From smartphones to air fryers, several popular gadgets have been found to'spy' on users. Now, it seems that not even the cinema is safe. British cinema chain, Vue, is quietly using artificial intelligence (AI) to track viewer habits and boost admissions. Affecting all of its 93 cinemas in the UK and Ireland, the system identifies the best locations and times to show films in order to maximise ticket sales. So if you saw'Gladiator II' or'Paddington in Peru' at a Vue cinema over the weekend, there's a chance your viewing habits have been used to inform future screening times.

Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. In 2013, after a decade in Silicon Valley, neuroscientist/designer duo Anand Chandrasekaran and Ashwini Asokan started Mad Street Den with the aim of taking computer vision technology it beyond the realm of scientific research. Today, through its Vue.ai business unit, the company helps retailers such as Diesel, Off-White and Tata CLiQ grow their businesses by reducing operational costs and increasing revenue through automation, and by creating personalized customer experiences. "Think of Vue.ai as a vertically integrated stack for the retail industry," said Asokan, who in addition to having co-founded of Mad Street Den serves as CEO of Vue.ai. "Today, a retailer has to shop across tens of vendors to avail a CDP, a recommendation system, a search engine, a styling and cross-sell solution, a marketing automation engine, A/B testing software, workflow automation -- the list is absolutely endless."

This is the final part of our 4-part series! In the previous 3, we covered the theory of collaborative filtering, how to build a Flask API, and how to deploy the API on AWS ECS. In this post, we'll build a simple Vue.js frontend that aims to simplify the movie recommendations as much as possible. Hence, we'll only ask the user to enter their favourite movie and recommend movies that are similar to it. Key functionalities of this project are the auto-search function for finding movie titles from MovieLens dataset and leveraging open-source scrapped movie posters to display recommended movies by the backend API.

If you want to land a great JavaScript job or catch up on important tech for 2020 and important technologies in the new decade, this post is for you. The point of this post is not to tell you which tech stacks and frameworks are the "best" or most loved or most popular -- but to shed some light on which ones give you the best odds of landing a great job in 2020 and beyond. We're going to look at data from a variety of sources: None of these metrics are perfect, but for our purposes, the npm download counts and job postings get the most weight, and when we look at the metrics in aggregate, they paint a clear and consistent picture of the JavaScript technology landscape and trends. Is it a good idea to select a framework to learn based on these metrics? That depends on your goal. Since we have a clear goal -- learning ROI for job seekers -- that makes the task much easier than trying to tell you what's best for everyone.

TATA CLiQ is India's leading e-commerce marketplace for all things fashion and is a pioneer of omnichannel retail. With innovations like'CliQ & PiQ', 'QuiQ Exchange' and Que Magazine, TATA CLiQ is a role model for e-commerce marketplaces across the globe. Housing over 1000 brands, the portal's focus is on sharing brand stories while helping their customers make informed choices and serving them with the best'phygital' experience. TATA CLiQ achieves this by leveraging the power of Artificial Intelligence. After two years of partnering with Vue.ai to help customers explore the platform better with their AI-powered personalization suite, TATA CLiQ now considers Vue.ai to be an integral part of the platform.

A maverick is a person of incredible vision, someone who challenges the norm and forces people to think beyond the ordinary. YourStory is going behind the scenes to uncover the inspirations and secrets of the ultimate maverick in the business world: the entrepreneur. For what is innovation but doing things differently? And as a woman in tech who doesn't code, Ashwini Asokan certainly does not all the stereotypes. At Mad Street Den, the AI startup which she launched in 2013 in Chennai along with her husband Anand Chandrasekaran, Ashwini fills in the shoes of the CEO (with her husband as the CTO).

We can say without a shadow of doubt that artificial intelligence (AI)--the ability of computers and other machines to work intelligently without human intervention--is bound to impact the world in stellar ways. When and where this impact will be felt can't be predicted, but one thing is certain: AI will disrupt traditional employment patterns. That said, AI is helping kill stereotypes too. Like with every other field, women in tech, too, faced the challenge of making a name for themselves in an industry full of men. But the space is now changing.

AI in commerce is a fast-growing -- and highly lucrative -- industry. A recent report published by Juniper Research predicts that global retail spending on AI and machine learning will reach $7.3 billion per year by 2022, up from an estimated $2 billion in 2018. Stores with and without brick-and-mortar presences cite cost savings, increased productivity, and more informed business decision-making as the leading adoption drivers; that's part of the reason why retail AI deployments experienced a sevenfold increase from 2016, according to Capgemini. Vue.ai is looking to cash in on the trend, and it's raising venture capital to lay the stage for its next round of growth. The Fremont, California-based subbrand of AI startup Mad Street Den today announced that it's raised $17 million in a series B financing round led by Falcon Edge Capital, with participation from Sequoia Capital India and KDDI Innovation Fund's Global Brain.

In this paper, a novel joint transmit power and resource allocation approach for enabling ultra-reliable low-latency communication (URLLC) in vehicular networks is proposed. The objective is to minimize the network-wide power consumption of vehicular users (VUEs) while ensuring high reliability in terms of probabilistic queuing delays. In particular, a reliability measure is defined to characterize extreme events (i.e., when vehicles' queue lengths exceed a predefined threshold with non-negligible probability) using extreme value theory (EVT). Leveraging principles from federated learning (FL), the distribution of these extreme events corresponding to the tail distribution of queues is estimated by VUEs in a decentralized manner. Finally, Lyapunov optimization is used to find the joint transmit power and resource allocation policies for each VUE in a distributed manner. The proposed solution is validated via extensive simulations using a Manhattan mobility model. It is shown that FL enables the proposed distributed method to estimate the tail distribution of queues with an accuracy that is very close to a centralized solution with up to 79\% reductions in the amount of data that need to be exchanged. Furthermore, the proposed method yields up to 60\% reductions of VUEs with large queue lengths, without an additional power consumption, compared to an average queue-based baseline. Compared to systems with fixed power consumption and focusing on queue stability while minimizing average power consumption, the reduction in extreme events of the proposed method is about two orders of magnitude.

Online fashion tech startup Vue.ai is selling technology that analyzes pieces of clothing and automatically generates an image of the garment on a person of any size, shape, or wearing any kind of shoes. Neural networks, the technology that GANs are built on, are an approximation of how our brain works: Millions of tiny, distributed neurons, processing data and passing them along to the next neuron. These networks are trained on thousands of images, and the neurons learn to distinguish different kinds of elbows, hips, and colors. Through trial and error, the two engineers figured out exactly the right neurons to alter the size, weight, or shape of a person, or the hardest part, the shoes they're wearing.