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涙ながらの証言によって警告の見落としや計画の欠如が詳述される中、遺族らはテキサス州のキャンプ場の再開に反対している。 昨夏の鉄砲水でキャンパー25人とカウンセラー2人が死亡したテキサス州ヒル・カントリーの女子専用キリスト教キャンプ場「キャンプ・ミスティック」のディレクターが、犠牲者の遺族に対し涙ながらに謝罪した。「あの夜、私たちは全力を尽くしました。しかし、お嬢さんたちを救うには不十分でした」と、エドワード・イーストランド（Edward Eastland）氏は、この凄惨な洪水を調査するためにオースティンで開かれたテキサス州上下両院の合同委員会パネルにおいて述べた。「本当に申し訳ありません」。 続きを読む...

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.