Learning from non-stationary data streams, also called Task-Free Continual Learning (TFCL) remains challenging due to the absence of explicit task information in most applications. Even though recently some algorithms have been proposed for TFCL, these methods lack theoretical guarantees. Moreover, there are no theoretical studies about forgetting during TFCL. This paper develops a new theoretical analysis framework that derives generalization bounds based on the discrepancy distance between the visited samples and the entire information made available for training the model. This analysis provides new insights into the forgetting behaviour in classification tasks. Inspired by this theoretical model, we propose a new approach enabled with the dynamic component expansion mechanism for a mixture model, namely Online Discrepancy Distance Learning (ODDL). ODDL estimates the discrepancy between the current memory and the already accumulated knowledge as an expansion signal aiming to ensure a compact network architecture with optimal performance. We then propose a new sample selection approach that selectively stores the samples into the memory buffer through the discrepancybased measure, further improving the performance. We perform several TFCL experiments with the proposed methodology, which demonstrate that the proposed approach achieves the state of the art performance.

For centuries, the Rub' al-Khali desert near Saudi Arabia and Dubai -- known as the Empty Quarter -- was dismissed as a lifeless sea of sand. In 2002, Sheikh Mohammed bin Rashid Al Maktoum, ruler of Dubai, spotted unusual dune formations and a large black deposit while flying over the desert. That led to the discovery of Saruq Al-Hadid, an archaeological site rich in remnants of copper and iron smelting, which is now believed to be part of a 5,000-year-old civilization buried beneath the sands. Researchers have now found traces of this ancient society approximately 10 feet beneath the desert surface, hidden in plain sight and long overlooked due to the harsh environment and shifting dunes of the Empty Quarter. This discovery brings fresh life to the legend of a mythical city known as'Atlantis of the Sands.'

Chatbots will be able to teach children more than twice as fast as teachers can within the next decade, the so-called godfather of AI has predicted. Geoffrey Hinton, who won a Nobel Prize for his work on the technology, also claimed AI personal tutors would'be much more efficient and less boring'. Speaking at Gitex Europe, the British computer scientist said: 'It's not there yet, but it's coming, and so we'll get much better education at many levels.' AI personal tutors are already being trialled in UK schools, with the technology now able to talk directly to the student and adapt lesson plans to their knowledge level. The government has already funnelled millions of pounds into AI education initiatives – though it has claimed the technology will'absolutely not' replace teachers.

Public pretraining is a promising approach to improve differentially private model training. However, recent work has noted that many positive research results studying this paradigm only consider in-distribution tasks, and may not apply to settings where there is distribution shift between the pretraining and finetuning data--a scenario that is likely when finetuning private tasks due to the sensitive nature of the data. In this work, we show empirically across three tasks that even in settings with large distribution shift, where both zero-shot performance from public data and training from scratch with private data give unusably weak results, public features can in fact improve private training accuracy by up to 67% over private training from scratch. We provide a theoretical explanation for this phenomenon, showing that if the public and private data share a low-dimensional representation, public representations can improve the sample complexity of private training even if it is impossible to learn the private task from the public data alone. Altogether, our results provide evidence that public data can indeed make private training practical in realistic settings of extreme distribution shift.

AI's energy use already represents as much as 20 percent of global data-center power demand, research published Thursday in the journal Joule shows. That demand from AI, the research states, could double by the end of this year, comprising nearly half of all total data-center electricity consumption worldwide, excluding the electricity used for bitcoin mining. The new research is published in a commentary by Alex de Vries-Gao, the founder of Digiconomist, a research company that evaluates the environmental impact of technology. De Vries-Gao started Digiconomist in the late 2010s to explore the impact of bitcoin mining, another extremely energy-intensive activity, would have on the environment. Looking at AI, he says, has grown more urgent over the past few years because of the widespread adoption of ChatGPT and other large language models that use massive amounts of energy. According to his research, worldwide AI energy demand is now set to surpass demand from bitcoin mining by the end of this year.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Had you scanned any of the latest headlines around the TAKE IT DOWN Act, legislation that President Donald Trump signed into law Monday, you would have come away with a deeply mistaken impression of the bill and its true purpose. The surface-level pitch is that this is a necessary law for addressing nonconsensual intimate images--known more widely as revenge porn. Obfuscating its intent with a classic congressional acronym (Tools to Address Known Exploitation by Immobilizing Technological Deepfakes on Websites and Networks), the TAKE IT DOWN Act purports to help scrub the internet of exploitative, nonconsensual sexual media, whether real or digitally mocked up, at a time when artificial intelligence tools and automated image generators have supercharged its spread. Enforcement is delegated to the Federal Trade Commission, which will give online communities that specialize primarily in user-generated content (e.g., social media, message boards) a heads-up and a 48-hour takedown deadline whenever an appropriate example is reported.

Fierce competition among some of the world's biggest tech companies has led to a profusion of AI tools that can generate humanlike prose and uncannily realistic images, audio, and video. While those companies promise productivity gains and an AI-powered creativity revolution, fears have also started to swirl around the possibility of an internet that's so thoroughly muddled by AI-generated content and misinformation that it's impossible to tell the real from the fake. Many leading AI developers have, in response, ramped up their efforts to promote AI transparency and detectability. Most recently, Google announced the launch of its SynthID Detector, a platform that can quickly spot AI-generated content created by one of the company's generative models: Gemini, Imagen, Lyria, and Veo. Originally released in 2023, SynthID is a technology that embeds invisible watermarks -- a kind of digital fingerprint that can be detected by machines but not by the human eye -- into AI-generated images.

A United Arab Emirates (UAE) academic lab today launched an artificial intelligence world model and agent, two large language models (LLMs) and a new research center in Silicon Valley as it ramps up its investment in the cutting-edge field. The UAE's Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) revealed an AI world model called PAN, which can be used to build physically realistic simulations for testing and honing the performance of AI agents. Eric Xing, President and Professor of MBZUAI and a leading AI researcher, revealed the models and lab at the Computer History Museum in Mountain View, California today. The UAE has made big investments in AI in recent years under the guidance of Sheikh Tahnoun bin Zayed al Nahyan, the nation's tech-savvy national security advisor and younger brother of president Mohamed bin Zayed Al Nahyan. Xing says the UAE's new center in Sunnyvale, California, will help the nation tap into the world's most concentrated source of AI knowledge and talent.

Elon Musk's so-called Department of Government Efficiency (DOGE) used artificial intelligence from Meta's Llama model to comb through and analyze emails from federal workers. Materials viewed by WIRED show that DOGE affiliates within the Office of Personnel Management (OPM) tested and used Meta's Llama 2 model to review and classify responses from federal workers to the infamous "Fork in the Road" email that was sent across the government in late January. The email offered deferred resignation to anyone opposed to changes the Trump administration was making to its federal workforce, including an enforced return to office policy, downsizing, and a requirement to be "loyal." To leave their position, recipients merely needed to reply with the word "resign." This email closely mirrored one that Musk sent to Twitter employees shortly after he took over the company in 2022.

Anthropic announced two new models, Claude 4 Opus and Claude Sonnet 4, during its first developer conference in San Francisco on Thursday. The pair will be immediately available to paying Claude subscribers. The new models, which jump the naming convention from 3.7 straight to 4, have a number of strengths, including their ability to reason, plan, and remember the context of conversations over extended periods of time, the company says. Claude 4 Opus is also even better at playing Pokémon than its predecessor. "It was able to work agentically on Pokémon for 24 hours," says Anthropic's chief product officer Mike Krieger in an interview with WIRED.