Online shopping is a complex multi-task, few-shot learning problem with a wide and evolving range of entities, relations, and tasks. However, existing models and benchmarks are commonly tailored to specific tasks, falling short of capturing the full complexity of online shopping. Large Language Models (LLMs), with their multi-task and few-shot learning abilities, have the potential to profoundly transform online shopping by alleviating task-specific engineering efforts and by providing users with interactive conversations.

Online shopping is a complex multi-task, few-shot learning problem with a wide and evolving range of entities, relations, and tasks. However, existing models and benchmarks are commonly tailored to specific tasks, falling short of capturing the full complexity of online shopping. Large Language Models (LLMs), with their multi-task and few-shot learning abilities, have the potential to profoundly transform online shopping by alleviating task-specific engineering efforts and by providing users with interactive conversations. Motivated by the potential and challenges, we propose Shopping MMLU, a diverse multi-task online shopping benchmark derived from real-world Amazon data. Shopping MMLU consists of 57 tasks covering 4 major shopping skills: concept understanding, knowledge reasoning, user behavior alignment, and multi-linguality, and can thus comprehensively evaluate the abilities of LLMs as general shop assistants.

Customers shouldn't have to explain intent. Businesses should monitor what they are up to, so that customers can expect the same personalised attention from online shopkeepers as they get when visiting their favourite farmers' market. Put simply, the online marketplace is fundamentally different to the physical marketplace. As customers, we're often alone online and need to find the products or services we'd like to purchase. If we can't find what we want, we simply navigate away without a trace. This is one reason why online marketing suffers from lacklustre conversion rates.

"If you want to keep a secret, you must also hide it from yourself." Following on from our review of the use of artificial intelligence (AI) in manufacturing and the Industry 4.0 Revolution, we look at the use of AI in the retail sector. As we will explore in this article, retailers are collecting data, and, combined with AI, are able to improve the efficiency, and the experience of shopping, making it easier for the consumer to find what they're looking for (even if they don't know they're looking for it), saving time and cutting retailers' costs. By using AI, retailers may begin to get an idea about what a particular consumer is looking for better, even, than consumers themselves. There may come a time where consumers could be under almost constant surveillance by retailers in terms of what they're looking at, buying and turning down – providing an insight into consumer preferences.

Something as simple as being welcomed to a shop with a "Good morning, how can I help you?" could soon be a thing of the past, or, at least, not hearing it from a person but a robot that customises the interaction according to the data the establishment has on the customer. Even now, both the role played by the shop assistant and the management of online information and purchases are fundamental. "Retailers are working to provide a seamless experience for customers, between online and offline shopping. We are now seeing customers browse online and pick up in the store; or visit a physical store to touch and feel the product and then purchase it online," explains Barbara Kahn, professor of marketing at The Wharton School, University of Pennsylvania. Building a strong brand that the consumer can identify with and a supportive brand experience is one of the approaches retailers are taking to adapt to these changes.

But both history and an understanding of human-machine interaction argue otherwise. Any number of forces may work towards the stratification of society, but the computer is not one of them. Computers, especially intelligent ones, are the great equalizers. Humanity has always recognized that the powers of mind are limited, and has always made devices to compensate for those limitations. Our most obvious cognitive limitation is memory, and writing is a device for storing information outside the head so that it does not have to be remembered.

We're a nation glued to our smartphones, according to a 2015 Ofcom report. Sixteen- to 24-year-olds are the biggest user group – 90% of them own one and look at it 387 times each day on average. It's clear that if recruiters are searching for a young, captive audience from which to source talent, they should be thinking mobile. Software companies are seeing growing demand from employers looking for gamified recruitment apps. Whether companies are seeking greater diversity or bubbly shop assistants, there's much in this new breed of digital tools to attract businesses.