Hardest

Tearful testimony details missed warnings and lack of plan as families oppose reopening of Texas campA director of Camp Mystic, the all-girls Christian camp in the Texas Hill Country where 25 campers and two counselors were killed in a flash flood last summer, has offered a tearful apology to victim’s families for the loss of life.“We tried our hardest that night. It wasn’t enough to save your daughters,” Edward Eastland said, at a joint Texas House and Senate committee panel in Austin investigating the deadly flooding. “I’m so sorry.” Continue reading...

As the war in Iran continues, ministers debate several options for extending support to households Middle East crisis – live updatesFamilies hardest hit by the looming energy crisis caused by the conflict in the Middle East could be given funds dispensed by local councils, under plans being considered by UK ministers keen to keep a lid on costs.As concerns increase about the impact of rising fuel and energy costs in response to a drawn-out war in Iran, a government official said several options for extending support were being debated inside Whitehall. Continue reading...

Researchers at Texas A&M University have developed what they describe as the most challenging AI benchmark test to date, with results that contradict earlier expectations about artificial intelligence capabilities. The comprehensive study involved a large team of scientists investigating how cutting-edge AI systems perform on extreme difficulty assessments as these models become increasingly sophisticated. The findings suggest that current AI systems are achieving unexpected performance levels on this rigorous evaluation framework, providing important insights into the true capabilities and limitations of advanced machine learning models.

Scientists at Texas A&M University created the most challenging AI test to date, involving over 50 collaborators, to evaluate advanced language models on complex reasoning tasks. Initial results showed even top AI systems struggling significantly, highlighting gaps in current capabilities despite acing simpler benchmarks. The benchmark, detailed in a peer-reviewed paper, pushes boundaries in AI safety and robustness research. Future iterations will incorporate multimodal challenges to further stress-test emerging models.