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 .

Artificial Intelligence (AI) has a long history, starting in the 1950s with the theoretical concept of the Turing test and gaining considerable momentum since the early 2000s. This development has been driven by the significant increase in computing power which makes computation-intensive Deep Learning algorithms easy and effortless to handle. The intelligent processing of images (computer vision) and speech (natural language processing) is becoming a routine part of our customers' daily lives as found in mass market applications such as Apple's Siri or Amazon's Alexa. Artificial Intelligence is defined as the imitation of human intelligence or intellectual processes by machines, especially computer systems. These processes include learning, reasoning, and automatic correction.

In Phoenix, cars are self-navigating the streets. In many homes, people are barking commands at tiny machines, with the machines responding. On our smartphones, apps can now recognize faces in photos and translate from one language to another. Artificial intelligence is here -- and it's bringing new possibilities, while also raising questions. Do these gadgets and services really behave as advertised? How will they evolve in the years ahead?

Inference will increasingly take place in apps on smartphones and other "edge" devices. While most phones have chips that can process rudimentary neural nets, additional performance beyond the CPU and GPU is needed for images and language processing. As a result, Huawei's latest Kirin 970 has what it calls a Neural Processing Unit, I believe supplied by Tensilica LLC. The iPhone X has the A11X Bionic chip with a custom silicon block for neural network processing to enable face detection and portrait photography with promises to do more in the future. The Qualcomm Snapdragon 835 accelerates TensorFlow, Caffe, Caffe2, MxNet and Android NNAPI across its CPU, GPU, and most importantly, its DSP.

Almost every major player in the smartphone industry now says that their devices use the power of artificial intelligence (AI), or more specifically, machine learning algorithms. Few devices, however, run their own AI software. That might soon change: thanks to a processor dedicated to machine learning for mobile phones and other smart-home devices, AI smartphones could one day be standard. British chip design firm ARM, the company behind virtually every chip in today's smartphones, now wants to put the power of AI into every mobile device. Currently, devices that run AI algorithms depend on servers in the cloud.