e e e e e e e e 测试测试一二主持人麦克检查一检查一二这是主持人麦克检查检查一二嗯是的所以我让我吸引你的注意力一秒钟抱歉抱歉关于那项目项目是的从这里开始好的好的大家好这样可以吗靠近一点大家好好的看我打破我摔了我的背包嗯所以我在做志愿者嗯和肖恩一起做这个也是的 10 9 8 7 6 5 4 3 2 1 嗯我其实现在不知道最后的演讲在 2 点 2 点 20 嗯是的 2 点 20 的演讲哦琳恩要来了真好琳恩不来了哦她不来了哦但她的助手来了哦好的好的他其实更棒不只是开玩笑可能不是说话但就像你知道的是的和大家一起变得极客很好地结束 um 是的我不确定关于替换我可以去问问我猜不我有 Swix 文本好的好的明白了好的 um 哦另外关于指南针的事情嗯你介意我在你手机上下载应用吗因为它必须保持在你身上嗯我可以发送给你链接和其他东西是的我会我也会在这里整个整个过程如果发生什么听起来不错指南针 um 其实你去 Safari 是一个链接我会链接你能发送给我吗是的我可以做到好的我很高兴你来得这么早 s 首先那八七六五四三二一零我认为想要说话大家好大家好这样更好吗超近测试测试好的好的很酷 e e e e e e e e e [音乐] [音乐] 我你你你你 [音乐] 你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你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你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你你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和速度将携手并进，如果我们想让它对人们来说近乎无缝地运作，我认为下一步是某种通用自然语言处理。所以，如果我们今天想到我们与软件的交互方式，你知道，你知道我们开始时是那种点击和键盘，我们已经转向了触摸，通过我们的移动设备，但实际上，你知道，你开始看到它的力量，而且你知道，我认为每个人都对 GPT 40 的发布感到非常兴奋，语音代理，我们，我认为我们还没有完全实现，但我认为我们已经展示了可能的艺术，通过他们能够用语音做什么，然后是那种混合交互。我会说，你知道，我们称之为某种 xrx，它就像任何类型的输入、推理和任何类型的输出。你知道，我喜欢告诉人们的例子是，如果你想订购某样东西，你可能想通过语音与代理交互，但你可能想在文本中看到响应。所以，想想如果你想预订你的理发，你想说，告诉我哪些时间可用，然后你知道，它告诉你，嗯，有上午 9 点、上午 11 点、下午 3 点半和下午 5 点半。如果它只是通过语音告诉你，这很难记住。所以你基本上想要这些多模态的交互，这触及了我第二点，而且我认为我们也将开始看到更多这样的界面变化。你知道，高级虚拟助手，这是复杂的任务调度。我认为今年下半年我们将看到很多的是，你知道，代理开始变得更加复杂，而且 LLM 提供商也高度关注，我认为是关于让，你知道，复杂的任务得到解决。今天很有趣，因为我们通常通过单次提示来衡量 LLM 的有效性，而且我认为我们这样做是因为，你知道，回到我们，你知道，对话的，这是性能障碍，但自然地，即使你今天采用任何现有的 LLM 并对其进行多次提示，其分数也会大大提高。最近有几篇论文出来，表明如果你有多代理协同解决问题，它们可以远远超过，你知道，参数较少的模型，它们可以通过某种多步推理或协同工作来与更高参数的模型竞争。所以，我认为随着速度的提高，我们将看到更多这样的情况，而且我认为那里有令人难以置信的令人难以置信的选择性。你知道，我们看到了第一个，我认为是第一个协作式 AI 代理的尝试，与苹果 AI 一起，你知道，在那里你可能会看到一些东西在设备上运行，与设备外的某些东西交互。我认为这是一个非常早期的实现，而且我认为这些东西将变得更加复杂和更好。你知道，我们花了很多时间在我们的职业生涯中，就像分析和预测分析一样。我认为今天一切都是，你知道，几乎是面向行动的，并且由人类行动驱动。所以，我认为如果我们进入一个速度提高的地方，它们可以变得更具预测性。你知道，这到底意味着什么？它只是一个一直在后台运行的代理，因为计算周期几乎是免费的。我们今天看不到这一点，但我认为随着我们沿着曲线向上发展，我们会到达那里。你知道，上下文感知也是如此。今天，我们再次，我们通常受限于我们可以提供的上下文量，而且我们甚至不得不，即使有了具有更大上下文窗口的模型，我们仍然需要，你知道，注意，你知道，我们将使用多少计算周期。但我认为，如果这变得几乎免费，它对我们来说将非常强大。你知道，创意工具和可定制内容。我将重点关注第二个，这是，这是我我认为我们许多人希望看到事情发展的领域。你知道，我总是喜欢告诉人们的例子，我最喜欢的节目之一是《宋飞正传》，显然，你知道，它已经不在播出了，但当我无聊时，我喜欢做的一件事就是进入你选择的 LLM，让它写一集《宋飞正传》，但由当今正在发生的事情组成。如果你曾经尝试过，它非常有趣，因为它在你给它的场景中识别出哪个角色会，你知道，发生有趣或奇怪的事情，做得非常出色。所以，将这个想法从某种写作扩展到多媒体形式，将是真正，你知道，非常非常强大的。你知道，复杂的决策制定。你知道，在我们公司被 Grok 收购之前，你知道，我们正在建立一家名为 Definitive Intelligence 的公司，所以我们在这个领域花了很多很多时间。不仅仅是进行某种自然语言到，你知道，SQL 的分析，很多人称之为德州 SQL。但是，你知道，里克，他在这里和我们一起，你知道，他为我们开发了一个非常酷的产品，叫做 Pioneer，这是一个自动化的数据科学代理，它真的 meant to run almost endlessly on a problem，而且，你知道，你定义了一个上限，你思考一个企业如何运营，一个企业有一堆 KPI，然后一个企业有一堆进来的数据，然后通常是人类在分析这些数据，KPI 并创建 PowerPoint 和电子表格，并告诉高管或世界他们做得有多好。没有理由这不应该自动发生，对吧？而且有一个代理不断地，你知道，查看新进来的数据，提出额外的问题，深入研究它。而且我认为我们有很多有趣的事情出现。你知道，我们让 Pioneer 处理了一组人类工人和他们的绩效评估数据，而我们看到的一件事是，它能够关联我们无法想到的非常有趣的事情，你知道，取决于你的年龄，取决于你的绩效评估，它真的影响了你的，我猜是你的产出，你的生产力。所以，我能够发现，如果你是某个年龄段的人，并且你得到了某种绩效评估，你的生产力就会下降。也许里克以后可以纠正我，但大概是这样，这对我来说一直是一个有趣的例子。然后显然有很多，你知道，关于动态优化的真正有趣的事情。你知道，这是我们之前熟悉的领域。你知道，当我们中的许多人在福特的时候，在 Autonomic 收购之后，我们真的看到了，你知道，对于供应链来说，如果你想想汽车是如何生产的，以及它们是如何运输的。你知道，有非常复杂的软件可以做到这一点，但它仍然效率不高，对吧？而且我认为，你知道，我们之前谈到的可能性的艺术，对于我们在福特的老同事来说，可能会非常非常有趣。我将简要提及几件事，然后留出几分钟时间提问，如果有的话。但边缘 AI 和去中心化 AI，这非常酷。你知道，有一个非常酷的项目叫做 Hyperspace.ai，他们正在做的是，他们实际上有很多，你知道，利用，你知道，某种像在家里的 SEI，甚至 Render，他们基本上允许人们将他们未使用的 GPU 计算提供给云。或者我猜是的。而且，为什么这很有趣呢？因为有一些用例不一定需要实时。所以，我认为我们将看到更多这样的情况。现在，这与我们从现有系统中获得更多吞吐量和更低延迟非常吻合。所以，我认为我们将看到更多这样的情况，尤其是因为所需的功耗，如果你将其分布式，那可能会非常有趣。还有几件事是增强的安全性和隐私性，这是一个很大的领域。你知道，我昨晚和我们的一位同事谈过，他遭受了一种非常非常可怕的，我猜是某种网络钓鱼电话，你知道，有人打电话进来，听起来非常正式，并且拥有大量信息。现在，你知道，我们都见过，你知道，有这些，你知道，经营诈骗电话中心的人，以及去攻击他们的人。但这些武装 AI 的人要复杂得多，因为他们可以创造比过去电话中心工人更深入的故事和叙述。而且，现在我认为为了保护自己免受这些系统的侵害，你几乎需要自己这边有一些东西。所以，你可以，你知道，你可以考虑一下，你知道，对于我们的同事来说，他只是非常困惑，因为叙述太好了。除了挂断电话，他唯一能真正确定这个人是骗子之外，就是让他发一些正式的消息。而且我们需要它们运行得非常快。而且，我认为这是最后一组，你知道，教育对我们来说非常重要。你知道，我们广义上在 Grok 考虑这个问题，我们考虑，你知道，让代币变得更便宜、更广泛，并且能够个性化。你知道，Salak Khan 几年前有一个非常好的 TED 演讲，他真的强调了，你知道，这是 Two Sigma 的演讲，他说你可以让任何学生，无论在哪个水平，最高水平，甚至表现较低的人，如果你给他们一个个性化的导师，他们可以将他们的考试分数提高两个标准差。所以，想象一下这样做，你知道，显然是使用 AI，它们，你知道，使用成本非常低，而且可以根据他们的学习体验进行个性化。你知道，我最近和一位正在为家庭学校建立 AI 服务的人交谈过。这项服务特别强大的是，假设你有一个年幼的孩子，他们非常喜欢独角兽或小马，你想教他们数学。所以，你知道，加法、减法、乘法，如果以这些东西为背景来解释，会容易得多。嘿，你知道，你有三匹小马乘以两只独角兽，你得到什么？所以，我以前从未想过这一点，但对于学习和根据个人的兴趣进行定制来说，这是非常强大的。所以，我们将看到更多这样的情况。然后是互操作性和兼容性，对吧？我认为如果你曾经接触过企业软件，那么部署和维护企业软件的大部分资金都与互连性、互操作性和兼容性有关。所以，你知道，拥有非常快速且廉价的 AI 技术将帮助我们真正减轻当今企业面临的巨大负担。所以，就是这样，希望你们喜欢这个。 [音乐] 不用谢，太棒了。我们从 Grok 那里听到了一个很棒的演讲。现在我们有一位来自 Cruzo 的产品经理，他将谈论加速专家混合。太棒了，谢谢。好的，好的，谢谢大家今天来参加我们的会议。AI 世界最近有很多非常有趣的事情发生，对吧？随着所有最近的模型开发和 GPU 开发，看到所有用例真的非常酷。然而，我想在这里谈谈基础设施以及我们如何支持最新的 GPU 模型和最新的机器学习模型，以及我们如何帮助一切顺利、快速、高效地运行。我的名字是 Yen Vinko，我是 Cruzo 的产品经理。我的主要职责是基础设施，特别是 AI GPU 网络基础设施，我们一直在寻找提高网络性能的方法，因为正如我们稍后在演示中将看到的，这样做确实很重要。关于 Cruzo，Cruzo 是一个 AI 云平台，它有一个我认为对我们所有人来说都非常重要的使命：将计算的未来与气候的未来对齐。目前对计算能力有非常强烈的需求，GPU 非常耗能，在数据中心领域进行了大量投资，当然，这给电网和能源来源带来了额外的压力。我们在 Cruzo 所做的就是试图利用被搁置的能源、浪费的能源和可再生能源来为我们的数据中心供电。我们真的想确保每次你训练模型，每次你使用 GPU 进行推理时，你都不会对气候造成任何负面影响。现在，当我们构建云时，AI 云，我们是基于三个重要支柱构建的。首先是高性能支柱。由于客户购买我们的服务并采购 GPU 时间并训练他们的模型，我们必须确保所有基础设施都针对此训练进行了优化。每次不优化时，每次出现延迟或故障或任何类型的停机或仅仅是性能不佳时，都会直接影响客户的底线，直接影响训练时间，并某种程度上增加了训练模型的成本。第二个我认为对这里的每个人都非常重要，那就是易于使用。我们真的想将自己与通用云区分开来。我们都知道它们，超大规模公司正在构建伟大的基础设施，并试图支持客户可能拥有的每一个云计算用例。然而，在我们的案例中，我们真的想专注于 AI 工程师的体验。所以，我们想确保我们提供一个更简单的用户界面，允许开发人员启动计算资源，部署模型，训练它们，使用它们进行推理等等。所有基础设施的底层复杂性都由我们隐藏起来，我相信这是我们的工作，以确保情况保持如此。现在，正如我之前提到的，我们是气候对齐的。这意味着我们公司确实致力于为我们 100% 的数据中心提供可再生能源、浪费能源和某种形式的被搁置能源，以确保我们从碳的角度实现净零排放，实现新的净零排放。我们在这方面有一个很大的故事，请随时在我们的网站上查看，或者到展厅的展位来，团队将很乐意与您交谈。现在，我们现在在哪里？我们在美国有多个数据中心，正如您所见，三个在美国大陆，它们通常靠近我之前提到的能源。所以，我们在德克萨斯州有一个，我们在该国北部中部有一个，在东部，我们还在冰岛建造一个大型数据中心，将由地热能供电。我再次认为，利用受限能源或可再生能源为数据中心供电是一种绝妙的方式。我们试图遵循这种模式，因此我们战略性地放置我们的数据中心。然而，冰岛数据中心的选址对我们的欧洲客户来说也非常重要，考虑到延迟和与欧洲的整体连接，我认为这对他们来说可能也有帮助。现在，我们的平台是什么？我说 Cruzo Cloud，但通常，当我们谈论任何云时，我们都在谈论三种主要类型的产品。首先，我们有计算。我们提供带有 GPU 的 VM。所以，每次客户想要启动，客户想要访问 GPU 时，他们都可以通过 VM 获得它，他们可以获得一堆 VM 连接在一起，并将它们用作一个单一的训练集群。我们还提供 CPU 实例，用于任何潜在的数据预处理或您可能拥有的任何通用计算任务，用于数据准备，用于卸载，无论您有什么。从存储的角度来看，我们在节点上提供 FML 和持久性磁盘。所以，这些是从放置 VM 的本地服务器上的 NVMe 提供的。我们还为客户提供持久性块存储解决方案，并且我们正在努力为客户提供托管文件系统，网络文件系统。在网络方面，当然，更传统、更典型的 VPC 网络，有时我们称之为前端网络，它用于将客户流量从互联网或客户环境（无论客户可能拥有数据源）传递到 VM。所以，这是您通往外部世界的主要连接路径。现在，我们提供许多附加服务，这不仅仅是连接。我们还有防火墙，我们很快将提供负载均衡器。但总的来说，我们试图遵循 VPC 网络和客户通常在那里有的需求的更传统路径。现在，更有趣的是，我们将在今天晚些时候更详细地讨论的是我们针对机架优化的 Infini 和集群网络。所以，对于那些不了解的人来说，通常 GPU 提供商会分离他们的网络。他们有用于通用流量的前端网络，但然后 GPU 之间的所有通信都发生在独立的网络上，该网络具有非常高的性能、低延迟和高带宽，并且整个拓扑都针对 GPU 到 GPU 通信进行了优化。最后但同样重要的是用户体验。正如我之前提到的，我们的主要客户，我们的主要用户，使用 Cruzer Cloud 的人是 AI 开发人员和机器学习工程师。所以，我们想确保他们拥有他们需要的东西，以便他们能够成功，而无需过多地考虑基础设施。我们提供 CLI，我们有 API，我们有 GUI，所以一切都可以自动化，一切都可以以您喜欢的方式进行消耗和配置。我们已经有很多客户了，在展会上看到一些客户在那里，并且他们正在谈论他们的解决方案，这对我来说非常有趣。这可能是我一生中第一次参加会议并在展位上，而不必与周围的所有人竞争。所以，我们看到所有展示他们解决方案的公司，现在他们是我们的合作伙伴。我们已经与其中许多公司合作了。我们有 Together AI 在这里，我们有 Ban AI，他们都在出于不同目的使用我们的基础设施。例如，Together AI 非常热衷于使用 Cruzer 的基础设施进行 ML 训练，用于微调他们的模型，有时也用于推理。C 是，他们正在使用我们的计算基础设施来训练新的基础模型。这真的很棒。我的意思是，如果你是 Together AI 或 Codium 等的客户，那么你很可能以某种方式接触过 Cruzer 的基础设施。分布式训练确实有一套非常具体的问题或挑战。其中有计算部分，当计算在 GPU 上进行时。但由于我们谈论的是分布式训练，这意味着有很多 GPU。在某些阶段，每当训练步骤完成时，所有 GPU 都必须交换信息，必须交换它们自己计算出的数据。这通常通过 all-reduce 或 all-get 通过 reduce 过程和协议完成。它包含前向路径和后向路径。但然后网络部分，如果没有优化，会占用训练时间的 25%-30%。这是你的 GPU 保持空闲的时间，它们无法计算任何东西，因为它们必须等待所有信息被收集起来。这对每个人来说都是一件坏事，对吧？这对客户来说是一件坏事，因为他们仍然为基础设施付费，他们仍然必须等待，这会延迟模型训练。但对我们来说也是一件坏事，因为我们的基础设施性能不够。我们可以做一些技巧。首先，计算和通信重叠允许你在计算仍在进行时开始网络交换或数据交换。但即使这样，当我们与客户合作时，我们也只看到了大约 10% 的减少。所以，大约 25% 的训练时间仍然花在网络上。作为基础设施方面的一名产品经理，我不断被问到，我们如何才能减少这一点，我们如何才能尽可能多地利用网络并缩小这个差距。所以，我们一直在关注这一点，并试图弄清楚正确的集群网络拓扑是什么，我们如何确保我们用于连接 GPU 的数据结构得到完全优化，并且能够提供所需的带宽和延迟。标准的 Fat Tree，你们中那些在数据中心基础设施方面工作过的人，这是我们多年来一直在做的很棒的方式，可以构建一个可扩展的、可能是非阻塞的 fabric。但它有很多问题。首先，如果我们连接下面的服务器到一个单一的叶子，这会引入一个单一的瓶颈以及一个单一的故障域。如果我们丢失了叶子，我们就丢失了连接到它的所有 GPU。那么，我们还在考虑什么呢？就像，看，我们有那个交换机，我们有那个交换机，抱歉，时间到了，好的。所以，我们有那个交换机，可以用于后端流量传播，为什么我们不从带宽的角度使用那个交换机，并且，你知道，有一个额外的路径。让我用一个简单的两节点例子来解释拓扑结构和我们如何使用它。所以，首先，每当有 GPU 想要在同一服务器内通信时，它们可以使用嵌入式的 NV Link 和 NV Switch，这提供了良好的通信。它们不必去外部 fabric。现在，每当有不同节点上的 GPU 之间的数据通信时，如果它们连接到单个叶子，我们就称之为单轨。这意味着通信流量将通过一个单一的叶子，只有一个跳，然后你将到达目的地。有趣的是，当我们想与不同的轨道对话时，我们必须一路走到 spine，这会引入额外的跳跃，除了可能导致额外延迟的带宽饱和问题，这对你的 all-reduce all-reduce 操作来说将非常重要。但幸运的是，Nvidia 在其最新的网卡版本中引入了一个名为 PXN 的功能，它允许你使用主机内部的 NV Switch 来跨轨道通信。所以，每当我们想让 GPU 0 与另一台主机上的 GPU 8 通信时，我们可以使用内部交换机进行 GPU 之间的流量交换，然后将其发送到连接它的叶子。所以，它仍然允许我们使用一个跳，并跨不同的 GPU 轨道进行访问。我们做了一些 Nickel 测试结果，并且看到了相当大的改进，小消息提高了 50%，大消息提高了 50%。这些数字当然是针对较小的消息，对于小消息来说，它们是关于延迟的，对于大消息来说，我们更关心带宽，因为延迟往往保持大致相同。这些数字很棒，对吧？每个人都会喜欢它们，但客户不会。而且这是有道理的，因为这些数字是合成的，更多地向你展示了应用于你网络的负载。客户关心的是训练特定模型的时间。我们以稀疏专家混合为例。而且，我不会深入探讨它是如何工作的，但本质上，稀疏网络，稀疏专家混合向你展示了专家层，并将它们之间的流量进行路由。当你将其部署在非常大的 GPU 集群上时，这会产生大量的流量，就像所有 GPU 都必须相互发送流量，它们必须扩展信息，网络上的负载非常可观。所以，我们使用了 Mixl 模型，开源的稀疏专家混合模型，它包含 8 个前馈块，70 亿个参数。我们使用微调来使用这个模型，在 240 个 H100 GPU 上对其进行微调。而且，当我们启用 PXN 时，我们看到了相当大的改进，而没有它。14% 的改进可以直接与训练模型的时间联系起来，可以直接与训练模型的成本联系起来，而这正是每个人都真正兴奋的。我肯定很兴奋，我们的客户也很兴奋，因为这向他们展示了他们可以从模型中获得的真正有价值的数字。这就是我这边的内容。抱歉，我讲得太快了。这是一个非常大的话题，很难谈论。但我很乐意回答任何其他问题，你们可能有的任何问题。谢谢。如果您想聊天，请随时到我们的展位来。我很乐意多聊聊。太棒了。至此，本次会议结束。请随意去吃午饭。太棒了，祝您有美好的一天。因为我想你，我一直在追逐错误的方向，试图抓住我找不到的东西，她没有抓住我的心。现在我知道我们在天堂的日落中，但现在有些事情不对劲了，你在继续前进。我发现自己处于劣势。你不会打电话，我们失去了一切，你渐渐消失。我正在收拾我破碎的心中的每一块碎片，并试图找回我自己，但却一无所知。我正在寻找运气，找不到一扇敞开的门。我正在失去所有的希望，感觉我被留在这里，因为我想你，因为我想你，哦，因为我想你，因为我想你，因为我想你。我正在，我正在，我正在收拾我破碎的心中的每一块碎片，并试图找回我自己，但却一无所知。我正在寻找运气，找不到一扇敞开的门。我正在失去所有的希望，感觉我被留在这里，因为我想你。我屏住呼吸，准备出发。我正一头栽进去，准备出发。我找到了我想要的，我知道我们在顶峰。所以，我会屏住呼吸，准备出发。我抓住你笑的样子，准备出发。你握着它，我准备好了。我们是一场夏日风暴，一种你无法忽视的感觉。你有没有停下来感受它，沉浸在光芒中？我会回到你的门前，让你知道你相信。夏日，我想要的一切。你知道，夏日，我们拥有了一切。我屏住呼吸，准备出发。我正一头栽进去，准备出发。我找到了我想要的，我知道我们在顶峰。所以，我会屏住呼吸，准备出发。我抓住你笑的样子，准备出发。你握着它，我准备好了。我的灵魂，我准备好了。我们把它再次点燃。天空。 [音乐] [掌声] 在人行道上跳舞，陷入完美的你。再次看到你的眼睛，当我最不期望的时候。你说你是我想要的一切。我们知道，我们拥有了一切。我屏住呼吸，准备出发。我正一头栽进去，准备出发。我找到了我想要的，我知道我们在顶峰。所以，我会屏住呼吸，准备出发。我抓住你笑的样子，准备出发。你握着它，我准备好了。我的灵魂，我准备好了。如果我发现自己在你门前，你会跟我去更好的地方吗？如果我发现自己在你门前，抓住钥匙，我们走吧。我想尝尝这个。如果你在我门前，我会跟你去更好的地方。如果你准备好了，我们走吧。我屏住呼吸，准备出发。我正一头栽进去，准备出发。我找到了我想要的，我知道我们在顶峰。所以，我会屏住呼吸，准备出发。我抓住你笑的样子，准备出发。你握着它，我准备好了。我的灵魂，我准备好了。 [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] [音乐] [掌声] 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and speed are going to go hand in hand, and if we're going to make that work kind of seamlessly for folks, I think next is kind of a a universal natural language processing. And so if we think about our interface today to software, it's, you know, you know, we started with sort of point and click and keyboards. Uh, we've gone to touch with our, you know, mobile devices. But really, you know, you start to see the power of this. And, you know, I think everyone's been super excited for the release of GPT 40. Uh, the voice agents, we, we, I don't think we fully got there yet, but I think we've showed the art of the possible there with what they were able to do with voice. And then that kind of mixed interaction, I would say, like, you know, we refer to as sort of like xrx, which it's like any type of input, reasoning, and any type of output. Um, you know, the example I like to tell people there, if you're trying to order something, you may want to interact with an agent in voice, but you may want to see the responses in text. And so think about if you're trying to book your haircut, and you want to say, well, tell me what times are available. And then, you know, it tells you, well, there's 9:00 a.m. and 11:00 a.m. and 3:30 and 5:30. That's hard to remember if it's just coming back to you in voice. So you want to basically have these interactions that are multimodal, that kind of touches on my second point there. And I think we're going to start to see a lot more of those, uh, interface changes as well. Um, you know, advanced virtual assistance, this is like complex task scheduling. I think a lot of what we'll see in the back half of just this year is, uh, you know, agents start to become much more, uh, complex. And a lot of focus from LLM providers as well, I think, on making, uh, you know, complex tasks something that are solved. It's, it's interesting today because we measure the efficacy of LLM through generally single shot. And I think we do that because of, you know, going back to that where we, you know, the, of the conversation, which is the performance barrier. But naturally, if you even take any existing LLM today and multi-shot it, its scores get a lot better. And there were a couple of papers that came out recently that showed if you just had multiple agents working together on a problem, they can far exceed, uh, of, of a less, you know, less parameter model, they can compete with higher parameter models just by doing sort of multi-shot reasoning or working together. And so I think we'll see a lot more of that as speed improves. And I think there's, there's an incredible, incredible optionality there. You know, we saw the first, um, I think first cut of collaborative AI agents with Apple AI, you know, where you see something maybe running on device interacting with something off device. It's, I think it's a very early implementation, and I think these things will get much more sophisticated and better. Um, an area, you know, we've spent a lot of time within our career is like analytics and predictive analytics. I think today everything is, uh, you know, pretty much action-oriented and driven off a human action. So I think if we get to a place where the speed goes up, they can be a lot more predictive. You know, what does that really mean? It's just an agent that's always running in the background because the compute cycles are next to free. We don't see that today, but I think we get there as we get, you know, higher up the curve. Um, you know, context aware as well. And today, we again, we are generally limited to how much context we can provide. And we're having to, even with with models with bigger context windows, we still have to, you know, be conscious of, you know, how much compute cycles we're going to use. But I think if that becomes next to free, it becomes quite powerful for us. Um, you know, creative tools and customizable content. Uh, I'll focus on the second one here. This is, this is an area where I think many of us would, would like to see things go. You know, the example I always like to, you know, one of my favorite shows was Seinfeld. And obviously, you know, it's not on anymore. But one of the things I like to do, you know, when, when I'm bored, is go into, you know, LLM of choice and have it write a Seinfeld episode, but made up of like modern-day things that are happening. And if you ever try that, it's super fun because it does an incredible job of, you know, identifying which character in those scenarios that you give it would, would have, you know, sort of the funny or odd thing happen to them. And so the idea of, you know, taking that beyond sort of writing and taking that to multimedia forms is, is going to be really, you know, really, really powerful going forward. Um, you know, complex decision making. You know, before our company was acquired by Grok, uh, you know, we were building a company called Definitive Intelligence. So we spent a lot of, a lot of time in this space. Um, not only, uh, doing sort of say, natural language to re, uh, you know, analysis of of SQL, uh, Texas SQL, as a lot of people would call it. But, uh, you know, Rick, who's sitting here with us, like, you know, he was working on this really cool product for us called Pioneer, which was an automated data science agent. Where it's really meant to run almost endlessly on a problem. And, uh, you know, you sort of define a cap, and you think about how a business runs. A business has a bunch of KPIs, and then a business has a bunch of data that's coming in. And then usually humans are taking that data and analyzing it, KPIs, and creating PowerPoints and spreadsheets, and telling either senior management or the world how well they're doing. Well, there's no reason that just shouldn't happen automatically, right? And where there's an agent just constantly, you know, looking at the new data that's coming in, asking additional questions, diving into it. And I think we had a lot of interesting things emerge. You know, we had let Pioneer loose on a data set of human workers and their performance reviews. And one of the things that we saw was it was able to correlate really interesting, uh, things that we couldn't think about in terms of, you know, depending on your age, and depending on your performance review, it really affected your, uh, I guess your output, your productivity. And so I was able to kind of discover that if you're of a certain age and you got a certain type of performance review, your productivity would fall off. And maybe Rick can correct me if I'm wrong later, but it was something along those lines, which I was always an interesting example for us. And then obviously, a lot of, you know, um, really interesting things around dynamic optimization. Um, you know, this, this an area we're familiar from before. Um, you know, when a bunch of us were at Ford, um, after the acquisition of Autonomic, we really saw, you know, for the supply chain, if you think about how, you know, cars are produced and how they're shipped. Um, um, you know, there's, you know, pretty sophisticated software that does this, but it's still not efficient, right? And I think, um, you know, the art of the possible with sort of what we were talking about earlier could be very, very interesting for some of our old, old colleagues at Ford. Um, I'll touch on a couple more things and then leave a couple minutes for questions if there's any. But Edge AI and decentralized AI, this is pretty cool. Um, you know, there's a, a really cool project called, you know, Hyperspace.ai. What they're doing is, um, they're actually have a lot of, uh, you know, taking, you know, sort of like SEI at home or even Render. And where they're basically allowing people to take their unused GPU compute and make it available in the cloud. Uh, or I guess, yeah. And, um, and why that's interesting is there's certain use cases that necessarily don't require something to be real-time. And so I think we'll see a lot more of that. Now, this intersects really well with us getting more throughput and getting lower latency out of existing, existing systems. So I think we'll see a lot more of that as well, especially because the amount of power consumption that's required, if you distribute it, that you could be really interesting. Um, and a couple more here is, uh, enhanced security and privacy. This is a big area. You know, I was, I was talking, uh, to one of our colleagues last night, and he was subject to a really, really scary type of, uh, I guess maybe fishing call where, um, you know, someone had called in, uh, sounded very formal, and had access to a lot of information. Now, you, you know, we've all seen, uh, there's, you know, these kind of, uh, people that run scam call centers and people that go and attack them. But th, these folks armed with AI are much more sophisticated because they can create stories and narratives that are much deeper than sort of the call center worker of past. And, uh, now I think in order to protect against these systems, you'll almost need to have something on your side, um, so that you can, you know, you can think about it. You know, with our colleague, he was just so confused because the narrative was so good. The only way he could really figure out that this person was a scammer, other than hanging up on them, was saying, me some kind of formal message. And we need them to run incredibly fast. And so, um, and I think this is the last set of them here is, uh, you know, education is, is something that's really important to us. You know, broadly at Grok, we, we think about this, and we think about, you know, making tokens available cheaper and more broadly, um, and being able to personalize. Uh, Salak Khan has a very good Ted Talk from a couple of years ago where he really highlights, um, you know, it's the Two Sigma talk. And he says, you can take any student at any level, the highest levels, or even someone performing lower, and if you give them a personalized tutor, they can improve their test scores to standard deviations. And so imagine doing that, you know, obviously with AIs that are, um, you know, can be one, very cheap to use, and that can be personalized to their learning experience. Um, you know, I was speaking to someone recently who was building, uh, an AI service for homeschooling. And what was, what was powerful about that particular service is, let's say you have a young child, and they're really into unicorns or ponies, and you want to teach them about, you know, math. And so, you know, math, subtraction, addition, multiplication, it's a lot easier if you frame it in the context of those things. Hey, you know, you have three ponies times two unicorns, and what do you get from it? And so I never thought about that before, but for learning and customizing that for the interest of the person is quite powerful. So we'll see more of that. Um, and then just, just interoperability and compatibility, right? I think this is an area, if you've ever been in enterprise software, the majority of money spent in deploying and maintaining enterprise software is really related to, you know, interconnectivity and interoperability and compatibility. And so, um, you know, having really fast and cheap, um, you know, AI technologies will help us really reduce a huge burden that exists on the enterprise today. So, um, that's it. Hopefully, you guys enjoyed that.

[Music]

Don't. Thank you so much. Awesome. We had such a great talk from Grok. Now we have a product manager from, uh, uh, Cruzo, and he's going to, uh, i j, and he'll be speaking about accelerating mixture of experts. Awesome. Thank you. Okay, just. Okay, great. Thank you for coming over to our session today. Lots of really interesting stuff is happening on the AI world these days, right? Uh, with all the recent model developments and the GPU developments, it is really cool to see all the use cases. Um, however, here I want to talk now a little bit about the infrastructure and the way how we can support the the newest models of the GPUs and the newest, um, the newest models, the machine learning models, and how we can help that everything is working smoothly and fast and and productive. So my name is Yen Vinko. I'm a product manager at Cruzo. Uh, my main responsibility is is the infrastructure, and specifically AI GPU networking infrastructure. And we are always looking for a way how we can increase the performance of that network, because as we will see later in the presentation, it is really important to do that. Now, a little bit about the Cruzo. Cruzo is an AI Cloud platform which has one, I think, very important mission for all of us. It's to align the future of computing with the future of climate. There is a really strong demand right now for the computing power. The GPUs are really energy hungry. There is a lot of investments being done in the data center area, and of course, that puts an additional pressure on the grid and on the energy sources. What we are trying to do here at Cruzo, we are trying to utilize the stranded energy sources, wasted energy sources, and renewables to power our data centers. We want really to be able to make sure that every time when you train your model, every time when you're using GPU for inference, you're not causing any negative impact on the climate. Now, uh, whenever we are building the cloud, right, the AI Cloud, we are building it based on three important pillars. First of all, there is a high performance pillar. Uh, as the customers are buying our services and procuring, you know, the GPU times and training their models, we have to ensure that all the infrastructure is optimized for this training. Every time when it's not optimized, every time when there is a delay or a glitch or any sort of outage, or simply not that great performance, it causes a direct impact on the customer's bottom line. It causes a direct impact on the time to train, and kind of raises the cost to train the model. Now, the second one, which I think is very important for everybody around here, is the easy to use. Uh, we want really to separate ourselves from the general purpose clouds. Uh, we do know all of them, the hyperscalers are building the great infrastructure and are trying to support each and every use case the customers might have for the cloud computing. However, in our case, we really want to focus on the experience of the AI engineer. So we want to make sure that we are providing a simpler user interface that allows developers to spin up the computer resources, to deploy the models, to train them, to use them, to infer inference, and and so on. Uh, all the underlying complexity of the infrastructure is being hidden by us. And I believe that's our job to make sure that that is that stays the case. And now, as I mentioned, we, as I mentioned before, we are climate aligned, which means, uh, we, as a company, really aiming to power 100% of our data centers with the renewable wasted energy sources, with some, some form of stranded energy sources, to ensure that we are, uh, we are being net zero emission, net new zero emissions from the carbon perspective. We have a big story around that, feel free to check it on our website or come over to our booth on the on the show floor, and the team will be happy to talk about that. Now, where are we present right now? We have a number of the data centers located across the US. Uh, as you see, three of them in the continental United States, and they are generally located close to the energy sources I was mentioning before. So we have the one in Texas, we have, uh, the one in the northern central part of the country, and on the East. We are also building right now one big data center in Iceland that will be powered by the geothermal energy. I mean, again, an amazing way to use the constrained energy sources or the renewables to power the data center. We are trying to follow that model, hence we are placing our data center strategically. The placement of the data center in Iceland, though, will be also very important for our EMEA customers, given the latency and the and the general connectivity to Europe. That is something I think, uh, might be helpful for them as well. Now, what is our platform, right? I say Cruzo Cloud, but generally, when, whenever we are talking about any cloud, we are talking about three general types of the products. First and foremost, we have the compute. We are offering the VMs with, uh, with GPUs attached to them. So every time when a customer wants to spin up, when a customer wants to get access to the GPUs, they're able to get it through the VM. They can get a bunch of VMs connected together and use them as one single training cluster. We also offer CPU instances for any potential data pre-processing or any general purpose compute tasks you might have for the data preparation, for the offload, whatever you have. Uh, from the storage perspective, we are offering FML and persistent disks on the node. So those are delivered from the NVMe on the local server where your VMs are being placed. We also have the persistent block storage solution available for our customers, and we are working on providing and delivering the managed file system, the network file systems for the customers. On the networking side, of course, more traditional, more typical VPC networking. That's the network, sometimes we call it front-end network, that is used to deliver the customer traffic from the internet or from the customer environment, wherever the customer might have the data sources, to deliver that towards the VM. So that's your kind of main connectivity, uh, path to the outside world. Now, uh, we do offer a number of the additional services on that. That's not simply connectivity. We also have the firewalls. We will be offering the load balancers soon. But generally, we are trying to follow more traditional path for the VPC networking and and the requirements the customers usually have there. Now, what is more interesting, and what we will be talking a little bit later today in greater details, is our rail optimized Infini and cluster networking. So for those of you who don't know, typically C, typically providers, the GPU providers are separating their network. They have the front-end network, which is used for general purpose traffic. But then all the communication between the GPUs is happening on the standalone separate network that is, uh, really high performance, low latency, and how bandwidth and the whole topology is optimized for the GPU to GPU communication. Uh, now, last but not least, the user experience. As I mentioned before, we are, our main customers, our main persona, the people who are using Cruzer Cloud are the AI developers and machine learning engineers. So we want to make sure they have what they need in order you know to be successful and not to think too much about infrastructure. We offer CLI, we have APIs, we have GUI, so everything can be automated, everything can be can be consumed and configured in the way you like it more. Uh, we do have a lot of customers already, and it was very fun for me to see on the floor that some of them are there, and some of them are talking about their solutions. Probably this is the first time in my life whenever I'm attending a conference and standing at the booth, I don't have to compete with all the people around us. So we do see all the companies that are presenting their solutions right now as our partners. Partners. We do partner with a bunch of them already. We have the Together AI here, we have the, uh, Ban AI, and all of them are using our infrastructure for different purposes. So Together AI, for example, they're really into using Cruzer infrastructure for the ML training, for the fine-tuning their models, and sometimes for inference. The C is, uh, they're training, they're using our compute infrastructure to train the new foundational models. This is really great. I mean, if you are the customer of Together AI, for example, or Codium, or what not, it is likely that you have been somehow exposed to the Cruzer infrastructure. Now, the distributed training has a very specific set of problems or issues, right? There is the compute part of it, when the computation is being done on the GPUs. But since we are talking about a distributed training, which means there are a lot of GPUs. At certain stages, whenever there is a, whenever there is a training step being completed, all the GPUs have to exchange the information, have to exchange the data that they calculated on their own. This is typically done through the all-reduce or or all all all-get, uh, um, through the reduce process and the protocols. And it contains a forward path, the backward path. But then the networking part takes, without any optimization, about 25-30% of the network of the time of the training time. This is the time where when your GPUs are staying idle, they're not being able to compute anything because they have to wait for all the information to be gathered, uh, together. This is kind of a bad thing for everybody, right? This is bad thing for the customers because they still pay for that infrastructure, they still have to wait, it delays the the model training. But it also bad for us because we have the infrastructure that is not being performant enough. There are a couple of tricks we can do. First of all, the computation and communication overlap allows you to start the network exchange or the data data exchange when the computation is still ongoing. But even with that, when we were working with the customers, we saw just the reduction, uh, of about 10%. So about 25% of the training time was still spent on the network. Me, as a product manager on the infrastructure side, am constantly being asked like, how can we reduce that? How can we use the network as much as possible and reduce that gap? So we, we have been looking into that, and we were trying to figure out what would be the right cluster networking topology. How can we make sure that our data fabric that is used for connecting the GPUs is being fully optimized and is being, uh, able to provide the bandwidth needed and the latency needed. The standard fat tree, those of you who have been working in the data center infrastructure before, that is something that we were traditionally doing for years. That's a great way to build a scalable, maybe non-blocking fabric, right? But there are a bunch of issues with that. First of all, if we will be connecting our servers that are shown below to a single leaf, that introduces a single choke point, as well as a single fold domain. Right? If we are losing the leaf, we are losing all the GPUs that are connected to it. Now, what else we were thinking about is like, look, we have that switch, we have that switch. Sorry, what is it? The time? Okay. So we have that switch that can be used for the backend traffic propagation. And why don't we use that switch from the bandwidth perspective and kind of, you know, have an additional path? Let me just use a simple two-node, uh, example to explain the topology and to explain how we are using it. So first of all, whenever we have the GPUs that want to communicate within one server, they can use their embedded NVLink and NV switch, and that provides a good communication. They don't have to go to the outside fabric anywhere and and whatnot. Now, whenever we have the data communication between the GPUs on the different nodes, if they collect, if they are connected to a single leaf, that's something we called one single rail. That means that the traffic communication will be passing through the, uh, through the one single leaf, just one hop away, and you will get to the destination. Now, what is interesting here is when we want to talk to the different rails, right? We have to go all the way to the spine, and that introduces an additional hop, besides the bandwidth saturation problems that may lead to the additional latency, which will be really important for all your, uh, all-reduce, all-reduce operations. But luckily for us, Nvidia, with the recent version of NIC, introduced a feature called PXN, which allows you to use the internal NV switch inside the host to communicate across the rail. So whenever we want to have the GPU zero to communicate with the GPU 8 on another host, we can use an an internal switch to do the traffic hub between the GPUs and then send it to the leaf where it is connected to. So it still allows us to use one single hop and have access across the different rails of the GPUs. Now, we did some Nickel test results, and we saw quite a significant improvement, 50% for the small messages, and 50% of the large messages. Now, those numbers here are of course for the smaller, uh, for the smaller messages are about latency. For larger ones, we care more about bandwidth because latency tends to stay roughly the same. Uh, those numbers are great, right? Everyone, everybody would love them, but not the customers. And it does make sense because those numbers are synthetic, and more are showing you the workload that is applied to your network. What customers care about is the time to train the particular model. So we use the sparse mixture of experts as an example. And, uh, I mean, I'm not going to dive into the details how how it works, but essentially, the sparse network, the, the sparse mixture of experts shows you, gives you different layers of the experts and routes the traffic between them. Whenever you're deploying that on the really large GPU cluster, that makes, uh, that creates a ton of traffic. Like all the GPUs have to send the traffic to each other, they have to extend the information. The workload on the network is pretty significant. So we use the Mixl model, the open source, uh, sparse mixture of experts, which is contained of 8 feed forward blocks, 8, 7 billion parameters. And we use the fine-tuning to use this model, to fine-tune it on 240 H100 GPUs. And we did a quite significant, we saw a quite significant improvement when we had the PXN enabled and without it. The 14% of improvement is something that can be directly connected to the time to train the model. That can be directly connected to the cost of training the model. And that is something that everybody really, uh, really got excited. I definitely got excited, and and our customers as well, because that shows them some real value numbers they can get with a model. Now, that was it from my side. Sorry for, uh, going through that. It's so fast. It's a very, you know, large topic. It's, it's hard to talk about that. But I'm happy to answer all the additional questions, anything you guys might have. Thank you. Feel free to come over to our booth if you want to talk. I'm, I'm happy to chat more. Awesome. With that, this track has concluded for now. So feel free to go for lunch. Awesome. Have a good day.

Because I'm missing you. I was chasing all the wrong sides, trying to hold on to something that I couldn't find. Which she didn't captivate my mind. Now I know we've in the sunsets in paradise, but now something went wrong. You're moving on. I found myself on the blind side. Now you won't call. We lost it all. You fade away. I'm picking up my heart from every piece that's broken. And trying to get back to myself, but don't have a clue. I'm looking for some luck. Can't find a door that's open. I'm losing all my hope. Feels like I'm left here because I'm missing you. Because I'm missing you. Oh, because I'm missing you. Because I'm missing you. Because I'm missing you. I'm in, I'm in, I'm in. Picking up my heart from every piece that's broken. Been trying to get back to myself, but don't have a clue. Looking for some luck. Can't find a door that's open. I'm losing all my hope. Feels like I'm left here because.

I'm holding my breath and I'm ready to go. I'm falling right in and I'm ready to go. I found what I want and I know that we're on top. So I'll take and I'm ready to. Hold my breath and I'm ready to go. I catch you laughing and I'm ready to go. You're holding it and I'm ready. We are a summer storm, feeling you can't ignore. Do you ever stop to feel it? Caught in the glow. I'll come back to your door to know that you believe. Summer, all that I want. You know, summer, we got it all. Holding my breath and I'm ready to. I'm falling right in and I'm ready to go. I found what I want and I know that we're on top. So I'll take and I'm ready to. Hold my breath and I'm ready to go. I catch you laughing and I'm ready to go. You're holding it and I'm ready. My soul and I'm ready to. We'll light it up again. Our silhouette. Dancing on the pavement, caught in a perfect stone. You and those eyes again when I least expected. Said you're all that I want. We know together, we got it all. Holding my breath and ready to go. I'm falling right in and I'm ready to go. I found what I want and I know that we're on top. So I'll take and I'm ready to. Hold my breath and I'm ready to go. I catch you laughing and I'm ready to go. You're holding it and I'm ready. My soul and I'm ready to. If I find myself at your door, would you follow me to better places? If I find myself at your door, grab the keys, let's go. I want to taste this. If you show up at my door, I would follow you to better places. If you show up ready, let's go, let's go. Oh, holding my breath and I'm ready to go. I'm falling right in and I'm ready to go. I found what I want and I know that we're on top. So I'll take and I'm ready to. Hold my breath and I'm ready to go. I catch you laughing and I'm ready to go. You're holding it and I'm ready. My soul and I'm ready to.

[Music]

Days kill me. We were the only ones. We were holding nothing back from the greatest nights we ever had. So the bard driving slow, stand in your car, singing along. Every night must to play that song 100 times. Made on fire. We were light and the summer breeze. Dancing the rec in your bedroom. Like a always on my mind. Music on, frozen and tied. Always on my mind. I feel it all come back in the moment. Breath spins like the ocean. So if you want to come with me, do it all. Play it all slow motion. You're my memory. So my heat feels like igniting up the night. When I'm alone, when I hear words. You made the bard driving slow in your heart, singing along. Every night must to play that song 100 times. Made on fire. And the breeze. Dancing the rec in your bedroom. Like the music in. Always on my mind. I feel it all come back in the moment. Back spins like the ocean. So if you want to come, the door is open. Play it all over in slow motion. Feel it all come back in the moment. That can spin away like the ocean. So if you want to come, the door is open.

[Music]

Back. Down. N. Down. Take the back down. Back down. N.

[Music]

Oh.

[Music]

I was watching you watch the sun come up. Vintage tee and worn through high times. These nights taste like gold, sweet with obsession. Show me something new. As each morning comes. We were out the night like we wear our clothes. Dancing right through the fire while we watch it sing. It on as we give up our ghosts. As a morning comes through the windows. We're riding all new light. Burning right through the pain. Tearing past all the life. You're wearing out my name. We wear our problems underneath the clothes like superheroes. Like superheroes. It's coming over now. It's a way down. A harmony of peace that only we can hear. A super sonic crush. You want to feel like us. It's forever young in America. Under your influence. A full moon waxing now. I couldn't see it until you showed me how. Feels like we're insane. We blame it all on love. Saturated. So we can't get in the. We were out the night like we wear our clothes. Dancing right through the fire while we were Chico. Singing an. As we give up our ghosts. As a new evening comes through the windows. It's coming over now. It's a way down. A harmony of peace that only we can hear. A super sonic crush. You are to feel like us. It's all forever. So young in America. It's coming over me. Electric symphony. Every night on fire. I know on master piece. A super sonic crush. You want to be like us. It's all forever. Young in. America. Don't hold back. Tonight is all we have. The sky is going so come with us. Don't hold back. Tonight is all we have. The sky is going black. So come with us. Don't hold back. Tonight is all we have. The sky is going. So come with us. Don't hold back. Tonight is all we have. The sky is going black. So come with us. It's coming over now. It's a way down. A harmony of peace that only we can hear. A super sonic crush. You want to feel like us. It's all forever. You're in America. It's coming over me. Electric symphony. Every night on fire. I know on master piece. A super sonic crush. You want to be like us. It's all forever. In. America. Come with us. Hold. Tonight is going. So come with us. Don't hold back. Tonight is going. So come with us. Don't hold back.

[Music]

More.

[Music]

Take one more breath beside you. So I could find strength to divide us. It's all we got. I know we did the best we could. If I could go back, undo the mess, I would. Minimize your face before I go. But this is how we go. Got to give it up sometimes. It's go. Know it when to kill your pride. There's no one to blame. Nothing really stays the same. This is how we go. Sometimes we hold on to. Let hold. Let. Sometimes we hold on to. Let go. There is nothing lost between us. And I know you have your reasons. Some days I'm a mess. But I know there's a rainbow over all of the past. Your head on my shoulder. But I know we're better on our own. But this is how we go. Got to give it up sometimes. It's go. Knowing when to kill your pride. There's no one to blame. Nothing really stays the same. This is how we grow. Sometimes we hold on to. Let go. Sometimes we hold on to. Let it go. Hold. How to give it up. Go. Know it when to kill your pride. There's no oh what the blame. Nothing really stays the same. This is how we go. This is how we.

[Music]

We know doors set open for us in a moment. Keeping light riding our. Keeping our sight. Everything we want. We catch our breath in the middle of it all. Taste it. Echoes. Echoes. Sun is coming up. All the rest is coming. Crystal vision. True believer. The why I can see on the horizon. All we can feel. Chasing the. See the forest for the trees. I'm keeping watch. All that storming. Waking up and turning on. Keeping light riding our. Keeping our sights. Everything we want. We catch our breath in the middle of it all. Chasing echoes. Is coming. All the is coming. Crystal vision. Like you looking on world. I can see on the horizon. All our we can feel. Chasing the. Stop. Ion. Let it. I don't. Let it go. Like a fever up. We're true believers. We're looking on the. I can see on the horizon. All our we can feel. True believer. We're looking on the world. I can see on the horizon. All the we can feel.

[Music]

E. E.

[Music]

Here. Would you? You. Would you? Would you? Would you? You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. 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You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. 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早晨来临，我们彻夜未眠，如同穿着我们的衣裳，在火焰中尽情舞蹈，当我们看着它在 R 上歌唱，当我们放弃我们的，当一个新的早晨透过窗户降临，我们骑着全新的生命，燃烧着红色穿过书页，撕裂过去所有的光，你戴着那张面具，我们把我们的问题藏在衣服下面，像超级英雄，它现在来了，它已经大大地压低了，只有我们能听到的和谐之声，一声超音速的诅咒，你想感受我们，它永远是静止的，你需要我的影响，满月正在升起，直到你告诉我感觉如何，我才看到它，感觉我们疯了，我们把这一切都归咎于爱，饱和了，所以我们进不去，我们彻夜未眠，如同穿着我们的衣裳，在火焰中尽情舞蹈，当我们唱着我们的圣歌，当我们放弃我们的目标，当一个新的夜晚降临，透过窗户，它现在来了，它已经大大地压低了，只有我们能听到的和谐之声，一声超音速的诅咒，你想感受我们，它永远是，你身在美国，它现在来了，每晚都在燃烧的电磁符号，我需要大师级的作品，一声超音速的诅咒，你想感受我们，它永远是，你身在美国，[音乐]今晚不要退缩，这是，所以和我们一起来，今晚不要退缩，这是我们拥有的一切，天空正在，所以和我们一起来，[音乐]今晚不要退缩，这是我们拥有的一切，天空正在，所以和我们一起来，今晚不要退缩，这是我们拥有的一切，天空正在，所以和我们一起来，它现在来了，它已经大大地压低了，只有我们能听到的和谐之声，一声超音速的诅咒，你想感受我们，它永远是，你身在美国，它现在来了，每晚都在燃烧的电磁符号，我需要大师级的作品，一声超音速的诅咒，你想感受我们，它永远是，你身在美国，[音乐]所以和我们一起来，今晚不要退缩，天空正在，所以和我们一起来，今晚不要退缩，天空正在，所以和我们一起来，今晚不要退缩，[音乐] [掌声] [音乐] 他 [音乐] 他 [音乐] 更多 [音乐] n 哦 [音乐] [音乐] 更多呼吸在你身边 [音乐] 所以我能找到力量来分开 [音乐] 我们拥有这一切，我知道我们尽力了，如果我能回去，我就会纠正这个混乱，在你走之前，我会看到你的脸，但这就是我们成长的过程，有时必须放弃，知道何时杀死你的骄傲，没有人可以责怪，没有什么真的会保持不变，这就是我们成长的方式，有时我们坚持不放 [音乐] 我们坚持不放 [音乐] 在我们之间 [音乐] 和哦，我知道你有你的理由，有些日子我一团糟，但我知道过去的一切都会有彩虹，你的头靠在我的肩膀上，但我知道我们在一起更好，但这就是我们成长的方式，有时必须放弃，知道何时杀死你的骄傲，没有人可以责怪，没有什么真的会保持不变，这就是我们成长的方式，有时我们坚持不放 [音乐] 坚持 [音乐] 放手，有时我们坚持 [音乐] 怎么做，有时必须放弃，知道何时杀死你的骄傲，没有人可以责怪，没有什么真的会保持不变，这就是我们成长的方式，这就是 [音乐] [音乐] n [音乐] [音乐] B [音乐] [音乐] 哦 [音乐] [音乐] B [音乐] [音乐] n [音乐] d [音乐] [音乐] [音乐] 我们知道的秘密，为我们打开的门，瞬间，保持光明，骑行，保持我们的视线，我们想要的一切，我们在这一切中间喘息，它正在到来，水晶般的视野，[音乐]像在海上一样，我在地平线上看到了，所有的，我们在森林里看到了树木，我一直在观察，所有的理论都在苏醒和开启，保持光明，骑行，保持我们的视线，我们想要的一切，我们在喘息中，混乱正在到来，其余的都是水晶般的视野，[音乐]像你一样，真正的信徒，我在地平线上看到了，我们都能感受到，混乱正在追逐，光明正在，[音乐]我停不下来，我也不会让它 [音乐] 放手，放手，就像在海上一样，我在地平线上看到了，我们所有的希望，我们都能感受到，追逐，[音乐]你是真正的信徒，我们正在，我在地平线上看到了，我们都能感受到，[音乐] [音乐] 测试，测试，一二，测试，一二，一二，三四，检查，一二，一二，三四，检查，一二，[音乐] [音乐] [音乐] [音乐] 他 [音乐] [音乐] [音乐] [音乐] [音乐] [音乐] [音乐] [音乐] [音乐] [掌声] [音乐] 我会把它交给你 [音乐] 问题，太棒了，太完美了 [音乐] [掌声] 应该为所有的人都这样做，第一张幻灯片，我只是 [音乐] 所以我们不需要 [音乐] [音乐] 我们都经历过，嗯，我们都经历过，太棒了，哦，我期待着 [音乐] 就像他一样，工作室，我知道，好的，听起来不错 [音乐] 是的，你，你 [音乐] 你，你，你，你，你会，你会，你会，你会，你会 [音乐] [音乐] [音乐] 好的，当然，房子说话，当然，还在做，我还没有得到你的声音，没有 [音乐] 问题，当然，我喜欢 [音乐] 哦，抱歉，我要，大家好，请忽略我，我正在做一些声音测试，我会一直说下去，直到我得到声音测试好的信号，哦，我希望就是这样，谢谢大家 [音乐] 你，你，你，你，你，你 [音乐] 你，你，你 [音乐] 我们有一个失眠症患者，眼睛紧闭，我们拥有我们需要的一切，然后多了一点，我知道你渴望一些无法触及的东西，你现在会对我诚实吗？有什么东西在暗流涌动，我能感觉到它正在涌上来，你不想感受它，它正在接管你的感官，你有没有感觉到，科技的脸，宝贝，和我一起逃跑吧，我会让你心动，你不想感受它吗？你不想吗？你不想吗？ [音乐] [音乐] 告诉我你想留下来，宝贝，只是不要走开，我现在需要你，淡出，我们所有独处的时间，在战斗中，不要让我失望，我现在需要你，因为我感到疲惫，它正在影响我，失去了一些心，试图站起来，陷入疯狂，我 somehow 感觉到你，不要放开我，我现在需要你，我想在你身边，你想在我身边，握着我们的纸做的爱心，淡出我们破碎的梦想，我想在你身边，你想在我身边，握着我们的纸做的爱心，淡出我们破碎的梦想，你想在我身边 [音乐] 你 [音乐] 告诉我你想留下来，宝贝，只是不要走开，我现在需要你，淡出，我们所有独处的时间，在战斗中，不要让我失望，我现在需要你，因为我感到疲惫，它正在影响我，失去了一些心，试图站起来，陷入疯狂，我 somehow 感觉到你，不要放开我，我现在需要你，我想在你身边，你想在我身边，握着我们的纸做的爱心，淡出我们破碎的梦想，我想在你身边，你想在我身边，握着我们的纸做的爱心，淡出我们破碎的梦想，你想在我身边 [音乐] [音乐] [掌声] 所以 [音乐] 我，进行一次小小的旅行，但大家好，我叫，我是的研究工程师，正如提到的，我是技术负责人，在我开始之前，我只想说，今天能和大家在一起真是太棒了，当我们构建 Gemma 时，我们的北极星，我们最兴奋的事情是构建一个能够赋能和加速 [音乐] 令人惊叹的东西，自从我们在二月份发布了第一个模型以来，我一直被 Gemma 之上已经构建的令人难以置信的项目、研究和创新所震撼，所以，我特别兴奋今天能和这么多开发者在一起，并特别高兴地发布 Gemma 模型系列的最新进展和补充，所以，废话不多说，我们将开始，正如你们中的许多人可能知道的，Google SCH 在过去十年中一直是 AI 和 ML 研究出版的先驱，包括发布了一些引发近期 AI 研究创新的关键研究，比如 Transformer SentencePiece Bert 等等，Google DeepMind 确实延续了这一传统，并积极致力于分享我们的研究，供世界验证、审查和在此基础上进行构建，但 Google 对开放社区在 AI 和 ML 方面的支持不仅仅限于发布研究，我们还长期致力于支持整个技术栈的 ML，从 TPU 的硬件突破（我想这对于这个人群和这个领域尤其相关）一直到 ML 框架的演进，从 TensorFlow 到 Jax，在所有这些开放开发中，对 Google 来说尤其关键，我们与开源社区合作的能力帮助我们所有人发现更多，更快地创新，并真正突破 AI 能力的极限，所以，这种对开源社区的长期支持，引领我们来到了今天，以及 Google 对开放模型 Gemma 的最新投资，Gemma 是 Google DeepMind 的开放源代码、轻量级、最先进的模型系列，我们从创建 Gemini 模型所使用的相同研究和技术构建了它，我非常抱歉，我想那是我的手机在这次演讲中响了，请随意翻找那个包，抱歉，各位，谢谢，哇，经验教训是，即使是演讲者也需要记住静音她的手机，好了，回到 Doah，Gemma 模型有几个关键优势我想强调一下，首先，Gemma 模型从设计之初就旨在负责任，我可以凭个人经验告诉你们，从开发 Gemma 模型的第一天起，安全就是重中之重，这意味着我们正在手动检查数据集，以确保我们不仅在最高质量的数据上进行训练，而且也在最安全的数据上进行训练，这意味着我们从最早的实验和消融研究开始评估我们的模型安全性，以便我们选择我们知道会产生更安全模型的训练方法，并在我们的开发结束时，我们的最终模型将根据我们评估 Gemini 模型所使用的相同严格的、最先进的安全评估进行评估，我们这样做是为了确保无论您在哪里或如何部署 Gemma 模型，您都可以确信您将拥有一个值得信赖且负责任的 AI 应用程序，无论您如何定制 Gemma 模型，您都可以相信它将是一个负责任的模型，Gemma 模型还实现了其规模模型前所未有的突破性性能，包括显著优于更大的模型，但我们很快就会对此进行更多介绍，我们还设计了 Gemma 模型，使其高度可扩展，以便您可以随时随地使用 Gemma 模型，这意味着它们针对 TPU 和 GPU 进行了优化，也适用于在您的本地设备上使用，它们支持许多框架，TensorFlow、Jax、Keras、PyTorch、Ollama、Transformers，您说得出的，Gemma 可能都在那里，最后，Gemma 模型真正的力量来自于它们的开放访问和开放许可，就是这样，这就是 Gemma 的强大之处，我们将最先进的技术交到您手中，以便您可以决定下一波创新是什么样的，当我们决定发布 Gemma 模型时，我们希望确保我们能够满足开发者的需求，这就是为什么 Gemma 模型随处可见，您可以在任何地方找到开放模型，我不会列出这个幻灯片上的所有框架，这只是今天您可以找到 Gemma 模型的地方的一小部分，这意味着您可以根据您的需要，在您偏好的开发工具中使用 Gemma，自二月份首次发布以来，我们为 Gemma 模型系列增加了一些不同的变体，当然，我们有最初的模型 Gemma 1.0，它们是我们的基础 LLM，我们随后发布了 Code Gemma，它们是 Gemma 1.0 模型，经过微调以提高代码生成和代码评估的性能，我特别兴奋的一个变体是 Recurrent Gemma，它是一种新颖的架构，一种状态空间模型，专为更快、更高效的推理而设计，尤其是在长上下文的情况下，自最初发布以来，我们还更新了所有这些模型，现在我们有了 Gemma 1.1，它在指令遵循和聊天方面做得更好，我们更新了 Code Gemma 以获得更强的代码性能，现在我们有了 Recurrent Gemma，它不仅有最初的 2B 大小，还有 90 亿参数大小，所以 Gemma 模型系列有很多内容，我特别兴奋地告诉你们我们最近的两次发布，第一个是我们自发布第一天起最受欢迎的功能，那就是多模态，所以我们发布了 Palema，P Gemma，哦，谢谢，我欣赏这一点，这就是我热爱开源社区的原因，真正最热情的开发者，P Gemma 是 Siglip Vision Encoder 和 Gemma 1.0 文本解码器的组合，这种组合使我们能够执行各种图像文本任务和功能，包括问答、图像和视频字幕、对象检测和对象分割，模型有几种不同的变体，目前只有 2B 大小，我们有可用于微调特定任务的预训练权重，我们还有几个不同的微调变体，它们已经针对对象检测和对象分割等内容进行了优化，我们还有传输检查点，这些模型专门针对一些学术基准，直到今天早上，这是我们最新的发布，但我非常兴奋今天能和大家在一起，因为今天是 Gemma V2 发布日，哇，我们一直在努力开发这些模型，自从 Gemma 1.0 发布以来，我们尽了最大努力收集社区的反馈，了解 1.0 和 1.1 模型在哪些方面不足，以及我们可以如何改进它们，所以我们创建了 Gemma 2，Gemma 2 有 90 亿参数大小和 270 亿参数大小，毫无疑问，这两个模型都是其规模中性能最好的，而且这两个模型都优于比它们大两到三倍的模型，但 Gemma 2 不仅仅强大，它还设计用于轻松集成到您已有的工作流程中，所以 Gemma 2 使用与 Gemma 1 相同的所有工具和框架，这意味着如果您已经开始使用 Gemma 1 进行开发，您只需几行代码就可以自动切换到使用 Gemma 2 模型，并为您的应用程序带来更高的性能和更强大的功能，我们还拥有相同的广泛框架兼容性，再次，TensorFlow、JAX、Transformers、Ollama，我之前提到的所有这些，我们都为 Gemma 2 提供了，我们还显著改进了文档，我们有更多的指南、教程，以便我们指导您如何开始，不仅是推理，还有高级和高效的微调，从第一天开始，最后，我们真的想将微调作为这些模型的一个关键功能，我们对核心建模决策如何影响用户进行下游微调的能力进行了广泛研究，所以我们相信这些模型将非常容易微调，因此您可以根据您的用例定制它们，除了让 Gemma 2 模型特别容易上手之外，我们还提供了 27B 模型，可以在 Google AI Studio 中使用，这意味着您可以访问 AI Studio 主页并选择 Gemma 2，如果您愿意，可以立即开始玩提示，您只需要做一件事，就是想出一个如何突破我们模型极限的想法，我特别期待看到你们用 AI Studio 和 Gemma 做些什么，我们有几种方式可以让我们知道你们在构建什么，我稍后会讲到，但如果您有想法，我今天都在这里，想听听你们用 Gemma 模型做什么，但让我们更深入地了解一下性能，我们对我们制造的模型感到非常自豪，正如我所提到的，它们毫无疑问是其规模中最好、性能最好的模型，并且与两到三倍大的模型相比也具有竞争力，所以我们的 27B 模型在性能上与 Llama 370B 相当，并且在许多基准测试中，在某些情况下，比 Grok 模型表现更好，但我认为学术基准测试只是我们评估 Gemma 模型的一部分，有时这些基准测试并不总是能说明模型在您手中时的表现，所以我们还进行了广泛的人工评估，我们发现 Gemma 模型始终比其他开放模型（包括更大的开放模型）更受欢迎，而且我很自豪地说，Gemma 27B 模型目前是其规模中排名第一的开放模型，并且目前在 LMS 上超越了 Llama 370B、Memotron 340B、Grok、Claude 3 以及许多其他模型，谢谢，哇，你们非常支持，我感谢你们，唯一比 Gemma 27B 模型表现更好的开放模型是 LMS 上的 E large 模型，所以我们希望您在玩它时会很有趣，尤其是在聊天应用程序方面，我们在评估中发现，Gemma 2 模型在指令遵循方面甚至更好，它们更具创造性，在事实性方面更好，总体上比 Gemma 1.0 和 1.1 模型更好，我们最近发布的另一个重要的事情是 Gemma Cookbook，目前 Gemma Cookbook 可以在 GitHub 上找到，其中包含 20 种不同的食谱，从简单的到非常高级的 Gemma 模型应用，我最兴奋的是 Gemma Cookbook 目前正在接受拉取请求，所以这是一个分享您正在使用 Gemma 模型构建的内容的好机会，这样我们就可以与世界分享，当然，我们也不介意您给存储库加星标，去看看，告诉我们您正在使用 Gemma 构建什么，所以有几种方法可以开始使用 Gemma 2 模型，当然，我刚才提到了 Cookbook，您还可以申请 GCP 积分以加速您使用 Gemma 2 的研究，我们有大量的资金支持研究，我强烈建议您填写申请表，无论您的项目大小如何，正如我所提到的，我们还显著改进了文档，我们有许多指南、教程、Colabs，涵盖了所有框架，因此您可以开始使用 Gemma 2 模型进行推理、微调和评估，您可以在任何开放模型可用的地方下载它们，请在 Discord 或其他社交媒体渠道上与我们聊天，以便我们了解您正在构建的内容，今天我就讲到这里了，我非常兴奋地看到你们用 Gemma 构建的一切，我从事这个项目已经快两年了，开始这个项目是因为我作为学术界的研究人员，对看到开放的基础 LLM 与我们看到的专有模型的快速改进相比有多么落后感到失望，所以这是我非常关心的事情，也是我希望在我积极参与开源社区时能够拥有的东西，所以我非常兴奋地看到你们用这些模型进行的项目和研究，请在社交媒体、GitHub、Hugging Face 以及这里的活动中与我们互动，并告诉我们您对模型的看法，告诉我们下次我们可以做得更好，非常感谢您的时间，太棒了，我们可能还有两分钟，您想说一下您希望用 Gemma 模型构建的令人兴奋的事情吗？是的，我喜欢这个问题，我认为，正如我在演讲开始时提到的，我认为安全和负责任是构建 Gemma 模型的一个非常关键的部分，但我认为这也是一个我们总体上研究不足的领域，我认为现在开源社区正在进行很多有趣的工作，关于如何构建更私密、更安全、尤其是有趣的模型，我真的很兴奋看到人们正在改编 Gemma 模型的一些新颖架构，用于像状态空间模型 Recurrent Gemma 这样的事情，我最兴奋的可能是那些我一无所知的研究领域，我真的很兴奋看到人们用我们从未想过的研究想法让我们大开眼界，这正是我最期待的，还有最后一个问题，B1 已经出来一段时间了，在过去的几个月里，有什么让您大吃一惊的事情吗？我最喜欢的 Gemma V1 模型用途之一是，Gemma 模型使用与 Gemini 模型相同的分词器，所以 Gemma 主要在英语数据上进行训练，而 Gemini 模型是多模态的，它们是多语言的，这意味着 Gemma 模型可以非常轻松地适应不同的语言，所以我最喜欢的项目之一，我们在 IO 中也重点介绍了它，是一个来自印度的研究团队，他们微调了 Gemma，在 200 多种印度语言变体上取得了最先进的性能，这以前从未实现过，让一个如此小的模型能够以如此高的质量覆盖如此多的语言，所以那真是太疯狂了，那是我最喜欢的一个，我们还有一个，它很快就会出现在 Cookbook 中，但我们有一个 Cookbook 菜谱，灵感来自一个用例，一个小型企业收到电子邮件，其中包含他们的新订单，电子邮件非常混乱，没有结构和格式，但有各种各样的需求清单，小型企业需要为这个订单制作什么，有一个小型企业微调了一个 Gemma 模型，用于接收这些电子邮件列表，并按优先级顺序输出需要何时处理的订单，所以我很高兴看到 Gemma 模型如此实用的应用，太棒了，好了，废话不多说，我们应该继续下一位演讲者了，来自 Fireworks AI，这里有 Dimma，首席技术官，首席执行官今天无法出席，但 Dimma 会给我们一个精彩的演讲，因为一些个人紧急情况，所以你得到了我，正如你所见，我们还没有 AI 来解决投影问题，但我们有 AI 来解决很多其他问题，所以今天我将谈论 Fireworks AI，总的来说，我将继续 Kin 开始的主题，关于开放模型，以及我们如何在 Fireworks 上专注于生产化模型推理，但首先，作为介绍，我们的背景是什么？所以 Fireworks 的创始团队来自 Meta 的 PCH 领导者和一些 Google AI 的老兵，我们加起来可能有十年的 AI 生产化经验，在世界上最大的公司中的一些，我自己个人在过去五年里一直是 P 的核心维护者，所以开源这个话题真的离我很近，自从我们通过我们在 Pyro 和一些 Google 技术上的工作引领了深度学习开源工具链的革命以来，我们真的相信开源模型也是 Gen 应用的未来，而我们在 Fireworks 的重点正是这一点，所以，我的意思是，听众中有多少人实际上使用 GPT 并将其部署到生产环境中，又有多少人使用开放模型？在生产中部署，哦，好的，我本来要说服你，开源模型的份额会随着时间增长，但看起来在这个听众中，它已经相当可观了，但尽管如此，所以，为什么，为什么基本上是这种权衡，为什么选择大的，为什么选择小的？目前，大部分生产推理仍然基于专有模型，而问题在于，那些模型确实很好，而且它们通常在许多领域都处于前沿，然而，问题在于，它是一个模型，它在很多事情上都很好，而且它通常以相同的方式提供服务，无论用例如何，这意味着，也许如果你在某个狭窄的领域进行批量推理，或者你有一些超级实时用例，你需要做语音助手之类的，那些通常是从相同的基础设施提供的，而没有在模型功能方面进行定制，这也意味着，是的，像 GPT4 很好，或者 Claude 很好，它可以处理很多事情，但你经常为不需要的额外功能支付很多费用，在这种特定情况下，你真的不需要客户支持聊天机器人知道 150 个宝可梦或者能够写你的诗，但你真的希望它在特定的狭窄领域做得很好，这种大型模型之间的这种差异会导致几个问题，一个是我提到的高延迟，因为使用大型模型意味着响应时间更长，这对于语音助手等实时用例尤其重要，随着技术的发展，它变得越来越重要，因为对于像下一个演讲将是 de 这样的事情，你需要为像代理一样的应用程序进行很多步骤，进行推理并多次调用模型，所以延迟非常重要，而且你经常可以看到，你可以选择像 Lar 或 Gemma 这样的小型模型，你刚刚谈论过，并在狭窄领域实现相同的质量，同时速度快十倍，例如，对于一些函数调用用例，例如伯克利进行的外部基准测试，你可以在 10 倍的速度下获得与微调的 Llama 3 相似的性能，成本也是一个问题，如果你在大量流量上运行一个大型模型，你知道，即使你可能有 5K 个 token 的提示和 10,000 个用户，他们每人每天调用 LM 20 次，你知道，即使是 GPT 4，即使是 GPT 40，每天也可能增加 10K 美元，或者每月几百万美元，每年 3000 万美元，这对于一家初创公司来说是一笔可观的费用，你可以用更小的模型轻松地削减这笔费用，而且我们经常看到这是寻求更小、更可定制模型的一种动力，但真正让开放模型闪耀的是领域适应性，这方面有两个方面，首先，有如此多的不同的微调和定制，我认为 KAIT 提到了 Gemma 在印度语言适应方面，有专门针对编码员、医学的模型的，如果你去 Hugging Face，有成千上万个不同的模型变体，因为权重是开放的，你总是可以根据你的特定用例进行定制，并专门为你需要的东西调整质量，所以开源模型很棒，那么挑战是什么？挑战确实来自三个方面，首先，我们通常看到人们尝试使用，你知道，开源模型，比如 Gem 或任何东西，或者 LB，你会遇到复杂的设置和维护，你需要去找到 GPU，你需要弄清楚在哪些框架上运行它们，你需要下载模型，也许进行一些性能优化调整，而且你每次模型更新或发布新版本时都必须重复这个过程，关于优化本身，特别是对于 LMS，但通常对于通用模型，有很多属性和设置，它们非常依赖于你的用例和需求，有人需要低延迟，有人需要高吞吐量，问题可能很短，问题可能很长，等等，选择跨堆栈的最优设置实际上并非易事，正如我稍后所示，在许多情况下，通过高效地完成这些工作，你可以获得多倍的改进，最后，仅仅是让它准备好生产实际上是困难的，如果你从实验过渡到生产，即使只是在公共云上租用 GPU 也不那么容易，因为 GPU 并不总是可靠的，但要达到企业规模需要，你知道，可扩展性技术、遥测、可观察性等等，这些是我们致力于在 Fireworks 上解决的问题，从效率开始，我们构建了自己的自定义服务堆栈，我们相信它是最快的之一，如果不是最快的，我们从头开始构建它，意味着从编写我们自己的 Cuda 内核一直到定制服务级别的部署和编排方式，这带来了多重优化，但最重要的是，我们真正专注于根据您的需求定制这个服务堆栈，这意味着根据您的自定义工作负载和您的自定义成本和延迟要求，我们可以根据这些设置进行调整，这在实践中意味着什么，以及定制在实践中意味着什么？例如，许多用例使用 Rag 和非常长的提示，所以有很多设置可以在运行时级别、部署级别进行调整，以优化长提示，这些提示通常是可重复的，所以缓存很有用，或者只是调整设置，这样吞吐量就更高，同时保持延迟，这是独立可衡量的，如果您去人工分析并选择长提示，其中 pbox 是最快的，甚至比那里的一些其他提供商还要快，而且我们不仅关注，我们不仅关注长提示，我们关注许多模态，例如，对于图像生成，我们是服务 SDXL 最快的提供商，我们也是唯一提供 SD3 稳定新模型的提供商，因为他们的 API 实际上路由到我们的服务器，最后，正如我提到的，像 LMS 这样的定制，特别是对于 LMS，定制非常重要，一种思考 LMS 性能的方法通常对用例很有用，就是考虑在特定延迟限制下的最大成本，我们经常有客户来找我们说，嘿，我需要我的交互式应用程序，我需要在两秒钟内生成这么多 token，而这正是跨堆栈优化闪耀的地方，通过调整到特定的延迟截止点并更改许多设置，您可以提供高得多的吞吐量，多倍的吞吐量，这意味着更少的 GPU 和更低的成本，在模型支持方面，我们支持最高质量的开源模型，我们听说了 Gemma，现在显然是 Llama，以及一些 ASR 和文本到语音模型，几乎来自许多提供商，我们还与模型开发者合作，例如，例如，e large 在美国也在 Fireworks 上提供服务，上周发布，并且作为平台功能，正如我提到的，我们有很多开源模型可以帮助您入门，或者我们自己进行一些微调，所以我将稍后谈论一些专门用于函数调用的模型，或者我们自己进行一些视觉语言模型融合，我们也发布了这些，当然，开源，开放模型开发的关键是它可以针对特定用例进行微调，所以我们提供了一个微调平台，无论您是带来您在其他地方收集的数据集，还是在我们的平台上通过反馈实时收集数据，特别是在定制方面，这是一个有趣的特性，很多人在尝试使用模型时发现很有趣，那就是如果你尝试微调并部署生成的模型，如何高效地服务它，结果是，如果你做 Lory 微调，很多人都会这样做，你可以做一些聪明的技巧，在同一个 GPU 上部署多个 Lory 模型，实际上是数千个，这意味着即使你有数千个模型变体在那里部署，我们仍然可以提供按 token 付费的无服务器推理，而无需支付任何固定成本，当然，单个模型都很棒，但我们越来越多地在应用程序中看到的是，模型本身不是产品，你需要一个更大的系统才能解决目标应用程序，原因是模型本身往往会产生幻觉，所以你需要一些基础，这就是 Rag 或访问外部知识库的用武之地，此外，我们行业中还没有跨所有模态的魔法多模态 AI，所以你经常需要将多种类型的模型链接起来，当然，你还有所有这些外部工具和外部操作，这些端到端应用程序可能想要以某种形式执行，所以我认为我喜欢的术语是数据砖流行的“复合 AI 系统”，但基本上越来越多地看到从模型本身是产品到这种组合，可能是 Rag 和函数调用和外部工具等构建在一起作为产品，这就是我们基本上看到这个领域发展的方向，随着时间的推移，所以从我们的角度来看这意味着什么，我们在这个案例中做什么？所以我们看到函数调用，就像代理一样，是这个新兴架构的核心，它可以连接到我们平台上直接提供的领域专用模型，或者可能针对不同的需求进行微调，并连接到外部工具，也许是代码解释器，也许是外部 API，具有这种中央代理视图，中央模型负责协调并尝试处理用户需求，如果它是一个聊天机器人之类的，你可能都听说过函数调用，最初由 OpenEd 推广，这基本上是相同的想法，所以，是的，Def Qu 是连接 LLM 与外部工具和外部元素的真正方法，这在实践中意味着什么？所以我们实际上专注于专门用于函数调用的模型，我们发布了一系列这样的模型，比如最新的 Fire Function V，两周前发布，你可以用它做什么？你可以点击这个按钮，这意味着你可以构建结合了自由式通用聊天功能和函数调用的应用程序，在这种情况下，这个 Fire Function 具有一些聊天功能，所以你可以看到，你可以问它你能做什么，它有一些自我反省来告诉你它能做什么，它还连接到这个演示应用程序中的一些外部工具，所以它可以查询股票报价，它可以绘制图表，所有这些外部 API，它还可以生成图像，但它真正需要弄清楚的是如何将用户查询转化为复杂的推理，转化为函数调用，例如，如果我们让它生成一个包含三大云提供商股票的条形图，它实际上需要做几个步骤，对吧？它需要理解“三大云提供商”意味着你知道 GCP 和 Azure，Azure 是微软旗下的，它需要然后去调用函数查询它们的股票价格，最后，它需要将这些信息结合起来，并发送到绘图 API，这就是刚才在后台发生的，另一个你必须做的重要方面是高效的函数调用聊天功能，你需要有上下文感知能力，所以如果我让它在这个图表中添加 Oracle，它需要理解我指的是什么，并且仍然保持之前的上下文并重新生成图像，最后，你知道，如果我切换到另一个话题，它就需要放弃之前的上下文，并理解“嘿，这个历史上下文不太重要，我将从头开始”，所以在猫的照片或任何东西中没有 Oracle，所以，你知道，这个特定的演示实际上是开源的，你可以去我们的 GitHub 尝试一下，它是用 Fire Function 和一些其他模型构建的，包括在我们平台上运行的 SDXL，函数调用模型本身是开源的，它在 Hugging Face 上，我的意思是，当然，你可以在 Fireworks 上调用它以获得最佳速度，但如果你愿意，你也可以在本地运行它，它使用了我们平台上的许多功能，例如，使用 JSON 模型语法模式进行结构化生成，我认为这与昨天在这里演讲的 Outline 的一些之前的演讲者类似，是的，所以最后，试试吧，总的来说，如何开始使用 Fireworks，如果你想了解 Fireworks，你会发现很多我提到的开源模型，它们在 Playground 中可用，在产品方面，我们有这种范围，可以带你从早期原型设计一直到大规模生产，你可以从无服务器推理开始，这与访问 OpenAI Playground 或类似的东西没有区别，你按 token 付费，你不需要担心硬件设置或其他任何事情，正如我提到的，你仍然可以进行微调，所以你可以在我们的平台上进行 P 微调，你可以带来你自己的 Lora 适配器，仍然可以无服务器地服务它，当你毕业时，也许是一家初创公司，一个新的毕业生，更具生产规模，你可能想要按需访问，这更像是专用的硬件，具有针对你的用例的更多设置和修改，你可以带来你自己的自定义模型，从头开始微调，或者在我们的平台上进行微调，最后，如果你扩展到更大的批量，并想达到企业级别，有折扣的长期合同，我们还将帮助你将其优化到我之前谈到的某些工作室，在用例方面，我的意思是，我们为许多公司提供生产服务，从小型初创公司到大型企业，我上次检查时每天处理超过 1500 亿个 token，你知道，像 Quora Build、Jet Bots、Po、Sour Draft 和 Cursor 这样的公司，我认为 Cursor 昨天在这里有一个演讲，他们将我们用于一些代码助手功能，他们的延迟非常重要，正如你可以想象的，你知道，像 Upstage 和 Liner 这样的公司正在构建不同的助手和代理，所以，我们绝对准备好生产了，去试试吧，最后，我们非常关心开发者，你们，所以实际上这是去年 LlamaChain AI 状态的外部数据，结果是，在 Hugging Face 之后，我们是最受欢迎的平台之一，人们在那里获取模型，这很棒，听到这个很高兴，再次，要开始，只需访问我们的网站，你可以在 Playground 中立即玩，例如，你可以运行 Llama 或 GMA 或任何东西，以最高速度运行，然后开始构建，我真的很兴奋看到你能用开放模型、Fire Function 或一些你自己微调的东西构建什么，是的，最后一点，我们与开放 API 兼容，所以你仍然可以使用你最喜欢的工具，相同的客户端，或者你可以使用 LlamaChain 或 Llama Index 等框架，所以，是的，我真的很兴奋能在这里，并稍微谈谈开源，开源模型，以及我们在 Fireworks 上如何专注于生产化和扩展它，去试试吧，你也可以在博览会的展位上找到我们，[掌声] 谢谢，那是一次很棒的演讲，现在，谁在这里听说过 Devon？大部分房间？嗯，所以这个标签对你们所有人来说都会非常有趣，这里有 Scott，[掌声] 哇，酷，好的，是的，我是 Scott，来自 Cognition AI，我将告诉你们一些关于，你知道，Devon 的早期制作，我们仍然非常非常早期，以及关于这个领域以及接下来会发生什么，你知道，我想先从演示开始，听起来你们中的一些人已经看过一些视频了，但我为今天的世界博览会带来了一个很棒的自定义视频，所以我将快速展示一下，在这里，我基本上说，嘿 Devon，这是今天早上，这太棒了，我说了，嘿 Devon，我想让你构建一个对移动设备友好的网站来玩名字游戏，我很难记住名字和脸，我不知道你们怎么样，但我基本上只是说，这里有一个 tsv 文件，里面有很多名字和脸，这些都是本周世界博览会的所有演讲者，我说，你能设置游戏，这样你就可以显示两个随机的脸，然后显示其中一个的名字，让我猜猜哪个是哪个，对吧？我给出了一些关于游戏如何工作的指示，所以 Devon 是一个完全自主的软件工程师，这意味着 Devon 拥有人类软件工程师在构建类似东西时所拥有的所有工具，所以 Devon 要做的第一件事是制定一个计划，你可以在这里看到，一个基本的计划正在出来，关于这一点有趣的一件事是，计划会随着时间变化很多，所以，你知道，当你获得新的信息或新的反馈时，你也会随之更新你的计划，之后，Devon 基本上就像人类一样运行，所以，如果你看一下，Devon 为名字游戏网站创建了一个新目录，启动了一个新的 React 应用程序，你知道，所有相同的基本元素，努力构建它，构建代码，你知道，读取 tsv 文件来查看这里发生了什么，它只是在努力完成它，并跳过，它出来并部署了第一个版本，几分钟后，我将快速拉出它，所以这就是它看起来的样子，它关闭了，但还没有完全完成，对吧？我的意思是，它显示了，它仍然显示名字，我想也许我没有完全指定它，但你知道，你可以点击名字，并且正确了，所以我就继续给它一些更多的反馈，用简单的英语，

所以我说，嘿，你知道，你能在我点击答案之前隐藏这两个名字吗？而且，你能重新设计一下“再玩一次”按钮吗？它在这个页面上感觉有点不对劲。我继续说，随着时间的推移，我给了它越来越多的反馈。我还问它，嘿，你能添加一个连胜计数器吗？你知道，你能跟踪我答对了多少题，然后重置为零吗？还有其他一些事情。最终部署的网站就是这个。所以，例如，这是 Justine，它会跟踪我的连胜记录，你可以看到它在不断增加。所以，如果我故意答错一题，你就会看到连胜记录会重置为零，然后继续。我实际上玩了这个游戏，学会了所有人的名字，这非常有帮助。你们也可以玩，就在这里，如果你想试试。这里有所有演讲者，我想在世博会上大约有 170 位演讲者。所以，这是一个很酷的例子。但我想强调一下，如果软件工程如此简单，世界会有多么不同。如果你能用简单的英语准确地解释你想要什么，然后实现它。这显然是一个玩具用例，可能很有用，但我们自己在使用 Devon 构建 Devon 时也经常使用它。顺便说一句，我显然不是自己做的这个网站，我只是说，嘿，你给我建一个带二维码的网站，等等。Devon 也建了那个。但这里有一个 Devon 的快速示例，我们自己正在生产中使用它。所以，如果你快速看一下，这里有一个搜索栏，还有所有的会议，你可以在会议中搜索。Devon 实际上在 Devon 仓库中做了那个。你可以在这里看到 Bryce 在我们的团队里，Bryce 问，嘿 Devon，你能进入 Devon 会议列表，在这里创建一个搜索栏组件吗？这是我需要你做的。这个有一些功能，特别适合在生产代码库中工作。你可以看到 Devon 从一个快照开始，我们加载了一个机器实例，它已经克隆了，它有一个 Playbook，所以它知道我们仓库的很多细节，而且它也能在我们的 git 环境中工作。所以你会看到它提交一个 PR，并与所有这些相同的工具进行交互。我将快速浏览一下。是的，Devon 说没问题，你知道，它提交了第一个 pull request。Bryce 继续说，你只是用简单的英语提供反馈，你说，这是一个很好的开始，你知道，现在你能添加一个放大镜吗？并且让它符合习惯，你知道，使用 phosphor lose seat，你知道，这取决于你。Devon 说，当然，我会做的。Bryce 说，哦，顺便说一下，不需要测试，你知道，我信任你。Devon 说，顺便说一下，我在登录过程中遇到一点问题，你知道，就像你和另一个工程师一起工作一样。Devon 和 Bryce 说，好的，兄弟。然后它就构建了所有东西，并得到了 PR，这个 PR 实际上被合并了，这就是搜索栏。同样，Devon 的很多 API 集成也是由 Devon 构建的，我们很多内部仪表板和指标跟踪也是由 Devon 构建的。看到 Devon 与公司一起构建公司，这很有趣。所以，很酷。我想稍微谈谈我们到目前为止的历程，以及这个领域正在发生的事情。我们从去年十一月开始，到现在大约七个月了。很有趣的是，我们开始在一个柏林的游戏黑客屋里，基本上我们很多人当时已经住在一起了。我们都在人工智能领域有过自己的经历，我们知道我们想一起构建一些东西，我们知道我们想在代码方面做些什么，构建一个编码代理。然后是湾区的黑客屋，之后是纽约的黑客屋，然后又是湾区的黑客屋。所以我们实际上在过去七个月里一直在纽约和湾区之间来回奔波。我想我们现在要定居在湾区了，但一直在来回奔波，每次都租一个稍大一点的 Airbnb，因为团队也越来越大。但为什么是 Devon 呢？这是一个我非常热衷的问题，你知道，语言模型已经很强大了，我认为这是公平的。我认为生成式人工智能的第一波是我通常称之为文本补全产品。如果你仔细想想，这是很自然的。语言模型的界面就是文本补全。你给它一个前缀，它就从那里完成。所以，如果你想想 ChatGPT，如果你想想很多问答产品，如果你想想写营销文案，回答客户支持，甚至 GitHub Copilot 和 Cursor 等产品，你知道，很多这些都是非常棒的产品，非常自然的用例，你有了到目前为止的前缀，然后你要求模型完成后缀的下一部分。它为你做到了，这是一个有用的工具。我认为我们正在进入一个新的浪潮，我们正在超越这一点，并引入一定程度的自主决策。在我们的领域，这通常被称为代理。你知道，你解锁了各种各样的新东西。你需要更高的可靠性，但你也解锁了新东西。所以，这很有趣，因为它既是一个非常核心的能力问题，如何让 Devon 解决这些问题，也是一个非常有趣的产品设计问题，因为我认为代理的用户体验是非常新的。为什么是代码呢？有几个不同的原因。我们都是编码迷，我们都是工程师，所以教人工智能编码的想法是我们能想到的最酷的事情之一。但除此之外，我认为有几个特别的原因，为什么代码与代理特别有效。一个原因是，显然，软件工程师的工作不仅仅是输入代码。你要做的很多工作是，你要查找一个 bug，你可能要查看代码库的不同文件，你可能要运行这个或那个命令，你可能要查阅文档，你可能要自己运行前端来重现 bug。你看这个东西，做这个编辑，再试一次。所有这些工作，显然，这就是软件工程的本质，比仅仅在文件中输入代码要多。这很自然地导向了代理工作流程。另一个密切相关的原因是代码反馈迭代的能力。我的意思是，如果你得到一个完整的生产代码库，并被告知，这个有一个 bug，我需要你修复它，这是 bug。假设有成千上万的文件，几十万行代码。说实话，对大多数人来说会很困难，对人工智能来说也会很困难。显然，我们在实践中的做法是，你添加打印语句，查看日志，检查监控。你在不同的文件之间来回跳转，试图诊断。你做的每件事，你都在做决定，然后运行实际代码来找出发生了什么。从那里，你能够迭代，它为你提供了一条更清晰的解决眼前问题的途径。同样，这也很适合代理。最后我想说的是，模型代理能力的提高速度。两年前，即使是像这个名字游戏演示这样简单的事情，我想也几乎是不可想象的。你再想想事情的发展方向，以及两年后会发生什么。我认为数据、正确的训练等等在这个领域正在迅速提高。然后，除了能力问题，还有一个非常深入的用户体验问题。从宏观上看，我认为当我们构建代理时，我认为我们这个领域的所有人都对代理很陌生。我认为立即想到的第一件事是映射到我们今天如何使用软件，以及我们如何与其他人交谈。所以，你知道，即使 Devon 的很多功能基本上也是在你自己的实习生旁边看，你可以看到他们的电脑，你可以看到他们运行什么命令，等等。问题是，我认为代理实际上与两者都不同。有很多并行工作、信息收集的细微差别和细节，它如何管理上下文等等，这些都非常非常不同。从产品角度来看，这是一个非常深刻的问题。为了让你对这一点有所了解，这里只是一个简短的列表，列出了一些我们已经构建到产品中的功能。所以，显然，Devon 可以使用 shell，编辑代码，浏览网页。但还有所有这些其他东西，能够 fork 和回滚会话，能够处理与 Slack 和 GitHub 的集成，能够处理 Playbook 来存储机器快照，跟踪秘密，能够使用正确的工具进行验证。所有这些都是实际产品迭代的一部分。我认为这本身已经是一个极其密集的问题。我认为实际上我们会随着时间的推移看到更多的迭代。我想展示一个我们最近发布的新功能，那就是使用 Devon 的机器的能力。这又是一种不太常见的东西，在我们今天的软件中没有非常接近的平行。但是，能够在 Devon 的机器上进行 VS Code 的实时共享，如果你想与 Devon 协作，说，哦，有这几行，你知道，你应该做这个编辑，我已经为你做了这个编辑，你可以和 Devon 交谈并做到这一点。所以，还有很多进步的空间，在这个领域有很多可以迭代的地方。我想提到的另一件事是，它如何改变我们自己的工作流程。你们看到了 Devon 构建搜索栏的简单示例，但我们实际上现在以一种更异步的方式处理任务。我认为 Devon 的一个很酷的功能是，如果作为一个工程师，你今天正在处理，比如说，四项不同的任务。你给 Devon 一号一个任务，给 Devon 二号第二个任务，给 Devon 三号第三个任务。你有四个 Devon 同时运行。这几乎就像把每个工程师都变成了一个工程经理。我认为 Devon 非常像热情的实习生。他们非常努力，他们显然不是什么都知道，他们会犯一些小错误，他们会问很多问题。但是，你和他们中的每一个都在合作，让他们迭代。所以，这是一个有趣的例子。这实际上是今天的例子。我们当时在讨论一个特定的功能，以及我们想构建什么。在这种情况下，改变颜色是一件很简单的事情。就像在 Slack 的对话中说，嘿，Devon，你能改变这个东西吗？然后 Devon 去提交一个 PR，然后你点击合并。所以，我们有很多时候，我们在健身房或在车里，现在你可以写 C 代码了，因为你知道，你可以告诉 Devon 你想做什么。你只是没有带上你的电脑，无法输入所有东西。但是，能够描述你想要什么给 Devon，然后能够事后审查代码，实际上效果很好。接下来是什么？我认为这是一个非常重要的问题。显然，我认为这项技术今天还非常早期。但几年后这些东西会去哪里？软件工程会发生什么？我认为关于这个问题有很多不确定性。随着我们越来越多地使用 Devon，我认为我们看到的一件大事是，这可能很明显，但 Devon 不是决定做什么或构建什么的那个。软件工程的核心部分，我描述它的方式是，各地的软件工程师都在同时做两份工作。第一份工作基本上是用代码解决问题。你被赋予一个问题，然后你准确地分解出你要构建的解决方案，你要使用的架构，可能会出现的各种流程、细节和边缘情况，并构建出你的确切解决方案。然后第二部分是，一旦你有了那个，你就需要处理调试，实现不同的函数，编写单元测试，以及所有其他进入你想要做的东西的实现的东西。我认为现在，平均软件工程师大约 10% 或 20% 的时间花在第一个思考部分，80% 或 90% 的时间花在实现部分。我认为 Devon 实际上让你有更多的时间去做第一部分。我认为 Devon 的未来，它还很早，但随着 Devon 变得更好，我们会看到更多这种情况，Devon 会为你节省实现时间。你不需要去弄清楚如何设置 Kubernetes，你不需要去调试所有这些坏掉的 API，你不需要去处理版本变更或迁移，以及所有这些在软件工程中占用大量时间的事情。但你实际上会花所有时间来弄清楚如何解决眼前的问题。这有点像技术架构师和产品经理的混合体。所以我认为，我们称之为软件工程的工作将会改变。但我认为实际上会有比以往更多的软件工程师。而且我认为也有很多先例。你知道，以前的编程意味着打孔卡，然后是汇编，然后是 C。随着这些事情的进展，大多数人不再使用打孔卡了，但实际上程序员比以前多了。我认为容易低估的是，还有多少代码需要编写。我想这很有趣，因为显然我们都热爱软件。我认为软件是过去 40 或 50 年来世界进步的最大驱动力。尽管如此，我认为我们对构建软件的需求实际上可能比我们目前所能提供的多 10 倍以上。所以，我认为会发生的是，我们将能够向更多人开放软件工程的力量，每个软件工程师都能提高 5 到 10 倍的效率，但我们实际上会做更多的软件工程。所以，这就是我所说的全部。但我们很乐意回答问题。前面这位，好问题。我们一直在逐步增加访问权限，每周都会让更多人加入。我们也一直在与我们的企业客户进行规模化合作。我们有很多等待名单要处理，所以我们正在尽我们所能。但我们希望尽快让你们获得访问权限。后面那位，红衣服的。在我们的代码库中，例如，Devon 拥有它所需的所有设置。它有一个机器实例，可以运行开发环境，可以运行服务器，可以运行前端。所以，如果你问它，我需要你调试这个特定的东西，它会自己把它调出来，然后重现它，然后调试它，再试一次。还有其他问题吗？当然。所以，有人问，随着所有这些更简单的任务都得到了解决，那么初级工程师或实习生怎么办呢？他们显然需要学习如何编码。我认为，需求将继续随着供应而增长，培训过程将略有改变。但我认为，很多核心基础知识，如果你认为某人是一个非常优秀的工程师，你通常不是指他们打字很快，尽管他们也可能这样做。你通常是指他们对问题有很好的理解，他们了解所有的架构，他们从不错过任何边缘情况。这些是基础知识，我认为它们将永远重要。我认为实习生或初级工程师将更早地接触到这些基础知识。有人问，实现未来套件愿景的最大挑战是什么？有很多，正如你可以想象的，几乎是所有事情。有速度，有一致性，有访问权限，有集成，有正确的产品用户体验。我认为所有这些事情中，很酷的一点是，到处都在上升。所以，我们当然会尽力而为，但每一次新的硬件发布对它来说都令人惊叹。每一个新的基础模型都令人惊叹。每一次新的代理研究。我认为这将是各种各样的事情，会有很多不同的优化出现在堆栈的不同部分，使这种代理流程变得越来越好。但它不会只是一个小事情，我认为它会很快。我们今天的时间就到这里了，非常感谢大家。谢谢大家。 [掌声]

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AI Engineer World’s Fair 2024 — GPUs & Inference Track

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