We study the problem of modeling purchase of multiple items and utilizing it to display optimized recommendations, which is a central problem for online e-commerce platforms. Rich personalized modeling of users and fast computation of optimal products to display given these models can lead to significantly higher revenues and simultaneously enhance the end user experience. We present a parsimonious multi-purchase family of choice models called the BundleMVL-K family, and develop a binary search based iterative strategy that efficiently computes optimized recommendations for this model. This is one of the first attempts at operationalizing multi-purchase class of choice models. We characterize structural properties of the optimal solution, which allow one to decide if a product is part of the optimal assortment in constant time, reducing the size of the instance that needs to be solved computationally. We also establish the hardness of computing optimal recommendation sets. We show one of the first quantitative links between modeling multiple purchase behavior and revenue gains. The efficacy of our modeling and optimization techniques compared to competing solutions is shown using several real world datasets on multiple metrics such as model fitness, expected revenue gains and run-time reductions. The benefit of taking multiple purchases into account is observed to be $6-8\%$ in relative terms for the Ta Feng and UCI shopping datasets when compared to the MNL model for instances with $\sim 1500$ products. Additionally, across $8$ real world datasets, the test log-likelihood fits of our models are on average $17\%$ better in relative terms. The simplicity of our models and the iterative nature of our optimization technique allows practitioners meet stringent computational constraints while increasing their revenues in practical recommendation applications at scale.

Over the last few years, there has been lots of conversation around how AI is going to affect our lives, and the economy. A quick google search returns multiple studies. For example, PWC says that the UK's economy GDP will increase by at least 5% as the result of AI. A report by McKinsey says that the annual productivity will be increasing by 1.2% each year. There are also some books that have started to come out on that subject. I am also talking about this topic in my upcoming book called Uncertainty.

Massachusetts-based supermarket chain Market Basket has announced plans for a second Rhode Island store. The 89,000-square-foot store in Warwick expected to open next year will be located at a site that was previously home to a Sam's Club and later an At Home store, according to a statement from Mayor Joseph Solomon and Market Basket President and CEO Arthur T. Demoulas. "Our city's central location in the state, combined with our growing business climate, continue to make Warwick a natural choice for multiple companies looking to expand their reach in the Ocean State," Solomon said in a statement. Privately-owned Market Basket currently has 81 stores in Massachusetts, New Hampshire and Maine. The company in March announced plans for a store in Johnston.

Amazon SageMaker is a fully managed service that provides developers and data scientists the ability to quickly build, train, and deploy machine learning (ML) models. Tens of thousands of customers, including Intuit, Voodoo, ADP, Cerner, Dow Jones, and Thomson Reuters, use Amazon SageMaker to remove the heavy lifting from the ML process. With Amazon SageMaker, you can deploy your ML models on hosted endpoints and get inference results in real time. You can easily view the performance metrics for your endpoints in Amazon CloudWatch, enable autoscaling to automatically scale endpoints based on traffic, and update your models in production without losing any availability. In many cases, such as e-commerce applications, offline model evaluation isn't sufficient, and you need to A/B test models in production before making the decision of updating models.

Amazon.com Inc. said it is halting law-enforcement use of its facial-recognition software, adding its voice to a growing chorus of companies, lawmakers and civil rights advocates calling for greater regulation of the surveillance technology amid widespread concern about its potential for racial bias. Facial-recognition technology has long been criticized for perceived bias, with studies showing most algorithms are more prone to misidentifying African-Americans' and other minorities' faces than Caucasians'.

Machine learning has moved from the stuff of science fiction to a staple of modern business, as organizations across nearly every industry vertical implement ML technologies. Doctors are using machine learning to more accurately diagnosis and treat their patients, retailers are using ML to get the right merchandise to the right stores at the right time, and researchers are utilizing the technology to develop effective new medicines. That is just a sliver of the use cases emerging, as all sectors -- from energy and utilities, to travel and hospitality, to manufacturing to logistics -- and the various functions within any given organization increasingly put machine learning to work. Machine learning is a subset of artificial intelligence, where computers use algorithms to learn from data, allowing the machines to identify patterns -- a capability that organizations can put to use in multiple ways. Experts said machine learning enables organizations to perform tasks on a scale and scope previously impossible to achieve.

A group of anonymous Walmart workers have raised concerns about the anti-shoplifting technology used to monitor the company's self-checkout kiosks. A group that calls themselves'Concerned Home Office Associates' has circulated a video documenting the system's flaws, including frequent failures to identify unscanned items, and incorrectly identifying personal items potentially shoplifted. In an email sent to company management at Walmart's headquarters in Bentonville, Arkansas, the group claims to be'past their breaking point,' saying the system's frequent false positives are irritating customers and putting workers at greater risk of COVID-19 exposure by unnecessarily having to verify customer's purchases at unsafe distances. An anonymous group of Walmart employees have raised concerns about anti-theft technology used at self-checkout kiosks, saying it's'a fake AI that just pretends to safeguard' 'It's like a noisy tech, a fake AI that just pretends to safeguard,' one of the Walmart employees, who asked to remain anonymous, told Wired. The system was originally designed by Everseen--an artificial intelligence and technology firm based in Cork, Ireland--and relies on overhead cameras, or'digital eyes,' that film customers as they scan objects into the register.

The landscape of traditional retail is experiencing a seismic shift. A rapidly evolving competitive environment, a global move towards digital shopping, and the ever-changing sentiments of highly informed buyers are forcing a new perspective in the industry. This competitive situation is forcing traditional retailers to innovate and adopt accelerated analytics, robotics, and deep learning. At Drishtic we are focused on improving the intelligence at each retail store by leveraging the data from Video cameras already deployed in the stores.

In a series of papers scheduled to be presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Amazon researchers propose complementary AI algorithms that could form the foundation of an assistant that helps customers shop for clothes. One lets people fine-tune search queries by describing variations on a product image, while another suggests products that go with items a customer has already selected. Meanwhile, a third synthesizes an image of a model wearing clothes from different product pages to demonstrate how items work together as an outfit. Amazon already leverages AI to power Style by Alexa, a feature of the Amazon Shopping app that suggests, compares, and rates apparel using algorithms and human curation. With style recommendations and programs like Prime Wardrobe, which allows users to try on clothes and return what they don't want to buy, the retailer is vying for a larger slice of sales in a declining apparel market while surfacing products that customers might not normally choose.

This paper describes the application of reinforcement learning (RL) to multi-product inventory management in supply chains. The problem description and solution are both adapted from a real-world business solution. The novelty of this problem with respect to supply chain literature is (i) we consider concurrent inventory management of a large number (50 to 1000) of products with shared capacity, (ii) we consider a multi-node supply chain consisting of a warehouse which supplies three stores, (iii) the warehouse, stores, and transportation from warehouse to stores have finite capacities, (iv) warehouse and store replenishment happen at different time scales and with realistic time lags, and (v) demand for products at the stores is stochastic. We describe a novel formulation in a multi-agent (hierarchical) reinforcement learning framework that can be used for parallelised decision-making, and use the advantage actor critic (A2C) algorithm with quantised action spaces to solve the problem. Experiments show that the proposed approach is able to handle a multi-objective reward comprised of maximising product sales and minimising wastage of perishable products.