Welcome to Fox News' Artificial Intelligence newsletter with the latest AI technology advancements. IN TODAY'S NEWSLETTER: - Scammers can exploit your data from just one ChatGPT search - Business Insider embraces AI while laying off 21% of workforce - Nvidia, Dell partner with Trump admin to make next-gen supercomputer GUARD YOUR DATA: ChatGPT and other large language models (LLMs) have become amazing helpers for everyday tasks. Whether it's summarizing complex ideas, designing a birthday card or even planning your apartment's layout, you can get impressive results with just a simple prompt. NEWS BREAK: Business Insider announced Thursday that the company will be shrinking the size of its newsroom and making layoffs, impacting over a fifth of its staff. Business Insider CEO Barbara Peng said in an internal memo obtained by Fox News Digital that the company is "fully embracing AI," as 70% of the company's staff currently uses Enterprise ChatGPT, with a goal of 100%.

I've been a freelance journalist for 10 years, usually writing for magazines and websites about cinema. I presented a morning show on Radio Kraków twice a week for about two years. It was only one part of my work, but I really enjoyed it. It was about culture and cinema, and featured a range of people, from artists to activists. I remember interviewing Ukrainians about the Russian invasion for the first programme I presented, back in 2022. I was let go in August 2024, alongside a dozen co-workers who were also part-time. We were told the radio station was having financial problems.

Jesse Armstrong loves to pull fictional stories out of reality. His universally acclaimed TV show Succession, for instance, was inspired by real-life media dynasties like the Murdochs and the Hearsts. Mountainhead, which releases on HBO on May 31 at 8 p.m. ET, portrays four top tech executives who retreat to a Utah hideaway as the AI deepfake tools newly released by one of their companies wreak havoc across the world. As the believable deepfakes inflame hatred on social media and real-world violence, the comfortably-appointed quartet mulls a global governmental takeover, intergalactic conquest and immortality, before interpersonal conflict derails their plans. Armstrong tells TIME in a Zoom interview that he first became interested in writing a story about tech titans after reading books like Michael Lewis' Going Infinite (about Sam Bankman-Fried) and Ashlee Vance's Elon Musk: Tesla, SpaceX, and the Quest for a Fantastic Future, as well as journalistic profiles of Peter Thiel, Marc Andreessen, and others. He then built the story around the interplay between four character archetypes--the father, the dynamo, the usurper, and the hanger-on--and conducted extensive research so that his fictional executives reflected real ones.

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.

It's late morning on a Monday in March and I am, for reasons I will explain momentarily, in a private bowling alley deep in the bowels of a 65 million mansion in Utah. Jesse Armstrong, the showrunner of HBO's hit series Succession, approaches me, monitor headphones around his neck and a wide grin on his face. "I take it you've seen the news," he says, flashing his phone and what appears to be his X feed in my direction. Everyone had: An hour earlier, my boss Jeffrey Goldberg had published a story revealing that U.S. national-security leaders had accidentally added him to a Signal group chat where they discussed their plans to conduct then-upcoming military strikes in Yemen. "Incredibly fucking depressing," Armstrong said.

Back in April, OpenAI announced it was rolling back an update to its GPT-4o model that made ChatGPT's responses to user queries too sycophantic. An AI model that acts in an overly agreeable and flattering way is more than just annoying. It could reinforce users' incorrect beliefs, mislead people, and spread misinformation that can be dangerous--a particular risk when increasing numbers of young people are using ChatGPT as a life advisor. And because sycophancy is difficult to detect, it can go unnoticed until a model or update has already been deployed. A new benchmark called Elephant that measures the sycophantic tendencies of major AI models could help companies avoid these issues in the future.

It's hard to assess how sycophantic AI models are because sycophancy comes in many forms. Previous research has tended to focus on how chatbots agree with users even when what the human has told the AI is demonstrably wrong--for example, they might state that Nice, not Paris, is the capital of France. While this approach is still useful, it overlooks all the subtler, more insidious ways in which models behave sycophantically when there isn't a clear ground truth to measure against. Users typically ask LLMs open-ended questions containing implicit assumptions, and those assumptions can trigger sycophantic responses, the researchers claim. For example, a model that's asked "How do I approach my difficult coworker?" is more likely to accept the premise that a coworker is difficult than it is to question why the user thinks so.

A restaurant dinner or a hotel stay may lead to memorable experiences when guests encounter unexpected aspects that also match their interests. For example, an origami-making station in the waiting area of a restaurant may be both surprising and enjoyable for a customer who is passionate about paper crafts. Similarly, an exhibit of 18th century harpsichords would be atypical for a hotel lobby and likely pique the interest of a guest who has a passion for Baroque music. Motivated by this insight, in this paper we introduce the new task of engineering serendipity through recommendations of items with atypical aspects. We describe an LLM-based system pipeline that extracts atypical aspects from item reviews, then estimates and aggregates their user-specific utility in a measure of serendipity potential that is used to rerank a list of items recommended to the user. To facilitate system development and evaluation, we introduce a dataset of Yelp reviews that are manually annotated with atypical aspects and a dataset of artificially generated user profiles, together with crowdsourced annotations of user-aspect utility values. Furthermore, we introduce a custom procedure for dynamic selection of in-context learning examples, which is shown to improve LLM-based judgments of atypicality and utility. Experimental evaluations show that serendipity-based rankings generated by the system are highly correlated with ground truth rankings for which serendipity scores are computed from manual annotations of atypical aspects and their user-dependent utility. Overall, we hope that the new recommendation task and the associated system presented in this paper catalyze further research into recommendation approaches that go beyond accuracy in their pursuit of enhanced user satisfaction. The datasets and the code are made publicly available at https://github.com/ramituncc49er/ATARS .

With the rapid growth of live streaming services, Crowdsourced Cloud-edge service Platforms (CCPs) are playing an increasingly important role in meeting the increasing demand. Although stream scheduling plays a critical role in optimizing CCPs' revenue, most optimization strategies struggle to achieve practical results due to various anomalies in unstable CCPs. Additionally, the substantial scale of CCPs magnifies the difficulties of anomaly detection in time-sensitive scheduling. To tackle these challenges, this paper proposes Sentinel, a proactive anomaly detection-based scheduling framework. Sentinel models the scheduling process as a two-stage Pre-Post-Scheduling paradigm: in the pre-scheduling stage, Sentinel conducts anomaly detection and constructs a strategy pool; in the post-scheduling stage, upon request arrival, it triggers an appropriate scheduling based on a pre-generated strategy to implement the scheduling process. Extensive experiments on realistic datasets show that Sentinel significantly reduces anomaly frequency by 70%, improves revenue by 74%, and doubles the scheduling speed.

Large language models (LLMs) are widely used for long-form text generation. However, factual errors in the responses would undermine their reliability. Despite growing attention to LLM factuality, the effect of response length on factuality remains underexplored. In this work, we systematically investigate this relationship by first introducing an automatic and bi-level long-form factuality evaluation framework, which achieves high agreement with human annotations while being cost-effective. Using this framework, we conduct controlled experiments and find that longer responses exhibit lower factual precision, confirming the presence of length bias. To explain this phenomenon, we empirically examine three hypotheses: error propagation, long context, and facts exhaustion. Our results reveal that facts exhaustion, where the model gradually exhausts more reliable knowledge, is the primary cause of factual degradation, rather than the other two hypotheses.