Training artificial intelligence models is costly. Researchers estimate that training costs for the largest frontier models will exceed $1 billion by 2027. Costs are incurred through hardware, including large data centers, energy needs and salaries for research and development staff. The massive training price tag limits the labs and researchers that can afford to work with this technology. Research from Dr. Tianbao Yang in Texas A&M University’s Department of Computer Science and Engineering may level the playing field. 

Resilience to disasters is not optional — it is essential to public safety, national security, economic prosperity and quality of life. Rice University’s civil and environmental engineering department (CEE) has established a national reputation for tackling one of society’s most pressing challenges: enhancing our understanding of and mitigating the risks posed by natural hazards to our communities and infrastructure. From pioneering work in probabilistic mechanics and structural system identification to breakthroughs in modeling infrastructure interdependencies and multihazard vulnerability, Rice CEE researchers have shaped the field and continue to push the boundaries of what is possible in disaster risk, reliability and resilience.

The partnership began by adapting robotic arc welding into a new form of large-scale metal 3D printing, enabling complex geometries, multi-material deposition and dramatically shorter production timelines. Joint research led to breakthroughs in real-time monitoring, materials qualification and multi-robot coordination, culminating in a system capable of depositing up to 100 pounds of metal per hour. Outcomes from the collaboration are already being applied across U.S. industry, including rapid fabrication of large molds, tooling and replacement parts that would traditionally require months or years to source overseas.