Security researchers discovered SessionReaper, a serious vulnerability in Magento and Adobe Commerce that allows hackers to hijack shopping sessions and steal data.

Using a technology called facial age estimation, John Lewis' online store can now check whether shoppers are over 18 without needing to see their ID.

With over 60 million PS5s sold, Sony is so far dominating this generation of game consoles. Four years since the PlayStation 5 debuted, the company has rounded out its gaming lineup with the refined PS5 Slim (both with and without a disc drive) and the more powerful (and expensive) PS5 Pro. At possibly the console's midlife, up against the Xbox Series S and X, ever-increasingly powerful gaming PCs, and Nintendo's Switch, it's a good time to reassess what the PS5, in pole position, is doing to hold gamers' attention spans and why the PS5 Slim is probably the right way to dive into Sony's rich gaming selection. Sony's PS5 redesign is a smaller console with slightly more storage and the same incredible selection of hit games. The PS5 Slim looks very similar to the original design but it's noticeably (thankfully!) smaller.

The term "AI" is inescapable right now, spreading its way across every facet of every market, whether it actually makes sense or not. And as it turns out, the US Federal Trade Commission (FTC) is as sick of all this AI-related branding as everyone else. A recent regulatory push dubbed "Operation AI Comply" now has the FTC cracking down on some of the more notable AI implementations, including at least three alleged scams. A press release from last week detailed five new cases that the FTC is taking on, specifically targeting firms that sprinkle AI-related claims into their businesses. It quotes chairperson Lina M. Khan, who's been particularly proactive since her appointment in 2021: "Using AI tools to trick, mislead, or defraud people is illegal. The FTC's enforcement actions make clear that there is no AI exemption from the laws on the books. By cracking down on unfair or deceptive practices in these markets, FTC is ensuring that honest businesses and innovators can get a fair shot and consumers are being protected."

The PRFashion24 dataset is a comprehensive Persian dataset collected from various online fashion stores, spanning from April 2020 to March 2024. With 767,272 reviews, it is the first dataset in its kind that encompasses diverse categories within the fashion industry in the Persian language. The goal of this study is to harness deep learning techniques, specifically Long Short-Term Memory (LSTM) networks and a combination of Bidirectional LSTM and Convolutional Neural Network (BiLSTM-CNN), to analyze and reveal sentiments towards online fashion shopping. The LSTM model yielded an accuracy of 81.23%, while the BiLSTM-CNN model reached 82.89%. This research aims not only to introduce a diverse dataset in the field of fashion but also to enhance the public's understanding of opinions on online fashion shopping, which predominantly reflect a positive sentiment. Upon publication, both the optimized models and the PRFashion24 dataset will be available on GitHub.

With each passing year, the phrase "Black Friday" becomes more of a misnomer. What was once a day of post-Thanksgiving special offers has become a month of sales promotions from retailers across the web. It's happening again in 2023: Target and Best Buy are already advertising their early Black Friday deals; Amazon is price matching many of those discounts and has its own "Holiday Deals" landing page; and Walmart says it'll kick off its first wave of Black Friday deals on November 8. Many other shops and manufacturers have (or will soon have) early deals as well. This barrage of sales promos can be aggravating, but it also presents a good opportunity to get your holiday shopping done at something closer to your own pace. To help, we've rounded up the best early Black Friday deals we can find below. There's always a chance we get bigger discounts on November 24, but we're already seeing all-time lows on Apple's AirPods Pro, Google's Pixel 7a, Amazon's Echo Show 5, LG's A2 OLED TV and other gadgets we like.

Companies are using machine learning to solve real-world problems and are developing hundreds to thousands of features in the process. They are building feature engineering pipelines as part of MLOps life cycle to transform data from various data sources and materialize the same for future consumption. Without feature stores, different teams across various business groups would maintain the above process independently, which can lead to conflicting and duplicated features in the system. Data scientists find it hard to search for and reuse existing features and it is painful to maintain version control. Furthermore, feature correctness violations related to online (inferencing) - offline (training) skews and data leakage are common. Although the machine learning community has extensively discussed the need for feature stores and their purpose [10, 11], this paper aims to capture the core architectural components that make up a managed feature store and to share the design learning in building such a system.

Amazon.com Inc. plans to bring ChatGPT-style product search to its web store, rivaling efforts by Microsoft Corp. and Google to weave generative artificial intelligence into their search engines. The e-commerce giant's ambitions appear in recent job postings reviewed by Bloomberg News. One listing seeking a senior software development engineer says the company is "reimagining Amazon Search with an interactive conversational experience" designed to help users find answers to questions, compare products and receive personalized suggestions. "We're looking for the best and brightest across Amazon to help us realize and deliver this vision to our customers right away," the company said in the listing, which was posted on its jobs board last month. "This will be a once in a generation transformation for Search."

The media makes it sound like magic. Reading this article will change that. It will give you an overview of the most common types of problems machine learning can be used for. And at the same time give you a framework to approach your future machine learning proof of concept projects. How is machine learning, artificial intelligence and data science different? These three topics can be hard to understand because there are no formal definitions. Even after being a machine learning engineer for over a year, I don't have a good answer to this question. I'd be suspicious of anyone who claims they do. To avoid confusion, we'll keep it simple. For this article, you can consider machine learning the process of finding patterns in data to understand something more or to predict some kind of future event.

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.