The viral "Hotel Lobby" meme, which lets users inhabit the bodies of Quavo and Takeoff using A.I., resembles a kind of digital blackface, but also speaks to a technology that makes appropriation the default. When I first heard "Versace," the 2013 breakout single by the Atlanta trio Migos, it was like seeing a rainbow for the first time. The hook is just Quavo saying the word "Versace" over and over, saccadic and uninterrupted: "Versace, Versace, Versace, Versace!" It's not the repetition of the designer name itself that gives the song gravitas but how Quavo and his bandmates, Offset and Takeoff, accent and manipulate the word into eighth-note triplets. They didn't invent this style of vocal performance, but they did revolutionize its use in hip-hop and popular music, with everyone from Taylor Swift to Ariana Grande adopting iterations of what came to be known simply as the "Migos flow." In the years following "Versace," the group notched two No. 1 albums, tied a Beatles chart record, and set off multiple online trends.

A seemingly simple set of rules kicks off a kind of mathematical magic trick, which has kept great minds busy since the 1930s. Almost a century ago, a mathematician came up with a puzzle that was so seemingly simple and yet so fiendishly difficult that it has been distracting other mathematicians ever since. It has become a meme that jumps from brain to brain, with many people claiming to have solved it, only to have their hopes dashed as the proof unravels. And be warned - once I explain the rules, you will immediately want to start playing around with it yourself, and I take no responsibility for how much of your time you waste. It starts a bit like a magic trick.

OpenAI's chatbot has some weird linguistic tics in Chinese that are driving users crazy. Are you even online in 2026 if you haven't experienced the verbal tics of ChatGPT? It loves goblins, em dashes, and "it's not A; it's B" sentence constructions. But what you might not know is that the chatbot also has plenty of strange phrases it loves to say in Chinese, and they are driving Chinese users crazy. ChatGPT does a decent job answering questions in Chinese, which is why it's widely used in China despite being blocked by the government.

The systems behind them--and the reasons we keep passing around war memes as entertainment--are more serious. As ceasefire announcements between the US and Iran --and separately between Israel and Lebanon --dominated headlines over the past two weeks, they also prompted a look back at how war spread online: through memes. There were jokes about conscription. Captions about getting drafted, but at least with a Bluetooth device. The song "Bazooka" went viral, with users lip-syncing to "Rest in peace my granny, she got hit by a bazooka."

May the 4th be with you The White House celebrates Star Wars Day. May the 4th be with you The White House celebrates Star Wars Day. Under Donald Trump, the White House has filled its social media with memes, wishcasting, nostalgia and deepfakes. Here's what you need to know to navigate the trolling I t started with an image of Trump as a king mocked up on a fake Time magazine cover. Since then it's developed into a full-blown phenomenon, one academics are calling "slopaganda" - an unholy alliance of easily available AI tools and political messaging.

Me Meme uses generative AI to combine a reference photo with templates. The feature is rolling out over the coming weeks. In Big Tech's never-ending quest to increase AI adoption, Google has unveiled a meme generator. The new Google Photos feature, Me Meme, lets you create personalized memes starring a synthetic version of you. Google describes Me Meme as a simple way to explore with your photos and create content that's ready to share with friends and family.

Why Everyone Is Suddenly in a'Very Chinese Time' in Their Lives It's a symbol of what Americans believe their own country has lost. In case you didn't get the memo, everyone is feeling very Chinese these days. Across social media, people are proclaiming that "You met me at a very Chinese time of my life," while performing stereotypically Chinese-coded activities like eating dim sum or wearing the viral Adidas Chinese jacket . The trend blew up so much in recent weeks that celebrities like comedian Jimmy O Yang and influencer Hasan Piker even got in on it. It has now evolved into variations like " Chinamaxxing " (acting increasingly more Chinese) and " u will turn Chinese tomorrow " (a kind of affirmation or blessing).

Multimodal classifiers function as opaque black box models. While several techniques exist to interpret their predictions, very few of them are as intuitive and accessible as natural language explanations (NLEs). To build trust, such explanations must faithfully capture the classifier's internal decision making behavior, a property known as faithfulness. In this paper, we propose CAuSE (Causal Abstraction under Simulated Explanations), a novel framework to generate faithful NLEs for any pretrained multimodal classifier. We demonstrate that CAuSE generalizes across datasets and models through extensive empirical evaluations. Theoretically, we show that CAuSE, trained via interchange intervention, forms a causal abstraction of the underlying classifier. We further validate this through a redesigned metric for measuring causal faithfulness in multimodal settings. CAuSE surpasses other methods on this metric, with qualitative analysis reinforcing its advantages. We perform detailed error analysis to pinpoint the failure cases of CAuSE. For replicability, we make the codes available at https://github.com/newcodevelop/CAuSE

Hateful memes have emerged as a particularly challenging form of online abuse, motivating the development of automated detection systems. Most prior approaches rely on direct detection, producing only binary predictions. Such models fail to provide the context and explanations that real-world moderation requires. Recent Explain-then-Detect approaches, using Chain-of-Thought prompting or LMM agents, perform worse than simple SFT baselines, and even advanced post-training methods such as GRPO fail to close the gap. Our analysis identifies two key issues of such systems: important policy-relevant cues such as targets and attack types are not hypothesized by the model as a likely explanation; and the binary reward signal is insufficient to guide reasoning. To address these challenges, we propose ExPO-HM (Explain-then-Detect Policy Optimization for Hateful Memes), inspired by the training and evaluation process of human annotators. ExPO-HM combines SFT warmup, GRPO with curriculum learning, and Conditional Decision Entropy (CDE) as both metric and reward for reasoning quality. Across three hateful meme benchmarks, ExPO-HM achieves state-of-the-art performance on binary detection, fine-grained classification, and reasoning quality, with up to 15\% and 17\% F1 improvement over the GRPO and DPO baselines, respectively. By moving hateful meme detection from simple binary alarms to explanation-driven detection, ExPO-HM provides accurate, interpretable, and actionable moderation support.