Qualcomm camera technology to be featured on products scheduled for release in 2018 will focus on high-resolution depth sensing. The semiconductor manufacturer announced Tuesday, its second generation Spectra ISP camera module. The updated technology is expected to further improve image quality and effects, not only for mobile cameras but also for iris scanning and facial recognition features as well as for AR and VR. Qualcomm has planned, announcements at IFA in September with more details pertaining to its new chips and technology. Consumers can expect future devices running the new Spectra ISP camera module to up the ante on trends that are already being tested.

With the progressing year, the technology diversified ways in which we could communicate and retrieve the information from the pocket fitting devices. Technologies such as IoT, automation, and cognitive computing moved beyond the conceptual stages in 2016. As the year takes up, companies throughout the world are developing their business strategies. In order to move forward in the competition, companies are turning towards major investments in technology. The world's biggest consumer technology convention, CES is one of the best places to find a handful of key technologies. CES 2017 finished another spectacular year with pioneering technology trends including smart homes to self-driving cars. This year is assumed to bring transformative technology trends for us to explore and invest in. AI, also known as Artificial Intelligence has been studied for decades and now the vision of transforming insentient objects into intelligence is gradually becoming a reality. AI based Innovations are now pondering into the market and becoming part of our daily lives with quick adaptability. Artificial intelligence assists humans and handles the tasks flawlessly, without interrupting your comfort. Whether to set an alarm, or remind you of something important, or to play your favorite music or to read out general news for you or to find your phone, AI can make the task more convenient and smart. Sit back and relax while you command your device to do things for you.

Call Detail Record (CDR) is the information captured by the telecom companies during Call, SMS, and Internet activity of a customer. Most of the telecom companies use CDR information for fraud detection by clustering the user profiles, reducing customer churn by usage activity, and targeting the profitable customers by using RFM analysis. The actual dataset contains 8 numerical features about SMS in and out activity, call in and out activity, Internet traffic activity, square grid ID where the activity has happened, country code, and timestamp information about when the activity has been started. Here, K-means is applied among "total activity and activity hours" to find the usage pattern with respect to the activity hours.

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.