特別節目·中文精讀

史蒂文·平克:理性時代的人工智慧

Steven Pinker: AI in the Age of Reason

認知科學家談語言與理性的演化基礎、AI 帶來的風險與機遇,以及啟蒙價值在當下的意義。

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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

埃隆·馬斯克說過,人工智慧比核武器危險得多,很可能摧毀人類文明。史蒂文·平克(Steven Pinker)的回應只有一句:這個類比從根本上就錯了。

核武器的全部設計目的就是摧毀東西,人工智慧的設計目的是解決問題。把它們放在一起比較,就像因為錘子和外科醫生都涉及"打擊"這個詞,就把它們歸成一類。

這場訪談裡,Lex Fridman 把平克請來,聊的正是這件事。平克是哈佛大學的認知心理學家,寫過《語言本能》《心智探奇》《白板》《人性中的善良天使》《當下的啟蒙》。他花了三十年研究語言、心智和暴力史,習慣用資料說話,也習慣對流行的恐慌潑冷水。AI威脅論逐漸成為矽谷某種預設共識的那幾年,他的立場顯得很不合群。

先回答一道關於生命意義的選擇題

訪談一開始,Lex 給平克出了一道選擇題。生命的意義是什麼?

A,像柏拉圖說的那樣追求知識。B,像尼采說的那樣追求權力。C,像歐內斯特·貝克爾(Ernest Becker)說的那樣逃避死亡。D,像達爾文說的那樣傳播基因。E,像虛無主義者說的那樣根本沒有意義。F,像平克二十年前在《心智探奇》裡推演的那樣,理解生命的意義超出了人類的認知能力。G,以上都不對。

平克的答案是A,但要修正一下。他認為最接近的是"追求知識",不過應該把它擴大為更寬泛的圓滿(fulfillment):生命、健康、刺激、接入活著的文化和社會世界。

他補了一句關鍵的區分:這是"我們的"生命意義。基因的生命意義是另一回事,它的任務是複製自己。基因所造就的大腦,會給自己設定另一套目標。兩套意義不必一致。

Lex 追問,追求知識到底是平克個人的偏好,還是人性的基本面?平克的回答是兩者都是。人類有能力追求知識,因為這是我們之所以為人的東西。人類在動物界格外突出的一點,就是獲取知識並用它來生存:我們造工具,用語言達成協議,提取毒物,預測動物行為,研究植物的功效。

這裡的"我們"指作為物種的人類,遍佈地球每一個角落,不分現代西方還是其他地區。正是這種能力使人類佔據了所有生態位,也把其他動物逼向滅絕。

平克說,用理性去追求人類福祉,去追求健康、幸福、社會豐富度和文化豐富度,是當下最重要的挑戰。說白了,就是用我們的智力搞清楚世界如何運轉、我們自身如何運轉,然後做出發現、達成協議,讓所有人長期過得更好。

生物神經網路裡那個說不清的東西

聊完意義,話題轉到智慧本身。

二十年前在《心智探奇》裡,平克推測人類思維可能是大量高度互聯的神經元網路的產物。互連之中湧現出思想。今天用來做機器學習的神經網路,跟這套生物神經網路相比,是不是有某種更復雜、更神秘、甚至更魔幻的東西?

平克的回答分兩層。

第一層,確實有點神秘。每個身為神經網路的人都知道自己是有意識的。這裡說的有意識,跟感知環境不是一回事,跟感知內部狀態也不是一回事,它指的是擁有主觀的第一人稱、現在時的體驗。看到紅色的時候,紅色不只是跟綠色不同,它還有一種"紅感",我能感覺到。

一個人工系統會不會有這種體驗,平克說:我不知道,我也不認為我能知道。這就是它神秘的地方。

假如有一個跟人類在行為上完全無法區分的機器人,我們會認為它有意識嗎?應該認為它有意識嗎?這個問題很難回答。

第二層,把意識這個偏哲學的問題放到一邊,還有另一個問題:今天的神經網路和人類大腦在組織方式上的差異,會不會意味著我們沿著現有軌跡走下去,永遠做不出一個行為上跟人無法區分的機器人?

平克認為兩者有重疊,但差異也很大。他給出的判斷是:現在的深度學習系統,實際上並沒有那麼深。它們非常擅長提取高階統計規律,但大多數系統沒有一個語義層,沒有那種"誰對誰做了什麼、為什麼、在哪、怎麼發生、什麼導致了什麼"的真正理解。

Lex 追問:如果網路更大、連線更豐富更怪異,這種語義層面的推理能力能不能湧現出來?

平克先把意識拆開。意識跟複雜度是兩回事。蝦的複雜度不高,但我們可以合理地追問蝦有沒有意識,它會不會感到疼痛。所以意識這塊先放一邊。

至於結構化的知識,單純的規模不夠,但如果是精心設計過的,那為什麼不行?自然選擇對人腦做了一種相當於工程設計的操作。矽基系統做人類大腦能做的事,在原理上沒有障礙。會不會真的發生,取決於我們工程做得多聰明,也取決於我們想不想,甚至取決於這件事本身是不是一個合理的目標。

木頭和棉花的啟示

平克在這裡講了一個很有意思的類比,用來解釋為什麼"造出和人腦完全一樣的東西"可能根本不是一個值得追求的目標。

你可以問,有沒有哪種移動方式比人更好?足式運動這件事,我們最終大概是想要超過人類的。沒有理由非得把人當作基準。工具比人更強,完全可能。

有些自然系統,我們造不出完全一樣的替代品,但原因並不神秘,只是複製它的每一步都不划算。

比如木頭。我們至今用木頭蓋房子、做傢俱。我們喜歡它的觀感,喜歡它的手感,它有某些合成材料沒有的特性。木頭裡面沒有什麼魔法,只是要合成出木頭的一切特性,一步接一步地做下去,太費勁了,而我們本來就有木頭。

再比如棉花。平克說他身上正穿著棉質衣服,感覺比滌綸舒服得多。棉花裡也沒有魔法,我們也不是永遠合成不出跟棉花一模一樣的東西,只是到了某個點,不值得。我們已經有棉花了。

平克把這套邏輯套到人類智慧上:想要造一個跟人腦一模一樣的人工系統,這個目標我們大概會一直追到某個程度就停下。因為如果你要的是比人幹得更好的工具,你根本不在乎它像不像人。診斷癌症、預測天氣,為什麼要以人類為基準?

但 Lex 接了一句:即便人不該是基準,也不該被模仿,研究人類對造AI仍然有大量可學之處。平克同意。造飛行機器的時候,我們想弄懂空氣動力學,包括鳥的飛行原理,但不去模仿鳥。因為鳥和飛機遵循的是同一套物理規律。

兩種AI末日論,平克說都不成立

接下來進入正題。

平克把關於AI的生存性威脅分成兩種,他認為兩種都站不住腳。

第一種是對AI接管的模糊恐懼。邏輯是:就像我們征服了動物和技術上不那麼先進的人群,如果我們造出比我們更先進的東西,它必然會把我們變成寵物、奴隸,或者某種被馴化的動物。

平克的診斷是:這種恐懼把智力和權力意志混為一談了。在人類身上,這兩種特質確實捆在一起,因為我們是自然選擇的產物,而自然選擇是一個競爭過程。跟我們解決問題的能力綁在一起的,還有一堆惡劣特質:支配、剝削、最大化權力、榮耀、資源和影響力。

但純粹的解決問題的能力,沒有理由把支配設為自己的目標之一。它的目標就是我們給它設定的目標。只要沒有人去造一個權力狂式的AI,就沒有理由認為它會自然朝那個方向演化。

有人可能會說,萬一我們給它設的目標就是最大化自己的權力呢?平克說,那是個蠢到家的目標,你不會給一個自主系統設這種目標。這屬於顯而易見的愚蠢。

回看歷史,有很多機會讓工程師把破壞力裝進某個系統,他們選擇不那麼做,因為這就是工程的自然過程。

Lex 插了一句:除非是武器。造武器的時候,它的目標就是殺人,所以有充分理由不去造某些型別的武器。平克順著說,造核武器就是一個巨大的錯誤。

核武器類比錯在哪

Lex 追問:這是說核武器在早期就該被攔住嗎?還是說它只是一個不幸的發明?有沒有可能攔住?

平克說,倒轉時鐘很難。核武器誕生在二戰背景下,當時擔心納粹先造出來。一旦因為這個理由啟動了,就很難停下來,尤其因為戰勝日本和納粹是當時每個負責任的人壓倒一切的目標,人們願意做任何事來確保勝利。

如果二戰沒有發生,核武器很可能不會被髮明出來。這沒法確知,但他不認為這是必然,就像其他一些被設想卻從未實施的武器系統一樣:像農用飛機那樣在城市上空播撒毒氣的飛機,在敵國製造地震和海嘯的系統,把天氣武器化,把太陽耀斑武器化。這些瘋狂的方案,我們最後都想清楚沒有去做。

然後他給出關鍵判斷:核武器和人工智慧之間的類比從根本上就是誤導的,因為核武器的全部意義就是摧毀東西,人工智慧的意義是解決問題。

這一點值得停下來想一想。當年關於AI威脅的公共討論裡,核武器是被引用最多的參照物。它給人強烈的畫面感:蘑菇雲、末日、不可逆。但平克指出的是,這個參照物在最基本的層面就不匹配。核彈不需要被"設計得善良",它的存在本身就是危險。一個AI系統的危險程度,幾乎完全取決於設計者給它設定了什麼目標。參照物選錯了,恐懼的傳播方式也就跟著錯。

回形針假設自己拆自己臺

第二種恐懼是附帶損傷。

我們給AI設定一個目標,比如生產回形針,它會以極高的效率追求這個目標,還沒等我們反應過來,它就把我們變成了回形針。我們給它設定治癒癌症的目標,它會把我們變成致命實驗的豚鼠。我們給它設定世界和平的目標,它理解的世界和平是沒有人類,因此沒有打鬥,於是它把我們全殺了。

平克說這些設想極其荒唐,而且本身就自我拆臺。它們首先假設我們聰明到能設計出一個治癒癌症的AI,同時又蠢到沒法把"治癒癌症"這件事說明白到它不會順手殺了我們。它假設這個系統聰明到能治癒癌症,又蠢到想不通我們說的治癒癌症不包括殺死所有人。

他還提到"價值對齊問題"(value alignment problem),認為這個附帶損傷的場景同樣基於誤解。

Lex 承認,目前我們既不知道怎麼造這兩種系統,也談不上接近知道。這些東西可能一夜之間改變。但此時此刻,往任何一個方向去理論化都很難。這正是問題的核心:缺少對眼前真實工程問題的推理能力時,想象力就會失控。

馬斯克為什麼一邊造車一邊害怕

Lex 問得更具體。他自己研究自動駕駛,認為特斯拉的自動駕駛是當今世界最大規模AI應用之一,對社會有潛在的非常正面的影響。那麼一個正在造這種出色"狹義AI"系統的人,為什麼會對"通用AI"如此擔憂?動機是什麼?

平克的回答很直接:這得去問他本人。他出了名地張揚、衝動,就像我們剛看到的那樣,甚至到了損害自己公司健康的程度。他也不知道馬斯克腦子裡在想什麼。

不過平克提到,馬斯克當時發過一條推文,大意是:如果連平克都搞不懂狹義AI(比如一輛車)和通用AI的區別,而後者算力是前者的一百萬倍、還帶一個開放式效用函式,那人類就麻煩大了。平克說,那不是馬斯克最後一次發火氣很大的推文。

但他不認為專用AI和所謂通用AI之間的區分是關鍵。專用AI也不會為了達成目標而做任何想得出來的事。所有工程系統都被設計成在多個目標之間做權衡。造車的時候,我們不會忘記裝剎車,因為車的目標是跑得快。人們想到了,是的,你希望它跑得快,但不是一直快,所以也裝了剎車。

同理,如果一輛自動駕駛車被程式設計為走最短路徑去機場,它不會走對角線碾過行人、樹木和柵欄,因為那不在我們對"最短路徑"的定義裡。這就是智慧系統的定義:它把多重約束考慮進去。

如果系統真是通用智慧,這一點更成立。它就不會一根筋地追求某個目標,把其他所有考慮和附帶效應都忽略掉。那不叫通用人工智慧,那叫通用人工愚蠢。

Lex 也表達了對自動駕駛改善人類福祉的信心。他說這個前景是驚人的,讓他驚訝的是媒體很少注意到:僅在美國,每年就有大約四萬人死於公路,遠遠超過死於恐怖襲擊的人數。我們花了一萬億美元打一場反恐戰爭,而美國平均每年死於恐怖襲擊的只有六個人左右。年復一年,四萬人在公路上被屠殺,而這個數字本可以降到接近零。

他補了一句個人的感受:作為一個造這些車的人,說工程師會蠢到不去把安全性設計進系統,讓他有點被冒犯。他常常熬夜想著那四萬人。他做的每一件事都是為了救這些人的命。他在深度學習文獻、CVPR 和 NIPS 會議上興奮的每一個新發明,落腳點都是讓它更安全、幫助人。

所以他看不出這條軌跡怎麼會突然滑向一個智慧產生極大負面影響的狀態。

被AI消滅的工作,本來就是糟糕的工作

平克同意自動駕駛的人道主義價值,並且認為這只是AI潛在人道收益的開端。

他指出了一個被忽略的角度。關於AI讓哪些人失業,人們投入了巨大的關注,但很少有人注意到:即將被淘汰的那些工作,本來就是糟糕的工作。

不再有人去摘莊稼、鋪床、開卡車、挖煤,這些是摧殘靈魂的工作。我們有一整套文學作品在同情那些被困在卑微、消磨心智、危險崗位上的人。如果能消滅這些崗位,對人類是天大的好事。

當然,解決一個問題會冒出另一個問題:怎麼讓這些人有體面的收入。但平克說,如果我們聰明到能發明鋪床、收碗、照顧病人的機器,我們大概也聰明到能想出怎麼重新分配收入,把巨大的經濟節約分一部分給那些不再需要鋪床的人。

這段話的分量在於,它把討論的框架換掉了。通常的討論是"AI會不會搶走工作",隱含的假設是那些工作值得保留。平克直接把假設翻過來問:那些工作真的值得人去做嗎?如果一臺機器能替你鋪床,你是失去了意義,還是獲得了自由?這個問題沒有統一答案,但它至少讓"失業焦慮"這個預設情緒變得可以被重新審視。

擔憂預算和想象力學

Lex 提到薩姆·哈里斯(Sam Harris)的觀點:AI最終成為生存性風險是顯而易見的。不知道什麼時候,但最終是顯然的。正因為不知道什麼時候,所以現在就該擔心。

Lex 覺得這個論證很有意思,問:我們該怎麼考慮時間尺度?怎麼考慮一個我們幾乎不瞭解的威脅,它可能明天就來,但很可能是一百年以後?

平克先反問:我們害怕的那個"它"到底是什麼?是AI奴役我們,還是把我們變成回形針?

Lex 說,從哈里斯的角度看,最有說服力的是回形針那種情形。

平克的回應:我覺得它完全是幻想。不要造那樣的系統,不要給它那樣的目標。工程的第一條守則就是,在測試之前不要部署一個擁有巨大控制權的系統。也許工程文化會徹底改變,那我會擔心。但我看不到任何跡象表明工程師會突然幹蠢事,比如讓一座電廠受一個沒測試過的系統控制。

他還指出,這些設想裡,AI 不僅被賦予近乎魔法的能力,包括治癒癌症(這本身可能是個不連貫的目標,因為癌症有很多種)或者帶來世界和平(這目標本身怎麼界定都是問題),還被賦予了控制宇宙中每一個分子的能力。這本身不太可能,而且我們也不會不測試就把這類系統接到基礎設施上,就像對待任何工程系統一樣。

也許會有不負責任的工程師,那就需要法律和監管上的責任機制,讓工程師不做按他們自己的標準來看也很蠢的事。但他從未見過一個足夠可信的生存性威脅場景,值得投入大量腦力去預防。

他還提到,AI 的進步確實可觀,尤其是最近十年。但"突然出現一個階躍函式、在我們意識到之前它就無所不能、還有某種遞迴式的自我改進"這種想法同樣是幻想。現在叫人印象深刻的這些技術,比如深度學習,需要拿幾十萬上百萬個樣本去訓練。而"治癒癌症"這種問題,根本沒有幾十萬個同類樣本可訓。過去五年讓AI進步的那些方法,不會導向那個指數級突然自我改進的幻想。這是魔法思維,不符合我們對AI實際運作方式的理解。

接著他談了"擔憂預算"這個概念。我們得排優先順序。得把接近確定性的威脅,比如氣候變化,跟那些僅僅可以想象但機率極低的威脅區分開。還要考慮人們的擔憂預算。你不可能什麼都擔心。如果你播撒恐懼、宿命論和絕望,它會導致一種麻木:這些問題太沉重了,工程師反正會把我們全殺了,那要麼摧毀整個科學技術的基礎設施,要麼趁還能享受就享受吧。

Lex 總結:存在一條界線,越過它,擔憂就變成了讓人癱瘓的恐懼,產出的成分沒了。平克說,正是如此。

他還指出,人類的努力跟風險是不匹配的。認知心理學的一個基本結論是:驅動風險感知、進而驅動恐懼感知的,是可想象性,資料起不了多少作用。

所以我們把巨量資源錯配到避免恐怖主義上,它平均每年殺死約六個美國人(9·11 那一年是例外)。我們入侵別的國家,設立全新的政府部門,耗費巨量資源和生命,去防範一個微不足道的風險。而確定會發生、必然發生的風險,比如交通事故,以及不在眼前但足夠可信、值得擔心的風險,比如大流行病、核戰爭,得到的關注少得多。總統辯論裡沒有關於如何最小化核戰爭風險的討論,卻有大量關於恐怖主義的討論。

平克說,把我們用於恐懼、擔憂、關注和規劃的預算,校準到實際的傷害機率上,至關重要。

Lex 提到了喬·羅根(Joe Rogan)。羅根是他喜歡的人之一,透過想象力探索理性的邊界。羅根曾經相信很多陰謀論,後來透過理性把其中不少信念剝掉了。看著他透過理性丟掉大腳怪和9·11之類的想法,很有意思。平克說,羅根已經成了一個真正的好力量。

平克上過羅根的播客,聊得很精彩,但沒怎麼談AI。他幾周後還要再去一次。羅根很擔心AI的生存性威脅,代表了很多從一萬英尺高空看AI這個話題的人。

怎麼跟普通人聊AI

Lex 問:如果讓你教我一個AI研究者,該怎麼跟羅根和公眾談AI,你會給什麼建議?

平克的短答案是:去讀《當下的啟蒙》裡他寫AI的那些章節。

長一點的答案是:強調工程文化。它確實是以安全為導向的。在《當下的啟蒙》裡,他畫了各種原因導致的意外死亡率曲線:飛機失事、車禍、職業事故,甚至被閃電擊中致死。它們全都在急劇下降,因為工程文化就是想辦法擠出致命風險。死於火災、溺水、窒息的比例全都大幅下降,靠的是工程進步。他說,在看到那些圖表之前,他並沒有真正體會到這一點。

而這正是因為有像 Lex 這樣的人,熬夜想著,天啊,我發明的東西會不會傷到人,並且調動聰明才智去防止它發生。他說自己在MIT待了22年,對工程文化有些瞭解,理解工程師就是這麼想的。至關重要的是,面對AI,這種文化不能突然被關掉。

Lex 說,確實需要更多工程師站出來,為這種興奮、為這種對人性的正面看法發聲。平克同意。他說,我們想發明的一切都是為了給世界帶來好處。

悲觀讓你顯得更聰明

Lex 問了一個關於心理學的問題:撇開具體話題不談,對未來悲觀似乎會讓你顯得比樂觀更聰明。這個觀察對不對?如果對,為什麼?

平克的回答引了諷刺作家湯姆·萊勒(Tom Lehrer)的一句話:永遠預測最壞的情況,你就會被譽為先知。

這可能跟我們整體的負面偏見有關。作為物種,我們對負面比對正面更敏感。我們害怕損失甚於享受收益。這就給"先知"們留出了空間,讓他們提醒我們那些可能被忽略的傷害、風險和損失。這種不對稱確實存在。

這段話其實點出了AI威脅論流行的部分心理機制。預言災難的人幾乎不用付出代價:說對了,他是先知;說錯了,沒人記得。而預言進展和好處的人,一旦落空就會被嘲笑天真。這種激勵結構本身就偏向悲觀敘事,跟實際的風險機率無關。

影響平克的書

訪談後半段轉向書。

Lex 說比爾·蓋茨也是平克的粉絲,說《當下的啟蒙》是他有史以來最喜歡的新書。他問平克,早年哪本書對他看世界的方式影響深遠?

《當下的啟蒙》受大衛·多伊奇(David Deutsch)的《無窮的開始》影響。那本書是對知識和知識改善人類處境之力的一次相當深刻的反思,裡面有這樣的智慧:問題是不可避免的,但只要有正確的知識,問題就是可解的;而解決方案會創造新的問題,需要被再次解決。平克說,這是關於人類處境的一種智慧,影響了這本書的寫作。

他還提到一些優秀但冷門的書。他讀過一本《武力的歷史》(The History of Force),是政治學家詹姆斯·佩恩(James Payne)自費出版的,講暴力的歷史性下降,是《人性中的善良天使》的靈感之一。

再往前,青少年時期,他喜歡一本叫《一、二、三……無窮大》(One, Two, Three, Infinity)的書,作者是物理學家喬治·伽莫夫(George Gamow)。那本書用非常通俗幽默的方式講了相對論、數論、維度、高維空間,出版七十年後讀起來仍然令人愉快。

他還喜歡《時代生活》科學系列。他母親訂閱的,每個月寄來一本,每本一個主題。一本講電,一本講森林,一本講進化,然後有一本講心智。他當時就被"可能存在一門關於心智的科學"這件事吸引住了,也會把那本書算作一個影響。

大學時期,他讀了喬姆斯基的《語言反思》。理查德·道金斯的兩本書《盲眼鐘錶匠》和《自私的基因》影響巨大,既因為內容,也因為寫作風格,那種用生動散文解釋抽象概念的能力。斯蒂芬·傑伊·古爾德的第一個文集《自達爾文以來》也是生動寫作的絕佳例子。心理學家喬治·米勒提出了人類記憶容量是七加減二個組塊這個觀點,可能是他最著名的貢獻,但他還寫了幾本關於語言和傳播的書,平克本科時讀過,同樣寫得漂亮,思想深刻。

訪談在輕鬆的氛圍裡收尾。平克道了謝,Lex 也道了謝。整場對話沒有得出什麼結論,但它提供了一套看AI恐慌的另一種眼光:把風險放回它該在的位置上,跟其他真實的風險一起,按機率排隊。

給讀者的行動結論

  • 下次看到關於AI末日的說法,先問一句"那個它到底是什麼"。是奴役,是回形針,還是別的什麼?把模糊的恐懼逼成一個具體的場景,很多恐慌就會自己散掉。
  • 給自己建一個擔憂預算。把氣候變化、大流行病、核戰爭這類機率高、後果重的風險,跟僅僅可以想象、機率極低的場景分開對待,別讓後者佔走你全部的注意力。
  • 警惕可想象性偏差。你害怕某件事的程度,往往取決於它有多容易被腦補出畫面,跟它實際發生的機率關係不大。恐怖襲擊和交通事故的對比就是最好的例子。
  • 別把智力等同於權力意志。一個系統解決問題的能力很強,不代表它天生就想支配別人。目標是外部給的,跟智慧本身無關。
  • 關注工程文化裡的安全本能。想理解AI為什麼會朝好的方向走,去看飛機失事率、車禍死亡率這些曲線,它們下降靠的是工程師的日常習慣,跟道德呼籲無關。
  • 如果想系統地瞭解這套反恐慌的論證,去讀平克《當下的啟蒙》裡關於AI的章節,還有大衛·多伊奇的《無窮的開始》。
  • 當你發現自己在為某個遙遠的災難焦慮時,先分清這是有產出的擔憂,還是讓你癱瘓的恐懼。前者推動你做事,後者只會讓你想躺平。
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。
You've studied the human mind, cognition, language, vision, evolution, psychology, from child to adult, from the level of individual to the level of our entire civilization. So I feel like I can start with a simple multiple choice question. What is the meaning of life? Is it A. to attain knowledge as Plato said, B. to attain power as Nietzsche said, C. to escape death as Ernest Becker said, D. to propagate our genes as Darwin and others have said, E. there is no meaning as the nihilists have said, F. knowing the meaning of life is beyond our cognitive capabilities as Stephen Pinker said, based on my interpretation 20 years ago, and G. none of the above. I'd say A. comes closest, but I would amend that to C. to attaining not only knowledge but fulfillment more generally, that is life, health, stimulation, access to the living cultural and social world. Now this is our meaning of life. It's not the meaning of life if you were to ask our genes. Their meaning is to propagate copies of themselves, but that is distinct from the meaning that the brain that they lead to sets for itself. So to you knowledge is a small subset or a large subset? It's a large subset, but it's not the entirety of human striving because we also want to interact with people. We want to experience beauty. We want to experience the richness of the natural world, but understanding what makes the universe tick is way up there. For some of us more than others, certainly for me that's one of the top five. So is that a fundamental aspect? Are you just describing your own preference or is this a fundamental aspect of human nature is to seek knowledge? In your latest book you talk about the power, the usefulness of rationality and reason and so on. Is that a fundamental nature of human beings or is it something we should just strive for? Both. We're capable of striving for it because it is one of the things that make us what we are, homo sapiens, wise men. We are unusual among animals in the degree to which we acquire knowledge and use it to survive. We make tools. We strike agreements via language. We extract poisons. We predict the behavior of animals. We try to get at the workings of plants. And when I say we, I don't just mean we in the modern West, but we as a species everywhere, which is how we've managed to occupy every niche on the planet, how we've managed to drive other animals to extinction. And the refinement of reason in pursuit of human wellbeing, of health, happiness, social richness, cultural richness is our main challenge in the present. That is using our intellect, using our knowledge to figure out how the world works, how we work in order to make discoveries and strike agreements that make us all better off in the long run. Right. And you do that almost undeniably and in a data driven way in your recent book, but I'd like to focus on the artificial intelligence aspect of things and not just artificial intelligence, but natural intelligence too. So 20 years ago in a book you've written on how the mind works, you conjecture again, am I right to interpret things? You can correct me if I'm wrong, but you conjecture that human thought in the brain may be a result of a massive network of highly interconnected neurons. So from this interconnectivity emerges thought compared to artificial neural networks, which we use for machine learning today, is there something fundamentally more complex, mysterious, even magical about the biological neural networks versus the ones we've been starting to use over the past 60 years and have become to success in the past 10? There is something a little bit mysterious about the human neural networks, which is that each one of us who is a neural network knows that we ourselves are conscious. Conscious not in the sense of registering our surroundings or even registering our internal state, but in having subjective first person, present tense experience. That is when I see red, it's not just different from green, but there's a redness to it that I feel. Whether an artificial system would experience that or not, I don't know and I don't think I can know. That's why it's mysterious. If we had a perfectly lifelike robot that was behaviorally indistinguishable from a human, would we attribute consciousness to it or ought we to attribute consciousness to it? And that's something that it's very hard to know. But putting that aside, putting aside that largely philosophical question, the question is, is there some difference between the human neural network and the ones that we're building in artificial intelligence will mean that we're on the current trajectory, not going to reach the point where we've got a lifelike robot indistinguishable from a human because the way their so called neural networks are organized are different from the way ours are organized. I think there's overlap, but I think there are some big differences that current neural networks, current so called deep learning systems are in reality not all that deep. That is, they are very good at extracting high order statistical regularities, but most of the systems don't have a semantic level, a level of actual understanding of who did what to whom, why, where, how things work, what causes what else. Do you think that kind of thing can emerge as it does? So artificial neural networks are much smaller, the number of connections and so on than the current human biological networks, but do you think sort of to go to consciousness or to go to this higher level semantic reasoning about things, do you think that can emerge with just a larger network with a more richly weirdly interconnected network? Separate it in consciousness because consciousness is even a matter of complexity. A really weird one. Yeah, you could sensibly ask the question of whether shrimp are conscious, for example, they're not terribly complex, but maybe they feel pain. So let's just put that part of it aside. But I think sheer size of a neural network is not enough to give it structure and knowledge, but if it's suitably engineered, then why not? That is, we're neural networks, natural selection did a kind of equivalent of engineering of our brains. So I don't think there's anything mysterious in the sense that no system made out of silicon could ever do what a human brain can do. I think it's possible in principle. Whether it'll ever happen depends not only on how clever we are in engineering these systems, but whether we even want to, whether that's even a sensible goal. That is, you can ask the question, is there any locomotion system that is as good as a human? Well, we kind of want to do better than a human ultimately in terms of legged locomotion. There's no reason that humans should be our benchmark. They're tools that might be better in some ways. It may be that we can't duplicate a natural system because at some point it's so much cheaper to use a natural system that we're not going to invest more brainpower and resources. So for example, we don't really have an exact substitute for wood. We still build houses out of wood. We still build furniture out of wood. We like the look. We like the feel. It has certain properties that synthetics don't. It's not that there's anything magical or mysterious about wood. It's just that the extra steps of duplicating everything about wood is something we just haven't bothered because we have wood. Likewise, say cotton. I'm wearing cotton clothing now. It feels much better than polyester. It's not that cotton has something magic in it. It's not that we couldn't ever synthesize something exactly like cotton, but at some point it's just not worth it. We've got cotton. Likewise, in the case of human intelligence, the goal of making an artificial system that is exactly like the human brain is a goal that we probably know is going to pursue to the bitter end, I suspect, because if you want tools that do things better than humans, you're not going to care whether it does something like humans. So for example, diagnosing cancer or predicting the weather, why set humans as your benchmark? But in general, I suspect you also believe that even if the human should not be a benchmark and we don't want to imitate humans in their system, there's a lot to be learned about how to create an artificial intelligence system by studying the human. Yeah, I think that's right. In the same way that to build flying machines, we want to understand the laws of aerodynamics, including birds, but not mimic the birds, but they're the same laws. You have a view on AI, artificial intelligence, and safety that, from my perspective, is refreshingly rational or perhaps more importantly, has elements of positivity to it, which I think can be inspiring and empowering as opposed to paralyzing. For many people, including AI researchers, the eventual existential threat of AI is obvious, not only possible, but obvious. And for many others, including AI researchers, the threat is not obvious. So Elon Musk is famously in the highly concerned about AI camp, saying things like AI is far more dangerous than nuclear weapons, and that AI will likely destroy human civilization. Human civilization. So in February, he said that if Elon was really serious about AI, the threat of AI, he would stop building self driving cars that he's doing very successfully as part of Tesla. Then he said, wow, if even Pinker doesn't understand the difference between narrow AI, like a car and general AI, when the latter literally has a million times more compute power and an open ended utility function, humanity is in deep trouble. So first, what did you mean by the statement about Elon Musk should stop building self driving cars if he's deeply concerned? Not the last time that Elon Musk has fired off an intemperate tweet. Well, we live in a world where Twitter has power. Yes. Yeah, I think there are two kinds of existential threat that have been discussed in connection with artificial intelligence, and I think that they're both incoherent. One of them is a vague fear of AI takeover, that just as we subjugated animals and less technologically advanced peoples, so if we build something that's more advanced than us, it will inevitably turn us into pets or slaves or domesticated animal equivalents. I think this confuses intelligence with a will to power, that it so happens that in the intelligence system we are most familiar with, namely homo sapiens, we are products of natural selection, which is a competitive process, and so bundled together with our problem solving capacity are a number of nasty traits like dominance and exploitation and maximization of power and glory and resources and influence. There's no reason to think that sheer problem solving capability will set that as one of its goals. Its goals will be whatever we set its goals as, and as long as someone isn't building a megalomaniacal artificial intelligence, then there's no reason to think that it would naturally evolve in that direction. Now, you might say, well, what if we gave it the goal of maximizing its own power source? That's a pretty stupid goal to give an autonomous system. You don't give it that goal. I mean, that's just self evidently idiotic. So if you look at the history of the world, there's been a lot of opportunities where engineers could instill in a system destructive power and they choose not to because that's the natural process of engineering. Well, except for weapons. I mean, if you're building a weapon, its goal is to destroy people, and so I think there are good reasons to not build certain kinds of weapons. I think building nuclear weapons was a massive mistake. You do. So maybe pause on that because that is one of the serious threats. Do you think that it was a mistake in a sense that it should have been stopped early on? Or do you think it's just an unfortunate event of invention that this was invented? Do you think it's possible to stop? I guess is the question. It's hard to rewind the clock because of course it was invented in the context of World War II and the fear that the Nazis might develop one first. Then once it was initiated for that reason, it was hard to turn off, especially since winning the war against the Japanese and the Nazis was such an overwhelming goal of every responsible person that there's just nothing that people wouldn't have done then to ensure victory. It's quite possible if World War II hadn't happened that nuclear weapons wouldn't have been invented. We can't know, but I don't think it was by any means a necessity, any more than some of the other weapon systems that were envisioned but never implemented, like planes that would disperse poison gas over cities like crop dusters or systems to try to create earthquakes and tsunamis in enemy countries, to weaponize the weather, weaponize solar flares, all kinds of crazy schemes that we thought the better of. I think analogies between nuclear weapons and artificial intelligence are fundamentally misguided because the whole point of nuclear weapons is to destroy things. The point of artificial intelligence is not to destroy things. So the analogy is misleading. So there's two artificial intelligence you mentioned. The first one I guess is highly intelligent or power hungry. Yeah, it's a system that we design ourselves where we give it the goals. Goals are external to the means to attain the goals. If we don't design an artificially intelligent system to maximize dominance, then it won't maximize dominance. It's just that we're so familiar with homo sapiens where these two traits come bundled together, particularly in men, that we are apt to confuse high intelligence with a will to power, but that's just an error. The other fear is that will be collateral damage that will give artificial intelligence a goal like make paper clips and it will pursue that goal so brilliantly that before we can stop it, it turns us into paper clips. We'll give it the goal of curing cancer and it will turn us into guinea pigs for lethal experiments or give it the goal of world peace and its conception of world peace is no people, therefore no fighting and so it will kill us all. Now I think these are utterly fanciful. In fact, I think they're actually self defeating. They first of all assume that we're going to be so brilliant that we can design an artificial intelligence that can cure cancer, but so stupid that we don't specify what we mean by curing cancer in enough detail that it won't kill us in the process and it assumes that the system will be so smart that it can cure cancer, but so idiotic that it can't figure out that what we mean by curing cancer is not killing everyone. I think that the collateral damage scenario, the value alignment problem is also based on a misconception. So one of the challenges, of course, we don't know how to build either system currently or are we even close to knowing? Of course, those things can change overnight, but at this time, theorizing about it is very challenging in either direction. So that's probably at the core of the problem is without that ability to reason about the real engineering things here at hand is your imagination runs away with things. Exactly. But let me sort of ask, what do you think was the motivation, the thought process of Elon Musk? I build autonomous vehicles, I study autonomous vehicles, I study Tesla autopilot. I think it is one of the greatest currently large scale application of artificial intelligence in the world. It has potentially a very positive impact on society. So how does a person who's creating this very good quote unquote narrow AI system also seem to be so concerned about this other general AI? What do you think is the motivation there? What do you think is the thing? Well, you probably have to ask him, but there, and he is notoriously flamboyant, impulsive to the, as we have just seen, to the detriment of his own goals of the health of the company. So I don't know what's going on in his mind. You probably have to ask him, but I don't think the, and I don't think the distinction between special purpose AI and so called general AI is relevant that in the same way that special purpose AI is not going to do anything conceivable in order to attain a goal. All engineering systems are designed to trade off across multiple goals. When we build cars in the first place, we didn't forget to install brakes because the goal of a car is to go fast. It occurred to people, yes, you want it to go fast, but not always. So you would build in brakes too. Likewise, if a car is going to be autonomous and program it to take the shortest route to the airport, it's not going to take the diagonal and mow down people and trees and fences because that's the shortest route. That's not what we mean by the shortest route when we program it. And that's just what an intelligence system is by definition. It takes into account multiple constraints. The same is true, in fact, even more true of so called general intelligence. That is, if it's genuinely intelligent, it's not going to pursue some goal singlemindedly, omitting every other consideration and collateral effect. That's not artificial and general intelligence. That's artificial stupidity. I agree with you, by the way, on the promise of autonomous vehicles for improving human welfare. I think it's spectacular. And I'm surprised at how little press coverage notes that in the United States alone, something like 40,000 people die every year on the highways, vastly more than are killed by terrorists. And we spent a trillion dollars on a war to combat deaths by terrorism, about half a dozen a year. Whereas year in, year out, 40,000 people are massacred on the highways, which could be brought down to very close to zero. So I'm with you on the humanitarian benefit. Let me just mention that as a person who's building these cars, it is a little bit offensive to me to say that engineers would be clueless enough not to engineer safety into systems. I often stay up at night thinking about those 40,000 people that are dying. And everything I tried to engineer is to save those people's lives. So every new invention that I'm super excited about, in all the deep learning literature and CVPR conferences and NIPS, everything I'm super excited about is all grounded in making it safe and help people. So I just don't see how that trajectory can all of a sudden slip into a situation where intelligence will be highly negative. You and I certainly agree on that. And I think that's only the beginning of the potential humanitarian benefits of artificial intelligence. There's been enormous attention to what are we going to do with the people whose jobs are made obsolete by artificial intelligence, but very little attention given to the fact that the jobs that are going to be made obsolete are horrible jobs. The fact that people aren't going to be picking crops and making beds and driving trucks and mining coal, these are soul deadening jobs. And we have a whole literature sympathizing with the people stuck in these menial, mind deadening, dangerous jobs. If we can eliminate them, this is a fantastic boon to humanity. Now granted, you solve one problem and there's another one, namely, how do we get these people a decent income? But if we're smart enough to invent machines that can make beds and put away dishes and handle hospital patients, I think we're smart enough to figure out how to redistribute income to apportion some of the vast economic savings to the human beings who will no longer be needed to make beds. Okay. Sam Harris says that it's obvious that eventually AI will be an existential risk. He's one of the people who says it's obvious. We don't know when the claim goes, but eventually it's obvious. And because we don't know when, we should worry about it now. This is a very interesting argument in my eyes. So how do we think about timescale? How do we think about existential threats when we don't really, we know so little about the threat, unlike nuclear weapons perhaps, about this particular threat, that it could happen tomorrow, right? So, but very likely it won't. Very likely it'd be a hundred years away. So how do we ignore it? How do we talk about it? Do we worry about it? How do we think about those? What is it? A threat that we can imagine. It's within the limits of our imagination, but not within our limits of understanding to accurately predict it. But what is the it that we're afraid of? Sorry. AI being the existential threat. AI. How? Like enslaving us or turning us into paperclips? I think the most compelling from the Sam Harris perspective would be the paperclip situation. Yeah. I mean, I just think it's totally fanciful. I mean, that is don't build a system. Don't give a, don't, first of all, the code of engineering is you don't implement a system with massive control before testing it. Now, perhaps the culture of engineering will radically change. Then I would worry, but I don't see any signs that engineers will suddenly do idiotic things, like put a electric power plant in control of a system that they haven't tested first. Or all of these scenarios, not only imagine almost a magically powered intelligence, including things like cure cancer, which is probably an incoherent goal because there's so many different kinds of cancer or bring about world peace. I mean, how do you even specify that as a goal? But the scenarios also imagine some degree of control of every molecule in the universe, which not only is itself unlikely, but we would not start to connect these systems to infrastructure without testing as we would any kind of engineering system. Now, maybe some engineers will be irresponsible and we need legal and regulatory and legal responsibility implemented so that engineers don't do things that are stupid by their own standards. But the, I've never seen enough of a plausible scenario of existential threat to devote large amounts of brain power to, to forestall it. So you believe in the sort of the power on mass of the engineering of reason, as you argue in your latest book of Reason and Science, to sort of be the very thing that guides the development of new technology so it's safe and also keeps us safe. You know, granted the same culture of safety that currently is part of the engineering mindset for airplanes, for example. So yeah, I don't think that that should be thrown out the window and that untested all powerful systems should be suddenly implemented, but there's no reason to think they are. And in fact, if you look at the progress of artificial intelligence, it's been, you know, it's been impressive, especially in the last 10 years or so, but the idea that suddenly there'll be a step function that all of a sudden before we know it, it will be all powerful, that there'll be some kind of recursive self improvement, some kind of fume is also fanciful. We, certainly by the technology that we, that we're now impresses us, such as deep learning, where you train something on hundreds of thousands or millions of examples, they're not hundreds of thousands of problems of which curing cancer is a typical example. And so the kind of techniques that have allowed AI to increase in the last five years are not the kind that are going to lead to this fantasy of exponential sudden self improvement. I think it's kind of a magical thinking. It's not based on our understanding of how AI actually works. Now give me a chance here. So you said fanciful, magical thinking. In his TED talk, Sam Harris says that thinking about AI killing all human civilization is somehow fun, intellectually. Now I have to say as a scientist engineer, I don't find it fun, but when I'm having beer with my non AI friends, there is indeed something fun and appealing about it. Like talking about an episode of Black Mirror, considering if a large meteor is headed towards Earth, we were just told a large meteor is headed towards Earth, something like this. And can you relate to this sense of fun? And do you understand the psychology of it? Yes. Good question. I personally don't find it fun. I find it kind of actually a waste of time because there are genuine threats that we ought to be thinking about like pandemics, like cyber security vulnerabilities, like the possibility of nuclear war and certainly climate change. You know, this is enough to fill many conversations. And I think Sam did put his finger on something, namely that there is a community, sometimes called the rationality community, that delights in using its brainpower to come up with scenarios that would not occur to mere mortals, to less cerebral people. So there is a kind of intellectual thrill in finding new things to worry about that no one has worried about yet. I actually think, though, that it's not only is it a kind of fun that doesn't give me particular pleasure, but I think there can be a pernicious side to it, namely that you overcome people with such dread, such fatalism, that there are so many ways to die, to annihilate our civilization, that we may as well enjoy life while we can. There's nothing we can do about it. If climate change doesn't do us in, then runaway robots will. So let's enjoy ourselves now. We've got to prioritize. We have to look at threats that are close to certainty, such as climate change, and distinguish those from ones that are merely imaginable but with infinitesimal probabilities. And we have to take into account people's worry budget. You can't worry about everything. And if you sow dread and fear and terror and fatalism, it can lead to a kind of numbness. Well, these problems are overwhelming, and the engineers are just going to kill us all. So let's either destroy the entire infrastructure of science, technology, or let's just enjoy life while we can. So there's a certain line of worry, which I'm worried about a lot of things in engineering. There's a certain line of worry when you cross, you're allowed to cross, that it becomes paralyzing fear as opposed to productive fear. And that's kind of what you're highlighting. Exactly right. And we've seen some, we know that human effort is not well calibrated against risk in that because a basic tenet of cognitive psychology is that perception of risk and hence perception of fear is driven by imaginability, not by data. And so we misallocate vast amounts of resources to avoiding terrorism, which kills on average about six Americans a year with one exception of 9 11. We invade countries, we invent entire new departments of government with massive, massive expenditure of resources and lives to defend ourselves against a trivial risk. Whereas guaranteed risks, one of them you mentioned traffic fatalities and even risks that are not here, but are plausible enough to worry about like pandemics, like nuclear war, receive far too little attention. In presidential debates, there's no discussion of how to minimize the risk of nuclear war. Lots of discussion of terrorism, for example. And so I think it's essential to calibrate our budget of fear, worry, concern, planning to the actual probability of harm. Yep. So let me ask this question. So speaking of imaginability, you said it's important to think about reason and one of my favorite people who likes to dip into the outskirts of reason through fascinating exploration of his imagination is Joe Rogan. Oh yes. So who has through reason used to believe a lot of conspiracies and through reason has stripped away a lot of his beliefs in that way. So it's fascinating actually to watch him through rationality kind of throw away the ideas of Bigfoot and 9 11. I'm not sure exactly. Kim Trails. I don't know what he believes in. Yes. Okay. But he no longer believed in. No, that's right. No, he's become a real force for good. Yep. So you were on the Joe Rogan podcast in February and had a fascinating conversation, but as far as I remember, didn't talk much about artificial intelligence. I will be on his podcast in a couple of weeks. Joe is very much concerned about existential threat of AI. I'm not sure if you're, this is why I was hoping that you would get into that topic. And in this way, he represents quite a lot of people who look at the topic of AI from 10,000 foot level. So as an exercise of communication, you said it's important to be rational and reason about these things. Let me ask, if you were to coach me as an AI researcher about how to speak to Joe and the general public about AI, what would you advise? Well, the short answer would be to read the sections that I wrote in enlightenment now about AI, but a longer reason would be I think to emphasize, and I think you're very well positioned as an engineer to remind people about the culture of engineering, that it really is safety oriented, that another discussion in enlightenment now, I plot rates of accidental death from various causes, plane crashes, car crashes, occupational accidents, even death by lightning strikes. And they all plummet because the culture of engineering is how do you squeeze out the lethal risks, death by fire, death by drowning, death by asphyxiation, all of them drastically declined because of advances in engineering that I got to say, I did not appreciate until I saw those graphs. And it is because exactly, people like you who stay up at night thinking, oh my God, is what I'm inventing likely to hurt people and to deploy ingenuity to prevent that from happening. Now, I'm not an engineer, although I spent 22 years at MIT, so I know something about the culture of engineering. My understanding is that this is the way you think if you're an engineer. And it's essential that that culture not be suddenly switched off when it comes to artificial intelligence. So, I mean, that could be a problem, but is there any reason to think it would be switched off? I don't think so. And one, there's not enough engineers speaking up for this way, for the excitement, for the positive view of human nature, what you're trying to create is positivity. Like everything we try to invent is trying to do good for the world. But let me ask you about the psychology of negativity. It seems just objectively, not considering the topic, it seems that being negative about the future makes you sound smarter than being positive about the future, irregardless of topic. Am I correct in this observation? And if so, why do you think that is? Yeah, I think there is that phenomenon that, as Tom Lehrer, the satirist said, always predict the worst and you'll be hailed as a prophet. It may be part of our overall negativity bias. We are as a species more attuned to the negative than the positive. We dread losses more than we enjoy gains. And that might open up a space for prophets to remind us of harms and risks and losses that we may have overlooked. So I think there is that asymmetry. So you've written some of my favorite books all over the place. So starting from Enlightenment Now to The Better Ages of Our Nature, Blank Slate, How the Mind Works, the one about language, Language Instinct. Bill Gates, big fan too, said of your most recent book that it's my new favorite book of all time. So for you as an author, what was a book early on in your life that had a profound impact on the way you saw the world? Certainly this book, Enlightenment Now, was influenced by David Deutsch's The Beginning of Infinity, a rather deep reflection on knowledge and the power of knowledge to improve the human condition. And with bits of wisdom such as that problems are inevitable but problems are solvable given the right knowledge and that solutions create new problems that have to be solved in their turn. That's I think a kind of wisdom about the human condition that influenced the writing of this book. There are some books that are excellent but obscure, some of which I have on a page on my website. I read a book called The History of Force, self published by a political scientist named James Payne on the historical decline of violence and that was one of the inspirations for The Better Angels of Our Nature. What about early on? If you look back when you were maybe a teenager? I loved a book called One, Two, Three, Infinity. When I was a young adult I read that book by George Gamow, the physicist, which had very accessible and humorous explanations of relativity, of number theory, of dimensionality, high multiple dimensional spaces in a way that I think is still delightful 70 years after it was published. I like the Time Life Science series. These are books that would arrive every month that my mother subscribed to, each one on a different topic. One would be on electricity, one would be on forests, one would be on evolution and then one was on the mind. I was just intrigued that there could be a science of mind and that book I would cite as an influence as well. Then later on... That's when you fell in love with the idea of studying the mind? Was that the thing that grabbed you? It was one of the things I would say. I read as a college student the book Reflections on Language by Noam Chomsky. I spent most of his career here at MIT. Richard Dawkins, two books, The Blind Watchmaker and The Selfish Gene, were enormously influential, mainly for the content but also for the writing style, the ability to explain abstract concepts in lively prose. Stephen Jay Gould's first collection, Ever Since Darwin, also an excellent example of lively writing. George Miller, a psychologist that most psychologists are familiar with, came up with the idea that human memory has a capacity of seven plus or minus two chunks. That's probably his biggest claim to fame. But he wrote a couple of books on language and communication that I read as an undergraduate. Again, beautifully written and intellectually deep. Wonderful. Stephen, thank you so much for taking the time today. My pleasure. Thanks a lot, Lex.

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