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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.
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随着临床试验的推进，APL-1702未来有望覆盖 CIN1-CIN3 全谱系宫颈上皮内病变，触达数百万量级的患者群体，实现市场空间的几何级扩容，推动专家共识的精准防控理念贯穿 CIN 全病程管理。