POMDP) model. First, we obtain an explicit expression for uncertainty of jammer dynamics, which paves the way for illuminating the performance metric of probability of being jammed for the radar beyond a conventional signal-to-noise ratio ($\mathsf{SNR}$) based analysis. Considering two frequency hopping strategies developed in the framework of reinforcement learning (RL), this performance metric is analyzed with deep Q-network (DQN) and long short term memory (LSTM) networks under various uncertainty values. Finally, the requirement of the target network in the RL algorithm for both network architectures is replaced with a softmax operator. Simulation results show that this operator improves upon the performance of the traditional target network.

In this work, we present a novel robust distributed beamforming (RDB) approach based on low-rank and cross-correlation techniques. The proposed RDB approach mitigates the effects of channel errors in wireless networks equipped with relays based on the exploitation of the cross-correlation between the received data from the relays at the destination and the system output and low-rank techniques. The relay nodes are equipped with an amplify-and-forward (AF) protocol and the channel errors are modeled using an additive matrix perturbation, which results in degradation of the system performance. The proposed method, denoted low-rank and cross-correlation RDB (LRCC-RDB), considers a total relay transmit power constraint in the system and the goal of maximizing the output signal-to-interference-plus-noise ratio (SINR). We carry out a performance analysis of the proposed LRCC-RDB technique along with a computational complexity study. The proposed LRCC-RDB does not require any costly online optimization procedure and simulations show an excellent performance as compared to previously reported algorithms.

The term "uncanny valley" refers to that unsettling feeling you get when looking at an android that has been made to appear human. Of course, the problem goes away when we can make robots that are indistinguishable from humans. A paper published last week by New Yawk University claims that "bots are more efficient than humans at certain human-machine interactions, but only if they are allowed to hide their non-human nature." In other words, once we're past that whole uncanny valley problem, we're better served letting people think they're interacting with a human when in fact it's just artificial intelligence perfected. This raises a very important question.