Acquisition of high dynamic range (HDR) images is thriving due to the increasing use of smart devices and the demand for high-quality output. Extensive research has focused on developing methods for reducing the luminance range in HDR images using conventional and deep learning-based tone mapping operators to enable accurate reproduction on conventional 8 and 10-bit digital displays. However, these methods often fail to account for pixels that may lie outside the target display's gamut, resulting in visible chromatic distortions or color clipping artifacts. Previous studies suggested that a gamut management step ensures that all pixels remain within the target gamut. However, such approaches are computationally expensive and cannot be deployed on devices with limited computational resources. We propose a generative adversarial network for fast and reliable chroma compression of HDR tone-mapped images. We design a loss function that considers the hue property of generated images to improve color accuracy, and train the model on an extensive image dataset. Quantitative experiments demonstrate that the proposed model outperforms state-of-the-art image generation and enhancement networks in color accuracy, while a subjective study suggests that the generated images are on par or superior to those produced by conventional chroma compression methods in terms of visual quality. Additionally, the model achieves real-time performance, showing promising results for deployment on devices with limited computational resources.

The Goldilocks theory of policy is simple enough. If Mummy Bear says your latest government bill is too hot, and Daddy Bear says your latest government bill is too cold, then you can tuck in knowing that the actual temperature is just right. Unfortunately, the Goldilocks theory sometimes fails. You learn that what you actually have in front of you is less a perfectly heated bowl of porridge and more a roast chicken you popped in the oven still frozen: frosty on the inside, burnt on the outside, and harmful to your health if you try to eat it. To its supporters, the online safety bill, which was dropped from the legislative calendar last Wednesday to make space for a no-confidence motion in the government, sits firmly in the Goldilocks zone. The bill is a monster piece of legislation, with its roots in a green paper published way back in October 2017.

It was with a strangely deflated feeling in his gut that Harvard biologist Mohammed AlQuraishi made his way to Cancun for a scientific conference in December. Strange because a major advance had just been made in his field, something that might normally make him happy. Deflated because the advance hadn't been made by him or by any of his fellow academic researchers. It had been made by a machine. DeepMind, an AI company that Google bought in 2014, had outperformed all the researchers who'd submitted entries to the Critical Assessment of Structure Prediction (CASP) conference, which is basically a fancy science contest for grown-ups. Every two years, researchers working on one of the biggest puzzles in biochemistry, known as the protein folding problem, try to prove how good their predictive powers are by submitting a prediction about the 3D shapes that certain proteins will take.

In 1950, Claude Shannon published his seminal work on how to program a computer to play chess. In Shannon's time, it would have seemed unlikely that only a scant 50 years would be needed to develop programs that play world-class backgammon, checkers, chess, Othello, and Scrabble. Computer games research is one of the important success stories of AI. This article reviews the past successes, current projects, and future research directions for AI using computer games as a research test bed.