Huawei used its annual pre-Mobile World Congress briefing to provide an update on its focus areas, including the role of artificial intelligence (AI) in networks and – unsurprisingly – 5G. Ryan Ding, president of the vendor's Carrier Business Group (pictured), said the company believes there is a need to embed AI "into our services, into our networks, to provide more flexible services, and also improve our operations experience". He talked-up the company's new AI platform, called Atlas, which he described as "Huawei's heterogeneous computing solution". AI, he said, had become a "general purpose technology" which was integrated into Huawei's products and solutions and "greatly improved the efficiency of live networks". Returning to one of the main themes of Huawei's 2017 Global Mobile Broadband Forum, the executive highlighted the role of AI in network management.

Modern mobile networks are facing unprecedented growth in demand due to a new class of traffic from Internet of Things (IoT) devices such as smart wearables and autonomous cars. Future networks must schedule delay-tolerant software updates, data backup, and other transfers from IoT devices while maintaining strict service guarantees for conventional real-time applications such as voice-calling and video. This problem is extremely challenging because conventional traffic is highly dynamic across space and time, so its performance is significantly impacted if all IoT traffic is scheduled immediately when it originates. In this paper, we present a reinforcement learning (RL) based scheduler that can dynamically adapt to traffic variation, and to various reward functions set by network operators, to optimally schedule IoT traffic. Using 4 weeks of real network data from downtown Melbourne, Australia spanning diverse traffic patterns, we demonstrate that our RL scheduler can enable mobile networks to carry 14.7% more data with minimal impact on existing traffic, and outpeforms heuristic schedulers by more than 2x. Our work is a valuable step towards designing autonomous, "self-driving" networks that learn to manage themselves from past data.

Big human mobility data are being continuously generated through a variety of sources, some of which can be treated and used as streaming data for understanding and predicting urban dynamics. With such streaming mobility data, the online prediction of short-term human mobility at the city level can be of great significance for transportation scheduling, urban regulation, and emergency management. In particular, when big rare events or disasters happen, such as large earthquakes or severe traffic accidents, people change their behaviors from their routine activities. This means people's movements will almost be uncorrelated with their past movements. Therefore, in this study, we build an online system called DeepUrbanMomentum to conduct the next short-term mobility predictions by using (the limited steps of) currently observed human mobility data. A deep-learning architecture built with recurrent neural networks is designed to effectively model these highly complex sequential data for a huge urban area. Experimental results demonstrate the superior performance of our proposed model as compared to the existing approaches. Lastly, we apply our system to a real emergency scenario and demonstrate that our system is applicable in the real world.

The Mist learning WLAN gives unprecedented visibility into the mobile user experience. To achieve this, Mist Access Points track over 100 pre- and post- connection states for every Wi-Fi client, which are sent to the Mist Cloud every few seconds where multiple machine learning algorithms use the data to provide actionable insights. In addition, machine learning in the Mist Cloud is used to calculate the location of mobile users with high accuracy and low latency (see figure 1 below for a network topology). The Mist Cloud consists of proprietary machine learning algorithms running on top of a variety of open source and in-house distributed systems. As one can imagine, scalability and reliability are critical to the Mist cloud, as is real-time performance to handle various different types of real-time streaming data.

Management teams often assume they can leapfrog best practices for basic data analytics by going directly to adopting artificial intelligence and other advanced technologies. But companies that rush into sophisticated artificial intelligence before reaching a critical mass of automated processes and structured analytics can end up paralyzed. They can become saddled with expensive start-up partnerships, impenetrable black-box systems, cumbersome cloud computational clusters, and open-source toolkits without programmers to write code for them. By contrast, companies with strong basic analytics -- such as sales data and market trends -- make breakthroughs in complex and critical areas after layering in artificial intelligence. For example, one telecommunications company we worked with can now predict with 75 times more accuracy whether its customers are about to bolt using machine learning.

Today, most enterprises using ML have only a handful of deployments and pilots under way, but, according to Deloitte Global, progress in five key areas should make it easier and faster to develop ML solutions. In response, technology vendors are creating compact ML software models to undertake tasks such as image recognition and language translation on portable devices. Semiconductor vendors are developing their own power-efficient AI chips to bring ML to mobile devices. With smartphones an increasingly viable deployment option for ML, the number of potential applications is growing. Collectively, the five vectors of ML progress should double the intensity with which enterprises are using this technology by the end of 2018.

If you've spent any time reading the round-up of 2018 technology predictions, you've likely seen artificial intelligence (AI) highlighted in nearly every one. The reason for this is that AI has a seemingly limitless number of applications and use cases for the enterprise. In fact, according to Gartner, over 85% of customer interactions will be managed without a human by 2020. While AI is definitely a hot topic to watch in 2018, there are also a few other tech areas that will have equally exciting momentum and just as big an impact on the enterprise in the year ahead. Following we'll take a deeper look at what some of those will be and how they might shape 2018.

With February finally here, it can't be ignored that Mobile World Congress (#MWC18) is just around the corner, taking place in Barcelona from 26 Feb to 1 Mar. The rate of attendees has been climbing up steadily year-on-year attracting over 108,000 attendees last year of which 74% is senior-level decision makers. Apart from the annual product launches by players like Huawei and Samsung, there's always a lot more going on and this year promises to be no different... AI has been a constant talking point in Tech for some time now and a number of interesting topics will be discussed by industry leaders at the event this year. AI technology has advanced rapidly raising questions about the ethical issues and effect on society at large. Social profiling, privacy and security are some of the most common concerns.

Amazon Alexa users can now send SMS messages through the voice assistant, the company announced via TechCrunch. The Amazon feature comes as Apple takes pre-orders for its own smart speaker: the HomePod. The Apple device will allow users to send iMessages and SMS text messages by using Siri. As of Tuesday, U.S. Alexa users can send text messages to their contacts in the Alexa app by simply asking the voice assistant. Alexa already has a voice calling and its own messaging system.

So much for needing your phone to send text messages. Thanks to a new software update, you can now use Alexa to deliver SMS text messages to any phone. There are some caveats, however. It only works for Alexa users in the US with an Android device that also have the Alexa app installed on their phone. You also can't use the feature to text 911 or participate in group text messages.