Chinese tech giants Baidu Inc. and Huawei Technologies Co. Ltd. announced today that they're teaming up to build an open artificial intelligence ecosystem for mobile devices. The two companies said they want to create platform that fosters the development of mobile AI that "knows you better." The new ecosystem will combine Baidu Brain, which is the catch-all term for Baidu's various AI tools, with Huawei's HiAI mobile computing platform. The project will also take advantage of the AI-focused hardware components that Huawei installs in its latest smartphone models. "The future is all about smart devices that will actively serve us, not just respond to what we tell them to do," Richard Yu (pictured, right), chief executive of Huawei's Consumer Business Group, said in a statement.

At the beginning of 2017 you could have been forgiven for thinking that smartphone innovation had died, with most phones looking the same and doing the same things, changing very little from the year before. But by the end of 2017 two things were clear: manufacturers needed to go all-screen or go home, and artificial intelligence had finally made its way into the phone, not just feeding everything you said to a server somewhere over the horizon. The Samsung Galaxy S8 introduced the new minimal bezel design in April, shrinking the non-screen parts of the front down to the bare minimum and as a result putting a bigger screen in the same sized smartphone. It was clearly the future. Even Apple agreed, launching the iPhone X in November.

Huawei and Baidu have formed an alliance with the intention of sharing their expertise to lead the new generation of AI-powered smartphones. Many flagship smartphones are launching with dedicated AI chips -- including, of course, Huawei's very own Mate 10. Over the course of the next year, we expect these chips to be available in most high-end devices. Research firm Counterpoint Technology forecasts that more than half a billion smartphones that will be shipped around the world in 2020 will have AI capabilities at the chipset level. The current abilities of these devices are impressive, but limited.

Relentless change; stubborn continuity… The predictions on tech trends for the New Year 2018 seem to be more drastic than any of its previous editions. Deloitte has just launched its annual Deloitte TMT Predictions for 2018, and it has some serious thought-provoking recommendations for marketing and advertising agencies -- focus on machine learning deployments, augmented reality tools, and live-streaming content. Fascinating as always, Deloitte TMT Predictions 2018 emphasize that the technology, media and entertainment, and telecommunications ecosystem are the top enterprise adopters of cutting-edge artificial intelligence capabilities, powered by new chips and better software tools. What should CMOs be expecting in 2018? Well, according to Deloitte's predictions, augmented reality will become more mainstream even as machine learning deployments begin to make a marked impact on marketing budgets.

In the race to get AI working faster on your smartphone, companies are trying all sorts of things. The company's adjacent market opportunities are growing at a CAGR of 18% for the next Adopting Artificial Intelligence for Internet of Things; Adopting Artificial Intelligence for Internet of working on platforms …

Huawei Technologies, the world's largest telecommunications equipment supplier, has forged an artificial intelligence (AI) alliance with Chinese internet search provider Baidu in a move that ratchets up the competition against Apple in leading the future of smartphones. The strategic cooperation with Huawei will kick off the AI-powered intelligent devices era, said Robin Li Yanhong, Baidu's chairman and chief executive, at the joint launch of their AI pact in Beijing on Thursday. There is much to look forward to an alliance between a smartphone company and an AI company, he said. The goal is to foster a new mobile and AI ecosystem by leveraging Huawei's hiAI platform and Baidu Brain, a compendium of the company's AI assets and services, and combine hardware and software to provide global consumers with new smart service experiences, according to the two companies. That would give Shenzhen-based Huawei, China's biggest smartphone brand, with an important edge in competing against Apple and other major players in the global mobile phone market, which will demand more devices with on-board AI capabilities over the next few years.

Customer retention campaigns increasingly rely on predictive models to detect potential churners in a vast customer base. From the perspective of machine learning, the task of predicting customer churn can be presented as a binary classification problem. Using data on historic behavior, classification algorithms are built with the purpose of accurately predicting the probability of a customer defecting. The predictive churn models are then commonly selected based on accuracy related performance measures such as the area under the ROC curve (AUC). However, these models are often not well aligned with the core business requirement of profit maximization, in the sense that, the models fail to take into account not only misclassification costs, but also the benefits originating from a correct classification. Therefore, the aim is to construct churn prediction models that are profitable and preferably interpretable too. The recently developed expected maximum profit measure for customer churn (EMPC) has been proposed in order to select the most profitable churn model. We present a new classifier that integrates the EMPC metric directly into the model construction. Our technique, called ProfTree, uses an evolutionary algorithm for learning profit driven decision trees. In a benchmark study with real-life data sets from various telecommunication service providers, we show that ProfTree achieves significant profit improvements compared to classic accuracy driven tree-based methods.

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.

A multiple classifiers fusion localization technique using received signal strengths (RSSs) of visible light is proposed, in which the proposed system transmits different intensity modulated sinusoidal signals by LEDs and the signals received by a Photo Diode (PD) placed at various grid points. First, we obtain some {\emph{approximate}} received signal strengths (RSSs) fingerprints by capturing the peaks of power spectral density (PSD) of the received signals at each given grid point. Unlike the existing RSSs based algorithms, several representative machine learning approaches are adopted to train multiple classifiers based on these RSSs fingerprints. The multiple classifiers localization estimators outperform the classical RSS-based LED localization approaches in accuracy and robustness. To further improve the localization performance, two robust fusion localization algorithms, namely, grid independent least square (GI-LS) and grid dependent least square (GD-LS), are proposed to combine the outputs of these classifiers. We also use a singular value decomposition (SVD) based LS (LS-SVD) method to mitigate the numerical stability problem when the prediction matrix is singular. Experiments conducted on intensity modulated direct detection (IM/DD) systems have demonstrated the effectiveness of the proposed algorithms. The experimental results show that the probability of having mean square positioning error (MSPE) of less than 5cm achieved by GD-LS is improved by 93.03\% and 93.15\%, respectively, as compared to those by the RSS ratio (RSSR) and RSS matching methods with the FFT length of 2000.

Most existing fingerprints-based indoor localization approaches are based on some single fingerprints, such as received signal strength (RSS), channel impulse response (CIR), and signal subspace. However, the localization accuracy obtained by the single fingerprint approach is rather susceptible to the changing environment, multi-path, and non-line-of-sight (NLOS) propagation. Furthermore, building the fingerprints is a very time consuming process. In this paper, we propose a novel localization framework by Fusing A Group Of fingerprinTs (FAGOT) via multiple antennas for the indoor environment. We first build a GrOup Of Fingerprints (GOOF), which includes five different fingerprints, namely, RSS, covariance matrix, signal subspace, fractional low order moment, and fourth-order cumulant, which are obtained by different transformations of the received signals from multiple antennas in the offline stage. Then, we design a parallel GOOF multiple classifiers based on AdaBoost (GOOF-AdaBoost) to train each of these fingerprints in parallel as five strong multiple classifiers. In the online stage, we input the corresponding transformations of the real measurements into these strong classifiers to obtain independent decisions. Finally, we propose an efficient combination fusion algorithm, namely, MUltiple Classifiers mUltiple Samples (MUCUS) fusion algorithm to improve the accuracy of localization by combining the predictions of multiple classifiers with different samples. As compared with the single fingerprint approaches, the prediction probability of our proposed approach is improved significantly. The process for building fingerprints can also be reduced drastically. We demonstrate the feasibility and performance of the proposed algorithm through extensive simulations as well as via real experimental data using a Universal Software Radio Peripheral (USRP) platform with four antennas.