Global Artificial Intelligence in Retail Market is Expected to Grow From US$ 712.6 Million in 2016 to US$ 27,238.6 Inception of exponential technologies such as sensors, robotics, virtual reality, and artificial intelligence in the retail industry has enabled the retailers to enhance their interactions with consumers and transformed the way retail operations were performed. This change in the industry is prominently driven by the seismic shift in the shopping pattern of the consumers, and their preferences backed by demographic dividend across regions. The report focuses on an in-depth segmentation of this market based by retail format, technology, and application. The geographic segmentation of the report covers five major regions including; North Americas, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA) and South America (SA).

Mr. Smith Goes to Washington 1939 TCM Tue. 7 p.m. Mean Streets 1973 Cinemax Sun. 6 a.m. Batman Begins 2005 AMC Sun. Throw Momma From the Train 1987 EPIX Sun. Die Hard 1988 IFC Sun. I Know What You Did Last Summer 1997 Starz Tue. Gone in 60 Seconds 2000 CMT Wed. 8 p.m., Thur. Total Recall 1990 Encore Thur. 2 a.m. A Fish Called Wanda 1988 Encore Thur. 2 p.m., 9 p.m. The World Is Not Enough 1999 EPIX Sat. 4 p.m. Look Who's Talking 1989 OVA Sun. Die Hard With a Vengeance 1995 IFC Thur. Oil-platform workers, including an estranged couple, and a Navy SEAL make a startling deep-sea discovery. A clueless politician falls in love with a waitress whose erratic behavior is caused by a nail stuck in her head. After glimpsing his future, an ambitious politician battles the agents of Fate itself to be with the woman he loves. To help a friend, a suburban baby sitter drives into downtown Chicago with her two charges and a neighbor. Two teenage baby sitters and a group of children spend a wild night ...

England fans know the drill all too well - the national team heads into a major international football tournament having qualified with a near-perfect record. Hopes are high, but then… well, you know what happens - lacklustre performances or penalty shoot-out heartbreak, followed by an early flight home. So what really determines the success or failure of a team going into a major international football tournament like the World Cup? Is it a side's quality (class) or its recent performances (form)? Reality Check has teamed up with the BBC's statistics department to try to answer one of the biggest debates in football - how much does form matter? To do this, we built a computer program that predicts football results by analysing ratings data.

Brazilian startup Cobli has specialized in technological solutions for vehicle fleet monitoring and management. It is currently focusing on safety and refining a tool to identify driver behavioral patterns by analyzing data collected by a solar-powered tracker. The project is based on machine learning, an application of artificial intelligence, and had the support) from the São Paulo Research Foundation - FAPESP through its Innovative Research in Small Business Program (PIPE http://www.bv.fapesp.br/en/3). "The algorithm uses the data collected to establish a driving profile with more than 90% accuracy," says engineer Rodrigo Mourad, a partner and co-founder of Cobli. According to Mourad, in one or two weeks of use, the system can glean a sufficient amount of data - on speed, acceleration, braking and curve angles - to produce a profile of the driver's vehicle handling habits.

In an increasingly complex scenario for network management, a solution that allows configuration in more autonomous way with less intervention of the network manager is expected. This paper presents an evaluation of similarity functions that are necessary in the context of using a learning strategy for finding solutions. The learning approach considered is based on Case-Based Reasoning (CBR) and is applied to a network scenario where different Bandwidth Allocation Models (BAMs) behaviors are used and must be eventually switched looking for the best possible network operation. In this context, it is required to identify and configure an adequate similarity function that will be used in the learning process to recover similar solutions previously considered. This paper introduces the similarity functions, explains the relevant aspects of the learning process in which the similarity function plays a role and, finally, presents a proof of concept for a specific similarity function adopted. Results show that the similarity function was capable to get similar results from the existing use case database. As such, the use of similarity functions with CBR technique has proved to be potentially satisfactory for supporting BAM switching decisions mostly driven by the dynamics of input traffic profile.

The variety of pedestrians detectors proposed in recent years has encouraged some works to fuse pedestrian detectors to achieve a more accurate detection. The intuition behind is to combine the detectors based on its spatial consensus. We propose a novel method called Content-Based Spatial Consensus (CSBC), which, in addition to relying on spatial consensus, considers the content of the detection windows to learn a weighted-fusion of pedestrian detectors. The result is a reduction in false alarms and an enhancement in the detection. In this work, we also demonstrate that there is small influence of the feature used to learn the contents of the windows of each detector, which enables our method to be efficient even employing simple features. The CSBC overcomes state-of-the-art fusion methods in the ETH dataset and in the Caltech dataset. Particularly, our method is more efficient since fewer detectors are necessary to achieve expressive results.

Models for audio source separation usually operate on the magnitude spectrum, which ignores phase information and makes separation performance dependant on hyper-parameters for the spectral front-end. Therefore, we investigate end-to-end source separation in the time-domain, which allows modelling phase information and avoids fixed spectral transformations. Due to high sampling rates for audio, employing a long temporal input context on the sample level is difficult, but required for high quality separation results because of long-range temporal correlations. In this context, we propose the Wave-U-Net, an adaptation of the U-Net to the one-dimensional time domain, which repeatedly resamples feature maps to compute and combine features at different time scales. We introduce further architectural improvements, including an output layer that enforces source additivity, an upsampling technique and a context-aware prediction framework to reduce output artifacts. Experiments for singing voice separation indicate that our architecture yields a performance comparable to a state-of-the-art spectrogram-based U-Net architecture, given the same data. Finally, we reveal a problem with outliers in the currently used SDR evaluation metrics and suggest reporting rank-based statistics to alleviate this problem.

A Tesla's autopilot function was engaged in the minutes before a fiery crash that killed its driver in California earlier this year, according to a federal inquiry. In the roughly 20 minutes before the vehicle slammed into a barrier near Mountain View and burst into flames, the car's autopilot feature was in "continuous operation", the National Transportation Safety Board (NTSB) found in its initial investigation. During the critical 60 seconds leading up to the crash, the NTSB reported, the car's driver repeatedly placed his hands on the steering wheel. Tesla crashes into parked police car in Autopilot mode Wall Street blasts Elon Musk's'truly bizarre' Tesla earnings call Tesla faces labour investigation after allegation of injury undercount But six seconds before the accident, evidence suggests the driver had removed his hands from the steering wheel. The vehicle also accelerated in the final three seconds.

Robots appear to be in the middle of a gradual but persistent transition from automated tools that perform specific tasks to artificially intelligent entities that we interact with socially and emotionally. It's not at all clear where this is going to end up--people toss around the idea of robot companionship and even robot love with some frequency, for example. What hasn't been explored nearly as much is the idea of robots in a religious context. We've seen a few examples of robots assisting in religious tasks, but what if robots could take things a step farther, and become sacred objects, embodying divinity within a robot itself? At the ACM/IEEE International Conference on Human Robot Interaction (HRI) in March, Gabriele Trovato from Waseda University in Japan (with colleagues from Pontificia Universidad Católica del Perú) presented a paper taking a look at whether divine robots might be possible, and why it could be useful to develop such robots in the first place.

Food tech is a booming industry and one that has witnessed some seriously staggering innovations that have completely transformed the food business. From hassle-free order placement to quick deliveries to curated options, the food tech space has everything covered. We spoke with José Daniel Leal Avila, CEO, and Founder at Foodstamp.tech, a promising young food tech brand, to shed light on the trends in the food tech space and to share valuable advice for businesses looking to startup in the food tech space. We started Foodstamp while studying for a project that involved understanding the issues that new restaurants faced. At the time, coincidentally, one of our favorite burger joints went out of business and we were puzzled as to why that happened.