Even as he's vowed to push the United States ahead in artificial intelligence research, President Donald Trump's threats to alter federal government contracts with chipmakers and slap new tariffs on the semiconductor industry may put new speed bumps in front of the tech industry. Since taking office, Trump has said he would place tariffs on foreign production of computer chips and semiconductors in order to return chip manufacturing to the U.S. The president and Republican lawmakers have also threatened to end the CHIPS and Science Act, a sweeping Biden administration-era law that also sought to boost domestic production. But economic experts have warned that Trump's dual-pronged approach could slow, or potentially harm, the administration's goal of ensuring that the U.S. maintains a competitive edge in artificial intelligence research. Saikat Chaudhuri, an expert on corporate growth and innovation at U.C. Berkeley's Haas School of Business, called Trump's derision of the CHIPS Act surprising because one of the biggest bottlenecks for the advancement of AI has been chip production. Most countries, Chaudhuri said, are trying to encourage chip production and the import of chips at favorable rates.

Agents with the U.S. Customs and Border Protection (CBP) will no longer wear body cameras during field operations after a social media post publicized how to identify individual agents. "All U.S. Border Patrol Agents will cease the use of body-worn cameras (BWC) in all operational environments," CBP said in a statement to NewsNation, which originally reported the news. The directive comes after a post on Reddit claimed that the mobile application BLE Radar, which uses Bluetooth to scan for low-energy devices such as phones, smartwatches and speakers, can also track CBP body cameras from a distance of 100 yards and can also trigger improvised explosive devices. A Border Patrol agent stands on a cliff looking for migrants that crossed the border wall between the U.S. and Mexico near the city of Sasabe, Arizona. CBP officials sent out a directive following the post informing agents of a "potential security risk" while immediately pulling body cameras from use in the field.

Russian drone strikes have damaged a thermal power plant in Mykolaiv in southern Ukraine overnight, leaving 46,000 consumers without heating as temperatures plunge below freezing, Ukrainian Prime Minister Denys Shmyhal said. "This was done deliberately to leave people without heat in sub-zero temperatures and create a humanitarian catastrophe," Shmyhal said on the Telegram messenger app. Russia attacked Ukraine with 143 drones overnight, but the Ukrainian military said it shot down 95 of them, while 46 did not reach their targets, likely thanks to the use of electromagnetic countermeasures that disrupt drone attacks. At least one person was injured in the overnight attacks which also damaged houses in the Kyiv region, Ukrainian officials said. The temperature in Mykolaiv is expected to fall to minus 7 degrees Celsius (19.4 Fahrenheit) on Sunday night.

The world's richest man, Elon Musk, has vowed to oversee a radical hollowing out of government agencies, asserting this week that some should be "deleted entirely" as he defunds public programs and lays off federal workers. While the immense cuts are framed as a means of removing waste, they may also become a boon to private companies – including Musk's own businesses – that the government increasingly relies on for many of its key initiatives. Musk and his allies in the "department of government efficiency" (Doge), the unofficial committee acting as the operations arm of his cost-cutting efforts, have targeted a range of major government departments. They have moved to close the United States Agency for International Development, slashed the Department of Education and taken over the General Services Administration that controls federal IT structures. Doge staffers have also gained access to the treasury department, as well as set their sights on the Department of Defense, energy department, Environmental Protection Agency and at least a dozen others.

Navalny's widow has moral authority but nowhere near his political skills. "All these… liberal figures have extremely low approval ratings," says academic Tatiana Stanovaya. Instead, she detects a consolidation of support for the Kremlin which she links to a surge in Ukrainian drone strikes on Russia. "People see that we are very vulnerable and they have to choose the strongest player to rely on," the analyst explains. "It's not because they like Putin or consider him a positive hero. It's because he can protect Russia in a very hostile environment."

Despite exceptional capabilities in knowledge-intensive tasks, Large Language Models (LLMs) face a critical gap in understanding how they internalize new knowledge, particularly how to structurally embed acquired knowledge in their neural computations. We address this issue through the lens of knowledge circuit evolution, identifying computational subgraphs that facilitate knowledge storage and processing. Our systematic analysis of circuit evolution throughout continual pre-training reveals several key findings: (1) the acquisition of new knowledge is influenced by its relevance to pre-existing knowledge; (2) the evolution of knowledge circuits exhibits a distinct phase shift from formation to optimization; (3) the evolution of knowledge circuits follows a deep-to-shallow pattern. These insights not only advance our theoretical understanding of the mechanisms of new knowledge acquisition in LLMs, but also provide potential implications for improving continual pre-training strategies to enhance model performance. Code and data will be available at https://github.com/zjunlp/DynamicKnowledgeCircuits.

This article explores the concept of technological singularity and the factors that could accelerate or hinder its arrival. The butterfly effect is used as a framework to understand how seemingly small changes in complex systems can have significant and unpredictable outcomes. In section II, we discuss the various factors that could hasten the arrival of technological singularity, such as advances in artificial intelligence and machine learning, breakthroughs in quantum computing, progress in brain-computer interfaces and human augmentation, and development of nanotechnology and 3D printing. In section III, we examine the factors that could delay or impede the arrival of technological singularity, including technical limitations and setbacks in AI and machine learning, ethical and societal concerns around AI and its impact on jobs and privacy, lack of sufficient investment in research and development, and regulatory barriers and political instability. Section IV explores the interplay of these factors and how they can impact the butterfly effect. Finally, in the conclusion, we summarize the key points discussed and emphasize the importance of considering the butterfly effect in predicting the future of technology. We call for continued research and investment in technology to shape its future and mitigate potential risks.

In this paper, we leverage recent advancements in feature monosemanticity to extract interpretable features from deep multimodal models, offering a data-driven understanding of modality gaps. Specifically, we investigate CLIP (Contrastive Language-Image Pretraining), a prominent visual-language representation model trained on extensive image-text pairs. Building upon interpretability tools developed for single-modal models, we extend these methodologies to assess multi-modal interpretability of CLIP features. Additionally, we introduce the Modality Dominance Score (MDS) to attribute the interpretability of each feature to its respective modality. Next, we transform CLIP features into a more interpretable space, enabling us to categorize them into three distinct classes: vision features (single-modal), language features (single-modal), and visual-language features (cross-modal). Our findings reveal that this categorization aligns closely with human cognitive understandings of different modalities. We also demonstrate significant use cases of this modality-specific features including detecting gender bias, adversarial attack defense and text-to-image model editing. These results indicate that large-scale multimodal models, equipped with task-agnostic interpretability tools, offer valuable insights into key connections and distinctions between different modalities.

In this paper, we introduce MASQRAD (Multi-Agent Strategic Query Resolution and Diagnostic tool), a transformative framework for query resolution based on the actor-critic model, which utilizes multiple generative AI agents. MASQRAD is excellent at translating imprecise or ambiguous user inquiries into precise and actionable requests. This framework generates pertinent visualizations and responses to these focused queries, as well as thorough analyses and insightful interpretations for users. MASQRAD addresses the common shortcomings of existing solutions in domains that demand fast and precise data interpretation, such as their incapacity to successfully apply AI for generating actionable insights and their challenges with the inherent ambiguity of user queries. MASQRAD functions as a sophisticated multi-agent system but "masquerades" to users as a single AI entity, which lowers errors and enhances data interaction. This approach makes use of three primary AI agents: Actor Generative AI, Critic Generative AI, and Expert Analysis Generative AI. Each is crucial for creating, enhancing, and evaluating data interactions. The Actor AI generates Python scripts to generate data visualizations from large datasets within operational constraints, and the Critic AI rigorously refines these scripts through multi-agent debate. Finally, the Expert Analysis AI contextualizes the outcomes to aid in decision-making. With an accuracy rate of 87\% when handling tasks related to natural language visualization, MASQRAD establishes new benchmarks for automated data interpretation and showcases a noteworthy advancement that has the potential to revolutionize AI-driven applications.

Large Language Models (LLMs) have achieved remarkable success in various domains but remain vulnerable to adversarial jailbreak attacks. Existing prompt-defense strategies, including parameter-modifying and parameter-free approaches, face limitations in adaptability, interpretability, and customization, constraining their effectiveness against evolving threats. To address these challenges, we propose ShieldLearner, a novel paradigm that mimics human learning in defense. Through trial and error, it autonomously distills attack signatures into a Pattern Atlas and synthesizes defense heuristics into a Meta-analysis Framework, enabling systematic and interpretable threat detection. Furthermore, we introduce Adaptive Adversarial Augmentation to generate adversarial variations of successfully defended prompts, enabling continuous self-improvement without model retraining. In addition to standard benchmarks, we create a hard test set by curating adversarial prompts from the Wildjailbreak dataset, emphasizing more concealed malicious intent. Experimental results show that ShieldLearner achieves a significantly higher defense success rate than existing baselines on both conventional and hard test sets, while also operating with lower computational overhead, making it a practical and efficient solution for real-world adversarial defense.