A determinantal point process (DPP) on a collection of $M$ items is a model, parameterized by a symmetric kernel matrix, that assigns a probability to every subset of those items. Recent work shows that removing the kernel symmetry constraint, yielding nonsymmetric DPPs (NDPPs), can lead to significant predictive performance gains for machine learning applications. However, existing work leaves open the question of scalable NDPP sampling. There is only one known DPP sampling algorithm, based on Cholesky decomposition, that can directly apply to NDPPs as well. Unfortunately, its runtime is cubic in $M$, and thus does not scale to large item collections. In this work, we first note that this algorithm can be transformed into a linear-time one for kernels with low-rank structure. Furthermore, we develop a scalable sublinear-time rejection sampling algorithm by constructing a novel proposal distribution. Additionally, we show that imposing certain structural constraints on the NDPP kernel enables us to bound the rejection rate in a way that depends only on the kernel rank. In our experiments we compare the speed of all of these samplers for a variety of real-world tasks.

At the start of the pandemic, many consumers began doing something they'd never done before: ordering groceries online. For people worried about getting sick, buying food online seemed safer than a trip inside a supermarket. Many grocers made it easy for shoppers to pick up their items curbside at appointed times. The phenomenon became so widespread, in fact, that grocery chains' call centers quickly became inundated with customers checking in on orders. "The call center volume at every grocer spiked," Karl Haller, partner at IBM's Consumer Industry Center of Competence, recalled in an interview.

When humans and machines work as one, will human values always triumph? If a crate of products is delivered to a store and left in the back room rather than unpacked and put on the shelf, an all-too-common problem in retail, the system records a decline in sales of the product. A reasonable analysis of the data assumes that demand for the product has declined and the product is either dropped by the retailer or reorders are reduced. Now along comes artificial intelligence (AI) and, using data, cameras or both, realizes that demand didn't drop, there's an operational problem that's causing sales to decline. If the system is a full suite of AI software, according to Bill Inzeo, Global Retail Technology Strategist of Zebra Technologies ZBRA, it will create and prioritize a list of tasks that need to be accomplished in the store and the left-behind crate problem will get addressed. That's one small example of how AI works; it finds problems and figures out how to solve them.

Fashion label Love, Bonito is using artificial intelligence and data analytics to fuel its expansion in Hong Kong and Japan as it battles regional rivals for space in Asian women's wardrobes.

"Ocado Technology, powering the future of retail through sustained disruptive technology innovation..." Ocado Technology is putting the world's retailers online using the cloud, robotics, AI, and IoT. We develop the innovative software and systems that power Ocado.com, the world's largest online-only grocery retailer as well as the global'Ocado Smart Platform'. With everything from websites to fully autonomous warehouse that we design in-house, our employees need to be specialists in a wide range of technologies to help drive our business. We champion a value-led culture to get our teams working at their very best and to help create a collaborative working environment that our people love. Core values of Trust, Autonomy, Craftsmanship, Collaboration and Learn Fast help drive our innovative culture.

This is the second post in the two-part series on how Tyson Foods Inc., is using computer vision applications at the edge to automate industrial processes inside their meat processing plants. In Part 1, we discussed an inventory counting application at packaging lines built with Amazon SageMaker and AWS Panorama . In this post, we discuss a vision-based anomaly detection solution at the edge for predictive maintenance of industrial equipment. Operational excellence is a key priority at Tyson Foods. Predictive maintenance is an essential asset for achieving this objective by continuously improving overall equipment effectiveness (OEE).

A casino is a location where specific forms of gambling are permitted. They've usually developed around or next to hotels, resorts, restaurants, retail stores, cruise ships, and other tourist destinations. A Lottery Retailer, on the other hand, is a person, organization, or corporate entity that has been contracted by the Lottery to sell Lottery items to the general public. In this sense, email marketing can be particularly effective if you run a gaming or casino business and want to advertise it online. It's a marketing technique that can assist you and your company in achieving incredible outcomes.

If you made the resolution to tidy up more regularly in 2022, a robot vacuum can help with that. And for those that hate cleaning, investing in a robot vacuum with self-emptying functionality can make it so you rarely have to interact with the machine. Two of iRobot's higher-end models with clean bases are on sale at Wellbots right now when you use the code ENGADGET250 at checkout -- both the Roomba s9 and the Roomba j7 will be $250 off, bringing them down to $850 and $600, respectively. Those are great deals, especially considering the prices are better than we saw during Cyber Monday at the end of last year. The Roomba j7 is the latest robo-vac from iRobot and it has new AI-driven computer vision technology the helps it detect objects and move around them as it cleans.

The Amazon Machine Learning Solutions Lab (MLSL) recently created a tool for annotating text with named-entity recognition (NER) and relationship labels using Amazon SageMaker Ground Truth. Annotators use this tool to label text with named entities and link their relationships, thereby building a dataset for training state-of-the-art natural language processing (NLP) machine learning (ML) models. Most importantly, this is now publicly available to all AWS customers. Booking.com is one of the world's leading online travel platforms. Understanding what customers are saying about the company's 28 million property listings on the platform is essential for maintaining a top-notch customer experience.

The chatbot revolution is upon us, and it's changing the way we interact with companies and brands. In fact, Servion predicts that, by 2025, AI will power 95% of all customer interactions. It's only a matter of time before the majority of people use chatbots for their business and social needs. The statistics show that it's not just a fad, but a serious business opportunity that's already being adopted by the biggest names in the industry. "I think chatbots and voicebots may become the future of commerce, as it relates to Gen Z." -- Tiffany Zhong, Founder & CEO of Zebra Intelligence Are you aware of these crucial chatbot statistics to have a better understanding of their future?