While deep generative models~(DGMs) have demonstrated remarkable success in capturing complex data distributions, they consistently fail to learn constraints that encode domain knowledge and thus require constraint integration. Existing solutions to this challenge have primarily relied on heuristic methods and often ignore the underlying data distribution, harming the generative performance. In this work, we propose a probabilistically sound approach for enforcing the hard constraints into DGMs to generate constraint-compliant and realistic data. This is achieved by our proposed gradient estimators that allow the constrained distribution, the data distribution conditioned on constraints, to be differentiably learned. We carry out extensive experiments with various DGM model architectures over five image datasets and three scientific applications in which domain knowledge is governed by linear equality constraints. We validate that the standard DGMs almost surely generate data violating the constraints. Among all the constraint integration strategies, ours not only guarantees the satisfaction of constraints in generation but also archives superior generative performance than the other methods across every benchmark.

The rapid advancement of large models, driven by their exceptional abilities in learning and generalization through large-scale pre-training, has reshaped the landscape of Artificial Intelligence (AI). These models are now foundational to a wide range of applications, including conversational AI, recommendation systems, autonomous driving, content generation, medical diagnostics, and scientific discovery. However, their widespread deployment also exposes them to significant safety risks, raising concerns about robustness, reliability, and ethical implications. This survey provides a systematic review of current safety research on large models, covering Vision Foundation Models (VFMs), Large Language Models (LLMs), Vision-Language Pre-training (VLP) models, Vision-Language Models (VLMs), Diffusion Models (DMs), and large-model-based Agents. Our contributions are summarized as follows: (1) We present a comprehensive taxonomy of safety threats to these models, including adversarial attacks, data poisoning, backdoor attacks, jailbreak and prompt injection attacks, energy-latency attacks, data and model extraction attacks, and emerging agent-specific threats. (2) We review defense strategies proposed for each type of attacks if available and summarize the commonly used datasets and benchmarks for safety research. (3) Building on this, we identify and discuss the open challenges in large model safety, emphasizing the need for comprehensive safety evaluations, scalable and effective defense mechanisms, and sustainable data practices. More importantly, we highlight the necessity of collective efforts from the research community and international collaboration. Our work can serve as a useful reference for researchers and practitioners, fostering the ongoing development of comprehensive defense systems and platforms to safeguard AI models.

Text-to-image diffusion models have recently enabled the creation of visually compelling, detailed images from textual prompts. However, their ability to accurately represent various cultural nuances remains an open question. In our work, we introduce CultDiff benchmark, evaluating state-of-the-art diffusion models whether they can generate culturally specific images spanning ten countries. We show that these models often fail to generate cultural artifacts in architecture, clothing, and food, especially for underrepresented country regions, by conducting a fine-grained analysis of different similarity aspects, revealing significant disparities in cultural relevance, description fidelity, and realism compared to real-world reference images. With the collected human evaluations, we develop a neural-based image-image similarity metric, namely, CultDiff-S, to predict human judgment on real and generated images with cultural artifacts. Our work highlights the need for more inclusive generative AI systems and equitable dataset representation over a wide range of cultures.

Sam Altman is leaving no room for doubt about his views on an Elon Musk-led bid to take control of OpenAI. In a letter to OpenAI staff Monday, the CEO put the words "bid" and "deal" in scare quotes and said the startup's board has no interest in the offer. "Our structure exists to ensure that no individual can take control of OpenAI," Altman wrote, according to two sources with knowledge of the letter. "Elon runs a competitive AI company, and his actions are not about OpenAI's mission or values." Altman has also told employees that OpenAI's board, which he sits on, has yet to receive an official offer from Musk and the other investors.

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.