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.
Дальневосточные биржи пережили падение из-за ситуации на Ближнем ВостокеФондовые индексы Японии и Южной Кореи резко снизились вслед за Европой
Reeves says her plan is working as growth forecast cut for this year
清代徐扬的《京师生春诗意图》，则将天上人间进行另一种连接。画面俯瞰紫禁城全貌以及京城多处地标性景观。从题诗可知画家希望绘制的是自腊月初一开始至上元节而止的一个时间轴，亦是彼时京师“饯腊迎春”景象的总图。画面巧妙安排节令时间与城市空间，宫俗和民俗在不同方位展开。画面东北角的山峦上方，隐藏着迎春的天象密码，是统筹全局、开启生春之旅的重要天文景观，如乾隆诗所述：“何处生春早，春生斗柄中。丽天居北宿，指艮转东风。玉杓垂如注，璇玑象已融。拱辰恒近极，率领众星丛。”这里用语言描绘了北斗七星的状态。这种天象预示了春之生，给予人间一种秩序的启示。