An accurate solar wind speed model is important for space weather predictions, catastrophic event warnings, and other issues concerning solar wind - magnetosphere interaction. In this work, we construct a model based on convolutional neural network (CNN) and Potential Field Source Surface (PFSS) magnetograms, considering a solar wind source surface of $R_{\rm SS}=2.5R_\odot$, aiming to predict the solar wind speed at the Lagrange 1 (L1) point of the Sun-Earth system. The input of our model consists of four Potential Field Source Surface (PFSS) magnetograms at $R_{\rm SS}$, which are 7, 6, 5, and 4 days before the target epoch. Reduced magnetograms are used to promote the model's efficiency. We use the Global Oscillation Network Group (GONG) photospheric magnetograms and the potential field extrapolation model to generate PFSS magnetograms at the source surface. The model provides predictions of the continuous test dataset with an averaged correlation coefficient (CC) of 0.52 and a root mean square error (RMSE) of 80.8 km/s in an eight-fold validation training scheme with the time resolution of the data as small as one hour. The model also has the potential to forecast high speed streams of the solar wind, which can be quantified with a general threat score of 0.39.

In decision-making methods, it is common to assume that the experts are honest and professional. However, this is not the case when one or more experts in the group decision making framework, such as the group analytic hierarchy process (GAHP), try to manipulate results in their favor. The aim of this paper is to introduce two heuristics in the GAHP, setting allowing to detect the manipulators and minimize their effect on the group consensus by diminishing their weights. The first heuristic is based on the assumption that manipulators will provide judgments which can be considered outliers with respect to those of the rest of the experts in the group. The second heuristic assumes that dishonest judgments are less consistent than the average consistency of the group. Both approaches are illustrated with numerical examples and simulations.

Social media has become extremely influential when it comes to policy making in modern societies, especially in the western world, where platforms such as Twitter allow users to follow politicians, thus making citizens more involved in political discussion. In the same vein, politicians use Twitter to express their opinions, debate among others on current topics and promote their political agendas aiming to influence voter behaviour. In this paper, we attempt to analyse tweets of politicians from three European countries and explore the virality of their tweets. Previous studies have shown that tweets conveying negative sentiment are likely to be retweeted more frequently. By utilising state-of-the-art pre-trained language models, we performed sentiment analysis on hundreds of thousands of tweets collected from members of parliament in Greece, Spain and the United Kingdom, including devolved administrations. We achieved this by systematically exploring and analysing the differences between influential and less popular tweets. Our analysis indicates that politicians' negatively charged tweets spread more widely, especially in more recent times, and highlights interesting differences between political parties as well as between politicians and the general population.

Cheapfake is a recently coined term that encompasses non-AI ("cheap") manipulations of multimedia content. Cheapfakes are known to be more prevalent than deepfakes. Cheapfake media can be created using editing software for image/video manipulations, or even without using any software, by simply altering the context of an image/video by sharing the media alongside misleading claims. This alteration of context is referred to as out-of-context (OOC) misuse of media. OOC media is much harder to detect than fake media, since the images and videos are not tampered. In this challenge, we focus on detecting OOC images, and more specifically the misuse of real photographs with conflicting image captions in news items. The aim of this challenge is to develop and benchmark models that can be used to detect whether given samples (news image and associated captions) are OOC, based on the recently compiled COSMOS dataset.

Midjourney, an AI image generator that creates realistic deepfakes, has been scrutinized recently for having a policy showing deference to China's communist government. The company enforces a rule that users can generate fake images of world leaders from President Biden to Vladimir Putin, but not Chinese President Xi Jinping. In a year-old message on the chat service Discord, the CEO of Midjourney, Inc. explained why the company has that rule. "I think we want to minimize drama," Midjourney CEO David Holz wrote last summer. He explained that the company did not immediately ban images of Xi, but it was triggered by abuse from users.

Elon Musk氏やSteve Wozniak氏を含む1800人以上のAIリーダーや技術者が、公開書簡を発表し、GPT-4よりも強力なAIシステムの開発を6ヶ月間一時停止するよう呼びかけた。彼らは、「社会と人類に深刻なリスクがある」としている。

About Indian Railways Indian Railways is the state-owned railway company of India, which is owned and operated by the Indian government. It is the fourth-largest railway network in the world and is responsible for providing transportation services to millions of passengers and freight across the country. Indian Railways was first established in 1853 when the first train ran from Bombay (now Mumbai) to Thane. Since then, it has grown to become a major contributor to the Indian economy, providing employment to over 1.3 million people, facilitating the transportation of goods and people, and promoting tourism. The railway network of Indian Railways is divided into 18 zones, each headed by a general manager. The zones are further divided into divisions, which are responsible for the management of train services and infrastructure in their respective areas.

Fox News correspondent Matt Finn has the latest on the impact of AI technology that some say could outpace humans on'Special Report.' A college student in the U.K. said she was able to get out of a parking ticket by using AI technology to draft a letter requesting to revoke the fine. "I was like, 'Oh I don't need this fine, I'm a student' but trying to articulate what I wanted to say was pretty difficult so I thought I'll just see if ChatGPT can do it for me," the 22-year-old student in York, Millie Houlton, told the BBC. Houlton said she asked OpenAI's ChatGPT to "please help me write a letter to the council, they gave me a parking ticket." The ticket was for £60, or roughly $74, that the young woman said was wrongly issued after she parked on her street.

Hollywood has colourful notions about artificial intelligence (AI). The popular image is a future where robot armies spontaneously turn to malevolence, pitching humanity in a battle against extinction. In reality, the risks posed by AI today are more insidious and harder to unpick. They are often a by-product of the technology's seemingly endless application in modern society and increasing role in everyday life, perhaps best highlighted by Microsoft's latest multi-billion-dollar investment into ChatGPT-maker OpenAI. Either way, it's unsurprising that AI generates so much debate, not least in how we can build regulatory safeguards to ensure we master the technology, rather than surrender control to the machines. Right now, we tackle AI using a patchwork of laws and regulations, as well as guidance that doesn't have the force of law. Against this backdrop, it's clear that current frameworks are likely to change – perhaps significantly.

Multiple factors have driven the development of artificial intelligence (AI) over the years. The ability to swiftly and effectively collect and analyze enormous amounts of data has been made possible by computing technology advancements, which have been a significant contributing factor. Another factor is the demand for automated systems that can complete activities that are too risky, challenging or time-consuming for humans. Also, there are now more opportunities for AI to solve real-world issues, thanks to the development of the internet and the accessibility of enormous amounts of digital data. Moreover, societal and cultural issues have influenced AI.