An illustration of how AI manipulates and changes images. An illustration of how AI manipulates and changes images. Ask Google's AI video tool to create a film of a time-travelling doctor who flies around in a blue British phone booth and the result, unsurprisingly, resembles Doctor Who . And if you ask OpenAI's technology to do the same, a similar thing happens. What's wrong with that, you may think?

The precarious work of training AI, which generally pays just a few dollars, has sparked a movement for better wages and conditions globally. Paris - For a generative artificial intelligence system to learn how to write an autopsy report, human workers must sort and annotate thousands of crime scene images. The precarious work of training AI, which generally pays just a few dollars, has sparked a movement for better wages and conditions stretching from Kenya to Colombia. You have to spend your whole day looking at dead bodies and crime scenes. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

OpenAI has temporarily stopped its artificial intelligence (AI) app Sora creating deepfake videos portraying Dr Martin Luther King Jr, following a request from his estate. It said disrespectful content had been generated about the civil rights campaigner. Sora has become popular in the US for making hyper-realistic AI-generated videos, which has led to people sharing clips of deceased celebrities and historical figures in outlandish and often offensive scenarios. OpenAI said it would pause images of Dr King as it strengthens guardrails for historical figures - but it continues to allow people to make clips of others. The firm has faced controversy over this stance, as videos featuring notable figures such as President John F. Kennedy, Queen Elizabeth II and Professor Stephen Hawking have been shared widely online.

In this era of AI slop, the idea that generative AI tools like Midjourney and Runway could be used to make art can seem absurd. But amid all the muck, there are people using AI tools with real consideration and intent. Some of them are finding notable success as AI artists: They are gaining huge online followings, selling their work at auction, and even having it exhibited in galleries and museums. This story is from our forthcoming print issue, which is all about the body. Plus, you'll also receive a free digital report on nuclear power. Take our quiz: How much do you know about antimicrobial resistance?

After launching in October in the US and Canada via invitation only, OpenAI's video app, Sora 2, hit 1m downloads in just five days. After launching in October in the US and Canada via invitation only, OpenAI's video app, Sora 2, hit 1m downloads in just five days. The video app can produce realistic deepfakes of Marx shopping and MLK Jr trolling. Some say using'historical figures' is the company's way of testing the legal waters L ast night I was flicking through a dating app. One guy stood out: "Henry VIII, 34, King of England, nonmonogamy".

Many will assume that OpenAI's Sora app represents a new era of social media. But that's wrong--all it does is reanimate our current one. As a purely creative instrument, Sora, the new AI video app from OpenAI, is a game changer. Dream up any scenario and it appears in an instant. Mr. Rogers teaching Tupac Shakur the lyrics to the legendary rap diss "Hit Em Up."

Like many nascent artistic movements, generative AI art has been widely criticized. But some artists are nevertheless pushing the creative limits of these new tools. In this era of AI slop, the idea that generative AI tools like Midjourney and Runway could be used to make art can seem absurd: What possible artistic value is there to be found in the likes of Shrimp Jesus and Ballerina Cappuccina? But amid all the muck, there are people using AI tools with real consideration and intent. Some of them are finding notable success as AI artists: They are gaining huge online followings, selling their work at auction, and even having it exhibited in galleries and museums. "Sometimes you need a camera, sometimes AI, and sometimes paint or pencil or any other medium," says Jacob Adler, a musician and composer who won the top prize at the generative video company Runway's third annual AI Film Festival for his work Total Pixel Space "It's just one tool that is added to the creator's toolbox."

ByteDance's Other AI Chatbot Is Quietly Gaining Traction Around the World ByteDance is paying for ads and partnering with influencers to promote its AI chatbot app Cici in countries like the UK, Mexico, and Indonesia. ByteDance, the parent company of TikTok, has built what is currently the most popular AI chatbot in China: Doubao . Launched in 2023, the app has risen to the top of the country's generative AI market, reaching more than 157 million monthly active users by August, according to Chinese analytics firm QuestMobile. But what's less known is that Doubao also has an overseas counterpart: Cici. It was released around the same time and features a nearly identical female cartoon avatar as its app icon, except Cici's has longer hair than Doubao's.

As the adoption of Generative AI in real-world services grow explosively, energy has emerged as a critical bottleneck resource. However, energy remains a metric that is often overlooked, under-explored, or poorly understood in the context of building ML systems. We present the ML$.$ENERGY Benchmark, a benchmark suite and tool for measuring inference energy consumption under realistic service environments, and the corresponding ML$.$ENERGY Leaderboard, which have served as a valuable resource for those hoping to understand and optimize the energy consumption of their generative AI services. In this paper, we explain four key design principles for benchmarking ML energy we have acquired over time, and then describe how they are implemented in the ML$.$ENERGY Benchmark. We then highlight results from the early 2025 iteration of the benchmark, including energy measurements of 40 widely used model architectures across 6 different tasks, case studies of how ML design choices impact energy consumption, and how automated optimization recommendations can lead to significant (sometimes more than 40%) energy savings without changing what is being computed by the model. The ML$.$ENERGY Benchmark is open-source and can be easily extended to various customized models and application scenarios.

While recent studies have examined the leaning impact of large language model (LLM) in educational contexts, the affective dynamics of LLM-mediated tutoring remain insufficiently understood. This work introduces the first ensemble-LLM framework for large-scale affect sensing in tutoring dialogues, advancing the conversation on responsible pathways for integrating generative AI into education by attending to learners' evolving affective states. To achieve this, we analyzed two semesters' worth of 16,986 conversational turns exchanged between PyTutor, an LLM-powered AI tutor, and 261 undergraduate learners across three U.S. institutions. To investigate learners' emotional experiences, we generate zero-shot affect annotations from three frontier LLMs (Gemini, GPT-4o, Claude), including scalar ratings of valence, arousal, and learning-helpfulness, along with free-text emotion labels. These estimates are fused through rank-weighted intra-model pooling and plurality consensus across models to produce robust emotion profiles. Our analysis shows that during interaction with the AI tutor, students typically report mildly positive affect and moderate arousal. Yet learning is not uniformly smooth: confusion and curiosity are frequent companions to problem solving, and frustration, while less common, still surfaces in ways that can derail progress. Emotional states are short-lived--positive moments last slightly longer than neutral or negative ones, but they are fragile and easily disrupted. Encouragingly, negative emotions often resolve quickly, sometimes rebounding directly into positive states. Neutral moments frequently act as turning points, more often steering students upward than downward, suggesting opportunities for tutors to intervene at precisely these junctures.