Craig Venter, and How Multiple, Parallel Paths Accelerate Science and Technology - AMS Headlines

image: Overview of memory-efficient training techniques for transformers The integration of deep learning—particularly Transformer-based models—has profoundly reshaped scientific research paradigms. In domains such as drug discovery, weather forecasting, and protein structure prediction, these models enable the extraction of complex patterns from large-scale data. However, as model resolution and parameter counts continue to grow, GPU memory consumption increases rapidly, creating a “memory wall” that confines large-scale training to costly, high-end hardware and limits broader adoption in scientific research.

image: Overview of memory-efficient training techniques for transformers The integration of deep learning—particularly Transformer-based models—has profoundly reshaped scientific research paradigms. In domains such as drug discovery, weather forecasting, and protein structure prediction, these models enable the extraction of complex patterns from large-scale data. However, as model resolution and parameter counts continue to grow, GPU memory consumption increases rapidly, creating a “memory wall” that confines large-scale training to costly, high-end hardware and limits broader adoption in scientific research.

Researchers at the MRC Laboratory of Medical Sciences (LMS) have developed a new way to make glowing biological reporters more accurate, allowing scientists to track gene activity in living plants over days, weeks and potentially even months and years. The approach could help researchers study how plants respond to disease, pests and environmental stress, while opening the door to similar applications in animal and human disease models.

The latest in space science: We look back at the Big Bang In this edition of Spacing Out, the team from Short Wave shares the latest developments in space science, including the theory on why the universe is made of matter In this edition of Spacing Out, the team from Short Wave discusses the latest space science news

A molecular modeling approach identifies boron- and oxygen-containing diamond defects as promising candidates for quantum technologies. The calculations focus on small atomic clusters while retaining the surrounding crystal’s influence, making sophisticated quantum chemistry more manageable. The predictions help screen candidates, but practical qubit performance still requires tests of coherence, stability and other properties. Tiny imperfections in diamond could offer new ways to store and read quantum information. The challenge is deciding which defects deserve the time and expense of experimental testing. A computational study now brings tools from molecular chemistry to that search, identifying promising boron- and oxygen-containing candidates. John Mark P. Martirez, a staff research scientist at the U. S. Department of Energy’s Princeton Plasma Physics Laboratory, reports the work in ACS Central Science. His approach concentrates demanding calculations on a small group of atoms surrounding a defect. Crucially, the model still accounts for the influence of the diamond crystal beyond that group.