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One challenge is having enough training data. Another is that the training data needs to be free of contamination. For a model trained up till 1900, there needs to be no information from after 1900 that leaks into the data. Some metadata might have that kind of leakage. While it’s not possible to have zero leakage - there’s a shadow of the future on past data because what we store is a function of what we care about - it’s possible to have a very low level of leakage, sufficient for this to be interesting.
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Diagnosed with a terminal lung condition in 2023, he spent many nerve-wracking months on the waiting list for an urgent double lung transplant.
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虽然苹果在此后紧急重组了技术架构，但关键人才的流失往往具有标志性意义。这也侧面说明了，为何后续版本的Siri会选择与谷歌Gemini等外部力量合作，这种技术路径的切换，本身就折射出苹果在自研核心模型上所面临的现实压力。