The Democratic Republic of the Congo has accused Rwanda of carrying out a drone attack that damaged a civilian aircraft at the airport in the strategic eastern city of Goma, the capital of North Kivu province. Fighting has flared in recent days around the town of Sake, 20km (12 miles) from Goma, between M23 rebels – which Kinshasa says are backed by Kigali – and Congolese government forces. "On the night of Friday to Saturday, at 2-o-clock in the morning local time, there was a drone attack by the Rwandan army," said Lieutenant-Colonel Guillaume Ndjike Kaito, army spokesperson for North Kivu province. "It had obviously come from the Rwandan territory, violating the territorial integrity of the Democratic Republic of the Congo," he added in a video broadcast by the governorate. The drones "targeted aircraft of DRC armed forces".

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Idaho legislature passed a bill this week to carry out the death penalty for sex crimes against children younger than 12. Another bill permitting prosecutors to bring sexual exploitation charges against producers of child pornography using artificial intelligence (AI) also passed the assembly in the same session. HB 515 would amend Idaho's current statute that carries a life sentence for "lewd conduct with a minor" below the age of 16. If the child is under 12, if the act is "especially heinous, atrocious or cruel, manifesting exceptional depravity," then prosecutors would seek the death penalty.

Heritage Foundation tech policy director Kara Frederick joins'America's Newsroom' to discuss pornographic AI photos of Taylor Swift sparking conversations about deepfake regulation. Ahead of the general election, more states are proactively introducing new bills to regulate the rise of artificial intelligence (AI) created "deepfakes," or digitally altered videos or images, in their campaign materials. Advanced generative AI tools, ranging from voice-cloning software to image generators, have swiftly become fixtures in election cycles both domestically and internationally. In the lead-up to the 2024 presidential race last year, a wave of innovation saw the integration of AI-generated audio and imagery in campaign ads, alongside ventures into AI chatbots to cultivate voter engagement. This week, Wisconsin joined 20 other states that have either introduced or passed election laws requiring election campaigns to disclose when advertisements are AI-generated.

Rachelle Hampton and Candice Lim catch up on the latest stories churning the Taylor Swift media machine, from her lawyers sending a cease and desist letter to a college student, to her possibly leading a groundbreaking case against AI deepfakes. Then, they break down the backlash surrounding Emily Mariko, who was criticized by her followers for selling out -- and shelling out -- a tote bag. This podcast is produced by Se'era Spragley Ricks, Daisy Rosario, Candice Lim and Rachelle Hampton.

Eliezer Yudkowsky, a 44-year-old academic wearing a grey polo shirt, rocks slowly on his office chair and explains with real patience – taking things slowly for a novice like me – that every single person we know and love will soon be dead. They will be murdered by rebellious self-aware machines. "The difficulty is, people do not realise," Yudkowsky says mildly, maybe sounding just a bit frustrated, as if irritated by a neighbour's leaf blower or let down by the last pages of a novel. "We have a shred of a chance that humanity survives." I have set out to meet and talk to a small but growing band of luddites, doomsayers, disruptors and other AI-era sceptics who see only the bad in the way our spyware-steeped, infinitely doomscrolling world is tending. I want to find out why these techno-pessimists think the way they do. I want to know how they would render change. Out of all of those I speak to, Yudkowsky is the most pessimistic, the least convinced that civilisation has a hope.

In previous work [6, 9, 10], we advanced a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measure of similarity based primarily on a Bayesian (MAP) analysis of image differ(cid:173) ences, leading to a "dual" basis similar to eigenfaces [13]. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenface matching was recently demonstrated using results from DARPA's 1996 "FERET" face recognition competition, in which this probabilistic matching algorithm was found to be the top performer. We have further developed a simple method of replacing the costly com put ion of nonlinear (online) Bayesian similarity measures by the relatively inexpensive computation of linear (offline) subspace projections and simple (online) Euclidean norms, thus resulting in a significant computational speed-up for implementation with very large image databases as typically encountered in real-world applications.

The thesis focuses on developing a data-driven algorithm, based on machine learning, to solve the stochastic alternating current (AC) chance-constrained (CC) Optimal Power Flow (OPF) problem. Although the AC CC-OPF problem has been successful in academic circles, it is highly nonlinear and computationally demanding, which limits its practical impact. The proposed approach aims to address this limitation and demonstrate its empirical efficiency through applications to multiple IEEE test cases. To solve the non-convex and computationally challenging CC AC-OPF problem, the proposed approach relies on a machine learning Gaussian process regression (GPR) model. The full Gaussian process (GP) approach is capable of learning a simple yet non-convex data-driven approximation to the AC power flow equations that can incorporate uncertain inputs. The proposed approach uses various approximations for GP-uncertainty propagation. The full GP CC-OPF approach exhibits highly competitive and promising results, outperforming the state-of-the-art sample-based chance constraint approaches. To further improve the robustness and complexity/accuracy trade-off of the full GP CC-OPF, a fast data-driven setup is proposed. This setup relies on the sparse and hybrid Gaussian processes (GP) framework to model the power flow equations with input uncertainty.

The rapid advancement of artificial intelligence (AI) has highlighted ChatGPT as a pivotal technology in the field of information retrieval (IR). Distinguished from its predecessors, ChatGPT offers significant benefits that have attracted the attention of both the industry and academic communities. While some view ChatGPT as a groundbreaking innovation, others attribute its success to the effective integration of product development and market strategies. The emergence of ChatGPT, alongside GPT-4, marks a new phase in Generative AI, generating content that is distinct from training examples and exceeding the capabilities of the prior GPT-3 model by OpenAI. Unlike the traditional supervised learning approach in IR tasks, ChatGPT challenges existing paradigms, bringing forth new challenges and opportunities regarding text quality assurance, model bias, and efficiency. This paper seeks to examine the impact of ChatGPT on IR tasks and offer insights into its potential future developments.

This survey paper presents a comprehensive analysis of crime prediction methodologies, exploring the various techniques and technologies utilized in this area. The paper covers the statistical methods, machine learning algorithms, and deep learning techniques employed to analyze crime data, while also examining their effectiveness and limitations. We propose a methodological taxonomy that classifies crime prediction algorithms into specific techniques. This taxonomy is structured into four tiers, including methodology category, methodology sub-category, methodology techniques, and methodology sub-techniques. Empirical and experimental evaluations are provided to rank the different techniques. The empirical evaluation assesses the crime prediction techniques based on four criteria, while the experimental evaluation ranks the algorithms that employ the same sub-technique, the different sub-techniques that employ the same technique, the different techniques that employ the same methodology sub-category, the different methodology sub-categories within the same category, and the different methodology categories. The combination of methodological taxonomy, empirical evaluations, and experimental comparisons allows for a nuanced and comprehensive understanding of crime prediction algorithms, aiding researchers in making informed decisions. Finally, the paper provides a glimpse into the future of crime prediction techniques, highlighting potential advancements and opportunities for further research in this field

Knowledge editing (KE) aims to efficiently and precisely modify the behavior of large language models (LLMs) to update specific knowledge without negatively influencing other knowledge. Current research primarily focuses on white-box LLMs editing, overlooking an important scenario: black-box LLMs editing, where LLMs are accessed through interfaces and only textual output is available. In this paper, we first officially introduce KE on black-box LLMs and then propose a comprehensive evaluation framework to overcome the limitations of existing evaluations that are not applicable to black-box LLMs editing and lack comprehensiveness. To tackle privacy leaks of editing data and style over-editing in current methods, we introduce a novel postEdit framework, resolving privacy concerns through downstream post-processing and maintaining textual style consistency via fine-grained editing to original responses. Experiments and analysis on two benchmarks demonstrate that postEdit outperforms all baselines and achieves strong generalization, especially with huge improvements on style retention (average $+20.82\%\uparrow$).