特別節目·中文精讀

格蘭特·桑德森:3Blue1Brown 與數學之美

Grant Sanderson: 3Blue1Brown and the Beauty of Mathematics

3Blue1Brown 創作者談如何用動畫講解數學,讓抽象概念變得直觀且富有感染力。

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如果宇宙中存在其他智慧生命,他們會發展出和我們一樣的數學嗎?

Grant Sanderson(格蘭特·桑德森)給的答案很乾脆:大機率非常不同。

他是 YouTube 頻道 3Blue1Brown 的創作者,用程式化動畫講線性代數、微積分和數學裡那些說不清道不明的東西。在這場對話裡,他從外星人一路聊到尤拉公式為什麼被高估,從模擬宇宙聊到無窮大為什麼沒那麼可怕,中間還承認自己做的影片其實並不能讓人真正學會數學。

外星人會有自然數,但未必會有 e 的 x 次方

Grant 認為有些東西是全宇宙通用的:一、二、三,自然數。理由很簡單,只要你會數數,你就有了“重複”這個概念,你可以數兩次、三次,重複數數的動作自然就帶來了加法和乘法。這部分他覺得沒什麼可爭議的,甚至連“自然數”這個名字本身就說明了問題。

真正有選擇空間的地方在後面。我們怎麼把數從自然數擴充套件到實數,這一步就帶著人為痕跡。他提到一個很少被提起的例子:超現實數(surreal numbers)。這是另一套數系,同樣能捕捉連續性的概念,同樣可以當作建模行星運動的數學底座。你換一套後端,前端看到的物理定律可以完全一樣。

換句話說,數學的“介面”可能是共通的,但“實現”未必只有一種。Grant 好奇的是,我們目前從不同數學家那裡看到的那些不同選擇,究竟只是冰山一角,還是已經接近了全部可能性。如果一種智慧生命的思考方式、與宇宙互動的方式都和我們完全不同,他們會走出多遠的分岔?

他最討厭的記號,是 e 的 x 次方

聊到記號(notation),Grant 說這東西的重要性被嚴重低估了。大家都以為它只是外衣,實際上它會反過來塑造你怎麼想問題。

他舉了自己最不喜歡的一個:把指數函式寫成 e 的 x 次方。這個寫法暗示你先有一個叫 e 的常數,然後把它反覆自乘。對整數指數來說這沒問題,對分數指數就開始含糊,到了 e 的 πi 次方,你得把這套“反覆相乘”的思路硬掰到複數上去。Grant 的原話是,這相當於把一個人的胳膊扭到背後按在桌上,然後命令它去處理複數。教學上毫無幫助。

如果你真的去拆解 e 的 πi 次方,它說的是:選一個原點,始終垂直於從原點到你的那個向量移動,走 π 倍半徑的距離,你就到了對面的半圈。就這麼回事。(格蘭特·桑德森)

這句話值得再讀一遍。它描述的其實是一個運動過程:速度方向始終垂直於位置向量,你就會繞圈走。這是複數指數運算真正想捕捉的現象。可“反覆乘以一個常數”這個框架根本裝不下它,因為這裡的操作是 90 度的轉動,跟縮放完全是兩碼事。

Grant 說,為了把 e 的 πi 次方硬塞進“重複乘法”的解釋框架,不知道浪費了多少聰明人的時間,包括科學家、電氣工程師和全世界的學生。如果一開始就用微分方程來引入這個函式,很多困惑本可以避免。

他還補了一個自己都覺得好笑的觀察:e 這個常數,是微積分的數值代言人,而微積分研究的是變化。用一個常數去代表“變化之學”,本身就帶著認知上的彆扭。他說自己從沒這麼想過,聽完覺得挺有道理。

尤拉公式被神話了,它只是把三個醜記號捆在一起

順著這條線,Lex 問出了那個著名的問題:尤拉公式被稱為數學中最美的等式,你怎麼看?

Grant 的反應是:美是美的,但沒有那麼神。它之所以顯得神秘,是因為記號太糟糕。

他拆了三處:第一,用了 e,而 e 是從人口增長、複利這些“自我強化的增長”場景裡長出來的,把它塞進旋轉的語境,位置很尷尬。第二,用了 π 而不是 τ,關於這件事他已經專門做過影片。第三,把虛數單位叫作“imaginary”,光這個名字就讓無數人以為它不真實。

三個容易製造混亂的記號,全部擠在一個等式裡,然後大家驚歎於它有多美。Grant 的說法是,剝掉記號這層皮,剩下的東西確實漂亮:如果你取一個 90 度旋轉的線性操作,再對它做某種指數化,你得到的是所有其他角度的旋轉。翻譯成人話就是,速度垂直於位置,你就走圓。這挺美,但算不上宇宙間最震撼的事。

他把 e 和 π 的關係形容成幾個孩子被塞進同一張床:它們確實有親緣關係,但沒那麼親,如果讓它們自己選,會想分開睡。真正的結構是這樣:有一個核心函式叫 exp,往實方向走,你碰到常數 e;往虛方向走,它呈現週期性,週期是 τ。兩個常數來自同一個函式的兩個正交方向,它們的關係被那個等式誇大了。

Grant 說他對這個公式已經“過了約會期”。他做過三個關於它的影片,還打算再做至少一個。但那種心跳加速的感覺沒有了,現在他看它像一個剛睡醒、頭髮亂糟糟、眼下有黑眼圈的人。

數學是發明的還是發現的:一個迴圈

回到外星人那個問題。Lex 把話頭引到了那個老問題上:數學是被發現的,還是被髮明的?

Grant 的答案是一個迴圈。你先從宇宙裡發現一些東西,這些發現告訴了你哪一套數學會有用;數學本身在某種意義上是發明出來的,但在所有可能的數學裡,是那些關於世界的發現替你做了篩選。

他拿勾股定理舉例。看那些用正方形拼來拼去的幾何證明,感覺像是發現。但如果你看我們怎麼把二維空間形式化成 R²(所有實數對的集合),怎麼在上面定義度量、定義距離,你會發現一件尷尬的事:我們是先定義了一個讓勾股定理成立的距離,然後再說勾股定理成立。這麼一看,那股發現的神聖感就淡了。

但 Grant 說,告訴我們該在 R² 上放哪個度量的,是物理觀察。你完全可以給 R² 定義別的度量,那套數學同樣自洽,只是它不描述我們生活的這個世界,因為在那個世界裡勾股定理不成立。

所以順序是這樣:先有一個貨真價實的發現,這個發現啟發了我們發明一套抽象表示;然後你開始單獨研究這套抽象表示,它自己會長出新問題、新發明、新謎團;這些謎團又可能帶來新發現,新發現再告訴你該發明什麼。數學在發明和發現之間來回切換角色,從來沒法二選一。

三類數學家,和一個說“數學是物理分支”的人

Lex 接著搬出了費曼式的問題:物理和數學的區別在哪?

Grant 先給了一個定義:數學研究的是抽象之上的模式,以及邏輯裡的純模式;物理則明確以理解我們所在的世界為目標。但他隨即說,這個問題你去問十個數學家,會得到十個答案,因為數學家本身分好幾類。

第一類是被純謎題吸引的人,他們喜歡組合數學,享受搭建一套解題工具、然後用在純模式上的過程。第二類是物理驅動的,他們主動去那些已知會和物理或電腦科學發生關係的方向上找問題,混沌理論就是個典型,它的表述完全是純數學的,但動機大量來自物理應用。第三類人熱愛抽象本身,喜歡把東西越抽越高,追求那種“能描述儘可能多事物”的力量感,拓撲學最早是他們搞出來的,後來他們又一頭扎進範疇論,聊起無窮範疇來能聊很久。

這三類人對“數學和物理什麼關係”的回答會完全不同。Grant 特別提到了 Vladimir Arnold(弗拉基米爾·阿諾德),寫了很多微分方程方面好書的那位,他的說法是:數學是物理的一個分支。當然,他本人研究的正是偏微分方程這類東西,動機來源就在那兒。

反過來,那些不太考慮物理的範疇論學者會覺得,抽象本身有力量,一般性就是價值,這套東西恰好能用來理解世界,是個令人愉快的巧合。

為什麼宇宙的規律可以被寫成方程

Lex 追問了一個更深的問題:為什麼夸克的基本規律可以被壓縮成那麼幹淨的方程?這些方程比它們本來可以有的樣子簡單得多。

Grant 說他不知道,但可以給幾個猜測。

第一個是篩選效應。物理學家覺得有意思的,本來就是那些簡單到能用數學描述的東西。一旦系統複雜到一定程度,它就被劃到生物學或者別的領域去了。這更像是學科分工的結果,宇宙未必在配合我們。

第二個是從資訊理論的角度看,你總能把一個東西洗掉不重要的部分,最後剩下資訊量最低的那個核心元件。

第三個是他自己也拿不準的地方:他很難想象“本質上覆雜的定律”到底是什麼意思。一組彼此無法解耦的方程?一個規則到處都不一樣、徹底分散式的世界?

Lex 補充說,我們幾乎把“物理定律處處相同”當成了理所當然,但另一種可能是,這個世界的執行規則在每個地方都不一樣,那方程就無從寫起了。

Grant 從這裡拐到了人擇原理上。他反問:一個定律不可壓縮的宇宙,能允許出現會問這種問題的存在嗎?也許宇宙本來就不可壓縮,只是我們的大腦在進化中被塑造成了只能感知可壓縮的那部分。Lex 提了喬姆斯基式的觀點,說我們不過是猿類的後代,是一臺很有限的生物計算機。

Grant 的回應很實在:但我們確實能造出飛船。能飛起來這件事本身就是個概念驗證,說明我們手上的定律沒那麼離譜。你可以說模型被過度簡化了,但基於這些模型去改造世界,世界確實按你預期的方式回應了。

模擬宇宙,和一個被忽略的物理限制

話題轉到了模擬假說。Grant 說他能接受這個想法的可能性,但他不接受那個流行的機率論證。

那個論證是這樣:如果模擬是可能的,被模擬的世界遲早也會造出自己的模擬,於是有第二層、第三層,層層套娃。那麼隨便扔一支飛鏢,扎中的大機率是某個模擬層,所以我們很可能生活在模擬裡。

Grant 的反駁是,這個論證預設了資訊處理能力沒有上限。但物理本身就給了上限:單位面積能儲存的資訊量是有限的,超過某個臨界點,那片區域會塌縮成黑洞。既然儲存有物理極限,那麼能套多少層就是個有限數。這時候問題就變成了:最高層宇宙擁有無限資訊處理能力和擁有有限資訊處理能力,哪個更可信?Grant 說他完全不知道怎麼給這個問題賦機率分佈。他覺得人們常常不自覺地給那個無限層級集合賦予了均勻先驗,這有點像帕斯卡的賭注,給了一個不該那麼低的先驗。

讓他真正在意的是另一件事:資訊是物理的,它有下限,也有上限。我們直覺上會覺得技術總能變得更好,總能往一小塊空間裡塞進更多資訊,但物理說不。這個事實本身就很值得琢磨。

無窮大沒那麼可怕,可怕的是過度泛化

Lex 承認自己對無窮大在心理上不舒服,問 Grant 怎麼看。

Grant 說他挺自在的,然後給了一個漂亮的解釋。他先問:你覺得存在多少個由字母組成的“詞”?

你不需要真的把所有的詞列出來。真正起作用的是那個性質:不管你列了多少個,在末尾加一個 a,你就得到了另一個可以想象的詞。讓無窮大成立的,就是這個“總能再加一個”的性質,你根本不必一次性把全體裝進腦子。

接著他把“抽象”這個詞重新定義了一遍。他說抽象不是玄學,也不是那種說不清道不明的東西。你每次稍微動一下臉,我視網膜上的畫素訊號就全變了,但我仍然有“這是 Lex”這個連貫的概念。這個能把無數種不同影象統一起來的東西,就是抽象。它的本質是壓縮,是從一堆具體情況裡提取出共同屬性,並且這個提取結果對變化有很強的抵抗力。

所以在他看來,無窮大和“Lex 這個人的概念”是同一類東西,沒有更多神秘色彩,也沒有更少。他甚至說,如果不用“抽象”這個詞,改用“一般”(general)會更容易理解,那麼 Lex 描述的那種危險其實就是“過度泛化”:一個概念如果泛到對任何具體情況都不適用,那它就失去了價值。

Grant 承認自己讀數學書時經常一頭霧水,感覺“這太抽象了,我想要點具體的東西抓住”。他說頻道的一大部分動機就是這股情緒:你花一個小時死磕兩頁紙,最後除了幾個定義和一點自我挫敗感,什麼都沒剩下。

做影片必須選一個例子,這是它最大的優勢

這引出了 Grant 關於視覺化最核心的一個觀點。

他主張,理解的過程走的是從具體往上的方向。教科書講線性代數,第一節課就給你向量空間的定義,列出一堆公理,然後說從這些性質出發我們推匯出後面的一切。Grant 認為這完全不符合人腦理解事物的方式。真正的方式是從你能觸及的最低層開始:不要想向量空間,想一個具體的向量,就是一列數字;更具體一點,想一支畫出來的箭頭,它有明確的 x 分量和 y 分量。

而視覺媒介強制你做出選擇。你在螢幕上畫出來的那支箭頭,是一個實實在在的箭頭,不是一個“可以適用於任何事物的所謂向量”。你在程式碼裡必須寫清楚它的方向、它的長度。每一次選擇,都是在展示一個具體例子;每一個具體例子,都在幫某個人往前走一步。

他順帶交代了自己寫指令碼時的通病:第一反應總是先講那個抽象的、一般的、威力強大的定義,然後再補例子。每一次他都這麼做,而每一次反過來做都更有效。

正確順序是:先給一堆例子,讓觀眾的大腦自己去做模式識別。腦子會感到它們之間有種相似性。等你再丟擲定義或公式的時候,公式只是在為一個已經形成的直覺命名,而不是扔給觀眾一堆需要靠例子填充的高認知負荷符號,還得指望他們沒在半路走神。

最美的數學,來自一本兒童小書的最後一頁

Lex 問:你見過最美的數學想法是什麼?

Grant 說,最美的東西往往是你只理解了一點點、但遠遠沒有全部理解的東西,那點殘留的神秘感本身就是美的來源。

然後他講了自己的啟蒙時刻。小時候他看過一套很小的書,好像叫 Wooden Books 系列,左頁一段極短的說明,右頁一張圖。在其中一本的最後一頁,某個不起眼的角落,印著一個公式:左邊是所有自然數的倒數的 s 次方之和,一路加到無窮;右邊是所有素數構成的某個乘積。

任何一個受過點數學薰陶的小孩,大概都被灌輸過素數有多混亂、多難捉摸,它們確實如此。然後你看到這個等式,一邊是你能想象的最容易理解的東西,就是數數的那些數字;另一邊是全體素數。那種感覺就是,等等,它們是這樣聯絡起來的?(格蘭特·桑德森)

這就是 Zeta 函式的尤拉乘積。Grant 後來才明白,這個等式本質上編碼了算術基本定理:每個數都能唯一地分解成一組素數。而它更深的意義在於,你可以用另一組數來描述素數,那組數就是 Zeta 函式的非平凡零點。

他說自己對黎曼猜想這件事依然常常力不從心。但每次多懂一點,它就更美一點,而且永遠留著足夠多的謎,讓它保持著那種美。

他描述學習路徑時很具體:多讀書,比如《普林斯頓數學指南》裡那篇解析數論的文章,文章後面還有一堆參考文獻,像一棵可以往上爬的樹。同時儘量把東西視覺化。他做那個 Zeta 函式的影片,起點就是程式設計時想看看某個東西長什麼樣,結果一看,被震住了,於是決定做一期影片。他還提到一個反覆出現的訊號:你會發現傅立葉變換的親戚們到處冒頭,那就說明值得專門花時間建立對傅立葉變換的深度直覺,哪怕那些文獻裡沒明說。

關於美,他給了一個更精確的說法:這些模式完全不可預測,極其精巧,但你不會覺得人類在研究物件的選擇上做過任何任意的決定。你感覺自己是在和模式本身對話,或者說,在和自然本身對話。任意的東西很難美,因為那就是“做作”這個詞的意思。不任意,意味著背後有乾淨的抽象和直覺可以把握它。

這也是他相信外星文明會研究同樣東西的理由:如果你真的遇到另一個智慧文明,他們大概也在研究 Zeta 函式的零點,或者某個明顯等價的親戚。

一個關於拓撲的影片,和一個只要三分鐘的冷知識

說到最喜歡的作品,Grant 提到了《Who Cares About Topology?》這期。

影片從一道至今未解的數學難題開始:內接正方形問題。隨便畫一條閉合曲線,問曲線上是否存在四個點能構成一個正方形。這問題完全沒用,不回答任何物理問題,它的趣味就在於我們居然答不上來,而這個問題看起來如此自然。

把它削弱一點:能不能總能找到四個點構成矩形?這依然很難,但是可解的。解法路徑要用到環面(甜甜圈那種帶一個洞的曲面),要用到莫比烏斯帶。

Grant 說他小時候學這些東西時的感受是:誰在乎啊?把一條紙帶擰一下粘起來,得到一個只有一個面、一條邊的形狀,然後呢?這能幫我解什麼題?我以為數學是用來解題的。所以這期影片想說明的正是,這些構造遠不止手工課上的紙帶遊戲,它們在某類問題裡會非常自然地冒出來,而且冒出來的方式很漂亮。

製作過程本身也有意思。這個拓撲論證非常抽象,而且是非構造性的。他當時用的是 macOS 自帶的 Grapher 畫三維曲面,必須額外做大量工作才能讓那個曲面真的出現在螢幕上。但一旦畫出來,他發現自己根本沒法預料它長什麼樣。

看起來像悉尼歌劇院,或者弗蘭克·蓋裡的某種設計。你知道它一定有點樣子,你能說出它的一些性質,它和那條曲線相接,它的邊界就是平面上那條曲線,整體都懸在平面之上。但在真正畫出來之前,你完全不知道它會是什麼樣。看到的那一刻,單純就是舒服。(格蘭特·桑德森)

他說那種滿足感來自一個事實:原論文的論證很美,但你不知道它長什麼樣,而“螢幕上必須出現一張圖”這個強迫函式,逼他補上了原論文沒有的那部分。

那個說“這很爛”的聲音,是他過去的自己

Lex 問他創作流程:選題、寫敘事、配視覺化,哪一段最煎熬。

Grant 說最痛苦的情況是,題目是你真想做的,但指令碼結構怎麼都想不出來。他有一期已經卡了兩三個月。他覺得正確的處理方式是先放一邊,去做那些指令碼自然就流出來的題目。因為敘事是不能硬磨的,你對它想得越多,就越難共情那個還沒聽懂的學生。

於是 Lex 問了那個很有意思的問題:你腦子裡那個評判者是誰?那個說“這很爛”或者“這個可以”的聲音,是什麼?

Grant 說,是他過去的自己。那是他唯一真正瞭解的人,你瞭解的其實只有自己的各個版本。所以他先取出那個還不懂這件事的自己,然後用新鮮的眼光去看一段指令碼、一個畫面:這東西有動機嗎?講得通嗎?

這個方法的副作用他也承認了。他做了四元數那期,真正想搞清楚的是:四元數在四維空間裡到底在做什麼?能不能看見?他找到了一種能讓自己滿意的解釋方式,用三維視覺化把四元數對四維球體做的事講清楚了。他很滿意,因為這正是過去的自己想要的。

但事後看,大多數想學四元數的人是機器人工程師或者圖形程式設計師,他們想懂的是怎麼用四元數描述三維旋轉。需求和過去的他並不一樣。他不會把那期影片推薦給這類人,會建議他們去看別的資源,除非某天他們突然想知道“等等,它們到底怎麼在四維裡起作用”,那時候再回來。

神經網路那期是另一個例子。同一個概念可以從物理、神經科學、機器人學、統計學等角度切入,每個角度都能做出一期好影片。他說自己關於尤拉公式已經做了三期,還會再做至少一期。

如果永生,你會要嗎

話題拐到了死亡上。Lex 說他覺得有限性是意義的關鍵:事情會結束,才讓它有意義。他甚至舉例,這個房間四十分鐘後就關門,所以他更愛這個房間。

Grant 的回應很誠實:如果給他永生,他會要。他承認自己人生裡只有很小一部分時間能意識到死亡這件事。可他大部分時間都很有動力去做東西、去和人相處、去體驗愛。這種動力是在死亡完全不在腦子裡的時候產生的,這有點奇怪。也許是因為他還年輕,也許那東西在潛意識裡,也許他構建了一個幻覺,讓自己可以透過享受當下繞開必死這件事。

Lex 半開玩笑地說,後者就是那種存在主義式的活法。Grant 說:如果拿槍指著頭逼我答,我不覺得是那樣。

中間有一段很好玩的交鋒。Lex 說自己有點俄羅斯血統,所以偏憂鬱,可能高估了苦難和死亡的價值,但這能寫出更好的小說,因為你需要某種存在性的威脅來推動情節。Grant 說,只要你不永生,數學裡永遠有足夠多的謎和樂趣,問題冒出來的速度遠快於答案冒出來的速度。

Lex 一直在旁邊推,Grant 最後說了一句可以直接拿去做簽名的話:

就因為你是在釣我,不代表我錯了。(格蘭特·桑德森)

怎麼判斷一個影片做完了

這個問題 Grant 說很簡單。指令碼要先寫好,裡面得有一個“啊哈”的時刻,最好整個指令碼都圍繞這個時刻展開。然後給每一句話配畫面,配音,剪輯。有了指令碼,終點就清楚了。動畫過程中他會改措辭,偶爾也改結構,但那時候已經是個相當確定性的流程。

難的是判斷指令碼什麼時候寫完。解題類影片相對簡單,你覺得一個原本不懂的人現在能懂了,就完了。像神經網路那種就麻煩得多,因為你有一堆切入角度。標準變成了:這個指令碼提出了一個有意義的問題,並且回答了它。

學數學最實際的一條建議:多做習題

Lex 問,對剛開始學數學的人,或者職業中途想重新撿起來的人,有什麼建議。

Grant 說,你的本能會驅使你花更多時間看講座、看書。你要強迫自己去做比本能願意做的更多的題。專注的時間應該花在解具體問題上,而且要去找那些題目被精心編排過的資源。

他給了一個很具體的操作:先翻到一章後面的習題,快速掃一遍。大部分你現在看不懂,沒關係。但總有幾個你能看懂的,那幾個就是你現階段最該想的題。然後回頭讀一點正文,再回來看題。在真正動手做完幾道題之前,不要認為這一章學完了。

然後他承認這有點虛偽,因為他自己發的影片幾乎從來不配習題。他把自己定位成生態裡的另一個環節,這等於承認:光看影片,學習只完成了一部分。

對完全零基礎的人,他推薦 Khan Academy,題庫足夠大,可以從最基礎的線性代數、微積分開始。另一個路徑是學程式設計,讓程式設計裡的動機推著你往前走。他說自己認識很多原本不喜歡數學的人,是透過程式設計對數學開竅的。他承認這可能是因為他住在灣區,樣本有偏,但他願意賭這條路更普適。

關於“教是最好的學”,他提到聽過一組數字:讀過的記住 10%,聽過的記住 20%,主動參與過的記住 70%,教過別人的記住 90%。他說這些數字可能沒有嚴謹出處,但它們感覺很對。如果換成教,學習效率可能比讀高九倍,這個估計甚至可能還保守了。所以去做點什麼來教別人,或者主動試著把一件事講清楚,對鞏固知識的作用巨大。

音樂、那本小書,和一句費曼的話

快結束時,Grant 提到音樂曾經在他生活裡佔很大比重。他說起一個真正開心的時刻:他和哥哥、一個朋友在滑雪場做了一場演出。觀眾要坐纜車上山頂,他們就在那兒演奏。下山的時候坐在纜車裡,幾個人又即興玩了起來,彈著玩著,纜車翻過山脊,城市的燈火一下子鋪在下面。

他說他不會把那一刻描述成“transformative”(有轉換性的)。他說不清為什麼,但那個畫面就是冒出來了,跟身邊的人關係不大,起作用的好像就是那件事本身,那種單純的、很高興的感覺。他說自己沒辦法給這個時刻一個更深的解釋。

Lex 說,作為一個也做音樂的人,他很想看到音樂重新回到 Grant 的創作裡。Grant 答應了,說 2020 年底之前一定做一個音樂影片,給自己留個長一點的視窗。他主修小提琴,吉他和鋼琴也喜歡瞎玩。Lex 說自己也彈吉他和鋼琴。

然後 Lex 引了 Paul Lockhart(保羅·洛克哈特)《一個數學家的嘆息》(A Mathematician's Lament)開頭那句話:首先要理解的是,數學是一門藝術。數學和音樂、繪畫等其他藝術的區別在於,我們的文化不承認它是藝術。Lex 說他代表幾百萬人,包括他自己,謝謝 Grant 把數學的藝術性展示出來。

Grant 說:哇,謝謝你這麼說。

這場對話

這場對話裡,最值得帶走的幾件事

整場對話沒有一句推銷,Grant 也沒有一次試圖把數學說得比它本身更玄。恰恰相反,他花了大量時間在拆解那些被神化的東西:尤拉公式沒那麼美,e 的記號是歷史的偶然,教科書從公理出發的講法反認知,他自己的影片也不能讓你真正學會數學。

把這些碎片拼起來,有一條主線很清楚:數學的困難,很大一部分來自表達方式,數學本身倒沒那麼難。

記號是表達,教科書的結構是表達,先講定義再講例子是表達。這些選擇在歷史上都有它的道理,e 之所以寫成自乘,是因為人類第一次遇見它是在複利和人口增長裡;線性代數之所以從公理開始,是因為那是整理知識最經濟的方式。“整理知識最經濟”和“讓人理解”經常衝突,衝突的時候吃虧的永遠是學習者。

Grant 做的事情,本質上是在糾正這個偏差。他每一次都往更具體的方向走,往能畫出來、能算出來、能看見的方向走。這算不上把數學變簡單,更像是把通往數學的路修平了一點。

另一條線是關於美。他說最美的東西往往是你只理解一半的東西,那點殘留的神秘感就是美的來源。他對 Zeta 函式的形容是“我還在和她約會”,有那種高多巴胺的部分。他對尤拉公式的形容是“過了約會期,現在看她頭髮亂糟糟的,眼下有黑眼圈”。

兩種關係都是真的,但只有前一種讓人想拍影片。他做影片的驅動力,可能更多來自“我好像摸到了一點,我想看看它到底是什麼樣子”,而非“我懂了,我講給你聽”。那個拓撲影片就是最好的例子:他必須把曲面畫出來才能知道它長什麼樣,而這個“必須畫出來”的約束,反過來讓他補上了原論文沒有的東西。

還有一條,關於學習。他說的那句話很樸素:你的本能會讓你多看、多聽,你要強迫自己多做。看講座和讀教科書的舒適感是真實的,學習卻不在那裡發生。學習發生的時候,你正盯著一道做不出來的題,不知道下一步該幹什麼。

最後,那個“教是最好的學”的說法,即便那組百分比是編的,邏輯也站得住。被動接收是線性的,主動輸出是指數的。你試著把一件事講清楚,講到別人能聽懂,你會清楚看到自己在哪一步其實是虛的。

如果你想把數學重新撿起來

  • 拿到一本教材,先翻到章節末尾的習題快速掃一遍,挑出那幾道你現在能看懂題意的。它們就是你的起點,不用等讀完正文。
  • 給自己定一條硬規矩:每讀完一節,必須動手做完至少兩道題。做不出來也算做完,但要在紙上留下你嘗試的痕跡。
  • 如果你本來會程式設計,從程式設計的角度切入數學。實現一次向量旋轉、寫一遍矩陣乘法、把傅立葉變換跑起來看到頻譜,比讀十頁定義管用。
  • 遇到一個概念,先自己找一個最小的具體例子:一個具體的函式、一組具體的數字、一支畫出來的箭頭。抽象定義留到你有直覺之後再回來看。
  • 試著把這個概念講給別人聽,或者寫下來。講的過程中卡住的地方,就是你真正沒懂的地方,回去補那一塊。
  • 對“最美公式”這類說法保持一點懷疑。任何一個讓你覺得神秘的東西,先問一句:會不會是表達方式的問題?換成另一種寫法,它是不是就變得平淡了?
  • 給自己留一點沒弄懂的東西。不必把每個謎都解開,留著它,你才有動力繼續往那兒走。
文稿來自公開 Kaggle 資料集(rajneesh231/lex-fridman-podcast-transcript),為早期自動轉錄存檔,可能存在識別錯誤。
The following is a conversation with Grant Sanderson. He's a math educator and creator of 3Blue1Brown, a popular YouTube channel that uses programmatically animated visualizations to explain concepts in linear algebra, calculus, and other fields of mathematics. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, give us five stars on Apple Podcast, follow on Spotify, support on Patreon, or simply connect with me on Twitter at Lex Friedman, spelled F R I D M A N. I recently started doing ads at the end of the introduction. I'll do one or two minutes after introducing the episode and never any ads in the middle that can break the flow of the conversation. I hope that works for you and doesn't hurt the listening experience. This show is presented by Cash App, the number one finance app in the App Store. I personally use Cash App to send money to friends, but you can also use it to buy, sell, and deposit Bitcoin in just seconds. Cash App also has an investing feature. You can buy fractions of a stock, say $1 worth, no matter what the stock price is. Broker services are provided by Cash App Investing, a subsidiary of Square, and member SIPC. I'm excited to be working with Cash App to support one of my favorite organizations called FIRST, best known for their FIRST Robotics and LEGO competitions. They educate and inspire hundreds of thousands of students in over 110 countries and have a perfect rating on Charity Navigator, which means the donated money is used to maximum effectiveness. When you get Cash App from the App Store at Google Play and use code LEXPODCAST, you'll get $10, and Cash App will also donate $10 to FIRST, which again is an organization that I've personally seen inspire girls and boys to dream of engineering a better world. And now, here's my conversation with Grant Sanderson. If there's intelligent life out there in the universe, do you think their mathematics is different than ours? Jumping right in. I think it's probably very different. There's an obvious sense the notation is different, right? I think notation can guide what the math itself is. I think it has everything to do with the form of their existence, right? Do you think they have basic arithmetic? Sorry, I interrupted. Yeah, so I think they count, right? I think notions like one, two, three, the natural numbers, that's extremely, well, natural. That's almost why we put that name to it. As soon as you can count, you have a notion of repetition, right? Because you can count by two, two times or three times. And so you have this notion of repeating the idea of counting, which brings you addition and multiplication. I think the way that we extend it to the real numbers, there's a little bit of choice in that. So there's this funny number system called the servial numbers that it captures the idea of continuity. It's a distinct mathematical object. You could very well model the universe and motion of planets with that as the back end of your math, right? And you still have kind of the same interface with the front end of what physical laws you're trying to, or what physical phenomena you're trying to describe with math. And I wonder if the little glimpses that we have of what choices you can make along the way based on what different mathematicians I've brought to the table is just scratching the surface of what the different possibilities are if you have a completely different mode of thought, right? Or a mode of interacting with the universe. And you think notation is a key part of the journey that we've taken through math. I think that's the most salient part that you'd notice at first. I think the mode of thought is gonna influence things more than like the notation itself. But notation actually carries a lot of weight when it comes to how we think about things, more so than we usually give it credit for. I would be comfortable saying. Do you have a favorite or least favorite piece of notation in terms of its effectiveness? Yeah, yeah, well, so least favorite, one that I've been thinking a lot about that will be a video I don't know when, but we'll see. The number e, we write the function e to the x, this general exponential function with a notation e to the x that implies you should think about a particular number, this constant of nature, and you repeatedly multiply it by itself. And then you say, oh, what's e to the square root of two? And you're like, oh, well, we've extended the idea of repeated multiplication. That's all nice, that's all nice and well. But very famously, you have like e to the pi i, and you're like, well, we're extending the idea of repeated multiplication into the complex numbers. Yeah, you can think about it that way. In reality, I think that it's just the wrong way of notationally representing this function, the exponential function, which itself could be represented a number of different ways. You can think about it in terms of the problem it solves, a certain very simple differential equation, which often yields way more insight than trying to twist the idea of repeated multiplication, like take its arm and put it behind its back and throw it on the desk and be like, you will apply to complex numbers, right? That's not, I don't think that's pedagogically helpful. So the repeated multiplication is actually missing the main point, the power of e to the x. I mean, what it addresses is things where the rate at which something changes depends on its own value, but more specifically, it depends on it linearly. So for example, if you have like a population that's growing and the rate at which it grows depends on how many members of the population are already there, it looks like this nice exponential curve. It makes sense to talk about repeated multiplication because you say, how much is there after one year, two years, three years, you're multiplying by something. The relationship can be a little bit different sometimes where let's say you've got a ball on a string, like a game of tetherball going around a rope, right? And you say, its velocity is always perpendicular to its position. That's another way of describing its rate of change is being related to where it is, but it's a different operation. You're not scaling it, it's a rotation. It's this 90 degree rotation. That's what the whole idea of like complex exponentiation is trying to capture, but it's obfuscated in the notation when what it's actually saying, like if you really parse something like e to the pi i, what it's saying is choose an origin, always move perpendicular to the vector from that origin to you, okay? Then when you walk pi times that radius, you'll be halfway around. Like that's what it's saying. It's kind of the, you turn 90 degrees and you walk, you'll be going in a circle. That's the phenomenon that it's describing, but trying to twist the idea of repeatedly multiplying a constant into that. Like I can't even think of the number of human hours of like intelligent human hours that have been wasted trying to parse that to their own liking and desire among like scientists or electrical engineers or students everywhere, which if the notation were a little different or the way that this whole function was introduced from the get go were framed differently, I think could have been avoided, right? And you're talking about the most beautiful equation in mathematics, but it's still pretty mysterious, isn't it? Like you're making it seem like it's a notational. It's not mysterious. I think the notation makes it mysterious. I don't think it's, I think the fact that it represents, it's pretty, it's not like the most beautiful thing in the world, but it's quite pretty. The idea that if you take the linear operation of a 90 degree rotation, and then you do this general exponentiation thing to it, that what you get are all the other kinds of rotation, which is basically to say, if your velocity vector is perpendicular to your position vector, you walk in a circle, that's pretty. It's not the most beautiful thing in the world, but it's quite pretty. The beauty of it, I think comes from perhaps the awkwardness of the notation somehow still nevertheless coming together nicely, because you have like several disciplines coming together in a single equation. Well, I think. In a sense, like historically speaking. That's true. You've got, so like the number E is significant. Like it shows up in probability all the time. It like shows up in calculus all the time. It is significant. You're seeing it sort of mated with pi, this geometric constant and I, like the imaginary number and such. I think what's really happening there is the way that E shows up is when you have things like exponential growth and decay, right? It's when this relation that something's rate of change has to itself is a simple scaling, right? A similar law also describes circular motion. Because we have bad notation, we use the residue of how it shows up in the context of self reinforcing growth, like a population growing or compound interest. The constant associated with that is awkwardly placed into the context of how rotation comes about, because they both come from pretty similar equations. And so what we see is the E and the pi juxtaposed a little bit closer than they would be with a purely natural representation, I would think. Here's how I would describe the relation between the two. You've got a very important function we might call exp. That's like the exponential function. When you plug in one, you get this nice constant called E that shows up in like probability and calculus. If you try to move in the imaginary direction, it's periodic and the period is tau. So those are these two constants associated with the same central function, but for kind of unrelated reasons, right? And not unrelated, but like orthogonal reasons. One of them is what happens when you're moving in the real direction. One's what happens when you move in the imaginary direction. And like, yeah, those are related. They're not as related as the famous equation seems to think it is. It's sort of putting all of the children in one bed and they'd kind of like to sleep in separate beds if they had the choice, but you see them all there and there is a family resemblance, but it's not that close. So actually thinking of it as a function is the better idea. And that's a notational idea. And yeah, and like, here's the thing. The constant E sort of stands as this numerical representative of calculus, right? Calculus is the like study of change. So at the very least there's a little cognitive dissonance using a constant to represent the science of change. I never thought of it that way. Yeah. Right? Yeah. It makes sense why the notation came about that way. Because this is the first way that we saw it in the context of things like population growth or compound interest. It is nicer to think about as repeated multiplication. That's definitely nicer. But it's more that that's the first application of what turned out to be a much more general function that maybe the intelligent life your initial question asked about would have come to recognize as being much more significant than the single use case, which lends itself to repeated multiplication notation. But let me jump back for a second to aliens and the nature of our universe. Okay. Do you think math is discovered or invented? So we're talking about the different kind of mathematics that could be developed by the alien species. The implied question is, yeah, is math discovered or invented? Is fundamentally everybody going to discover the same principles of mathematics? So the way I think about it, and everyone thinks about it differently, but here's my take. I think there's a cycle at play where you discover things about the universe that tell you what math will be useful. And that math itself is invented in a sense, but of all the possible maths that you could have invented, it's discoveries about the world that tell you which ones are. So like a good example here is the Pythagorean theorem. When you look at this, do you think of that as a definition or do you think of that as a discovery? From the historical perspective, right, it's a discovery because they were, but that's probably because they were using physical object to build their intuition. And from that intuition came the mathematics. So the mathematics wasn't in some abstract world detached from physics, but I think more and more math has become detached from, you know, when you even look at modern physics from string theory to even general relativity, I mean, all math behind the 20th and 21st century physics kind of takes a brisk walk outside of what our mind can actually even comprehend in multiple dimensions, for example, anything beyond three dimensions, maybe four dimensions. No, no, no, no, higher dimensions can be highly, highly applicable. I think this is a common misinterpretation that if you're asking questions about like a five dimensional manifold, that the only way that that's connected to the physical world is if the physical world is itself a five dimensional manifold or includes them. Well, wait, wait, wait a minute, wait a minute. You're telling me you can imagine a five dimensional manifold? No, no, that's not what I said. I would make the claim that it is useful to a three dimensional physical universe, despite itself not being three dimensional. So it's useful meaning to even understand a three dimensional world, it'd be useful to have five dimensional manifolds. Yes, absolutely, because of state spaces. But you're saying there in some deep way for us humans, it does always come back to that three dimensional world for the usefulness that the dimensional world and therefore it starts with a discovery, but then we invent the mathematics that helps us make sense of the discovery in a sense. Yes, I mean, just to jump off of the Pythagorean theorem example, it feels like a discovery. You've got these beautiful geometric proofs where you've got squares and you're modifying the areas, it feels like a discovery. If you look at how we formalize the idea of 2D space as being R2, right, all pairs of real numbers, and how we define a metric on it and define distance, you're like, hang on a second, we've defined a distance so that the Pythagorean theorem is true, so that suddenly it doesn't feel that great. But I think what's going on is the thing that informed us what metric to put on R2, to put on our abstract representation of 2D space, came from physical observations. And the thing is, there's other metrics you could have put on it. We could have consistent math with other notions of distance, it's just that those pieces of math wouldn't be applicable to the physical world that we study because they're not the ones where the Pythagorean theorem holds. So we have a discovery, a genuine bonafide discovery that informed the invention, the invention of an abstract representation of 2D space that we call R2 and things like that. And then from there, you just study R2 as an abstract thing that brings about more ideas and inventions and mysteries which themselves might yield discoveries. Those discoveries might give you insight as to what else would be useful to invent and it kind of feeds on itself that way. That's how I think about it. So it's not an either or. It's not that math is one of these or it's one of the others. At different times, it's playing a different role. So then let me ask the Richard Feynman question then, along that thread, is what do you think is the difference between physics and math? There's a giant overlap. There's a kind of intuition that physicists have about the world that's perhaps outside of mathematics. It's this mysterious art that they seem to possess, we humans generally possess. And then there's the beautiful rigor of mathematics that allows you to, I mean, just like as we were saying, invent frameworks of understanding our physical world. So what do you think is the difference there and how big is it? Well, I think of math as being the study of abstractions over patterns and pure patterns in logic. And then physics is obviously grounded in a desire to understand the world that we live in. I think you're gonna get very different answers when you talk to different mathematicians because there's a wide diversity in types of mathematicians. There are some who are motivated very much by pure puzzles. They might be turned on by things like combinatorics. And they just love the idea of building up a set of problem solving tools applying to pure patterns. There are some who are very physically motivated, who try to invent new math or discover math in veins that they know will have applications to physics or sometimes computer science. And that's what drives them. Like chaos theory is a good example of something that's pure math, that's purely mathematical. A lot of the statements being made, but it's heavily motivated by specific applications to largely physics. And then you have a type of mathematician who just loves abstraction. They just love pulling it to the more and more abstract things, the things that feel powerful. These are the ones that initially invented like topology and then later on get really into category theory and go on about like infinite categories and whatnot. These are the ones that love to have a system that can describe truths about as many things as possible. People from those three different veins of motivation into math are gonna give you very different answers about what the relation at play here is. Cause someone like Vladimir Arnold, who has written a lot of great books, many about like differential equations and such, he would say, math is a branch of physics. That's how he would think about it. And of course he was studying like differential equations related things because that is the motivator behind the study of PDEs and things like that. But you'll have others who, like especially the category theorists who aren't really thinking about physics necessarily. It's all about abstraction and the power of generality. And it's more of a happy coincidence that that ends up being useful for understanding the world we live in. And then you can get into like, why is that the case? It's sort of surprising that that which is about pure puzzles and abstraction also happens to describe the very fundamentals of quarks and everything else. So why do you think the fundamentals of quarks and the nature of reality is so compressible into clean, beautiful equations that are for the most part simple, relatively speaking, a lot simpler than they could be? So you have, we mentioned somebody like Stephen Wolfram who thinks that sort of there's incredibly simple rules underlying our reality, but it can create arbitrary complexity. But there is simple equations. What, I'm asking a million questions that nobody knows the answer to, but. I have no idea, why is it simple? It could be the case that there's like a filter iteration at play. The only things that physicists find interesting are the ones that are simple enough they could describe it mathematically. But as soon as it's a sufficiently complex system, like, oh, that's outside the realm of physics, that's biology or whatever have you. And of course, that's true. Maybe there's something where it's like, of course there will always be something that is simple when you wash away the like non important parts of whatever it is that you're studying. Just from like an information theory standpoint, there might be some like, you get to the lowest information component of it. But I don't know, maybe I'm just having a really hard time conceiving of what it would even mean for the fundamental laws to be like intrinsically complicated, like some set of equations that you can't decouple from each other. Well, no, it could be that sort of we take for granted that the laws of physics, for example, are for the most part the same everywhere or something like that, right? As opposed to the sort of an alternative could be that the rules under which the world operates is different everywhere. It's like a deeply distributed system where just everything is just chaos, not in a strict definition of chaos, but meaning like just it's impossible for equations to capture, for to explicitly model the world as cleanly as the physical does. I mean, we almost take it for granted that we can describe, we can have an equation for gravity, for action at a distance. We can have equations for some of these basic ways the planet's moving. Just the low level at the atomic scale, how the materials operate, at the high scale, how black holes operate. But it doesn't, it seems like it could be, there's infinite other possibilities And then on the gondola ride down, we decided to just jam a little bit. And it was just like, I don't know, the gondola sort of came over a mountain and you saw the city lights and we're just like jamming, like playing some music. I wouldn't describe that as transformative. I don't know why, but that popped into my mind as a moment of, in a way that wasn't associated with people I love, but more with like a thing I was doing, something that was just, it was just happy and it was just like a great moment. I don't think I can give you anything deeper than that. Well, as a musician myself, I'd love to see, as you mentioned before, music enter back into your work, back into your creative work. I'd love to see that. I'm certainly allowing it to enter back into mine. And it's a beautiful thing for a mathematician, for a scientist to allow music to enter their work. I think only good things can happen. All right, I'll try to promise you a music video by 2020. By 2020? By the end of 2020. Okay, all right, good. Give myself a longer window. All right, maybe we can like collaborate on a band type situation. What instruments do you play? The main instrument I play is violin, but I also love to dabble around on like guitar and piano. Beautiful, me too, guitar and piano. So in a mathematician's lament, Paul Lockhart writes, the first thing to understand is that mathematics is an art. The difference between math and the other arts, such as music and painting, is that our culture does not recognize it as such. So I think I speak for millions of people, myself included, in saying thank you for revealing to us the art of mathematics. So thank you for everything you do and thanks for talking today. Wow, thanks for saying that. And thanks for having me on. Thanks for listening to this conversation with Grant Sanderson. And thank you to our presenting sponsor, Cash App. Download it, use code LEXPodcast. You'll get $10 and $10 will go to FIRST, a STEM education nonprofit that inspires hundreds of thousands of young minds to become future leaders and innovators. If you enjoy this podcast, subscribe on YouTube, give it five stars on Apple Podcast, support on Patreon, or connect with me on Twitter. And now, let me leave you with some words of wisdom from one of Grant's and my favorite people, Richard Feynman. Nobody ever figures out what this life is all about, and it doesn't matter. Explore the world. Nearly everything is really interesting if you go into it deeply enough. Thank you for listening, and hope to see you next time. where none of it could be compressible into such equations. So it just seems beautiful. It's also weird, probably to the point you're making, that it's very pleasant that this is true for our minds. So it might be that our minds are biased to just be looking at the parts of the universe that are compressible. And then we can publish papers on and have nice E equals empty squared equations. Right, well, I wonder would such a world with uncompressible laws allow for the kind of beings that can think about the kind of questions that you're asking? That's true. Right, like an anthropic principle coming into play in some weird way here? I don't know, like I don't know what I'm talking about at all. Maybe the universe is actually not so compressible, but the way our brain, the way our brain evolved we're only able to perceive the compressible parts. I mean, we are, so this is the sort of Chomsky argument. We are just descendants of apes over like really limited biological systems. So it totally makes sense that we're really limited little computers, calculators, that are able to perceive certain kinds of things and the actual world is much more complicated. Well, but we can do pretty awesome things, right? Like we can fly spaceships and we have to have some connection of reality to be able to take our potentially oversimplified models of the world, but then actually twist the world to our will based on it. So we have certain reality checks that like physics isn't too far a field simply based on what we can do. Yeah, the fact that we can fly is pretty good. It's great, yeah, like it's a proof of concept that the laws we're working with are working well. So I mentioned to the internet that I'm talking to you and so the internet gave some questions. So I apologize for these, but do you think we're living in a simulation that the universe is a computer or the universe is a computation running on a computer? It's conceivable. What I don't buy is, you know, you'll have the argument that, well, let's say that it was the case that you can have simulations. Then the simulated world would itself eventually get to a point where it's running simulations. And then the second layer down would create a third layer down and on and on and on. So probabilistically, you just throw a dart at one of those layers, we're probably in one of the simulated layers. I think if there's some sort of limitations on like the information processing of whatever the physical world is, like it quickly becomes the case that you have a limit to the layers that could exist there because like the resources necessary to simulate a universe like ours clearly is a lot just in terms of the number of bits at play. And so then you can ask, well, what's more plausible? That there's an unbounded capacity of information processing in whatever the like highest up level universe is, or that there's some bound to that capacity, which then limits like the number of levels available. How do you play some kind of probability distribution on like what the information capacity is? I have no idea. But I don't, like people almost assume a certain uniform probability over all of those meta layers that could conceivably exist when it's a little bit like a Pascal's wager on like you're not giving a low enough prior to the mere existence of that infinite set of layers. Yeah, that's true. But it's also very difficult to contextualize the amount. So the amount of information processing power required to simulate like our universe seems like amazingly huge. But you can always raise two to the power of that. Yeah, like numbers get big. And we're easily humbled by basically everything around us. So it's very difficult to kind of make sense of anything actually when you look up at the sky and look at the stars and the immensity of it all, to make sense of the smallness of us, the unlikeliness of everything that's on this earth coming to be, then you could basically anything could be, all laws of probability go out the window to me because I guess because the amount of information under which we're operating is very low. We basically know nothing about the world around us, relatively speaking. And so when I think about the simulation hypothesis, I think it's just fun to think about it. But it's also, I think there is a thought experiment kind of interesting to think of the power of computation, whether the limits of a Turing machine, sort of the limits of our current computers, when you start to think about artificial intelligence, how far can we get with computers? And that's kind of where the simulation hypothesis used with me as a thought experiment is the universe just a computer? Is it just a computation? Is all of this just a computation? And sort of the same kind of tools we apply to analyzing algorithms, can that be applied? If we scale further and further and further, will the arbitrary power of those systems start to create some interesting aspects that we see in our universe? Or is something fundamentally different needs to be created? Well, it's interesting that in our universe, it's not arbitrarily large, the power, that you can place limits on, for example, how many bits of information can be stored per unit area. Right, like all of the physical laws, you've got general relativity and quantum coming together to give you a certain limit on how many bits you can store within a given range before it collapses into a black hole. The idea that there even exists such a limit is at the very least thought provoking, when naively you might assume, oh, well, technology could always get better and better, we could get cleverer and cleverer, and you could just cram as much information as you want into like a small unit of space, that makes me think, it's at least plausible that whatever the highest level of existence is doesn't admit too many simulations or ones that are at the scale of complexity that we're looking at. Obviously, it's just as conceivable that they do and that there are many, but I guess what I'm channeling is the surprise that I felt upon learning that fact, that there are, that information is physical in this way. There's a finiteness to it. Okay, let me just even go off on that. From a mathematics perspective and a psychology perspective, how do you mix, are you psychologically comfortable with the concept of infinity? I think so. Are you okay with it? I'm pretty okay, yeah. Are you okay? No, not really, it doesn't make any sense to me. I don't know, like how many words, how many possible words do you think could exist that are just like strings of letters? So that's a sort of mathematical statement as beautiful and we use infinity in basically everything we do, everything we do in science, math, and engineering, yes. But you said exist, the question is, you said letters or words? I said words. Words. To bring words into existence to me, you have to start like saying them or like writing them or like listing them. That's an instantiation. Okay, how many abstract words exist? Well, the idea of an abstract. The idea of abstract notions and ideas. I think we should be clear on terminology. I mean, you think about intelligence a lot, like artificial intelligence. Would you not say that what it's doing is a kind of abstraction? That like abstraction is key to conceptualizing the universe? You get this raw sensory data. I need something that every time you move your face a little bit and they're not pixels, but like analog of pixels on my retina changed entirely, that I can still have some coherent notion of this is Lex, I'm talking to Lex, right? What that requires is you have a disparate set of possible images hitting me that are unified in a notion of Lex, right? That's a kind of abstraction. It's a thing that could apply to a lot of different images that I see and it represents it in a much more compressed way and one that's like much more resilient to that. I think in the same way, if I'm talking about infinity as an abstraction, I don't mean nonphysical woo woo, like ineffable or something. What I mean is it's something that can apply to a multiplicity of situations that share a certain common attribute in the same way that the images of like your face on my retina share enough common attributes that I can put the single notion to it. Like in that way, infinity is an abstraction and it's very powerful and it's only through such abstractions that we can actually understand like the world and logic and things. And in the case of infinity, the way I think about it, the key entity is the property of always being able to add one more. Like no matter how many words you can list, you just throw an A at the end of one and you have another conceivable word. You don't have to think of all the words at once. It's that property, the oh, I could always add one more that gives it this nature of infiniteness in the same way that there's certain like properties of your face that give it the Lexness, right? So like infinity should be no more worrying than the I can always add one more sentiment. That's a really elegant, much more elegant way than I could put it. So thank you for doing that as yet another abstraction. And yes, indeed, that's what our brain does. That's what intelligent systems do. That's what programming does. That's what science does is build abstraction on top of each other. And yet there is at a certain point abstractions that go into the quote woo, right? Sort of, and because we're now, it's like we built this stack of, you know, the only thing that's true is the stuff that's on the ground. Everything else is useful for interpreting this. And at a certain point you might start floating into ideas that are surreal and difficult and take us into areas that are disconnected from reality in a way that we could never get back. What if instead of calling these abstract, how different would it be in your mind if we called them general? And the phenomenon that you're describing is overgeneralization. When you try to have a concept or an idea that's so general as to apply to nothing in particular in a useful way, does that map to what you're thinking of when you think of? First of all, I'm playing little just for the fun of it. Devil's advocate. And I think our cognition, our mind is unable to visualize. So you do some incredible work with visualization and video. I think infinity is very difficult to visualize for our mind. We can delude ourselves into thinking we can visualize it, but we can't. I don't, I mean, I don't, I would venture to say it's very difficult. And so there's some concepts of mathematics, like maybe multiple dimensions, we could sort of talk about that are impossible for us to truly intuit, like, and it just feels dangerous to me to use these as part of our toolbox of abstractions. On behalf of your listeners, I almost fear we're getting too philosophical. Right? Heck no. Heck no. I think to that point for any particular idea like this, there's multiple angles of attack. I think the, when we do visualize infinity, what we're actually doing, you know, you write dot, dot, dot, right? One, two, three, four, dot, dot, dot, right? Those are symbols on the page that are insinuating a certain infinity. What you're capturing with a little bit of design there is the I can always add one more property, right? I think I'm just as uncomfortable with you are if you try to concretize it so much that you have a bag of infinitely many things that I actually think of, no, not one, two, three, four, dot, dot, dot, one, two, three, four, five, six, seven, eight. I try to get them all in my head and you realize, oh, you know, your brain would literally collapse into a black hole, all of that. And I honestly feel this with a lot of math that I try to read where I don't think of myself as like particularly good at math in some ways. Like I get very confused often when I am going through some of these texts. And often what I'm feeling in my head is like, this is just so damn abstract. I just can't wrap my head around it. I just want to put something concrete to it that makes me understand. And I think a lot of the motivation for the channel is channeling that sentiment of, yeah, a lot of the things that you're trying to read out there, it's just so hard to connect to anything that you spend an hour banging your head against a couple of pages and you come out not really knowing anything more other than some definitions maybe and a certain sense of self defeat, right? One of the reasons I focus so much on visualizations is that I'm a big believer in, I'm sorry, I'm just really hampering on this idea of abstraction, being clear about your layers of abstraction, right? It's always tempting to start an explanation from the top to the bottom, okay? You give the definition of a new theorem. You're like, this is the definition of a vector space. For example, that's how we'll start a course. These are the properties of a vector space. First from these properties, we will derive what we need in order to do the math of linear algebra or whatever it might be. I don't think that's how understanding works at all. I think how understanding works is you start at the lowest level you can get at where rather than thinking about a vector space, you might think of concrete vectors that are just lists of numbers or picturing it as like an arrow that you draw, which is itself like even less abstract than numbers because you're looking at quantities, like the distance of the x coordinate, the distance of the y coordinate. It's as concrete as you could possibly get and it has to be if you're putting it in a visual, right? It's an actual arrow. It's an actual vector. You're not talking about like a quote unquote vector that could apply to any possible thing. You have to choose one if you're illustrating it. And I think this is the power of being in a medium like video or if you're writing a textbook and you force yourself to put a lot of images is with every image, you're making a choice. With each choice, you're showing a concrete example. With each concrete example, you're aiding someone's path to understanding. I'm sorry to interrupt you, but you just made me realize that that's exactly right. So the visualizations you're creating while you're sometimes talking about abstractions, the actual visualization is an explicit low level example. Yes. So there's an actual, like in the code, you have to say what the vector is, what's the direction of the arrow, what's the magnitude of the, yeah. So that's, you're going, the visualization itself is actually going to the bottom of that. And I think that's very important. I also think about this a lot in writing scripts where even before you get to the visuals, the first instinct is to, I don't know why, I just always do, I say the abstract thing, I say the general definition, the powerful thing, and then I fill it in with examples later. Always, it will be more compelling and easier to understand when you flip that. And instead, you let someone's brain do the pattern recognition. You just show them a bunch of examples. The brain is gonna feel a certain similarity between them. Then by the time you bring in the definition, or by the time you bring in the formula, it's articulating a thing that's already in the brain that was built off of looking at a bunch of examples with a certain kind of similarity. And what the formula does is articulate what that kind of similarity is, rather than being a high cognitive load set of symbols that needs to be populated with examples later on, assuming someone's still with you. What is the most beautiful or awe inspiring idea you've come across in mathematics? I don't know, man. Maybe it's an idea you've explored in your videos, maybe not. What just gave you pause? What's the most beautiful idea? Small or big. So I think often, the things that are most beautiful are the ones that you have a little bit of understanding of, but certainly not an entire understanding. It's a little bit of that mystery that is what makes it beautiful. What was the moment of the discovery for you personally, almost just that leap of aha moment? So something that really caught my eye, I remember when I was little, there were these, I think the series was called like wooden books or something, these tiny little books that would have just a very short description of something on the left and then a picture on the right. I don't know who they're meant for, but maybe it's like loosely children or something like that. But it can't just be children, because of some of the things I was describing. On the last page of one of them, somewhere tiny in there was this little formula that on the left hand had a sum over all of the natural numbers. It's like one over one to the S plus one over two to the S plus one over three to the S on and on to the infinity. Then on the other side had a product over all of the primes and it was a certain thing had to do with all the primes. And like any good young math enthusiast, I'd probably been indoctrinated with how chaotic and confusing the primes are, which they are. And seeing this equation where on one side you have something that's as understandable as you could possibly get, the counting numbers. And on the other side is all the prime numbers. It was like this, whoa, they're related like this? There's a simple description that includes all the primes getting wrapped together like this. This is like the Euler product for the Zeta function, as I like later found out. The equation itself essentially encodes the fundamental theorem of arithmetic that every number can be expressed as a unique set of primes. To me still there's, I mean, I certainly don't understand this equation or this function all that well. The more I learn about it, the prettier it is. The idea that you can, this is sort of what gets you representations of primes, not in terms of primes themselves, but in terms of another set of numbers. They're like the non trivial zeros of the Zeta function. And again, I'm very kind of in over my head in a lot of ways as I like try to get to understand it. But the more I do, it always leaves enough mystery that it remains very beautiful to me. So whenever there's a little bit of mystery just outside of the understanding that, and by the way, the process of learning more about it, how does that come about? Just your own thought or are you reading? Reading, yeah. Or is the process of visualization itself revealing more to you? Visuals help. I mean, in one time when I was just trying to understand like analytic continuation and playing around with visualizing complex functions, this is what led to a video about this function. It's titled something like Visualizing the Riemann Zeta Function. It's one that came about because I was programming and tried to see what a certain thing looked like. And then I looked at it and I'm like, whoa, that's elucidating. And then I decided to make a video about it. But I mean, you try to get your hands on as much reading as you can. You know, in this case, I think if anyone wants to start to understand it, if they have like a math background like they studied some in college or something like that, like the Princeton Companion to Math has a really good article on analytic number theory. And that itself has a whole bunch of references and you know, anything has more references and it gives you this like tree to start piling through. And like, you know, you try to understand, I try to understand things visually as I go. That's not always possible, but it's very helpful when it does. You recognize when there's common themes, like in this case, Cousins of the Fourier Transform that come into play and you realize, oh, it's probably pretty important to have deep intuitions of the Fourier Transform, even if it's not explicitly mentioned in like these texts. And you try to get a sense of what the common players are. But I'll emphasize again, like, I feel very in over my head when I try to understand the exact relation between like the zeros of the Riemann Zeta function and how they relate to the distribution of primes. I definitely understand it better than I did a year ago. I definitely understand it on 100th as well as the experts on the matter do, I assume. But the slow path towards getting there is, it's fun, it's charming, and like to your question, very beautiful. And the beauty is in the, what, in the journey versus the destination? Well, it's that each thing doesn't feel arbitrary. I think that's a big part, is that you have these unpredictable, no, yeah, these very unpredictable patterns or these intricate properties of like a certain function. But at the same time, it doesn't feel like humans ever made an arbitrary choice in studying this particular thing. So, you know, it feels like you're speaking to patterns themselves or nature itself. That's a big part of it. I think things that are too arbitrary, it's just hard for those to feel beautiful because this is sort of what the word contrived is meant to apply to, right? And when they're not arbitrary means it could be, you can have a clean abstraction and intuition that allows you to comprehend it. Well, to one of your first questions, it makes you feel like if you came across another intelligent civilization, that they'd be studying the same thing. Maybe with different notation. Certainly, yeah, but yeah. Like that's what, I think you talked to that other civilization, they're probably also studying the zeros of the Riemann Zeta function or like some variant thereof that is like a clearly equivalent cousin or something like that. But that's probably on their docket. Whenever somebody does a lot of something amazing, I'm gonna ask the question that you've already been asked a lot and that you'll get more and more asked in your life. But what was your favorite video to create? Oh, favorite to create. One of my favorites is, the title is Who Cares About Topology? You want me to pull it up or no? If you want, sure, yeah. It is about, well, it starts by describing an unsolved problem that's still unsolved in math called the inscribed square problem. You draw any loop and then you ask, are there four points on that loop that make a square? Totally useless, right? This is not answering any physical questions. It's mostly interesting that we can't answer that question. And it seems like such a natural thing to ask. Now, if you weaken it a little bit and you ask, can you always find a rectangle? You choose four points on this curve, can you find a rectangle? That's hard, but it's doable. And the path to it involves things like looking at a torus, this surface with a single hole in it, like a donut, or looking at a mobius strip. In ways that feel so much less contrived to when I first, as like a little kid, learned about these surfaces and shapes, like a mobius strip and a torus. Like what you learn is, oh, this mobius strip, you take a piece of paper, put a twist, glue it together, and now you have a shape with one edge and just one side. And as a student, you should think, who cares, right? Like, how does that help me solve any problems? I thought math was about problem solving. So what I liked about the piece of math that this was describing that was in this paper by a mathematician named Vaughn was that it arises very naturally. It's clear what it represents. It's doing something. It's not just playing with construction paper. And the way that it solves the problem is really beautiful. So kind of putting all of that down and concretizing it, right? Like I was talking about how when you have to put visuals to it, it demands that what's on screen is a very specific example of what you're describing. The construction here is very abstract in nature. You describe this very abstract kind of surface in 3D space. So then when I was finding myself, in this case, I wasn't programming, I was using a grapher that's like built into OSX for the 3D stuff to draw that surface, you realize, oh man, the topology argument is very non constructive. I have to make a lot of, you have to do a lot of extra work in order to make the surface show up. But then once you see it, it's quite pretty and it's very satisfying to see a specific instance of it. And you also feel like, ah, I've actually added something on top of what the original paper was doing that it shows something that's completely correct. That's a very beautiful argument, but you don't see what it looks like. And I found something satisfying in seeing what it looked like that could only ever have come about from the forcing function of getting some kind of image on the screen to describe the thing I was talking about. So you almost weren't able to anticipate what it's gonna look like. I had no idea. I had no idea. And it was wonderful, right? It was totally, it looks like a Sydney Opera House or some sort of Frank Gehry design. And it was, you knew it was gonna be something and you can say various things about it. Like, oh, it touches the curve itself. It has a boundary that's this curve on the 2D plane. It all sits above the plane. But before you actually draw it, it's very unclear what the thing will look like. And to see it, it's very, it's just pleasing, right? So that was fun to make, very fun to share. I hope that it has elucidated for some people out there where these constructs of topology come from, that it's not arbitrary play with construction paper. So let's, I think this is a good sort of example to talk a little bit about your process. You have a list of ideas. So that's sort of the curse of having an active and brilliant mind is I'm sure you have a list that's growing faster than you can utilize. Now I'm ahead, absolutely. But there's some sorting procedure depending on mood and interest and so on. But okay, so you pick an idea and then you have to try to write a narrative arc that sort of, how do I elucidate? How do I make this idea beautiful and clear and explain it? And then there's a set of visualizations that will be attached to it. Sort of, you've talked about some of this before, but sort of writing the story, attaching the visualizations. Can you talk through interesting, painful, beautiful parts of that process? Well, the most painful is if you've chosen a topic that you do want to do, but then it's hard to think of, I guess how to structure the script. This is sort of where I have been on one for like the last two or three months. And I think that ultimately the right resolution is just like set it aside and instead do some other things where the script comes more naturally. Because you sort of don't want to overwork a narrative. The more you've thought about it, the less you can empathize with the student who doesn't yet understand the thing you're trying to teach. Who is the judger in your head? Sort of the person, the creature, the essence that's saying this sucks or this is good. And you mentioned kind of the student you're thinking about. Can you, who is that? What is that thing? That says, the perfectionist that says this thing sucks. You need to work on that for another two, three months. I don't know. I think it's my past self. I think that's the entity that I'm most trying to empathize with is like you take who I was, because that's kind of the only person I know. Like you don't really know anyone other than versions of yourself. So I start with the version of myself that I know who doesn't yet understand the thing, right? And then I just try to view it with fresh eyes, a particular visual or a particular script. Like, is this motivating? Does this make sense? Which has its downsides, because sometimes I find myself speaking to motivations that only myself would be interested in. I don't know, like I did this project on quaternions where what I really wanted was to understand what are they doing in four dimensions? Can we see what they're doing in four dimensions, right? And I came up with a way of thinking about it that really answered the question in my head that made me very satisfied and being able to think about concretely with a 3D visual, what are they doing to a 4D sphere? And so I'm like, great, this is exactly what my past self would have wanted, right? And I make a thing on it. And I'm sure it's what some other people wanted too. But in hindsight, I think most people who wanna learn about quaternions are like robotics engineers or graphics programmers who want to understand how they're used to describe 3D rotations. And like their use case was actually a little bit different than my past self. And in that way, like, I wouldn't actually recommend that video to people who are coming at it from that angle of wanting to know, hey, I'm a robotics programmer. Like, how do these quaternion things work to describe position in 3D space? I would say other great resources for that. If you ever find yourself wanting to say like, but hang on, in what sense are they acting in four dimensions? Then come back. But until then, that's a little different. Yeah, it's interesting because you have incredible videos on neural networks, for example. And from my sort of perspective, because I've probably, I mean, I looked at the, is sort of my field and I've also looked at the basic introduction of neural networks like a million times from different perspectives. And it made me realize that there's a lot of ways to present it. So you were sort of, you did an incredible job. I mean, sort of the, but you could also do it differently and also incredible. Like to create a beautiful presentation of a basic concept requires sort of creativity, requires genius and so on, but you can take it from a bunch of different perspectives. And that video on neural networks made me realize that. And just as you're saying, you kind of have a certain mindset, a certain view, but from a, if you take a different view from a physics perspective, from a neuroscience perspective, talking about neural networks or from a robotics perspective, or from, let's see, from a pure learning, statistics perspective. So you can create totally different videos. And you've done that with a few actually concepts where you've have taken different cuts, like at the Euler equation, right? You've taken different views of that. I think I've made three videos on it and I definitely will make at least one more. Right? Never enough. Never enough. So you don't think it's the most beautiful equation in mathematics? Like I said, as we represent it, it's one of the most hideous. It involves a lot of the most hideous aspects of our notation. I talked about E, the fact that we use pi instead of tau, the fact that we call imaginary numbers imaginary, and then, hence, I actually wonder if we use the I because of imaginary. I don't know if that's historically accurate, but at least a lot of people, they read the I and they think imaginary. Like all three of those facts, it's like those are things that have added more confusion than they needed to, and we're wrapping them up in one equation. Like boy, that's just very hideous, right? The idea is that it does tie together when you wash away the notation. Like it's okay, it's pretty, it's nice, but it's not like mind blowing greatest thing in the universe, which is maybe what I was thinking of when I said, like once you understand something, it doesn't have the same beauty. Like I feel like I understand Euler's formula, and I feel like I understand it enough to sort of see the version that just woke up that hasn't really gotten itself dressed in the morning that's a little bit groggy, and there's bags under its eyes. So you're past the dating stage, you're no longer dating, right? I'm still dating the Zeta function, and like she's beautiful and right, and like we have fun, and it's that high dopamine part, but like maybe at some point we'll settle into the more mundane nature of the relationship where I like see her for who she truly is, and she'll still be beautiful in her own way, but it won't have the same romantic pizzazz, right? Well, that's the nice thing about mathematics. I think as long as you don't live forever, there'll always be enough mystery and fun with some of the equations. Even if you do, the rate at which questions comes up is much faster than the rate at which answers come up, so. If you could live forever, would you? I think so, yeah. So you think, you don't think mortality is the thing that makes life meaningful? Would your life be four times as meaningful if you died at 25? So this goes to infinity. I think you and I, that's really interesting. So what I said is infinite, not four times longer. I said infinite. So the actual existence of the finiteness, the existence of the end, no matter the length, is the thing that may sort of, from my comprehension of psychology, it's such a deeply human, it's such a fundamental part of the human condition, the fact that there is, that we're mortal, that the fact that things end, it seems to be a crucial part of what gives them meaning. I don't think, at least for me, it's a very small percentage of my time that mortality is salient, that I'm aware of the end of my life. What do you mean by me? I'm trolling. Is it the ego, is it the id, or is it the superego? The reflective self, the Wernicke's area that puts all this stuff into words. Yeah, a small percentage of your mind that is actually aware of the true motivations that drive you. But my point is that most of my life, I'm not thinking about death, but I still feel very motivated to make things and to interact with people, experience love or things like that. I'm very motivated, and it's strange that that motivation comes while death is not in my mind at all. And this might just be because I'm young enough that it's not salient. Or it's in your subconscious, or that you've constructed an illusion that allows you to escape the fact of your mortality by enjoying the moment, sort of the existential approach to life. Could be. Gun to my head, I don't think that's it. Yeah, another sort of way to say gun to the head is sort of the deep psychological introspection of what drives us. I mean, that's, in some ways to me, I mean, when I look at math, when I look at science, is a kind of an escape from reality in a sense that it's so beautiful. It's such a beautiful journey of discovery that it allows you to actually, it sort of allows you to achieve a kind of immortality of explore ideas and sort of connect yourself to the thing that is seemingly infinite, like the universe, right? That allows you to escape the limited nature of our little, of our bodies, of our existence. What else would give this podcast meaning? That's right. If not the fact that it will end. This place closes in 40 minutes. And it's so much more meaningful for it. How much more I love this room because we'll be kicked out. So I understand just because you're trolling me doesn't mean I'm wrong. But I take your point. I take your point. Boy, that would be a good Twitter bio. Just because you're trolling me doesn't mean I'm wrong. Yeah, and sort of difference in backgrounds. I'm a bit Russian, so we're a bit melancholic and seem to maybe assign a little too much value to suffering and mortality and things like that. Makes for a better novel, I think. Oh yeah, you need some sort of existential threat to drive a plot. So when do you know when the video is done when you're working on it? That's pretty easy actually, because I'll write the script. I want there to be some kind of aha moment in there. And then hopefully the script can revolve around some kind of aha moment. And then from there, you're putting visuals to each sentence that exists, and then you narrate it, you edit it all together. So given that there's a script, the end becomes quite clear. And as I animate it, I often change certainly the specific words, but sometimes the structure itself. But it's a very deterministic process at that point. It makes it much easier to predict when something will be done. How do you know when a script is done? It's like, for problem solving videos, that's quite simple. It's once you feel like someone who didn't understand the solution now could. For things like neural networks, that was a lot harder because like you said, there's so many angles at which you could attack it. And there, it's just at some point you feel like this asks a meaningful question and it answers that question, right? What is the best way to learn math for people who might be at the beginning of that journey? I think that's a question that a lot of folks kind of ask and think about. And it doesn't, even for folks who are not really at the beginning of their journey, like there might be actually deep in their career, some type they've taken college or taken calculus and so on, but still wanna sort of explore math. What would be your advice instead of education at all ages? Your temptation will be to spend more time like watching lectures or reading. Try to force yourself to do more problems than you naturally would. That's a big one. Like the focus time that you're spending should be on like solving specific problems and seek entities that have well curated lists of problems. So go into like a textbook almost and the problems in the back of a textbook kind of thing, back of a chapter. So if you can take a little look through those questions at the end of the chapter before you read the chapter, a lot of them won't make sense. Some of them might, and those are the best ones to think about. A lot of them won't, but just take a quick look and then read a little bit of the chapter and then maybe take a look again and things like that. And don't consider yourself done with the chapter until you've actually worked through a couple exercises. And this is so hypocritical, right? Cause I like put out videos that pretty much never have associated exercises. I just view myself as a different part of the ecosystem, which means I'm kind of admitting that you're not really learning, or at least this is only a partial part of the learning process if you're watching these videos. I think if someone's at the very beginning, like I do think Khan Academy does a good job. They have a pretty large set of questions you can work through. Just the very basics, sort of just picking up, getting comfortable with the very basic linear algebra, calculus or so on, Khan Academy. Programming is actually I think a great, like learn to program and like let the way that math is motivated from that angle push you through. I know a lot of people who didn't like math got into programming in some way and that's what turned them on to math. Maybe I'm biased cause like I live in the Bay area, so I'm more likely to run into someone who has that phenotype. But I am willing to speculate that that is a more generalizable path. So you yourself kind of in creating the videos are using programming to illuminate a concept, but for yourself as well. So would you recommend somebody try to make a, sort of almost like try to make videos? Like you do as a way to learn? So one thing I've heard before, I don't know if this is based on any actual study. This might be like a total fictional anecdote of numbers, but it rings in the mind as being true. You remember about 10% of what you read, you remember about 20% of what you listen to, you remember about 70% of what you actively interact with in some way, and then about 90% of what you teach. This is a thing I heard again, those numbers might be meaningless, but they ring true, don't they, right? I'm willing to say I learned nine times better if I'm teaching something than reading. That might even be a low ball, right? So doing something to teach or to like actively try to explain things is huge for consolidating the knowledge. Outside of family and friends, is there a moment you can remember that you would like to relive because it made you truly happy or it was transformative in some fundamental way? A moment that was transformative. Or made you truly happy? Yeah, I think there's times, like music used to be a much bigger part of my life than it is now, like when I was a, let's say a teenager, and I can think of some times in like playing music. There was one, like my brother and a friend of mine, so this slightly violates the family and friends, but it was the music that made me happy. They were just accompanying. We like played a gig at a ski resort such that you like take a gondola to the top and like did a thing.

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