Big Data enabled AI will begin to change the game for online marketing and website presence – and amazing tools are being launched now to democratize access. While it is easy to get blasé about the impact of cheaper data in general, consider this: In 2017, online shopping finally came online en masse in India thanks to technological advances in the domestic telecommunications network and a drop in the price of data transmission. This means that India last year began to see a world that western audiences, in particular, have now grown blasé too. Except that there are 700 million Indians. That means a country several times the size of the United States is able to go shopping online for the first time. And boy, has that made a difference.

Saudi Gazette A world without smartphones, passwords, and flying cars might seem like a sci-fi movie but it's not very far off and might come into reality "in a blink", according to predictions made by leading networking company Cisco that considers itself shaping the Internet since the 1980's. One prediction is that by 2027, texting by thinking will be a form of communication. Dubai's plans to launch the first self-driving drone taxi in a couple of years from now aims to lead the way to flying automobiles. Complete simulations by the human brain will be possible before the 2030's where new jobs will be common such as avatar manager, body part maker, climate change reversal specialist and nano medic, to name a few. The 2040's will look dramatically different when the average home PC will have the computing power of one billion brains, virtual telepathy will dominate telecommunications and artificial intelligence could become smarter than humans.

Investors know that robotics and artificial intelligence (NYSE:AI) are disrupting traditional paradigms. But they may be surprised by just how much disruptive technologies are impacting our daily lives. I know that I was a little taken aback when I looked at my day. Typically, I wake up and check my smartphone to see what's going on in the world through social media (SOCL). I get my caffeine fix via a Wi-Fi enabled coffee maker (SNSR).

"ML techniques, so far, are more or less related to prediction, classification and decision-making in the networking area," says Qiang Cheng of the Artificial Intelligence Industry Association of the China Academy of ICT. Cheng was highlighting the current state of play in Machine Learning's contribution to ICT networking at an ITU workshop in Geneva, 29 January 2018. The workshop brought together experts in machine learning and ICT networking to discuss the challenges and opportunities on the agenda of the new ITU Focus Group on Machine Learning for 5G, which met for the first time from 30 January to 2 February 2018. Looking to the future, Huawei, KT, ZTE and Deutsche Telekom see great potential for Machine Learning to assist the design, operation and optimization of 5G networks. Machine Learning is expected to assist the ICT industry in meeting the challenges brought on by 5G and IoT, shifts representative of considerable increases in network complexity and the diversity of device requirements.

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.