“没有海量真实场景数据的‘喂养’，再强的芯片也只是空谈。”一位从蔚来智驾部门离职的核心算法工程师向虎嗅回忆，“为了适配神玑，我们重构了底层架构，进度一度滞后，直接错失了端到端大模型落地的最佳窗口期。在模型泛化能力上，我们与拥有百万级车队的对手差距明显。”
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The converse is also worth asking — whether simulating artificial environments (for instance a 3d representation of a Youtube video) might have unintended negative consequences. Fei-Fei Li’s startup World Labs, which aims to make the leading “world model” — an alternative to language models based on tokenizing physical space rather than words — recently raised a substantial amount of money. As consumer-facing robots become more plausible, the business case for such a model is obvious. But what physical spaces are “world” models actually being trained on? The contemporary physical environment, sound-proofed, plastic-coated, and artificially-colored, is radically different from the environment that Homo sapiens evolved to excel in.
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