Even after TikTok removed swastika jewelry from its online shop, I was algorithmically nudged toward a web of Nazi-related products during searches, like "double lightning bolt" and "ss" necklaces. My journey on TikTok Shop started out with a search for "hip hop jewelry." It's an innocuous search query multiple users have likely typed in, hoping to find something to wear. While browsing the cheap jewelry, I was struck by what TikTok's algorithm repeatedly suggested that I might also be interested in: jewelry with blatant Nazi symbolism. TikTok continues to struggle with moderation as its in-app ecommerce store gains traction with younger users.

A Swedish man discovered the 12th century buried treasure near his summer home. Breakthroughs, discoveries, and DIY tips sent every weekday. It only costs a few dollars to buy a tub of bait worms for fishing, but many people are fine with sourcing them straight from the ground. There's always a chance you may find more in the dirt than wriggling invertebrates. Take a recent example near Stockholm, Sweden: According to county officials last month, an unnamed fisherman scrounging for worms at his summer house discovered a corroded copper cauldron containing around 13 pounds of treasure from the Middle Ages.

Jewelry recognition is a complex task due to the different styles and designs of accessories. Precise descriptions of the various accessories is something that today can only be achieved by experts in the field of jewelry. In this work, we propose an approach for jewelry recognition using computer vision techniques and image captioning, trying to simulate this expert human behavior of analyzing accessories. The proposed methodology consist on using different image captioning models to detect the jewels from an image and generate a natural language description of the accessory. Then, this description is also utilized to classify the accessories at different levels of detail. The generated caption includes details such as the type of jewel, color, material, and design. To demonstrate the effectiveness of the proposed method in accurately recognizing different types of jewels, a dataset consisting of images of accessories belonging to jewelry stores in C\'ordoba (Spain) has been created. After testing the different image captioning architectures designed, the final model achieves a captioning accuracy of 95\%. The proposed methodology has the potential to be used in various applications such as jewelry e-commerce, inventory management or automatic jewels recognition to analyze people's tastes and social status.

I was dreading buying Christmas gifts this year. My family tends to buy things they need as they go, and my sister would kill me if I bought her another sweater. So when my editor suggested I use ChatGPT to plan my Christmas shopping for me and write about it, I jumped at the opportunity. And I figured it was a win-win. If its suggested gifts were good, I wouldn't need to worry about coming up with present ideas for another 12 months! If they were a disaster, it would be a good opportunity to showcase how rudimentary artificial intelligence is (I'm extremely skeptical about the predictions of AI enslaving us in the future).

In mass manufacturing of jewellery, the gross loss is estimated before manufacturing to calculate the wax weight of the pattern that would be investment casted to make multiple identical pieces of jewellery. Machine learning is a technology that is a part of AI which helps create a model with decision-making capabilities based on a large set of user-defined data. In this paper, the authors found a way to use Machine Learning in the jewellery industry to estimate this crucial Gross Loss. Choosing a small data set of manufactured rings and via regression analysis, it was found out that there is a potential of reducing the error in estimation from +-2-3 to +-0.5 using ML Algorithms from historic data and attributes collected from the CAD file during the design phase itself. To evaluate the approach's viability, additional study must be undertaken with a larger data set.

Late last year, Valerie Peter, a twenty-three-year-old student in Manchester, England, realized that she had an online-shopping problem. It was more about what she was buying than how much. A fashion trend of fuzzy leg warmers had infiltrated Peter's social-media feeds--her TikTok For You tab, her Instagram Explore page, her Pinterest recommendations. She'd always considered leg warmers "ugly, hideous, ridiculous," she told me recently, and yet soon enough she "somehow magically ended up with a pair of them," which she bought online at the push of a button, on an almost subconscious whim. "They're in the back of my closet," she said.)

Apple's HomePod smart speaker will be discontinued according to a statement the company provided to TechCrunch this evening. Existing users will receive software updates and support through Apple Care according to the company. On the U.S. website, the space gray color is listed as "Sold Out" but there are still models available in white. However, this move will not signal the end of the HomePod product line. Apple's HomePod Mini will continue to be sold. HomePod mini has been a hit since its debut last fall, offering customers amazing sound, an intelligent assistant, and smart home control all for just $99.

Nash Social Welfare (NSW) is a classical economics allocation idea. I'll explain what NSW has to do with machine learning shortly. NSW is best explained by example. Suppose you have three people: Adam, Brad, Carl. And suppose you have five pieces of jewelry which are made from different materials: gold, jade, opal, ruby, wood. Your…

This forecast is part of the Risk-Conscious Package, as one of I Know First's equity research solutions. We determine our aggressive stock picks by screening our algorithm daily for higher volatility stocks that present greater opportunities but are also riskier. Package Name: Aggressive Stocks Forecast Recommended Positions: Long Forecast Length: 3 Months (8/28/2019 – 11/28/2019) I Know First Average: 37.51% The algorithm correctly predicted 7 out 10 of the suggested trades in the Aggressive Stocks Forecast Package for this 3 Months forecast. Among the top-performing market predictions in this forecast was FRAN, which registered a return of 277.67%. MHLD and OMI also performed well for this time horizon with returns of 53.16% and 42.33%, respectively.

Jewelry has been an integral part of human culture since ages. One of the most popular styles of jewelry is created by putting together precious and semi-precious stones in diverse patterns. While technology is finding its way in the production process of such jewelry, designing it remains a time-consuming and involved task. In this paper, we propose a unique approach using optimization methods coupled with machine learning techniques to generate novel stone jewelry designs at scale. Our evaluation shows that designs generated by our approach are highly likeable and visually appealing.