Chinese proposals mandating AI reinforce socialist values and viewpoints make the technology more dangerous to humanity, China researcher Gordon Chang says. Chinese Communist Party socialist values reinforced through artificial intelligence are a threat to humanity, but the country's restrictions on the technology may also hamper its development, China expert Gordon Chang told Fox News. "Artificial intelligence poses a direct threat to humanity, but it'll become even more a threat to humanity if China masters it before we do," said Chang. "We see this, for instance, in the gene editing of humans." The Cyberspace Administration of China recently proposed AI measures to ensure the technology supports socialist values.

Orthodox Catholic philosopher Joe Vukov discusses the moral implications of using artificial intelligence to cope with the loss of loved ones. HereAfter AI is giving mourning families a space to talk with digital replicas of their deceased loved ones in what some are calling an eerie blurring of the lines between fantasy and reality. The interactive app is the latest venture in the rapidly-advancing tech space, allowing mourners to keep the voice and personality of their deceased loved ones alive and chat with them using artificial intelligence. While the innovation might sound unique and comforting to some, others say the development's moral implications could restrict the significance of life to simple characteristics that attempt to replace a once-living person. ARTIFICIAL INTELLIGENCE IN HEALTH CARE: NEW PRODUCT ACTS AS'COPILOT FOR DOCTORS' "The motivation driving this sort of conversation is clear – we want to keep people around who we've lost," Orthodox Catholic philosopher Joe Vukov said Sunday on "Fox & Friends Weekend."

An AI-Generated news presenter has been introduced by Kuwait News, an online news organization connected to the Kuwait Times. Fedha, the host, made her debut in a brief 13-second film during which she introduced herself in Arabic. She also solicited feedback from the audience regarding their preferred source of news. The outlet's Twitter account published the video. Additionally, Fedha will reportedly use a typical Kuwaiti accent to provide news updates on the website's social media accounts. A new revolution has begun in the media industry!

Ian Sansavera, a software architect at a New York startup called Runway AI, typed a short description of what he wanted to see in a video. "A tranquil river in the forest," he wrote. Less than two minutes later, an experimental internet service generated a short video of a tranquil river in a forest. The river's running water glistened in the sun as it cut between trees and ferns, turned a corner and splashed gently over rocks. Runway, which plans to open its service to a small group of testers this week, is one of several companies building artificial intelligence technology that will soon let people generate videos simply by typing several words into a box on a computer screen.

Consciousness And Light Are Explored. The Inter Mind Bridges The Gap Between The Physical Mind And The Conscious Mind.

Art is standing on the Precipice of Revolution never before seen in human history. From cave painting to the creation of paints from natural material, to Illuminated Manuscripts, to the Gutenberg Printing Press, to Renaissance painters and sculptors, to synthetic paints, to photography, videography, and spherography, to music composition, to digital representation and storage, all guided by the creativity of the human mind. We are accelerating into the Age of Artificial (General) Intelligence Artists. Optionally combined with advances in robotics, AI Artists will discover and use new creative insights. They will optionally place bush to canvas, or chisel to marble, or digit to piano key, or become a photographer, videographer, or spherographer, and compete with -- and eventually eclipse-- human artists.

Recent studies have demonstrated promising potential of ChatGPT for various text annotation and classification tasks. However, ChatGPT is non-deterministic which means that, as with human coders, identical input can lead to different outputs. Given this, it seems appropriate to test the reliability of ChatGPT. Therefore, this study investigates the consistency of ChatGPT's zero-shot capabilities for text annotation and classification, focusing on different model parameters, prompt variations, and repetitions of identical inputs. Based on the real-world classification task of differentiating website texts into news and not news, results show that consistency in ChatGPT's classification output can fall short of scientific thresholds for reliability. For example, even minor wording alterations in prompts or repeating the identical input can lead to varying outputs. Although pooling outputs from multiple repetitions can improve reliability, this study advises caution when using ChatGPT for zero-shot text annotation and underscores the need for thorough validation, such as comparison against human-annotated data. The unsupervised application of ChatGPT for text annotation and classification is not recommended.

Robotic technology can support the creation of new tools that improve the creative process of cinematography. It is crucial to consider the specific requirements and perspectives of industry professionals when designing and developing these tools. In this paper, we present the results from exploratory interviews with three cinematography practitioners, which included a demonstration of a prototype robotic system. We identified many factors that can impact the design, adoption, and use of robotic support for cinematography, including: (1) the ability to meet requirements for cost, quality, mobility, creativity, and reliability; (2) the compatibility and integration of tools with existing workflows, equipment, and software; and (3) the potential for new creative opportunities that robotic technology can open up. Our findings provide a starting point for future co-design projects that aim to support the work of cinematographers with collaborative robots.

Misinformation is considered a threat to our democratic values and principles. The spread of such content on social media polarizes society and undermines public discourse by distorting public perceptions and generating social unrest while lacking the rigor of traditional journalism. Transformers and transfer learning proved to be state-of-the-art methods for multiple well-known natural language processing tasks. In this paper, we propose MisRoB{\AE}RTa, a novel transformer-based deep neural ensemble architecture for misinformation detection. MisRoB{\AE}RTa takes advantage of two transformers (BART \& RoBERTa) to improve the classification performance. We also benchmarked and evaluated the performances of multiple transformers on the task of misinformation detection. For training and testing, we used a large real-world news articles dataset labeled with 10 classes, addressing two shortcomings in the current research: increasing the size of the dataset from small to large, and moving the focus of fake news detection from binary classification to multi-class classification. For this dataset, we manually verified the content of the news articles to ensure that they were correctly labeled. The experimental results show that the accuracy of transformers on the misinformation detection problem was significantly influenced by the method employed to learn the context, dataset size, and vocabulary dimension. We observe empirically that the best accuracy performance among the classification models that use only one transformer is obtained by BART, while DistilRoBERTa obtains the best accuracy in the least amount of time required for fine-tuning and training. The proposed MisRoB{\AE}RTa outperforms the other transformer models in the task of misinformation detection. To arrive at this conclusion, we performed ample ablation and sensitivity testing with MisRoB{\AE}RTa on two datasets.

Recommendation models are typically trained on observational user interaction data, but the interactions between latent factors in users' decision-making processes lead to complex and entangled data. Disentangling these latent factors to uncover their underlying representation can improve the robustness, interpretability, and controllability of recommendation models. This paper introduces the Causal Disentangled Variational Auto-Encoder (CaD-VAE), a novel approach for learning causal disentangled representations from interaction data in recommender systems. The CaD-VAE method considers the causal relationships between semantically related factors in real-world recommendation scenarios, rather than enforcing independence as in existing disentanglement methods. The approach utilizes structural causal models to generate causal representations that describe the causal relationship between latent factors. The results demonstrate that CaD-VAE outperforms existing methods, offering a promising solution for disentangling complex user behavior data in recommendation systems.