OpenAI on Friday rejected a 97.4bn bid from a consortium led by billionaire Elon Musk for the ChatGPT maker, saying the startup is not for sale. The unsolicited approach is Musk's latest attempt to block the startup he co-founded with CEO Sam Altman – but later left – from becoming a for-profit firm, as it looks to secure more capital and stay ahead in the AI race. "OpenAI is not for sale, and the board has unanimously rejected Mr Musk's latest attempt to disrupt his competition. Any potential reorganization of OpenAI will strengthen our nonprofit and its mission to ensure AGI benefits all of humanity," OpenAI said on X, quoting its chair Bret Taylor, on behalf of its board. On Tuesday, Altman told news website Axios that OpenAI was not for sale.

Elon Musk launched a 97.4 billion bid to take control of OpenAI. The Wall Street Journal reported a group of investors led by Musk's xAI submitted an unsolicited offer to the company's board of directors on Monday. The group wants to buy the nonprofit that controls OpenAI's for-profit arm. When asked for comment, an OpenAI spokesperson pointed Engadget to an X post from CEO Sam Altman. "No thank you but we will buy twitter for 9.74 billion if you want," Altman wrote on the social media platform Musk owns.

The Guardian Media Group, owner of The Guardian and The Observer newspapers, is partnering with OpenAI. The deal will see reporting from The Guardian appear as a news source within ChatGPT, alongside article extracts and short summaries. In return, OpenAI will provide the Guardian Media Group with access to ChatGPT Enterprise, which the company says it will use to develop new products, features and tools. "This new partnership with OpenAI reflects the intellectual property rights and value associated with our award-winning journalism, expanding our reach and impact to new audiences and innovative platform services," said Keith Underwood, chief financial and operating officer of the Guardian Media Group. The Guardian Media Group joins a growing list of news publishers that are now working with OpenAI after an initial period of uncertainty over the company and its business model.

Apple's artificial intelligence features for iPhones could be available in China as early as May, according to Bloomberg. The company reportedly established several teams in China and the US to make that happen, and it's also teaming up with local companies for its generative AI needs in the country. Joe Tsai, Alibaba Group's Chairman, recently confirmed that Apple will use his company's generative AI technology for Chinese iPhones during an event. Tsai didn't say when Apple intends to roll out the AI features that use Alibaba's tech, but The Information previously reported that the companies had already submitted them for approval to the country's regulators. Bloomberg says Apple will use Alibaba's technology for its on-device AI models, specifically as a layer on top that can censor certain materials and information for the Chinese government.

Chip designer Arm plans to unveil its own processor this year with Meta as the launch customer, The Financial Times reported. The chip would be a CPU designed for servers in data centers and would have the potential to be customized for clients. Manufacturing would be outsourced to a contract fab plant like TSMC (Taiwan Semiconductor Manufacturing Co.) and the first in-house chip could be revealed as early as this summer, according to the FT's sources. Last month, Arm parent Softbank announced the Stargate project, a partnership with OpenAI to build up to 500 billion worth of AI infrastructure. Arm, along with Microsoft and NVIDIA, is a key technology partner for the project.

Is Elon Musk's 97.4 billion offer to buy OpenAI genuine--or an irresistible opportunity to troll Sam Altman? Subscribe to Slate Plus to access ad-free listening to the whole What Next family and all your favorite Slate podcasts. Subscribe today on Apple Podcasts by clicking "Try Free" at the top of our show page. Sign up now at slate.com/whatnextplus to get access wherever you listen.

Two of Silicon Valley's most prominent tech titans, Elon Musk and his former protégé Sam Altman, are in the middle of a very public feud over the future of OpenAI, the company behind the groundbreaking ChatGPT. Musk – the world's richest man and CEO of Tesla and SpaceX – has filed multiple lawsuits over the past year to stop Altman from restructuring OpenAI from a hybridised nonprofit into a for-profit company. Earlier this week, Musk raised the stakes by offering to buy the nonprofit for 97.4bn to preserve the original mission of the AI research lab – ensuring that "artificial general intelligence benefits all of humanity". Musk's proposal was quickly rebuffed by Altman. In the latest development, Musk said through his lawyers on Wednesday that he would drop his offer if OpenAI remains a nonprofit, which would prevent the company from accessing potentially billions of dollars in funding.

Proteins are dynamic molecular machines whose biological functions, spanning enzymatic catalysis, signal transduction, and structural adaptation, are intrinsically linked to their motions. Designing proteins with targeted dynamic properties, however, remains a challenge due to the complex, degenerate relationships between sequence, structure, and molecular motion. Here, we introduce VibeGen, a generative AI framework that enables end-to-end de novo protein design conditioned on normal mode vibrations. VibeGen employs an agentic dual-model architecture, comprising a protein designer that generates sequence candidates based on specified vibrational modes and a protein predictor that evaluates their dynamic accuracy. This approach synergizes diversity, accuracy, and novelty during the design process. Via full-atom molecular simulations as direct validation, we demonstrate that the designed proteins accurately reproduce the prescribed normal mode amplitudes across the backbone while adopting various stable, functionally relevant structures. Notably, generated sequences are de novo, exhibiting no significant similarity to natural proteins, thereby expanding the accessible protein space beyond evolutionary constraints. Our work integrates protein dynamics into generative protein design, and establishes a direct, bidirectional link between sequence and vibrational behavior, unlocking new pathways for engineering biomolecules with tailored dynamical and functional properties. This framework holds broad implications for the rational design of flexible enzymes, dynamic scaffolds, and biomaterials, paving the way toward dynamics-informed AI-driven protein engineering.

Recent advancements in Generative AI have significantly improved the efficiency and adaptability of natural language processing (NLP) systems, particularly through Retrieval-Augmented Generation (RAG), Low-Rank Adaptation (LoRA), and Weight-Decomposed Low-Rank Adaptation (DoRA). RAG integrates external knowledge to enhance factual consistency in generative outputs, while LoRA enables parameter-efficient fine-tuning of large language models (LLMs). DoRA further refines this process by optimizing fine-tuning through adaptive parameter ranking and domain-aware weight adjustments, improving learning efficiency while maintaining inference performance. This paper presents a large-scale empirical evaluation of RAG, LoRA, and DoRA, with model fine-tuning and generation performance assessed on 20,000 FAQ-based queries, while the knowledge base spans 400,000 entries. The study analyzes key performance metrics such as accuracy, relevance, and inference latency. Experimental results demonstrate that DoRA achieves the highest accuracy (90.1%), relevance score (0.88), and lowest latency (110 ms per query), outperforming both LoRA and RAG in real-world, domain-specific generative AI applications. Furthermore, this study examines the trade-offs between fine-tuning efficiency, computational cost, and real-time adaptability across different models. Findings highlight RAG's effectiveness in knowledge grounding, LoRA's cost-efficient domain adaptation, and DoRA's ability to balance fine-tuning efficiency with model precision. These insights provide practical guidance for deploying AI-driven generative systems in accuracy-critical domains such as healthcare, finance, and legal services, ensuring scalability, reliability, and optimal performance in dynamic environments.

Elon Musk says he will abandon his 97.4bn offer to buy the non-profit behind OpenAI if the ChatGPT maker drops its plan to convert into a for-profit company. "If OpenAI, Inc's Board is prepared to preserve the charity's mission and stipulate to take the'for sale' sign off its assets by halting its conversion, Musk will withdraw the bid," lawyers for the billionaire said in a filing to a California court on Wednesday. "Otherwise, the charity must be compensated by what an arms-length buyer will pay for its assets." Musk and a group of investors made their offer earlier this week, in the latest twist to a dispute with the artificial intelligence company that he helped found a decade ago. OpenAI is controlled by a non-profit board bound to its original mission of safely building "better-than-human" AI for public benefit.