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.

NVIDIA Deep Learning Institute, together with SGInnovate, is hosting the practical Deep Learning Fundamentals training. In this full-day workshop, you will start with the basic concepts of deep learning and quickly move to learning how to solve real-word problems using deep learning. NVIDIA Deep Learning Institute Certified Instructors will blend lecture and hands-on, real-world exercises to explore how to solve the most challenging problems with deep learning. You will receive a Beginner Level certificate from Deep Learning Institute! Aik Beng, Ng is a Senior Solutions Architect, Deep Learning at NVIDIA APAC.

Artificial intelligence will play an important role in reshaping an array of major industries such as retail, manufacturing and healthcare. Leading senior executives told the 4th World Internet Conference in Wuzhen, eastern China, that rapid technological changes will transform companies and society. Robin Li, chief executive of Baidu, felt that in comparison with mobile internet technology, which revolutionised consumer services, artificial intelligence (AI) would have a far bigger influence on how companies ran their businesses. "For instance, Baidu is leveraging AI to help supermarkets better manage their supply of fresh food, by analysing and predicting which products are most popular," said Li, who runs China's largest search engine. He pointed out that such solutions had effectively reduced food waste and boosted profit growth at pilot stores.

Last week, Qualcomm announced the Snapdragon 845, which sends AI tasks to the most suitable cores. There's not a lot of difference between the three company's approaches -- it ultimately boils down to the level of access each company offers to developers, and how much power each setup consumes. Before we get into that though, let's figure out if an AI chip is really all that much different from existing CPUs. A term you'll hear a lot in the industry with reference to AI lately is "heterogeneous computing." It refers to systems that use multiple types of processors, each with specialized functions, to gain performance or save energy.

If nothing else, AI continues to climb the technology hype curve. It was impossible to read the news, browse the web, attend a conference, or even watch television without seeing a reference to how AI is making our lives better. Since Alan Turing declared "what we want is a machine that can learn from experience" in a 1947 lecture to the London Mathematical Society, the imaginations of computer scientists and engineers have run wild with visions of a computer that can answer questions on par with a human. Today, almost everyone in business is looking at how to leverage AI, and there is no shortage of vendors looking to capitalize on the trend. Venture Scanner currently tracks more than 2,000 AI startups that have received more than $26 billion in funding.

In cased you missed it, we got driven down an actual highway while wearing a VR headset, did cartwheels in a next-generation VR device and heard all about T-Mobile launching a TV service. That last one has nothing to do with VR. Its "world-scale" tracking is surprisingly solid. After fully unveiling the Vive Focus, HTC finally allowed lucky folks like Richard Lai to properly test out the six-degrees-of-freedom (6DoF) standalone VR headset. For the first time, you can actually walk around in VR without being tethered to a PC or confined to a fixed space.

With federal regulators poised to repeal net neutrality rules this week, your internet service provider would be allowed to speed up delivery of some online content to your home or phone. Whether those fast lanes are coming, and what they ultimately deliver for Americans, is unclear. The concept, known as paid prioritization, involves a telecommunications company charging an additional fee to transport a video stream or other content at a higher speed through its network. The fee would most likely come from deals struck with websites such as Netflix willing to pay for a competitive advantage over an online rival. Or the fee could be charged to a company providing services that require reliably fast connections, such as self-driving vehicles or remote health monitoring of people with serious illnesses.

ZDNet's Sandra Vogel posted a formal review of the Huawei Mate 10 Pro, giving it an outstanding 9/10 rating. I've been spending quality time with this business-ready powerhouse and think that the AI found in the camera is worth discussing in a bit more detail. Huawei's partnership with Leica has resulted in some fantastic cameras and performance that is tough to beat. DxOMark awarded the Mate 10 Pro it second highest overall score, 97, and best still image score, 100. Keep in mind, these scores are not scaled to 100.