Collaborating Authors


Amazon tests its "Just Walk Out" shopping tech at two Whole Foods stores


Amazon on Wednesday said it's bringing its "Just Walk Out" shopping technology to two Whole Foods stores, giving it an opportunity to test the cashierless payment system in a larger retail space. Next year, with the system in place at stores in Washington, DC, and Sherman Oaks, Calif., shoppers will have the option to skip the checkout line. Amazon acquired Whole Foods for $13.7 billion in 2017. Meanwhile, the tech giant first introduced the "Just Walk Out" system at its first Amazon Go store in 2016. The system uses computer vision, sensor fusion and deep learning to eliminate checkout lines.

The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

Europe's answer to Amazon Go


Helen, a stay-at-home mum living in the north of Lisbon, has just done a weekly grocery shop. But instead of paying for her items at the cash register, she's walked straight out of the store without going to a checkout. The 34-year-old is one of the early customers of Europe's first autonomous store, which uses computer vision and machine learning to enable customers to shop without queuing, paying with a cashier or even getting their wallet or phone out. "I'm glad this store opened in my neighbourhood," she told Sifted on a recent visit. "It's so much more convenient to shop when you have a baby stroller. The store, which is a partnership between technology provider Sensei and the physical retailer Continente, is an early example of what Sensei hopes will soon be used by retailers across the continent. It aims to be Europe's answer to Amazon Go, the checkoutless shop created by Amazon which launched in London this March after testing the water in the US over the past three years. Sensei's bet is that ultimately all shops will be compelled to adopt this technology in pursuit of improved customer experience. "There's no hassle, no friction in the experience: if you forgot to buy water, you just go in, buy your water, come out –– it's super fast!

Amazon opens till-free grocery store in London - the online retailer's first physical store outside the US


Amazon will open its first physical store outside the US today - but the shopping experience will be a bit different. Amazon Fresh is in Ealing, London, and it is much smaller than a supermarket. It will sell prepared meals, some groceries, and Amazon devices, as well as having a counter for collecting and returning online orders. Shoppers will scan a smartphone QR code to open the store's gates and their purchases will be tallied using ceiling cameras and shelf weight sensors. The technology can also register when someone has put an item back on the shelf, if they change their mind, for instance.

Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness Machine Learning

Due to accessible big data collections from consumers, products, and stores, advanced sales forecasting capabilities have drawn great attention from many companies especially in the retail business because of its importance in decision making. Improvement of the forecasting accuracy, even by a small percentage, may have a substantial impact on companies' production and financial planning, marketing strategies, inventory controls, supply chain management, and eventually stock prices. Specifically, our research goal is to forecast the sales of each product in each store in the near future. Motivated by tensor factorization methodologies for personalized context-aware recommender systems, we propose a novel approach called the Advanced Temporal Latent-factor Approach to Sales forecasting (ATLAS), which achieves accurate and individualized prediction for sales by building a single tensor-factorization model across multiple stores and products. Our contribution is a combination of: tensor framework (to leverage information across stores and products), a new regularization function (to incorporate demand dynamics), and extrapolation of tensor into future time periods using state-of-the-art statistical (seasonal auto-regressive integrated moving-average models) and machine-learning (recurrent neural networks) models. The advantages of ATLAS are demonstrated on eight product category datasets collected by the Information Resource, Inc., where a total of 165 million weekly sales transactions from more than 1,500 grocery stores over 15,560 products are analyzed.

The Future Of Work Now: AutoML At 84.51 And Kroger


One of the most frequently-used phrases at business events these days is "the future of work." It's increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they're already present in many organizations for many different jobs. The job and incumbents described below are an example of this phenomenon. Steve Miller of Singapore Management University and I are collaborating on these stories.

Artificial Intelligence: Research Impact on Key Industries; the Upper-Rhine Artificial Intelligence Symposium (UR-AI 2020) Artificial Intelligence

The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Inteeligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

Kroger enlists artificial intelligence to cut down self-checkout errors


The Kroger Co. plans to roll out Everseen's Visual AI technology chainwide to detect and reduce customer errors at self-checkout stations. Ireland-based Everseen said its artificial intelligence and machine learning platform began deployment in Kroger stores in March and is slated to be installed at 2,500 stores in the coming months. The Visual AI platform watches video in real time to recognize regular processes and "intelligently" step in whenever something is amiss, Evergreen explained. For Kroger shoppers, the technology flags errors occasionally experienced at self-checkout and enables customers to self-correct or, if they're unable to rectify the problem, an associate is summoned to help. For example, if a customer scanning groceries at the self-checkout kiosk has an item that doesn't scan properly, Evergreen's solution identifies the non-scan incident and alerts a store associate via a mobile device to intervene and rescan the item.

GPT-3 Creative Fiction


What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.

AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types Artificial Intelligence

Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question answering, and it is natural to wonder if a KG can contain information about products offered at online retail sites. There have been several successful examples of generic KGs, but organizing information about products poses many additional challenges, including sparsity and noise of structured data for products, complexity of the domain with millions of product types and thousands of attributes, heterogeneity across large number of categories, as well as large and constantly growing number of products. We describe AutoKnow, our automatic (self-driving) system that addresses these challenges. The system includes a suite of novel techniques for taxonomy construction, product property identification, knowledge extraction, anomaly detection, and synonym discovery. AutoKnow is (a) automatic, requiring little human intervention, (b) multi-scalable, scalable in multiple dimensions (many domains, many products, and many attributes), and (c) integrative, exploiting rich customer behavior logs. AutoKnow has been operational in collecting product knowledge for over 11K product types.