The electroretinogram (ERG) is a clinical test that records the retina's electrical response to light. The ERG is a promising way to study different neurodevelopmental and neurodegenerative disorders, including autism spectrum disorder (ASD) - a neurodevelopmental condition that impacts language, communication, and reciprocal social interactions. However, in heterogeneous populations, such as ASD, where the ability to collect large datasets is limited, the application of artificial intelligence (AI) is complicated. Synthetic ERG signals generated from real ERG recordings carry similar information as natural ERGs and, therefore, could be used as an extension for natural data to increase datasets so that AI applications can be fully utilized. As proof of principle, this study presents a Generative Adversarial Network capable of generating synthetic ERG signals of children with ASD and typically developing control individuals. We applied a Time Series Transformer and Visual Transformer with Continuous Wavelet Transform to enhance classification results on the extended synthetic signals dataset. This approach may support classification models in related psychiatric conditions where the ERG may help classify disorders.

The British landscape painter John Constable is considered foundational for the Realist movement in 19th-century European painting. Constable's painted skies, in particular, were seen as remarkably accurate by his contemporaries, an impression shared by many viewers today. Yet, assessing the accuracy of realist paintings like Constable's is subjective or intuitive, even for professional art historians, making it difficult to say with certainty what set Constable's skies apart from those of his contemporaries. Our goal is to contribute to a more objective understanding of Constable's realism. We propose a new machine-learning-based paradigm for studying pictorial realism in an explainable way. Our framework assesses realism by measuring the similarity between clouds painted by artists noted for their skies, like Constable, and photographs of clouds. The experimental results of cloud classification show that Constable approximates more consistently than his contemporaries the formal features of actual clouds in his paintings. The study, as a novel interdisciplinary approach that combines computer vision and machine learning, meteorology, and art history, is a springboard for broader and deeper analyses of pictorial realism.

An actual, if elderly and ailing, Havanese is looking up at me as I work, and an Avedon portrait book is open on my desk. What could be more beguiling than combining the two? Then my laptop stutters and pauses, and there it is, eerily similar to what Richard Avedon would have done if confronted with a Havanese. The stark expression, the white background, the implicit anxiety, the intellectual air, the implacable confrontational exchange with the viewer--one could quibble over details, but it is close enough to count. My Havedon is, of course, an image produced by an artificial-intelligence image generator--DALL-E 2, in this case--and the capacity of such systems to make astonishing images in short order is, by now, part of the fabric of our time, or at least our pastimes.

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.