李东生已连续多年关注中国先进制造业发展问题。“要培育世界一流企业，必须强化资本支撑。既要依靠企业自身盈利积累，也要充分利用资本市场融资功能，为企业持续发展注入动力。”李东生表示，“建议监管机构配合国家产业政策发展的需要，出台相关政策，支持高科技、重资产、长周期产业发展，为该类型项目设置特别的融资规则和通道，以更好地推动资本市场，支持先进制造业发展。”
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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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