A large number of ancient mosaics have not reached us because they have been destroyed by erosion, earthquakes, looting or even used as materials in newer construction. To make things worse, among the small fraction of mosaics that we have been able to recover, many are damaged or incomplete. Therefore, restoration and reconstruction of mosaics play a fundamental role to preserve cultural heritage and to understand the role of mosaics in ancient cultures. This reconstruction has traditionally been done manually and more recently using computer graphics programs but always by humans. In the last years, Artificial Intelligence (AI) has made impressive progress in the generation of images from text descriptions and reference images. State of the art AI tools such as DALL-E2 can generate high quality images from text prompts and can take a reference image to guide the process. In august 2022, DALL-E2 launched a new feature called outpainting that takes as input an incomplete image and a text prompt and then generates a complete image filling the missing parts. In this paper, we explore whether this innovative technology can be used to reconstruct mosaics with missing parts. Hence a set of ancient mosaics have been used and reconstructed using DALL-E2; results are promising showing that AI is able to interpret the key features of the mosaics and is able to produce reconstructions that capture the essence of the scene. However, in some cases AI fails to reproduce some details, geometric forms or introduces elements that are not consistent with the rest of the mosaic. This suggests that as AI image generation technology matures in the next few years, it could be a valuable tool for mosaic reconstruction going forward.

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Most of us have a music, movie or video-game library – possibly all three – but few have an art collection or even know what their favourite works of art are. Next year, that will change as art moves from the inaccessible to the everyday, thanks to AI. Art hasn't felt accessible to many for a long time. Our main experience of it involves visiting galleries and museums or feeling out our depth in art history classes. At a gallery, we spend a couple of hours looking at a lot of seemingly important pieces, but then we leave and the artworks stay where they are. They don't draw us in, like a favourite album, movie or video game, and we know we can't afford to take them home with us.

Beauty, it is said, resides in the eye of the beholder. What if that beholder is a machine learning model being trained to describe and classify fine works of art? That's what AI researchers at Zhejiang University of Technology in China are attempting to find out by comparing the ability of different models trained on a growing list of image data sets to classify artwork by genre and style. Whether these models can be trained to respond emotionally remains to be seen. Preliminary results from one study published earlier this month in the journal of the Public Library of Science highlighted the utility of using convolutional neural networks (CNNs) for demanding tasks like art classification.

Microsoft has teamed with New York City's Metropolitan Museum of Art on a series of "hackathon" projects. The projects pair the technology company's AI expertise with the Met's collection of artwork to "transform future connections between people and art," according to Microsoft. The "Met x Microsoft x MIT" partnership came out of a collaboration last December, where a two-day hackathon combined Microsoft's AI technology and the museum's data. It's a part of the Met's Open Access Program, designed to make the museum's collections more accessible on the Internet. A number of Microsoft Azure cloud services were used, including Azure Cognitive Services, Azure Machine Learning, conversational AI, and more.

Microsoft has teamed up with the Tate's IK Prize winner for 2016, Italian-based communication research centre Fabrica, to surface images from Tate's extensive collection of more than 30,000 images that relate to other photos, supplied by Reuters, of current events as part of an art installation at the Tate called Recognition. The search engine uses Microsoft's Cognitive Services AI to recognize expressions, themes, context and style similarities between pictures in the news and Tate's massive collection, merging the modern and historical. The algorithm is not only able to match pictures, but also to explain why it thinks they are a matches, for example having similar objects, colours or compositions, and builds on technology such as HowOldAmI.net The exhibition is a show-case of Microsoft's AI technology and is also designed to improve public perception of Artificial Intelligence. "From a Microsoft perspective AI is the future – we're betting the company on this technology that extends the reach of humanity," said Dave Coplin, chief envisaging officer at the tech brand.