Elon Musk has launched a 97.4 billion bid for AI darling OpenAI. The Wall Street Journal reported that a group of investors led by Musk's xAI submitted an unsolicited offer to the company's board of directors on Monday. It's a bid for the non-profit that controls OpenAI's for-profit arm. OpenAI is not a traditional company, and the non-profit structure Sam Altman and others at the company want it to get away from may, in fact, protect it from Musk's offer. There's further drama around all this: Musk had sued OpenAI and Sam Altman for allegedly ditching its non-profit mission around this time last year.

Grindr's AI wingman, currently in beta testing with around 10,000 users, arrives at a pivotal moment for the software company. With its iconic notification chirp and ominous mask logo, the app is known culturally as a digital bathhouse for gay and bisexual men to swap nudes and meet with nearby users for sex, but Grindr CEO George Arison sees the addition of a generative AI assistant and machine intelligence tools as an opportunity for expansion. "This is not just a hookup product anymore," he says. "There's obviously no question that it started out as a hookup product, but the fact that it's become a lot more over time is something people don't fully appreciate." Grindr's product road map for 2025 spotlights multiple AI features aimed at current power users, like chat summaries, as well as dating and travel-focused tools.

Artificial Intelligence (AI) can be a force for good in our future, that much is obvious from the fact that it's being utilized to advance things like medical research. The thought that somewhere out there, there's a James Bond-like villain in an armchair stroking a cat and using generative AI to hack your PC may seem like fantasy but, quite frankly, it's not. Cyber security experts are already scrambling to thwart millions of threats by hackers that have used generative AI to hack PCs, steal money, credentials, and data, and, with the rapid proliferation of new and improved AI tools, it's only going to get worse. The type of cyberattacks hackers are using aren't necessarily new. They're just more prolific, sophisticated, and effective now that they have weaponized AI.

In general, Microsoft's Copilot and Google's Gemini had more significant issues than OpenAI's ChatGPT and Perplexity, which counts Jeff Bezos as one of its investors. Normally, the BBC blocks its content from AI chatbots, but it opened its website up for the duration of the tests in December 2024. The report said that as well as containing factual inaccuracies, the chatbots "struggled to differentiate between opinion and fact, editorialised, and often failed to include essential context." The BBC's Programme Director for Generative AI, Pete Archer, said publishers "should have control over whether and how their content is used and AI companies should show how assistants process news along with the scale and scope of errors and inaccuracies they produce."

A group of investors led by Elon Musk is offering about 97.4 billion to buy the nonprofit behind OpenAI, escalating a dispute with the artificial intelligence company that Musk helped found a decade ago. Musk and his own AI startup, xAI, and a consortium of investment firms want to take control of the ChatGPT maker and revert it to its original charitable mission as a nonprofit research lab, according to Musk's attorney Marc Toberoff. OpenAI CEO Sam Altman quickly rejected the unsolicited bid on Musk's social platform X, saying, "no thank you but we will buy Twitter for 9.74 billion if you want." Musk bought Twitter, now called X, for 44 billion in 2022. Musk and Altman, who together helped start OpenAI in 2015 and later competed over who should lead it, have been in a long-running feud over the startup's direction since Musk resigned from its board in 2018.

A consortium led by Elon Musk said on Monday it has offered 97.4 billion to buy the nonprofit that controls OpenAI, another salvo in the billionaire's fight to block the artificial intelligence startup from transitioning to a for-profit firm. Musk's bid is likely to ratchet up longstanding tensions with OpenAI CEO Sam Altman over the future of the ChatGPT maker at the heart of a boom in generative AI technology. Altman on Monday promptly posted on X: "no thank you but we will buy twitter for 9.74 billion if you want." Musk cofounded OpenAI with Altman in 2015 as a nonprofit, but left before the company took off. He founded the competing AI startup xAI in 2023.

Leveraging mathematical Large Language Models (LLMs) for proof generation is a fundamental topic in LLMs research. We argue that the ability of current LLMs to prove statements largely depends on whether they have encountered the relevant proof process during training. This reliance limits their deeper understanding of mathematical theorems and related concepts. Inspired by the pedagogical method of "proof by counterexamples" commonly used in human mathematics education, our work aims to enhance LLMs' ability to conduct mathematical reasoning and proof through counterexamples. Specifically, we manually create a high-quality, university-level mathematical benchmark, CounterMATH, which requires LLMs to prove mathematical statements by providing counterexamples, thereby assessing their grasp of mathematical concepts. Additionally, we develop a data engineering framework to automatically obtain training data for further model improvement. Extensive experiments and detailed analyses demonstrate that CounterMATH is challenging, indicating that LLMs, such as OpenAI o1, have insufficient counterexample-driven proof capabilities. Moreover, our exploration into model training reveals that strengthening LLMs' counterexample-driven conceptual reasoning abilities is crucial for improving their overall mathematical capabilities. We believe that our work offers new perspectives on the community of mathematical LLMs.

The increasing deployment of unmanned surface vehicles (USVs) require computational support and coverage in applications such as maritime search and rescue. Unmanned aerial vehicles (UAVs) can offer low-cost, flexible aerial services, and ground stations (GSs) can provide powerful supports, which can cooperate to help the USVs in complex scenarios. However, the collaboration between UAVs and GSs for USVs faces challenges of task uncertainties, USVs trajectory uncertainties, heterogeneities, and limited computational resources. To address these issues, we propose a cooperative UAV and GS based robust multi-access edge computing framework to assist USVs in completing computational tasks. Specifically, we formulate the optimization problem of joint task offloading and UAV trajectory to minimize the total execution time, which is in the form of mixed integer nonlinear programming and NP-hard to tackle. Therefore, we propose the algorithm of generative artificial intelligence-enhanced heterogeneous agent proximal policy optimization (GAI-HAPPO). The proposed algorithm integrates GAI models to enhance the actor network ability to model complex environments and extract high-level features, thereby allowing the algorithm to predict uncertainties and adapt to dynamic conditions. Additionally, GAI stabilizes the critic network, addressing the instability of multi-agent reinforcement learning approaches. Finally, extensive simulations demonstrate that the proposed algorithm outperforms the existing benchmark methods, thus highlighting the potentials in tackling intricate, cross-domain issues in the considered scenarios.

Prompt caching in large language models (LLMs) results in data-dependent timing variations: cached prompts are processed faster than non-cached prompts. These timing differences introduce the risk of side-channel timing attacks. For example, if the cache is shared across users, an attacker could identify cached prompts from fast API response times to learn information about other users' prompts. Because prompt caching may cause privacy leakage, transparency around the caching policies of API providers is important. To this end, we develop and conduct statistical audits to detect prompt caching in real-world LLM API providers. We detect global cache sharing across users in seven API providers, including OpenAI, resulting in potential privacy leakage about users' prompts. Timing variations due to prompt caching can also result in leakage of information about model architecture. Namely, we find evidence that OpenAI's embedding model is a decoder-only Transformer, which was previously not publicly known.

Multimodal Large Language Models (MLLMs) represent the cutting edge of AI technology, with DeepSeek models emerging as a leading open-source alternative offering competitive performance to closed-source systems. While these models demonstrate remarkable capabilities, their vision-language integration mechanisms introduce specific vulnerabilities. We implement an adapted embedding manipulation attack on DeepSeek Janus that induces targeted visual hallucinations through systematic optimization of image embeddings. Through extensive experimentation across COCO, DALL-E 3, and SVIT datasets, we achieve hallucination rates of up to 98.0% while maintaining high visual fidelity (SSIM > 0.88) of the manipulated images on open-ended questions. Our analysis demonstrates that both 1B and 7B variants of DeepSeek Janus are susceptible to these attacks, with closed-form evaluation showing consistently higher hallucination rates compared to open-ended questioning. We introduce a novel multi-prompt hallucination detection framework using LLaMA-3.1 8B Instruct for robust evaluation. The implications of these findings are particularly concerning given DeepSeek's open-source nature and widespread deployment potential. This research emphasizes the critical need for embedding-level security measures in MLLM deployment pipelines and contributes to the broader discussion of responsible AI implementation.