This study investigates the cognitive plausibility of a pretrained multimodal model, CLIP, in recognizing emotions evoked by abstract visual art. We employ a dataset comprising images with associated emotion labels and textual rationales of these labels provided by human annotators. We perform linguistic analyses of rationales, zero-shot emotion classification of images and rationales, apply similarity-based prediction of emotion, and investigate color-emotion associations. The relatively low, yet above baseline, accuracy in recognizing emotion for abstract images and rationales suggests that CLIP decodes emotional complexities in a manner not well aligned with human cognitive processes. Furthermore, we explore color-emotion interactions in images and rationales. Expected color-emotion associations, such as red relating to anger, are identified in images and texts annotated with emotion labels by both humans and CLIP, with the latter showing even stronger interactions. Our results highlight the disparity between human processing and machine processing when connecting image features and emotions.

In recent years, we've seen an influx of self-proclaimed "cozy games," video games explicitly designed to invoke good vibes. To help those who could use some help winding down, we've rounded up a selection of games that purposefully deemphasize fail states, violence, overwhelming grinds, intense competition and other aggressive urges, but aren't overly cute for the sake of it or so stripped-down that they're boring. This open-ended sim has you fix up a dilapidated farm and interact with nearby townsfolk. Apart from being one of our favorite couch co-op games, the farming life sim Stardew Valley is also notable for its relaxing qualities. It's a game that's willing to meet you at your pace: If you want to putter around your farm, casually chat up townsfolk, brew beer or fish for a few hours, you can.

The possibilities have been endless, the opportunity for meme-making infinite. It should not be surprising that a great many artists who have spent a lifetime honing their skills are a little put out by this latest disruption. Are companies going to keep hiring designers when they can produce prototypes themselves for free? Will budgets stretch to include animators if their hand can be imitated from a simple text description? Advocates of AI have insisted that creatives should have nothing to worry about and can adapt their process to incorporate or work around technological advances, much like the modernists did with the invention of photography. But if those historical greats were alive and working today, would they also be watching their backs? And could a computer ever hope to reproduce the emotional depth that gives great art its charm and meaning? To find out, we set a challenge for three art experts: Bendor Grosvenor, art historian and presenter of the BBC's Britain's Lost Masterpieces; JJ Charlesworth, art critic and editor of ArtReview; and Pilar Ordovas, founder of the Mayfair gallery Ordovas. Each was invited to look at pairs of artworks of a similar style and period over Zoom to see if they could tell which was generated by a machine.

From abstract art, digital painting, complex sculpture, architectural visualisation or 5 years old hand drawing, whatever you ask for, the AI makes it, or at least tries its best to. Does that cheapen the value of someone who puts their hands in researching, planning, sketching, developing their artworks; versus an AI just learning that from having studied millions of existing images and figuring out what we humans like to look at, and replicating that in mere minutes? You can create artwork without specifying colour or even form of your subject, and simply allowing the AI to take the reins, while you only provide inspirations and mood you wanted to achieve. It's not perfect though, as you will quickly notice; it can not, at the moment at least, understand nuance interactions between human culture, and everything that is presented in the resulting image is based on learning from what people created. Allowing it to run without supervision will in turn create a visual language in our society that might encourage bigotry and biases, and limit true creativity that inspire progression through diversity.

Image datasets are commonly used in psychophysical experiments and in machine learning research. Most publicly available datasets are comprised of images of realistic and natural objects. However, while typical machine learning models lack any domain specific knowledge about natural objects, humans can leverage prior experience for such data, making comparisons between artificial and natural learning challenging. Here, we introduce DELAUNAY, a dataset of abstract paintings and non-figurative art objects labelled by the artists' names. This dataset provides a middle ground between natural images and artificial patterns and can thus be used in a variety of contexts, for example to investigate the sample efficiency of humans and artificial neural networks. Finally, we train an off-the-shelf convolutional neural network on DELAUNAY, highlighting several of its intriguing features.

While homo sapiens were capable of abstract thought almost 100,000 years ago, it took much longer for the human mind to invent abstract painting. It wasn't until the beginning of the 20th century that artists such as Wassily Kandinsky, Kazimir Malevich, and Hilma af Klint created abstract works with no identifiable references to the physical world. Abstraction quickly became the lodestar driving artistic production, a trend that largely continues to this day. But just how popular is abstract art with collectors and art enthusiasts? To try and answer this question, we assembled a database of 112,600 Instagram posts made last December for which the geolocation and/or hashtags indicated that the user was in Miami during Art Basel in Miami Beach. Eliminating selfies and other extraneous pictures yielded approximately 74,760 images, which represents the collective visual record of all the artworks Instagram users saw in person that they also elected to share with their followers.

Luckily for those of you fearing an impending robocalypse, the aim of the study is to further the progress of machine-created art, rather than creating an artificial intelligence that knows about our greatest fears. If a machine can identify emotions as related to art, it could conceivably create art with the intensity of a being able to be passionate. Simon Colton of the Imperial College of London notes that art as a whole could benefit from machine-created art, because machines can do certain things humans cannot, such as scanning the entirety of a social network for inspiration. Penn State University's James Wang suggests applications for machines understanding artistic emotion that aren't about creating art, such as an image search being able to accurately categorize results by emotion, or computer parental controls that have an emotions filter. We simply hope this study will lead toward robots being able to make us some comfort food when they identify we're feeling down.

The image above is what happens when you take a satellite photo of Earth and use an artificial-intelligence program to give it a makeover in the style of an early 20th-century painter. It's just one of many remarkable images recently posted to Instagram by Bill Morris, a cartographer at Faraday, a data analytics company in Burlington, Vermont. Morris says he was inspired by Meredith Scheff-King, an artist who recently made watercolor paintings of satellite imagery. He wanted to try something similar but didn't have that kind of artistic talent. That's where the AI came in.

Are art and science of distinctly different cultures? The former often seems fixated on human experience, the latter on physical processes. In his most recent book, Reductionism in Art and Brain Science: Bridging the Two Cultures, published this year, the Nobel Prize-winning neuroscientist Eric Kandel argues that such a separation no longer exists. The best-known abstractionists, like Mark Rothko, Jackson Pollock, Dan Flavin, and Willem de Kooning, Kandel writes, effectively created "new rules for visual processing." Abstract art, says Kandel, is therefore the key to understanding both how art and science inform one another, and together, they might open up entirely new ways of seeing and imagining.