Chinese landscape painting has a unique and artistic style, and its drawing technique is highly abstract in both the use of color and the realistic representation of objects. Previous methods focus on transferring from modern photos to ancient ink paintings. However, little attention has been paid to translating landscape paintings into modern photos. To solve such problems, in this paper, we (1) propose DLP-GAN (Draw Modern Chinese Landscape Photos with Generative Adversarial Network), an unsupervised cross-domain image translation framework with a novel asymmetric cycle mapping, and (2) introduce a generator based on a dense-fusion module to match different translation directions. Moreover, a dual-consistency loss is proposed to balance the realism and abstraction of model painting. In this way, our model can draw landscape photos and sketches in the modern sense. Finally, based on our collection of modern landscape and sketch datasets, we compare the images generated by our model with other benchmarks. Extensive experiments including user studies show that our model outperforms state-of-the-art methods.

The consistent mapping from poems to paintings is essential for the research and restoration of traditional Chinese gardens. But the lack of firsthand ma-terial is a great challenge to the reconstruction work. In this paper, we pro-pose a method to generate garden paintings based on text descriptions using deep learning method. Our image-text pair dataset consists of more than one thousand Ming Dynasty Garden paintings and their inscriptions and post-scripts. A latent text-to-image diffusion model learns the mapping from de-scriptive texts to garden paintings of the Ming Dynasty, and then the text description of Jichang Garden guides the model to generate new garden paintings. The cosine similarity between the guide text and the generated image is the evaluation criterion for the generated images. Our dataset is used to fine-tune the pre-trained diffusion model using Low-Rank Adapta-tion of Large Language Models (LoRA). We also transformed the generated images into a panorama and created a free-roam scene in Unity 3D. Our post-trained model is capable of generating garden images in the style of Ming Dynasty landscape paintings based on textual descriptions. The gener-ated images are compatible with three-dimensional presentation in Unity 3D.

Take for instance, Bengaluru-based artist Raghava K K, who is in the US at the moment, collaborating with painting robots, scientists and technologists to create an artwork, six years in the making. Just this week, Terrain.art unveiled India's first artificial intelligence non-fungible token (NFT) art exhibition'Intertwined Intelligences', which explores the relationship between artificial intelligence (AI) and human creativity. The show, which features international AI artists, is curated by Bengaluru-based Harshit Agrawal who shot to fame with his work'The Anatomy of Dr Algorithm', where he fed images of surgeries into an algorithm and used AI to create Rembrandt-inspired art based on the images of everything from organs to fibroids. What AI does is that it uses algorithms to predict behaviour through patterns. So the more patterns give a machine for a painting, the greater precision with which the requests are processed to get a final outcome.

Researchers in the United States have discovered a painting hidden under the famous "The Crouching Beggar" (La Misereuse Accroupie) artwork by Pablo Picasso. According to a report by the Guardian, researchers used a non-invasive imagine technique to inspect the Picasso painting which resulted in them finding a landscape painting hidden underneath it. The new study also provided more information on certain hidden features discovered in the painting earlier. Marc Walton of Northwestern University, who presented the study at a meeting of the American Association for the Advancement of Science in Texas on Saturday, said, "This is where technology allows us to get into the mind of the artist, so we can actually understand the creative process of Picasso and how he actually started producing this work of art." When an x-ray image -- also known as x-radiograph -- of the Picasso painting was taken for a background research, the gallery team of the Art Gallery of Ontario, Canada -- where the painting is now kept -- found the hidden landscape. In order to further delve into the subject, the gallery team was joined by researchers from Northwester University, Illinois.

Nvidia chief executive Jen-Hsun Huang announced that the company has created a new chip, the Tesla P100, with 15 billion transistors for deep-learning computing. It's the biggest chip ever made, Huang said. Huang made the announcement during his keynote at the GPUTech conference in San Jose, California. He unveiled the chip after he said that deep-learning artificial intelligence chips have already become the company's fastest-growing business. "We are changing so many things in one project," Huang said.

Nvidia chief executive Jen-Hsun Huang announced that the company has created a new chip, the Tesla P100, with 15 billion transistors for deep-learning computing. It's the biggest chip ever made, Huang said. Huang made the announcement during his keynote at the GPUTech conference in San Jose, Calif. He unveiled the chip after he said that deep learning artificial intelligence chips have already become the company's fastest-growing business. "We are changing so many things in one project," Huang said.