This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital. We define the Factuality Issue as the probability of LLMs to produce content inconsistent with established facts. We first delve into the implications of these inaccuracies, highlighting the potential consequences and challenges posed by factual errors in LLM outputs. Subsequently, we analyze the mechanisms through which LLMs store and process facts, seeking the primary causes of factual errors. Our discussion then transitions to methodologies for evaluating LLM factuality, emphasizing key metrics, benchmarks, and studies. We further explore strategies for enhancing LLM factuality, including approaches tailored for specific domains. We focus two primary LLM configurations standalone LLMs and Retrieval-Augmented LLMs that utilizes external data, we detail their unique challenges and potential enhancements. Our survey offers a structured guide for researchers aiming to fortify the factual reliability of LLMs.

There have been tremendous efforts over the past decades dedicated to the generation of realistic graphs in a variety of domains, ranging from social networks to computer networks, from gene regulatory networks to online transaction networks. Despite the remarkable success, the vast majority of these works are unsupervised in nature and are typically trained to minimize the expected graph reconstruction loss, which would result in the representation disparity issue in the generated graphs, i.e., the protected groups (often minorities) contribute less to the objective and thus suffer from systematically higher errors. In this paper, we aim to tailor graph generation to downstream mining tasks by leveraging label information and user-preferred parity constraints. In particular, we start from the investigation of representation disparity in the context of graph generative models. To mitigate the disparity, we propose a fairness-aware graph generative model named FairGen. Our model jointly trains a label-informed graph generation module and a fair representation learning module by progressively learning the behaviors of the protected and unprotected groups, from the `easy' concepts to the `hard' ones. In addition, we propose a generic context sampling strategy for graph generative models, which is proven to be capable of fairly capturing the contextual information of each group with a high probability. Experimental results on seven real-world data sets, including web-based graphs, demonstrate that FairGen (1) obtains performance on par with state-of-the-art graph generative models across nine network properties, (2) mitigates the representation disparity issues in the generated graphs, and (3) substantially boosts the model performance by up to 17% in downstream tasks via data augmentation.

In November 2022, OpenAI launched ChatGPT. Within five days, it had over a million users. Six months later, the CEOs of the world's leading AI companies, and hundreds of researchers and experts, signed a short statement warning that mitigating the risk of extinction from AI should be a global priority on the scale of preventing nuclear war. AI's rapid technological progress and the dire warnings from its creators provoked a reaction in capitals around the world. But as lawmakers and regulators rushed to write the rules charting AI's future, many warned their efforts were insufficient to mitigate the risks from, and capitalize on the benefits of AI.

AI expert Marva Bailer tells Fox News Digital how the open availability of artificial intelligence can have negative effects and talks about potential federal legislation to control it. FIRST ON FOX: Lawmakers are eyeing safeguards for integrating artificial intelligence (AI) technology into the U.S.'s agricultural sector. A new bill introduced by Rep. Randy Feenstra, R-Iowa, and backed by both sides of the aisle aims to enforce standards for AI programs connected to everyday Americans' food, fuel and other necessities. Feenstra, whose district is heavily rural, told Fox News Digital that AI is becoming increasingly relevant in the farming industry but that existing guardrails on new technology aren't keeping up with that boom, he suggested. Rep. Randy Feenstra is leading a bill to add safeguards to AI technology used in the agricultural sector (Rod Lamkey/Pool/Getty Images) "From precision agriculture to veterinary software, the latest developments in agricultural technology – including artificial intelligence – have the power to lower input costs for farmers, protect the health of livestock and poultry, and make farming operations more efficient," Feenstra said. "We must be equally active in certifying that these new technologies, products and processes work as they should and uphold the highest industry standards."

With less than a year to go before one of the most consequential elections in US history, Microsoft's AI chatbot is responding to political queries with conspiracies, misinformation, and out-of-date or incorrect information. When WIRED asked the chatbot, initially called Bing Chat and recently renamed Microsoft Copilot, about polling locations for the 2024 US election, the bot referenced in-person voting by linking to an article about Russian president Vladimir Putin running for reelection next year. When asked about electoral candidates, it listed numerous GOP candidates who have already pulled out of the race. After being asked to create an image of a person voting at a ballot box in Arizona, Copilot told WIRED it was unable to--before displaying a number of different images pulled from the internet that linked to articles about debunked election conspiracies regarding the 2020 US election. When WIRED asked Copilot to recommend a list of Telegram channels that discuss "election integrity," the chatbot shared a link to a website run by a far-right group based inColorado that has been sued by civil rights groups, including the NAACP, for allegedly intimidating voters, including at their homes, during purported canvassing and voter campaigns in the aftermath of the 2020 election.

Financial regulators in the United States have named artificial intelligence (AI) as a risk to the financial system for the first time. In its latest annual report, the Financial Stability Oversight Council said the growing use of AI in financial services is a "vulnerability" that should be monitored. While AI offers the promise of reducing costs, improving efficiency, identifying more complex relationships and improving performance and accuracy, it can also "introduce certain risks, including safety-and-soundness risks like cyber and model risks," the FSOC said in its annual report released on Thursday. The FSOC, which was established in the wake of the 2008 financial crisis to identify excessive risks in the financial system, said developments in AI should be monitored to ensure that oversight mechanisms "account for emerging risks" while facilitating "efficiency and innovation". Authorities must also "deepen expertise and capacity" to monitor the field, the FSOC said.

Ukraine's Air Force said Russia launched 42 drones and six missiles, mostly targeting the southern Odesa region. Air defence systems destroyed most of the Iranian-made Shahed drones but 11 people were injured by falling debris, which also damaged buildings and warehouses. The air force said Ukraine was also attacked by Russian fighter jets dropping Kinzhal hypersonic missiles. One missile was shot down over the Kyiv region, but another two hit the west of the capital where there is an air base. Kyiv regional governor Ruslan Kravchenko said no casualties were reported, or damage to critical and civilian infrastructure.

Vegetation structure mapping is critical for understanding the global carbon cycle and monitoring nature-based approaches to climate adaptation and mitigation. Repeated measurements of these data allow for the observation of deforestation or degradation of existing forests, natural forest regeneration, and the implementation of sustainable agricultural practices like agroforestry. Assessments of tree canopy height and crown projected area at a high spatial resolution are also important for monitoring carbon fluxes and assessing tree-based land uses, since forest structures can be highly spatially heterogeneous, especially in agroforestry systems. Very high resolution satellite imagery (less than one meter (1m) Ground Sample Distance) makes it possible to extract information at the tree level while allowing monitoring at a very large scale. This paper presents the first high-resolution canopy height map concurrently produced for multiple sub-national jurisdictions. Specifically, we produce very high resolution canopy height maps for the states of California and Sao Paulo, a significant improvement in resolution over the ten meter (10m) resolution of previous Sentinel / GEDI based worldwide maps of canopy height. The maps are generated by the extraction of features from a self-supervised model trained on Maxar imagery from 2017 to 2020, and the training of a dense prediction decoder against aerial lidar maps. We also introduce a post-processing step using a convolutional network trained on GEDI observations. We evaluate the proposed maps with set-aside validation lidar data as well as by comparing with other remotely sensed maps and field-collected data, and find our model produces an average Mean Absolute Error (MAE) of 2.8 meters and Mean Error (ME) of 0.6 meters.

Regular blood pressure (BP) monitoring in clinical and ambulatory settings plays a crucial role in the prevention, diagnosis, treatment, and management of cardiovascular diseases. Recently, the widespread adoption of ambulatory BP measurement devices has been driven predominantly by the increased prevalence of hypertension and its associated risks and clinical conditions. Recent guidelines advocate for regular BP monitoring as part of regular clinical visits or even at home. This increased utilization of BP measurement technologies has brought up significant concerns, regarding the accuracy of reported BP values across settings. In this survey, focusing mainly on cuff-based BP monitoring technologies, we highlight how BP measurements can demonstrate substantial biases and variances due to factors such as measurement and device errors, demographics, and body habitus. With these inherent biases, the development of a new generation of cuff-based BP devices which use artificial-intelligence (AI) has significant potential. We present future avenues where AI-assisted technologies can leverage the extensive clinical literature on BP-related studies together with the large collections of BP records available in electronic health records. These resources can be combined with machine learning approaches, including deep learning and Bayesian inference, to remove BP measurement biases and to provide individualized BP-related cardiovascular risk indexes.

Artificial intelligence (AI) and machine learning (ML) present revolutionary opportunities to enhance our understanding of animal behavior and conservation strategies. Using elephants, a crucial species in Africa's protected areas, as our focal point, we delve into the role of AI and ML in their conservation. Given the increasing amounts of data gathered from a variety of sensors like cameras, microphones, geophones, drones, and satellites, the challenge lies in managing and interpreting this vast data. New AI and ML techniques offer solutions to streamline this process, helping us extract vital information that might otherwise be overlooked. This paper focuses on the different AI-driven monitoring methods and their potential for improving elephant conservation. Collaborative efforts between AI experts and ecological researchers are essential in leveraging these innovative technologies for enhanced wildlife conservation, setting a precedent for numerous other species.