AI is a retailer's automated helper, acting as a smart assistant to suggest the perfect position for products in stores, accurately predict consumer demand, automate order fulfillment in warehouses, and much more. The technology can help retailers grow their top line, potentially improving net profit margins from 2 percent to 6 percent -- and adding $1 trillion in profits to the industry globally -- according to McKinsey Global Institute analysis. It can also help them hold on to more of what they already have by reducing shrinkage -- the loss of inventory due to theft, shoplifting, ticket switching at self-checkout lanes, etc. -- which costs retailers $62 billion annually, according to the National Retail Federation. For retailers, the ability to deploy, manage and scale AI across their entire distributed edge infrastructure using a single, unified platform is critical. Managing these many devices is no small feat for IT teams as the process can be time-consuming, expensive and complex.

Have you ever ordered a large package and found that it would be shipped to you the very next day? Ever wonder how it was possible that the minute you buy something online, it's already found, registered, and in the process of getting to you? It's all thanks to warehouses that are using some special tech to make the process as efficient as it can be. Machine learning is revolutionary in almost every field, but the warehouse industry in particular has seen huge gains thanks to some of the advancements this technology has provided. It's completely changed the way warehouses can track, manage, and ship items by streamlining and automating many common tasks to the point where the process is almost completely autonomous.

You can get the Echo Show 5 for under $50 right now, plus get cool extras. Purchases you make through our links may earn us a commission. Life gets so hectic sometimes it helps to have an assistant. With the Echo Show 5, that's essentially what you'll be getting: A remarkably resourceful buddy who's always down to help you with your errands and to-do list--all you have to do is ask. As one of Amazon's early Prime Day 2020 deals, Prime members can get this truly marvelous smart display at a major discount--and that's just where the fun begins.

Effective October 1st, 2020, we're reducing the prices for ml.p3 and ml.p2 instances in Amazon SageMaker by up to 18% so you can maximize your machine learning (ML) budgets and innovate with deep learning using these accelerated compute instances. The new price reductions apply to ml.p3 and ml.p2 instances of all sizes for Amazon SageMaker Studio notebooks, on-demand notebooks, processing, training, real-time inference, and batch transform. Customers including Intuit, Thomson Reuters, Cerner, and Zalando are already reducing their total cost of ownership (TCO) by at least 50% using Amazon SageMaker. Amazon SageMaker removes the heavy lifting from each step of the ML process and makes it easy to apply advanced deep learning techniques at scale. Amazon SageMaker provides lower TCO because it's a fully managed service, so you don't need to build, manage, or maintain any infrastructure and tooling for your ML workloads.

This early Prime Day 2020 deal on a Shark robot vacuum will save you $70. Purchases you make through our links may earn us a commission. By now, you're probably already well aware that an affordable robot vacuum can be a real lifesaver when it comes to cleaning up pet hair, snack crumbs and pretty much any mess in between. If you're looking for a machine to help you prolong the hassle of busting out the broom and mop for daily touch-ups to your floors, you're in luck: The Shark ION AV751 robot vacuum just so happens to be at a big discount ahead of Amazon Prime Day 2020. Not only is this machine highly-rated by Amazon shoppers for its powerful cleaning and high-tech features, it's a cool 32% off right now, falling from $219.99 to $149.99.

Product descriptions in e-commerce platforms contain detailed and valuable information about retailers assortment. In particular, coding promotions within digital leaflets are of great interest in e-commerce as they capture the attention of consumers by showing regular promotions for different products. However, this information is embedded into images, making it difficult to extract and process for downstream tasks. In this paper, we present an end-to-end approach that classifies promotions within digital leaflets into their corresponding product categories using both visual and textual information. Our approach can be divided into three key components: 1) region detection, 2) text recognition and 3) text classification. In many cases, a single promotion refers to multiple product categories, so we introduce a multi-label objective in the classification head. We demonstrate the effectiveness of our approach for two separated tasks: 1) image-based detection of the descriptions for each individual promotion and 2) multi-label classification of the product categories using the text from the product descriptions. We train and evaluate our models using a private dataset composed of images from digital leaflets obtained by Nielsen. Results show that we consistently outperform the proposed baseline by a large margin in all the experiments.

Recent advances in neural forecasting have produced major improvements in accuracy for probabilistic demand prediction. In this work, we propose novel improvements to the current state of the art by incorporating changes inspired by recent advances in Transformer architectures for Natural Language Processing. We develop a novel decoder-encoder attention for context-alignment, improving forecasting accuracy by allowing the network to study its own history based on the context for which it is producing a forecast. We also present a novel positional encoding that allows the neural network to learn context-dependent seasonality functions as well as arbitrary holiday distances. Finally we show that the current state of the art MQ-Forecaster (Wen et al., 2017) models display excess variability by failing to leverage previous errors in the forecast to improve accuracy. We propose a novel decoder-self attention scheme for forecasting that produces significant improvements in the excess variation of the forecast.

AWS DeepComposer provides a creative and hands-on experience for learning generative AI and machine learning (ML). We recently launched the Edit melody feature, which allows you to add, remove, or edit specific notes, giving you full control of the pitch, length, and timing for each note. In this post, you can learn to use the Edit melody feature to collaborate with the autoregressive convolutional neural network (AR-CNN) algorithm and create interesting Bach-style compositions. Through human-AI collaboration, we can surpass what humans and AI systems can create independently. For example, you can seek inspiration from AI to create art or music outside their area of expertise or offload the more routine tasks, like creating variations on a melody, and focus on the more interesting and creative tasks.

For decades humans have been cultivating the concept that "teams" are more powerful and effective than the sole individual. Professor Leigh Thompson of the Kellogg School of Management at Northwestern University defined a team as "a group of people who are interdependent with respect to information, resources, knowledge, and skills and who seek to combine their efforts to achieve a common goal." Robert E. Cole of the Haas School of Business at the University of California, Berkeley studied the evolution of the team concept in industrialized countries from the 1960s to the 1980s. Whether we call them "autonomous workgroups" or just "teams," the result is often higher productivity with more superb quality. From my experience managing engineering teams, my preferred high-tech strategy has been to form "small teams that move fast."

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.