Bayesian nonparametric methods are naturally suited to the problem of out-of-distribution (OOD) detection. However, these techniques have largely been eschewed in favor of simpler methods based on distances between pre-trained or learned embeddings of data points. Here we show a formal relationship between Bayesian nonparametric models and the relative Mahalanobis distance score (RMDS), a commonly used method for OOD detection. Building on this connection, we propose Bayesian nonparametric mixture models with hierarchical priors that generalize the RMDS. We evaluate these models on the OpenOOD detection benchmark and show that Bayesian nonparametric methods can improve upon existing OOD methods, especially in regimes where training classes differ in their covariance structure and where there are relatively few data points per class.

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.

Large Language Models (LLMs) have shown proficiency in generating persuasive dialogue, yet concerns about the fluency and sophistication of their outputs persist. This paper presents a multi-LLM communication framework designed to enhance the generation of persuasive data automatically. This framework facilitates the efficient production of high-quality, diverse linguistic content with minimal human oversight. Through extensive evaluations, we demonstrate that the generated data excels in naturalness, linguistic diversity, and the strategic use of persuasion, even in complex scenarios involving social taboos. The framework also proves adept at generalizing across novel contexts. Our results highlight the framework's potential to significantly advance research in both computational and social science domains concerning persuasive communication.

The aviation industry has always been a first mover in adopting technological advancements. This early adoption offers valuable insights because of its stringent regulations and safety - critical procedures. As a result, the aviation industry provides an optimal platform to counter AI vulnerabilities through its tight regulation s, standardization processes, and certification of new technologies . Keywords: AI in aviation; aviation safety; standardization; certifiable AI; regulations 2 Introduction The aviation industry has always been a trailblazer in embracing innovation, constantly driving safer air travel through various technological revolutions from the early days of pioneer flights to the modern era. T he latest frontier lies in the rise of arti ficial intelligence (AI) and it s potential to reshape aviation in extraordinary ways from pre - flight arrangements to in - flight operations and analyze post - flight data . In real - time, AI - powered assistants in cockpits can analyze vast amounts of data to alert pilots of changing weather conditions and determine optimal flight routes . Moreover, AI can vastly improve business intelligence by predicting and mitigating potential delays, reducing congestion, and ensuring smoother operations and safety . As AI continues to develop, the policy landscape on its role and application will evolve. In 1956, computer science researchers across the United States gathered at Dartmouth College in New Hampshire to discuss the formative concepts and ideas on a new branch of computing pegged artificial intelligence. The end goal of this gathering was to advance AI to the point that human assistance and intervention was no longer needed to perform a task. The evolution of AI since this meeting has resulted in decades of research and investment in the AI ecosystem -- a group of AI systems which are linked togethe r to achieve common goals .

Drought is a frequent and costly natural disaster in California, with major negative impacts on agricultural production and water resource availability, particularly groundwater. This study investigated the performance of applying different machine learning approaches to predicting the U.S. Drought Monitor classification in California. Four approaches were used: a convolutional neural network (CNN), random forest, XGBoost, and long short term memory (LSTM) recurrent neural network, and compared to a baseline persistence model. We evaluated the models' performance in predicting severe drought (USDM drought category D2 or higher) using a macro F1 binary classification metric. The LSTM model emerged as the top performer, followed by XGBoost, CNN, and random forest. Further evaluation of our results at the county level suggested that the LSTM model would perform best in counties with more consistent drought patterns and where severe drought was more common, and the LSTM model would perform worse where drought scores increased rapidly. Utilizing 30 weeks of historical data, the LSTM model successfully forecasted drought scores for a 12-week period with a Mean Absolute Error (MAE) of 0.33, equivalent to less than half a drought category on a scale of 0 to 5. Additionally, the LSTM achieved a macro F1 score of 0.9, indicating high accuracy in binary classification for severe drought conditions. Evaluation of different window and future horizon sizes in weeks suggested that at least 24 weeks of data would result in the best performance, with best performance for shorter horizon sizes, particularly less than eight weeks.

Large language models (LLMs) can produce erroneous responses that sound fluent and convincing, raising the risk that users will rely on these responses as if they were correct. Mitigating such overreliance is a key challenge. Through a think-aloud study in which participants use an LLM-infused application to answer objective questions, we identify several features of LLM responses that shape users' reliance: explanations (supporting details for answers), inconsistencies in explanations, and sources. Through a large-scale, pre-registered, controlled experiment (N=308), we isolate and study the effects of these features on users' reliance, accuracy, and other measures. We find that the presence of explanations increases reliance on both correct and incorrect responses. However, we observe less reliance on incorrect responses when sources are provided or when explanations exhibit inconsistencies. We discuss the implications of these findings for fostering appropriate reliance on LLMs.

Safety concerns are out, optimism is in: that was the takeaway from a major artificial intelligence summit in Paris this week, as leaders from the U.S., France, and beyond threw their weight behind the AI industry. Although there were divisions between major nations--the U.S. and the U.K. did not sign a final statement endorsed by 60 nations calling for an "inclusive" and "open" AI sector--the focus of the two-day meeting was markedly different from the last such gathering. Last year, in Seoul, the emphasis was on defining red-lines for the AI industry. The concern: that the technology, although holding great promise, also had the potential for great harm. The final statement made no mention of significant AI risks nor attempts to mitigate them, while in a speech on Tuesday, U.S. Vice President J.D. Vance said: "I'm not here this morning to talk about AI safety, which was the title of the conference a couple of years ago. I'm here to talk about AI opportunity."

Over the past few weeks, Donald Trump has positioned himself as an unabashed bull on America's need to dominate AI. Yet the president has also tied this newfound and futuristic priority to a more traditional mission of his: to go big with fossil fuels. A true AI revolution will need "double the energy" that America produces today, Trump said in a recent address to the World Economic Forum, days after declaring a national energy emergency. And he noted a few ways to supply that power: "We have more coal than anybody. We also have more oil and gas than anybody."

A researcher affiliated with Elon Musk's startup xAI has found a new way to both measure and manipulate entrenched preferences and values expressed by artificial intelligence models--including their political views. The work was led by Dan Hendrycks, director of the nonprofit Center for AI Safety and an adviser to xAI. He suggests that the technique could be used to make popular AI models better reflect the will of the electorate. "Maybe in the future, [a model] could be aligned to the specific user," Hendrycks told WIRED. But in the meantime, he says, a good default would be using election results to steer the views of AI models.

Can simplifying AI rules in Europe create competition for US and China? Can simplifying AI rules in Europe create competition for US and China? Europe to cut red tape to make artificial intelligence advancements easier.Read more The Artificial Intelligence Action Summit in Paris has drawn nearly 100 world leaders and tech firms, and the consensus is that 2025 is not the year for new AI regulations. France says it is time to simplify the rules in Europe to allow AI advances – or risk being left behind. Which countries have banned DeepSeek and why? list 2 of 3 Elon Musk-led group makes 97.4bn bid for OpenAI list 3 of 3 In January, Chinese start-up DeepSeek disrupted Wall Street and Silicon Valley.