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人工智慧達特茅斯夏季研究項目提案(1955年8月31日)中英對照版(36k字)

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華盛頓人 發表於 2019-6-9 12:26 | 顯示全部樓層 |閱讀模式
  2019-06-08 16:23

  科學Sciences導讀:人工智慧達特茅斯夏季研究項目提案(1955年8月31日)中英對照版。全文分為六大部分:一、提案說明,二、C.E.香農(C.E. Shannon)的研究提案,三、M.L.明斯基(M. L. Minsky)的研究提案,四、N.羅切斯特(N. Rochester)的研究提案,五、約翰·麥卡錫(JohnMcCarthy)的研究提案,六、對人工智慧問題感興趣的人。譯后只校對了一遍,不妥之處請看後面附的原文再次校正或留言。公號輸入欄發送「AI達特茅斯1955提案」獲取本PDF資料;歡迎大家讚賞支持科普、下載學習科技知識。

  人工智慧達特茅斯夏季研究項目提案(1955年8月31日)中英對照版(36k字)

  目錄

  人工智慧達特茅斯夏季研究項目提案(1955年8月31日)中譯版

  A PROPOSAL FOR THEDARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL INTELLIGENCE

  素材(880字)

  

  人工智慧達特茅斯夏季研究項目提案(1955年8月31日)中譯版

  APROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL INTELLIGENCE

  文|麥卡錫、明斯基、羅徹斯特、香農等,譯|秦隴紀,科學Sciences©20190607Fri

  J.麥卡錫(J.McCarthy),達特茅斯學院

  M.L.明斯基(M.L. Minsky),哈佛大學

  N.羅切斯特(N.Rochester),I.B.M公司

  C.E.香農(C.E. Shannon),貝爾電話實驗室

  1955年8月31日

  我們建議在1956年夏天在新罕布希爾州漢諾威的達特茅斯學院進行為期2個月、10人的人工智慧研究。該研究是在假設的基礎上進行的,即學習的每個方面或任何其他智能特徵原則上都可以如此精確地描述,以便可以使機器模擬它。將嘗試找到如何使機器使用語言,形成抽象和概念,解決現在為人類保留的各種問題,並改進自己。我們認為,如果一個經過精心挑選的科學家團隊在一起工作一個夏天,就可以在一個或多個這些問題上取得重大進展。

  以下是人工智慧問題的一些方面:

  1.自動計算機

  如果一台機器可以做一項工作,則一台可編程自動計算機器能用來模擬這台機器。現有計算機的速度和內存容量可能不足以模擬人腦的許多高級功能,但主要障礙不是缺乏機器容量,而是我們無法編寫充分利用我們所擁有優勢的程序。

  2.如何使用語言對計算機進行編程

  可以推測,人類思想的很大一部分包括根據推理規則和猜想規則來操控單詞。從這個角度來看,形成包括承認一個新詞和一些規則的概括,其中含有它的句子暗示並被其他人暗示。這個想法從未如此精確地制定,也沒有制定出實例。

  3.神經網路

  如何安排一組(假設的)神經元以形成概念。烏特利·拉什夫斯基(Uttley,Rashevsky)和他的團隊,法利(Farley)和克拉克(Clark),匹茲(Pitts)和麥卡洛克(McCulloch),明斯基(Minsky),羅切斯特(Rochester)和霍蘭德(Holland)等人在這個問題上做了大量的理論和實驗工作。已經獲得了部分結果,但問題是需要更多的理論工作。

  4.計算大小的理論

  如果給出一個定義明確的問題(可以用機械方式測試提出的答案是否是有效答案),解決問題的方法是按順序嘗試所有可能的答案。這種方法效率低,要排除它,必須有一些計算效率的標準。一些考慮將表明,為了測量計算的效率,有必要手頭有一種測量計算裝置複雜性的方法,如果有一個具有功能複雜性的理論,則可以這樣做。香農(Shannon)和麥卡錫(McCarthy)也獲得了關於這個問題的部分結果。

  5.自我改進

  可能真正智能的機器將開展可以最好地描述為自我改進的活動。已經提出了一些這樣做的方案,值得進一步研究。這個問題似乎也可以抽象地進行研究。

  6.抽象

  許多類型的「抽象」可以明確定義,而其他幾個則不那麼明顯。直接嘗試對這些進行分類並描述從感官數據和其他數據形成抽象的機器方法似乎是值得的。

  7.隨機性和創造力

  一個相當有吸引力但又不完全不完整的猜想是,創造性思維和缺乏想象力的能力思維之間的區別在於注入一些隨機性。隨機性必須由直覺引導才能有效。換句話說,受過教育的猜測或預感包括在其他有序思維中的受控隨機性。

  除了上述集體制定的研究問題外,我們還要求參與其中的個人描述他們將要開展的工作。附上項目的四個發起人的聲明。

  我們建議如下組織小組的工作。

  潛在參與者將被發送此提案的副本,並詢問他們是否願意處理該組中的人工智慧問題,如果是,他們希望如何工作。邀請將由組委會根據個人對小組工作潛在貢獻的估計作出。成員們將在小組工作期間的幾個月內分發他們以前的工作和他們對受到攻擊的問題的看法。

  會議期間將定期舉辦研討會,讓會員有機會單獨和非正式的小組工作。

  該提案的創始人是:

  1.C.E.香農(C.E. Shannon),數學家,貝爾電話實驗室。香農(Shannon)開發了信息統計理論,命題演算在開關電路中的應用,並且有關於開關電路的有效合成,學習機器的設計,密碼學和圖靈機理論的結果。他和J.麥卡錫(J. McCarthy)共同編輯了「自動機理論」的數學年鑒研究。

  2.M.L.明斯基(M. L. Minsky),哈佛大學數學與神經學初級研究員。明斯基已經建立了一個用於通過神經網路模擬學習的機器,並且已經寫了一篇名為「神經網路和腦模型問題」的普林斯頓博士論文,其中包括學習理論和隨機神經網路理論的結果。

  3.N.羅切斯特(N. Rochester),IBM公司信息研究經理,紐約波基普西。羅切斯特七年來一直關注雷達和計算機械的發展。他和另一位工程師共同負責IBM Type 701的設計,這是目前廣泛使用的大型自動計算機。他研究了一些當今廣泛使用的自動編程技術,並且一直關注如何讓機器完成以前只能由人來完成的任務相關問題。他還致力於模擬神經網路,特彆強調使用計算機測試神經生理學的理論。

  4.J.麥卡錫(J.McCarthy),達特茅斯學院數學助理教授。麥卡錫研究了許多與思維過程的數學本質相關的問題,包括圖靈機的理論,計算機的速度,大腦模型與環境的關係,以及機器對語言的使用。這項工作的一些成果包含在即將出版的香農(Shannon)和麥卡錫(McCarthy)編輯的「年鑒研究(Annals Study)」中。麥卡錫的其他工作一直是微分方程領域。

  洛克菲勒基金會被要求在以下基礎上為該項目提供財政支持:

  1.每個教師級別參與者1200美元的薪水,他們沒有得到他自己組織的支持。例如,預計來自貝爾實驗室和IBM公司的參與者將得到這些組織的支持,而來自達特茅斯和哈佛的參與者將需要基金會的支持。

  2.最多兩名研究生的700美元薪水。

  3.遠方參與者的鐵路票花費。

  4.為同時在其他地方租住的人租房。

  5.秘書費用650美元,秘書費500美元,複製費用150美元。

  6.組織費用200美元。(包括由參與者複製初步工作的費用和組織目的所需的旅行費用。

  7.兩三個人短期訪問的費用。

  預計花費

  6份1200的薪資$7200

  2份700的薪資1400

  8份旅行和租金費用平均300 2400

  秘書和組織費用850

  額外旅行費用600

  意外事件550

  ----

  $13,500

  C.E.香農(C.E. Shannon)的研究提案

  我想將我的研究投入到下面列出的一個或兩個主題中。雖然我希望這樣做,但出於個人考慮,我可能無法參加完整的兩個月。儘管如此,我打算在任何時間都在那裡。

  1.將資訊理論概念應用於計算機器和腦模型。信息理論中的基本問題是在嘈雜的通道上可靠地傳輸信息。計算機器中的類似問題是使用不可靠元件的可靠計算。這個問題已經由馮諾依曼研究的謝弗行程元件(Sheffer strokeelements),香農(Shannon)和摩爾(Moore)研究了繼電器(relays);但仍有許多懸而未決的問題。幾個要素的問題,類似於通道容量的概念的發展,對所需冗餘的上限和下限的更尖銳的分析等都是重要的問題。另一個問題涉及信息網路理論,其中信息在許多閉環中流動(與通信理論中通常考慮的簡單單向通道形成對比)。延遲問題在閉環情況下變得非常重要,似乎有必要採用一種全新的方法。當已知消息集合的過去歷史的一部分時,這可能涉及諸如部分熵(partial entropies)之類的概念。

  2.匹配環境——自動機的大腦模型方法。通常,機器或動物只能適用於在有限的一類環境中操作。即使是複雜的人類大腦也首先適應其環境的簡單方面,並逐漸建立起更複雜的特徵。我建議通過一系列匹配(理論上)環境的并行開發來研究腦模型的合成。這裡的重點是澄清環境模型並將其表示為數學結構。探索定理、寫音樂或下棋。我在這裡建議從簡單開始,當環境不是敵對(只是漠不關心)或複雜時,並通過一系列簡單階段向這些高級活動方向努力。

  M.L.明斯基(M. L. Minsky)的研究提案

  設計具有以下學習類型的機器並不困難。該機器具有輸入和輸出通道以及內部裝置,可以對輸入提供不同的輸出響應,使得機器可以通過「反覆試驗和錯誤」過程「訓練」以獲得一個範圍輸入輸出功能這樣的機器,當放置在適當的環境中並且被賦予「成功」或「失敗」的標準時,可以被訓練成表現出「追求目標」的行為。除非機器具有或能夠開發一種抽象感覺材料的方式,否則它只能通過緩慢的緩慢步驟在複雜的環境中前進,並且通常不會達到高水平的行為。

  現在讓成功的標準不僅僅是在機器的輸出通道上出現所需的活動模式,而是在給定環境中給定操作的性能。然後在某些方面,該動作機(motor)狀況似乎是感覺狀況的雙重情形,只有當機器同樣能夠組裝「動作機抽象」集合並將其輸出活動與環境變化聯繫起來時,進展才能相當快。這種「動作機抽象」只有當它們與環境的變化相關時才有價值,這些變化可以被機器檢測為感覺狀況的變化,即,如果它們通過環境結構,機器正在使用的感覺類型的抽象。

  我已經研究了這樣的系統一段時間並且覺得如果可以設計一種機器,其中可以使感覺和運動抽象形成,以滿足某些關係,可以產生高度的行為。這些關係涉及配對、動作機抽象與感官抽象,以產生新的感覺情境,表示如果相應的運動機行為實際發生可能預期的環境變化。

  將要尋找的重要結果是機器傾向於在其自身內部構建一個放置它的環境的抽象模型。如果遇到問題,它可以首先在內部抽象環境模型中探索解決方案,然後嘗試外部實驗。由於這項初步的內部研究,這些外部實驗似乎相當聰明,而且這種行為必須被視為「富有想象力」。

  我的論文中描述了一個關於如何做到這一點的非常初步的建議,我打算在這個方向上做進一步的工作。我希望到1956年夏天,我能夠將這種機器的模型與計算機編程階段相當接近。

  N.羅切斯特(N. Rochester)的研究提案

  機器性能的獨創性

  在編寫用於自動計算器的程序時,通常為機器提供一組規則以涵蓋可能出現並面對機器的每個意外事件。有人期望機器能夠盲目地遵循這套規則,而顯得沒有任何原創性或常識。此外,當機器感到困惑時,一個人只會對自己感到惱火,因為他為機器提供的規則有點矛盾。最後,在為機器編寫程序時,有時必須以非常費力的方式處理問題,而如果機器只有一點點直覺或者可以做出合理的猜測,問題的解決方案可能是非常直接的。本文描述了一個關於如何使機器在上面建議的一般領域中以更複雜的方式表現的設想。本文討論了我偶爾工作了大約五年的問題,希望明年夏天在人工智慧項目中進一步研究這個問題。

  發明或發現的過程

  生活是在給我們提供了解決許多問題的程序(procedures)之文化環境中。這些程序的工作原理尚不清楚,但我將根據Craik1建議的模型討論問題的這一方面。他認為,心理行為基本上包括在大腦內構建小型引擎,可以模擬並預測與環境相關的抽象。因此,已理解問題的解決方案如下:

  1.該環境提供形成某些抽象的數據。

  2.抽象以及某些內部習慣或驅動提供:

  2.1 根據將來要實現的期望條件來定義問題,目標。

  2.2 建議的解決問題的措施。

  2.3 刺激引起大腦引擎回應這種情況。

  3.然後該引擎運行以預測這種環境狀況和擬議的反應將導致什麼。

  4.如果預測對應於目標,則個體繼續按照指示行事。

  
時代小人物. 但也有自己的思想,情感. 和道德.

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 樓主| 華盛頓人 發表於 2019-6-9 12:27 | 顯示全部樓層
如果生活在他的文化環境中為個人提供問題的解決方案,則該預測將對應於該目標。關於作為存儲程序計算器的個人,該程序包含用於覆蓋該特定意外事件的規則。

  對於更複雜狀況,其規則可能更複雜。該規則可能要求測試一組可能的操作中的每一個,以確定提供解決方案的操作。更複雜的一套規則可能會提供環境的不確定性,例如在玩tic tac toe(三子棋:九宮格中的三連棋遊戲,一款休閑益智遊戲,秦注)時,不僅要考慮他的下一步動作,還要考慮環境的各種可能動作(他的對手)。

  現在考慮一個問題,在這個問題中,文化中的任何個體都沒有解決方案,並且抵制在解決方案上的努力。這可能是當前未解決的典型科學問題。個人可能會嘗試解決它,並發現每一個合理的行為都會導致失敗。換句話說,存儲的程序包含解決此問題的規則,但規則略有錯誤。

  為了解決這個問題,個人將不得不做一些不合理或意想不到的事情,正如文化所積累的智慧傳統所判斷的那樣。他可以通過隨機嘗試不同的事情來獲得這種行為,但這種方法通常效率太低。通常有太多可能的行動方案,其中只有一小部分是可以接受的。個人需要預感,這是意想不到的,但並非完全合理。一些問題,通常是相當新的問題,並且沒有抵抗很多努力,只需要一點點隨機性。其他人,通常是那些長期抵制解決方案的人,需要與傳統方法進行真正奇怪的偏離。解決方案需要原創性的問題可能會產生一種涉及隨機性的解決方法。

  就Craik1的模型而言,應該模擬環境的引擎首先就無法正確模擬。因此,有必要嘗試對該引擎進行各種修改,直到找到使其完成所需的動作。

  不是根據他的文化中的個體來描述問題,而是可以根據對不成熟個體的學習來描述。當個人被提出超出其經驗範圍的問題時,他必須以類似的方式克服它。

  迄今為止,在問題的機器解決方案中使用該方法的最近實用方法是蒙特卡羅方法的擴展。在適用於蒙特卡羅的通常問題中,存在一種嚴重誤解的情況,其中存在太多可能的因素,並且無法確定在制定分析解決方案時忽略哪些因素。所以數學家有機器做了幾千個隨機實驗。這些實驗的結果提供了關於答案可能是什麼的粗略猜測。蒙特卡羅方法的擴展是使用這些結果作為指導,以確定忽略什麼,以便簡化問題,足以獲得近似的解析方案。

  可能會問為什麼該方法應該包括隨機性。為什麼不應該按照當前知識狀態預測其成功的概率的順序來嘗試每種可能性?對於被他的文化所提供的環境所包圍的科學家來說,可能只有一位科學家不可能在他的生活中解決問題,因此需要許多人的努力。如果他們使用隨機性,他們可以立即在其上工作,而無需完全重複工作。如果他們使用系統,他們將需要不可能的詳細通信。對於在與其他個體競爭中成熟的個體,混合策略(使用博弈論術語)的要求有利於隨機性。對於機器,可能需要隨機性來克服程序員的短視和偏見。雖然隨機性的必要性顯然尚未得到證實,但有許多證據表明它是有利的。

  具有隨機性的機器

  為了編寫程序使自動計算器使用原創性,而不使用洞見(forsight)來引入隨機性。例如,如果一個人編寫了一個程序,那麼每10,000個步驟中就會產生一個隨機數,並將其作為指令執行,結果可能會很混亂。然後在一定程度的混亂之後,機器可能會嘗試禁止或執行停止指令,實驗將結束。

  然而,有兩種方法似乎是合理的。其中之一是找到大腦如何設法做這種事情並複製它。另一種是在解決方案中採取某些需要原創性的實際問題,並試圖找到一種方法來編寫程序以在自動計算器上解決它們。這些方法中的任何一種都可能最終成功。然而,目前尚不清楚哪個會更快或者需要多少年或幾代。到目前為止,我在這些方面的大部分努力都是採用前一種方法,因為我覺得最好掌握所有相關科學知識,以便解決這個難題,而且我已非常了解目前計算器的狀況和為其編程的工藝。

  大腦的控制機制與今天的計算器中的控制機制明顯不同。表現其差異之一的是失敗方式。計算器的失敗很有特徵性地產生了一些非常不合理的東西。內存或數據傳輸中的錯誤,可能至少就在最重要的數字中。控制中的錯誤幾乎可以做任何事情。它可能執行錯誤的指令或操作錯誤的輸入輸出單元。另一方面,語言中的人為錯誤往往會導致幾乎有意義的陳述(考慮一個幾乎睡著,稍微醉酒,或稍微發燒的人)。也許大腦的機制是這樣的,推理中的輕微錯誤會以正確的方式引入隨機性。也許控制行為2中的序列順序的機制引導隨機因素,以便提高想象過程相對於純隨機性的效率。

  在我們的自動計算器上模擬神經網路已經完成了一些工作。一個目的是看是否有可能以適當的方式引入隨機性。事實證明,神經元的活動與解決問題之間存在太多未知的聯繫,這種方法尚未完成。結果對網和神經元的行為有所啟發,但尚未找到解決需要創意的問題的方法。

  這項工作的一個重要方面是努力使機器形成和操縱概念,抽象,概括和名稱。試圖測試大腦是如何做到的理論3。第一組實驗引發了對該理論某些細節的修訂。第二組實驗正在進行中。到明年夏天,這項工作將完成,並將編寫最終報告。

  我的程序是嘗試下一個編寫程序來解決問題,這些問題是在解決方案中需要原創性的一些有限類問題的成員。現在預測明年夏天將會是什麼階段還是僅僅是;然後我將如何定義直接問題。但是,本文中描述的潛在問題是我打算追求的。用一句話來說,問題是:我怎樣才能製造出能夠在問題解決方案中展現獨創性的機器?

  參考

  1.K.J.W. Craik,「解釋的本質」,劍橋大學出版社,1943年(轉載於1952年),92頁。

  2.K.S. Lashley,「行為中的序列順序問題」,「行為中的腦機制」,Hixon Symposium,L.A.Jeffress編輯,John Wiley&Sons,紐約,第112-146頁,1951年。

  3.D. O. Hebb,行為組織,John Wiley&Sons,紐約,1949年

  1.K.J.W. Craik, The Nature of Explanation, Cambridge University Press,1943 (reprinted 1952), p. 92.

  2. K.S.Lashley, ``The Problem of Serial Order in Behavior'', in Cerebral Mechanismin Behavior, the Hixon Symposium, edited by L.A. Jeffress, John Wiley &Sons, New York, pp. 112-146, 1951.

  3. D.O. Hebb, The Organization of Behavior, John Wiley & Sons, New York,1949

  約翰·麥卡錫(John McCarthy)的研究提案

  在明年和夏季人工智慧研究項目期間,我建議研究語言與智力的關係。似乎很清楚,將試驗和錯誤方法直接應用於感覺數據和運動活動之間的關係不會導致任何非常複雜的行為。相反,試驗和錯誤方法必須應用於更高的抽象層次。人類的思想顯然使用語言作為處理複雜現象的手段。較高級別的試錯過程經常採用制定猜想和測試的形式。英語有許多屬性,目前所描述的每種形式語言都缺乏這些屬性。

  1.用非正式數學補充的英語論證可以簡明扼要。

  2.英語是普遍的,因為它可以在英語中設置任何其他語言,然後在適當的地方使用該語言。

  3.英語用戶可以在其中引用自己並制定關於他在解決他正在處理的問題方面的進展的陳述。

  4.除了舉證規則外,如果完全制定英語則會有猜想規則。

  迄今為止制定的邏輯語言要麼是指令列表,要麼使計算機進行預先指定的計算,要麼正式化數學部分。後者的構建如下:

  1.在非正式數學中容易描述,

  2.允許將非正式數學的陳述翻譯成語言,

  3.輕鬆爭論是否證明(???)

  沒有嘗試用人工語言製作像非正式證據一樣簡短的證據。因此,似乎希望嘗試構造一種人工語言,計算機可以編程用於需要猜測和自我引用的問題。它應該與英語相對應,因為關於給定主題的簡短英語陳述應該在語言中有短記者,因此應該簡短的論點或推測論證。我希望嘗試製定一種具有這些屬性的語言,並且除了包含物理對象,事件等的概念之外,希望使用這種語言可以對機器進行編程以學習如何很好地玩遊戲以及其他任務。

  對人工智慧問題感興趣的人

  這個名單的目的,是讓那些人知道誰有興趣接收有關問題的文件。名單中的人將獲得達特茅斯人工智慧夏季項目報告的副本。[1996年註:沒有報告。]

  該名單由參與或參觀達特茅斯人工智慧夏季研究項目或已知對該主題感興趣的人組成。它被發送給本名單和其他幾個人。

  就目前的目的而言,人工智慧問題被認為是使機器以一種被稱為智能的方式運行,如果人類如此表現的話。

  修訂后的名單將很快發布,以便任何有興趣進入名單的人或希望更改其地址的任何人都應寫信給:

  約翰·麥卡錫

  數學系

  達特茅斯學院

  新罕布希爾州漢諾威

  [1996年註:並非所有這些人都參加了達特茅斯會議。他們是我們認為可能對人工智慧感興趣的人。](秦隴紀註:47人)

  該清單包括:

  阿德爾森,馬文;休斯飛機公司;機場站,洛杉磯,加利福尼亞州

  阿什比,W.R.;巴恩伍德之家;格洛斯特,英格蘭

  巴克斯,約翰;IBM公司;麥迪遜大街590號,紐約州紐約市

  伯恩斯坦,亞歷克斯;IBM公司;麥迪遜大街590號,紐約州紐約市

  比奇洛,J.H.;高等研究院;新澤西州普林斯頓

  伊萊亞斯,彼得;麻省理工學院R.L.E.;馬薩諸塞州劍橋市

  杜達,W.L.;IBM研究實驗室;紐約州波基普西市

  戴維斯,保羅M.;第18街1317號;加利福尼亞州洛杉磯市

  法諾,R.M.;麻省理工學院R.L.E.;馬薩諸塞州劍橋市

  法利,B.G.;公園大道324號;馬薩諸塞州阿靈頓

  加蘭特,E.H.;賓夕法尼亞大學;賓夕法尼亞州費城

  蓋爾森特,赫伯特;IBM研究院;紐約州波基普西市

  格拉肖,哈維A.;奧利維亞街1102號;安娜堡,密歇根州

  戈爾扎爾,赫伯特;西11街330號;紐約州紐約市

  哈格爾巴格;貝爾電話實驗室;新澤西州默里希爾

  米勒,喬治A.;紀念館;哈佛大學;馬薩諸塞州劍橋市

  哈蒙,萊昂D.;貝爾電話實驗室;新澤西州默里希爾

  霍蘭德,約翰H.;E.R.I.密歇根大學;安娜堡,密歇根州

  霍爾特,阿納托爾;農村巷7358號;賓夕法尼亞州費城

  考茨,威廉H.;斯坦福研究所;加州門洛帕克

  盧斯,R.D.;西117街427號;紐約州紐約市

  麥凱,唐納德;物理系;倫敦大學;倫敦,WC2,英格蘭

  麥卡錫,約翰;達特茅斯學院;新罕布希爾州漢諾威

  麥卡洛克,沃倫S.;麻省理工學院R.L.E.;馬薩諸塞州劍橋市

  梅爾扎克,Z.A.;密歇根大學數學系;安娜堡,密歇根州

  明斯基,M.L.;紐伯里街112號;馬薩諸塞州波士頓

  莫特,特倫查德;麻省理工學院電氣工程系;馬薩諸塞州劍橋市

  納什,約翰;高等研究院;新澤西州普林斯頓

  紐厄爾,艾倫;卡內基理工學院工業管理系;匹茲堡,賓夕法尼亞州

  羅賓遜,亞伯拉罕;多倫多大學數學系;多倫多,安大略省,加拿大

  羅切斯特,納撒尼爾;IBM公司工程研究實驗室;紐約州波基普西市

  羅傑斯,哈特利,小Jr;MIT數學系;馬薩諸塞州劍橋市

  羅森布利斯,沃爾特;麻省理工學院R.L.E.;馬薩諸塞州劍橋市

  羅斯坦,傑羅姆;東卑爾根廣場21號;新澤西州紅銀行

  賽爾,大衛;IBM公司;麥迪遜大街590號;紐約州紐約市

  肖爾康,J.J.;麻省理工學院C-380林肯實驗室;馬薩諸塞州列剋星敦

  沙普利,L.;蘭德公司;1700大街;加利福尼亞州聖莫尼卡

  舒特澤伯格Schutzenberger,M.P;麻省理工學院R.L.E.;馬薩諸塞州劍橋市

  塞爾弗里奇,O.G.;麻省理工學院林肯實驗室;馬薩諸塞州列剋星敦

  香農,C.E.;麻省理工學院R.L.E.;馬薩諸塞州劍橋市

  夏皮羅,諾曼;蘭德公司;1700大街;加利福尼亞州聖莫尼卡

  西蒙,赫伯特A.;工業管理系;卡內基理工學院;匹茲堡,賓夕法尼亞州

  索洛莫諾夫,雷蒙德J.;技術研究組;17聯合廣場西;紐約州紐約市

  斯蒂爾,J.E.,上尉,美國空軍;B區8698盒;萊特-帕特森空軍基地;俄亥俄州

  韋伯斯特,弗雷德里克;柯立芝大道62號;馬薩諸塞州劍橋市

  摩爾,E.F.;貝爾電話實驗室;新澤西州默里希爾

  凱梅尼,約翰G.;達特茅斯學院;新罕布希爾州漢諾威

  關於這份文件......

  約翰麥卡錫

  周四4月3日星期三19:48:31

  

  
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原文如下,來自http://www-formal.stanford.edu/j ... outh/dartmouth.html

  Next:About this document

  A PROPOSAL FOR THEDARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL INTELLIGENCE

  J. McCarthy, Dartmouth College

  M. L. Minsky, Harvard University

  N. Rochester, I.B.M. Corporation

  C.E. Shannon, Bell Telephone Laboratories

  August 31, 1955

  We propose that a 2 month, 10 man study of artificial intelligence be carried outduring the summer of 1956 at Dartmouth College in Hanover, New Hampshire. Thestudy is to proceed on the basis of the conjecture that every aspect oflearning or any other feature of intelligence can in principle be so preciselydescribed that a machine can be made to simulate it. An attempt will be made tofind how to make machines use language, form abstractions and concepts, solvekinds of problems now reserved for humans, and improve themselves. We thinkthat a significant advance can be made in one or more of these problems if acarefully selected group of scientists work on it together for a summer.

  The following are some aspects of the artificial intelligenceproblem:

  1. AutomaticComputers

  If amachine can do a job, then an automatic calculator can be programmed to simulatethe machine. The speeds and memory capacities of present computers may beinsufficient to simulate many of the higher functions of the human brain, butthe major obstacle is not lack of machine capacity, but our inability to writeprograms taking full advantage of what we have.

  2. HowCan a Computer be Programmed to Use a Language

  It maybe speculated that a large part of human thought consists of manipulating wordsaccording to rules of reasoning and rules of conjecture. From this point ofview, forming a generalization consists of admitting a new word and some ruleswhereby sentences containing it imply and are implied by others. This idea hasnever been very precisely formulated nor have examples been worked out.

  3. NeuronNets

  How cana set of (hypothetical) neurons be arranged so as to form concepts.Considerable theoretical and experimental work has been done on this problem byUttley, Rashevsky and his group, Farley and Clark, Pitts and McCulloch, Minsky,Rochester and Holland, and others. Partial results have been obtained but theproblem needs more theoretical work.

  4. Theoryof the Size of a Calculation

  If weare given a well-defined problem (one for which it is possible to testmechanically whether or not a proposed answer is a valid answer) one way ofsolving it is to try all possible answers in order. This method is inefficient,and to exclude it one must have some criterion for efficiency of calculation.Some consideration will show that to get a measure of the efficiency of a calculationit is necessary to have on hand a method of measuring the complexity ofcalculating devices which in turn can be done if one has a theory of thecomplexity of functions. Some partial results on this problem have beenobtained by Shannon, and also by McCarthy.

  5. Self-lmprovement

  Probablya truly intelligent machine will carry out activities which may best bedescribed as self-improvement. Some schemes for doing this have been proposedand are worth further study. It seems likely that this question can be studiedabstractly as well.

  6. Abstractions

  Anumber of types of ``abstraction'' can be distinctly defined and several othersless distinctly. A direct attempt to classify these and to describe machinemethods of forming abstractions from sensory and other data would seemworthwhile.

  7. Randomnessand Creativity

  Afairly attractive and yet clearly incomplete conjecture is that the differencebetween creative thinking and unimaginative competent thinking lies in theinjection of a some randomness. The randomness must be guided by intuition tobe efficient. In other words, the educated guess or the hunch includecontrolled randomness in otherwise orderly thinking.

  Inaddition to the above collectively formulated problems for study, we have askedthe individuals taking part to describe what they will work on. Statements bythe four originators of the project are attached.

  Wepropose to organize the work of the group as follows.

  Potentialparticipants will be sent copies of this proposal and asked if they would liketo work on the artificial intelligence problem in the group and if so what theywould like to work on. The invitations will be made by the organizing committeeon the basis of its estimate of the individual's potential contribution to thework of the group. The members will circulate their previous work and theirideas for the problems to be attacked during the months preceding the workingperiod of the group.

  Duringthe meeting there will be regular research seminars and opportunity for themembers to work individually and in informal small groups.

  The originators of this proposal are:

  1. C.E. Shannon, Mathematician, Bell Telephone Laboratories. Shannon developedthe statistical theory of information, the application of propositional calculusto switching circuits, and has results on the efficient synthesis of switchingcircuits, the design of machines that learn, cryptography, and the theory ofTuring machines. He and J. McCarthy are co-editing an Annals of MathematicsStudy on ``The Theory of Automata'' .

  2. M.L. Minsky, Harvard Junior Fellow in Mathematics and Neurology. Minsky hasbuilt a machine for simulating learning by nerve nets and has written a PrincetonPhD thesis in mathematics entitled, ``Neural Nets and the Brain Model Problem''which includes results in learning theory and the theory of random neural nets.

  3. N. Rochester, Manager of Information Research,IBM Corporation, Poughkeepsie, New York. Rochester was concerned with thedevelopment of radar for seven years and computing machinery for seven years.He and another engineer were jointly responsible for the design of the IBM Type701 which is a large scale automatic computer in wide use today. He worked outsome of the automatic programming techniques which are in wide use today andhas been concerned with problems of how to get machines to do tasks whichpreviously could be done only by people. He has also worked on simulation ofnerve nets with particular emphasis on using computers to test theories inneurophysiology.

  4. J. McCarthy, Assistant Professor of Mathematics,Dartmouth College. McCarthy has worked on a number of questions connected withthe mathematical nature of the thought process including the theory of Turingmachines, the speed of computers, the relation of a brain model to itsenvironment, and the use of languages by machines. Some results of this workare included in the forthcoming ``Annals Study'' edited by Shannon and McCarthy.McCarthy's other work has been in the field of differential equations.

  TheRockefeller Foundation is being asked to provide financial support for theproject on the following basis:

  1.Salaries of $1200 for each faculty level participant who is not being supportedby his own organization. It is expected, for example, that the participantsfrom Bell Laboratories and IBM Corporation will be supported by theseorganizations while those from Dartmouth and Harvard will require foundationsupport.

  2. Salariesof $700 for up to two graduate students.

  3.Railway fare for participants coming from a distance.

  4. Rentfor people who are simultaneously renting elsewhere.

  5.Secretarial expenses of $650, $500 for a secretary and $150 for duplicatingexpenses.

  6.Organization expenses of $200. (Includes expense of reproducing preliminarywork by participants and travel necessary for organization purposes.

  7.Expenses for two or three people visiting for a short time.

  EstimatedExpenses

  6 salaries of 1200 $7200

  2 salaries of 700 &1400

  8 traveling and rent expenses averaging 300 &2400

  Secretarial and organizational expense &850

  Additional traveling expenses &600

  Contingencies &550

  &----&

  $13,500

  PROPOSAL FOR RESEARCH BY C.E.SHANNON

  I would like to devote my research to one or both of the topicslisted below. While I hope to do so, it is possible thatbecause of personal considerations I may not be able to attend for the entiretwo months. I, nevertheless, intend to be there for whatever time is possible.

  1.Application of information theory concepts to computing machines and brainmodels. A basic problem in information theory is that of transmittinginformation reliably over a noisy channel. An analogous problem in computingmachines is that of reliable computing using unreliable elements. This problemhas been studies by von Neumann for Sheffer stroke elements and by Shannon andMoore for relays; but there are still many open questions. The problem forseveral elements, the development of concepts similar to channel capacity, thesharper analysis of upper and lower bounds on the required redundancy, etc. areamong the important issues. Another question deals with the theory ofinformation networks where information flows in many closed loops (ascontrasted with the simple one-way channel usually considered in communicationtheory). Questions of delay become very important in the closed loop case, anda whole new approach seems necessary. This would probably involve concepts suchas partial entropies when a part of the past history of a message ensemble isknown.

  2. Thematched environment - brain model approach to automata. In general a machine oranimal can only adapt to or operate in a limited class of environments. Eventhe complex human brain first adapts to the simpler aspects of its environment,and gradually builds up to the more complex features. I propose to study thesynthesis of brain models by the parallel development of a series of matched(theoretical) environments and corresponding brain models which adapt to them.The emphasis here is on clarifying the environmental model, and representing itas a mathematical structure. Often in discussing mechanized intelligence, wethink of machines performing the most advanced human thought activities-provingtheorems, writing music, or playing chess. I am proposing here to start at thesimple and when the environment is neither hostile (merely indifferent) norcomplex, and to work up through a series of easy stages in the direction ofthese advanced activities.

  PROPOSAL FOR RESEARCH BY M.L.MINSKY

  It isnot difficult to design a machine which exhibits the following type oflearning. The machine is provided with input and output channels and aninternal means of providing varied output responses to inputs in such a waythat the machine may be ``trained'' by a ``trial and error'' process to acquireone of a range of input-output functions. Such a machine, when placed in anappropriate environment and given a criterior of ``success'' or ``failure'' canbe trained to exhibit ``goal-seeking'' behavior. Unless the machine is providedwith, or is able to develop, a way of abstracting sensory material, it canprogress through a complicated environment only through painfully slow steps,and in general will not reach a high level of behavior.

  Now letthe criterion of success be not merely the appearance of a desired activitypattern at the output channel of the machine, but rather the performance of agiven manipulation in a given environment. Then in certain ways the motorsituation appears to be a dual of the sensory situation, and progress can bereasonably fast only if the machine is equally capable of assembling anensemble of ``motor abstractions'' relating its output activity to changes in theenvironment. Such ``motor abstractions'' can be valuable only if they relate tochanges in the environment which can be detected by the machine as changes inthe sensory situation, i.e., if they are related, through the structure of theenvironrnent, to the sensory abstractions that the machine is using.

  I havebeen studying such systems for some time and feel that if a machine can bedesigned in which the sensory and motor abstractions, as they are formed, canbe made to satisfy certain relations, a high order of behavior may result.These relations involve pairing, motor abstractions with sensory abstractionsin such a way as to produce new sensory situations representing the changes inthe environment that might be expected if the corresponding motor act actuallytook place.

  Theimportant result that would be looked for would be that the machine would tendto build up within itself an abstract model of the environment in which it isplaced. If it were given a problem, it could first explore solutions within theinternal abstract model of the environment and then attempt externalexperiments. Because of this preliminary internal study, these externalexperiments would appear to be rather clever, and the behavior would have to beregarded as rather ``imaginative''

  A verytentative proposal of how this might be done is described in my dissertationand I intend to do further work in this direction. I hope that by summer 1956 Iwi11 have a model of such a machine fairly close to the stage of programming ina computer.

  PROPOSAL FOR RESEARCH BY N. ROCHESTER

  Originality in Machine Performance

  Inwriting a program for an automatic calculator, one ordinarily provides themachine with a set of rules to cover each contingency which may arise andconfront the machine. One expects the machine to follow this set of rulesslavishly and to exhibit no originality or common sense. Furthermore one isannoyed only at himself when the machine gets confused because the rules he hasprovided for the machine are slightly contradictory. Finally, in writingprograms for machines, one sometimes must go at problems in a very laboriousmanner whereas, if the machine had just a little intuition or could makereasonable guesses, the solution of the problem could be quite direct. Thispaper describes a conjecture as to how to make a machine behave in a somewhatmore sophisticated manner in the general area suggested above. The paperdiscusses a problem on which I have been working sporadically for about fiveyears and which I wish to pursue further in the ArtificialIntelligence Projectnext summer.

 
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he Process of Invention or Discovery

Livingin the environment of our culture provides us with procedures for solving manyproblems. Just how these procedures work is not yet clear but I shall discussthis aspect of the problem in terms of a model suggested by Craik . He suggests that mental action consists basically ofconstructing little engines inside the brain which can simulate and thuspredict abstractions relating to environment. Thus the solution of a problemwhich one already understands is done as follows:

1.Theenvironment provides data from which certain abstractions are formed.

2.Theabstractions together with certain internal habits or drives provide:

2.1 Adefinition of a problem in terms of desired condition to be achieved in thefuture, a goal.

2.2 Asuggested action to solve the problem.

2.3 Stimulationto arouse in the brain the engine which corresponds to this situation.

3.Thenthe engine operates to predict what this environmental situation and theproposed reaction will lead to.

4.Ifthe prediction corresponds to the goal the individual proceeds to act asindicated.

Theprediction will correspond to the goal if living in the environment of hisculture has provided the individual with the solution to the problem. Regardingthe individual as a stored program calculator, the program contains rules tocover this particular contingency.

For amore complex situation the rules might be more complicated. The rules mightcall for testing each of a set of possible actions to determine which providedthe solution. A still more complex set of rules might provide for uncertainty aboutthe environment, as for example in playing tic tac toe one must not onlyconsider his next move but the various possible moves of the environment (hisopponent).

Nowconsider a problem for which no individual in the culture has a solution andwhich has resisted efforts at solution. This might be a typical currentunsolved scientific problem. The individual might try to solve it and find thatevery reasonable action led to failure. In other words the stored programcontains rules for the solution of this problem but the rules are slightlywrong.

Inorder to solve this problem the individual will have to do something which isunreasonable or unexpected as judged by the heritage of wisdom accumulated bythe culture. He could get such behavior by trying different things at randombut such an approach would usually be too inefficient. There are usually toomany possible courses of action of which only a tiny fraction are acceptable.The individual needs a hunch, something unexpected but not altogether reasonable.Some problems, often those which are fairly new and have not resisted mucheffort, need just a little randomness. Others, often those which have longresisted solution, need a really bizarre deviation from traditional methods. Aproblem whose solution requires originality could yield to a method of solutionwhich involved randomness.

Interms of Craik's S model, the engine which should simulate the environment atfirst fails to simulate correctly. Therefore, it is necessary to try variousmodifications of the engine until one is found that makes it do what is needed.

Insteadof describing the problem in terms of an individual in his culture it couldhave been described in terms of the learning of an immature individual. Whenthe individual is presented with a problem outside the scope of his experiencehe must surmount it in a similar manner.

So farthe nearest practical approach using this method in machine solution ofproblems is an extension of the Monte Carlo method. In the usual problem which isappropriate for Monte Carlo there is a situation which is grossly misunderstoodand which has too many possible factors and one is unable to decide whichfactors to ignore in working out analytical solution. So the mathematician hasthe machine making a few thousand random experiments. The results of theseexperiments provide a rough guess as to what the answer may be. The extensionof the Monte Carlo Method is to use these results as a guide to determine whatto neglect in order to simplify the problem enough to obtain an approximateanalytical solution.

Itmight be asked why the method should include randomness. Why shouldn't themethod be to try each possibility in the order of the probability that thepresent state of knowledge would predict for its success? For the scientistsurrounded by the environment provided by his culture, it may be that onescientist alone would be unlikely to solve the problem in his life so theefforts of many are needed. If they use randomness they could all work at onceon it without complete duplication of effort. If they used system they wouldrequire impossibly detailed communication. For the individual maturing incompetition with other individuals the requirements of mixed strategy (usinggame theory terminology) favor randomness. For the machine, randomness willprobably be needed to overcome the shortsightedness and prejudices of theprogrammer. While the necessity for randomness has clearly not been proven,there is much evidence in its favor.

TheMachine With Randomness

Inorder to write a program to make an automatic calculator use originality itwill not do to introduce randomness without using forsight. If, for example,one wrote a program so that once in every 10,000 steps the calculator generateda random number and executed it as an instruction the result would probably bechaos. Then after a certain amount of chaos the machine would probably trysomething forbidden or execute a stop instruction and the experiment would beover.

Twoapproaches, however, appear to be reasonable. One of these is to find how thebrain manages to do this sort of thing and copy it. The other is to take someclass of real problems which require originality in their solution and attemptto find a way to write a program to solve them on an automatic calculator.Either of these approaches would probably eventually succeed. However, it isnot clear which would be quicker nor how many years or generations it wouldtake. Most of my effort along these lines has so far been on the former approachbecause I felt that it would be best to master all relevant scientificknowledge in order to work on such a hard problem, and I already was quiteaware of the current state of calculators and the art of programming them.

Thecontrol mechanism of the brain is clearly very different from the controlmechanism in today's calculators. One symptom of the difference is the mannerof failure. A failure of a calculator characteristically produces somethingquite unreasonable. An error in memory or in data transmission is as likely tobe in the most significant digit as in the least. An error in control can donearly anything. It might execute the wrong instruction or operate a wronginput-output unit. On the other hand human errors in speech are apt to resultin statements which almost make sense (consider someone who is almost asleep,slightly drunk, or slightly feverish). Perhaps the mechanism of the brain issuch that a slight error in reasoning introduces randomness in just the rightway. Perhaps the mechanism that controls serial order in behavior guides the random factor so as to improve the efficiency ofimaginative processes over pure randomness.

Somework has been done on simulating neuron nets on our automatic calculator. Onepurpose was to see if it would be thereby possible to introduce randomness inan appropriate fashion. It seems to have turned out that there are too manyunknown links between the activity of neurons and problem solving for thisapproach to work quite yet. The results have cast some light on the behavior ofnets and neurons, but have not yielded a way to solve problems requiringoriginality.

Animportant aspect of this work has been an effort to make the machine form andmanipulate concepts, abstractions, generalizations, and names. An attempt wasmade to test a theory3 of how the brain does it. The first set ofexperiments occasioned a revision of certain details of the theory. The secondset of experiments is now in progress. By next summer this work will befinished and a final report will have been written.

Myprogram is to try next to write a program to solve problems which are membersof some limited class of problems that require originality in their solution.It is too early to predict just what stage I will be in next summer, or just;how I will then define the immediate problem. However, the underlying problemwhich is described in this paper is what I intend to pursue. In a singlesentence the problem is: how can I make a machine which will exhibitoriginality in its solution of problems?

REFERENCES

1.K.J.W. Craik, The Nature of Explanation, Cambridge University Press,1943 (reprinted 1952), p. 92.

2. K.S.Lashley, ``The Problem of Serial Order in Behavior'', in Cerebral Mechanismin Behavior, the Hixon Symposium, edited by L.A. Jeffress, John Wiley &Sons, New York, pp. 112-146, 1951.

3. D.O. Hebb, The Organization of Behavior, John Wiley & Sons, New York,1949
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