The singles of today's dating horticulture are not happy. A vast majority of young people report they're burned out by app dating, and many are also struggling to date IRL. Even with the bevy of options and tools we have abetted around dating, singles from their 20s to their 40s told me that finding meaningful, long-term relationships is becoming harder than ever. There's no one reason, but a culmination of many factors that result in the current perilous state of modern courtship: A loneliness epidemic has been exacerbated by the COVID pandemic, which has led to poor socializing skills, and there's a surplus of unvetted suitors that the major dating apps like Tinder, Hinge, and Bumble push onto users while hiding their most authentic matches behind a paywall. Serious intention is also seriously lacking in today's climate.

Putting yourself out there always comes at a cost: you have to be vulnerable, open yourself up and risk rejection. These days it can also come with a hefty price tag. It's not just the cost of drinks or dinner to consider. Before you've even got to the awkward, age-old dance of who is going to foot the bill, you might have already forked out hundreds of pounds on a dating site to be in with the shot for a date. While some dating services are free, many now include tempting extra features that they claim will help you find more compatible connections, get noticed sooner and go on more dates.

A man who pretended to be a paramedic has been found guilty of raping and sexually assaulting women he met on an online dating website. Jamie Kadolski, 24, of Ladysmith Road, Norwich, was found guilty of committing nine sexual offences over an 18-month period. During the trial at Norwich Crown Court he denied the charges made by four different women, which he met on Tinder. The court had previously heard how the former ambulance call handler had told the women he was a paramedic and had used stickers to hide his real role on his work ID card.SuppliedKadolski worked in medical sector but never as a paramedic Kadolski worked as a call handler for the East of England Ambulance Service. The prosecution told the jury that he used stickers to hide his more junior role, so he could claim to the women he met that he was a paramedic.

Voice assistants have a reputation for finicky activation, such as responding to their prompts that are spoken in an ad. Apple may have a fix for that in the works. A part of code called AdBlocker appears to use the Shazam API to match audio captured by a device's microphones against audio fingerprints downloaded from Apple. When there's a match, the usual Siri trigger command is disabled. In theory, this means Apple could have devices not react to the "Hey Siri" prompt when it's spoken as part of the company's keynote presentations or TV advertisements.

Apple fans have slammed CEO Tim Cook for congratulating Donald Trump on his victory after beating Kamal Harris in the 2024 US Election. Cook said on X that the tech giant looks forward to working with him to'help make sure the US continues to lead with and be fueled by ingenuity, innovation, and creativity.' However, users flooded the post with comments criticizing the CEO for'interfering with the election since 2016,' citing claims that Siri only showed a picture of Harris' face when asked about the 2024 general election. Many of the comments urged Apple to start manufacturing its products in the US, which has been expressed by Trump along the campaign trail. 'We will reclaim our nation's destiny as the No. 1 manufacturing superpower in the world,' he said in August.

Although it has been argued that under-threes should not have any screen time at all, research has found that digital tech can offer "rich opportunities" for young children's development. A two-year study, Toddlers, Tech and Talk, funded by the Economic and Social Research Council and led by researchers from Manchester Metropolitan University (MMU), working with Lancaster, Queen's Belfast, Strathclyde and Swansea universities, looked at children's interactions with everything from Amazon Alexa to Ring doorbells, in diverse communities across the UK, to find out how tech was influencing 0- to three-year-olds' early talk and literacy. It examined how children use technology with parents or by themselves, whether taking photos and videos, using learning apps and playing games, listening and singing to songs, talking about favourite characters, or chatting on video calls. The researchers found that children were not only interacting with smart devices and appliances when very young, but also that digital tech could have benefits for language development and other skills. "The evidence generated through this study suggests that young children's digital activity often involves sensory exploration through touch, vision, hearing, movement and embodied cognition," the report said.

Video summarization techniques have been proven to improve the overall user experience when it comes to accessing and comprehending video content. If the user's preference is known, video summarization can identify significant information or relevant content from an input video, aiding them in obtaining the necessary information or determining their interest in watching the original video. Adapting video summarization to various types of video and user preferences requires significant training data and expensive human labeling. To facilitate such research, we proposed a new benchmark for video summarization that captures various user preferences. Also, we present a pipeline called Video Summarization with Language (VSL) for user-preferred video summarization that is based on pre-trained visual language models (VLMs) to avoid the need to train a video summarization system on a large training dataset. The pipeline takes both video and closed captioning as input and performs semantic analysis at the scene level by converting video frames into text. Subsequently, the user's genre preference was used as the basis for selecting the pertinent textual scenes. The experimental results demonstrate that our proposed pipeline outperforms current state-of-the-art unsupervised video summarization models. We show that our method is more adaptable across different datasets compared to supervised query-based video summarization models. In the end, the runtime analysis demonstrates that our pipeline is more suitable for practical use when scaling up the number of user preferences and videos.

Achieving a provable exponential quantum speedup for an important machine learning task has been a central research goal since the seminal HHL quantum algorithm for solving linear systems and the subsequent quantum recommender systems algorithm by Kerenidis and Prakash. These algorithms were initially believed to be strong candidates for exponential speedups, but a lower bound ruling out similar classical improvements remained absent. In breakthrough work by Tang, it was demonstrated that this lack of progress in classical lower bounds was for good reasons. Concretely, she gave a classical counterpart of the quantum recommender systems algorithm, reducing the quantum advantage to a mere polynomial. Her approach is quite general and was named quantum-inspired classical algorithms. Since then, almost all the initially exponential quantum machine learning speedups have been reduced to polynomial via new quantum-inspired classical algorithms. From the current state-of-affairs, it is unclear whether we can hope for exponential quantum speedups for any natural machine learning task. In this work, we present the first such provable exponential separation between quantum and quantum-inspired classical algorithms. We prove the separation for the basic problem of solving a linear system when the input matrix is well-conditioned and has sparse rows and columns.

Somehow it's already November, which means Thanksgiving and Black Friday are right around the corner. Amazon is already running early Black Friday deals on some of our picks for best smart speakers. There's a range of Amazon products on sale, including the Echo Pop, which is available for 18, down from 40. The 55 percent discount brings the Amazon Echo Pop to a record-low price. Amazon launched the Echo Pop in May 2023 as a new entry-level option for Alexa-powered devices.

As recommender systems become increasingly prevalent, the environmental impact and energy efficiency of training large-scale models have come under scrutiny. This paper investigates the potential for energy-efficient algorithm performance by optimizing dataset sizes through downsampling techniques in the context of Green Recommender Systems. We conducted experiments on the MovieLens 100K, 1M, 10M, and Amazon Toys and Games datasets, analyzing the performance of various recommender algorithms under different portions of dataset size. Our results indicate that while more training data generally leads to higher algorithm performance, certain algorithms, such as FunkSVD and BiasedMF, particularly with unbalanced and sparse datasets like Amazon Toys and Games, maintain high-quality recommendations with up to a 50% reduction in training data, achieving nDCG@10 scores within approximately 13% of full dataset performance. These findings suggest that strategic dataset reduction can decrease computational and environmental costs without substantially compromising recommendation quality. This study advances sustainable and green recommender systems by providing insights for reducing energy consumption while maintaining effectiveness.