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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.

Are we losing critical thinking skills to artificial intelligence? Using generative AI can limit its users' critical thinking when doing tasks. People using generative AI also think less critically when they trust the AI to do a task, such as developing an argument for a paper or presentation. The researchers behind the findings say the solution is to adapt the technology, rather than to limit its use. How does ChatGPT work and do AI-powered chatbots "think" like us? Lev Tankelevitch at Microsoft Research and his colleagues asked 319 workers to take part in a survey.

In an age of breathless predictions and sky-high valuations, cognitive scientist Gary Marcus has emerged as one of the best-known skeptics of generative artificial intelligence (AI). In fact, he recently wrote a book about his concerns, Taming Silicon Valley, in which he made the case that "we are not on the best path right now, either technically or morally." Marcus--who has spent his career examining both natural and artificial intelligence--explained his reasoning in a recent conversation with Leah Hoffmann. You've written about neural networks in everything from your 1992 monograph on language acquisition to, most recently, your book Taming Silicon Valley. Your thoughts about how AI companies and policies fall short have been well covered in your U.S. Senate testimony and other outlets (including your own Substack).

This is another salvo in the ongoing war between the people that make stuff and the AI algorithms that mimic the stuff that people make. Additionally, the startup has been accused of passing off large segments of entire articles to its users without proper attribution. "Rather than create their own content, they're stealing ours to compete with us without our permission, without compensation, and undermining our very business that feeds their machines in the first place," said Danielle Coffey, CEO of the News Media Alliance, which organized the lawsuit on behalf of its members. The suit also says the company has engaged in trademark infringement, suggesting that the algorithm would send articles to users with proper attribution, using the publisher's name, but the article itself would be filled with hallucinated and incorrect information. One example given in the suit involves a piece that The Guardian published about Hamas's attack on the Nova music festival in Israel, only the AI conflated the terror attack with a 2020 shooting in Nova Scotia, Canada. Members of the News Media Alliance are suing the AI company Cohere, accusing it of stealing their journalism without permission to train its generative AI model.

OpenAI's CEO Sam Altman wrote a social media post with an update on the roadmap for ChatGPT. In it, he explained that they've halted the launch of its upcoming o3 reasoning model to instead focus more on a streamlined yet monolithic version of GPT-5. "We want AI to'just work' for you; we realize how complicated our model and product offerings have gotten. We hate the model picker as much as you do and want to return to magic unified intelligence. We will next ship GPT-4.5, the model we called Orion internally, as our last non-chain-of-thought model.

Apple will use Alibaba's generative AI to power artificial intelligence features for iPhones meant for sale in the Chinese market. Joe Tsai, Alibaba Group's Chairman, has confirmed the companies' partnership at the World Governments Summit in Dubai. He revealed that Apple talked to a number of other companies in China for a potential partnership, but it decided to team up with Alibaba in the end. Apple Intelligence features are not accessible in China at the moment, and even those who purchased their iPhones outside the country will not be able to use those features once they change their region to mainland China. As CNBC explains, the country has strict regulations surrounding AI, including requiring large language models to get approval for commercial use.