人工智能达特茅斯夏季研究项目提案(1955年8月31日)中英对照版

科学Sciences导读：人工智能达特茅斯夏季研究项目提案(1955年8月31日)中英对照版。全文分为六大部分：一、提案说明，二、C.E.香农(C.E. Shannon)的研究提案，三、M.L.明斯基(M. L. Minsky)的研究提案，四、N.罗切斯特(N. Rochester)的研究提案，五、约翰·麦卡锡(JohnMcCarthy)的研究提案，六、对人工智能问题感兴趣的人。译后只校对了一遍，不妥之处请看后面附的原文再次校正或留言。公号输入栏发送“AI达特茅斯1955提案”获取本PDF资料；欢迎大家赞赏支持科普、下载学习科技知识。

人工智能达特茅斯夏季研究项目提案(1955年8月31日)中英对照版(36k字)

目录

人工智能达特茅斯夏季研究项目提案(1955年8月31日)中译版

A PROPOSAL FOR THEDARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL INTELLIGENCE

素材(880字)

人工智能达特茅斯夏季研究项目提案(1955年8月31日)中译版

APROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL INTELLIGENCE

文|麦卡锡、明斯基、罗彻斯特、香农等，译|秦陇纪，科学Sciences©20190607Fri

J.麦卡锡(J.McCarthy)，达特茅斯学院

M.L.明斯基(M.L. Minsky)，哈佛大学

N.罗切斯特(N.Rochester)，I.B.M公司

C.E.香农(C.E. Shannon)，贝尔电话实验室

1955年8月31日

我们建议在1956年夏天在新罕布什尔州汉诺威的达特茅斯学院进行为期2个月、10人的人工智能研究。该研究是在假设的基础上进行的，即学习的每个方面或任何其他智能特征原则上都可以如此精确地描述，以便可以使机器模拟它。将尝试找到如何使机器使用语言，形成抽象和概念，解决现在为人类保留的各种问题，并改进自己。我们认为，如果一个经过精心挑选的科学家团队在一起工作一个夏天，就可以在一个或多个这些问题上取得重大进展。

以下是人工智能问题的一些方面：

1.自动计算机

如果一台机器可以做一项工作，则一台可编程自动计算机器能用来模拟这台机器。现有计算机的速度和内存容量可能不足以模拟人脑的许多高级功能，但主要障碍不是缺乏机器容量，而是我们无法编写充分利用我们所拥有优势的程序。

2.如何使用语言对计算机进行编程

可以推测，人类思想的很大一部分包括根据推理规则和猜想规则来操控单词。从这个角度来看，形成包括承认一个新词和一些规则的概括，其中含有它的句子暗示并被其他人暗示。这个想法从未如此精确地制定，也没有制定出实例。

3.神经网络

如何安排一组(假设的)神经元以形成概念。乌特利·拉什夫斯基(Uttley，Rashevsky)和他的团队，法利(Farley)和克拉克(Clark)，匹兹(Pitts)和麦卡洛克(McCulloch)，明斯基(Minsky)，罗切斯特(Rochester)和霍兰德(Holland)等人在这个问题上做了大量的理论和实验工作。已经获得了部分结果，但问题是需要更多的理论工作。

4.计算大小的理论

如果给出一个定义明确的问题(可以用机械方式测试提出的答案是否是有效答案)，解决问题的方法是按顺序尝试所有可能的答案。这种方法效率低，要排除它，必须有一些计算效率的标准。一些考虑将表明，为了测量计算的效率，有必要手头有一种测量计算装置复杂性的方法，如果有一个具有功能复杂性的理论，则可以这样做。香农(Shannon)和麦卡锡(McCarthy)也获得了关于这个问题的部分结果。

5.自我改进

可能真正智能的机器将开展可以最好地描述为自我改进的活动。已经提出了一些这样做的方案，值得进一步研究。这个问题似乎也可以抽象地进行研究。

6.抽象

许多类型的“抽象”可以明确定义，而其他几个则不那么明显。直接尝试对这些进行分类并描述从感官数据和其他数据形成抽象的机器方法似乎是值得的。

7.随机性和创造力

一个相当有吸引力但又不完全不完整的猜想是，创造性思维和缺乏想象力的能力思维之间的区别在于注入一些随机性。随机性必须由直觉引导才能有效。换句话说，受过教育的猜测或预感包括在其他有序思维中的受控随机性。

除了上述集体制定的研究问题外，我们还要求参与其中的个人描述他们将要开展的工作。附上项目的四个发起人的声明。

我们建议如下组织小组的工作。

潜在参与者将被发送此提案的副本，并询问他们是否愿意处理该组中的人工智能问题，如果是，他们希望如何工作。邀请将由组委会根据个人对小组工作潜在贡献的估计作出。成员们将在小组工作期间的几个月内分发他们以前的工作和他们对受到攻击的问题的看法。

会议期间将定期举办研讨会，让会员有机会单独和非正式的小组工作。

该提案的创始人是：

1.C.E.香农(C.E. Shannon)，数学家，贝尔电话实验室。香农(Shannon)开发了信息统计理论，命题演算在开关电路中的应用，并且有关于开关电路的有效合成，学习机器的设计，密码学和图灵机理论的结果。他和J.麦卡锡(J. McCarthy)共同编辑了“自动机理论”的数学年鉴研究。

2.M.L.明斯基(M. L. Minsky)，哈佛大学数学与神经学初级研究员。明斯基已经建立了一个用于通过神经网络模拟学习的机器，并且已经写了一篇名为“神经网络和脑模型问题”的普林斯顿博士论文，其中包括学习理论和随机神经网络理论的结果。

3.N.罗切斯特(N. Rochester)，IBM公司信息研究经理，纽约波基普西。罗切斯特七年来一直关注雷达和计算机械的发展。他和另一位工程师共同负责IBM Type 701的设计，这是目前广泛使用的大型自动计算机。他研究了一些当今广泛使用的自动编程技术，并且一直关注如何让机器完成以前只能由人来完成的任务相关问题。他还致力于模拟神经网络，特别强调使用计算机测试神经生理学的理论。

4.J.麦卡锡(J.McCarthy)，达特茅斯学院数学助理教授。麦卡锡研究了许多与思维过程的数学本质相关的问题，包括图灵机的理论，计算机的速度，大脑模型与环境的关系，以及机器对语言的使用。这项工作的一些成果包含在即将出版的香农(Shannon)和麦卡锡(McCarthy)编辑的“年鉴研究(Annals Study)”中。麦卡锡的其他工作一直是微分方程领域。

洛克菲勒基金会被要求在以下基础上为该项目提供财政支持：

1.每个教师级别参与者1200美元的薪水，他们没有得到他自己组织的支持。例如，预计来自贝尔实验室和IBM公司的参与者将得到这些组织的支持，而来自达特茅斯和哈佛的参与者将需要基金会的支持。

2.最多两名研究生的700美元薪水。

3.远方参与者的铁路票花费。

4.为同时在其他地方租住的人租房。

5.秘书费用650美元，秘书费500美元，复制费用150美元。

6.组织费用200美元。(包括由参与者复制初步工作的费用和组织目的所需的旅行费用。

7.两三个人短期访问的费用。

预计花费

6份1200的薪资 $7200

2份700的薪资 1400

8份旅行和租金费用平均300 2400

秘书和组织费用 850

额外旅行费用 600

意外事件 550

----

$13,500

C.E.香农(C.E. Shannon)的研究提案

我想将我的研究投入到下面列出的一个或两个主题中。虽然我希望这样做，但出于个人考虑，我可能无法参加完整的两个月。尽管如此，我打算在任何时间都在那里。

1.将信息论概念应用于计算机器和脑模型。信息理论中的基本问题是在嘈杂的信道上可靠地传输信息。计算机器中的类似问题是使用不可靠元件的可靠计算。这个问题已经由冯诺依曼研究的谢弗行程元件(Sheffer strokeelements)，香农(Shannon)和摩尔(Moore)研究了继电器(relays)；但仍有许多悬而未决的问题。几个要素的问题，类似于信道容量的概念的发展，对所需冗余的上限和下限的更尖锐的分析等都是重要的问题。另一个问题涉及信息网络理论，其中信息在许多闭环中流动(与通信理论中通常考虑的简单单向信道形成对比)。延迟问题在闭环情况下变得非常重要，似乎有必要采用一种全新的方法。当已知消息集合的过去历史的一部分时，这可能涉及诸如部分熵(partial entropies)之类的概念。

2.匹配环境——自动机的大脑模型方法。通常，机器或动物只能适用于在有限的一类环境中操作。即使是复杂的人类大脑也首先适应其环境的简单方面，并逐渐建立起更复杂的特征。我建议通过一系列匹配(理论上)环境的并行开发来研究脑模型的合成。这里的重点是澄清环境模型并将其表示为数学结构。探索定理、写音乐或下棋。我在这里建议从简单开始，当环境不是敌对(只是漠不关心)或复杂时，并通过一系列简单阶段向这些高级活动方向努力。

M.L.明斯基(M. L. Minsky)的研究提案

设计具有以下学习类型的机器并不困难。该机器具有输入和输出通道以及内部装置，可以对输入提供不同的输出响应，使得机器可以通过“反复试验和错误”过程“训练”以获得一个范围输入输出功能这样的机器，当放置在适当的环境中并且被赋予“成功”或“失败”的标准时，可以被训练成表现出“追求目标”的行为。除非机器具有或能够开发一种抽象感觉材料的方式，否则它只能通过缓慢的缓慢步骤在复杂的环境中前进，并且通常不会达到高水平的行为。

现在让成功的标准不仅仅是在机器的输出通道上出现所需的活动模式，而是在给定环境中给定操作的性能。然后在某些方面，该动作机(motor)状况似乎是感觉状况的双重情形，只有当机器同样能够组装“动作机抽象”集合并将其输出活动与环境变化联系起来时，进展才能相当快。这种“动作机抽象”只有当它们与环境的变化相关时才有价值，这些变化可以被机器检测为感觉状况的变化，即，如果它们通过环境结构，机器正在使用的感觉类型的抽象。

我已经研究了这样的系统一段时间并且觉得如果可以设计一种机器，其中可以使感觉和运动抽象形成，以满足某些关系，可以产生高度的行为。这些关系涉及配对、动作机抽象与感官抽象，以产生新的感觉情境，表示如果相应的运动机行为实际发生可能预期的环境变化。

将要寻找的重要结果是机器倾向于在其自身内部构建一个放置它的环境的抽象模型。如果遇到问题，它可以首先在内部抽象环境模型中探索解决方案，然后尝试外部实验。由于这项初步的内部研究，这些外部实验似乎相当聪明，而且这种行为必须被视为“富有想象力”。

我的论文中描述了一个关于如何做到这一点的非常初步的建议，我打算在这个方向上做进一步的工作。我希望到1956年夏天，我能够将这种机器的模型与计算机编程阶段相当接近。

N.罗切斯特(N. Rochester)的研究提案

机器性能的独创性

在编写用于自动计算器的程序时，通常为机器提供一组规则以涵盖可能出现并面对机器的每个意外事件。有人期望机器能够盲目地遵循这套规则，而显得没有任何原创性或常识。此外，当机器感到困惑时，一个人只会对自己感到恼火，因为他为机器提供的规则有点矛盾。最后，在为机器编写程序时，有时必须以非常费力的方式处理问题，而如果机器只有一点点直觉或者可以做出合理的猜测，问题的解决方案可能是非常直接的。本文描述了一个关于如何使机器在上面建议的一般领域中以更复杂的方式表现的设想。本文讨论了我偶尔工作了大约五年的问题，希望明年夏天在人工智能项目中进一步研究这个问题。

发明或发现的过程

生活是在给我们提供了解决许多问题的程序(procedures)之文化环境中。这些程序的工作原理尚不清楚，但我将根据Craik1建议的模型讨论问题的这一方面。他认为，心理行为基本上包括在大脑内构建小型引擎，可以模拟并预测与环境相关的抽象。因此，已理解问题的解决方案如下：

1.该环境提供形成某些抽象的数据。

2.抽象以及某些内部习惯或驱动提供：

2.1 根据将来要实现的期望条件来定义问题，目标。

2.2 建议的解决问题的措施。

2.3 刺激引起大脑引擎回应这种情况。

3.然后该引擎运行以预测这种环境状况和拟议的反应将导致什么。

4.如果预测对应于目标，则个体继续按照指示行事。

如果生活在他的文化环境中为个人提供问题的解决方案，则该预测将对应于该目标。关于作为存储程序计算器的个人，该程序包含用于覆盖该特定意外事件的规则。

对于更复杂状况，其规则可能更复杂。该规则可能要求测试一组可能的操作中的每一个，以确定提供解决方案的操作。更复杂的一套规则可能会提供环境的不确定性，例如在玩tic tac toe(三子棋：九宫格中的三连棋游戏，一款休闲益智游戏，秦注)时，不仅要考虑他的下一步动作，还要考虑环境的各种可能动作(他的对手)。

现在考虑一个问题，在这个问题中，文化中的任何个体都没有解决方案，并且抵制在解决方案上的努力。这可能是当前未解决的典型科学问题。个人可能会尝试解决它，并发现每一个合理的行为都会导致失败。换句话说，存储的程序包含解决此问题的规则，但规则略有错误。

为了解决这个问题，个人将不得不做一些不合理或意想不到的事情，正如文化所积累的智慧传统所判断的那样。他可以通过随机尝试不同的事情来获得这种行为，但这种方法通常效率太低。通常有太多可能的行动方案，其中只有一小部分是可以接受的。个人需要预感，这是意想不到的，但并非完全合理。一些问题，通常是相当新的问题，并且没有抵抗很多努力，只需要一点点随机性。其他人，通常是那些长期抵制解决方案的人，需要与传统方法进行真正奇怪的偏离。解决方案需要原创性的问题可能会产生一种涉及随机性的解决方法。

就Craik1的模型而言，应该模拟环境的引擎首先就无法正确模拟。因此，有必要尝试对该引擎进行各种修改，直到找到使其完成所需的动作。

不是根据他的文化中的个体来描述问题，而是可以根据对不成熟个体的学习来描述。当个人被提出超出其经验范围的问题时，他必须以类似的方式克服它。

迄今为止，在问题的机器解决方案中使用该方法的最近实用方法是蒙特卡罗方法的扩展。在适用于蒙特卡罗的通常问题中，存在一种严重误解的情况，其中存在太多可能的因素，并且无法确定在制定分析解决方案时忽略哪些因素。所以数学家有机器做了几千个随机实验。这些实验的结果提供了关于答案可能是什么的粗略猜测。蒙特卡罗方法的扩展是使用这些结果作为指导，以确定忽略什么，以便简化问题，足以获得近似的解析方案。

可能会问为什么该方法应该包括随机性。为什么不应该按照当前知识状态预测其成功的概率的顺序来尝试每种可能性？对于被他的文化所提供的环境所包围的科学家来说，可能只有一位科学家不可能在他的生活中解决问题，因此需要许多人的努力。如果他们使用随机性，他们可以立即在其上工作，而无需完全重复工作。如果他们使用系统，他们将需要不可能的详细通信。对于在与其他个体竞争中成熟的个体，混合策略(使用博弈论术语)的要求有利于随机性。对于机器，可能需要随机性来克服程序员的短视和偏见。虽然随机性的必要性显然尚未得到证实，但有许多证据表明它是有利的。

具有随机性的机器

为了编写程序使自动计算器使用原创性，而不使用洞见(forsight)来引入随机性。例如，如果一个人编写了一个程序，那么每10,000个步骤中就会产生一个随机数，并将其作为指令执行，结果可能会很混乱。然后在一定程度的混乱之后，机器可能会尝试禁止或执行停止指令，实验将结束。

然而，有两种方法似乎是合理的。其中之一是找到大脑如何设法做这种事情并复制它。另一种是在解决方案中采取某些需要原创性的实际问题，并试图找到一种方法来编写程序以在自动计算器上解决它们。这些方法中的任何一种都可能最终成功。然而，目前尚不清楚哪个会更快或者需要多少年或几代。到目前为止，我在这些方面的大部分努力都是采用前一种方法，因为我觉得最好掌握所有相关科学知识，以便解决这个难题，而且我已非常了解目前计算器的状况和为其编程的工艺。

大脑的控制机制与今天的计算器中的控制机制明显不同。表现其差异之一的是失败方式。计算器的失败很有特征性地产生了一些非常不合理的东西。内存或数据传输中的错误，可能至少就在最重要的数字中。控制中的错误几乎可以做任何事情。它可能执行错误的指令或操作错误的输入输出单元。另一方面，语言中的人为错误往往会导致几乎有意义的陈述(考虑一个几乎睡着，稍微醉酒，或稍微发烧的人)。也许大脑的机制是这样的，推理中的轻微错误会以正确的方式引入随机性。也许控制行为2中的序列顺序的机制引导随机因素，以便提高想象过程相对于纯随机性的效率。

在我们的自动计算器上模拟神经网络已经完成了一些工作。一个目的是看是否有可能以适当的方式引入随机性。事实证明，神经元的活动与解决问题之间存在太多未知的联系，这种方法尚未完成。结果对网和神经元的行为有所启发，但尚未找到解决需要创意的问题的方法。

这项工作的一个重要方面是努力使机器形成和操纵概念，抽象，概括和名称。试图测试大脑是如何做到的理论3。第一组实验引发了对该理论某些细节的修订。第二组实验正在进行中。到明年夏天，这项工作将完成，并将编写最终报告。

我的程序是尝试下一个编写程序来解决问题，这些问题是在解决方案中需要原创性的一些有限类问题的成员。现在预测明年夏天将会是什么阶段还是仅仅是;然后我将如何定义直接问题。但是，本文中描述的潜在问题是我打算追求的。用一句话来说，问题是：我怎样才能制造出能够在问题解决方案中展现独创性的机器？

参考

1.K.J.W. Craik，“解释的本质”，剑桥大学出版社，1943年(转载于1952年)，92页。

2.K.S. Lashley，“行为中的序列顺序问题”，“行为中的脑机制”，Hixon Symposium，L.A.Jeffress编辑，John Wiley＆Sons，纽约，第112-146页，1951年。

3.D. O. Hebb，行为组织，John Wiley＆Sons，纽约，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

约翰·麦卡锡(John McCarthy)的研究提案

在明年和夏季人工智能研究项目期间，我建议研究语言与智力的关系。似乎很清楚，将试验和错误方法直接应用于感觉数据和运动活动之间的关系不会导致任何非常复杂的行为。相反，试验和错误方法必须应用于更高的抽象层次。人类的思想显然使用语言作为处理复杂现象的手段。较高级别的试错过程经常采用制定猜想和测试的形式。英语有许多属性，目前所描述的每种形式语言都缺乏这些属性。

1.用非正式数学补充的英语论证可以简明扼要。

2.英语是普遍的，因为它可以在英语中设置任何其他语言，然后在适当的地方使用该语言。

3.英语用户可以在其中引用自己并制定关于他在解决他正在处理的问题方面的进展的陈述。

4.除了举证规则外，如果完全制定英语则会有猜想规则。

迄今为止制定的逻辑语言要么是指令列表，要么使计算机进行预先指定的计算，要么正式化数学部分。后者的构建如下：

1.在非正式数学中容易描述，

2.允许将非正式数学的陈述翻译成语言，

3.轻松争论是否证明(???)

没有尝试用人工语言制作像非正式证据一样简短的证据。因此，似乎希望尝试构造一种人工语言，计算机可以编程用于需要猜测和自我引用的问题。它应该与英语相对应，因为关于给定主题的简短英语陈述应该在语言中有短记者，因此应该简短的论点或推测论证。我希望尝试制定一种具有这些属性的语言，并且除了包含物理对象，事件等的概念之外，希望使用这种语言可以对机器进行编程以学习如何很好地玩游戏以及其他任务。

对人工智能问题感兴趣的人

这个名单的目的，是让那些人知道谁有兴趣接收有关问题的文件。名单中的人将获得达特茅斯人工智能夏季项目报告的副本。[1996年注：没有报告。]

该名单由参与或参观达特茅斯人工智能夏季研究项目或已知对该主题感兴趣的人组成。它被发送给本名单和其他几个人。

就目前的目的而言，人工智能问题被认为是使机器以一种被称为智能的方式运行，如果人类如此表现的话。

修订后的名单将很快发布，以便任何有兴趣进入名单的人或希望更改其地址的任何人都应写信给：

约翰·麦卡锡

数学系

达特茅斯学院

新罕布什尔州汉诺威

[1996年注：并非所有这些人都参加了达特茅斯会议。他们是我们认为可能对人工智能感兴趣的人。](秦陇纪注：47人)

该清单包括：

阿德尔森，马文；休斯飞机公司；机场站，洛杉矶，加利福尼亚州

阿什比，W.R.；巴恩伍德之家；格洛斯特，英格兰

巴克斯，约翰；IBM公司；麦迪逊大街590号，纽约州纽约市

伯恩斯坦，亚历克斯；IBM公司；麦迪逊大街590号，纽约州纽约市

比奇洛，J.H.；高等研究院；新泽西州普林斯顿

伊莱亚斯，彼得；麻省理工学院R.L.E.；马萨诸塞州剑桥市

杜达，W.L.；IBM研究实验室；纽约州波基普西市

戴维斯，保罗 M.；第18街1317号；加利福尼亚州洛杉矶市

法诺，R.M.；麻省理工学院R.L.E.；马萨诸塞州剑桥市

法利，B.G.；公园大道324号；马萨诸塞州阿灵顿

加兰特，E.H.；宾夕法尼亚大学；宾夕法尼亚州费城

盖尔森特，赫伯特；IBM研究院；纽约州波基普西市

格拉肖，哈维A.；奥利维亚街1102号；安娜堡，密歇根州

戈尔扎尔，赫伯特；西11街330号；纽约州纽约市

哈格尔巴格；贝尔电话实验室；新泽西州默里希尔

米勒，乔治A.；纪念馆；哈佛大学；马萨诸塞州剑桥市

哈蒙，莱昂D.；贝尔电话实验室；新泽西州默里希尔

霍兰德，约翰H.；E.R.I.密歇根大学；安娜堡，密歇根州

霍尔特，阿纳托尔；农村巷7358号；宾夕法尼亚州费城

考茨，威廉H.；斯坦福研究所；加州门洛帕克

卢斯，R.D.；西117街427号；纽约州纽约市

麦凯，唐纳德；物理系；伦敦大学；伦敦，WC2，英格兰

麦卡锡，约翰；达特茅斯学院；新罕布什尔州汉诺威

麦卡洛克，沃伦S.；麻省理工学院R.L.E.；马萨诸塞州剑桥市

梅尔扎克，Z.A.；密歇根大学数学系；安娜堡，密歇根州

明斯基，M.L.；纽伯里街112号；马萨诸塞州波士顿

莫特，特伦查德；麻省理工学院电气工程系；马萨诸塞州剑桥市

纳什，约翰；高等研究院；新泽西州普林斯顿

纽厄尔，艾伦；卡内基理工学院工业管理系；匹兹堡，宾夕法尼亚州

罗宾逊，亚伯拉罕；多伦多大学数学系；多伦多，安大略省，加拿大

罗切斯特，纳撒尼尔；IBM公司工程研究实验室；纽约州波基普西市

罗杰斯，哈特利，小Jr；MIT数学系；马萨诸塞州剑桥市

罗森布利斯，沃尔特；麻省理工学院R.L.E.；马萨诸塞州剑桥市

罗斯坦，杰罗姆；东卑尔根广场21号；新泽西州红银行

赛尔，大卫；IBM公司；麦迪逊大街590号；纽约州纽约市

肖尔康，J.J.；麻省理工学院C-380林肯实验室；马萨诸塞州列克星敦

沙普利，L.；兰德公司；1700大街；加利福尼亚州圣莫尼卡

舒特泽伯格Schutzenberger，M.P；麻省理工学院R.L.E.；马萨诸塞州剑桥市

塞尔弗里奇，O.G.；麻省理工学院林肯实验室；马萨诸塞州列克星敦

香农，C.E.；麻省理工学院R.L.E.；马萨诸塞州剑桥市

夏皮罗，诺曼；兰德公司；1700大街；加利福尼亚州圣莫尼卡

西蒙，赫伯特A.；工业管理系；卡内基理工学院；匹兹堡，宾夕法尼亚州

索洛莫诺夫，雷蒙德J.；技术研究组；17联合广场西；纽约州纽约市

斯蒂尔，J.E.，上尉，美国空军；B区8698盒；莱特-帕特森空军基地；俄亥俄州

韦伯斯特，弗雷德里克；柯立芝大道62号；马萨诸塞州剑桥市

摩尔，E.F.；贝尔电话实验室；新泽西州默里希尔

凯梅尼，约翰G.；达特茅斯学院；新罕布什尔州汉诺威

关于这份文件......

约翰麦卡锡

周四4月3日星期三19:48:31

原文如下，来自http://www-formal.stanford.edu/jmc/history/dartmouth/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 Project next summer.

The 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

PROPOSAL FOR RESEARCH BY JOHN MCCARTHY

Duringnext year and during the Summer Research Project on Artificial Intelligence, Ipropose to study the relation of language tointelligence. It seems clear that the direct application of trialand error methods to the relation between sensory data and motor activity willnot lead to any very complicated behavior. Rather it is necessary for the trialand error methods to be applied at a higher level of abstraction. The humanmind apparently uses language as its means of handling complicated phenomena.The trial and error processes at a higher level frequently take the form offormulating conjectures and testing them. The English language has a number ofproperties which every formal language described so far lacks.

1. Arguments in English supplemented byinformal mathematics can be concise.

2. English is universal in the sense thatit can set up any other language within English and then use that languagewhere it is appropriate.

3. The user of English can refer tohimself in it and formulate statements regarding his progress in solving theproblem he is working on.

4. In addition to rules of proof, Englishif completely formulated would have rules of conjecture .

Thelogical languages so far formulated have either been instruction lists to makecomputers carry out calculations specified in advance or else formalization ofparts of mathematics. The latter have been constructed so as:

1. to be easily described in informalmathematics,

2. to allow translation of statements frominformal mathematics into the language,

3. to make it easy to argue about whetherproofs of (???)

Noattempt has been made to make proofs in artificial languages as short asinformal proofs. It therefore seems to be desirable to attempt to construct anartificial language which a computer can be programmed to use on problemsrequiring conjecture and self-reference. It should correspond to English in thesense that short English statements about the given subject matter should haveshort correspondents in the language and so should short arguments orconjectural arguments. I hope to try to formulate a language having theseproperties and in addition to contain the notions of physical object, event,etc., with the hope that using this language it will be possible to program amachine to learn to play games well and do other tasks.

PEOPLE INTERESTED IN THE ARTIFICIAL INTELLIGENCE PROBLEM

Thepurpose of the list is to let those on it know who is interested in receivingdocuments on the problem. The people on the 1ist wlll receive copies of thereport of the Dartmouth Summer Project on Artificial Intelligence. [1996 note:There was no report.]

Thelist consists of people who particlpated in or visited the Dartmouth SummerResearch Project on Artificlal Intelligence, or who are known to be interestedin the subject. It is being sent to the people on the 1ist and to a few others.

For thepresent purpose the artificial intelligence problem is taken to be that ofmaking a machine behave in ways that would be called intelligent if a humanwere so behaving.

Arevised list will be issued soon, so that anyone else interested in getting onthe list or anyone who wishes to change his address on it should write to:

John McCarthy

Dapartment of Mathematics

Dartmouth College

Hanover, NH

[1996note: Not all of these people came to the Dartmouth conference. They werepeople we thought might be interested in Artificial Intelligence.] (Mr. Qinlongji notes 47 p.)

The list consists of:

Adelson,Marvin

HughesAircraft Company, Airport Station, Los Angeles, CA

Ashby, W.R.

BarnwoodHouse, Gloucester, England

Backus,John

IBMCorporation, 590 Madison Avenue, New York, NY

Bernstein,Alex

IBMCorporation, 590 Madison Avenue, New York, NY

Bigelow,J. H.

Institutefor Advanced Studies, Princeton, NJ

Elias,Peter

R. L.E., MIT, Cambridge, MA

Duda, W.L.

IBMResearch Laboratory, Poughkeepsie, NY

Davies,Paul M.

1317 C. 18thStreet, Los Angeles, CA.

Fano, R.M.

R. L.E., MIT, Cambridge, MA

Farley,B. G.

324 ParkAvenue, Arlington, MA.

Galanter,E. H.

Universityof Pennsylvania, Philadelphia, PA

Gelernter,Herbert

IBMResearch, Poughkeepsie, NY

Glashow,Harvey A.

1102Olivia Street, Ann Arbor, MI.

Goertzal,Herbert

330 West11th Street, New York, New York

Hagelbarger,D.

BellTelephone Laboratories, Murray Hill, NJ

Miller,George A.

MemorialHall, Harvard University, Cambridge, MA.

Harmon,Leon D.

BellTelephone Laboratories, Murray Hill, NJ

Holland,John H.

E. R. I., University of Michigan

AnnArbor, MI

Holt,Anatol, 7358 Rural Lane, Philadelphia, PA

Kautz,William H.

StanfordResearch Institute, Menlo Park, CA

Luce, R.D.

427 West117th Street, New York, NY

MacKay,Donald

Departmentof Physics, University of London, London, WC2, England

McCarthy,John

DartmouthCollege, Hanover, NH

McCulloch,Warren S.

R.L.E., M.I.T., Cambridge, MA

Melzak,Z. A.

MathematicsDepartment, University of Michigan

AnnArbor, MI

Minsky,M. L. , 112 Newbury Street, Boston, MA

More,Trenchard

Departmentof Electrical Engineering, MIT, Cambridge, MA

Nash, John

Institutefor Advanced Studies, Princeton, NJ

Newell,Allen

Departmentof Industrial Administration, Carnegie Institute of Technology, Pittsburgh, PA

Robinson,Abraham

Departmentof Mathematics, University of Toronto, Toronto, Ontario, Canada

Rochester,Nathaniel

EngineeringResearch Laboratory, IBM Corporation, Poughkeepsie, NY

Rogers,Hartley, Jr.

Departmentof Mathematics, MIT, Cambridge, MA.

Rosenblith,Walter

R.L.E.,M.I.T. , Cambridge, MA.

Rothstein,Jerome

21 EastBergen Place, Red Bank, NJ

Sayre,David

IBMCorporation, 590 Madison Avenue, New York, NY

Schorr-Kon,J.J.

C-380Lincoln Laboratory, MIT, Lexington, MA

Shapley,L.

RandCorporation, 1700 Main Street, Santa Monica, CA

Schutzenberger,M.P.

R.L.E.,M.I.T. , Cambridge, MA

Selfridge,O. G.

LincolnLaboratory, M.I.T. , Lexington, MA

Shannon,C. E.

R.L.E.,M.I.T. , Cambridge, MA

Shapiro,Norman

RandCorporation, 1700 Main Street, Santa Monica, CA

Simon,Herbert A.

Departmentof Industrial Administration, Carnegie Institute of Technology, Pittsburgh, PA

Solomonoff,Raymond J.

TechnicalResearch Group, 17 Union Square West, New York, NY

Steele,J. E., Capt. USAF

Area B.,Box 8698, Wright-Patterson AFB, Ohio

Webster,Frederick

62Coolidge Avenue, Cambridge, MA

Moore, E.F.

BellTelephone Laboratory, Murray Hill, NJ

Kemeny,John G.

DartmouthCollege, Hanover, NH

[Mr. Qin notes: 13 pages original paper (PDF).]

Aboutthis document ...

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素材(880字)

1. J. McCarthy, Dartmouth College; M. L. Minsky,Harvard University; N. Rochester, I.B.M. Corporation; C.E. Shannon, BellTelephone Laboratories. A PROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECT ONARTIFICIAL INTELLIGENCE. [EB/OL], stanford, http://jmc.stanford.edu/articles/dartmouth.htmll, August 31, 1955, visit date: 2019-06-07

2. J. McCarthy, Dartmouth College; M. L. Minsky,Harvard University; N. Rochester, I.B.M. Corporation; C.E. Shannon, BellTelephone Laboratories. A PROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECT ONARTIFICIAL INTELLIGENCE. [EB/OL], stanford, http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html,August 31, 1955, visit date: 2019-06-07

3. 秦陇纪. 人工智能起源与发展正史. [EB/OL], 科学Sciences. http://weixin.qq.com/, 2019-06-06, visit date: 2019-06-07

x. 秦陇纪. 西方哲学与人工智能、计算机; 数据科学与大数据技术专业概论; 人工智能研究现状及教育应用; 数据资源概论; 文本数据溯源与简化; 大数据简化技术体系; 数据简化社区概述. [EB/OL], 数据简化DataSimp(微信公众号),https://dsc.datasimp.org/, http://www.datasimp.org, 2017-06-06

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