AI might slowly be taking over the world but it is still lagging behind at online dating, a study claims. Singletons are far more likely to swipe right on a real-life profile compared to one written by ChatGPT. The results found 36 per cent of women said'Yes' on Tinder to an AI-generated male but this rose to almost 64 per cent who did so for genuine life details. But it was far closer for men, who swiped right for 46 per cent of AI summaries and 54 per cent of those written by humans. Alex Limanowka, a relationship coach and psychotherapist, said: 'This gender disparity suggests that men often rely more on photos and may swipe right without reading the woman's profile.'

The background-checking tool used by Match Group to offer a safety feature for Tinder users is shutting down. The non-profit and female-founded Garbo, which the dating app conglomerate has partnered with since 2019, will shut down its consumer tool at the end of August. "Most tech companies just see trust and safety as good PR," Kathryn Kosmides, Garbo's founder and CEO, told The Wall Street Journal, which published a report on the severed partnership. "I'd rather Garbo shift focus to our other efforts than allow the vision of Garbo to be compromised and relegated to a piece of big corporations' marketing goals." A Match Group spokesperson supplied a statement to Engadget.

Kurt "The CyberGuy" Knutsson provides tips on how to limit the device's notifications. Do you ever feel like your Alexa device is listening to you a little too much? Do you wish you could have more control over what it says and when it says it? It's great that they can be set up to lock our doors, turn off our lights or give us the weather. STAY UPDATED WITH KURT'S FREE CYBERGUY NEWSLETTER TO GET SECURITY ALERTS, QUICK TIPS, TECH REVIEWS AND EASY HOW-TO'S TO MAKE YOU SMARTER What I don't appreciate about Alexa devices, however, is that they sometimes can give us extra information that we never asked for, which can be annoying.

Amazon's virtual assistant Alexa has been accused of sexism after being unable to respond to a question about the Lionesses' World Cup semi-final. British academic Dr Joanne Rodda asked Alexa for the result of Wednesday's match against Australia, which England won 3-1. But the supposedly'smart' technology didn't even know the match had taken place as it was only familiar with the men's game, the BBC reports. Astonishingly, when Dr Rodda asked'for the result of the England-Australia football match', Alexa said there was no such match. Amazon admitted the mistake was due to an'error' – although it didn't specify the cause – and that Alexa will get better at learning over time.

Microsoft has been filling up Windows builds with questionably necessary add-on programs for decades, and for just as long, power users have been stripping them out. Some have gone so far as to create custom installation media with trimmed-down builds. But in an upcoming Windows 11 release, you won't need to go to such drastic lengths, at least for some of the most common occupants of the cutting room floor. A new Windows Insider build lets users remove more default apps without any third-party tools. In particular, Canary build 25931 lets users uninstall the default Windows programs Camera, Photos, People, and the Remote Desktop Client via the default uninstall interface in Settings, as well as the recently decommissioned Cortana virtual assistant.

Dr Rodda, a senior lecturer in psychiatry at Kent and Medway Medical School - with an interest in artificial intelligence (AI), said she had only been able to get an answer from Alexa when she specified it was women's football she was interested in,.

Recommender systems have been gaining increasing research attention over the years. Most existing recommendation methods focus on capturing users' personalized preferences through historical user-item interactions, which may potentially violate user privacy. Additionally, these approaches often overlook the significance of the temporal fluctuation in item popularity that can sway users' decision-making. To bridge this gap, we propose Popularity-Aware Recommender (PARE), which makes non-personalized recommendations by predicting the items that will attain the highest popularity. PARE consists of four modules, each focusing on a different aspect: popularity history, temporal impact, periodic impact, and side information. Finally, an attention layer is leveraged to fuse the outputs of four modules. To our knowledge, this is the first work to explicitly model item popularity in recommendation systems. Extensive experiments show that PARE performs on par or even better than sophisticated state-of-the-art recommendation methods. Since PARE prioritizes item popularity over personalized user preferences, it can enhance existing recommendation methods as a complementary component. Our experiments demonstrate that integrating PARE with existing recommendation methods significantly surpasses the performance of standalone models, highlighting PARE's potential as a complement to existing recommendation methods. Furthermore, the simplicity of PARE makes it immensely practical for industrial applications and a valuable baseline for future research.

In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data. Consequently, tackling bias has emerged as a major challenge in the field of recommendation systems. Recently, Doubly Robust Learning (DR) has gained significant attention due to its remarkable performance and robust properties. However, our experimental findings indicate that existing DR methods are severely impacted by the presence of so-called Poisonous Imputation, where the imputation significantly deviates from the truth and becomes counterproductive. To address this issue, this work proposes Conservative Doubly Robust strategy (CDR) which filters imputations by scrutinizing their mean and variance. Theoretical analyses show that CDR offers reduced variance and improved tail bounds.In addition, our experimental investigations illustrate that CDR significantly enhances performance and can indeed reduce the frequency of poisonous imputation.

Neural-based multi-task learning (MTL) has gained significant improvement, and it has been successfully applied to recommendation system (RS). Recent deep MTL methods for RS (e.g. MMoE, PLE) focus on designing soft gating-based parameter-sharing networks that implicitly learn a generalized representation for each task. However, MTL methods may suffer from performance degeneration when dealing with conflicting tasks, as negative transfer effects can occur on the task-shared bottom representation. This can result in a reduced capacity for MTL methods to capture task-specific characteristics, ultimately impeding their effectiveness and hindering the ability to generalize well on all tasks. In this paper, we focus on the bottom representation learning of MTL in RS and propose the Deep Task-specific Bottom Representation Network (DTRN) to alleviate the negative transfer problem. DTRN obtains task-specific bottom representation explicitly by making each task have its own representation learning network in the bottom representation modeling stage. Specifically, it extracts the user's interests from multiple types of behavior sequences for each task through the parameter-efficient hypernetwork. To further obtain the dedicated representation for each task, DTRN refines the representation of each feature by employing a SENet-like network for each task. The two proposed modules can achieve the purpose of getting task-specific bottom representation to relieve tasks' mutual interference. Moreover, the proposed DTRN is flexible to combine with existing MTL methods. Experiments on one public dataset and one industrial dataset demonstrate the effectiveness of the proposed DTRN.

Google will stop supporting Assistant on smartwatches running Wear OS 2 in the near future. "Google Assistant support on this watch is ending soon," reads a message in the latest version of the Wear OS companion app, as spotted by 9to5 Google. "Please upgrade to a newer watch that supports Google Assistant and runs Wear OS 3 or later." The companion app is only needed for devices that use Wear OS 2 or earlier versions of the operating system. There's a dedicated Assistant app for Wear OS 3 devices, such as the Samsung Galaxy Watch 4, Fossil smartwatches and, of course, the Pixel Watch.