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How Artificial Intelligence will change the future of Online Shopping?


The days when switching from a brick-and-mortar store to an online store was considered a significant modification to the business model are long gone. Thanks to artificial intelligence (AI), the hottest trend in online shopping, the surge of new technologies has radically changed the way people shop. Artificial intelligence (AI) is not a future technology; rather, it is a very real and unavoidable part of the modern era. Artificial intelligence is transforming the retail sector. Retailers may use AI to communicate with customers and operate more efficiently, from deploying cutting-edge tools to customize marketing campaigns to implementing ML for inventory management.



In our increasingly digitized world, enterprise software applications can better serve your business and your customers. By connecting every department within a company, enterprise systems allow companies to improve productivity and efficiency. These systems are generally created with specific goals in mind and serve many users at the same time, typically over a computer network instead of an end-user application. From payment processing and online shopping to automated billing, interactive product catalogs, business process management, content management, security, and more, the services such software systems provide are built to satisfy the needs of businesses, schools, clubs, charities, and government organizations alike. These systems are frequently enhanced to meet the changing needs and opportunities of the particular entity for which they were written.

Financial-Technology Firms Tap AI to Reach More Borrowers


OppFi Inc., a 10-year-old fintech platform based in Chicago, targets U.S. households with an average of $50,000 in annual income that need extra cash for car repairs, medical bills, student loans and other expenses. Todd Schwartz, the company's chief executive, said its customers are employed and have bank accounts but are otherwise "locked out of mainstream financial services." The Morning Download delivers daily insights and news on business technology from the CIO Journal team. OppFi, which made its public-market debut last summer, uses an AI model, real-time data analytics and a proprietary scoring algorithm to automate the underwriting process. It generates a credit score by analyzing a loan applicant's online shopping habits, income and employment information, among other data sources.

The 12 Industries Amazon Could Disrupt Next - CB Insights Research


Since 1999, Amazon's disruptive bravado has made "getting Amazoned" a fear for executives in any sector the tech giant sets its sights on. Here are the industries that could be under threat next. Jeff Bezos once famously said, "Your margin is my opportunity." Today, Amazon is finding opportunities in industries that would have been unthinkable for the company to attack even a few years ago. Throughout the 2000s, Amazon's e-commerce dominance paved a path of destruction through books, music, toys, sports, and a range of other retail verticals. Big box stores like Toys "R" Us, Sports Authority, and Barnes & Noble -- some of which had thrived for more than a century -- couldn't compete with Amazon's ability to combine uncommonly fast shipping with low prices. Today, Amazon's disruptive ambitions extend far beyond retail. With its expertise in complex supply chain logistics and competitive advantage in data collection, Amazon is attacking a whole host of new industries. The tech giant has ...

AI in retail and the rise of the purpose-driven consumer - Journey to AI Blog


That retail has experienced extreme disruption in recent years is beyond questioning. Even before Covid turned the world on its head, headlines about the so-called "retail apocalypse" were near-ubiquitous in the media. Since then, we've seen lockdowns, fluctuating openings and closings, some firms going out of business altogether, celebrations of essential retail workers and a surge in online shopping that brought record profits while yielding more ambiguous results for others. And now, with ongoing supply chain disruption, inflation and a tight labor market, it's clear that the retail sector still faces substantial challenges. But these challenges also represent opportunity, and harnessing the power of digital transformation will remain central to every business leader serious about thriving in the post-Covid world.

Bias, racism and lies: facing up to the unwanted consequences of AI


The phrase "artificial intelligence" can conjure up images of machines that are able to think, and act, just like humans, independent of any oversight from actual, flesh and blood people. Movies versions of AI tend to feature super-intelligent machines attempting to overthrow humanity and conquer the world. The reality is more prosaic, and tends to describe software that can solve problems, find patterns and, to a certain extent, "learn". This is particularly useful when huge amounts of data need to be sorted and understood, and AI is already being used in a host of scenarios, particularly in the private sector. Examples include chatbots able to conduct online correspondence; online shopping sites which learn how to predict what you might want to buy; and AI journalists writing sports and business articles (this story was, I can assure you, written by a human).

Utilizing Textual Reviews in Latent Factor Models for Recommender Systems Machine Learning

Most of the existing recommender systems are based only on the rating data, and they ignore other sources of information that might increase the quality of recommendations, such as textual reviews, or user and item characteristics. Moreover, the majority of those systems are applicable only on small datasets (with thousands of observations) and are unable to handle large datasets (with millions of observations). We propose a recommender algorithm that combines a rating modelling technique (i.e., Latent Factor Model) with a topic modelling method based on textual reviews (i.e., Latent Dirichlet Allocation), and we extend the algorithm such that it allows adding extra user- and item-specific information to the system. We evaluate the performance of the algorithm using datasets with different sizes, corresponding to 23 product categories. After comparing the built model to four other models we found that combining textual reviews with ratings leads to better recommendations. Moreover, we found that adding extra user and item features to the model increases its prediction accuracy, which is especially true for medium and large datasets.

Walmart deliveries arrive via autonomous Ford cars


For some Walmart customers, online shopping will soon come with an autonomous twist. The retailer will be partnering with Ford and its self-driving unit, Argo AI, to drop off deliveries to customers' homes in Miami, Austin, and Washington, D.C. Argo AI has been testing its autonomous vehicles in those cities already, but in the coming months it'll drop off groceries and other supplies customers buy through the Walmart app or website. Autonomous delivery pricing will be the same as Walmart's normal delivery fees. Instead of the usual delivery method in a human-powered vehicle, items will be transported in a self-driving car (with a safety driver up front still). Customers will retrieve bags from the car once it arrives.

End-to-End Conversational Search for Online Shopping with Utterance Transfer Artificial Intelligence

Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers. However, building such systems from scratch faces real word challenges from both imperfect product schema/knowledge and lack of training dialog data.In this work we first propose ConvSearch, an end-to-end conversational search system that deeply combines the dialog system with search. It leverages the text profile to retrieve products, which is more robust against imperfect product schema/knowledge compared with using product attributes alone. We then address the lack of data challenges by proposing an utterance transfer approach that generates dialogue utterances by using existing dialog from other domains, and leveraging the search behavior data from e-commerce retailer. With utterance transfer, we introduce a new conversational search dataset for online shopping. Experiments show that our utterance transfer method can significantly improve the availability of training dialogue data without crowd-sourcing, and the conversational search system significantly outperformed the best tested baseline.

Top 7 AI Stocks to Watch for Rapid Growth In 2022


Today, artificial intelligence innovation is all over the place. From online shopping to the data used for scholastic tasks. From sectors like automotive, manufacturing to finance and healthcare, AI has become an integral part of human life. Particularly with regards to financial investment, in addition to the fact that artificial intelligence allows you to settle on the right choices by looking over the feasible options available, but also opens unlimited opportunities for monetary escalation. In this segment, you will learn about the top AI stocks in 2022 to invest in.