On User-Generated Content (UGC) platforms, recommendation algorithms significantly impact creators' motivation to produce content as they compete for algorithmically allocated user traffic. This phenomenon subtly shapes the volume and diversity of the content pool, which is crucial for the platform's sustainability. In this work, we demonstrate, both theoretically and empirically, that a purely relevance-driven policy with low exploration strength boosts short-term user satisfaction but undermines the long-term richness of the content pool. In contrast, a more aggressive exploration policy may slightly compromise user satisfaction but promote higher content creation volume. Our findings reveal a fundamental trade-off between immediate user satisfaction and overall content production on UGC platforms. Building on this finding, we propose an efficient optimization method to identify the optimal exploration strength, balancing user and creator engagement. Our model can serve as a pre-deployment audit tool for recommendation algorithms on UGC platforms, helping to align their immediate objectives with sustainable, long-term goals.

'The Big Weekend Show' co-hosts discuss Tinder user traffic peaking during'Dating Sunday.' Spencer Rascoff, the CEO of Tinder parent company Match Group, is promising to change the reputation of Tinder as a casual hookup app into a more serious dating app. They don't drink as much alcohol, they don't have as much sex," Rascoff said to a group of investors, according to The Wall Street Journal. "We need to adapt our products to accept that reality." Unlike the millennial generation, which helped popularize Tinder and shaped the dating app into a domestic and international success, Gen Z appears to be less interested in purely casual dating experiences. Some commentators believe that Gen Z is a generation that is tired of "ghosting," which is defined as suddenly cutting off communications with another person without warning, and instead seeking more authentic dating experiences.

For the quartet of tech billionaires in Jesse Armstrong's Mountainhead, ideas are so powerful that nothing else seems real. Holed up in a resplendent snowy retreat built by meditation-app developer Hugo Van Yalk (Jason Schwartzman), they're glued to their phones as the outside world is erupting into chaos, thanks in no small part to the wildfire spread of A.I. deepfakes on the social media platform owned by the world's richest man, Venis Parish (Cory Michael Smith). People in Gujarat are being burned alive after being falsely accused of desecrating religious symbols, and Midwestern Americans are machine-gunning each other over minor disagreements, but for these four men, the widespread devastation is in some ways proof of concept that they're as important as they believe themselves to be. And besides, those bodies going up in flames are just images on a tiny screen, so distant they might as well be theoretical. As he trudges through the snow with Randall (Steve Carell), the venture capitalist who serves as the group's self-appointed philosopher king, Venis asks him, "Do you … believe in other people?"

At the Google I/O 2025 event on May 20, Google announced the release of Veo 3, a new AI video generation model that makes 8-second videos. Within hours of its release, AI artists and filmmakers were showing off shockingly realistic videos. You may have even seen some of these videos in your social media feeds and not realized they were artificially generated. To be blunt: We've never seen anything like Veo 3 before. And it's only going to get better.

Existing benchmarks for evaluating LLMs' cultural sensitivities are limited to a single language or collected from online sources such as Wikipedia, which do not reflect the mundane everyday lifestyles of diverse regions. That is, information about the food people eat for their birthday celebrations, spices they typically use, musical instruments youngsters play, or the sports they practice in school is common cultural knowledge but uncommon in easily collected online sources, especially for underrepresented cultures.

Last week, Match Group (the conglomerate that owns Tinder, Hinge, OkCupid, and many others) announced it acquired the sapphic dating app HER. HER founder Robyn Exton posted on LinkedIn that, "I'm incredibly proud to say that Match Group sees the value in this incredible space we've built. Maybe even more importantly, they see the power and presence of the sapphic community." "We are not a niche. We are not an afterthought. We are a global audience with a voice, a culture, and a future. And now we have the backing to build for that future with more care, more depth, and even more ambition," Exton continued.

A majority of small businesses are using artificial intelligence and finding out it can save time and money. So, you want to start making money using AI but you're not trying to build Skynet or learn 15 coding languages first? Good, because neither am I. You don't need to become the next Sam Altman or have a Ph.D. in machine learning to turn artificial intelligence into real income. What you do need is curiosity, a dash of creativity, and the right prompts.

Tinder's incoming CEO wants to rid the app of its hookup app reputation, but the app is testing a pretty superficial preference: height. In recent days, users have started noticing a height "filter" in the app. Another dating app, Hinge, already had a height filter for premium users. Both Tinder and Hinge are owned by Match Group. Apparently, though, height is being tested as a paid preference, not a hard filter.

Continual learning aims to sequentially learn new tasks without forgetting previous tasks' knowledge (catastrophic forgetting). One factor that can cause forgetting is the interference between the gradients on losses from different tasks. When the gradients on the current task's loss are in opposing directions to those on previous tasks' losses, updating the model for the current task may cause performance degradation on previous tasks. In this paper, we first identify causes of the above interference, and hypothesize that correlations between data representations are a key factor of interference. We then propose a method for promoting appropriate correlations between arbitrary tasks' data representations (i.e., global alignment) in individual task learning. Specifically, we learn the data representation as a taskspecific composition of pre-trained token representations shared across all tasks.

Imagine a single rogue line of code slipping past your tired eyes - and suddenly your entire app is compromised. AI coding agents could be the silent saboteurs of the next big cybersecurity crisis.