On July the 13th the company that developed the generative AI art tool Midjourney opened its closed beta. To access it, you just need to enter the discord channel. Playing with this model immediately gives the feeling of something very powerful with a wide comprehension of text prompts. It works especially well with environments and characters, and from what I have seen, by default it has a bias towards artistic paintings. For example, with the prompt "enchanted jungle" you get: To thoroughly understand its power, I decided to test it by mixing in various ways two things that never existed at the same time: Pokémon and pre-1990 art movements.

Dr. Mohammed Elhoseiny, assistant professor of computer science at KAUST, has carved a career out of teaching machines the art of creating art. After finishing his doctoral degree at Rutgers University in 2016, Elhoseiny went on to work for Adobe Research, Baidu Research, Facebook and now KAUST. His latest research paper, Creative Walk Adversarial Networks: Novel Art Generation with Probabilistic Random Walk Deviation from Style Norms, was accepted at the premiere conference on computational creative artificial intelligence (AI), the International Conference on Computational Creativity (ICCC) 2022. The paper covers the work of Elhoseiny and his team VISION CAIR on the use of Creative Walk Adversarial Networks (CWAN) for novel, or original, art generation. CWAN learns about existing art styles in its training by being exposed to a large repository of paintings from various art movements and styles, from 5000 years ago to present times.

A work made by AI image generation, part of the'Promptism' art movement, reflecting on a new generation merged with Artificial Intelligence, that we now witness its emergence. Inspired by sci-fi stories, visions of the future, acceleration of technology, and sense of apocalypse. Where will it all take us? A work made by AI image generation, part of the'Promptism' art movement, reflecting on a new generation merged with Artificial Intelligence, that we now witness its emergence. Inspired by sci-fi stories, visions of the future, acceleration of technology, and sense of apocalypse.

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.

With rapid progress in artificial intelligence (AI), popularity of generative art has grown substantially. From creating paintings to generating novel art styles, AI based generative art has showcased a variety of applications. However, there has been little focus concerning the ethical impacts of AI based generative art. In this work, we investigate biases in the generative art AI pipeline right from those that can originate due to improper problem formulation to those related to algorithm design. Viewing from the lens of art history, we discuss the socio-cultural impacts of these biases. Leveraging causal models, we highlight how current methods fall short in modeling the process of art creation and thus contribute to various types of biases. We illustrate the same through case studies. To the best of our knowledge, this is the first extensive analysis that investigates biases in the generative art AI pipeline from the perspective of art history. We hope our work sparks interdisciplinary discussions related to accountability of generative art.

Pushing the envelope of Neural Language Processing model GPT-3 by OpenAI I explore its capability to interpret art -- and to create. One might say, "GPT-3" is still far away from AGI. One might say, it's "competence without comprehension". But during my experiments, I was already convinced about the creative power of GPT-3 -- mixed with human perception. In my texts, I always mark AI-generated contents as such.

Conditional Generative Adversarial Networks (CGANs) are a recent and popular method for generating samples from a probability distribution conditioned on latent information. The latent information often comes in the form of a discrete label from a small set. We propose a novel method for training CGANs which allows us to condition on a sequence of continuous latent distributions $f^{(1)}, \ldots, f^{(K)}$. This training allows CGANs to generate samples from a sequence of distributions. We apply our method to paintings from a sequence of artistic movements, where each movement is considered to be its own distribution. Exploiting the temporal aspect of the data, a vector autoregressive (VAR) model is fitted to the means of the latent distributions that we learn, and used for one-step-ahead forecasting, to predict the latent distribution of a future art movement $f^{{(K+1)}}$. Realisations from this distribution can be used by the CGAN to generate "future" paintings. In experiments, this novel methodology generates accurate predictions of the evolution of art. The training set consists of a large dataset of past paintings. While there is no agreement on exactly what current art period we find ourselves in, we test on plausible candidate sets of present art, and show that the mean distance to our predictions is small.

Artificial intelligence has exploded onto the art scene over the past few years, with everybody from artists to tech giants experimenting with the new tools that technology provides. While the generative adversarial networks (GANs) that power the likes of Google's BigGAN are capable of creating spectacularly strange images, they require a large degree of human interaction and guidance. Not so with the AICAN system developed by Professor Ahmed Elgammal and his team at Rutgers University's AI & Art Lab. It's a nearly autonomous system trained on 500 years worth of Western artistic aesthetics that produces its own interpretations of these classic styles. AICAN stands for "Artificial Intelligence Creative Adversarial Network" and while it utilizes the same adversarial network architecture as GANs, it engages them differently.

Most agree that AI will be the defining technology of our time, but our predictions tend to differ wildly. Either AI will become the perfect servant, ushering in a new era of productivity and leisure one weather report at a time (Hi, Alexa), or it'll master us, consigning humanity to the ash heap of biological history (I see you, Elon). But there's a slice of gray in between we should consider: What if AI became a peer and a collaborator, rather than a servant or an overlord? Let's use art as an example. The history of art and the history of technology have always been intertwined. In fact, artists--and whole movements--are often defined by the tools available to make the work.

Recent advances in imaging and multimedia technologies have paved the way for automatic analysis of visual art. Despite notable attempts, extracting relevant patterns from paintings is still a challenging task. Different painters, born in different periods and places, have been influenced by different schools of arts. However, each individual artist also has a unique signature, which is hard to detect with algorithms and objective features. In this paper we propose a novel dictionary learning approach to automatically uncover the artistic style from paintings. Specifically, we present a multi-task learning algorithm to learn a style-specific dictionary representation. Intuitively, our approach, by automatically decoupling style-specific and artist-specific patterns, is expected to be more accurate for retrieval and recognition tasks than generic methods. To demonstrate the effectiveness of our approach, we introduce the DART dataset, containing more than 1.5K images of paintings representative of different styles. Our extensive experimental evaluation shows that our approach significantly outperforms state-of-the-art methods.