Looking at the benefits data can provide, the smart city concept is incomplete without its use. Today, smart cities are unlocking the potential of data in terms of preventing and predicting crime and terrorism. Not to mention, the two elements can bring devastating changes to life in cities. According to sources, today, the most successful smart cities are those that are utilising the gathered data to predict and prevent crime and terrorism. Along the way, they are strengthening the connected infrastructure that is key to establishing a secure urban environment.

The hardware segment is projected to witness the highest growth rate during the forecast period. Algorithm Segment Review Based on algorithm, it is classified into deep learning, querying method, natural language processing, and context aware processing. The deep learning segment is projected to grow at the highest CAGR during the forecast period, owing to increase in use of signal reduction, data mining, and image recognition, which are integral components of most AI protocols. Global AI in healthcare Market: Key Geographic Segment Based on region, the AI in healthcare market is divided into North America, Europe, Asia-Pacific, and LAMEA. North America accounted for the largest market share in the AI in healthcare market in 2016, and is expected to retain its dominance throughout the forecast period.

Demand forecasting, for all of its importance in business, has had a mixed run in retail. Even in fairly predictable categories in general merchandise, it's far too easy for retailers to start the current year's plan by loading in all the assumptions made from the year before, rather than starting clean with a new demand forecast. In fact, according to RSR Research's benchmark, even though 68% of better-performing retailers ("Retail Winners") and 53% of all other retailers believe that starting with a demand forecast as the basis for the next year's plan is very valuable, only 49% of Winners and 29% of their peers actually do so today. Part of the reason why is because forecast error in retail is high, as high as 32% according to some estimates. And, the more sporadic or non-repeatable the demand is, the more forecast error occurs – thus, grocery retailers operating a replenishment strategy have a far easier time using a forecast than a fashion retailer introducing a high-fashion item that responds to a new trend. Additionally, not all products face the same demand profiles.

As part of Data Science Nigeria's (DSN) vision to build a world-class Artificial Intelligence (AI) ecosystem, it has released a first-of-its-kind AI …

Model predictive control (MPC) has become one of the well-established modern control methods for three-phase inverters with an output LC filter, where a high-quality voltage with low total harmonic distortion (THD) is needed. Though it is an intuitive controller easy to understand and implement, it has the significant disadvantage of requiring a large number of online calculations for solving the optimization problem. On the other hand, the application of model-free approaches such as artificial neural network-based (ANN-based) approaches is currently growing rapidly in the area of power electronics and drives. This paper presents a new control scheme for a two-level converter based on combining MPC with feed-forward ANN, with the aim of getting lower THD and improving the steady and dynamic performance of the system for different types of loads. First, MPC is used, as an expert, in the training phase to generate data required for training the proposed neural network. Then, once the neural network is fine-tuned, it can be successfully used online for voltage tracking purpose, without the need of using MPC. The proposed ANN-based control strategy is validated through simulation, using MATLAB/Simulink tools, taking into account different loads conditions. Moreover, the performance of the ANN-based controller is evaluated, on several samples of linear and non-linear loads under various operating conditions, and compared to that of MPC, demonstrating the excellent steady-state and dynamic performance of the proposed ANN-based control strategy.

Traditional clustering algorithms such as K-means rely heavily on the nature of the chosen metric or data representation. To get meaningful clusters, these representations need to be tailored to the downstream task (e.g. cluster photos by object category, cluster faces by identity). Therefore, we frame clustering as a meta-learning task, few-shot clustering, which allows us to specify how to cluster the data at the meta-training level, despite the clustering algorithm itself being unsupervised. We propose Centroid Networks, a simple and efficient few-shot clustering method based on learning representations which are tailored both to the task to solve and to its internal clustering module. We also introduce unsupervised few-shot classification, which is conceptually similar to few-shot clustering, but is strictly harder than supervised* few-shot classification and therefore allows direct comparison with existing supervised few-shot classification methods. On Omniglot and miniImageNet, our method achieves accuracy competitive with popular supervised few-shot classification algorithms, despite using *no labels* from the support set. We also show performance competitive with state-of-the-art learning-to-cluster methods.

Statistical uncertainties are rarely incorporated in machine learning algorithms, especially for anomaly detection. Here we present the Bayesian Anomaly Detection And Classification (BADAC) formalism, which provides a unified statistical approach to classification and anomaly detection within a hierarchical Bayesian framework. BADAC deals with uncertainties by marginalising over the unknown, true, value of the data. Using simulated data with Gaussian noise, BADAC is shown to be superior to standard algorithms in both classification and anomaly detection performance in the presence of uncertainties, though with significantly increased computational cost. Additionally, BADAC provides well-calibrated classification probabilities, valuable for use in scientific pipelines. We show that BADAC can work in online mode and is fairly robust to model errors, which can be diagnosed through model-selection methods. In addition it can perform unsupervised new class detection and can naturally be extended to search for anomalous subsets of data. BADAC is therefore ideal where computational cost is not a limiting factor and statistical rigour is important. We discuss approximations to speed up BADAC, such as the use of Gaussian processes, and finally introduce a new metric, the Rank-Weighted Score (RWS), that is particularly suited to evaluating the ability of algorithms to detect anomalies.

Frank n Raf Market Research LLP As per Frank n Raf latest Research report, The global industrial robotics market size is expected to reach USD 41.23 billion by 2020., increasing at a CAGR of 7.0% through the forecast period. The accelerated expansion of the automotive industry worldwide and increasing adoption of robotics in the non-automotive industry including chemicals, food & beverage, rubber & plastics, and electronics/electrical are stoking the growth of the market. Companies executing industrial robots are frequently realizing substantial financial advantages, which is pointing to a surge in installation of robots in modern manufacturing plants. Combination of robots with production processes help increase productivity, reduces overheads, contributes a high degree of flexibility, improves quality, and reduces waste to a large range as compared to the outcome of manual labor, which consequently drives the market. Industrial robots have been effective for the formation of a new ecosystem distinguished by rewarding, lucrative, and high-paying jobs.

The next big thing in the social sector has officially arrived. Machine learning is now at the center of international conferences, $25 million dollar funding competitions, fellowships at prestigious universities, and Davos-launched initiatives. Yet amidst all of the hype, it can be difficult to understand which social sector problems machine learning is best positioned to solve, how organizations can practically use it to enhance their impact, and what kind of sector-wide investments can enable the ambitious use of it for social good in the future. Our work at IDinsight, a nonprofit that uses data and evidence to help leaders in the social sector combat poverty, and the work of other organizations offer some insights into these questions. Machine learning uses data (usually a lot) and statistical algorithms to predict something unknown.

WINTER PARK, Fla.--(BUSINESS WIRE)--Feb 20, 2019--COPC Inc., a global consulting firm that helps companies improve operations to transform the customer experience, and Execs In The Know, a global community of customer experience professionals, have announced the release of the 2018 Corporate Edition of the Customer Experience Management Benchmark (CXMB) Series. The report, The CX Journey: Understanding Corporate Strategies and Best Practices, provides customer experience management insights from the corporate perspective. A key finding is that since 2017, companies have dramatically increased their use of artificial intelligence (AI)-powered solutions for customer service. "Our new corporate report shows that companies see tremendous potential in AI-powered solutions for customer care, both in applications that are customer-facing and in those that assist call center agents with their work. However, we also know from previous research that customers want a quick and easy way out of any AI-powered solution to reach a live person. Our findings overwhelmingly show that companies are keenly aware of this necessity in any customer-facing application. And while customers still want that personal interaction, we think that AI-powered solutions will find their appropriate place in the service journey," said Kyle Kennedy, president and chief operating officer, COPC Inc.