第 373 期·中文精讀

馬諾利斯·凱利斯:人類文明的演化與超級智慧 AI

Manolis Kellis: Evolution of Human Civilization and Superintelligent AI

計算生物學家從基因組與演化視角談人類文明的走向,以及超級智慧 AI 可能帶來的變革與風險。

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中文精讀由 AI 基於原始訪談改寫,非官方翻譯,可能存在偏差或錯誤;表述以原音訊與官方文稿為準。說明

馬諾利斯·凱利斯(Manolis Kellis)在 MIT 領導計算生物學組,這是他第五次上 Lex Fridman 的播客。他區分人和 AI 的那個說法,比絕大多數討論都硬:

AI 只有新皮層。它沒有邊緣系統,沒有恐懼、逃跑、飢餓這一整套東西。(馬諾利斯·凱利斯)

人的大腦是在舊系統上一層層加蓋起來的。最上面是新皮層,負責推理;中間壓著情緒;最底下是恐懼、戰慄、轉身就跑這些反應。AI 把這套底層全跳過,直接拿到了最上面那一層。

被跳過的部分,恰恰是創造力的來源之一。凱利斯說得很直接:

我有時會生氣,有時會餓,這些可能就是我的創造力來源。我不想變成一個沒有情緒的機器人。我想惹點麻煩,想說錯話,想冒出一句尷尬的評論,把別人頂到有反應的位置上,讓別人換個角度去想事情。(馬諾利斯·凱利斯)

他甚至覺得當下社會正在滑向一個沒有幽默感的空間:所有人都怕說錯話,於是集體沉默,然後開始不喜歡自己的工作。

你身體裡跑著三億年前的程式碼

他講人體演化層積的方式,比任何教科書都直觀:

我每一次細胞分裂,我就是酵母,就是一個單細胞生物。我每一次吸氣、肺擴張、每一次心跳,我就是一條魚,心臟幾乎沒怎麼變過,血在血管裡流、氧氣、這些東西變了一點點。免疫系統,我們是靈長類。社會行為,我們是獼猴。你追蹤每一個行為,都能在演化裡找到出處。(馬諾利斯·凱利斯)

這些舊程式一直沒被"替換掉"。它們還在執行,還在分裂、代謝、抵禦病原體、爭奪地位。新皮層只是搭在最上面的一層薄殼。

軟體工程裡最常見的做法是推倒重寫,把 v2.0 從零開始。演化從來不這麼幹,它只會往上打補丁,而且補丁打得極其聰明。三億年前的程式碼今天還在你的細胞裡跑,還跑得很好。任何一個被要求維護過祖傳程式碼庫的工程師,都能體會這有多難。

這層認知也順手給了凱利斯一個倫理立場。既然體內每一條魚、每一隻獼猴、每一個單細胞都還在工作,那麼殺戮的邊界就不只是"別殺人":

所有那些生物過程從來沒有停止存在,它們一直在我們體內活著。所以別再殺別的物種了。(馬諾利斯·凱利斯)

演化越複雜,反而越快

他提出了一個看著矛盾的現象。1960 年代人們設計遺傳演算法,把複雜度一路加上去,很快就卡死了。原因不難推:隨著複雜度提高,把你從"能用"推到"不能用"的突變數量指數級增長,把你推向"更好"的突變數量指數級下降。複雜度越高,演化出更好東西的機率越趨近於零。

生物演化幾乎是反過來的,越複雜,看起來越快。

時間線擺出來是這樣的:幾十億年才搞出一個會分裂的細胞,然後是多細胞、複雜的身體結構、精密的感官。蝙蝠在幾百萬年之內演化出飛行,還順帶演化出聲吶。幾百萬年在演化的尺度上,是眨一下眼。

凱利斯的解釋是,系統學會了演化。演化本身被演化出來了。

模組化、層級結構、可複用元件,這些東西讓有意義的改動可以發生,同時不把整個系統弄崩。真正的門檻落在變異能不能被容納上,能不能變異反倒好辦。一個系統如果一改就全廢,它的可演化性就是零。反過來,一個系統如果設計成"改動區域性不影響全域性",它就能在複雜度不斷上升的同時保持演化的速度。

人類今天寫軟體、搭組織、建公司,反覆撞上的就是這堵牆。

四十億年只做了一件事:處理資訊

如果要把整條演化軌跡壓縮成一句話,凱利斯選的是資訊處理。

最早的一步是趨化性(chemotaxis)。細菌沿著化學梯度移動,濃度高一點就往那邊走。這是最原始的資訊處理,也是捕食的第一步。

下一步是感光。所有已知的生命都演化在這塊每 24 小時轉一圈的岩石上,亮、暗、亮、暗。光既是能量,也是資訊:光告訴你上面在哪。有了這個,就能演化出感光細胞,再演化出複雜的眼睛、多隻眼睛,然後識別形狀、運動、方向。有了這些輸入,才有在上面搭認知基礎設施的可能,去建模整個環境。

按這條線看,人類的出現是下一步。

有些動物在很多維度上比我們強得多,但在認知這個維度上,我們在這個星球上無人能及。(馬諾利斯·凱利斯)

我們沒有爪子、沒有獠牙、跑得不快、嗅覺一般,卻成了優勢物種。原因就是那條主線:資訊處理能力一直在往上加。

下一層演化,用自己搭自己

沿著這條線往前推,凱利斯看到一個可能性:下一層演化,是自己搭自己。

人類用這套舊硬體,把認知能力推到極致,然後創造出了技術。技術能做很多原本只有認知才能做的任務,卻不背上前面所有演化層留下的包袱。再往前,就是我們用現在的聰明才智去設計更好的程式語言,用程式語言去訓練 ChatGPT,再讓它去寫下一層軟體,加速下一輪。

他注意到一個能耗上的落差。大多數動物把 80% 的清醒時間花在找食物和搭窩上,人類大約只花 1%,剩下全給了創造性的活動。AI 連這一層都不用操心,它整個活在認知空間裡。

把這理解成演化的下一個自然階段,其實挺順的。(馬諾利斯·凱利斯)

還有一層讓凱利斯覺得漂亮:造出這一層的東西,還活著,還在旁邊看著,還能參與引導。他希望這種共存關係能一直持續下去。

讀自己的程式碼,是內省的最後一層

人類開始讀懂自己的基因組,這件事在凱利斯眼裡意義特殊。它是內省的終極形態:不只是觀察自己的念頭,而是直接讀那套生成念頭的原始碼。

讀懂之後就有干預的可能。不只是和 AI 協作來增強能力,還包括看清哪些神經通路支撐著共情、判斷、正義感這類東西,然後透過神經元層面的、化學的、電的干預去調整。再往後,理解的不只是神經元的連線和功能,還有基因本身。

終點的清單很實在:能不能消掉精神疾病,能不能消掉神經退行,能不能消掉痴呆。

凱利斯的答案在這裡拐了個彎。他不想要一個沒有包袱的升級版。

憤怒、飢餓、坐立不安,這些東西混在一起才讓人有意思。一個永遠冷靜、永遠得體、永遠說正確的話的人,不會冒出新想法。他覺得那種"更理性"的人,其實是把人身上最像人的那部分抑制掉了,代價是創造力和新鮮感一起沒了。

同一個模型,換幾個提示詞就換了個人

凱利斯有一個特別有意思的類比。他說在 ChatGPT 出來之前,他跟學生說:

你就問自己,馬諾利斯現在會怎麼說?你們每個人腦子裡都已經有一個還不錯的我在跑。(馬諾利斯·凱利斯)

這東西的正式名字叫橡皮鴨除錯法(rubber duck debugging)。你對著桌上那隻一動不動的橡皮鴨,一步步解釋你寫了什麼程式碼、為什麼會出現這個 bug。解釋著解釋著,你自己就想通了。鴨子的作用在於提供一個"你必須把話講清楚"的框架,它自己一個字都不用說。

他講了一個夢。他夢見自己在一個禮堂講課,一位朋友正在給他講癌症基因組和癌細胞如何演化的深刻洞見,講得極其精彩。醒來他意識到,那些洞見全都是他自己的神經元產出的,只是被投射到了夢裡的朋友身上。

我的神經元本來就有這個能力,但只有在"你現在是某某教授,你是這個領域的專家,你怎麼說"這樣的提示下,它才會啟動。(馬諾利斯·凱利斯)

他還有更小的例子。遇到不知道怎麼辦的時候,他就問自己:

我不知道該怎麼做,讓我問問虛擬的 Lex,你會怎麼做?虛擬的 Lex 會說,要善良。我說,哦對。(馬諾利斯·凱利斯)

他自己在沒被提示的狀態下未必做得到,但提示一給,答案就出來了。

他最常用的提示詞是"一步一步想"。這招對他十歲的兒子也管用。孩子做數學題總想一步到位,凱利斯完全知道他會錯在哪一步。但只要說一句"請一步一步來",孩子就進入狀態了。

ChatGPT 的"think step by step"能起效,本質上是一回事。人類在訓練模型時用的那套基於人類反饋的強化學習(RLHF),也是把同樣的東西反向灌了回去。

模型把知識、語境和風格拆開了

還有一個讓凱利斯覺得美的發現。語言模型在極度過引數化之後,自己學會了某件事:知識、語境和形式是可以分離的。

你可以用俳句寫科學知識,也可以用莎士比亞的腔調講同一件事。模型能在幾個提示之內,穩定地切換成"莎士比亞混搭大衛·鮑伊"的風格,而且切換得令人信服。這種變換的幅度和穩定度,人很難做到。

照這個思路推下去,人的心智裡,同樣的三個維度大概也是可分離的。這給心理學提供了一批新詞彙。今天我們形容一個人的說話方式,只能說"他讓我想起某位朋友",含含糊糊。如果模型能幫我們精確地命名這些維度,這些概念在人的心智裡也會變得更清晰,然後再被更好地編碼回機器。

解釋性這件事上,卷積網路好辦。它有區域性性,有層級結構,不同層看不同尺度的東西,啟用特徵能對上影象裡的具體部位。語言模型難辦得多,但凱利斯覺得還是能做。辦法是"消融":把網路的這一塊去掉,看它會生成什麼樣的提示詞,然後一點點逼近一套能描述這些維度的語言。

基座模型是精神病院,也是思想史

Lex 提到一個想法:去看看沒經過 RLHF 對齊、沒有安全審查的基座模型,探索它能生成多少仇恨言論,能復活多少人類自己幹過的事,能切換成共產主義、法西斯主義、資本主義中的哪一種,看看哪些部分被啟用。

凱利斯順著往下講了一層。他說從某個角度講,精神病院住的正是這些人:心智失去了控制,漂到自己 psyche 裡某個特定的位置出不來了。他看那些描繪錯亂心智的電影,常常在裡面看到自己的某個版本,沒那麼極端而已。躁鬱、精神分裂、抑鬱、自閉,每個人身上都有這些方向上的投影,只是向量短很多。

這些行為都在每個人的向量張成空間裡,大多數人的向量幅度小,因為他們沒遺傳到那組特定的變異,或者沒暴露在那樣的環境裡,或者成長過程中的那套對齊機制對他們沒起效。(馬諾利斯·凱利斯)

順著這個邏輯,嬰兒就是沒對齊的模型。凱利斯說,每個嬰兒基本上都是一個怒火中燒的自戀者。

但接著他補了一句:也不全是。他觀察自己三個孩子長大,性格里有一部分從頭到尾沒變過,另一部分在不同階段劇烈擺動。有個孩子永遠要更大的那塊,有個孩子要求絕對平均,第三個說沒關係我可以拿小的,你們別擔心我。這些差異在很早很早就出現了。

你選的朋友,正在寫你的明天

人也是可以被提示的。給一個人一個環境,他的行為就會跟著變。

老話是,給我看你的朋友,我告訴你你是誰。其實更像是,給我看你的朋友,我告訴你你會變成誰。(馬諾利斯·凱利斯)

第一步當然是你傾向於選像自己的人。更值得琢磨的是第二步:你的圈子裡哪些行為算正常,你就會開始採用哪些行為。

這是一種元層面的演化。你每做一次某個行為,不只是得到今天的結果,還在為明天鋪路,因為你強化了那條神經通路。凱利斯把它講成了自我實現的預言:你按你希望成為的樣子去行動,去挑那些和你想成為的樣子一致的人,那些行為就真的會變成你的常態。

一個人的生活本身,就是一套極其複雜的提示機制。你選的朋友、你選的環境、你怎麼改造這個環境,全都在持續給你下指令。

教養能掰回來的,比你想的少

凱利斯是遺傳學家,也是三個孩子的父親,他在這件事上的坦白很不留情面。

作為父母,他和妻子花了巨大的精力去"修"後天那部分:教孩子分享,教他們善良,教他們開放,教他們互相信任,教他們跨過囚徒困境。所有人都只為自己打算,大家就都住在一個糟糕的地方;稍微利他一點,所有人都過得更好。他們並沒有區別對待孩子,可孩子生下來就是不一樣。

作為一個遺傳學家,我必須承認,後天能做的就那麼多。先天肯定佔了很大一塊。(馬諾利斯·凱利斯)

他給了一個統計學上的細節來解釋為什麼。常見的基因變異一般效應都很弱,因為選擇會把效應強的變異清除掉。一個大幅提高精神分裂風險的變異,不可能升到很高的頻率。能留下來的常見變異,按定義都是效應溫和的那些。

那麼問題來了。如果性格、認知、行為的差異全都由成千上萬個弱效應變異決定,按大數定律,孩子的特徵應該穩穩地落在父母兩人的中間。可現實裡兄弟姐妹的差異大得驚人。在層常見變異之上,還壓著一批以孟德爾方式遺傳的罕見變異,效應強得多,直接決定了很多行為、生理和心理的走向。

同一套邏輯也能解釋同卵雙胞胎。同卵雙胞胎的基因組幾乎完全一樣,但其中一個確診精神分裂,另一個確診的機率只有大約 50%。剩下那一半,來自發育過程中的暴露、環境、創傷,以及所有那些說不清的東西。

天才的孩子,大機率是普通人

"迴歸"這個詞的來源,凱利斯講得很有意思。二戰時戰鬥機飛行員在某次空戰裡表現特別好,回去被表揚了一通,下一次出戰往往表現變差。軍方當時的解讀是:表揚把他們毀了。

統計學上的解讀叫均值迴歸(regression to the mean)。那位飛行員受過極好的訓練,實力被推到了很高的位置,某一次他發揮到了自己分佈的上沿。下一次再抽一次樣,最可能的結果是往中間靠一點。跟表揚沒有關係。

把這個套到人身上就有點殘酷了。愛因斯坦是人類智力的某種極致,但這不意味著他的孩子和孫輩會是天才。更可能的解釋是,他本身是常見變異和罕見變異的一次極端組合,是分佈尾部的稀有采樣。他的後代從同一個底層分佈裡重新抽樣,落點大機率會往中間挪。

這套框架的用處是校正直覺。孩子的表現可以從父母均值為起點去理解,再疊加上他們繼承到的那組罕見變異帶來的偏移。

先天和後天在這裡攪成了一團。你說這是天生的,可能只是環境使然;你說這是教養出來的,可能只是同一套基因在同一個環境裡的表達。父母不僅在塑造自己的環境,同時也在塑造孩子將要出生的那個環境。孩子在那個環境裡出生,再帶著自己那一袋基因變異開始新一輪。

先天能影響後天,後天能影響先天,兩者至少是相關的、可互相預測的。想把它們拆開,非常非常難。(馬諾利斯·凱利斯)

他還順帶提了一個演化尺度上的觀察。人類作為物種,演化速度慢得可怕,一代人二三十年,拿來做選擇迴圈是很糟的設計。真正高效的選擇需要更緊的反饋迴路。免疫系統就是例子:免疫細胞在分裂時做 V(D)J 重組,幾百萬種抗體在幾天之內被造出來、被篩選、被擴增。那是嵌在人體內部、跑得比人類演化快好幾個數量級的另一套演化。

受精那一刻也一樣。幾十萬個精子在競爭,只有一個能進去。隨機性確實存在,但隨機性背後是一整套篩選機制在極小的尺度上高速運轉。

讓 AI 去做數學,讓人類負責不一樣

回到 AI。凱利斯覺得這件事最值得期待的地方,是它可能把智力追求從一條窄門裡解放出來。

過去的教育體系在篩選一種人:數學特別好的人。所有人擠向同一批學校,同一批專業,同一批崗位,彷彿人類的智力只有一個可測量的方向。可如果有 AI 能瞬間完成數學推導、寫程式碼、跑分析,那麼"數學特別好"這件事本身就不再稀缺。稀缺的會變成另一種東西:想法特別不一樣的人。

AI 可以做數學,那我們就該去找一群彼此差異極大的人湊在一起。那才是創新的真正搖籃。(馬諾利斯·凱利斯)

這個判斷背後的邏輯很硬。創新的本質是把兩個原本不相干的點連起來,而連線的可能性來自差異。十個數學天才坐一桌,他們大機率會討論同一個問題,用同一套語言,得出同一類結論。一個生物學家、一個木匠、一個詩人、一個機械工程師坐一桌,反而更可能撞出點東西。過去我們沒法這麼組織,因為專業分工要求每個人在單一維度上足夠深。現在深度可以外包,廣度就變得值錢了。

他還提到另一條路徑:語言模型可以用幾個提示詞就被推離中心。你讓它扮演某種特定的思路,它就會穩定地沿著那個方向去思考和表達。這和人類被環境塑造是一回事。

一個模型用幾個提示詞就能變成"莎士比亞混搭大衛·鮑伊",而且變完還能穩得住、說得像。人類做不到這麼快、這麼徹底。這個差距本身說明了點東西:風格、語境、知識這三樣東西在模型內部是解耦的,而在人身上它們被攪在一起,難拆難換。

情緒的詞有多老,人的心就有多老

凱利斯還想到一個繞得挺遠的辦法,去追問人類心理的底層結構:從語言的演化史裡找線索。

善良、憤怒、嫉妒,這些詞在不同語言里長得像不像?如果高度相似,說明它們非常古老,可能在人類分化之前就已經存在於共同祖先的語言裡。如果差異極大,說明每個語言群體是各自獨立地發明了這些概念。

把語言的分化樹和人類遷徙的路線圖疊在一起看,遺傳學已經能相當精確地追蹤後者的時間線。兩條線對齊,就能推斷出某個心理特質大概在什麼時候出現、沿著什麼路徑傳播。

這條路能走通,靠的是語言本身也是一層演化產物。它和基因組一樣會變異、會繼承、會分化,只不過傳得更快。

人被意識形態抓住,因為人本來就容易被提示

Lex 提了一個他很好奇的思想實驗:把沒對齊的基座模型放出來,讓它生成共產主義、法西斯主義、資本主義的文字,看哪些部分被啟用,看這些意識形態在神經網路裡究竟長什麼樣。

凱利斯的回應繞回了"可提示性"這個概念。人之所以會被某套理念整個抓住,因為人天然就能被提示。給一套詞彙、一套框架,人就會沿著它去想、去說、去行動。意識形態就是一套高度濃縮的提示詞包,它把複雜世界壓縮成一句口號,然後人就開始按那句口號生活。

也許共產主義、法西斯主義、資本主義、民主制度,在底層是連著的。人的心天然就被意識形態吸引,被一箇中心化的想法吸引。也許我們需要一個神經網路來提醒我們這一點。(馬諾利斯·凱利斯)

這個說法有點冷,但成立。人被概念捕獲的速度,比人自己意識到的快得多。你讀到一句足夠簡潔的解釋,感覺"原來如此",那一刻你就已經被提示了。

基因能改,包袱別扔

回到他那個最大的分歧點。技術在往"可以修改自己的硬體"的方向走,讀懂基因組、干預神經通路、消掉疾病,這條路的盡頭是什麼?

凱利斯的答案很明確:他可以接受技術去消除疾病,但不接受用同樣的思路去消除人的情緒包袱。

我不想變成一個沒有情緒的人。我想惹麻煩,想說錯話,想冒出一句尷尬的評論。(馬諾利斯·凱利斯)

他說得很認真,理由也是功能性的:那些看起來是缺陷的東西,正是創造力的燃料。一個永遠冷靜、永遠精確、永遠說正確的話的頭腦,輸出會越來越可預測。可預測意味著可以被最佳化掉。真正不可替代的部分,恰恰是那些說不清從哪裡冒出來的、帶著點偏差和火氣的東西。

他甚至認為當下社會正在往一個無趣的方向漂:所有人都在擔心說錯話,於是選擇不說,於是集體變得沉默,然後開始不喜歡自己在做的事。

順著這個邏輯,他還講了一件讓很多人不舒服的事:人的多樣性不是可以被抹平的,因為它就是硬體本身。

我們在 99.9% 的程度上是同一個物種。如果把這件事真正想明白,人類大概就不會再互相打了。(馬諾利斯·凱利斯)

但剩下那 0.1% 裡,藏著六百萬個常見變異和一堆罕見變異組合出的無窮可能。兄弟姐妹之間、父母子女之間,差異大得讓"教養"這個詞顯得力不從心。凱利斯和妻子的六個兄弟姐妹,從遠處看像同一個模具倒出來的,走近了看每個人都是另一個物種。有意思的地方在於,這些差異又小到不足以破壞溝通。大家還是同一種東西,只不過各自偏了不同的方向。

他說自己願意相信,人類在高維空間裡的那個"質心"是空的。沒有一個標準的、平均的人。維度多到某種程度之後,中心區域根本沒被佔據,每個人都落在稀疏的外圍。所謂"正常人",只是一個統計上的虛構。

給讀者的行動清單

這期對話資訊量很大,但如果只留下可以立刻用起來的東西,大概是下面這些。

  • 把"一步一步想"當成預設設定。遇到複雜問題,無論是自己思考、跟同事討論,還是向 AI 提問,都先要求拆成步驟。跳過步驟的思考,錯誤率會高得離譜。
  • 給自己建幾個"虛擬顧問"。針對你常遇到的問題,想清楚誰會給出最好的答案,然後在心裡向他提問。人的神經元具備這個能力,只是需要一個提示才會啟動。
  • 刻意選擇你的提示環境。你經常待的圈子、反覆看的賬號、日常對話的語氣,都在持續給你下指令。這些輸入會決定你明天是誰,選的時候認真一點。
  • 別再追求在所有維度上中庸。中心位置是空的,往某個方向偏出去,才是你真正的位置。用 AI 補上你不擅長的深度,把省下來的時間投到你的偏斜上。
  • 接受先天和後天攪在一起、拆不開這個事實。教育孩子、管理團隊、評估自己的時候,把"我努力了所以他該變"
  • 接受先天和後天攪在一起、拆不開這個事實。教育孩子、管理團隊、評估自己的時候,把"我努力了所以他該變"這個預期調低一點。人能做的比想象中少,但也沒必要因此放棄所有努力。
  • 保留你的情緒包袱。憤怒、飢餓、坐立不安,這些不是需要被最佳化掉的 bug。真正該修的只有一種:讓你無法正常運轉的那些。剩下的,那是你的燃料。
  • 對意識形態保持一步距離。任何一句足夠簡潔、能讓你立刻"原來如此"的解釋,都值得警惕。它可能確實說對了一部分,也可能只是一套高效的提示詞包,成功地把你的思考方向鎖定了。
  • 去找那些真的和你不一樣的人。別隻找技能互補的搭檔,要找思路、語言、判斷方式都跟你錯開的人。AI 能補齊深度,補不了這種錯位。

凱利斯這期講得最重的一句話,其實藏在他的自嘲裡。他說自己不想變成那種永遠正確的人,想惹點麻煩。這話聽著像玩笑,落到實踐上卻很具體:一個人在什麼環境下會突然冒出新想法?多半是在他覺得安全、能說錯話、不怕被糾正的時候。反過來,一個把所有不確定都藏起來、每句話都先過一遍審查的人,輸出會越來越像模板。

AI 把知識、語境和風格拆開了,這件事正在重排人的價值。能被拆解、能被複制、能被打包成提示詞的那部分能力,正在快速貶值。剩下的是那些拆不開的:你為什麼會在這個問題上固執,你為什麼對某件小事莫名憤怒,你為什麼在所有人都說對的時候覺得哪裡不對。這些偏斜沒有理由,也不高效,卻恰好構成了別人沒法從你這裡拿走的那一小塊。

四十億年的演化在人體內一層層打補丁,酵母、魚、獼猴、新皮層,全部同時線上執行。凱利斯提醒人們別急著關掉底下那幾層,道理到最後就是這麼樸素。真正稀缺的是那個願意在正確和有趣之間選後者的判斷力,精確倒從來不稀缺。

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Lex Fridman Podcast #373 – Manolis Kellis: Evolution of Human Civilization and Superintelligent AI Machine transcription by Qwen3-ASR 0.6B (MLX, local, Apache-2.0). May contain recognition errors. The following is a conversation with Manolis Kalis, his fifth time on this podcast. He's a professor at MIT and head of the MIT Computational Biology Group. He's one of the greatest living scientists in the world, but he's also a humble, kind, caring human being that I have the greatest of honors and pleasures of being able to call a friend. And now a quick few second mention of each sponsor. Check them out in the description. It's the best way to support this podcast. We got Eight Sleep for naps, NetSuite for business management software, ExpressVPN for privacy and security on the interwebs, and InsideTracker for biological data. Choose wisely, my friends. Also, if you want to work with our amazing team, we're always hiring. Go to lexfreeman dot com slash hiring. And now onto the full ad reads. As always, no ads in the middle. I try to make this interesting. I often fail, but I try. And if you must skip them, please still check out our sponsors. I enjoy their stuff. Maybe you will too. This episode is brought to you by Eight Sleep and its new Pod Three mattress, which I think of as a teleportation device, into a land of dreams, a place where the mind goes to escape. The spacetime physics of this reality of the waking world. Anything is possible in the place of dreams. The darkness that lurks in the Jungian shadow is possible. The hope, the triumph, symbolized as the light at the end of the tunnel is possible. All of it is possible. It's all up to you. I'm kind of somebody that likes both the the good and the bad of dreaming. There's a cleansing aspect of a bad dream. You wake up freaking out a little bit, but then you realize how awesome this life is. That whatever happened in the dream world is not real. It's kind of dress rehearsal for a bad event that happens in reality, but it doesn't. It's like in a video game you get to save, do a dangerous thing, screw it up, and then you get to load and try again. That's what a dream is. Anyway, I love dreaming. I love sleeping. I love naps, and an eight sleep mattress. Is the best place to teleport into that dream world. Check them out and get special savings when you go to eightsleep dot com slash lex. This show is also brought to you by NetSuite, an all in one cloud business management system. It manages financials, human resources, inventory, e commerce, and many business related details. All things that I have to start figuring out. I put up a job position for somebody to help me with financials. All all of it. 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This show is also brought to you by ExpressVPN, my comrade, my friend, the piece of software that has accompanied me through darkness and light for many years, way before I had a podcast, way before I have found my way, though I am still forever lost, and you if you too are forever lost perhaps it will also warm your heart as a dead mind first of all practically speaking let's put the romantic stuff aside you should be using a VPN on the internet and ExpressVPN is the VPN I've used and can highly highly highly recommend by the way I apologize for the coarseness of my voice I've been feeling a little bit under the weather whatever the heck that expression actually means there's always ChatGPT that can ask the question but I'm not going to I'm just going to go with it going to wing it going to wing it is another funny expression right wing it what does that mean probably has to do with birds and the fact that bird flight is a kind of chaotic process that's not amenable to clear dynamical system modeling that for example an airplane is but let us return to the piece of software you should be using to warm your heart and to protect your privacy on the internet go to expressvpn dot com slash lex pod for an extra three months free this show is also brought to you by InsideTracker a service I used to track biological data that comes from my body there's that song it's my party and I'll cry if I want to and I used to think it said it's my body and I'll cry if I want to I don't know what I thought that actually meant it's a good song it's a silly song it's my party and I'll cry if I want to cry if I want to cry if I want to you would cry too if it happened to you anyway speaking of which we're going hard on the tangents today I like data that comes from my body that is then used in machine learning algorithms to make decisions or recommendations of what I should do with said body lifestyle choices diet maybe in the future be career advice all kinds of stuff dating friends anything you know but basically health stuff medicine I think and that's the obvious way you should be figuring out what to do with your body is at least in large part based on the data that comes from your body and not just once but many times over and over and over and over InsideTracker pioneering the data collection sort of the blood test then then can extract all kinds of information give you advice I highly recommend them get special savings for a limited time when you go to insidetracker dot com slash lex this is a lex Freeman podcast to support it please check out our sponsors in the description and now dear friends here's Manolis Kalis good to see you first of all Lex I've missed you I think you've changed the lives of so many people that I know and it's truly like such a pleasure to be back such a pleasure to see you grow to sort of reach so many different aspects of your own personality thank you for the love you always give me so much support and love I just can't I I I'm forever grateful for that it's lovely to see a fellow human being who has that love who basically does not judge people and there's so many judgmental people out there and it's just so nice to see this beacon of openness so what makes me one instantiation of human irreplaceable do you think as we enter this increasingly capable age of increasingly capable AI I have to ask what do you think makes humans irreplaceable so humans are you replaceable because of the baggage that we talked about so we talked about baggage we talked about the fact that every one of us has effectively relearned all of human civilization in their own way so every single human has a unique set of genetic variants that they've inherited some common some rare and some make us think differently some make us have different personalities they say that a a parent with one child believes in genetics a parent with multiple children understands genetics just how different kids are and and my three kids have dramatically different personalities ever since the beginning so one thing that makes us unique is that every one of us has a different hardware the second thing that makes us unique is that every one of us has a different software uploading of all of human society all of human civilization all of human knowledge we don't we're not born knowing it we're not like I don't know birds that learn how to make a nest through genetics and will make a nest even if they've never seen one we are constantly relearning all of human civilization so that's the second thing and the third one that actually makes humans very different from AI is that the baggage we carry is not experiential baggage it's also evolutionary baggage so we have evolved through rounds of complexity so just like ogres have layers and shrek has layers humans have layers there's the cognitive layer which is sort of the outer you know most the the latest evolutionary innovation this enormous neocortex that we have evolved and then there's the emotional baggage underneath that and then there's all of the fear and fright and flight and all of these kinds of behaviors so AI only has a neocortex AI doesn't have a lymbic system it doesn't have this complexity of human emotions which make us so I think beautifully complex so beautifully intertwined with our emotions with our instincts with our you know sort of gut reactions and all of that so I think when humans are trying to suppress that aspect the sort of quote unquote more human aspect towards a more cerebral aspect I think we lose a lot of the creativity we lose a lot of the you know freshness of humans and I think that's quite irreplaceable so we can look at the entirety of people that are alive today maybe all humans who have ever lived and map them in this high dimensional space and there's probably a center a center of mass for that mapping and a lot of us deviate in different directions so the the variety of directions in which we all deviate from that center is vast I would like to think that the center is actually empty yes that basically humans are just so diverse from each other that there's no such thing as an average human that every one of us has some kind of complex baggage of emotions intellectual you know motivational behavioral traits that it's not just one sort of normal distribution we deviate from it there's so many dimensions that we're kind of hitting the sort of sparseness the the curse of dimensionality where it's actually quite sparsely populated and I don't think you have an average human being so what makes us unique in part is the diversity and the capacity for diversity and the capacity of the diversity comes from the entire evolutionary history so there's just so many ways we can vary from each other yeah I would say not just the capacity but the inevitability of diversity basically it's in our hardware we are wired differently from each other my siblings and I are completely different my kids from each other are completely different my my wife has she's like number two of six siblings from from a distance they look the same but then you get to you know you get to know them every one of them is completely different but sufficiently the same that the differences interplay with each other so that's the interesting thing where the diversity is functional it's useful so it's like we're close enough to where we notice the diversity and it doesn't completely destroy the possibility of like effective communication and interaction so we're still the same kind of thing so what I said in one of our earlier podcasts is that if humans realize that we're 99 point nine percent identical we would basically stop fighting with each other like we are really one human species and we are so so similar to each other and if you look at the alternative if you look at the next thing outside humans like it's been six million years that we haven't had a relative so it's it's truly extraordinary that that we're we're kind of like this dot in outer space compared to the rest of life on Earth when you think about evolving through rounds of complexity can you maybe elaborate such a beautiful phrase beautiful thought that there's layers of complexity that make so so with software sometimes you're like oh let's like build version two from scratch but this doesn't happen in evolution in evolution you layer in additional features on top of old features so basically when I like every single time my cells divide I'm a yeast like I'm a unicellular organism and then cell division is basically identical every time I breathe in and my lungs expand I'm basically you know like every time my heart beats I'm a fish so basically that I still have the same heart like very very little has changed the blood going through my veins the oxygen the you know our immune system we're basically primates our social behavior we're basically neural monkeys and all neural monkeys we're basically this this concept that every single one of these behaviors can be traced somewhere in evolution and that all of that continues to live within us is also a testament to not just not killing other humans for God's sake but like not killing other species either like just to realize just how united we are with nature and that all of these biological processes have never ceased to exist they're continuing to live within us and then just the neocortex and all of the reasoning capabilities of humans are built on top of all of these other species that continue to live breathe divide metabolize fight off pathogens all continue inside us so you think the neocortex the whatever reasoning is that's the the latest feature in the in the latest version of this journey it's it's extraordinary that humans have evolved so much in so little time again if you look at the the timeline of evolution you basically have billions of years to even get to a dividing cell and then a multicellular organism and then a complex body plan and then these incredible senses that we have for perceiving the world the fact that bats can fly and they evolved flight the evolved sonar in the span of a few million years I mean it's just extraordinary how much evolution has kind of sped up and all of that comes through this evolvability the fact that we took a while to get good at evolving and then once you get good at evolving you can sort of you have modularity built in you have hierarchical organizations built in you have all of these constructs that allow meaningful changes to occur without breaking the system completely if you look at a traditional genetic algorithm the way that humans designed them in the 60s you can only evolve so much and as you evolve a certain amount of complexity the number of mutations that move you away from something functional exponentially increases and the number of mutations that move you to something better exponentially decreases so the probability of evolving something so complex becomes infinitesimally small as you get more complex but with evolution it's almost the opposite almost the exact opposite that it appears that it's speeding up exactly as complex complexity is increasing and I think that's just the system getting good at evolving where do you think it's all headed do you ever think about where try to visualize the entirety of the evolutionary system and see if there's an arrow to it and a destination to it so the best way to understand the future is to look at the past if you look at the trajectory then you can kind of learn something about the direction in which we're heading and if you look at the trajectory of life on Earth it's really about information processing so the the concept of the senses evolving one after the other you know being like bacteria are able to do chemotaxis basically means moving towards a chemical gradient and that's the first thing that you need to sort of hunt down food the next step after that is being able to actually perceive light so all life on this planet and all life that we know about evolved on this rotating rock every 24 hours you get sunlight and dark sunlight and dark and light is a source of energy light is also information about where is up light is all kinds of you know things so you can you can basically now start perceiving light and then perceiving shapes beyond just the sort of single photoreceptor you can now have complex eyes or multiple eyes and then start perceiving motion or perceiving direction perceiving shapes and then you start building infrastructure on the cognitive apparatus to start processing this information and making sense of the environment building more complex models of the environment so if you look at that trajectory of evolution what we're experiencing now and humans are basically according to this sort of information theoretic view of evolution humans are basically the next natural step and it's perhaps no surprise that we became the dominant species of the planet because yes there's so many dimensions in which some animals are way better than we are but at least on the cognitive dimension we're just simply unsurpassed on this planet and and perhaps the universe but the the concept that if you now trace this forward we talked a little bit about evolvability and how things get better at evolving one possibility is that the next layer of evolution builds the next layer of evolution and what we're looking at now with humans and AI is that having mastered this information capability that humans have from this quote unquote old hardware this basically you know biological evolved system that kind of you know somehow in the environment of Africa and then in subsequent environments of sort of dispersing through the globe was evolutionarily advantageous that has now created technology which now has a capability of solving many of these cognitive tasks it doesn't have all the baggage of the previous evolutionary layers but maybe the next round of evolution on Earth is self replicating AI where we're actually using our current smarts to build better programming languages and the programming languages to build you know ChatGPT and that to then build the next layer of software that will then sort of help AI speed up and it's lovely that we're coexisting with this AI that sort of the creators of this next layer of evolution in this next stage are still around to help guide it and hopefully will be for the rest of eternity as partners but it's also nice to think about it as just simply the next stage of evolution where you've kind of extracted away the biological needs like if you look at animals most of them spend 80 of their waking hours hunting for food or building shelter humans maybe one percent of that time and then the rest is left to creative endeavors and AI doesn't have to worry about shelter etc so basically it's all living in the cognitive space so in a way it might just be a very natural sort of next step to think about evolution and that's that's on the on the sort of purely cognitive side if you now think about humans themselves the ability to understand comprehend our own genome again the ultimate layer of introspection. Gives us now the ability to even mess with this hardware, not just augment our capabilities through interacting and collaborating with AI, but also perhaps understand the neural pathways that are necessary for you know empathetic thinking for for justice for this and this and that and sort of help augment human capabilities through you know neuronal interventions through chemical interventions through electrical interventions to basically help steer the human you know bag of hardware that we kind of evolved with into greater capabilities, and then ultimately by understanding not just the wiring of neurons and the function of neurons but even the genetic code we could even at one point in the future start thinking about well can we get rid of psychiatric disease can we get rid of neurodegeneration can we get rid of dementia and start perhaps even augmenting human capabilities not just getting rid of disease can we tinker with the genome with the hardware or getting closer to the hardware without having to deeply understand the baggage in the way we've disposed of the baggage in our software systems with AI to some degree not fully but to some degree can we do the same with the genome or is the genome deeply integrated into this bag I wouldn't want to get rid of the the baggage the baggage would make us awesome so the fact that I'm sometimes angry and sometimes hungry and sometimes hungry is perhaps contributing to my creativity I don't want to be dispassionate I don't want to be another like you know Robert I you know I want to get in trouble and I want to sort of say the wrong thing and I want to sort of you know make an awkward comment and sort of push myself into you know reactions and responses and things that can get just people thinking differently and I think our society is moving towards a humorless space where everybody's so afraid to say the wrong thing that people kind of start quitting on mass and start like not liking their jobs and stuff like that maybe we should be kind of embracing that human aspect a little bit more in all of that baggage aspect and not necessarily thinking about replacing it on the contrary like embracing it and sort of this coexistence of the cognitive and the emotional hardware so embracing and celebrating the diversity that springs from the baggage versus kind of pushing towards and empowering this kind of pull towards conformity yeah and in fact with the advent of AI I would say and these seemingly extremely intelligent systems that sort of can perform can perform tasks that we thought of as extremely intelligent at the blink of an eye this might democratize intellectual pursuits instead of just simply wanting the same type of brains that you know carry out specific ways of thinking we can like instead of just always only wanting say the mathematically extraordinary to go to the same universities what you could see simply say like who needs that anymore you know we now have AI maybe what we should really be thinking about is the diversity and the power that comes with the diversity where AI can do the math and then we should be getting a bunch of humans that sort of think extremely differently from each other and maybe that's the true cradle of innovation but AI can also these large language models can also be with just a few prompts essentially fine tuned to be diverse from the center so the prompts can really take you away into unique territory you can ask the model to act in a certain way and it will start to act in that way is that possible that the language models could also have some of the magical diversity that makes us so damn interesting so I would say humans are the same way so basically when you when you sort of prompt humans to basically you know you know give an environment to act a particular way they change their own behaviors and you know the old saying is show me your friends and I'll tell you who you are more like show me your friends and I'll tell you who you'll become so it's not necessarily that you choose friends that are like you but I mean that's the first step but then the second step is that you know the kind of behaviors that you find normal in your circles are the behaviors that you'll start espousing and that type of met evolution where every action we take not only shapes our current action and the result of this action but also shapes our future actions by shaping the environment in which their future actions will be taken every time you carry out a particular behavior it's not just a consequence for today but it's also a consequence for tomorrow because you're reinforcing that neural pathway so in a way self discipline is a self fulfilling prophecy and by behaving the way that you want to behave and choosing people that are like you and sort of exhibiting those behaviors that are sort of desirable you end up creating that that environment as well so it is a kind of life itself is a kind of prompting mechanism super complex the friends you choose the environments you choose the way you modify the environment that you choose yes but that seems like that process is much less efficient than a large language model you can literally get a large language model through a couple of prompts to be a mix of Shakespeare and David Bowie right you can very aggressively change in a way that's stable and convincing you really transform through a couple of prompts the the behavior of the model into something very different from the original so well before ChatGPT I would tell my students just ask you know what would Manoli say right now and you you guys all have a pretty good emulator of me right now yes and I don't know if you know the programming paradigm of the Robert Ducklin where you basically explain to the Robert Ducklin that's just sitting there exactly what you did with your code and why you have a bug and just by the act of explaining you'll kind of figure it out I woke up one morning from a dream where I was giving a lecture in the San Fy theater and one of my friends was basically giving me some deep evolutionary insight on how cancer genomes and cancer cells evolve and I woke up with a very elaborate discussion that I was giving and a very elaborate set of insights that he had that I was projecting onto my friend in my sleep and obviously this was my dream so my own neurons were capable of doing that but they only did that under the prompt of you are now Pusgupta you are a professor in cancer genomics you're an expert in that field what do you say so I feel that we all have that inside us that we have that capability of basically saying I don't know what the right thing is but let me ask my virtual legs what would you do and virtual legs would say be kind I'm like oh yes or something like that and even though I myself might not be able to do it unprompted and the my favorite prompt is think step by step and I'm like you know this also works on my ten year old when he tries to solve a math equation all in one step I know exactly what mistake you'll make but if I prompt it with oh please think step by step then he sort of gets you know mindset and I think it's also part of the way that ChatGPT was actually trained this whole sort of human in the loop reinforcement learning has probably reinforced a set of behaviors whereby having this feedback loop you kind of aligned AI better to the prompting opportunities by humans yeah prompting human like reasoning steps the step by step kind of thinking yeah but it does seem to be I suppose it just puts a mirror to our own capabilities and so we can be truly impressed by our own cognitive capabilities because the variety of what you can try because we don't usually have this kind of we can play with our own mind rigorously through Python code right yeah so this allows us to really play with all all of human wisdom and knowledge or at least knowledge at our fingertips and then mess with that little mind that can think and speak in all kinds of ways what's unique is that as I mentioned earlier every one of us was trained by different subset of human culture and ChatGPT was trained on all of it yeah and the difference there is that it probably has the ability to emulate almost any every one of us the fact that you can figure out where that is in cognitive behavioral space just by a few prompts is pretty impressive but the fact that that exists somewhere is you know absolutely beautiful and the fact that it's encoded in an orthogonal way from the knowledge I think is also beautiful the fact that somehow through this extreme overparameterization of AI models it was able to somehow figure out that context knowledge and form are separable and that you can sort of describe scientific knowledge in a haiku in the form of I don't know Shakespeare or something that tells you something about the the decoupling and the decouplability of these types of aspects of human psyche and that's part of the science of this whole thing so these large language models are you know days old in terms of this kind of leap that they've taken and it would be interesting to do this kind of analysis on them of context of the separation of context form and knowledge where exactly does that happen there's already sort of initial investigations but it's very hard to figure out where is there a particular parameter set of parameters that are responsible for a particular piece of knowledge or particular context or particular style speaking so with convolutional neural networks interpretability had many good advances because we can kind of understand them there's a structure to them there's a locality to them and we can kind of understand that different layers have different sort of ranges that they're looking at so we can look at activation features and basically see where you know where does that correspond to with large language models it's perhaps a little more complicated in but I think it's still achievable in the sense that we could kind of ask well what kind of prompts does this generate if I sort of drop out this part of the network then what happens and sort of start getting at a language to even describe these types of aspects of human behavioral psychology if you wish from the spoken part in the language part and the advantage of that is that it might actually teach us something about humans as well like you you know we might not have words to describe these types of aspects right now but when somebody speaks in particular way it might remind us of a friend that we know from here or there and if we had better language for describing that these concepts might become more apparent in our own human psyche and then we might be able to encode them better in machines themselves I well probably you and I would have certain interest with the base model with OpenAI is the base model which is before the the alignment of the reinforcement learning with human feedback and and before the AI safety based kind of censorship of the model it would be fascinating to explore to investigate the ways that the model can generate hate speech the kind of hate that humans are capable of it would be fascinating or the kind of of course like sexual language or the kind of romantic language or the all kinds of ideologies can I get it to be a communist can I get it to be a fascist can I get it to be a capitalist can I get it to be all these kinds of things and see which parts get activated and not because it would be fascinating to sort of explore at the individual mind level and at a societal level where do these ideas take hold what is the fundamental core of those ideas maybe the communism fascism capitalism democracy are all actually connected by the fact that the human heart the human mind is drawn to ideology to to a centralizing idea and maybe we need a neural network to remind us of that I like the concept that the human mind is somehow tied to ideology and I think that goes back to the the promptability of ChatGPT the fact that you can kind of say well think in this particular way now and the fact that humans have invented words for encapsulating these types of behaviors and it's hard to know how much of that is innate and how much of that was like passed on from language to language but basically if you look at the evolution of language you can kind of see how young are these words in the history of language evolution that describe these types of behaviors like you know kindness and anger and jealousy et cetera if these words are very similar from language to language it might suggest that they're very ancient if they're very different it might suggest that this concept may have emerged independently in each different language and so on so forth so looking at the phylogeny the history the evolutionary traces of language at the same time as people moving around that we can now trace thanks to genetics is a fascinating way of understanding the human psyche and also understanding sort of how these types of behaviors emerge and to go back to your idea about sort of exploring the system unfiltered I mean in a way the psychiatric hospitals are full of those full of those people so basically people whose mind is uncontrollable who have kind of gone adrift in specific locations of their psyche and I I do find this fascinating basically you know watching movies that are trying to capture the essence of troubled minds I think is teaching us so much about our everyday selves because many of us are able to sort of control our minds and are able to somehow somehow hide these emotions and but every time I see somebody who's troubled I I see versions of myself maybe not as extreme but I can sort of empathize with these behaviors and you know I see bipolar I see schizophrenia I see depression I see autism I see so many different aspects that we kind of have names for and crystallize in specific individuals and I think all of us have that all of us have sort of just this multidimensional brain and genetic variations that push us in these directions environmental exposures and traumas that push us in these directions environmental behaviors that are reinforced by the kind of friends that we chose or friends that we were stuck with because of the environment that we grew up in so in a way a lot of these types of behaviors are within the vector span of every human it's just that the magnitude of those vectors is generally smaller for most people because they haven't inherited that particular set of genetic variants or because they haven't been exposed to those environments basically or something about the mechanism of reinforcement learning with human feedback didn't quite work for them so it's fascinating to think about that's what we do we have this capacity to have all these psychiatric psychiatric or behaviors associated with psychiatric disorders but we through the alignment process as we grow up with parents we kind of we notice suppress them yeah we know how to control every human that grows up in this in this world spends several decades being shaped into place and without that you know maybe we would have the unfiltered ChatGPT for every baby is basically a raging narcissist not all of them not all of them believe it or not it's it's remarkable like I remember like watching my kids grow up and again like yes part of their personality has stays the same but also in different phases through their life they've gone through these dramatically different types of behaviors and you know my daughter basically saying you know basically one one kid saying oh I want the bigger piece the other one saying oh everything must be exactly equal and the third one saying I'm okay you you know I might have to have the smaller part don't worry about me even in the early days in the early days of development yeah it's just extraordinary to sort of see these dramatically different like I mean my wife and I you know are are very different from each other but we also have you know six million variants six million low side each if you wish if you just look at common variants we also have a bunch of rare variants that are inherited in a more Mendelian fashion and now you have you know an infinite number of possibilities for each of the kids so basically it's due to the six million just from the common variants and then if you like layering the the rare variants so let me talk a little bit about common variants and rare variants so if you look at just common variants they're generally weak effect because selection selects against strong effect variants so if something like has a big risk for schizophrenia it won't rise to high frequency so the ones that are common are by definition by selection only the ones that had relatively weak effect and if all of the variants associated with personality with cognition and all aspects of human behavior were weak effect variants then kid would basically be just averages of their parents if it was like thousands of low side just by low of large numbers the average of two large numbers would be you know very robustly close to that middle but what we see is that kids are dramatically different from each other so that basically means that in the context of that common variation you basically have rare variants that are inherited in a more Mendelian fashion that basically then sort of govern likely many different aspects of human behavior human biology and human psychology and that's again if like if you look at sort of a person with schizophrenia their identical twin has only 50 chance of actually being diagnosed with schizophrenia so that basically means there's probably developmental exposures environmental exposures trauma all kinds of other aspects that can shape that and if you look at siblings for the common variants it kind of drops off exponentially as you would expect with you know sharing 50 of your genome 25 of your genome You know, twelve point five percent of your genome et cetera, with more and more distant cousins. But the fact that siblings can differ so much in their personalities that we observe every day, it can't all be nurture. Basically, you know, we we've like again as parents we we spend enormous amount of energy. Trying to fix quote unquote the nurture part, trying to you know get them to share, get them to be kind, get them to be open, get them to trust each other, like you know like overcome the prisoner's dilemma. Of you know if if everyone fends from themselves, we're all going to live in a horrible place, but if we're a little more altruistic, then we're all going to be in a better place. And I think it's not like we treat our kids differently, but but they're they're just born differently. So in a way, as a geneticist, I have to admit that there's only so much I can do with nurture. That nature definitely played a big component. The the selection of variants we have the common variants and the rare variants. What what can we say about the landscape of possibility they create? If you can just linger on that, so the the selection of rare variants is divine. How how do we get the ones that we get? Is it just laden in that giant evolutionary baggage? So I'm going to talk about regression. Why do we call it regression? And the concept of regression to the mean. The fact that when fighter pilots in a dog fight did amazingly well, they would give them rewards, and then the next time they're in dog fight they would do worse. So then you know the navy basically realized that wow, or at least interpreted that as wow, we're ruining them by praising them, and then they're going to perform worse. The statistical interpretation of that is regression to the mean. The fact that you're an extraordinary pilot, you've been trained in an extraordinary fashion. That pushes your mean further and further to extraordinary achievement, and then in some dog fights you'll just do extraordinarily well. The probability that the next one will be just as good is almost nil, because this is the peak of your performance. And just by statistical odds, the next one will be another sample from the same underlying distribution, which is going to be a little closer to the mean. So regression analysis takes its name from this type of realization in the statistical world. Now if you now take humans, you basically have people who have achieved extraordinary achievements. Einstein, for example, you know you would call him for example the epitome of human intellect. Does that mean that all of his children and grandchildren will be extraordinary geniuses? It probably means that they're sampled from the same underlying distribution, but he was probably a rare combination of extremes in addition to these common variants. So you can basically interpret your kid's variation for example as well. Of course, they're going to be some kind of sampled from the average of the parents, with some kind of deviation according to the specific combination of rare variants that they have that they have inherited. So you know, given all that, the you know the possibilities are endless as to where you should be, but you should always interpret that with well, it's probably an alignment of nature. And nurture, and then nature has both the common variants that are acting kind of like the law of large numbers and the rare variants that are acting more in a Mendelian fashion, and then you layer in the nurture, which again in everyday action we make, we shape our future environment. But the genetics we inherit are shaping the future environment of not only us but also our children. So there's this weird nature nurture interplay and self reinforcement, where you're kind of shaping your own environment, but you're also shaping the environment of your kids. And your kids are going to be born in the context of your environment that you've shaped, but also with a bag of genetic variants that they have inherited. And there's just so much complexity associated with that. When we start blaming something on nature, it might just be nurture. It might just be that well, yes, they inherited the genes from the parents, but they also you know were shaped by the same environment. So it's very very hard to untangle the two, and you should always always realize that nature can influence nurture, nurture can influence nature. Or at least be correlated with and predictive of and so on and so forth. So I love thinking about that distribution that you mentioned, and here's where I can be my usual ridiculous self. And I sometimes think about that army of sperm cells, however many hundreds of thousands there are, and I kind of think of all the possibilities there. Because there's a lot of variation, and one gets to win. Is is that not a random one? Is it a totally ridiculous way to think about? No, not at all. So I would say evolutionarily we are a very slow evolving species. Basically, the generations of humans are a terrible way to do selection. What you need is processes that allow you to do selection in a smaller tighter loop. And part of what. If you look at our immune system for example, it evolves at a much faster pace than humans evolve, because there is actually an evolutionary process that happens within our immune cells. As they're dividing, there's basically VDJ recombination that basically creates this extraordinary wealth of antibodies and antigens against the the

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