Modelling the dynamics of urban venues is a challenging task as it is multifaceted in nature. Demand is a function of many complex and nonlinear features such as neighborhood composition, real-time events, and seasonality. Recent advances in Graph Convolutional Networks (GCNs) have had promising results as they build a graphical representation of a system and harness the potential of deep learning architectures. However, there has been limited work using GCNs in a temporal setting to model dynamic dependencies of the network. Further, within the context of urban environments, there has been no prior work using dynamic GCNs to support venue demand analysis and prediction. In this paper, we propose a novel deep learning framework which aims to better model the popularity and growth of urban venues. Using a longitudinal dataset from location technology platform Foursquare, we model individual venues and venue types across London and Paris. First, representing cities as connected networks of venues, we quantify their structure and note a strong community structure in these retail networks, an observation that highlights the interplay of cooperative and competitive forces that emerge in local ecosystems of retail businesses. Next, we present our deep learning architecture which integrates both spatial and topological features into a temporal model which predicts the demand of a venue at the subsequent time-step. Our experiments demonstrate that our model can learn spatio-temporal trends of venue demand and consistently outperform baseline models. Relative to state-of-the-art deep learning models, our model reduces the RSME by ~ 28% in London and ~ 13% in Paris. Our approach highlights the power of complex network measures and GCNs in building prediction models for urban environments. The model could have numerous applications within the retail sector to better model venue demand and growth.

The Encyclopedia Britannica defines Artificial Intelligence or A.I. as "the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings." Intelligent beings are those who can adapt to changing circumstances. The most forward-thinking companies are investing in Artificial Intelligence, as they already realized the importance of A.I. in business, and the impact A.I. will have, while it is becoming a key component of organizations' strategies as digital disruption increases. I am sharing here today an overview of the top 5 companies in the world according to Fortune 2020) and some examples of how these companies are using A.I. to empower their business. Walmart has been in business since the 1960s, but the company is still developing ways to revolutionize retail operations and enhance customer service.

Explainable deep learning models are advantageous in many situations. Prior work mostly provide unimodal explanations through post-hoc approaches not part of the original system design. Explanation mechanisms also ignore useful textual information present in images. In this paper, we propose MTXNet, an end-to-end trainable multimodal architecture to generate multimodal explanations, which focuses on the text in the image. We curate a novel dataset TextVQA-X, containing ground truth visual and multi-reference textual explanations that can be leveraged during both training and evaluation. We then quantitatively show that training with multimodal explanations complements model performance and surpasses unimodal baselines by up to 7% in CIDEr scores and 2% in IoU. More importantly, we demonstrate that the multimodal explanations are consistent with human interpretations, help justify the models' decision, and provide useful insights to help diagnose an incorrect prediction. Finally, we describe a real-world e-commerce application for using the generated multimodal explanations.

Chartbusters is a global challenge in which developers use AWS DeepComposer to create original compositions and compete in monthly challenges to showcase their machine learning (ML) and generative artificial intelligence (AI) skills. Regardless of your background in music or ML, one of the two new challenges will be right for you. You can choose between two different challenges this season. In the basic challenge, Melody-Go-Round, you can use any of the generative AI models available in the AWS DeepComposer Music studio to create new compositions. In the advanced challenge, Melody Harvest, you train a custom generative AI model with your own dataset using Amazon SageMaker.

Thinkers360: Firstly, what is omnichannel commerce? Since the topic has been around for a while, what is unique about PROS' perspective on this topic and why now? Well, the topic's been around for a while now, largely thanks to patterns in the consumer world. The first thing that kind of comes to mind is what happens in a typical business to consumer or retail experience where you might purchase online but pick up in-store. And thanks to COVID right now, we've seen the rising popularity of curbside pickup.

Counterfeiting is a big business. Nearly $509 billion of fake and pirated products were sold internationally in 2016. In that year, the latest for which data was available, counterfeit goods made up 3.3% of international trade, up from 2.5% three years earlier, according to the Organization for Economic Cooperation and Development. That figure, which does not include domestic trade in fakes, not only means companies are losing revenue and consumers are not getting their money's worth; counterfeiting also helps fund organized crime. Because it skirts safety regulations, makers of counterfeits could use toxic materials or produce unsafe products.

Nowadays, almost everybody is aware of the effect Artificial Intelligence (AI) has on our every day lives. AI is already a part of many people's lives and maybe already a part of your life too -- whether you realize it or not. Alexa), Google Home, and Apple's HomePod (with Siri) are perhaps the three most popular products in the thriving field of AI assistants. It's estimated that Amazon has sold about 25 million Echo devices up to now, and they expect that number to go double or more by 2020. These AI assistants products understand spoken commands and speak in humanlike voices using natural language.

Amazon Lookout for Vision is a machine learning (ML) service that spots defects and anomalies in visual representations using computer vision (CV). With Amazon Lookout for Vision, manufacturing companies can increase quality and reduce operational costs by quickly identifying differences in images of objects at scale. Many enterprise customers want to identify missing components in products, damage to vehicles or structures, irregularities in production lines, minuscule defects in silicon wafers, and other similar problems. Amazon Lookout for Vision uses ML to see and understand images from any camera as a person would, but with an even higher degree of accuracy and at a much larger scale. Amazon Lookout for Vision eliminates the need for costly and inconsistent manual inspection, while improving quality control, defect and damage assessment, and compliance. In minutes, you can begin using Amazon Lookout for Vision to automate inspection of images and objects--with no ML expertise required.

A fleet of 3,000 washing machine-like robots working inside Ocado's London warehouse can fill a 50-item grocery order in just five minutes, the firm claims. They travel along a grid inside the 563,000 square foot London warehouse and are controlled'like pieces on a chessboard' by an AI air traffic controller. As they move along the board, coming within a fraction of an inch of each other, they grab items and prepare them to be delivered to the customer in a process that can take just 15 minutes to process an order with 99 per cent accuracy, Ocado claims. The British online supermarket is now as much a technology company as it is a grocer, licensing its automation system to retailers around the world. Ocado's chief of advanced technology, Alex Harvey, said the eventual goal is to become fully automated and have items'out the door without a single human touch.'

From cutting costs to improving customer experience, forecasting is the crux of retail supply chain management (SCM) and the key to better supply chain performance. Several retailers are using AI/ML models to gather datasets and provide forecast guidance in applications such as Cognitive Demand Forecasting, Product End-of-Life, Forecasting, and Demand Integrated Product Flow. Early work in these areas looked at classical algorithms to improve on a gamut of challenges such as network flow and graphs. But the recent disruptions have made it critical for supply chains to have the resiliency to handle unexpected events. The biggest challenge lies in matching supply with demand. Reinforcement Learning (RL) with its ability to train systems to respond to unforeseen environments, is being increasingly adopted in SCM to improve forecast accuracy, solve supply chain optimization challenges, and train systems to respond to unforeseen circumstances. Companies like UPS and Amazon have developed RL algorithms to define winning AI strategies and keep up with rising consumer delivery expectations. While there are many ways to build RL algorithms for supply chain use cases, the OpenAI Gym toolkit is becoming the preferred choice because of the robust framework for event-driven simulations. This white paper explores the application of RL in supply chain forecasting and describes how to build suitable RL models and algorithms by using the OpenAI Gym toolkit.