Что думаешь? Оцени!
周健工：谷歌在收购DeepMind之后，有一天哈萨比斯突然跟佩奇在讲，说我们将战胜围棋的世界冠军。然后对方就很吃惊地说，多长时间？他随口就说了两年。
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Турция сообщила о перехвате баллистического снаряда из Ирана14:52
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.