Фото: Roman Naumov / URA.RU / Globallookpress.com
2026一开年，何小鹏就抛出重磅言论——跳过L3级有条件自动驾驶，直奔L4级自动驾驶。他认为，从L4开始责任主体划分会更加明确。在今天全球科技发展的情况下，L2的下一个台阶就是L4，中间专门加一个L3，对于硬件、软件、法律法规都是挑战。
，详情可参考电影
Compute grows much faster than data . Our current scaling laws require proportional increases in both to scale . But the asymmetry in their growth means intelligence will eventually be bottlenecked by data, not compute. This is easy to see if you look at almost anything other than language models. In robotics and biology, the massive data requirement leads to weak models, and both fields have enough economic incentives to leverage 1000x more compute if that led to significantly better results. But they can't, because nobody knows how to scale with compute alone without adding more data. The solution is to build new learning algorithms that work in limited data, practically infinite compute settings. This is what we are solving at Q Labs: our goal is to understand and solve generalization.
但这个逻辑在2025年悄悄失效了。
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