Things to Do in L.A. Tap to enable a layout that focuses on the article. As Compton students ace tests, educators are baffled by Rep. Maxine Waters' snub of school bond Students walk on campus at Dominguez High School in Compton. A bond measure would provide millions of dollars to rebuild the school. This is read by an automated voice. Please report any issues or inconsistencies here .

Membership in ACM includes a subscription to Communications of the ACM (CACM), the computing industry's most trusted source for staying connected to the world of advanced computing. In an era defined by rapid technological advancement, particularly in fields such as artificial intelligence (AI), there is a growing discourse surrounding the pivotal role of academia and the impact of federal funding on innovation. The following conversation sheds light on an often-underdiscussed facet of this relationship: the profound influence of academic research on the formation and continued success of large technology companies such as Google. The participants include Magda Balazińska (MB) and three senior Google engineers--Urs Hölzle (UH), Jeff Dean (JD), and Parthasarathy Ranganathan (PR)--who collectively have more than a century of experience spanning both academia and industry, and between them represent different disciplines across the computing stack (distributed systems, AI, hardware). The discussion delves into the foundational role of academia in Google's inception, the long-term impact of federally funded research, the stories behind key innovations, and the grand challenges that lie ahead for academic research.

x402 enables Hypertext Transfer Protocol (HTTP) services like application programming interfaces (APIs), data feeds, and inference providers to accept cryptocurrency payments for access. As agents increasingly consume these services, discovery becomes critical: which swap interface should an agent trust? Which data provider is the most reliable? We introduce TraceRank, a reputation-weighted ranking algorithm where payment transactions serve as endorsements. TraceRank seeds addresses with precomputed reputation metrics and propagates reputation through payment flows weighted by transaction value and temporal recency. Applied to x402's payment graph, this surfaces services preferred by high-reputation users rather than those with high transaction volume. Our system combines TraceRank with semantic search to respond to natural language queries with high quality results. We argue that reputation propagation resists Sybil attacks by making spam services with many low-reputation payers rank below legitimate services with few high-reputation payers. Ultimately, we aim to construct a search method for x402 enabled services that avoids infrastructure bias and has better performance than purely volume based or semantic methods.

This paper presents an adversary model and a simulation framework specifically tailored for analyzing attacks on distributed systems composed of multiple distributed protocols, with a focus on assessing the security of blockchain networks. Our model classifies and constrains adversarial actions based on the assumptions of the target protocols, defined by failure models, communication models, and the fault tolerance thresholds of Byzantine Fault Tolerant (BFT) protocols. The goal is to study not only the intended effects of adversarial strategies but also their unintended side effects on critical system properties. We apply this framework to analyze fairness properties in a Hyperledger Fabric (HF) blockchain network. Our focus is on novel fairness attacks that involve coordinated adversarial actions across various HF services. Simulations show that even a constrained adversary can violate fairness with respect to specific clients (client fairness) and impact related guarantees (order fairness), which relate the reception order of transactions to their final order in the blockchain. This paper significantly extends our previous work by introducing and evaluating a mitigation mechanism specifically designed to counter transaction reordering attacks. We implement and integrate this defense into our simulation environment, demonstrating its effectiveness under diverse conditions.

Dr Mehmet Oz reportedly told federal staffers that artificial intelligence models may be better than frontline human physicians in his first all-staff meeting this week. Oz told staffers that if a patient went to the doctor for a diabetes diagnosis it would cost roughly 100 an hour, compared with 2 an hour for an AI visit, according to unnamed sources who spoke to Wired magazine. He added that patients may prefer an AI avatar. Oz also spent a portion of his first meeting with employees arguing they had a "patriotic duty" to remain healthy, with the goal of decreasing costs to the health insurance system. He made a similar argument at his confirmation hearing.

Blockchain is increasingly offered as blockchain-as-a-service (BaaS) by cloud service providers. However, configuring BaaS appropriately for optimal performance and reliability resorts to try-and-error. A key challenge is that BaaS is often perceived as a ``black-box,'' leading to uncertainties in performance and resource provisioning. Previous studies attempted to address this challenge; however, the impacts of both vertical and horizontal scaling remain elusive. To this end, we present machine learning-based models to predict network reliability and throughput based on scaling configurations. In our evaluation, the models exhibit prediction errors of ~1.9%, which is highly accurate and can be applied in the real-world.

This research describes a novel approach to evaluating hedging behaviour in large language models (LLMs), specifically in the context of human rights as defined in the Universal Declaration of Human Rights (UDHR). Hedging and non-affirmation are behaviours that express ambiguity or a lack of clear endorsement on specific statements. These behaviours are undesirable in certain contexts, such as queries about whether different groups are entitled to specific human rights; since all people are entitled to human rights. Here, we present the first systematic attempt to measure these behaviours in the context of human rights, with a particular focus on between-group comparisons. To this end, we design a novel prompt set on human rights in the context of different national or social identities. We develop metrics to capture hedging and non-affirmation behaviours and then measure whether LLMs exhibit demographic parity when responding to the queries. We present results on three leading LLMs and find that all models exhibit some demographic disparities in how they attribute human rights between different identity groups. Futhermore, there is high correlation between different models in terms of how disparity is distributed amongst identities, with identities that have high disparity in one model also facing high disparity in both the other models. While baseline rates of hedging and non-affirmation differ, these disparities are consistent across queries that vary in ambiguity and they are robust across variations of the precise query wording. Our findings highlight the need for work to explicitly align LLMs to human rights principles, and to ensure that LLMs endorse the human rights of all groups equally.

The paper considers an approach of using Google Search API and GPT-4o model for qualitative and quantitative analyses of news through retrieval-augmented generation (RAG). This approach was applied to analyze news about the 2024 US presidential election process. Different news sources for different time periods have been analyzed. Quantitative scores generated by GPT model have been analyzed using Bayesian regression to derive trend lines. The distributions found for the regression parameters allow for the analysis of uncertainty in the election process. The obtained results demonstrate that using the GPT models for news analysis, one can get informative analytics and provide key insights that can be applied in further analyses of election processes.

Days after Vice President Kamala Harris launched her presidential bid, a video -- created with the help of artificial intelligence -- went viral. "I ... am your Democrat candidate for president because Joe Biden finally exposed his senility at the debate," a voice that sounded like Harris' said in the fake audio track used to alter one of her campaign ads. "I was selected because I am the ultimate diversity hire." Billionaire Elon Musk -- who has endorsed Harris' Republican opponent, former President Trump-- shared the video on X, then clarified two days later that it was actually meant as a parody. His initial tweet had 136 million views.

Former FBI Special Agent Nicole Parker joins'Cavuto Live' to weigh in on the cancellation of the Taylor Swift concerts in Vienna due to a terror plot. Former President Trump promoted images on Sunday, including some generated through artificial intelligence, showing apparent support from singer Taylor Swift and her fans, triggering a widespread media outcry. Trump posted a collage of Swift-related images to his Truth Social account showing apparent support from the pop star and her diehard fans known as "Swifties." One doctored image played off the classic Uncle Sam recruiting posters, showing Swift in red, white and blue with the caption, "Taylor Swift Wants You To Vote For Donald Trump." Over the images, he wrote, "I accept!"