In this paper, we revisit Johanna Drucker's question, "Is there a digital art history?" -- posed exactly a decade ago -- in the light of the emergence of large-scale, transformer-based vision models. While more traditional types of neural networks have long been part of digital art history, and digital humanities projects have recently begun to use transformer models, their epistemic implications and methodological affordances have not yet been systematically analyzed. We focus our analysis on two main aspects that, together, seem to suggest a coming paradigm shift towards a "digital" art history in Drucker's sense. On the one hand, the visual-cultural repertoire newly encoded in large-scale vision models has an outsized effect on digital art history. The inclusion of significant numbers of non-photographic images allows for the extraction and automation of different forms of visual logics. Large-scale vision models have "seen" large parts of the Western visual canon mediated by Net visual culture, and they continuously solidify and concretize this canon through their already widespread application in all aspects of digital life. On the other hand, based on two technical case studies of utilizing a contemporary large-scale visual model to investigate basic questions from the fields of art history and urbanism, we suggest that such systems require a new critical methodology that takes into account the epistemic entanglement of a model and its applications. This new methodology reads its corpora through a neural model's training data, and vice versa: the visual ideologies of research datasets and training datasets become entangled.

Artificial intelligence (AI) has made remarkable progress creating images that are not only breathtaking, but astonishingly diverse in style. Ten years ago, such an achievement would have been deemed unlikely by experts. Today, AI can create images using specific artistic styles, such as Van Gogh's unique approach, with an infinite range of variations. This raises an intriguing question. A very accurate, detailed, and faithful reproduction of a scene, where realism is aimed for: Ave Caesar!

People get up in arms whenever the hand of the artist is detached from the final artwork. "Are photographs real art?" they muttered in the 19th Century. "God I hate this Pollock guy," cried haters witnessing a splattered canvas that the artist seemingly never touched. So it's no wonder that AI image-generators have got art historians in a twist, as more artists make use of these tools to inform their practice. I love diving into what gets people's blood boiling in the art world, and this summer AI crept its way onto the leaderboard of irritants.

Comparison of Georgia the cat with a painting by Samuel van Hoogstraten, created using Pet Portraits, a feature of the Google Arts & Culture App. In 2018, Google put their impressive artificial intelligence to use in a new way. People took selfies and uploaded snaps on the Google Arts & Culture app. The app then analyzed your appearance and found doppelgängers throughout hundreds of years of art history. Even celebrities jumped on the trend by posting their unlikely look-alikes.

People tend to rejoice in the disclosure of a secret. Or, at the very least, media outlets have come to realize that news of "mysteries solved" and "hidden treasures revealed" generate traffic and clicks. So I'm never surprised when I see AI-assisted revelations about famous masters' works of art go viral. Over the past year alone, I've come across articles highlighting how artificial intelligence recovered a "secret" painting of a "lost lover" of Italian painter Modigliani, "brought to life" a "hidden Picasso nude", "resurrected" Austrian painter Gustav Klimt's destroyed works and "restored" portions of Rembrandt's 1642 painting "The Night Watch."The As an art historian, I've become increasingly concerned about the coverage and circulation of these projects.

People tend to rejoice in the disclosure of a secret. Or, at the very least, media outlets have come to realize that news of "mysteries solved" and "hidden treasures revealed" generate traffic and clicks. So I'm never surprised when I see AI-assisted revelations about famous masters' works of art go viral. Over the past year alone, I've come across articles highlighting how artificial intelligence recovered a "secret" painting of a "lost lover" of Italian painter Modigliani, "brought to life" a "hidden Picasso nude", "resurrected" Austrian painter Gustav Klimt's destroyed works and "restored" portions of Rembrandt's 1642 painting "The Night Watch." As an art historian, I've become increasingly concerned about the coverage and circulation of these projects.

Recent advances in machine learning have led to an acceleration of interest in research on artificial intelligence (AI). This fostered the exploration of possible applications of AI in various domains and also prompted critical discussions addressing the lack of interpretability, the limits of machine intelligence, potential risks and social challenges. In the exploration of the settings of the "human versus AI" relationship, perhaps the most elusive domain of interest is the creation and understanding of art. Many interesting initiatives are emerging at the intersection of AI and art, however comprehension and appreciation of art is still considered to be an exclusively human capability. Rooted in the idea that the existence and meaning of art is indeed inseparable from human-to-human interaction, the motivation behind this paper is to explore how bringing AI in the loop can foster not only advances in the fields of digital art and art history, but also inspire our perspectives on the future of art. The variety of activities and research initiatives related to "AI and Art" can generally be divided into two categories: 1) AI is used in the process of analyzing existing art; or 2) AI is used in the process of creating new art. In this paper, relevant aspects and contributions of these two categories are discussed, with a particular focus on the relation of AI to visual arts. In recent years, there has been a surge of interest among artists, technologists and researchers in exploring the creative potential of AI technologies. The use of AI in the process of creating visual art was significantly accelerated with the emergence of Generative Adversarial Networks (GAN) [56].

The process of identifying and understanding art styles to discover artistic influences is essential to the study of art history. Traditionally, trained experts review fine details of the works and compare them to other known works. To automate and scale this task, we use several state-of-the-art CNN architectures to explore how a machine may help perceive and quantify art styles. This study explores: (1) How accurately can a machine classify art styles? (2) What may be the underlying relationships among different styles and artists? To help answer the first question, our best-performing model using Inception V3 achieves a 9-class classification accuracy of 88.35%, which outperforms the model in Elgammal et al.'s study by more than 20 percent. Visualizations using Grad-CAM heat maps confirm that the model correctly focuses on the characteristic parts of paintings. To help address the second question, we conduct network analysis on the influences among styles and artists by extracting 512 features from the best-performing classification model. Through 2D and 3D T-SNE visualizations, we observe clear chronological patterns of development and separation among the art styles. The network analysis also appears to show anticipated artist level connections from an art historical perspective. This technique appears to help identify some previously unknown linkages that may shed light upon new directions for further exploration by art historians. We hope that humans and machines working in concert may bring new opportunities to the field.

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.

Using our prior work on quantifying creativity, AICAN can judge how creative its individual pieces are. Since it has also learned the titles used by artists and art historians in the past, the algorithm can even give names to the works it generates. It named one Orgy; it called another The Beach at Pourville. The algorithm favors generating more abstract works than figurative ones. Our research on how the machine is able to understand the evolution of art history could offer an explanation.