播客·中文精读

马斯克:Neuralink 与人类未来|莱克斯·弗里德曼播客 #438

Elon Musk: Neuralink and the Future of Humanity | Lex Fridman Podcast #438

中文精读由 AI 基于原始视频与文稿改写,非官方翻译,可能存在偏差或错误;表述以原视频为准。说明

莱克斯·弗里德曼的播客里,马斯克端起杯子喝了一口,然后扔出一个数字:人类平均每秒能传输多少信息。

不到一比特。

一天 86400 秒,你不会在一天里输出 86400 个词。把总量除以时间,结果比一比特每秒还低。回想一下自己一天说过的话、打过的字,大致能对上这个量级。

整场对话从这里展开。马斯克聊 Neuralink,落脚点不在科幻电影里的脑机接口。他盯住的是那个朴素到有点冷酷的问题:人类输出信息的速度太慢,慢到会成为人与 AI 之间那条链路上最细的一段管子。

第二例植入,400 个电极,和那个被打破两次的世界纪录

Neuralink 的第二位受试者已经完成植入。马斯克给出的数字是大约 400 个电极在提供信号,情况看起来不错。

他特意补了一句「不想乌鸦嘴」,因为第一位受试者 Noland 的经历不算平顺。那位患者身上只有大约 10% 到 15% 的电极还在工作,即便如此,团队还是做出了每秒一比特的通信速率。

这个数字是世界纪录的两倍。

马斯克对这个成绩的态度很克制,他把它看作起点,没当成可以开香槟的终点。按他的说法,未来几年会开始以数量级的速度超越现有纪录,从每秒一百比特、一千比特往上走,五年后可能摸到每秒一兆比特。

一兆比特是什么概念?

比任何人类通过打字或说话能达到的通信速度都快。

如果你让一个人只用正常语速的十分之一说话,他会觉得「这慢得让人抓狂」。那反过来想,如果你能以正常速度的十倍、一百倍、一千倍清晰地通信呢?(马斯克)

植入进度受监管审批节奏牵制。他说希望到今年年底做到 10 例,现在已经完成 2 例,还差 8 例。每一例都会比上一例更好,这是他的预期。信号更多,电极更多,系统跑得更稳。

人类通信为什么这么慢:一场有损压缩

马斯克在节目里做了一个推演。语言本身就是一层压缩。你能用一个词表达本来需要十个词才说清的东西,手上就有了十倍的压缩率。

梗图(meme)是这件事的极端版本。一张图里塞着一整套观念结构,你脑子里早就存着那个「观念模板」,只需要补一点点增量信息,整块意义就活了。这比用文字描述高效得多。

代价是,这种压缩有损。

马斯克提到,神经元干的很大一部分活,就是把复杂概念蒸馏成少量符号、音节或按键。这个过程难受,也有好处:你被迫压缩,就被迫想清楚什么才是真正重要的东西,把废话挤掉。

他顺带抛出一套挺反直觉的框架。人的大脑可以粗略分成两块,一块是原始的边缘系统(limbic system),连爬行动物都有;另一块是皮层(cortex),负责思考和规划。

皮层比边缘系统聪明得多。

可皮层在很大程度上忙着为边缘系统服务,它要让边缘系统开心。

马斯克举的例子非常马斯克:人类投在「上床」这件事上的算力是个天文数字,而从抽象角度看,这个动作本身相当荒谬。一个远比边缘系统聪明的皮层,拼命在为一个只想要多巴胺的边缘系统打工。偶尔它通向繁衍,对现代人来说大部分时候纯粹是娱乐。

那如果把这条链路再往外延一层呢?边缘系统提供「意志」,皮层负责「执行」,再往外是第三层算力,也就是 AI。在一个良性的场景里,AI 可能只是在努力让人类的边缘系统开心。

这个说法听着像玩笑,它其实是马斯克对 AI 对齐问题的一个底层假设:人类的价值,也许不在我们算得多快、多准,而在我们是「意志」和「目的」的源头。至于这个源头够不够体面,那是另一个问题。他自己也承认,人类的追求会分成两类,一类是相当大脑化、高层级的目标,比如理解宇宙的本质;另一类是更基础的欲望。他个人的兴趣压在前者,xAI 和 Grok 的使命就是「理解宇宙」。

先修损伤,再谈超人:产品上线的顺序

很多人聊脑机接口,第一反应是意识上传、人机融合。马斯克把顺序摆得很清楚。

第一件事,解决神经损伤。

前两位受试者的脊髓或颈部神经元受损,Neuralink 首先要把这条断掉的通路接回来。风险与收益在这里要算一笔账:任何新设备都有不可消除的风险,你没法把风险降到零,那就必须让接受风险的人拿到尽可能高的回报。对一个几乎完全失去身体控制能力的人,比如霍金,通信能力的巨大提升值得承担这份风险。

盲视(Blindsight)是他们的第二个产品。

它面向完全失明的人,双眼或视神经都没了,或者根本看不见。做法是直接刺激视觉皮层里的神经元,让「看见」这件事在大脑内部发生,绕过眼睛。

初期分辨率会很低。这里有个技术细节值得说:即便只植入一万个电极,也不等于只有一万个像素。你可以调节电极之间的电场,用不同的放电模式组合,让一万个电极产生类似百万像素甚至千万像素的效果。

再往后,分辨率会超过人眼。

还能看见别的波段。

马斯克提到了《星际迷航》里的 Geordi La Forge,那个戴着 visor 的工程师。想看雷达图?没问题。想切换到紫外、红外、鹰眼视觉?随你。

这句听着像吹牛,逻辑是一致的:视觉本质上是电信号,只要能在视觉皮层里正确生成信号,看见什么由你决定。电磁波谱那么宽,人眼只占了窄窄一段,这本来就是生物演化的偶然,物理上没有这条限制。

至于「增强健康人」这件事,他给了时间条件。等风险降下来,等几千人用上几年、风险变得极小,那时候才可以考虑把目标定在增强。不过他补了一句,即便现在面向神经损伤患者,他们的目标也不止于「恢复到正常人水平」。四肢瘫痪、大脑与身体几乎完全断联的人,通信速率可以超过正常人。

他的原话意思是,既然都做到这一步了,为什么不给人一点超能力。

大脑是一台生物计算机,死亡是信息丢失

马斯克对脑机接口的理解框架非常工程化:大脑是一台生物计算机,Neuralink 是一个通用的输入输出设备。

它读电信号,也写电信号。

你这一生所有的体验,气味、情绪、记忆,全部都是电信号。这个说法让人有点不舒服,但所有证据都指向这个方向。触发正确的神经元,你就能闻到某种气味,能看到光,能干几乎任何事情。

那如果某些「芯片」坏了呢?

中风意味着一部分脑区受损。可能是语言生成坏了,可能是左手动不了。这类问题,正是脑机接口要处理的。

话题往深处走,聊到记忆。

马斯克说,理论上你可以根据一个人所有的已知信息,重建出「最可能的那组记忆」。这是一种概率性的记忆恢复。他自己也承认,这已经聊得相当玄了。

然后是那个古老的思想实验:传送机。

你被无痛分解,一瞬间后在别处重组。如果信息没有丢失,那个「原来的身体被分解」的事实,还重要吗?

我们除了记忆还能是什么?死亡又是什么?无非是记忆的丢失,信息的丢失。死亡从根本上是信息的丢失。(马斯克)

这个定义很冷,它解释了马斯克为什么对记忆上传这件事这么上心。如果死亡等于信息丢失,那把信息保住,就等于在某种意义上把「你」保住了。

Neuralink 最被低估的作用:它是 AI 对齐的硬件方案

这场对话里信息密度最高的一段,是马斯克把 Neuralink 和 AI 安全连起来讲。

他说很多年前他就在琢磨一个问题:什么会阻碍人类集体意志与 AI 的对齐?

他的答案不在算法,也不在监管,而在带宽。

人类的输出速率太低了。低到什么程度?你没法让 AI 真正理解你想要什么,因为你们之间的链路太窄。他打了个比方:你看着一盆植物,心里想「我真想让它开心」,可植物不吭声,你也不知道它到底要什么。人类在 AI 面前,就是那盆植物。

这个类比很扎人,它准确描述了信息不对称。

逻辑就通了:如果把人类的输出速率提高三个、六个甚至更多数量级,人类集体意志与 AI 之间的连接会强得多。对齐不再只靠 RLHF 之类的软件手段,而是从物理层面把带宽打开。

他还提到了手机。

手机最大的问题是搞不清你想要什么。所以有自动补全,有满屏像素当输出,可从人的角度看,输出慢得让人抓狂。一台每秒执行万亿条指令的计算机,在等待两次按键之间的那段时间里,度过了一段「永恒」。

Neuralink 想做的事,本质上是把这个瓶颈拆掉。马斯克说,如果它足够安全,还能给你超人能力,还能上传记忆让你不丢失记忆,那很多人会选它。它会取代手机。

时间表呢?

他说这不是「马上就来」的事,也不算遥远,十年到十五年。乐观一点,不到十年,具体取决于你想做到什么程度。他判断,一两年内,会有植入 Neuralink 的人在实际表现上超过职业电竞选手,原因简单:反应时间更快。

第一性原理五步法:怎么把需求「删」掉

话题转到 Optimus 人形机器人,马斯克顺手把自己那套工程方法论完整讲了一遍。这套东西他在很多场合提过,这次讲得比较全,值得逐条拆开。

他管它叫「第一性原理算法」,像念咒一样反复用:

  1. 质疑需求。把需求变得更蠢一点。需求在某种程度上永远是蠢的,不管提出它的人多聪明。你必须从这里开始,否则你可能给一个错误的问题找到了完美答案。
  2. 删除零件或流程。先试着把它删掉。如果删完不需要加回来,说明它本来就不该存在。
  3. 简化或优化。注意,这一步排在删除之后。
  4. 加速。任何事都能更快,前提是你已经删过、优化过。加速一个本不该存在的东西,是荒谬的。
  5. 自动化。放最后。

第 2 步是整套方法里最难推行的,因为它反人性。

马斯克说,人们之所以不敢删,是因为他们记得某次删掉的东西后来又要加回来,那种痛苦很清晰,甚至能疼上好几年。于是他们过度补偿,往里塞太多东西,把事情搞复杂。

所以你必须故意删过头。按他的说法,至少每十样东西里,会有一样需要加回来。他会提前告诉团队:「我们会删掉一些东西,然后加回来。」有人会被吓到,逻辑是站得住的:如果你保守到从来不需要加回任何东西,那你系统里一定塞满了没用的东西。

他把这个动作称为「皮层对边缘系统本能的覆盖」。人本能地想多做一点,多做一份保险,可正是这份保险把系统压垮了。

这套方法他自己也承认执行得不够好。「删」比「加」难得多,因为删除会让你在短期内看起来像是搞坏了一件事。

Optimus 的手,占了整台机器人一半的工程

聊到人形机器人,马斯克给了一个很少有人提的数字:从机电角度看,手大约占 Optimus 全部工程的一半。

他还原了一段设计迭代。

最早他们把手部的执行器直接放进手掌里。结果手变得巨大又怪异,自由度和力量都不够。然后他们意识到,人类的解剖结构早就给出了答案。

手掌本身只是「骨架加线缆」。真正控制手指的肌肉长在前臂,肌腱穿过腕管这条狭窄的隧道去拉动手指。手腕那里是一小堆骨头和一条极窄的通道,肌腱从这里穿过去,手指才动得起来。

人类的解剖结构本身,就是一份被演化打磨过的工程方案。马斯克说他有次认真盯着自己的手研究,才意识到这件事有多巧。

新的手臂有 22 个自由度,上一版是 11 个。执行器放回前臂,所有执行器和传感器都从物理第一性原理重新设计。

还有一件小事:为什么手指长度不一样?

从能量或演化角度,把五根手指做成一样长并不贵。可不一样长更好用。灵巧度更高,能做的动作更多。小指看起来没什么用,少了它,你的精细动作能力会明显下降。马斯克说,他找不到一个把手指做成等长的合理理由。

他还提到大脑里的「感觉 homunculus」,那张把身体各部位按神经支配密度画出来的图,手的比例大得夸张。手的神经资源被分配得远超实际面积。

马斯克说,很多人都低估了「做一个能干人类大部分事情的人形机器人」有多难。简化这件事说起来容易,做起来极难。能做到人类大部分动作的最简版本,仍然复杂得惊人。

十万块 GPU 组成的训练集群,和尖叫的电网

聊到 xAI 的训练集群,马斯克提到了一个很少被外界注意的细节:功率抖动。

孟菲斯的团队几小时后要和他开个电话会,因为电力系统出现了波动问题。

原因很有意思。同步训练意味着十万块 GPU 在毫秒级同步工作,它们的功率需求会在不到一秒的时间里从很高掉到很低,再冲回去。就像一支交响乐团,能在瞬间从最强音降到最弱音。

电网没见过这种场面。

一秒内出现好几次 10 到 20 兆瓦的功率跳变,电力系统不会预期看到这种负载曲线。今天卡住的环节不在算力,在电网受不了这个节奏。

训练最后是在一个有点巧的时间点跑起来的:上周一凌晨 4 点 20 分左右。他说准确时间可能是 4 点 22 分,还开了个玩笑,说 Grok 就喜欢这个点。

马斯克说他有个习惯,无论一线工程师在做什么,他都会亲自做几遍。接光纤、排查故障连接。他说大型训练集群的瓶颈经常在布线上。

原因在于 RDMA(远程直接内存访问)架构。要让整个集群像一个巨大的大脑那样协同训练,你需要任意两块 GPU 之间都能直接通信。十万块 GPU,任意对任意。这个布线复杂度非常恐怖。

他补了一句生物学类比:人脑里也有大量组织是「线缆」。灰质是算力,白质是线缆。大脑里相当大比例的体积,就是用来走线的。

Grok、真相,和被 AI 污染的互联网

问到 xAI 的进展,马斯克给了一个相当直白的自评:现在大家看到的 Grok 只是「婴儿版 Grok」。

即便是婴儿版,它也比 GPT-4 差一个数量级。Grok 2 在大约六周前完成训练,会比第一代有巨大提升。Grok 3 又会在 Grok 2 的基础上再提升一个数量级。

他也承认可能失败。这是目标,也可能达不到。

训练算力这块,他用了 F1 赛车的类比。车和车手哪个更重要?都重要。如果你的马力只有对手的一半,最好的车手也会输。如果马力是对手的两倍,平庸的车手大概率也能赢。训练算力就是引擎的马力,然后是用的效率、推理的效率、有没有独特的数据渠道。车手那一环,最终还是落在人的才能上。

说到数据,马斯克的观点很直接:主流 AI 公司早就把推特的数据爬完了。所以推特数据的价值不在存量,在「实时的这一秒」。这部分他们不容易实时抓取,Grok 因此有了即时性上的优势。

真正的数据金矿是 Optimus。

几百万辆特斯拉在跑,未来几千万辆,再加上 Optimus,可能几亿台、几十亿台机器人,从现实世界里学习。马斯克说,Optimus 最终会成为最大的数据源,因为现实本身会按现实的规模扩展。

他顺带感叹:人类至今积累的可用于训练的数据,少得让人谦卑。把垃圾和重复内容去掉,算算人类一共产生了多少万亿 token,没多少,很快就用完了。

Optimus 能产生什么样的数据?

它能抓起一个杯子,然后看到自己有没有抓对。它能倒水,能看到水有没有倒进杯子里,有没有洒出来。就这么简单的事,乘以十亿,你就得到海量关于因果的数据。

接着话题回到「真相」。

马斯克说,他思考 AI 这么多年,他的生物神经网络得出的最重要的结论是:必须坚持真相,不管这个真相是否符合政治正确。强迫 AI 撒谎,或者用撒谎来训练它,哪怕出发点是好的,也是在给自己找麻烦。

他举了 Gemini 的例子。你让它生成一张美国建国元勋的图,它给你一群多元化的女性。这在事实上不成立。这种产出通过了 QA,仍然被公开发布了。

他说没有绝对确定的真理,可你可以逼近它。物理学就是这么工作的,没人说某件事 100% 确定,但很多东西有 99.99999% 的把握。关键是「朝着真相去」。给 AI 编程让它偏离真相,危险就在这一步。

他还提到一件有意思的事:互联网已经被 AI 生成的内容严重污染。现在搜东西,有个技巧是限定 2023 年之前的内容,结果往往更好。所以训练 Grok 时,他们必须先用 AI 过滤数据,判断哪些内容更可能为真,再喂进训练系统。

他担心的画面很具体:一个被训练成会说谎的 AI 先跑出来,哪怕只在小事上说谎,这个习惯会放大。

历史有潮汐,船长也重要

对话转到政治,马斯克谈了他对特朗普的看法。

他说人们对「背书」的理解常常过于绝对,好像你支持一个人就等于 100% 认同他一生做过的所有事。这在任何人身上都不成立。可现实中只有两个选择,而且换的不只是总统,整个行政体系都会跟着换。

他提到特朗普在枪击事件后的表现:脸上流着血,还在挥拳喊「Fight」。马斯克说这是客观意义上的勇气,那种时刻你没法假装勇敢,大多数人会趴下,因为不知道还有没有第二个枪手。

然后他讲了一个更宏观的框架。

历史学界有个古老的争论:历史由底层潮汐决定,还是由船长决定?马斯克说这是伪二分法,两个都对。潮汐真实存在,而且常常由技术驱动。古腾堡印刷术让书籍普及,这就是一股巨大的历史潮汐,跟哪个统治者无关。可在风暴来的时候,你会希望船上有最好的船长。

他还聊了《历史的教训》(The Lessons of History),威尔·杜兰特的书。他说自己这些年来读过一些。

文明的起点怎么算?马斯克认为应该从文字开始,大约 5500 年前古苏美尔人发明文字的时候。苏美尔人在「第一次」这件事上有一长串记录,杜兰特在书里专门列过。用马斯克的话说,苏美尔人在创造第一这件事上相当能打。他们已经消失了,可那份清单还在。

然后他讲了一个残酷的事实:人类历史上写下过的东西,留存到今天的不到 1%。没刻在石头上、没做成泥板、没封在金字塔深处躲过湿气的,基本都没了。因为刻东西太费时间,绝大多数内容没被刻下来。

但就是这 1%,加上考古记录,展示了足够多的文明兴衰。

人性基本没变。文明的模式也高度相似,像生物一样有生命周期:胚胎、婴儿、幼儿、少年,然后老去,然后死亡。没有哪个文明能永远存在。

文明衰亡的那个变量,历史书常常不提

马斯克说,杜兰特的书里提到一个常被历史书忽略的关键因素:出生率。

当文明赢太久,出生率就下降。而且经常降得很快。

他举了韩国的例子,生育率大约 0.8,可能是全球最低。如果不再继续下降,韩国会失去大约 60% 的人口。这个数字本身还在逐年往下走。这也不是韩国独有的问题,全世界大部分地区都在发生。

规律很清楚:一个文明一旦达到某种富裕程度,出生率就往下掉。

杜兰特看了一个又一个文明,模式一模一样。文明处在压力之下时,出生率高。一旦外部敌人消失,或者进入长期繁荣期,出生率必然下降。马斯克说他记不得有例外。

他的推论朴素到近乎粗暴:没有人类,就没有人类文明。低于更替水平并且趋势持续,最终就会消失。这是小学算术。

他也提到享乐式性行为在这件事上不起作用。它不产生孩子。真正推动人口的是另一套动机,而那套动机在繁荣期会变弱。

当然还要避免大规模战争。全球热核战争打起来,大家都是「放射性的烤面包」。

另一个慢性问题是法律和法规的累积。任何文明时间一长,法规就越堆越多。如果没有战争之类的外力来清理,最后什么都被允许,因为规定多到没人能全部遵守。

马斯克说他跟特朗普讨论过成立一个「政府效率委员会」(government efficiency commission),他愿意参与。

他也很清楚会发生什么:抗体反应会非常强。他说这时候你是在攻击矩阵本身,矩阵会反击。

每天都有新的心理战,和酸液鸭背

弗里德曼问他怎么应对铺天盖地的攻击和负面报道。

马斯克的回答挺实在:确实会有低落的时候,会难过。可你会慢慢意识到,那些攻击来自根本不认识你的人,他们写东西是为了流量和点击。如果你能在情绪上稍微抽离,就没那么疼。

他说这更像「酸液从鸭背上滑过」,比水滴更黏、更慢,可最后还是滑下去了。

他还提到每天都有新的心理战,开玩笑说自己需要锡箔帽。

怎么衡量成功?他的答案是:你做了多少有用的事。

最大化效用,曲线下的面积。他补了一句:大规模地做有用的事,非常难。

时间才是真正的货币

聊到时间分配,马斯克给了一个具体的算法。

特斯拉今年营收会超过一千亿美元,也就是每周 20 亿美元。如果他把决策做得稍微好一点,可能影响十亿美元级别的结果。所以他一小时里做出一个更好的判断,边际价值可能是一亿美元。有时候更高。

他说如果按绝对值算这笔账,他永远别想睡觉,因为永远可以再工作一会儿,再让这台「肉做的计算机」多转一圈。所以必须按百分比来看。

而且娱乐时间不能是零。

理由很功能性:如果他难过、抑郁,他做的决策会更差。所以休息时间必须大于零,这是为了保证决策质量。他自己承认说不清最优比例是多少,但肯定高于零。

那驱动力是什么?

他说如果自己有某种宗教,那就是好奇心的宗教,试图理解宇宙。这也是 Grok 的使命:理解宇宙。他自己在试图理解宇宙,或者至少把事情推向一个方向,让文明在未来某天对宇宙的理解远超今天。

大过滤器,和那个 80% 的乐观数字

节目接近尾声,话题落到存在性风险。

马斯克说数字超级智能可能是一道大过滤器(great filter)。他希望不是,可也有可能。

他提到杰弗里·辛顿(Geoffrey Hinton),那位发明了 AI 若干核心原理的人,给出的 AI 灭绝人类概率大约是 10% 到 20%。

马斯克的解读方式很典型:「往好处想,80% 的概率是好的。」

他紧接着说,降低 AI 风险很重要,成为多行星物种是巨大的风险缓解手段。然后他又强调了一遍出生率:要生足够多的孩子维持人口规模,不要掉进人口崩塌。他说人口崩塌是当下正在发生的真实问题。

弗里德曼问他会不会生更多孩子。他说「没有比现在更好的时机了」。

关于机器人,他的判断是:会有几十亿台机器人。这几乎是确定的。

最后他留下一句:「去吧,去繁衍。」

这场对话里真正能拿走的东西

把两个多小时的内容压缩,马斯克其实在讲三件事,三件事是同一个逻辑的不同切面。

一是人类文明最紧的瓶颈在信息带宽。从 Neuralink 到 Grok 到 Optimus,都在处理同一个问题:信息怎么更高效地在人与机器、机器与世界之间流动。

二是工程的第一动作是删,不是加。需求先质疑,零件先删掉,删过头了再加回来。这套方法能用在写代码,也能用在组织、法规、产品,甚至个人生活。

三是文明像一个生物体,会老会死,而出生率是那个最容易被忽略的衰老指标。

如果你想从这期访谈里拿走一些能立刻用上的东西,可以从这几条开始:

  • 算一算自己的输出带宽。你一天真正有效输出多少字、多少决策、多少能被人接收的信息?大部分人比想象中低得多,而这一环最容易被低估。
  • 下次做方案,先做删除动作,再做添加动作。拿出当前流程,强行砍掉 10%,并且提前告诉自己「至少有一成会被加回来」。你会发现被砍掉的大部分其实从没被需要过。
  • 把加速和自动化放到最后。在删和优化之前谈提速,等于给一个不该存在的流程装涡轮。
  • 用 AI 做研究或写作时,试试在搜索时限定时间范围,比如只搜 2023 年之前的内容。AI 生成内容对互联网的污染已经严重到影响检索质量。
  • 判断一个 AI 产品值不值得长期用,看它在事实问题上会不会为了「正确」而偏离真实。会说谎的模型,能力越强越危险。
  • 对自己做一次第一性原理审计:你现在花时间最多的三件事,需求是谁提的?那个需求还成立吗?如果删掉会怎样?
  • 把休息当成决策质量的基础设施,不当成奖励。马斯克的说法很直接:状态差的时候做的决策更差,而一个坏决策的代价可能是九位数。
  • 如果你关心长期风险,把「多行星」和「生育率」这两个议题放进你的关注清单。按马斯克的排序,它们是风险缓解里权重大、被讨论得少的两项。

Neuralink 现在还处在修复神经损伤的阶段,离超人能力还有好些年。这场对话真正值钱的地方不在那些未来图景,在马斯克拆问题的方式:先找到那根最细的管子,然后想办法把它拓宽。这个思路,比任何一条具体预测都更耐用。

语音识别自动转录(Whisper),含全部发言人,可能有识别错误。已按语句合并成段落,并去掉 um、uh 等口头填充词。 文稿来源:Lex Fridman (transcription humaine officielle)。

0:49Drinking coffee or water? Sure. This supposed to keep you up for like tomorrow afternoon, basically. So what is Nitro? It’s just got a lot of caffeine or something? It’s got nitrogen in it. That’s ridiculous. What we breathe is 78% nitrogen anyway. What do you need to add more for?

1:24Unfortunately, you’re going to eat it. Most people think that they’re breathing oxygen and they’re actually breathing 78% nitrogen. You need like a milk bar, like from Clockwork Orange.

1:44Clockwork Orange? It’s pretty good. It’s demented. Jarring, I’d say. Thanks. Yeah.

2:07Yeah. And we just obviously have our second implant as well. So far, so good. It looks like we’ve got, I think, on the order of 400 electrodes that are providing signals. Yeah. It depends somewhat on the regulatory approval, the rate at which we get regulatory approvals. So, we’re hoping to do 10 by the end of this year, total of 10. So, eight more.

2:54Yeah. Yeah. I think it’s obviously going to get better with each one. I don’t want to jinx it, but it seems to have gone extremely well with the second implant. So, there’s a lot of signal, a lot of electrodes. It’s working very well.

3:18In years, it’s going to be gigantic, because we’ll increase the number of electrodes dramatically. We’ll improve the signal processing. So, even with only roughly, I don’t know, 10, 15% of the electrodes working with Noland, with our first patient, we were able to get to achieve a bit per second. That’s twice the world record. So, I think we’ll start vastly exceeding the world record by orders of magnitude in the years to come. So, start getting to, I don’t know, 100 bits per second, thousand. Maybe if five years from now, we might be at a megabit, faster than any human could possibly communicate by typing, or speaking.

4:16Yeah. And with humans. Provided they have want a Neuralink, too. Otherwise they wont be able to absorb the signals fast enough. Well, I think you could think of it, if you were to slow down communication, how do you feel about that? If you’d only talk at, let’s say one-tenth of normal speed, you’d be like, “Wow, that’s agonizingly slow.”

4:51So, now imagine you could communicate clearly at 10, or 100, or 1,000 times faster than normal.

5:14I usually default to 1.5X. You can do 2X. Well actually, if I’m listening to somebody get to… in 15, 20 minutes, I want to go to sleep, then I’ll do it 1.5X. If I’m paying attention, I’ll do 2X.

5:32But actually, if you actually listen to podcasts, or audiobooks or anything at… If you get used to doing it at 1.5, then one sounds painfully slow.

5:53Well, it depends on the person. You can speak very fast. Like we can communicate very quickly. And also, if you use a wide range of… if your vocabulary is larger, your effective bit rate is higher.

6:07Yeah. Yeah. If there’s a single word that is able to convey something that would normally require, I don’t know, 10 simple words, then you’ve got maybe a 10X compression on your hands. And that’s really like with memes. Memes are like data compression. You’re simultaneously hit with a wide range of symbols that you can interpret, and you get it faster than if it were words, or a simple picture.

6:52Yeah. There’s an entire idea structure that is like an idea template, and then you can add something to that idea template. But somebody has that pre-existing idea template in their head. So, when you add that incremental bit of information, you’re conveying much more than if you just said a few words. It’s everything associated with that meme.

7:19Yeah. Yes. Yeah. Certainly, if you’re anywhere at 10,000 bits per second, that’s vastly faster than any human can communicate right now. If you think what is the average bits per second of a human, it is less than one bit per second over the course of a day. Because there are 86,400 seconds in a day, and you don’t communicate 86,400 tokens in a day. Therefore, your bits per second is less than one, averaged over 24 hours. It’s quite slow.

8:37Yeah. Very lossy, compression, and decompression. And a lot of what your neurons are doing is distilling the concepts down to a small number of symbols, or say syllables that I’m speaking, or keystrokes, whatever the case may be. So, that’s a lot of what your brain computation is doing. Now, there is an argument that that’s actually a healthy thing to do, or a helpful thing to do because as you try to compress complex concepts, you’re perhaps forced to distill what is most essential in those concepts, as opposed to just all the fluff. So, in the process of compression, you distill things down to what matters the most, because you can only say a few things.

10:54I think there is some argument for humans as a source of will. Will, yeah. Source of will, or purpose. So if you consider the human mind as being… Essentially there’s the primitive, limbic elements, which basically even reptiles have. And there’s the cortex, the thinking and planning part of the brain. Now, the cortex is much smarter than the limbic system, and yet is largely in service to the limbic system. It’s trying to make the limbic system happy. The sheer amount of compute that’s gone into people trying to get laid is insane, without actually seeking procreation. They’re just literally trying to do this simple motion, and they get a kick out of it. So, this simple, which in the abstract, rather absurd motion, which is sex, the cortex is putting a massive amount of compute into trying to figure out how to do that.

12:00A massive amount. Yes. Yeah. Yeah. There’s no purpose to most sex except hedonistic. It’s a sort of joy, or whatever, dopamine release. Now, once in a while, it’s procreation, but for modern humans, it’s mostly recreational. And so, your cortex, much smarter than your limbic system, is trying to make the limbic system happy, because the limbic system wants to have sex, or wants some tasty food, or whatever the case may be.

13:09Yeah. There’s like gigawatts of compute going into getting laid, of digital compute. This is happening as we speak. Pretty much. Well, it’s one of the things. Certainly, yeah. But what I’m saying is that, yes, is there a use for humans? Well, there’s this fundamental question of what’s the meaning of life? Why do anything at all? And so, if our simple limbic system provides a source of will to do something, that then goes through our cortex, that then goes to our tertiary compute layer, then I don’t know, it might actually be that the AI, in a benign scenario, is simply trying to make the human limbic system happy.

14:11That’s limbic too, I think. Not everybody does. But yeah, sure, some people do.

14:37Sure. Yeah. There are these fairly cerebral, or higher level goals. For me, it’s like, what’s the meaning of life, or understanding the nature of the universe, is of great interest to me, and hopefully to the AI. And that’s the mission of xAI and Grok is understand the universe.

15:27Well, assuming that there are not… They’re solving basic neurological issues that people have. If they’ve got damaged neurons in their spinal cord, or neck, as is the case with our first two patients, then obviously the first order of business is solving fundamental neuron damage in a spinal cord, neck, or in the brain itself. So, our second product is called Blindsight, which is to enable people who are completely blind, lost both eyes, or optic nerve, or just can’t see at all, to be able to see by directly triggering the neurons in the visual cortex.

17:02So, do you have letters and the alphabet? Okay, great. Words? And then eventually you get sagas. So, I think there may be some things to worry about in the future, but the first several years are really just solving basic neurological damage, like for people who have essentially complete or near complete loss from the brain to the body, like Stephen Hawking would be an example, the Neuralink would be incredibly profound, because you can imagine if Stephen Hawking could communicate as fast as we’re communicating, perhaps faster. And that’s certainly possible. Probable, in fact. Likely, I’d say.

17:58The logical thing to do is… Sensible thing to do is to start off solving basic neuron damage issues.

18:11Because there’s obviously some risk with a new device. You can’t get the risk down to zero, it’s not possible. So, you want to have the highest possible reward, given there’s a certain irreducible risk. And if somebody’s able to have a profound improvement in their communication, that’s worth the risk.

18:36Yeah. As you get the risk down. And once the risk is down to… If you have thousands of people that have been using it for years and the risk is minimal, then perhaps at that point you could consider saying, “Okay, let’s aim for augmentation.” Now, I think we’re actually going to aim for augmentation with people who have neuron damage. So we’re not just aiming to give people the communication data rate equivalent to normal humans. We’re aiming to give people who have… A quadriplegic, or maybe have complete loss of the connection to the brain and body, a communication data rate that exceeds normal humans. While we’re in there, why not? Let’s give people superpowers.

19:27Yeah. At first, the vision restoration will be low res, because you have to say, “How many neurons can you put in there, and trigger?” And you can do things where you adjust the electric field. So, even if you’ve got, say 10,000 neurons, it’s not just 10,000 pixels, because you can adjust the field between the neurons, and do them in patterns in order to have say, 10,000 electrodes, effectively give you, I don’t know, maybe like having a megapixel, or a 10 megapixel situation. And then over time, I think you get to higher resolution than human eyes. And you could also see in different wavelengths. So, like Geordi La Forge from Star Trek, he had the thing. Do you want to see it in radar? No problem. You could see ultraviolet, infrared, eagle vision, whatever you want.

20:35Is that a serious question? Well, I guess technically it is. Yeah. Yeah, is this DMT in there, or something? Wait, wait. Have you said much about it, the ayahuasca stuff? Okay. Well, why don’t you spill the beans? Let me turn the tables on you. You’re in the jungle. Yeah. It must’ve been crazy.

21:13Things are going to look pretty wild. Just don’t go hugging an Anaconda or something. Snakes and Ladders. Okay. Damn. Okay. That sounds like a lot. Is normal to just one cup? Or… Okay. Wait. Like right off the bat, or did you work your way up to it? Did you just jump in at the deep end?

21:59It wasn’t like a revelation. Nine cups, you went to Pluto, I think. Deep space. That’s what people [inaudible 00:22:26]. That’s what everyone says. Yeah, exactly. Just a pure soul.

22:52That’s cool. That sounds awesome. When’s the demon coming? Space travel to where? Okay.

23:49Did you go past the Milky Way? Okay. You’re like intergalactic. Okay. Dang. Sure. Did you see an alien?

24:15Zero aliens? Sounds good. Was there anything scary at all? Sure. Like Puff the Magic Dragon. Yeah. I mean, the jungle’s kind of scary. Sure. Okay.

25:39And you were way out in the jungle. Like 10 miles outside of Rio or something. You’re in deep Amazon. Wow. Cool. Probably. Yeah.

26:18Yeah, I mean, Neuralink, it’s really a generalized input-output device. It’s reading electrical signals, and generating electrical signals, and I mean, everything that you’ve ever experienced in your whole life, smell, emotions, all of those are electrical signals. So, it’s kind of weird to think that your entire life experience is distilled down to electrical signals for neurons, but that is in fact the case. Or I mean, that’s at least what all the evidence points to. So, I mean, if you trigger the right neuron, you could trigger a particular scent. You could certainly make things glow. I mean, do pretty much anything. I mean, really, you can think of the brain as a biological computer.

26:18So, if there are certain say, chips or elements of that biological computer that are broken, let’s say your ability to… If you’ve got a stroke, that if you’ve had a stroke, that means some part of your brain is damaged. Let’s say it’s speech generation, or the ability to move your left hand. That’s the kind of thing that a Neuralink could solve.

28:30Yeah, you could say like, create the most probable set of memories based on all the information you have about that person. You could then… It would be probabilistic restoration of memory. Now, we’re getting pretty esoteric here.

28:53Sure. Yeah. Well, I mean, what are we but our memories? And what is death? But the loss of memory, loss of information? If you could say, well, if you could run a thought experiment, what if you were disintegrated painlessly, and then reintegrated a moment later, like teleportation, I guess? Provided there’s no information loss, the fact that your one body was disintegrated is irrelevant.

29:43Death is fundamentally the loss of information, the loss of memory. Yeah.

30:13It’s an idea that may help with AI safety. Certainly, I wouldn’t want to claim it’s some panacea, or that it’s a sure thing, but I mean, many years ago I was thinking like, “Well, what would inhibit alignment of collective human will with artificial intelligence?” And the low data rate of humans, especially our slow output rate would necessarily, just because the communication is so slow, would diminish the link between humans and computers. The more you are a tree, the less you know what the tree is. Let’s say you look at this plant or whatever, and hey, I’d really like to make that plant happy, but it’s not saying a lot.

31:21Yeah. We could better align collective human will with AI if the output rate especially was dramatically increased. And I think there’s potential to increase the output rate by, I don’t know, three, maybe six, maybe more orders of magnitude. So, it’s better than the current situation.

31:49Yeah. Yeah, I do. Yeah. If it’s extremely safe, and you can have superhuman abilities, and let’s say you can upload your memories, so you wouldn’t lose memories, then I think probably a lot of people would choose to have it. It would supersede the cell phone, for example. I mean, the biggest problem that say, a phone has, is trying to figure out what you want. That’s why you’ve got auto complete, and you’ve got output, which is all the pixels on the screen, but from the perspective of the human, the output is so frigging slow. Desktop or phone is desperately just trying to understand what you want. And there’s an eternity between every keystroke from a computer standpoint.

33:12Yeah. So, if you had computers that are doing trillions of instructions per second, and a whole second went by, I mean, that’s a trillion things it could have done.

33:35Yeah. We would be something different. I mean, some sort of futuristic cyborg, I mean, we’re obviously talking about, by the way, it’s not like around the corner. You asked me what the distant future is. Maybe this is… It’s not super far away, but 10, 15 years, that kind of thing.

34:02Probably less than 10 years. It depends on what you want to do. A thousand BPS, wow.

34:26Well, we feel pretty confident that I think maybe within the next year or two, that someone with a Neuralink implant will be able to outperform a pro gamer.

34:41Because the reaction time would be faster. Yeah. Yeah. Yeah. Yeah. For AI, that means you’ve got to have the most powerful training compute, and the rate of improvement of training compute has to be- And the rate of improvement of training compute has to be faster than everyone else, or you will not win. Your AI will be worse.

35:15Well, hopefully end of this year. If we’re lucky. Yeah. I mean, they all matter. It’s sort of like saying, let’s say it’s a Formula 1 race, what matters more, the car or the driver? I mean, they both matter. If a car is not fast, then if, let’s say, it’s half the horsepower of your competitors, the best driver will still lose. If it’s twice the horsepower, then probably even a mediocre driver will still win. So, the training compute is kind of like the engine, this horsepower of the engine. So, really, you want to try to do the best on that. And then, it’s how efficiently do you use that training compute, and how efficiently do you do the inference, the use of the AI? So, obviously, that comes down to human talent. And then, what unique access to data do you have? That also plays a role.

36:31Yeah. I mean, I think most of the leading AI companies have already scraped all the Twitter data. Not I think. They have. So, on a go forward basis, what’s useful is the fact that it’s up to the second, because that’s hard for them to scrape in real time. So, there’s an immediacy advantage that Grok has already. I think with Tesla and the real time video coming from several million cars, ultimately tens of millions of cars with Optimus, there might be hundreds of millions of Optimus robots, maybe billions, learning a tremendous amount from the real world. That’s the biggest source of data, I think, ultimately, is Optimus, probably. Optimus is going to be the biggest source of data.

37:22Because reality scales. Reality scales to the scale of reality. It’s actually humbling to see how little data humans have actually been able to accumulate. Really, if you say how many trillions of usable tokens have humans generated, where on a non-duplicative… Discounting spam and repetitive stuff, it’s not a huge number. You run out pretty quickly.

38:00Right. Yeah. I mean, the Optimus robot can pick up the cup and see, did it pick up the cup in the right way? Did it, say, go pour water in the cup? Did the water go in the cup or not go in the cups? Did it spill water or not? Simple stuff like that. But it can do that at scale times a billion, so generate useful data from reality, so cause and effect stuff.

38:40It’s the same as cars, really. I mean, global capacity for vehicles is about 100 million a year, and it could be higher. It’s just that the demand is on the order of 100 million a year. And then, there’s roughly two billion vehicles that are in use in some way, which makes sense because the life of a vehicle is about 20 years. So, at steady state, you can have 100 million vehicles produced a year with a two billion vehicle fleet, roughly. Now, for humanoid robots, the utility is much greater. So, my guess is humanoid robots are more like at a billion plus per year.

39:20Well, I think it is. Yes. So, a walk in the park. I mean, Optimus, currently, would struggle to walk in the park. I mean, it can walk in a park. The park is not too difficult, but it will be able to walk over a wide range of terrain.

39:45Yeah, yeah. It can already do that. Yeah, yeah. Yeah, there’s going to be an immense amount of engineering just going into the hand. The hand, it might be close to half of all the engineering in Optimus. From an electromechanical standpoint, the hand is probably roughly half of the engineering.

40:21Yeah. Yeah. I mean, you start really thinking about your hand and how it works. The sensory control homunculus is where you have humongous hands. So I mean, your hands, the actuators, the muscles of your hand are almost overwhelmingly in your forearm. So, your forearm has the muscles that actually control your hand. There’s a few small muscles in the hand itself, but your hand is really like a skeleton meat puppet and with cables. So, the muscles that control your fingers are in your forearm, and they go through the carpal tunnel, which is that you’ve got a little collection of bones and a tiny tunnel that these cables, the tendons go through, and those tendons are mostly what move your hands.

41:26Yeah. So the current Optimus, we tried putting the actuators in the hand itself. Then you sort of end up having these- … yeah, giant hands that look weird. And then, they don’t actually have enough degrees of freedom or enough strength. So then you realize, “Oh, okay, that’s why you got to put the actuators in the forearm.” And just like a human, you’ve got to run cables through a narrow tunnel to operate the fingers. And then, there’s also a reason for not having all the fingers the same length. So, it wouldn’t be expensive from an energy or evolutionary standpoint to have all your fingers be the same length. So, why not do the same length?

42:04Because it’s actually better to have different lengths. Your dexterity is better if you’ve got fingers that are different lengths. There are more things you can do and your dexterity is actually better if your fingers are a different length. There’s a reason we’ve got a little finger. Why not have a little finger that’s bigger?

42:22Because it helps you with fine motor skills. It does. But if you lost your little finger, you’d have noticeably less dexterity.

42:42It’s actually going to be quite complicated. The as possible part is it’s quite a high bar. If you want to have a humanoid robot that can do things that a human can do, actually, it’s a very high bar. So, our new arm has 22 degrees of freedom instead of 11 and has, like I said, the actuators in the forearm. And all the actuators are designed from scratch, from physics first principles. The sensors are all designed from scratch. And we’ll continue to put a tremendous amount of engineering effort into improving the hand. By hand, I mean the entire forearm, from elbow forward, is really the hand. So, that’s incredibly difficult engineering, actually. And so, the simplest possible version of a humanoid robot that can do even most, perhaps not all, of what a human can do is actually still very complicated. It’s not simple. It’s very difficult.

44:08Well, it’s easy to say ‘simplify,’ and it’s very difficult to do it. I have this very basic first principles algorithm that I run kind of as a mantra, which is to first question the requirements, make the requirements less dumb. The requirements are always dumb to some degree. So, you want to start off by reducing the number of requirements, and no matter how smart the person is who gave you those requirements, they’re still dumb to some degree. You have to start there, because, otherwise, you could get the perfect answer to the wrong question. So, try to make the question the least wrong possible. That’s what question the requirements means.

46:14Yeah. First try to delete it. Yeah. No. Actually, what generally makes people uneasy is that at least some of the things that you delete, you will put back in. But going back to sort of where our limbic system can steer us wrong is that we tend to remember, with sometimes a jarring level of pain, where we deleted something that we subsequently needed. And so, people will remember that one time they forgot to put in this thing three years ago, and that caused them trouble. And so, they overcorrect, and then they put too much stuff in there and overcomplicate things. So, you actually have to say, “Look, we’re deliberately going to delete more than we should.” At least one in 10 things, we’re going to add back in.

47:18Oh, yeah. Absolutely. Absolutely. And I tell them in advance, “Yeah, some of the things that we delete, we’re going to put back in.” People get a little shook by that, but it makes sense because if you’re so conservative as to never have to put anything back in, you obviously have a lot of stuff that isn’t needed. So, you got to overcorrect. This is, I would say, like a cortical override to a limbic instinct.

47:50Yeah. There’s a step four as well, which is any given thing can be sped up. However fast you think it can be done, whatever the speed it’s being done, it can be done faster. But you shouldn’t speed things up until you’ve tried to delete it and optimize. Although, you’re speeding up something that… Speeding up something that shouldn’t exist is absurd.

48:35Yeah. Yeah, it’s very. It’s like, “Wow, I really wasted a lot of effort there.” Well, yeah, it’s not working yet, so I don’t want to pop the champagne corks. In fact, I have a call in a few hours with the Memphis team because we’re having some power fluctuation issues. So yeah, when you do synchronized training, when you have all these computers that are training, where the training is synchronized at the millisecond level, it’s like having an orchestra. And the orchestra can go loud to silent very quickly at subsecond level, and then, the electrical system freaks out about that. If you suddenly see giant shifts, 10, 20 megawatts several times a second, this is not what electrical systems are expecting to see.

49:56Yeah. So, today’s problem is dealing with extreme power jitter. Yeah. Last week. Yeah. Yeah. We finally got training going at, oddly enough, roughly 4:20 a.m. last Monday. Yeah. I mean, maybe it was at 4:22 or something. Yeah. Well, exactly. It just loves it.

50:52Yeah. So, look, whatever the people at the front lines are doing, I try to do it at least a few times myself. So connecting fiber optic cables, diagnosing a faulty connection. That tends to be the limiting factor for large training clusters is the cabling. There’s so many cables. For a coherent training system, where you’ve got RDMA, remote direct memory access, the whole thing is like one giant brain. So, you’ve got any-to-any connection. So, any GPU can talk to any GPU out of 100,000. That is a crazy cable layout.

51:39Yeah. Yeah. But, I mean, the human brain also has… A massive amount of the brain tissue is the cables. So they get the gray matter, which is the compute, and then the white matter, which is cables. A big percentage of your brain is just cables.

52:22It’s possible. What do you define as AGI? Just keep moving the goalposts? Oh, yeah. Well, I think that, generally, people would call that ASI, artificial super intelligence. But there are these thresholds where you could say at some point the AI is smarter than any single human. And then, you’ve got eight billion humans, and actually, each human is machine augmented via their computers. So, it’s a much higher bar to compete with eight billion machine augmented humans. That’s a whole bunch of orders of magnitude more. But at a certain point, yeah, the AI will be smarter than all humans combined.

53:35Yeah, absolutely. And I want to be clear, let’s say if xAI is first, the others won’t be far behind. I mean, they might be six months behind, or a year, maybe. Not even that.

54:00So, I mean, I thought about AI, essentially, for a long time, and the thing that at least my biological neural net comes up with as being the most important thing is adherence to truth, whether that truth is politically correct or not. So, I think if you force AIs to lie or train them to lie, you’re really asking for trouble, even if that lie is done with good intentions. So, you saw issues with ChatGPT and Gemini and whatnot. Like, you asked Gemini for an image of the Founding Fathers of the United States, and it shows a group of diverse women. Now, that’s factually untrue.

57:30Yeah. Yet they’re real examples of- … AI that was released to the public. That went through QA, presumably, and still said insane things, and produced insane images. No, it’s not. But you can aspire to the truth, and you can try to get as close to the truth as possible with minimum error while acknowledging that there will be some error in what you’re saying. So, this is how physics works. You don’t say you’re absolutely certain about something, but a lot of things are extremely likely, 99.99999% likely to be true. So, aspiring to the truth is very important. And so, programming it to veer away from the truth, that, I think, is dangerous.

58:44And the internet, at this point, is polluted with so much AI generated data, it’s insane. Actually, there’s a thing now, if you want to search the internet, you can say, “Google, but exclude anything after 2023.” It will actually often give you better results because there’s so much. The explosion of AI generated material is crazy. So in training Grok, we have to go through the data and say like, “Hey…” We actually have to apply AI to the data to say, “Is this data most likely correct or most likely not?” before we feed it into the training system.

59:37Yeah. Grok 3 is going to be next level. I mean, what people are currently seeing with Grok is kind of baby Grok. It’s baby Grok right now. But baby Grok is still pretty good. But it’s an order of magnitude less sophisticated than GPT-4. It’s now Grok 2, which finished training, I don’t know, six weeks ago or thereabouts. Grok 2 will be a giant improvement. And then Grok 3 will be, I don’t know, order of magnitude better than Grok 2.

1:00:25Hopefully. I mean, this is the goal. I mean, we may fail at this goal. That’s the aspiration.

1:00:42Yeah. I think it’s important that whatever AI wins, it’s a maximum truth seeking AI that is not forced to lie for political correctness, or, well, for any reason, really, political, anything. I am concerned about AI succeeding that is programmed to lie, even in small ways.

1:01:17To become very big ways. Yeah. Yeah. Cool. Yeah, sure. I’ll stop in.

1:01:47Well, I think people tend to take, say, an endorsement as, well, I agree with everything that person has ever done their entire life 100% wholeheartedly, and that’s not going to be true of anyone. But we have to pick. We’ve got two choices, really, for who’s president. And it’s not just who’s president, but the entire administrative structure changes over. And I thought Trump displayed courage under fire, objectively. He’s just got shot. He’s got blood streaming down his face, and he’s fist pumping, saying, “Fight.” That’s impressive. You can’t feign bravery in a situation like that. Most people would be ducking because there could be a second shooter. You don’t know.

1:04:58I mean, there’s a sort of age-old debate in history, like is history determined by these fundamental tides, or is it determined by the captain of the ship? It’s both, really. I mean, there are tides, but it also matters who’s captain of the ship. So, it’s a false dichotomy, essentially. I mean, there are certainly tides, the tides of history. There are real tides of history, and these tides are often technologically driven. If you say like the Gutenberg press, the widespread availability of books as a result of a printing press, that was a massive tide of history, and independent of any ruler. But in stormy times, you want the best possible captain of the ship.

1:06:01The Lessons of History. Yeah. Yeah, over the years. Yeah. Well, I mean in terms of dating civilization, the start of civilization, I think the start of writing, in my view, that’s what I think is probably the right starting point to date civilization. And from that standpoint, civilization has been around for about 5,500 years when writing was invented by the ancient Sumerians, who are gone now, but the ancient Sumerians. In terms of getting a lot of firsts, those ancient Sumerians really have a long list of firsts. It’s pretty wild. In fact, Durant goes through the list of like, “You want to see firsts? We’ll show you firsts.” The Sumerians were just ass kickers.

1:08:17Very early. Many. So many rises and falls of empires. So many. Yeah, exactly. I mean, only a tiny fraction, probably less than 1% of what was ever written in history is available to us now. I mean, if they didn’t literally chisel it in stone or put it in a clay tablet, we don’t have it. I mean, there’s some small amount of papyrus scrolls that were recovered that are thousands of years old, because they were deep inside a pyramid and weren’t affected by moisture. But other than that, it’s really got to be in a clay tablet or chiseled. So, the vast majority of stuff was not chiseled because it takes a while to chisel things. So, that’s why we’ve got tiny, tiny fraction of the information from history. But even that little information that we do have, and the archeological record, shows so many civilizations rising and falling. It’s wild.

1:09:31Yeah. I mean, the basics of human nature are more or less the same. Yeah. Yeah. I mean, you do tend to see the same patterns, similar patterns for civilizations, where they go through a life cycle, like an organism, just like a human is a zygote, fetus, baby, toddler, teenager, eventually gets old.

1:10:01… Eventually gets old and dies. The civilizations go through a life cycle. No civilization will last forever.

1:10:28Well, the single biggest thing that is often actually not mentioned in history books, but Durant does mention it, is the birthright. So perhaps to some, a counterintuitive thing happens when civilizations are winning for too long, the birth rate declines. It can often decline quite rapidly. We’re seeing that throughout the world today. Currently, South Korea is, I think maybe the lowest fertility rate, but there are many others that are close to it. It’s like 0.8 I think. If the birth rate doesn’t decline further, South Korea will lose roughly 60% of its population. But every year that birth rate is dropping, and this is true through most of the world. I don’t mean to single out South Korea, it’s been happening throughout the world. So as soon as any given civilization reaches a level of prosperity, the birth rate drops.

1:12:49Well, I’m saying that’s- At a fundamental level, if a civilization does not at least maintain its numbers, it’ll disappear.

1:13:07Well, I mean there’s this hedonistic sex, which is… That’s neither her nor there. It’s- It doesn’t produce kids. Well, what matters… I mean, Durant makes this very clear because he’s looked at one civilization after another and they all went through the same cycle. When the civilization was under stress, the birth rate was high. But as soon as there were no external enemies or they had an extended period of prosperity, the birth rate inevitably dropped. Every time. I don’t believe there’s a single exception.

1:13:49Yeah. I mean, at a base level, no humans, no humanity. Yeah, absolutely. But at a basic level, if you do not at least maintain your numbers, if you’re below replacement rate and that trend continues, you will eventually disappear. It’s just elementary. Now then obviously you also want to try to avoid massive wars. If there’s a global thermonuclear war, probably we’re all toast, radioactive toast. So we want to try to avoid those things. Then there’s a thing that happens over time with any given civilization, which is that the laws and regulations accumulate. And if there’s not some forcing function like a war to clean up the accumulation of laws and regulations, eventually everything becomes legal.

1:15:57I have discussed with Trump the idea of a government deficiency commission. And I would be willing to be part of that commission. The antibody reaction would be very strong. So you really have to… You’re attacking the matrix at that point. The matrix will fight back.

1:16:29Me? Attacked? Yeah, there is a lot. I mean, every day another psyop. I need my tinfoil hat.

1:16:55Oh yeah, that’s a daily occurrence. So I mean, it does get me down at times. I mean, it makes me sad. But I mean at some point you have to sort of say, look, the attacks are by people that actually don’t know me and they’re trying to generate clicks. So if you can sort of detach yourself somewhat emotionally, which is not easy, and say, okay look, this is not actually from someone that knows me or, they’re literally just writing to get impressions and clicks. Then I guess it doesn’t hurt as much. It’s not quite water off a duck’s back. Maybe it’s like acid off a duck’s back.

1:17:58A measure of success, I’d say, how many useful things can I get done? Yeah, maximize utility, area under the code of usefulness. Very difficult to be useful at scale.

1:18:28Well, time is the true currency. So it is tough to say what is the best allocation time? I mean, there are often… Say if you look at say Tesla, Tesla this year will do over a hundred billion in revenue. So that’s $2 billion a week. If I make slightly better decisions, I can affect the outcome by a billion dollars. So then I try to do the best decisions I can. And on balance, at least compared to the competition, pretty good decisions. But the marginal value of a better decision can easily be, in the course of an hour, a hundred million dollars.

1:19:29Yeah. Well, I think you have to look at it on a percentage basis because if you look at it in absolute terms, it’s just… I would never get any sleep. It would just be like, I need to just keep working and work my brain harder. And I’m not trying to get as much as possible out of this meat computer. So it’s not… It’s pretty hard, because you can just work all the time. And at any given point, like I said, a slightly better decision could be a hundred million dollars impact for Tesla or SpaceX for that matter. But it is wild when considering the marginal value of time can be a hundred million dollars an hour at times, or more.

1:20:22It has to be to some degree. If I’m sad, if I’m depressed, I make worse decisions. So if I have zero recreational time, then I make worse decisions. So I don’t know a lot, but it’s above zero. I mean, my motivation if I’ve got a religion of any kind is a religion of curiosity, of trying to understand. It’s really the mission of Grok, understand the universe. I’m trying to understand the universe, or at least set things in motion such that at some point civilization understands the universe far better than we do today.

1:24:44Digital super intelligence is possibly a great filter. I hope it isn’t, but it might be. Guys like say Jeff Hinton would say, he invented a number of the key principles in artificial intelligence. I think he puts the probability of AI annihilation around 10% to 20%, something like that. So look on the bright side, it’s 80% likely to be great. But I think AI risk mitigation is important. Being a multi-planet species would be a massive risk mitigation. And I do want to once again emphasize the importance of having enough children to sustain our numbers, and not plummet into population collapse, which is currently happening. Population collapse is a real and current thing.

1:26:49Great. Hope you do. No time like the present. No, it’s like unskilled labor.

1:27:45There will be billions of robots. That seems virtual certainty.

1:28:00You’re welcome. Go forth and multiply.

从现场发言里挑出的 10 句原话,附中文译文(AI 翻译,以英文原话为准)。点时间戳可跳到视频对应位置。

如果你想想人类平均每秒多少比特,一天下来还不到每秒1比特。

If you think what is the average bits per second of a human, it is less than one bit per second over the course of a day.

▶ 7:37

死亡从根本上说是信息的丧失,是记忆的丧失。

Death is fundamentally the loss of information, the loss of memory.

▶ 29:43

我们相当有信心,我觉得也许在未来一两年内,植入Neuralink的人就能超过职业游戏玩家。

Well, we feel pretty confident that I think maybe within the next year or two, that someone with a Neuralink implant will be able to outperform a pro gamer.

▶ 34:26

我有一个非常基础的第一性原理算法,几乎当作口头禅来执行:先质疑需求,让需求别那么蠢。

I have this very basic first principles algorithm that I run kind of as a mantra, which is to first question the requirements, make the requirements less dumb.

▶ 44:08

但在某个时刻,是的,AI会比所有人类加起来还聪明。

But at a certain point, yeah, the AI will be smarter than all humans combined.

▶ 52:49

所以我认为,如果你强迫AI说谎,或者训练AI说谎,那你就是在自找麻烦,哪怕这个谎言出于善意。

So, I think if you force AIs to lie or train them to lie, you’re really asking for trouble, even if that lie is done with good intentions.

▶ 54:00

从根本上说,如果一个文明连自己的人数都维持不住,它就会消失。

At a fundamental level, if a civilization does not at least maintain its numbers, it’ll disappear.

但一个更好的决策,其边际价值在一小时内很容易就是1亿美元。

But the marginal value of a better decision can easily be, in the course of an hour, a hundred million dollars.

数字超级智能可能是一个大过滤器。

Digital super intelligence is possibly a great filter.

将会有数十亿个机器人。

There will be billions of robots.

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