Researchers found that multimodal AI models cannot tell time. The more variation there was in the clock face, the more the chatbot being tested was likely to misread the clock. MLLMs only recognize patterns they have already seen and clocks, calendars or anything that requires spatial reasoning don't fit into that.

【ニューヨーク=谷本克之】人工知能（AI）の開発や運用に使うデータセンターの建設が米国で急増し、サーバーの冷却に使う水の大量消費への懸念が広がっている。米データセンターの年間水使用量は日本で50万人分の生活水に相当する660億リットルと9年で3倍となり、増え続けている。建設地では住民が地下水枯渇と干ばつにおびえている。巨大産業団地の半分がデータセンターに米西部ネバダ州の砂漠に位置するタホ・リ

Researchers found that multimodal AI models cannot tell time. The more variation there was in the clock face, the more the chatbot being tested was likely to misread the clock. MLLMs only recognize patterns they have already seen and clocks, calendars or anything that requires spatial reasoning don't fit into that.

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Lyft has rearchitected its machine learning platform LyftLearn into a hybrid system, moving offline workloads to AWS SageMaker while retaining Kubernetes for online model serving. Its decision to choose managed services where operational complexity was highest, while maintaining custom infrastructure where control mattered most, offers a pragmatic alternative to unified platform strategies. By Eran Stiller

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