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.
ADB can do a lot of interesting things.
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互有过错的船舶，对碰撞造成的船舶以及船上货物和其他财产的损失，依照前款规定的比例承担赔偿责任。碰撞造成第三人财产损失的，各船的赔偿责任均不超过其应当承担的比例。
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Иран начал новую волну ракетных атак. Об этом сообщает агентство Irib.