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“自动驾驶行业将跳过L3，直接从L2迈向L4级全自动驾驶”，何小鹏认为，L3的本质是“过渡性技术陷阱”，为规避风险而堆砌的大量规则，使其沦为“看似安全却限制进化”的存在。与其如此，不如集中攻克L4难题，以真正的技术创新来解决技术发展中的问题。
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8年攻坚、5年过渡，中国以成功实践进一步向世界表明：本着滴水穿石、一张蓝图绘到底的韧性、恒心和奋斗精神，贫困不仅是可以战胜的，更是可以阻断、不再复发的。
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.