Mayor Michael Melham of Belleville, New Jersey, gives an update on the mysterious drone sightings across the Garden State on'The Faulkner Focus.' Sen. Rand Paul, R-Ky., blocked a Senate bill Wednesday that would have authorized resources for state and local authorities to track drones that have mystified residents across New Jersey and the Northeast in recent weeks. Paul objected to the passage of the bill, citing his long-standing concerns over expanding governmental powers. "This body must not rush to grant sweeping surveillance powers without proper consideration and debate by the committees of jurisdiction," he said. Sen. Rand Paul, ranking member of the Senate Homeland Security and Governmental Affairs Committee, blocked a bill Wednesday that would have allowed local law enforcement agencies to track aerial drones.

U.S. Army Staff Sergeant Payton May joins'Fox & Friends Weekend' and sheds light on being reunited with his former military service dog Yyacob. The Senate voted to pass the 895 billion annual defense policy bill that includes a pay raise for U.S. servicemembers and a provision that restricts transgender care. The bill passed 85 to 14, and now heads to President Biden's desk for his signature. The legislation scored a more bipartisan vote in the Senate than it did in the House, where more Democrats voted no on the legislation in protest of the transgender provisions. The bill prohibits military health care provider Tricare from paying for transgender care "that could result in sterilization" for children under 18.

Raj Goyle, CEO of intelligence firm Bodhala and former Democratic Kansas state representative, told Fox News Digital it is encouraging to see members of both parties come together to try and determine the source of these drones. SpaceX and Tesla founder Elon Musk speaks during an America PAC town hall on October 26, 2024, in Lancaster, Pennsylvania. AI WARS: OpenAI is pushing back against Elon Musk's latest attempt to rework his lawsuit against the artificial intelligence giant that seeks to prevent the company from moving to a for-profit structure, noting in a blog post and legal filing that Musk had argued for it to do so years ago. AGE OF AI: OpenAI CEO Sam Altman is joining the list of U.S. tech titans donating to President-elect Trump's inaugural fund, a spokesperson exclusively told Fox News Digital. ARTIFICIAL INTELLIGENCE: The House task force on artificial intelligence is urging the U.S. government to aim for "a flexible sectoral regulatory framework" for the technology in a nearly 300-page report released Tuesday morning.

On October 7, as Israel began its latest war on Gaza following Hamas's incursion into southern Israel, the European Union's position was immediately clear. "Israel has a right to defend itself – today and in the days to come," European Commission President Ursula von der Leyen posted on X, alongside an image of her office's headquarters lit up with Israel's flag. "The European Union stands with Israel." Israel has since been placed on trial for genocide at the International Court of Justice (ICJ) at The Hague and its leaders – as well as a top Hamas commander – have been indicted by the International Criminal Court (ICC). Yet the EU continues to partner with Israeli institutions under its "Horizon" scheme, a programme that funds research and innovation.

With the growth of social media and large language models, content moderation has become crucial. Many existing datasets lack adequate representation of different groups, resulting in unreliable assessments. To tackle this, we propose a socio-culturally aware evaluation framework for LLM-driven content moderation and introduce a scalable method for creating diverse datasets using persona-based generation. Our analysis reveals that these datasets provide broader perspectives and pose greater challenges for LLMs than diversity-focused generation methods without personas. This challenge is especially pronounced in smaller LLMs, emphasizing the difficulties they encounter in moderating such diverse content.

Transfer learning aims to transfer knowledge or information from a source domain to a relevant target domain. In this paper, we understand transfer learning from the perspectives of knowledge transferability and trustworthiness. This involves two research questions: How is knowledge transferability quantitatively measured and enhanced across domains? Can we trust the transferred knowledge in the transfer learning process? To answer these questions, this paper provides a comprehensive review of trustworthy transfer learning from various aspects, including problem definitions, theoretical analysis, empirical algorithms, and real-world applications. Specifically, we summarize recent theories and algorithms for understanding knowledge transferability under (within-domain) IID and non-IID assumptions. In addition to knowledge transferability, we review the impact of trustworthiness on transfer learning, e.g., whether the transferred knowledge is adversarially robust or algorithmically fair, how to transfer the knowledge under privacy-preserving constraints, etc. Beyond discussing the current advancements, we highlight the open questions and future directions for understanding transfer learning in a reliable and trustworthy manner.

How are AI assistants being used in the real world? While model providers in theory have a window into this impact via their users' data, both privacy concerns and practical challenges have made analyzing this data difficult. To address these issues, we present Clio (Claude insights and observations), a privacy-preserving platform that uses AI assistants themselves to analyze and surface aggregated usage patterns across millions of conversations, without the need for human reviewers to read raw conversations. We validate this can be done with a high degree of accuracy and privacy by conducting extensive evaluations. We demonstrate Clio's usefulness in two broad ways. First, we share insights about how models are being used in the real world from one million Claude.ai Free and Pro conversations, ranging from providing advice on hairstyles to providing guidance on Git operations and concepts. We also identify the most common high-level use cases on Claude.ai (coding, writing, and research tasks) as well as patterns that differ across languages (e.g., conversations in Japanese discuss elder care and aging populations at higher-than-typical rates). Second, we use Clio to make our systems safer by identifying coordinated attempts to abuse our systems, monitoring for unknown unknowns during critical periods like launches of new capabilities or major world events, and improving our existing monitoring systems. We also discuss the limitations of our approach, as well as risks and ethical concerns. By enabling analysis of real-world AI usage, Clio provides a scalable platform for empirically grounded AI safety and governance.

Recently machine unlearning (MU) is proposed to remove the imprints of revoked samples from the already trained model parameters, to solve users' privacy concern. Different from the runtime expensive retraining from scratch, there exist two research lines, exact MU and approximate MU with different favorites in terms of accuracy and efficiency. In this paper, we present a novel hybrid strategy on top of them to achieve an overall success. It implements the unlearning operation with an acceptable computation cost, while simultaneously improving the accuracy as much as possible. Specifically, it runs reasonable unlearning techniques by estimating the retraining workloads caused by revocations. If the workload is lightweight, it performs retraining to derive the model parameters consistent with the accurate ones retrained from scratch. Otherwise, it outputs the unlearned model by directly modifying the current parameters, for better efficiency. In particular, to improve the accuracy in the latter case, we propose an optimized version to amend the output model with lightweight runtime penalty. We particularly study the boundary of two approaches in our frameworks to adaptively make the smart selection. Extensive experiments on real datasets validate that our proposals can improve the unlearning efficiency by 1.5$\times$ to 8$\times$ while achieving comparable accuracy.

Human trafficking (HT) remains a critical issue, with traffickers increasingly leveraging online escort advertisements (ads) to advertise victims anonymously. Existing detection methods, including Authorship Attribution (AA), often center on text-based analyses and neglect the multimodal nature of online escort ads, which typically pair text with images. To address this gap, we introduce MATCHED, a multimodal dataset of 27,619 unique text descriptions and 55,115 unique images collected from the Backpage escort platform across seven U.S. cities in four geographical regions. Our study extensively benchmarks text-only, vision-only, and multimodal baselines for vendor identification and verification tasks, employing multitask (joint) training objectives that achieve superior classification and retrieval performance on in-distribution and out-of-distribution (OOD) datasets. Integrating multimodal features further enhances this performance, capturing complementary patterns across text and images. While text remains the dominant modality, visual data adds stylistic cues that enrich model performance. Moreover, text-image alignment strategies like CLIP and BLIP2 struggle due to low semantic overlap and vague connections between the modalities of escort ads, with end-to-end multimodal training proving more robust. Our findings emphasize the potential of multimodal AA (MAA) to combat HT, providing LEAs with robust tools to link ads and disrupt trafficking networks.

The capabilities of recent large language models (LLMs) to generate high-quality content indistinguishable by humans from human-written texts rises many concerns regarding their misuse. Previous research has shown that LLMs can be effectively misused for generating disinformation news articles following predefined narratives. Their capabilities to generate personalized (in various aspects) content have also been evaluated and mostly found usable. However, a combination of personalization and disinformation abilities of LLMs has not been comprehensively studied yet. Such a dangerous combination should trigger integrated safety filters of the LLMs, if there are some. This study fills this gap by evaluation of vulnerabilities of recent open and closed LLMs, and their willingness to generate personalized disinformation news articles in English. We further explore whether the LLMs can reliably meta-evaluate the personalization quality and whether the personalization affects the generated-texts detectability. Our results demonstrate the need for stronger safety-filters and disclaimers, as those are not properly functioning in most of the evaluated LLMs. Additionally, our study revealed that the personalization actually reduces the safety-filter activations; thus effectively functioning as a jailbreak. Such behavior must be urgently addressed by LLM developers and service providers.