There's something truly nice about not having to get up when you realize that light across the room is still on. If you're looking for that ease then check out the current sale on our favorite smart plug. The Kasa Smart Plug Mini EP25 four-pack is on sale for 32.58, down from 50. The initial deal cuts its price to 37.58 with a 5 coupon available at checkout (though its limited to one per order). The set is down to a new record-low price.

Men who moan that dating apps favor women are now using AI to'even up the odds' - by creating irresistible pickup lines. The singles are using apps like Rizz and MGAI that were specifically designed as dating tools that follow text prompts - you can tell the AI to write a message'with the the charm of Leonardo DiCaprio.' 'The first day I used the AI, I matched with a baddie, got a reply, scored a date, and we hooked up - all in under 24 hours.' Paul, 30, used AI'wingman' apps to generate openers. 'The first day I used the AI, I matched with a baddie, got a reply, scored a date, and we hooked up - all in under 24 hours' 'I have about a hundred better things to do than use my brain to come up with a witty text that's most likely going to a 16-year-old Indian kid posing as a hot chick so he can scam me for 20,' said Paul, who is from Miami. Alexandr Zhadan, 23, matched with 5,000 women on Tinder and used a modified version of the AI software to whittle those down to a shortlist of 100 who he then dated.

Last month, a new dating app called Volar launched in New York City, with the promise "We go on blind dates. So you don't have to." To sign up, you enter your name and phone number, then submit yourself to a brief interview with a chatbot matchmaker. When I made an account, Volar's bot asked what line of work I was in. "I'm a book critic," I replied.

How to Do It is Slate's sex advice column. Send it to Jessica and Rich here. I'm (35F) very interested in having a threesome and have been working the apps to try to find the right person to help make this happen. I've had a few bites. I was sexting with one guy for days on end about our joint fantasy of making this happen, and I found a second guy, who said he'd like to join us.

With the surge in mobile gaming, accurately predicting user spending on newly downloaded games has become paramount for maximizing revenue. However, the inherently unpredictable nature of user behavior poses significant challenges in this endeavor. To address this, we propose a robust model training and evaluation framework aimed at standardizing spending data to mitigate label variance and extremes, ensuring stability in the modeling process. Within this framework, we introduce a collaborative-enhanced model designed to predict user game spending without relying on user IDs, thus ensuring user privacy and enabling seamless online training. Our model adopts a unique approach by separately representing user preferences and game features before merging them as input to the spending prediction module. Through rigorous experimentation, our approach demonstrates notable improvements over production models, achieving a remarkable \textbf{17.11}\% enhancement on offline data and an impressive \textbf{50.65}\% boost in an online A/B test. In summary, our contributions underscore the importance of stable model training frameworks and the efficacy of collaborative-enhanced models in predicting user spending behavior in mobile gaming.

As the amount of information and content available to users continues to explode, recommender systems play an essential role in enhancing users' experience in areas ranging from e-commerce and entertainment to social media and personalized content delivery. These systems are designed to balance the huge amount of content available with the individual preferences of users to maximize the interaction-utility ratio of the users. Among the various paradigms in recommendation systems, collaborative filtering (CF) stands out as a widely adopted approach known for its effectiveness in delivering valuable and personalized recommendations to users Shi et al. (2014). By leveraging the collective wisdom of users' preferences and behaviors, collaborative filtering recommends items based on the similarity of users' tastes and interactions. Despite its practical success, much of the knowledge surrounding collaborative filtering remains largely empirical, leaving a gap in our comprehensive understanding of the underlying characteristics of the filtering problem and the intricacies of this specific approach. Unraveling the inner workings of collaborative filtering is a major challenge due to its inherent complexity. The interactions between users and items within a recommendation system generate large and intricate datasets, making extracting meaningful patterns and underlying mechanisms difficult. To address these challenges, researchers are increasingly turning to interdisciplinary approaches that combine insights from data science, machine learning, and the social sciences Chen et al. (2023). By integrating theories and methods from these diverse fields, they aim to gain a more holistic understanding of how users' social interactions, psychology, and preferences influence the collaborative filtering process.

Collaborative filtering (CF) based recommendations suffer from mainstream bias -- where mainstream users are favored over niche users, leading to poor recommendation quality for many long-tail users. In this paper, we identify two root causes of this mainstream bias: (i) discrepancy modeling, whereby CF algorithms focus on modeling mainstream users while neglecting niche users with unique preferences; and (ii) unsynchronized learning, where niche users require more training epochs than mainstream users to reach peak performance. Targeting these causes, we propose a novel end-To-end Adaptive Local Learning (TALL) framework to provide high-quality recommendations to both mainstream and niche users. TALL uses a loss-driven Mixture-of-Experts module to adaptively ensemble experts to provide customized local models for different users. Further, it contains an adaptive weight module to synchronize the learning paces of different users by dynamically adjusting weights in the loss. Extensive experiments demonstrate the state-of-the-art performance of the proposed model. Code and data are provided at \url{https://github.com/JP-25/end-To-end-Adaptive-Local-Leanring-TALL-}

I've found myself at a loss for words when trying to explain the Humane AI Pin to my friends. The best description so far is that it's a combination of a wearable Siri button with a camera and built-in projector that beams onto your palm. But each time I start explaining that, I get so caught up in pointing out its problems that I never really get to fully detail what the AI Pin can do. Or is meant to do, anyway. Yet, words are crucial to the Humane AI experience. Your primary mode of interacting with the pin is through voice, accompanied by touch and gestures. Without speaking, your options are severely limited. The company describes the device as your "second brain," but the combination of holding out my hand to see the projected screen, waving it around to navigate the interface and tapping my chest and waiting for an answer all just made me look really stupid. When I remember that I was actually eager to spend 700 of my own money to get a Humane AI Pin, not to mention shell out the required 24 a month for the AI and the company's 4G service riding on T-Mobile's network, I feel even sillier. In the company's own words, the Humane AI Pin is the "first wearable device and software platform built to harness the full power of artificial intelligence." There are basically two parts to the device: the Pin and its magnetic attachment.

Despite the many recent achievements in developing and deploying social robotics, there are still many underexplored environments and applications for which systematic evaluation of such systems by end-users is necessary. While several robotic platforms have been used in gerontological healthcare, the question of whether or not a social interactive robot with multi-modal conversational capabilities will be useful and accepted in real-life facilities is yet to be answered. This paper is an attempt to partially answer this question, via two waves of experiments with patients and companions in a day-care gerontological facility in Paris with a full-sized humanoid robot endowed with social and conversational interaction capabilities. The software architecture, developed during the H2020 SPRING project, together with the experimental protocol, allowed us to evaluate the acceptability (AES) and usability (SUS) with more than 60 end-users. Overall, the users are receptive to this technology, especially when the robot perception and action skills are robust to environmental clutter and flexible to handle a plethora of different interactions.

Modern dating can be severed into two eras: before the swipe, and after. When Tinder and other dating apps took off in the early 2010s, they unleashed a way to more easily access potential love interests than ever before. By 2017, about five years after Tinder introduced the swipe, more than a quarter of different-sex couples were meeting on apps and dating websites, according to a study led by the Stanford sociologist Michael Rosenfeld. Suddenly, saying "We met on Hinge" was as normal as saying "We met in college" or "We met through a friend." The share of couples meeting on apps has remained pretty consistent in the years since his 2017 study, Rosenfeld told me.