Ubisoft, one of the world's largest games developers, says it's working to fix an apparent hack on popular online shooter Rainbow Six Siege. Servers for the tactical multiplayer game were taken offline on Saturday and Sunday after in-game currency thought to be worth millions of pounds was distributed to players. The company has since restored service, but suspended the game's marketplace until further notice and warned players they may face queues when trying to log on. In a statement on X, Ubisoft said it would continue to make investigations and corrections over the next two weeks. Rainbow Six Siege, commonly referred to as R6, has been a success story for Ubisoft, which is also behind the Assassin's Creed and Far Cry series.

Paul Pierce explains how LeBron's absence has actually been good for the Lakers. Los Angeles Lakers shooting guard Bronny James has been the center of debate from the moment he was drafted in June. The 20-year-old said he tries to filter it all out, but he sees it all. "My first thought about everything is I always try to just let it go through one ear and out the other, put my head down and come to work and be positive every day. I see everything that people are saying, and people think, like, I'm a f---ing robot, like I don't have any feelings or emotions," James said via The Athletic.

Jules White used to believe his 11-year-old son needed to know how to code to be successful. Now, though, the Vanderbilt computer science professor says it's more crucial for James to learn a new, more useful skill: how to prompt artificial intelligence (AI) chatbots. The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. Since OpenAI released ChatGPT in 2022, White has been showing his son the ropes of generative AI.

Knowledge editing has become a promising approach for efficiently and precisely updating knowledge embedded in large language models (LLMs). In this work, we focus on Same-Subject Editing, which involves modifying multiple attributes of a single entity to ensure comprehensive and consistent updates to entity-centric knowledge. Through preliminary observation, we identify a significant challenge: Current state-of-the-art editing methods struggle when tasked with editing multiple related knowledge pieces for the same subject. To address the lack of relevant editing data for identical subjects in traditional benchmarks, we introduce the $\text{S}^2\text{RKE}$(Same-Subject Related Knowledge Editing) benchmark. Our extensive experiments reveal that only mainstream locate-then-edit methods, such as ROME and MEMIT, exhibit "related knowledge perturbation," where subsequent edits interfere with earlier ones. Further analysis reveals that these methods over-rely on subject information, neglecting other critical factors, resulting in reduced editing effectiveness.

Alignment of large language models (LLMs) with human values and preferences, often achieved through fine-tuning based on human feedback, is essential for ensuring safe and responsible AI behaviors. However, the process typically requires substantial data and computation resources. Recent studies have revealed that alignment might be attainable at lower costs through simpler methods, such as in-context learning. This leads to the question: Is alignment predominantly superficial? In this paper, we delve into this question and provide a quantitative analysis. We formalize the concept of superficial knowledge, defining it as knowledge that can be acquired through easily token restyling, without affecting the model's ability to capture underlying causal relationships between tokens. We propose a method to extract and isolate superficial knowledge from aligned models, focusing on the shallow modifications to the final token selection process. By comparing models augmented only with superficial knowledge to fully aligned models, we quantify the superficial portion of alignment. Our findings reveal that while superficial knowledge constitutes a significant portion of alignment, particularly in safety and detoxification tasks, it is not the whole story. Tasks requiring reasoning and contextual understanding still rely on deeper knowledge. Additionally, we demonstrate two practical advantages of isolated superficial knowledge: (1) it can be transferred between models, enabling efficient offsite alignment of larger models using extracted superficial knowledge from smaller models, and (2) it is recoverable, allowing for the restoration of alignment in compromised models without sacrificing performance.

Care and Feeding is Slate's parenting advice column. Have a question for Care and Feeding? How should we guard against cheating with AI? Long explanation: My 13-year-old rising 8th grader had minimal summer homework to complete. The homework was reading with related writing and it was not difficult. One of the books he had to read was The Sea of Monsters by Rick Riordan.

When you run a major app, all it takes is one mistake to put countless people at risk. Such is the case with Diksha, a public education app run by India's Ministry of Education that exposed the personal information of around 1 million teachers and millions of students across the country. The data, which included things like full names, email addresses, and phone numbers, was publicly accessible for at least a year and likely longer, potentially exposing those impacted to phishing attacks and other scams. Speaking of cybercrime, the LockBit ransomware gang has long operated under the radar, thanks to its professional operation and choice of targets. But over the past year, a series of missteps and drama have thrust it into the spotlight, potentially threatening its ability to continue operating with impunity.

When it comes to artificial intelligence, Dr. Cornelius James thinks media and pop culture too often portray it as something dangerous that shouldn't be trusted. "One of the challenges is that people go to extremes," says James, a primary care physician and clinical assistant professor at Michigan Medicine. He says that people think of The Terminator and other villainous portrayals. In reality, artificial intelligence is much less harmful than we imagine -- in fact, it's quite helpful, particularly in medicine. The hard part is convincing others that's the case.

Robots relying on AI to learn a new task generally require a laborious and repetitious training process. University of California, Berkeley researchers are attempting to simplify and shorten that with an innovative learning technique that has the robot filling in the gaps rather than starting from scratch. The team shared several lines of work with TechCrunch to show at TC Sessions: Robotics today and in the video below you can hear about them -- first from UC Berkeley researcher Stephen James. "The technique we're employing is a kind of contrastive learning setup, where it takes in the YouTube video and it kind of patches out a bunch of areas, and the idea is that the robot is then trying to reconstruct that image," James explained. "It has to understand what could be in those patches in order to then generate the idea of what could be behind there; it has to get a really good understand of what's going on in the world." Of course it doesn't learn just from watching YouTube, as common as that is in the human world.

For about 20 years I've written and produced documentaries, one of the most rewarding ways of telling stories ever invented. It's a privilege to plunge into different cultures and eras and put together deeply human narratives that can be enjoyed by everyone. My clients include National Geographic, Discovery, PBS, and other broadcasters in the US and Europe. My long fascination with Artificial Intelligence came to a head in 2000, when I interviewed inventor Ray Kurzweil, roboticist Rodney Brooks, and sci-fi legend Arthur C. Clarke. Kurzweil and Brooks were casually optimistic about a future they considered inevitable - a time when we will share the planet with intelligent machines.