AI tagged posts

Astronomers have recently found hundreds of “polluted” white dwarf stars in our home galaxy, the Milky Way. These are white dwarfs caught actively consuming planets in their orbit. They are a valuable resource for studying the interiors of these distant, demolished planets. They are also difficult to find.

Historically, astronomers have had to manually review mountains of survey data for signs of these stars. Follow-up observations would then prove or refute their suspicions.

By using a novel form of artificial intelligence, called manifold learning, a team led by University of Texas at Austin graduate student Malia Kao has accelerated the process, leading to a 99% success rate in identification...

A research team led by Professor Jang-Sik Lee from the Department of Materials Science and Engineering and the Department of Semiconductor Engineering at Pohang University of Science and Technology (POSTECH) has significantly enhanced the data storage capacity of ferroelectric memory devices. By utilizing hafnia-based ferroelectric materials and an innovative device structure, their findings, published on June 7 in the international journal Science Advances, mark a substantial advancement in memory technology.

With the exponential growth in data production and processing due to advancements in electronics and artificial intelligence (AI), the importance of data storage technologies has surge...

Powerful new artificial intelligence models sometimes, quite famously, get things wrong—whether hallucinating false information or memorizing others’ work and offering it up as their own. To address the latter, researchers led by a team at The University of Texas at Austin have developed a framework to train AI models on images corrupted beyond recognition.

DALL-E, Midjourney and Stable Diffusion are among the text-to-image diffusion generative AI models that can turn arbitrary user text into highly realistic images. All three are now facing lawsuits from artists who allege generated samples replicate their work...

Researchers look to machine learning to optimize the design and control of stellarators and tokamaks. Researchers are using artificial intelligence to perfect the design of the vessels surrounding the super-hot plasma, optimize heating methods and maintain stable control of the reaction for increasingly long periods. A new article explains how a researcher team used machine learning to avoid magnetic perturbations, or disruptions, which destabilize fusion plasma.

The intricate dance of atoms fusing and releasing energy has fascinated scientists for decades. Now, human ingenuity and artificial intelligence are coming together at the U.S...