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歡迎回到On Insight。如果我們要選一個節點作為全球AI產業,從學術夢幻期徹底跨入現實暴擊期的分水裡,我敢打賭,絕不會是哪家大廠又在PPT裡發布了多大規模參數的模型,基準測試又突破了多高的分數。

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真正的变局往往发生在那些

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看起来极度安静

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甚至带着几分诡异的高层人事变动力

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就在最近

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谷歌内部发生了一场

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足以重塑整个硅谷底层格局的震荡

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在谷歌效力了整整27年

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被全球程序员

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尊为代码之神的谷歌首席科学家

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Jeff Dean正式宣布离职

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如果这只是一位老将退隐的话

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倒也不至于让硅谷各大投资机构

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和安全部门彻夜未免

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真正认识背后发紧的是

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Jav Dien离职的时候

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带走了一个堪称

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硅谷全明星的豪华降维打击阵容

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这其中包括与Jav Dien合作

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20多年的底层系统传奇搭档

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桑杰·格马瓦特

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还有Google DeepMind研究副总裁

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Jemline模型最核心的技术操盘手

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奥里奥尔·维尼亚尔斯

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以及AutoML领域先驱

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和Google Brain的联合创始人黎光莲

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这四个人加在一起

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几乎就是半个谷歌AI

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与底层分布式系统的技术灵魂

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他们组建了一家名为Discovery Loop的新公司

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按理说

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面对如此核心的技术骨干离职

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谷歌应该爆发一场血腥的公关战

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对吧

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但诡异的事情来了

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在继续后面的内容之前

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我想先请你帮我一个小忙

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如果你经常观看我的视频

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但还没有订阅我的频道

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欢迎点击订阅

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这样你就不会错过后续更多的科学资讯和深度解析

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那如果你觉得这期视频对你有所帮助

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也请帮忙点赞和分享

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让更多人看到这些内容

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那如果你希望给予我更多的支持

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也欢迎加入我的会员

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每月一杯咖啡的钱

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就是对我持续创作最大的支持

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好的我们继续

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这个诡异的事情就是

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谷歌不仅没有阻拦

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反而第一时间成为了Discovery Loop的初始投资方

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并且双手奉上了Google Cloud的超级算力集群

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双方直接签下了长期云服务绑定协议

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不仅如此

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就在同一天

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Google DeepMind内部也完成了一场

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近乎手术刀级别的高层重组

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DeepMind的灵魂人物Demith Hossabies

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正式卸任DeepMind CEO的日常管理

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身任Alphabet的首席科学家

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去操盘更为宏大的AGI理论中举

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而接替他全盘操盘Google DeepMind的新帅

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是原DevMind的CTO

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DQN与WaveNet的发明人

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科雷·卡伍克·乔鲲鲁

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他越过了所有中间管理层

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直接向谷歌CEO桑德尔PX1汇报

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听到这里

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你脑海里一定盘旋着一个极其巨大的悬念

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就是谷歌为什么会心甘情愿的

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让自己的顶尖大脑集体外逃

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甚至还要掏钱掏算力

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帮他们去创业呢

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而那位既懂DQN强化学习

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又懂Vivnet原生语音的工程狂人

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柯雷接棒之后

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到底要把Gemla带向何方呢

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今天我们就有一期节目

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彻底拆解这场看似是高层离职潮

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实则是谷歌AI技术大分流的

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硅谷终极棋局

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要解开这个谜团

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我们首先得搞清楚

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Jeff Dean带着这帮神仙阵容杀出去

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到底要去干一件什么样的事情

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在Discovery Loop的官宣里

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Jeff Dean直接描绘了公司的愿景

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那就是构建自动化的科学方法闭环

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简单来说

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过去几百年

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人类探索未知的方式

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本质上都是串行实验

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就是科学家提出假设

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亲手写代码

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跑一次实验

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等几天看结果

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分析数据

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然后再手动修改参数

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跑下一次

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在这套流程里

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最瓶颈的变量不是算力

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而是人类脑力的思考速度与手动操作的物理实验

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而Discovery Loop要构建一套完全自主运行的AIA层架构

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做到自动提出科研假设

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自动编写实验代码

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自动部署分布式计算

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自动分析实验指标

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最后自动递归迭代

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你注意看Discovery Loop的首期应用场景选择

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他们没有第一天就跑去见物理实验室

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而是选择吃自己的狗粮

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把机器学习研发本身作为第一个实验战场

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也就是说他们要用AI去自动设计

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自动搜索

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自动验证下一代超越Transformer的模型架构

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很多朋友可能会问

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用AI自动写代码做研究

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之前日本Sakana AI的项目不是也做过吗

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这正是我们接下来要拆解的关键

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Sakana AI的项目本质上是一个

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应用层的agent流程封装

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它是拿现成的GPT4O或者Cloud外包一层prompt

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去模拟科学家写代码跑实验写论文

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它的上限完全被底下调用的通用大模型给封死了

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而Discovery Loop完全是另一个维度的存在

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Jeff Dean和桑杰·格瓦马特

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是写出MapReduce Bigtable的超级系统架构师

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李光莲是AutoML的鼻祖

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奥里尔尔维尼亚尔斯

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是顶尖的神经网络搜索与多模态专家

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他们要做的是从底层分布式计算

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硬件算力调度

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神经架构搜索到算法闭环的系统级重构

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他们不是在用AI写论文

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他们是在用超级计算机集群

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让AI去做数以万计的硬件级与架构级实验

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直接在算法底层寻找下一个Transformer

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那么回到我们开头的悬念

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就是谷歌为什么不把这个超级团队留在内部

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而是支持他们独立成立一家公共利益公司呢

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因为谷歌算透了一笔账

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像AI自主研发下一代AI这种前沿探索

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风险极高

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烧钱极大

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失败率也极高

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如果放在谷歌Digemine内部

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研究员们每天要面对华尔街的财报审查和季度KPI

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就会被无限牵扯精力去搞产品适配

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通过把JFDN团队外部化

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谷歌既让这一帮顶尖天才

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摆脱了大公司病和商业汇报的枷锁

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又能通过初始投资方和独家GoCloud算力的身份

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牢牢锁定未来潜在的技术回流与云服务收益

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这一招叫做前沿高风险战略的外部结偶

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如果说JFDN的离职是把远期高风险探索拆分了出去

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那么接下来发生的事情

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才是对Jamlight产品生态影响最深远

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也是最暴力的一环

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接掌Google D.My的全局率颖的

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科雷·卡伍克·乔武

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是一个与Demis Hossabies

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风格截然不同的工程技术派

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Demis是国际象棋神童和神经科学家

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他充满着理想主义

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而科雷是把深度强化学习

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真正推向AlphaGo破局

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把WaveNet实施语音

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真正变成极易手机里语音引擎的超级工程操盘手

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在科雷掌舵之后

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Google G-Mind释放出了一个极其明确的信号

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就是全面放弃学术包袱

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转向极致工程眼镜与大规模商业化暴击

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在他的主持下

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Gamlight的底层架构

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首先,最根本的變化是,徹底打破對預训訓參數崇拜的單位幻想,全面轉向後訓練與推理期計算的深度融合。

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在过去两年 行业盲目堆叠预训练参数

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但预训练越往后 编辑效应递减越严重

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算力成本高到令人发指

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柯雷结合他在DQN强化学习领域的积累

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给Gemla引入了过程奖励模型PRM

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与蒙特卡洛书搜索MCTS

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打个比方 传统的语言模型

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就像在考场上没有任何草稿纸

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靠直觉备答案的考生

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遇到简单的题那是没问题的

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但遇到复杂逻辑推导或者长流程编程

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中间要是错一步后面就全乱了

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而PRM与MCTS的融合

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相当于给GEMLA配了一张无线扩展的虚拟草稿纸

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和一位微观监考官

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在推理的每一步

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PRM都会对它的逻辑合理性进行毫秒级评分

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同时MCTS数搜索会在后台预判

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未来几十步的走向

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如果发现某个分支走向死胡同

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模型会在推理阶段自动撤回并重新搜索最右键

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这意味着在不用无限膨胀预讯链参数的前提下

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GEMLINE处理高难度编程

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复杂数学推导和A乘的规划的成功率

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称指数级提升

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紧接着第二种转变在于

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系统级推理效率的极限拉链

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那就是构建GEMINI Spark端云协同分层架构

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这里稍微提一下我个人对Spark的使用体验

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虽然它还是Beta版本

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但先说结论

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我觉得它超级好用

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我会单独出一期视频

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聊一下Gemla Spark和Hermis Agent的配合使用

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作为Google订阅的长期体验者

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我明显感觉最近Gemla的使用限额提高了

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他们虽然没有发布像Fable 5这类的模型

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但是无论是Gemla 3.6 Flash

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还是Gemla 3.1 Pro都变得更聪明了

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00:10:09,440 --> 00:10:11,740
好 我们继续这个第二重转变

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在商业高并发场景中

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用户问一句今天天气怎么样

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00:10:16,420 --> 00:10:20,660
如果都要调用一次千亿参数的单体大模型

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00:10:20,660 --> 00:10:23,820
显存和电力开销能把大厂烧穿

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克雷推动的Gemlet Spark架构

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把整套AI系统做成了像特种部队一样的分层调度

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前端部署及轻量及低延迟的Gemlet Spark代理模型

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负责高频意图识别

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上下门过滤和轻量任务

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只有当前线判定

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涉及复杂长链条逻辑的时候

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才会无缝调动云端期间大模型

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这种分层架构

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直接把单位Token成本打了下来

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这才是支撑数亿并发的商业硬实力

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而第三种变化

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则是科雷将他的看家本领

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Vivnet原生音频与流逝生成

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深度融合到了詹姆賴的全模态底座中

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以往的多模态AI

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往往是文本模型外挂语音识别

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与语音合成模块的拼接车

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延迟高 语调生硬

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而柯雷主导下的詹姆賴

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实现了真正的原生全模态流逝交互

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不仅能实现毫秒级

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实时音视频双向对打

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还能在听懂说话的同时

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自主执行代码

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甚至在手机屏幕上动态生成

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UI界面与交互空间

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这已经不是在聊天了

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这是直接把自然语言变成了实时软件生成的界面

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那底层架构演进的再漂亮

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如果不能转化为商业流水

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在今天也是一张废纸

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科雷·卡伍克·乔鲁这次最让人惊悚的调整

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在于他不仅管科研

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还同时兼任了Gemline产品与开发者生态的最高指挥官

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在过去的大厂体制里

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科研实验室和商品产品部门是完全脱节的

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实验室发了论文

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产品部门要花大半年去沟通打包

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而柯雷一把抓之后

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直接祭出了商业落地的三大杀招

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在终端入口上

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他们开始加速清洗传统的语音助手

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全面用GM来替换掉Android和VLOS上

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那个指挥社闹钟的assistant

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赋予他操作系统级的最高上下文读取权限

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与跨应用API调用能力

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直接把它打造成操作系统的中央控制大脑

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比如在ValOS手表或者车载系统里

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你只需要说帮我安排今晚与张总的商务晚餐

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并准备一份他公司最新财报的背景简报

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Gamlight就会自动调取邮件和地图订餐厅

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同时在云端提取财报生成简报推送给你

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与此同时

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他们还大幅压缩了科研成果

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转化到商业API的时间

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在谷歌云Vertex AI和开发者API上

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前沿团队在Achic代码生成

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自动模型评估和推理加速上的突破

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能以周级别的速度直接部署上线

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让科研成果在第一秒

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直接转化为商业账单

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形成技术产品与算力的超级费轮

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更关键的是

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谷歌正在悄无声息地改变自己的盈利模式

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当用户不再翻页点击搜索广告链接

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而是让Gamlet给出最终答案时

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谷歌的解法是

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直接占据agent决策路径的入口

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当Gamlet在每日简报或者个人助理里

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帮你规划行程推荐服务时

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它在生成方案的同时

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精准接入商业API入口

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谷歌由此从一个信息检索广告商

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变成了全局智能决策的开门人

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抽取服务分账和佣金

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拆决员Jeff Dean的离职创业

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与柯磊掌舵下的Deep Mind重组

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00:14:02,140 --> 00:14:06,440
我们终于能看清这场硅谷AI大戏的全貌了

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硅谷的AI战争正是告别了依靠概念和论文引用量

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获取高估值的学术竞赛阶段

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全面跨入了拼推理成本

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拼系统稳定性

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拼终端控制力

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以及拼商业造学能力的工程与商业深水去

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对于我们所有在做AI研发架构设计

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00:14:27,800 --> 00:14:29,920
或者技术创业的朋友来说

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谷歌的这一系列辩证

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00:14:31,720 --> 00:14:34,860
留下了三条极其宝贵的工程启发

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首先不要盲目迷信

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00:14:36,940 --> 00:14:39,580
纯域训练参数的单位堆叠

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多去研究推理器计算

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通过过程奖励模型打分与蒙特卡洛数搜索

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让模型在推理阶段学会自我纠错

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这是低成本实现逻辑跨越的唯一解法

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其次要学会拥抱端云分层架构

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平气无论任务大小

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00:14:58,520 --> 00:15:01,620
一力调用千亿大模型的粗暴思维

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00:15:01,620 --> 00:15:03,400
学习Jamler Spark的设计

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用轻量级模型做好前置意图路由

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才能在高并发任务中守住成本底线

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最后我们需要重新思考软件入口的定义

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未来的软件竞争不再是争夺

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手机屏幕上的APP图标位置

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而是争夺能否被系统及agent

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API决策链所调用

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技术不相信幻觉

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商业不相信眼泪

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这里是All Insight

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我们下期见