Generate plans, code, and reviews that reflect how your system actually works
Блогеру Арсену Маркаряну дали срок14:50
。关于这个话题，Safew下载提供了深入分析
В КСИР выступили с жестким обращением к США и Израилю22:46
Россия поставит Индии ракеты для «Штиля»UNI: Россия поставит Индии ракеты для корабельных ЗРК «Штиль-1»
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Что думаешь? Оцени!
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.。业内人士推荐heLLoword翻译官方下载作为进阶阅读