Chithrai is the Chief Technology and Innovation Officer (CTIO) for InfoVision. As online shopping replaces more and more brick-and-mortar retail stores, AI in retail is taking center stage for enterprises. In a digital era where consumers are constantly seeking personalized products and services, artificial intelligence solutions in retail are helping retailers align their offerings with the expectations of their customers. The disruptive impact of artificial intelligence in retail is seen across the value chain and is emerging as a powerful tool for retail brands to gain a strategic advantage over their competition. Marketing research firm Tractica has predicted that global AI revenues will grow from $643.7 million in 2016 to an excess of $36.8 billion in 2025.

Amazon Kendra is a highly accurate and easy-to-use enterprise search service powered by machine learning (ML). As your users begin to perform searches using Kendra, you can fine-tune which search results they receive. For example, you might want to prioritize results from certain data sources that are more actively curated and therefore more authoritative. Or if your users frequently search for documents like quarterly reports, you may want to display the more recent quarterly reports first. Relevance tuning allows you to change how Amazon Kendra processes the importance of certain fields or attributes in search results.

In this paper we investigate to what extent long short-term memory neural networks (LSTMs) are suitable for demand forecasting in the e-grocery retail sector. For this purpose, univariate as well as multivariate LSTM-based models were developed and tested for 100 fast-moving consumer goods in the context of a master's thesis. On average, the developed models showed better results for food products than the comparative models from both statistical and machine learning families. Solely in the area of beverages random forest and linear regression achieved slightly better results. This outcome suggests that LSTMs can be used for demand forecasting at product level. The performance of the models presented here goes beyond the current state of research, as can be seen from the evaluations based on a data set that unfortunately has not been publicly available to date.

How a dominant design, appropriate form factors, and awareness of the IKEA effect will help enterprises realize their AI strategiesContinue reading on Towards Data Science » Source

The digital transformation of the retail industry has been ongoing for some years. Thanks to high-level data and visitor analytics systems, companies are making well-informed and data-driven business decisions that help increase sales, marketing efficiency, and revenue across each branch. None of those insights would have been possible without artificial intelligence. AI in retail has empowered businesses to leverage advanced data to improve their retail operations and find new business opportunities. As brands compete to remain relevant, understanding why AI has become a go-to solution boils down to some key factors.

Citibot is a technology company that builds AI-powered chat solutions for local governments such as Fort Worth, Texas; Charleston, South Carolina; and Arlington, Virginia. With Citibot, local residents can quickly get answers to city-related questions, report issues, and receive real-time alerts via text responses. To power these interactions, Citibot uses Amazon Lex, a service for building conversational interfaces for text and voice applications. Citibot built the chatbot to handle basic call queries, which allows government employees to allocate more time to higher-impact community actions.

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to analyze text documents and identify insights such as sentiment, entities, and topics from text. You can use Custom Entity Recognition to identify terms that are specific to your domain. For example, you can instantly extract product names, financial entities or any term relevant to you from unstructured text documents. Starting today, Amazon Comprehend is adding support for the following five new languages to Custom Entity Recognition: French, German, Italian, Portuguese, and Spanish.

When it comes to online returns, the stats aren't pretty. Consumers return 30% of ecommerce purchases and a study from Shopify reveals that 40% of consumers buy variations of a product online intending to send back most of the order. In many cases, returned items cannot be put back on shelves due to product obsolescence (this is especially true with fast fashion and technology items) or slight wear and tear. This ever-increasing number of online returns is causing profits to take a massive hit. While 2019 saw a rise in retailers cracking down on serial returners and implementing a more efficient and sustainable approach to the handling of returned and excess stock, the issue isn't being solved at a fast-enough rate.

Amazon Comprehend now supports Amazon Virtual Private Cloud (Amazon VPC) endpoints via AWS PrivateLink so you can securely initiate API calls to Amazon Comprehend from within your VPC and avoid using the public internet. Amazon Comprehend is a fully managed natural language processing (NLP) service that uses machine learning (ML) to find meaning and insights in text. You can use Amazon Comprehend to analyze text documents and identify insights such as sentiment, people, brands, places, and topics in text. Using AWS PrivateLink, you can access Amazon Comprehend easily and securely by keeping your network traffic within the AWS network, while significantly simplifying your internal network architecture. It enables you to privately access Amazon Comprehend APIs from your VPC in a scalable manner by using interface VPC endpoints.

With the fear of contracting the coronavirus pervading most aspects of daily life, consumers are embracing contactless systems for transactions and spurring the growth of artificial intelligence-driven smart stores. Ecommerce had been growing steadily before the pandemic hit, and the contagion only accelerated the trend away from brick-and-mortar operations. Total online sales reached $73.2 billion in June, up 76.2% from one year ago, Adobe Analytics reported. Coresight Research said the pandemic has forced retailers to innovate, leveraging technology to create a more efficient supply chain and generate growth. At the same time, it has become necessary to reduce contact between employees and consumers, forcing retailers to embrace curbside pickup, cashier-less stores, contactless payment and vending machines.