前几天刷视频，看到一个博主分享她的云南旅行。一个人在大理的咖啡馆发呆，在沙溪古镇的田埂上走了两小时，晚上回到青旅，和刚认识的陌生人拼了顿火锅。这个视频的标题中，有个词吸引了我的目光——solo trip。
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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.