One Huawei employee, identified in the indictment only as "R.Y," wrote in a January 2013 email to Huawei China that, "Once again, we CAN'T ask TMO any questions about the robot. TMO is VERY angry the questions that we asked. Sorry we can't deliver any more information to you." The employee suggested Huawei China send its own engineer to the Seattle lab.

Embattled Chinese telecom giant Huawei has some new problems. The US Department of Justice on Monday unsealed a 13-count indictment against Huawei and its CFO, Meng Wanzhou, alleging the company misled banking partners about violations of US sanctions against Iran. The charges include bank fraud, wire fraud, money laundering, and obstruction of justice. Meng, who is also the daughter of Huawei founder Ren Zhengfei, was arrested in Canada last month and is awaiting extradition to the US. In a separate case, the DOJ indicted Huawei for stealing intellectual property related to a cell-phone-testing robot from T-Mobile in 2012.

TL;DR: I had a pleasure to be a part of the first AI conference in Somaliland, organised by Shaqodoon, HarHub and Elmi Academy, and featuring speakers from Google, MIT, major Somaliland telecoms, banks, University of Hargeisa and Ministry of Telecommunication & Technology of Somaliland. See slides (lectures workshops) and event program for details. Below are my personal notes and pictures from this trip. Whenever I tell this story people seem to be surprised with choice of spending vacation time in Somaliland and running an AI-related event there. So let me share some first-hand experience with you and explain why trips and events like this are useful, fun and safe.

TechInAfrica – The implementation of Artificial Intelligence (AI) by organizations for the past four years has rapidly increased, around 270 percent, according to the 2019 CIO survey by Gartner, Inc. The organizations that implemented AI were across all industries and predicted to keep increasing in 2019. Chris Howard, Vice President of Gartner said: "Four years ago, AI implementation was rare, only 10% of survey respondents reported that their organizations had deployed AI or would do so shortly. For 2019, that number has leaped to 37% -- a 270% increase in four years. He continued, "If you are a CIO and your organization doesn't use AI, chances are high that your competitors do and this should be a concern." The survey aims at helping IT leaders, especially CIOs to set and manage their agendas in the upcoming year. It took data from over 3,000 CIO respondents across 89 countries from main industries that represented $15 trillion of revenue and $284 billion of IT spending. In 2018, the implementation of Artificial Intelligence (AI) in many organizations across various industries grew 25%. And the number increased to 37% at the beginning of 2019. The growing number of organizations that implement AI due to its significant capabilities resulting in more organizations want to deploy it. AI has been a part of the digital strategy in many industries nowadays since it offers sustainable digital transformation and task automation. The survey reveals that around 52% of telecommunications companies apply chatbots and about 38% of healthcare providers use computer-assisted diagnostics. Meanwhile, other organizations use AI for fraud prevention and consumer determination. However, the more organizations utilize Artificial Intelligence (AI), the more challenges await them. There were around 54% of survey respondents found that the lack of skill was their biggest challenge. Howard commented on the findings, "In order to stay ahead, CIOs need to be creative.

Chinese telecom giant Huawei is at the centre of an increasingly tense standoff between China and the US. What began as a trade spat and grievances over corporate intellectual property theft has developed into a global standoff involving "hostage diplomacy", death sentences and allegations of Chinese espionage. Huawei's senior executive Meng Wanzhou, was arrested in Canada in December over allegations of sanctions violations, and awaits extradition to the US. Meanwhile, three Canadians remain in police custody in China – with one of them sentenced to death this month. Washington, meanwhile, has said it will file a formal extradition request for Meng by the 30 January deadline.

Bing has been blocked in China, Microsoft has said. The outage makes the search engine the latest of a whole host of foreign technologies to be taken down by China's "great firewall", which controls what people can see from within the country. Bing was the only major foreign search engine available in the country. "We've confirmed that Bing is currently inaccessible in China and are engaged to determine next steps," the company said in a statement. It is the U.S. technology giant's second setback in China since November 2017 when its Skype internet phone call and messaging service was pulled from Apple and Android app stores.

When it comes to artificial intelligence (AI), most organizations will have to make one of two choices. They can either invest in the technologies required to build their own AI platform or leverage one that has already been built. Verizon is betting most organizations will opt for the latter. Verizon this week launched Digital Customer Experience, a managed service through which Verizon will provide organizations access to bots developed by Verizon that can be trained to automate a wide variety of processes relating to customer service spanning chats, texts, email, social media and, of course, a phone. Alla Reznik, director of customer experience, global products and services for Verizon, says the underlying AI platform employed to deliver AI-enhanced customer interactions is the same one the telecommunications carrier relies on to automate its own customer interactions.

Besides the potential of foldable/expandable devices and 5G, what else can we expect to see at Mobile World Congress next month? According to LG's invite to its Premiere event on February 24th, we'll say "Goodbye Touch." The video clip shows a hand summoning and dismissing text with a simple wave, similar to things we've seen from tech like Samsung's Air Gesture that arrived in the Galaxy S4. Presumably LG's implementation will be far more advanced than what we experienced back in 2013, but we'll have to wait and see.

Next-generation wireless networks (NGWN) have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of machine learning by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning, respectively. Furthermore, we investigate their employment in the compelling applications of NGWNs, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various machine learning algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.

Wireless Internet access has brought legions of heterogeneous applications all sharing the same resources. However, current wireless edge networks that cater to worst or average case performance lack the agility to best serve these diverse sessions. Simultaneously, software reconfigurable infrastructure has become increasingly mainstream to the point that dynamic per packet and per flow decisions are possible at multiple layers of the communications stack. Exploiting such reconfigurability requires the design of a system that can enable a configuration, measure the impact on the application performance (Quality of Experience), and adaptively select a new configuration. Effectively, this feedback loop is a Markov Decision Process whose parameters are unknown. The goal of this work is to design, develop and demonstrate QFlow that instantiates this feedback loop as an application of reinforcement learning (RL). Our context is that of reconfigurable (priority) queueing, and we use the popular application of video streaming as our use case. We develop both model-free and model-based RL approaches that are tailored to the problem of determining which clients should be assigned to which queue at each decision period. Through experimental validation, we show how the RL-based control policies on QFlow are able to schedule the right clients for prioritization in a high-load scenario to outperform the status quo, as well as the best known solutions with over 25% improvement in QoE, and a perfect QoE score of 5 over 85% of the time.