This paper explores the image synthesis capabilities of GPT-4, a leading multi-modal large language model. We establish a benchmark for evaluating the fidelity of texture features in images generated by GPT-4, comprising manually painted pictures and their AI-generated counterparts. The contributions of this study are threefold: First, we provide an in-depth analysis of the fidelity of image synthesis features based on GPT-4, marking the first such study on this state-of-the-art model. Second, the quantitative and qualitative experiments fully reveals the limitations of the GPT-4 model in image synthesis. Third, we have compiled a unique benchmark of manual drawings and corresponding GPT-4-generated images, introducing a new task to advance fidelity research in AI-generated content (AIGC). The dataset is available at: \url{https://github.com/rickwang28574/DeepArt}.

Figure 1: Examples of the established deepart detection database (DDDB). The examples of LAION-5B Schuhmann et al. (2022) are conventional artworks (conarts), and the rest examples (i.e., StableDiff Rombach et al. (2021),DALL-E 2 Ramesh et al. (2022),Imagen Saharia et al. (2022),Midjourney Holz (2022), and Parti Yu et al. (2022)) are deepfake artworks (deeparts) produced by generative models. Our data and code will be released. Deepfake technologies have been blurring the boundaries between the real and unreal, likely resulting in malicious events. By leveraging newly emerged deepfake technologies, deepfake researchers have been making a great upending to create deepfake artworks (deeparts), which are further closing the gap between reality and fantasy. This database enables us to explore once-for-all deepart detection and continual deepart detection. For the two new problems, we suggest four benchmark evaluations and four families of solutions on the constructed DDDB. The comprehensive study demonstrates the effectiveness of the proposed solutions on the established benchmark dataset, which is capable of paving a way to more interesting directions of deepart detection. The constructed benchmark dataset and the source code will be made publicly available. There has been a propensity to view deepfake technologies as destructive to the supposed boundaries between the real and unreal, leading to potentially detrimental effects. Despite this, deepfake researchers are continuing to make breakthroughs by wielding newly emerged deepfake technologies to create artworks, which are called deeparts throughout this paper. The new deepart techniques include Stable DiffusionRombach et al. (2021), DALL-E Ramesh et al. (2021; 2022), Imagen Saharia et al. (2022), Midjourney Holz (2022), and Parti Yu et al. (2022) As shown in Figure 1, compared to conventional deepfakes, deeparts have been making the boundary between reality and fantasy much more blurry.

Job automation, algorithmic bias, and technological development are the first thoughts that spring to mind when we think of Artificial Intelligence. But at the same time, AI can be used in many fun and interesting ways. Here, we discuss ten fun AI tools that you must try out. Besides being a great way to kill boredom, they demonstrate how advanced AI has already become. Semantris is one of the many Google-powered AI experiments.

As research pushes the boundaries of what's possible with AI, the popularity of art created by algorithms -- generative art -- continues to grow. From creating paintings to inventing new art styles, AI-based generative art has been showcased in a range of applications. But a new study from researchers at Fujitsu investigates whether biases might creep into the AI tools used to create art. Leveraging models, they claim that current AI methods fail to take into account socioeconomic impacts and exhibit clear prejudices. In their work, the researchers surveyed academic papers, online platforms, and apps that generate art using AI, selecting examples that focused on simulating established art schools and styles.

When some people hear the term'artificial intelligence' their initial reaction is to imagine a dystopian future where robots have risen up and overthrown humanity. The truth is, application of AI technology in our day-to-day lives is a lot less sinister. It might not be long before these technologies become common in our everyday lives. It's currently assisting with medical diagnosis, the creation of autonomous cars and to help improve businesses by analysing data and creating accurate forecasts of client or market behaviour. The application of AI is becoming more and more popular in businesses worldwide, with the potential to improve our lives in unexpected ways.

Our mission is to provide a novel artistic painting tool that allows everyone to create and share artistic pictures with just a few clicks. We are five researchers working at the interface of neuroscience and artificial intelligence, based at the University of Tübingen (Germany), École polytechnique fédérale de Lausanne (Switzerland) and Université catholique de Louvain (Belgium).

A programmer recently turned to artificial intelligence to create positively charming images of so-called "botanical dinosaurs," representations of tyrannosaurs, stegosaurs, triceratops and others, all constructed entirely out of flowers. To generate the unusual effect, coder Chris Rodley used a web app that employs a technique known as style transfer, in which an algorithm "learns" a specific visual style and re-creates an image in that style. In this case, the algorithm re-created a selection of dinosaurs in the style of botanical illustrations, using all the visual elements that you'd expect to find in a field guide to local flora -- stems, leaves and blossoms in a variety of colors. Style transfer isn't new, but the unlikely pairing of dinosaurs and flowers was unusual and eye-catching enough to garner quite a bit of attention online. As of today (June 21), Rodley's June 15 tweet sharing the images has gathered over 32,000 likes and about 14,000 retweets.

Escher may have just lost its lucrative stranglehold on the dorm room poster market thanks to artist Chris Rodley, who used a deep learning algorithm to merge a book of dinosaurs with a book of flower paintings. The results are magnificent, and deserve a spot on the walls of our finest art galleries. This isn't the first time Rodley has dabbled with a deep learning A.I. to create art. Using a website called Deepart.io, which is powered by an algorithm developed by Leon Gatys and a team from the University of Tübingen in Germany, Rodley previously merged a Trump family photo and various Muppet characters, with nightmarish results. The Deepart.io algorithm differs from what Google's Deep Dream does by applying features of an artist's visual style to another image, preserving recognizable details and features and using them to rebuild the target image from scratch.