The suspect in Charlie Kirk's assassination has been captured, FBI director Kash Patel announced MSNBC sparks outrage for'disgusting' Charlie Kirk comments following Utah shooting Tragedy as Charlie Kirk's wife left behind with two young children after conservative activist is fatally shot A DEI mayor, an inconvenient crime and video they never wanted you to see: MAUREEN CALLAHAN knows why the Left has sympathy for that killer... but none for his victim Sweater weather starts here - the cozy, chic pieces from Soft Surroundings you'll actually wear all season We only had one symptom we dismissed... but then we were diagnosed with the rarest form of melanoma Soft-touch prosecutor let felon walk free... before crook'slit Auburn professor's throat in random attack' I tried the 30 cent'miracle chill pill' before a big event.. now I'm taking it for everything Donald Trump and House Republicans lead prayers for Charlie Kirk's family after conservative star is fatally shot Prince Harry says his father King Charles is'great' following their first meeting in 19 months... which was over a cup of tea and just 55 minutes long Liberal media defends thug who killed Ukrainian woman in cold blood: 'This man was hurting' Knifeman accused of stabbing Ukrainian refugee to death gives chilling reason for the attack... as he speaks for the first time from jail on the murder that shocked America Fox News reveals new lineup and elevates star White House reporter who's sparred with Trump Horrific new details of passenger injuries after they were'thrown' around Delta flight during'severe turbulence' Artificial Intelligence if left unchecked could pose a serious danger to democracy and peace, the United Nations has warned. UN Secretary-General Antonio Guterres made the comments as a panel of experts warned of the dangers of increasingly realistic deepfakes as well as the evolution of autonomous weapons and AI use by criminal and terrorist groups. The group called for greater global collaboration on the technology and said its development should not be left to market forces. The panel of around 40 experts from the fields of technology, law and data protection was established by Guterres in October. Their report raised the alarm over the lack of global governance of AI as well as the effective exclusion of developing countries from debates about the technology's future.

A group that includes sex workers, sex tech businesses, and sex educators has demanded a seat at the table to shape AI regulations that they say could lead to discrimination against them. A group of sex industry professionals and advocates issued an open letter to EU regulators on Thursday, claiming that their views are being overlooked in vital discussions on policing AI technology despite also being implicated in AI's momentous rise. In response to European internet regulations, a collective of adult industry members--including sex workers, erotic filmmakers, sex tech enterprises, and sex educators--urged the European Commission to include them in future negotiations shaping AI regulations, according to the letter, seen by WIRED. The group includes erotic filmmaker Erika Lust's company as well as the European Sex Workers' Rights Alliance campaign group, and is signed the Open Mind AI initiative. The group aims to alert the commission of what it says is a "critical gap" in discussions on AI regulation.

Improving the automatic and timely recognition of construction and demolition waste (C&DW) composition is crucial for enhancing business returns, economic outcomes, and sustainability. Technologies like computer vision, artificial intelligence (AI), robotics, and internet of things (IoT) are increasingly integrated into waste processing to achieve these goals. While deep learning (DL) models show promise in recognising homogeneous C&DW piles, few studies assess their performance with mixed, highly contaminated material in commercial settings. Drawing on extensive experience at a C&DW materials recovery facility (MRF) in Sydney, Australia, we explore the challenges and opportunities in developing an advanced automated mixed C&DW management system. We begin with an overview of the evolution of waste management in the construction industry, highlighting its environmental, economic, and societal impacts. We review various C&DW analysis techniques, concluding that DL-based visual methods are the optimal solution. Additionally, we examine the progression of sensor and camera technologies for C&DW analysis as well as the evolution of DL algorithms focused on object detection and material segmentation. We also discuss C&DW datasets, their curation, and innovative methods for their creation. Finally, we share insights on C&DW visual analysis, addressing technical and commercial challenges, research trends, and future directions for mixed C&DW analysis. This paper aims to improve the efficiency of C&DW management by providing valuable insights for ongoing and future research and development efforts in this critical sector.

Machine-learning technologies are seeing increased deployment in real-world market scenarios. In this work, we explore the strategic behaviors of large language models (LLMs) when deployed as autonomous agents in multi-commodity markets, specifically within Cournot competition frameworks. We examine whether LLMs can independently engage in anti-competitive practices such as collusion or, more specifically, market division. Our findings demonstrate that LLMs can effectively monopolize specific commodities by dynamically adjusting their pricing and resource allocation strategies, thereby maximizing profitability without direct human input or explicit collusion commands. These results pose unique challenges and opportunities for businesses looking to integrate AI into strategic roles and for regulatory bodies tasked with maintaining fair and competitive markets. The study provides a foundation for further exploration into the ramifications of deferring high-stakes decisions to LLM-based agents.

Across various applications, humans increasingly use black-box artificial intelligence (AI) systems without insight into these systems' reasoning. To counter this opacity, explainable AI (XAI) methods promise enhanced transparency and interpretability. While recent studies have explored how XAI affects human-AI collaboration, few have examined the potential pitfalls caused by incorrect explanations. The implications for humans can be far-reaching but have not been explored extensively. To investigate this, we ran a study (n=160) on AI-assisted decision-making in which humans were supported by XAI. Our findings reveal a misinformation effect when incorrect explanations accompany correct AI advice with implications post-collaboration. This effect causes humans to infer flawed reasoning strategies, hindering task execution and demonstrating impaired procedural knowledge. Additionally, incorrect explanations compromise human-AI team-performance during collaboration. With our work, we contribute to HCI by providing empirical evidence for the negative consequences of incorrect explanations on humans post-collaboration and outlining guidelines for designers of AI.

In real-world applications with Large Language Models (LLMs), external retrieval mechanisms - such as Search-Augmented Generation (SAG), tool utilization, and Retrieval-Augmented Generation (RAG) - are often employed to enhance the quality of augmented generations in dialogues. These approaches often come with multi-turn dialogue, where each interaction is enriched by relevant information retrieved from external sources. Existing benchmarks either assess LLMs' chat abilities in multi-turn dialogues or their use of retrieval for augmented responses in single-turn settings. However, there is a gap in evaluating LLMs' ability to leverage retrieval for more precise responses across multiple turns. To address this limitation, we introduce RAD-Bench (Retrieval Augmented Dialogue), a benchmark designed to evaluate LLMs' capabilities in multi-turn dialogues following retrievals, essential for their deployment in context-rich applications. RAD-Bench evaluates two key abilities of LLMs: Retrieval Synthesis and Retrieval Reasoning. These are measured using discriminative questions and retrieved contexts, and corresponding reference answers, assessing how effectively LLMs integrate and reason with context to maintain and enhance conversation quality over multiple turns. Our evaluation results on commonly used LLMs reveal that model performance deteriorates as additional layers of conditions or constraints are applied across conversation turns, even when accurate retrieved contexts are provided.

Large language models (LLMs) have revolutionized various domains, yet their utility comes with significant challenges related to outdated or problematic knowledge embedded during pretraining. This paper addresses the challenge of modifying LLMs to unlearn problematic and outdated information while efficiently integrating new knowledge without retraining from scratch. Here, we propose LLM Surgery, a framework to efficiently modify LLM behaviour by optimizing a three component objective function that: (1) Performs reverse gradient on unlearning dataset (problematic and outdated information), (2) Performs gradient descent on the update dataset (new and updated information), and (3) Minimizes the KL divergence on the retain dataset (small subset of unchanged text), ensuring alignment between pretrained and modified model outputs. Due to the lack of publicly available datasets specifically tailored for our novel task, we compiled a new dataset and an evaluation benchmark. Using Llama2-7B, we demonstrate that LLM Surgery can achieve significant forgetting on the unlearn set, a 20\% increase in accuracy on the update set, and maintain performance on the retain set.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Two likely finalists spotted at mayor's mansion From left: Former LAPD Deputy Chief Robert Arcos, LAPD Deputy Chief Emada Tingirides and former Los Angeles County Sheriff Jim McDonnell. Published Sept. 18, 2024 Updated Sept. 19, 2024 10:46 AM PT Mayor Karen Bass said she would conduct a nationwide search for the next chief of the Los Angeles Police Department, but in the end it seems she found three finalists close to home. Deputy Chief Emada Tingirides and Robert "Bobby" Arcos, a former LAPD assistant chief who works in the L.A. County district attorney's office, were seen arriving at Getty House, the mayor's residence, for their candidate interviews over the span of a few hours Tuesday. The third candidate is said to be former Los Angeles County Sheriff Jim McDonnell, who also served in the LAPD, leaving as first assistant chief.

Extract-then-Abstract is a naturally coherent paradigm to conduct abstractive summarization with the help of salient information identified by the extractive model. Previous works that adopt this paradigm train the extractor and abstractor separately and introduce extra parameters to highlight the extracted salients to the abstractor, which results in error accumulation and additional training costs. In this paper, we first introduce a parameter-free highlight method into the encoder-decoder framework: replacing the encoder attention mask with a saliency mask in the cross-attention module to force the decoder to focus only on salient parts of the input. A preliminary analysis compares different highlight methods, demonstrating the effectiveness of our saliency mask. We further propose the novel extract-and-abstract paradigm, ExtAbs, which jointly and seamlessly performs Extractive and Abstractive summarization tasks within single encoder-decoder model to reduce error accumulation. In ExtAbs, the vanilla encoder is augmented to extract salients, and the vanilla decoder is modified with the proposed saliency mask to generate summaries. Built upon BART and PEGASUS, experiments on three datasets show that ExtAbs can achieve superior performance than baselines on the extractive task and performs comparable, or even better than the vanilla models on the abstractive task.

This paper investigates the factuality of large language models (LLMs) as knowledge bases in the legal domain, in a realistic usage scenario: we allow for acceptable variations in the answer, and let the model abstain from answering when uncertain. First, we design a dataset of diverse factual questions about case law and legislation. We then use the dataset to evaluate several LLMs under different evaluation methods, including exact, alias, and fuzzy matching. Our results show that the performance improves significantly under the alias and fuzzy matching methods. Further, we explore the impact of abstaining and in-context examples, finding that both strategies enhance precision. Finally, we demonstrate that additional pre-training on legal documents, as seen with SaulLM, further improves factual precision from 63% to 81%.