特斯拉AI Day 2022全解读：能走会动的Tesla Bot，DOJO超算明年量产、还有FSD新进展

电动星球News2022-10-01

几个小时前，特斯拉正式举办了 2022 AI Day，一场全球汽车、人工智能、信息科技行业翘首以待足足 13 个月的发布会。严格意义上 AI Day 不像是「发布会」，而是「交流会」——马斯克本人也在推特上说，「此活动旨在招聘 AI 和机器人工程师，因此技术含量很高」——换句话说，这是马斯克的高山流水，为特斯拉的锺子期而开。不过这并不妨碍我们以比较轻松的视角，记录下这场科技狂欢。因为特斯拉团队几乎...

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千里独行侠
·2022-10-03
👍👍👍對對對ᥬ🌚᭄
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Paggie
·2022-10-03
閱
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Paggie
·2022-10-02
閱
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Yap Wai Hong
·2022-10-02
Palo Alto
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hhyw
·2022-10-02
好
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UC震惊部主编
·2022-10-02
按這種發展勢頭，特斯拉未來有望超越蘋果成爲新的科技股老大
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7
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奥龙
·2022-10-02
最酷的公司招聘會
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胆大如老虎
·2022-10-02
。
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1
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- Yap Wai Hong:
  老虎
  2022-10-02
  Reply
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嘞撸虎7
·2022-10-02
閱
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mimi pua
·2022-10-02
👍
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alvin240576
·2022-10-02
👍👍
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2
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- mei leng:
  👍
  2022-10-02
  Reply
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JJJjohn
·2022-10-02
路還是很長
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2
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- hhyw:
  好
  2022-10-02
  Reply
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alexande007
·2022-10-02
👏👏👏
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Douglas92
·2022-10-01
[微笑]
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1
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雨点尔
·2022-10-01
感謝分享
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Fattkhong
·2022-10-01
[惊讶]
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1
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- Fattkhong:
  [惊讶]
  2022-10-01
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niuniu688
·2022-10-01
偉大的公司
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似懂费懂
·2022-10-01
這場發佈會是不是低於很多人預期
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4ac7d3ff
·2022-10-01
這樣就會漲嗎？
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BenWong78
·2022-10-01
😀
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挑选尽可能保证成本+效率的原材料。「我们不会用碳纤维、钛合金这样的原材料。因为它们虽然很优秀，但像肩膀这样的易损部位，制造和维修成本都太贵了」。除此以外，制造 Optimus 的中心思想，也基本和智能汽车相当：减少线束长度、计算和电子控制单元中心化，等等。2. 仿生学既然是类人机器人 humanoid，设计自然要借鉴人类仿生学。特斯拉用了几个例子解释 Optimus 的仿生学，首先是膝关节。特斯拉表示 Optimus 的关节希望尽量复刻生物学上的「非线性」逻辑，也就是贴合膝关节直立到完全弯曲时的受力曲线。为此，Optimus 的膝关节使用了类似于平面四杆机构的设计，最终发力效果会更接近人类。紧接着，我们创造人类文明的双手，才是 Optimus 类人之路更大的 boss。Optimus 光手掌区域就用了 6 个促动器，具有 11 档的自由度。拥有自适应的抓握角度、20 磅（9 公斤）负荷、工具使用能力、小物件精准抓握能力等等。此外，Optimus 的手掌用的是「non-backdrivable」无法反向驱动的指尖促动器。学术界的看法是，这样的促动器可以提升在「开放环境」下的性能。最后是让 Optimus 学着像人类一样走路——这里用到的仿生学设计叫做「运动重心控制」。为什么有外壳的  Optimus 还不会走？其中一个原因就是重量变了，运动重心控制算法需要重新调试。事实上，Optimus 不仅要做到会走路，还要做到别摔倒。所以它不仅需要控制走路的重心，还要稳住受到外力（比如推搡）时的随机动态重心。训练 FSD 用到的神经网络和在线仿真模拟，这次在 Optimus 身上大显身手。路径规划、视觉融合、视觉导航等等熟悉的名词都被「灌输」到 Optimus 脑子里。这样的努力下，Optimus 今年 4 月迈出了它的第一步；7 月份解锁了骨盆活动；8 月走路时可以摆手臂了——发布会前几周，实现了脚趾离地的类人行走动作。3. 「肌肉」我们通过结缔组织包裹着的肌肉完成运动，机器人的「肌肉」则叫做促动器 actuator。如上图所示，橙色部分均为 Optimus 的促动器，这些促动器也都是特斯拉完全自研的。特斯拉为  Optimus 从力度大小的角度，设计了 6 种各自独特的促动器——这其实是很小的数字，业界平均是 20-30，甚至 50 种，目的是覆盖尽可能多的人类活动细节。为什么特斯拉的促动器种类这么少？原因还是 FSD 体系。特斯拉举了 28 种人类常见活动，比如抬举手臂、弯曲右膝等。通过分析这些活动反馈的云数据，找出各类运动的相对共同点，然后就可以尽量减少专门设计促动器的种类。虽然只是轻描淡写的一张 PPT，但我认为促动器从 50 种减少到 6 种，意义实际上远大于借鉴特斯拉电机经验的促动器本体——因为它代表着数据为王的新工业时代。不过促动器种类大幅度减少，也意味着 Optimus 前期的实际效果可能会没有那么「类人」，当然还是得等最终交付了。最后来说一个数字：2 万美元（约 14 万元）。这笔钱买不到半台 Model 3，但却是马斯克口中 Optimus 的目标售价。「它会彻底改变人类社会的效率，就像无人交通可以彻底改变运输效率」。二、DOJO 的终极形态？本来发布会的第二部分是 FSD，但那部分过于硬核，我决定先让大家看点激动人心的数字。去年 DOJO 惊艳全世界，但遗憾的是有太多细节未公布。D1 芯片是怎么组成 EXA POD 超算系统的？理论性能爆炸，能代表实际应用吗？这部分，特斯拉举了大量的数据，证明自己已经是计算领域的新巨头。首先是散热。先别发问号，超算平台的散热，一直是衡量超算制造者系统工程能力的重要维度。比如谷歌、华为、英伟达在公布自家方案的时候，都会花大篇幅讲散热。DOJO POD 的散热可以用两个词概括：高集成度、高自研率。特斯拉在 DOJO POD 上使用了全自研的 VRM（电压调节模组），单个 VRM 模组可以在不足 25 美分硬币面积的电路上，提供超过 1000A 的电流。高集成度带来的问题，是热膨胀系数 CTE。DOJO 堪称极限的体积集成率和发热，意味着 CTE 稍微失控，都会对系统结构造成巨大破坏（也就是会撑爆）。为此，这套自研 VRM 在过去两年内迭代了 14 个版本，最终才完全符合特斯拉对 CTE 指标的要求。目前 DOJO POD 已经进入负载测试阶段——单机柜 2.2MW 的负载，相当于 6 台 Model Y 双电机全力输出。解决了散热，才有资格说集成度。一个 DOJO POD 机柜由两层计算托盘和存储系统组成。每一层托盘都有 6 个 D1 Tile 计算「瓦片」——两层 12 片 组成的一个机柜，就可以提供 108PFLOPS 算力的深度学习性能。对了，DOJO POD 的供电模组也是 52V 电压的，Optimus 母亲实锤了。每层托盘都连接着超高速存储系统：640GB 运行内存可以提供超过 18TB 每秒的运算带宽，另外还有超过 1TB 每秒的网络交换。为了适配训练软件以及运营/维护，每个托盘还配备了专属的管理计算中心。最终，可以提供1.1E 算力、13TB 运存、1.3TB 缓存的 EXA POD，将于 2023 年 Q1，正式量产——这也是今天发布会唯一一个有确定日期的特斯拉产品。意大利炮有了，能不能轰下县城？特斯拉表示，配合专属的编译器，DOJO 的训练延迟，最低可以做到同等规模 GPU 的1/50！最终，特斯拉的目标是到 2023 年 Q1 量产时，DOJO 可以实现相比英伟达 A100，最高 4.4 倍的单芯片训练速度——甚至能耗和成本都更低。三、FSD 的新进化文章来到这里，大家的手指应该已经划了很多次屏幕。这也说明，看到这里依然兴致勃勃的你，一定是特斯拉老粉——那就聊点更「无聊」、更硬核的吧。篇幅有限，本届 AI Day 关于 FSD 的进展，我们只聊三个点：Occupancy Network、Training Optimization、Lanes。1. Occupancy Network先聊一个概念：矢量图。做设计的朋友一定很熟悉，这是一种精度（分辨率）可以做到无限，但占用存储空间很小的数字绘图。Occupancy Network，就是将 3D 向量数据绘制成矢量图的、 2019 年开始兴起的一种三维重建表达方法。有意思的是，特斯拉用了最 Occupancy Network 的方式，表达他们对 Occupancy Network 的应用：网格（方块）化的 3D 模拟。其实 FSD 眼中的世界并不是这样 Minecraft 化的，但 Occupancy Network 的本质特征，就是用「决策边界」描绘「物体边缘」。尽管 Occupancy Network 效率很高，但实际训练规模依然足够可观。目前特斯拉公布的数据是超过14.4 亿帧视频数据，需要超过10 万个 GPU 训练小时，实际视频缓存超过30PB——而且全程 90℃ 满负载。二、因此，Training Optimization 训练优化尤为重要。去年 Andrej 公布了特斯拉的千人 in-house 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