Covariant, an AI robotics company, has announced it has raised an additional $75 million in Series C funds, bringing its total funding to $222 million. Returning investors Radical Ventures and Index Ventures co-led the round, which also saw additional funding from returning investors Canada Pension Plan Investment Board and Amplify Partners. The round also welcomed new investors Gates Frontier Holdings, AIX Ventures, and Northgate Capital. The funding will be used to ensure today's leading retailers and their logistics providers are able to deploy robotic picking quickly and without disruption to their current operations, Covariant stated. This comes at a time when retail executives are eager to invest in AI-powered robotic automation: according to a Covariant-led research survey from February 2023, more than 80% of retail leaders see automation as a key solution for navigating operational uncertainty in an unpredictable marketplace – and 98% plan to further invest in AI Robotics in 2023 despite current economic conditions.

Customers shopping for a used car can sometimes feel overwhelmed digging through countless specs and reviews, but CarMax, the largest used car retailer in the U.S., is making it easier for customers to find the most useful information. Thanks to powerful AI language models, potential buyers can now see summaries of customer reviews for every make, model and year of vehicle that CarMax sells, about 5,000 combinations in a vast inventory of approximately 45,000 cars. The summaries provide easy-to-read takeaways from real customer reviews: whether it's a great family car, how comfortable the ride is or if there's enough space to pack for weekend adventures. CarMax has also used the models to create new website content that allows customers to easily see what's new for each version of a car, helping them decide whether new features are worth splurging on. CarMax generated the massive amount of original content in just a few months -- a rate previously impossible -- with powerful GPT-3 natural language models built by the company OpenAI.

Playing as Kurt, the sole human employee of an Amazon-like online retailer called Jüngle, you spend your days keeping pace with an army of robotic drones as they sort millions of packages for delivery. Then an activist group wrangles you into a scheme to bring down the giant corporation, whereupon both Kurt's world and the game's central premise begin to fall apart. Initially, The Last Worker is built around a light simulation of Kurt's daily routine. Using a hovering cart, you must locate assigned packages among the endless shelving units, and either transport them to a delivery chute or send them for recycling. Packages vary in size, weight, and condition, all of which must be checked before dispatch.

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We study an online joint assortment-inventory optimization problem, in which we assume that the choice behavior of each customer follows the Multinomial Logit (MNL) choice model, and the attraction parameters are unknown a priori. The retailer makes periodic assortment and inventory decisions to dynamically learn from the realized demands about the attraction parameters while maximizing the expected total profit over time. In this paper, we propose a novel algorithm that can effectively balance the exploration and exploitation in the online decision-making of assortment and inventory. Our algorithm builds on a new estimator for the MNL attraction parameters, a novel approach to incentivize exploration by adaptively tuning certain known and unknown parameters, and an optimization oracle to static single-cycle assortment-inventory planning problems with given parameters. We establish a regret upper bound for our algorithm and a lower bound for the online joint assortment-inventory optimization problem, suggesting that our algorithm achieves nearly optimal regret rate, provided that the static optimization oracle is exact. Then we incorporate more practical approximate static optimization oracles into our algorithm, and bound from above the impact of static optimization errors on the regret of our algorithm. At last, we perform numerical studies to demonstrate the effectiveness of our proposed algorithm.

Tele-medicine has recently gained popularity in healthcare [9]. Digital sessions reduce preparation and travel time for patients and has been found to improve patient outcomes and reduce hospital visits [3]. Other digital solutions now allow treatments to occur in the patients home. Remote physio therapy has been tested where patients meet with a physio therapist, via tele-conference, to view live demonstratations ofcorrect exercise techniques, and then access a database of exercises with reference images and videos to follow while performing rehabilitation exercises without remote human supervision [11]. The digital skills divide among older adults can limit their access to health services, as well as other services that rely on digital technology [5]. This has the potential to exclude them from the ever-increasing number of services and advancements such as those mentioned above. Digital technology has been found to be empowering for elderly users when they are designed with the elderly in mind [2], specifically by facilitating daily activities [6]. Many physical challenging life factors, such as reduced mobility and social contact, can be addressed through the use of digital technology, motivating many programs aimed at increasing digital literacy in the elderly population such as [8]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.

Welcome to the 21st edition of the AWS Serverless ICYMI (in case you missed it) quarterly recap. Every quarter, we share all the most recent product launches, feature enhancements, blog posts, webinars, live streams, and other interesting things that you might have missed! In case you missed our last ICYMI, check out what happened last quarter here. Artificial intelligence (AI) technologies, ChatGPT, and DALL-E are creating significant interest in the industry at the moment. Find out how to integrate serverless services with ChatGPT and DALL-E to generate unique bedtime stories for children.

Sarcasm is an advanced linguistic expression often found on various online platforms. Sarcasm detection is challenging in natural language processing tasks that affect sentiment analysis. This article presents the inventive method of the semigraph, including semigraph construction and sarcasm detection processes. A variation of the semigraph is suggested in the pattern-relatedness of the text document. The proposed method is to obtain the sarcastic and non-sarcastic polarity scores of a document using a semigraph. The sarcastic polarity score represents the possibility that a document will become sarcastic. Sarcasm is detected based on the polarity scoring model. The performance of the proposed model enhances the existing prior art approach to sarcasm detection. In the Amazon product review, the model achieved the accuracy, recall, and f-measure of 0.87, 0.79, and 0.83, respectively.

Echo smart displays and speakers aren't the only devices on sale on Amazon this weekend, the retailer has also discounted Ring doorbells, cameras and alarm systems. Nearly every product Ring offers is part of the promotion, including the Ring Video Doorbell Pro 2. Thanks to a 30 percent discount, you can get the smart doorbell for $175, instead of $250 at its usual price. Other notable discounts include a $40 price drop on both the plug-in and battery models of Ring Spotlight Cam Plus. You can also save $40 on Ring Floodlight Cam Wired Pro, in either black and white. The Ring Video Doorbell Pro 2 offers a 150-degree field of view, two-way audio and advanced motion detection.