Amazon Locker is a self-service delivery or pickup location where customers can pick up packages and drop off returns. A basic first-come-first-served policy for accepting package delivery requests to lockers results in lockers becoming full with standard shipping speed (3-5 day shipping) packages, and leaving no space left for expedited packages which are mostly Next-Day or Two-Day shipping. This paper proposes a solution to the problem of determining how much locker capacity to reserve for different ship-option packages. Yield management is a much researched field with popular applications in the airline, car rental, and hotel industries. However, Amazon Locker poses a unique challenge in this field since the number of days a package will wait in a locker (package dwell time) is, in general, unknown. The proposed solution combines machine learning techniques to predict locker demand and package dwell time, and linear programming to maximize throughput in lockers. The decision variables from this optimization provide optimal capacity reservation values for different ship options. This resulted in a year-over-year increase of 9% in Locker throughput worldwide during holiday season of 2018, impacting millions of customers.

This study investigates the forecasting accuracy of human experts versus Large Language Models (LLMs) in the retail sector, particularly during standard and promotional sales periods. Utilizing a controlled experimental setup with 123 human forecasters and five LLMs, including ChatGPT4, ChatGPT3.5, Bard, Bing, and Llama2, we evaluated forecasting precision through Mean Absolute Percentage Error. Our analysis centered on the effect of the following factors on forecasters performance: the supporting statistical model (baseline and advanced), whether the product was on promotion, and the nature of external impact. The findings indicate that LLMs do not consistently outperform humans in forecasting accuracy and that advanced statistical forecasting models do not uniformly enhance the performance of either human forecasters or LLMs. Both human and LLM forecasters exhibited increased forecasting errors, particularly during promotional periods and under the influence of positive external impacts. Our findings call for careful consideration when integrating LLMs into practical forecasting processes.

The Sonos Era 100 wireless bookshelf speaker is back down to $199, matching prices found during Black Friday and Cyber Monday. That's a discount of around $50, as the MSRP for this speaker is $250. If you squint, it's almost like the holiday spending season never ended. This sale is for the both the black and white colorways, suiting those with diverging aesthetic preferences. The Sonos Era 100 boasts touch controls and voice controls, like many modern speakers.

Robotic-based compact storage and retrieval systems provide high-density storage in distribution center and warehouse applications. In the system, items are stored in bins, and the bins are organized inside a three-dimensional grid. Robots move on top of the grid to retrieve and deliver bins. To retrieve a bin, a robot removes all bins above one by one with its gripper, called bin digging. The closer the target bin is to the top of the grid, the less digging is required to retrieve the bin. In this paper, we propose a policy to optimally arrange the bins in the grid while processing bin requests so that the most frequently accessed bins remain near the top of the grid. This improves the performance of the system and makes it responsive to changes in bin demand. Our solution approach identifies the optimal bin arrangement in the storage facility, initiates a transition to this optimal set-up, and subsequently ensures the ongoing maintenance of this arrangement for optimal performance. We perform extensive simulations on a custom-built discrete event model of the system. Our simulation results show that under the proposed policy more than half of the bins requested are located on top of the grid, reducing bin digging compared to existing policies. Compared to existing approaches, the proposed policy reduces the retrieval time of the requested bins by over 30% and the number of bin requests that exceed certain time thresholds by nearly 50%.

Kurt "The CyberGuy" Knutsson explains how you can keep unwanted rodents from entering through the cat flap. Despite rising concerns over artificial intelligence, pet owners may have actually found a new best friend in AI for their best friend. Cats are known to be proud predators who often show their affection and prowess through hunting, killing and bringing their prey back home. While a lovely gesture, this primal feline habit often leaves bewildered pet owners with bloody messes or partial or fully live birds, rodents and bugs in their homes. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER Many pet owners love their furry companions, but they also want to keep their homes clean and safe from unwanted visitors.

There's something so satisfying about waiting to buy a newly released product and then seeing it go on sale. Take the Apple Watch Series 9, which debuted in September at $399 before dropping to $329 for Black Friday. If you still waited, there's good news: the Apple Watch Series 9 in (Product) Red and Storm Blue is even cheaper now, at a new all-time low of $310. The great deal comes courtesy of an eight percent discount and a $59 coupon that will activate at checkout. It's down to an new all-time low thanks to a sale and a coupon thrown in at checkout.

Embedding-based Retrieval Models (ERMs) have emerged as a promising framework for large-scale text retrieval problems due to powerful large language models. Nevertheless, fine-tuning ERMs to reach state-of-the-art results can be expensive due to the extreme scale of data as well as the complexity of multi-stages pipelines (e.g., pre-training, fine-tuning, distillation). In this work, we propose the PEFA framework, namely ParamEter-Free Adapters, for fast tuning of ERMs without any backward pass in the optimization. At index building stage, PEFA equips the ERM with a non-parametric k-nearest neighbor (kNN) component. At inference stage, PEFA performs a convex combination of two scoring functions, one from the ERM and the other from the kNN. Based on the neighborhood definition, PEFA framework induces two realizations, namely PEFA-XL (i.e., extra large) using double ANN indices and PEFA-XS (i.e., extra small) using a single ANN index. Empirically, PEFA achieves significant improvement on two retrieval applications. For document retrieval, regarding Recall@100 metric, PEFA improves not only pre-trained ERMs on Trivia-QA by an average of 13.2%, but also fine-tuned ERMs on NQ-320K by an average of 5.5%, respectively. For product search, PEFA improves the Recall@100 of the fine-tuned ERMs by an average of 5.3% and 14.5%, for PEFA-XS and PEFA-XL, respectively. Our code is available at https://github.com/amzn/pecos/tree/mainline/examples/pefa-wsdm24.

Cyber Monday may have come and gone, but quite a few of the deals are still live. We're also seeing new discounts and bundles pop up that weren't previously listed. If you didn't get everything you need during the frenzy of Black Friday sales, you can still save on Amazon Echos, Dyson vacs, Google Nests and Sony headphones. Amazon has the most deals remaining at the moment, but other retailers, including Sonos, Wellbots, Target and Walmart, still have some worthy sale prices too. There's no telling how long these leftover savings will last, so you may not want to wait much longer to shop. Here are the best Cyber Monday tech deals you can still get today. The Echo Dot smart speaker is down to $23, which is 54 percent off and matches the low price it hit for previous sales at Amazon. The Echo Dot is Amazon's most popular smart speaker and for Cyber Monday, it's down to $23.

Are you thinking about getting a new job? Well, it's time to revisit your resume, then. But rather than do the work yourself, take advantage of this Cyber Monday deal on AI Resume Builder! This tool combines an AI writer and an ATS-friendly resume builder in a drag-and-drop layout system that makes creating a fully optimized resume easy. You can rearrange elements of your resume with easy toggle controls and advanced design tools to make your resume stand out from the crowd.

Amazon.com is rolling out a workplace chatbot called Amazon Q, designed to help corporate customers search for information, write code and review business metrics. Amazon Web Services, the retailer's cloud-computing division, is infusing generative artificial intelligence into more products, expanding its efforts to reclaim ground in a field led by its main rivals. Microsoft and Alphabet's Google have announced similar moves. Existing chatbots powered by generative AI are "genuinely super useful for consumers," AWS CEO Adam Selipsky said Tuesday at re:Invent, the company's conference in Las Vegas.