Artificial intelligence (AI) and machine learning are two of the latest networking buzzwords being thrown around the industry. The problem is many enterprise network managers remain confused about the real value of these vastly useful technologies. Emerging network analytics services, powered by AI and machine learning promise to transform traditional infrastructure management models by simplifying operations, lowering costs, and giving unprecedented insights into the user experience – improving the productivity of both IT professionals and their users. For network staff, the concept and value of these technologies is extremely powerful if applied to the right problems. One big problem is today's operational challenge in dealing with the mass of user, device, application and network service data traversing the enterprise access infrastructure.

Nordstrom is transforming the way that they do business and the cloud is the key to enabling speed and hyper personalized customer experiences.

Comcast's voice remote for its X1 platform has become an increasingly popular feature, and the operator has kept it stocked with a constant flow of updates that, for example, support voice commands for special events such as the Super Bowl or the Winter Olympics. The technology that underpins that platform is also branching well beyond the TV. Comcast has already started to integrate the X1 voice remote with Xfinity Home, its home security and automation service. Comcast also recently added another voice wrinkle with a Phone Finder feature for Xfinity Mobile. Those customers can activate it by saying, "Xfinity Mobile, find my phone" into the X1 voice remote, or by uttering their 10-digit phone number into the remote.

Huawei has really upped its game in recent years, especially with the launch of its Huawei Mate 10 Series late last year. Its sleek build aside, its specifications are pretty outstanding. In fact, it boasts Kirin 970 – which Huawei designed and built itself – the world's first AI processor for smartphones with a dedicated Neural-Network Processing Unit (NPU). This chipset pretty much fuels the smartphone's machine-learning features and includes a special module dedicated to AI calculations. AI is becoming increasingly important in today's smartphone experience, and coupled with machine learning, they are the reasons why our phones are getting better and more powerful every year.

This is the second in a two-part series of articles about recruiting trends for 2018. This installment addresses data analytics and artificial intelligence. Advances in talent data analytics and artificial intelligence (AI) will provide talent acquisition professionals in 2018 with the tools they need to be more strategic and insightful when making hiring decisions and streamline the transactional side of recruiting. Over 9,000 recruiters and hiring managers across the globe identified these trends, among others, as being the most impactful when surveyed by LinkedIn for the professional networking site's Global Recruiting Trends 2018 report. LinkedIn found that most companies are already using data to some degree to solve talent issues and that most recruiting professionals expect AI will eventually transform their roles.

The invitation for Samsung's 2018 S9 event, at which the newest Galaxy phone is expected to be unveiled. Spring is almost here which means its almost time for Samsung to release its latest Galaxy. Whereas Apple uses the Fall to announce new iPhones, Samsung prefers to release new Galaxies in the Spring. And with a press event scheduled at Mobile World Congress in Barcelona on Feb. 25, it looks like 2018 will be no different. Here's what we expect to see in the Galaxy S9.

Networks are a fundamental model of complex systems throughout the sciences, and network datasets are typically analyzed through lower-order connectivity patterns described at the level of individual nodes and edges. However, higher-order connectivity patterns captured by small subgraphs, also called network motifs, describe the fundamental structures that control and mediate the behavior of many complex systems. We develop three tools for network analysis that use higher-order connectivity patterns to gain new insights into network datasets: (1) a framework to cluster nodes into modules based on joint participation in network motifs; (2) a generalization of the clustering coefficient measurement to investigate higher-order closure patterns; and (3) a definition of network motifs for temporal networks and fast algorithms for counting them. Using these tools, we analyze data from biology, ecology, economics, neuroscience, online social networks, scientific collaborations, telecommunications, transportation, and the World Wide Web.

So while many parts of the ARCloud will involve hosting big data and serving web APIs and training machine learning models, just like today's cloud, there will need to be a very big rethink of how do we support real-time applications and AR interactions at massive scale. Basic AR use-cases like: streaming live 3D models of our room while we "AR Skype"; updating the data & applications connected to things, presented as I go by on public transport; streaming (rich graphical) data to me that changes depending on where my eyes are looking, or who walks near to me; maintaining & updating the real-time application state of every person & application in a large crowd at a concert. Without this type of UX, there's no real point to AR. Let's just stick with smartphone apps. Supporting this for eventually billions of people will be a huge opportunity. If history is any guide, some if not most of today's incumbents who have massive investments in the cloud infrastructure of today will not cannibalize those investments to adapt to this new world.

Mobile network that millions of people use every day is one of the most complex systems in real world. Optimization of mobile network to meet exploding customer demand and reduce CAPEX/OPEX poses greater challenges than in prior works. Learning to solve complex problems in real world to benefit everyone and make the world better has long been ultimate goal of AI. However, it still remains an unsolved problem for deep reinforcement learning (DRL), given imperfect information in real world, huge state/action space, lots of data needed for training, associated time/cost, multi-agent interactions, potential negative impact to real world, etc. To bridge this reality gap, we proposed a DRL framework to direct transfer optimal policy learned from multi-tasks in source domain to unseen similar tasks in target domain without any further training in both domains. First, we distilled temporal-spatial relationships between cells and mobile users to scalable 3D image-like tensor to best characterize partially observed mobile network. Second, inspired by AlphaGo, we used a novel self-play mechanism to empower DRL agent to gradually improve its intelligence by competing for best record on multiple tasks. Third, a decentralized DRL method is proposed to coordinate multi-agents to compete and cooperate as a team to maximize global reward and minimize potential negative impact. Using 7693 unseen test tasks over 160 unseen simulated mobile networks and 6 field trials over 4 commercial mobile networks in real world, we demonstrated the capability of our approach to direct transfer the learning from one simulator to another simulator, and from simulation to real world. This is the first time that a DRL agent successfully transfers its learning directly from simulation to very complex real world problems with incomplete and imperfect information, huge state/action space and multi-agent interactions.

Time for an update on my One-Shot learning approach using a Siamese LSTM-based Deep Neural Network we developed for telecommunication network fault identification through traffic analysis. A lot of small details had to change as we upgraded our machine to the latest TensorFlow and Keras. That alone introduced a few new behaviors… As well as we obtained new data for new examples and found out some problems with our model. I don't intend to go through all changes, but some of the main ones as well as some interesting findings. It feels a lot like potty training a cat… If you are new to this series, you can refer to my previous posts: "Do Telecom Networks Dreams of Siamese Memories?" and "What Siamese Dreams are made of…" First, Batch Normalization in Keras is now on my black magic list .