Churn prediction is big business. It minimizes customer defection by predicting which customers are likely to cancel a subscription to a service. Though originally used within the telecommunications industry, it has become common practice across banks, ISPs, insurance firms, and other verticals. The prediction process is heavily data-driven and often utilizes advanced machine learning techniques. In this post, we'll take a look at what types of customer data are typically used, do some preliminary analysis of the data, and generate churn prediction models -- all with Spark and its machine learning frameworks.

Oh the times they are a changin'. Yes, it's more than 50 years since Bob Dylan crooned about the ever-evolving world, and half a century on, things have only quickened in their rate of development. That's no truer than in the technology space, where new developments are constantly lurking, waiting for their moment in the spotlight. With personal computers, TVs and portable audio players having all had their day, it's now the smartphone's time to shine, and thanks to manufactures such as Huawei, the glow of these pocket powerhouses is only getting brighter. Huawei's flagship smartphones, such as the brilliant Huawei Mate 9 and stunning new Huawei P10, aren't just following the trends; they're actively contributing to and enabling a more immersive, connected world for consumers.

An artificially intelligent robot that can understand sarcasm in social media posts better than humans has been developed by scientists. The algorithm can decipher the tone of tweets, and researchers say it could be used to tackle online abuse. By interpreting emoji used alongside a post's text, the robot can understand emotional subtext and identify if sarcasm is being used. A robot that can understand sarcasm in social media posts better than humans has been developed by scientists. By interpreting emoji used alongside a post's text, the AI can understand emotional subtext and identify if sarcasm is being used (stock image) Researchers created the AI, known as DeepMoji, by feeding it 1.2 billion tweets. The robot analysed each tweet to understand how 64 popular emoji were used in them to express meaning.

The predictions for AI use cases have been prolific and wide-ranging in recent years. From humanoid robots to predictive analytics for legal institutions, hedge funds, and more, there has rarely been more excitement generated by a technology than the current buzz emanating from AI software. Application of this technology extends to mobile and telecommunications too. Here, it has become an important next step in helping operators' transition from Communications Service Providers into more advanced Digital Service Providers that can predict their customers wants and needs. AI is empowering service providers with a range of new capabilities such as deep learning, natural language processing, and cognitive computing to create a digital interface that will essentially deal directly with human beings, addressing and resolving customer service issues.

But just such a change appears to be happening now. In a great wave of technological change, sensors are spreading through factories and warehouses, software is predicting the need for maintenance before a machine breaks down, power grids and loading docks are becoming intelligent, and custom-designed parts are being produced on demand. The leaders of the next industrial revolution are companies making advances in fields such as robotics, machine learning, digital fabrication (including 3D printing), the Industrial Internet, the Internet of Things (IoT), data analytics and blockchain (a system of decentralized, automated transaction verification). Because these technologies all reinforce the others' impact, they are leading to a new level of proficiency, and to new types of opportunities and challenges for business and for society at large. One key indicator is that conventional boundaries between industries are eroding. It's getting harder to tell the difference between, say, a telecommunications company and an entertainment producer, or between a retail bank and a retail store. The relationships among suppliers, producers, and consumers are also blurring, more rapidly than many business decision makers are prepared for. The foundation of business strategy has long been the classic value chain, which links together raw materials producers, manufacturers, distributors, and (in the end) consumers through a well-established commercial infrastructure characterized by a stable set of transactions. But the rise of digital technology enables individuals to connect outside the value chain and deliver more efficient, effective products and services. This will reduce the importance of economies of scale and conventional divisions of labor. Relationships among companies will be more fluid and the price and cost of goods and services more volatile than they are today.

Wireless systems perform rate adaptation to transmit at highest possible instantaneous rates. Rate adaptation has been increasingly granular over generations of wireless systems. The base-station uses SINR and packet decode feedback called acknowledgement/no acknowledgement (ACK/NACK) to perform rate adaptation. SINR is used for rate anchoring called inner look adaptation and ACK/NACK is used for fine offset adjustments called Outer Loop Link Adaptation (OLLA). We cast the OLLA as a reinforcement learning problem of the class of Multi-Armed Bandits (MAB) where the different offset values are the arms of the bandit. In OLLA, as the offset values increase, the probability of packet error also increase, and every user equipment (UE) has a desired Block Error Rate (BLER) to meet certain Quality of Service (QoS) requirements. For this MAB we propose a binary search based algorithm which achieves a Probably Approximately Correct (PAC) solution making use of bounds from large deviation theory and confidence bounds. In addition to this we also discuss how a Thompson sampling or UCB based method will not help us meet the target objectives. Finally, simulation results are provided on an LTE system simulator and thereby prove the efficacy of our proposed algorithm.

The idea is to develop microprocessors configured more like human brains than traditional silicon chips with the aim of making computers more astute about the environment; this is seen as step-forwards with artificial intelligence. Neuroinformatics refers to the creation of neuromorphic chips that can replicate the brain's information processing capabilities in real-time. Key players in the development of neuromoprhic computing are Qualcomm, IBM, HRL Laboratories and the Human Brain Project. The Human Brain Project is a 10-year project seeking to simulate a complete human brain in a supercomputer using biological data. With the commercial developments, a neuromorphic chip made by IBM contains five times as many transistors as a standard Intel processor, Wired reports, yet it consumes only 70 milliwatts of power.

COLUMBUS, Ohio – July 19, 2017 – DeviceBits, a leading artificial intelligence (AI) software company that offers predictive, self-learning platforms that help companies adopt self-service customer support materials, announced today its Academy offering will host digital self-support materials for customers of Nex-Tech Wireless. Nex-Tech Wireless focuses on providing its customers cutting-edge technology including 4G LTE data and mobile services, as well as the latest wireless equipment and competitive wireless plans that provide nationwide coverage. As part of the agreement, Nex-Tech Wireless has selected DeviceBits to deploy the Nex-Tech Wireless Academy and Support Predict Platform for self-service digital customer support materials. It will offer customers a destination that will include an enhanced self-service digital experience on their website and mobile devices. This experience will support the top-selling device models offered by Nex-Tech Wireless and guide customers with FAQ's, guides, tutorials and videos that are intelligently linked to predict user journeys which will provide a positive customer experience.

Softbank may be best known as one of Japan's top phone carriers, but their recent behind-the-scenes investments make it clear they want something more. Bloomberg Technology reports that the Japanese company has invested in iRobot, the manufacturer of the robot vacuum Roomba, with a stake of less than 5 percent. This isn't the first robotics company that Softbank has put some money into. Just last month, the phone carrier purchased Boston Dynamics from Google, along with all of its robots. Softbank also has found success with its companion robot, Pepper.

Qualcomm's decided to open up its year-old AI, by making its Neural Processing Engine (NPE) available to all. The Snapdragon NPE first landed last year, with the company pitching capabilities including "scene detection, text recognition, object tracking and avoidance, gesturing, face recognition, and natural language processing". Announcing the open release, the chip-slinger cites Facebook as a user, with the Social Network working to use the NPE in its camera app "to accelerate Caffe2-powered [augmented reality] features." TensorFlow is also name-checked in the announcement, and since the SDK's page also mentions convolutional neural network support, Vulture South reckons Cuda ConvaNet (part of last year's announcement) is also in there somewhere. As well as runtimes and libraries, the SDK provides APIs, sample code, debugging/benchmarking tools, documentation, and offline model conversion tools.