Before talking about prospects for AI in the retail and CG sector, let's first look at some macro trends triggered by Covid-19, that point at a tectonic shift in the contours and operating model of this industry. Statista predicts that global e-commerce sales is expected to touch $6.5 trillion by 2023 which is staggering especially when the first documented online sale happened just 25 years ago in 1994. Not just pre-Covid online buyers increasing their online buy by 45% as per a recent BCG study, the same report also suggests that a staggering 20 % of buyers are first time online buyers. Myntra sees a 86% increase in customers from Tier2 and Tier3 cities and as per Unicommerce data, Tier3 cities clocked a 53% increase in online buying. Unicommerce further predicts that the unique number of online shoppers annually in tier-II and beyond cities to go up to 170 million over the next three years from the current 50 million.

Tiliter, a Sydney based retail tech startup has now secured $7.5 million in funding. Tiliter is founded by Marcel Herz, Martin Karafilis, in the year 2017. It is an Artificial Intelligence provider whose technology uses computer vision to recognize products without barcodes. Its technology automatically identifies items without the need for barcodes, price stickers, and packaging, which makes it easier for shoppers to manage at the time of self-checkout. CEO and co-founder of Tiliter Marcel Herz, said, "As an industry, we're just at the beginning of how AI combined with computer vision will shape the future for brick-and-mortar and online shopping. It was important that we partner with investors that understand the new dynamics in retail innovation and the massive opportunity arising from this change".

One of the most frequently-used phrases at business events these days is "the future of work." It's increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they're already present in many organizations for many different jobs. The job and incumbents described below are an example of this phenomenon. Steve Miller of Singapore Management University and I are collaborating on these stories.

If you've ever started a sentence with "Alexa…" or "Siri…", you'll know that we humans are now well used to communicating with machines through natural human language. Chatbots are underpinned by the same technology as voice interface systems like Siri, but instead of responding to spoken commands, chatbots interact with users via a written chat interface, such as Facebook Messenger or a web-based application. Like many other AI-driven technologies, chatbots have become a key technology trend. Today, businesses big and small are using chatbots to interact with their customers, drive sales, solve user problems, and more. In fact, chatbots are being used in a wide range of business functions – customer service, sales, marketing, tech support, HR – across a surprisingly diverse range of industries.

Amazon Prime Day was the unofficial start to the holiday shopping season, and if you're tired of scrolling your phone for things to buy, don't worry. You can get everything you need using Amazon Alexa voice shopping to buy the best gifts for everyone on your list. Alexa voice shopping is an amazing service that lets you find and buy products on Amazon's website without doing any real work. Here's how to use it. From a smart screen to a smartphone, there are plenty of ways you can get your holiday shopping done with the help of Amazon Alexa.

Heyday co-founders Steve Desjarlais, left, and Étienne Mérineau stand in the company's Montreal office on Sept. 30, 2020. When COVID-19 hit, forcing people to stay home, Heyday AI knew its chatbot technology would be vital for retailers that could no longer converse with their customers in person. But the Montreal-based company also knew landing new clients would be a challenge given the cancellation of big tech events where it often drums up business face-to-face. "A lot of [those] sales ... bank on relationships, and therefore you need to meet in person," says Étienne Mérineau, who co-founded Heyday in 2017 with Steve Desjarlais, David Bordeleau and Hugues Rousseau. Heyday uses artificial intelligence to help retailers communicate with customers on websites and across various apps and programs such as Facebook Messenger and Google Maps.

"An AI system needs data in order to become smart. And the more data it has, the smarter it gets," says Gaylene Meyer, Vice President Global Marketing & Communications at RFID company Impinj (PI), whose products allow retailers to track trillions of items of inventory in real time and respond quickly to changes in demand. "When you can see everything moving through a system, you gain a new view of the system as a whole. So you can find the pain points and eliminate them." That's crucial, as inconvenience is the enemy of sales; the easier the transaction, the more likely people are to complete it.

The problem of basket recommendation~(BR) is to recommend a ranking list of items to the current basket. Existing methods solve this problem by assuming the items within the same basket are correlated by one semantic relation, thus optimizing the item embeddings. However, this assumption breaks when there exist multiple intents within a basket. For example, assuming a basket contains \{\textit{bread, cereal, yogurt, soap, detergent}\} where \{\textit{bread, cereal, yogurt}\} are correlated through the "breakfast" intent, while \{\textit{soap, detergent}\} are of "cleaning" intent, ignoring multiple relations among the items spoils the ability of the model to learn the embeddings. To resolve this issue, it is required to discover the intents within the basket. However, retrieving a multi-intent pattern is rather challenging, as intents are latent within the basket. Additionally, intents within the basket may also be correlated. Moreover, discovering a multi-intent pattern requires modeling high-order interactions, as the intents across different baskets are also correlated. To this end, we propose a new framework named as \textbf{M}ulti-\textbf{I}ntent \textbf{T}ranslation \textbf{G}raph \textbf{N}eural \textbf{N}etwork~({\textbf{MITGNN}}). MITGNN models $T$ intents as tail entities translated from one corresponding basket embedding via $T$ relation vectors. The relation vectors are learned through multi-head aggregators to handle user and item information. Additionally, MITGNN propagates multiple intents across our defined basket graph to learn the embeddings of users and items by aggregating neighbors. Extensive experiments on two real-world datasets prove the effectiveness of our proposed model on both transductive and inductive BR. The code is available online at https://github.com/JimLiu96/MITGNN.

Artificial Intelligence (AI) has become the semiconductor equivalent of software -- pervasive, intangible, and capable of transforming the fiber of society and business. It is integrated into a growing number of applications and systems today in a manner that is both seamless and transformational. From Amazon's Alexa to self-driving vehicles, the development of AI has been revolutionary to the point that it seems to mimic human features, intelligence, and behavior. Although experts and scientists have warned against the dangers and hazards associated with highly mature AI machines, the market is expected to expand rapidly. According to Forbes, AI is a strategic priority of 83% of businesses today and is expected to drive global sales from nearly $8 billion in 2016 to more than $47 billion by 2020.

Investor demand for innovative emerging companies remains strong with Australian AI tech startup Tiliter completing a $7.5 million capital raise, led by Investec Emerging Companies (IEC). Eleanor Venture, a tech investment syndicate for angel investors, and New York's Cornell University also participated in the funding round. Tiliter is a leading artificial intelligence (AI) provider whose technology uses computer vision to recognise products without barcodes. Its technology automatically identifies items, such as fresh produce, without the need for barcodes, packaging, and price stickers, making it easier for shoppers to manage during self-checkout. Tiliter is currently focused on the Supermarket vertical and its camera and software system uses AI to pre-select items and remove the need for manual entry, with over 99% accuracy and in under one second.