Foundation models have demonstrated remarkable success across various scientific domains, motivating our exploration of their potential in solar physics. In this paper, we present Solaris, the first foundation model for forecasting the Sun's atmosphere. We leverage 13 years of full-disk, multi-wavelength solar imagery from the Solar Dynamics Observatory, spanning a complete solar cycle, to pre-train Solaris for 12-hour interval forecasting. Solaris is built on a large-scale 3D Swin Transformer architecture with 109 million parameters. We demonstrate Solaris' ability to generalize by fine-tuning on a low-data regime using a single wavelength (1700 {\AA}), that was not included in pre-training, outperforming models trained from scratch on this specific wavelength. Our results indicate that Solaris can effectively capture the complex dynamics of the solar atmosphere and transform solar forecasting.

NASA scientists are using artificial intelligence to calibrate photographs of the Sun to improve data for solar studies. NASA's Solar Dynamics Observatory (SDO) has been providing high-definition photos of the Sun for nearly a decade since its launch on February 11, 2010. The photos have offered an in-depth examination of a variety of solar phenomena. SDO's Atmospheric Imaging Assembly (AIA) observes the Sun continuously and generates a lot of data about our Sun that has never been possible before. AIA degrades over time as a result of continual looking, and the data must be calibrated frequently.

Infrared images of the gas giant Jupiter show the massive planet's churning atmosphere like never before - beyond what we can see with the human eye. Hawaii's Gemini North observatory and the NASA Hubble space telescope captured the largest planet in the solar system in a range of light wavelengths. The images show the planet at infrared, visible, and ultraviolet, revealing details of the atmosphere of the gas giant not visible without specialist observatories. These views reveal a range of details in atmospheric features such as the Great Red Spot, superstorms, and gargantuan cyclones stretching across the planet's disk. Viewing planets at different wavelengths of light allows scientists to glean otherwise unavailable insights such as features of storms previously hidden, the team said.

The first exoplanet was discovered in 1992 and nearly 30 years later, the feat has been accomplished by artificial intelligence (AI). Scientists developed a new machine-learning algorithm that uncovered 50 potential Martian worlds beyond our solar system. The technology is capable of separating real planets from fake ones in samples of thousands of candidates spotted by NASA telescope missions, such as TESS and Kepler. The team trained the AI to recognize exoplanets using a database of confirmed cosmic orbs and false positives shown in data. The first exoplanet was discovered in 1992 (artist's impression) and nearly 30 years later, the feat has been accomplished by artificial intelligence (AI).

The first exoplanet was discovered in 1992 and nearly 30 years later, the feat has been accomplished by artificial intelligence (AI). Scientists developed a new machine learning algorithm that uncovered 50 potential Martian worlds beyond our solar system. The technology is capable of separating real planets from fake ones in samples of thousands of candidates spotted by NASA telescope missions, such as TESS and Kepler. The team trained the AI to recognize exoplanets using a database of confirmed cosmic orbs and false positives shown in data. The first exoplanet was discovered in 1992 (artist's impression) and nearly 30 years later, the feat has been accomplished by artificial intelligence (AI).

A new Chameleon paint which allows people to change the colour of their cars, phone cases and trainers as many times as they want has been created by MIT researchers. The reprogrammable ink changes the colour of an object after it is exposed to ultraviolet and visible light sources at different wave lengths. A team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) designed the system, which has been dubbed'PhotoChromeleon'. One researcher said the tool will allow users to personalise their appearance and belongings multiple times in a variety of styles and colours. The chameleon paint was designed by a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) which has been dubbed'PhotoChromeleon' They created the ink by mixing cyan, magenta, and yellow photochromic dyes into a single sprayable solution.

The color-changing capabilities of chameleons have long bewildered willing observers. The philosopher Aristotle himself was long mystified by these adaptive creatures. But while humans can't yet camouflage much beyond a green outfit to match grass, inanimate objects are another story. A team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) has brought us closer to this chameleon reality, by way of a new system that uses reprogrammable ink to let objects change colors when exposed to ultraviolet (UV) and visible light sources. Dubbed "PhotoChromeleon," the system uses a mix of photochromic dyes that can be sprayed or painted onto the surface of any object to change its color -- a fully reversible process that can be repeated infinitely.

What better way to build smarter computer chips than to mimic nature's most perfect computer – the human brain? Being able to store, delete and process information is crucial for computing, and the brain does this extremely efficiently. Our new electronic chip uses light to create and modify memories, moving us closer towards artificial intelligence (AI) that can replicate the human brain's sophistication. To develop this, we drew inspiration from a new technique called optogenetics, to develop a device that replicates the way the brain stores (and loses) information. Optogenetics involves using light to control cells in living tissue, typically nerve cells (neurons).