图灵1951年报告Intelligent Machinery,A Heretical Theory中英文，公号回复“图灵1951报告”下载PDF双语典藏版

图灵1951年报告Intelligent Machinery,A Heretical Theory中英文，公号回复“图灵1951报告”下载PDF双语典藏版

原创 2018-04-21 阿兰·图灵 数据简化DataSimp

数据简化DataSimp导读：柳渝博士在不肯定性的困惑与NP理论分享http://blog.sciencenet.cn/u/liuyu2205博客，组织了一个图灵论文翻译行动。这是著名AI科学家图灵的文章“Intelligentmachinery, a heretical theory”译文，附英文原文，全文由刘德欣主译，数据简化社区获受权转发并再校对，部分译文及解读见智能哲学：逆源图灵的思想看人、机关系。

用数学方法描述世界、解决问题，是科学发展的核心动力。知识是如何被发现产生出来，以及不一样知识间的渊源和启发关系，比记住不少知识更重要。对于人类来讲，文字知识是记录人类智能和思想的手段，而非终点。把文字考试做为教育目标，是极其简单粗暴不负责任的。从启迪思想来讲，应试是舍本逐末。这方面，愚昧落后的教育者要负责任。AI时代来临，在大多数研究者仍然没有掌握计算机设计制做技术精髓的状况下，很难相信如何跨过计算机科学“弯道超车”步入人工智能科学。咱们必须理解计算机、人工智能是如何诞生，背后的科学思想和原理是什么？加油！只会空想空谈喊口号表忠心可不行，而浪费人财物时间精力投入骗经费的则可耻。（秦陇纪，2018）

 

01图灵1951年报告IntelligentMachinery, A Heretical Theory汉译文 (4461字)

经典人工智能文章《智能机器，被视为异端的理论》（Intelligent machinery, a heretical theory）来自于1951年，阿兰·图灵在BBC电台节目The'51 Society中为大众作的报告^[1]，旨在介绍“智能机器（Intelligent machinery）”的原理。

在文章中，图灵用“learn byexperience（经过经验学习）”来阐释“智能机器”：从一个相对而言较为简单的机器来开始这一切，经过让其经历一系列所谓的“经验”，逐步将其转化为一套精巧得多的机器，并且还可以被用来处理更为普遍的各种偶发事件。^[2]

图灵这里所说的“智能机器”与人工智能的核心“Machine Learning（机器学习）”密切相关，但愿图灵这篇文章能追本溯源为理解“机器学习”这一基本而复杂的概念带来启发性的帮助。

这里刊载的全文由刘德欣主译，部分译文及解读见智能哲学：逆源图灵的思想看人、机关系。

参考文献：

［1］Alan Turing, Intelligentmachinery, aheretical theory，1951，http://viola.informatik.uni-bremen.de/typo/fileadmin/media/lernen/Turing-_Intelligent_Machinery.pdf

［2］B. Jack. Copeland, The Essential Turing,2004

智能机器，被视为异端的理论Intelligent machinery, aheretical theory

A.M.图灵，1951年BBC的The'51 Society电台节目

“你不可能制造出一台能够为你思考的机器”，这彷佛是一个已经被广泛接受的再也不被质疑的观点。

本文的目的就是要质疑这个彷佛已经被广为接受的观点。

大多数为商业用途开发的机器是被用于完成一些特定领域的特定工做，而且以可观的速度得到肯定的结果。一般状况下，机器被用来完成一系列一样且重复的工做。这个基于咱们实际所能获得的机器的这个事实，成为了不少持如上标语所示观点的人们的一个强大论据。然而，对一个数理学家来讲，这个论据是明显站不住脚的。由于有证据代表，存在这样的机器，理论上是能够用很是接近人类思考的方式来完成一些事情。举例来讲，这种能力能够被用来检验《数学原理》体系当中出现的正式证据的有效性，甚至可被用来断定这个《数学原理》体系当中的公式究竟是可被证实为真仍是被证实为假。可是，在公式自己不能被证明真假的状况下，这样的机器的表现确定是不可以使人满意的。由于它会持续不断工做，而根本不产生任何有效结果，但这一点不能被视为与数学家在相似情境中的反应有很大的不一样，好比对照在过去数百年间，数学家在证实费尔马大定理之真伪的过程的实际表现。对这类机器来讲，一个更为精巧微妙的论据支持无疑是必要的。根据著名的哥德尔定理，或者一些相似的根据，人们能够证实：尽管这类机器能够被建造出来，但确定有一些事例，机器对此是不可能给出一个答案的，但一个数学家却能够。从另一个角度来讲，机器的确拥有一些相对于数学家的优点。只要不出现所谓的机械“系统崩溃”，那么其实际所为是能够被信赖的，而与此相对照，数学家却老是会犯必定比例的错误。我倾向于相信数学家会犯错的这种所谓的“危险”的一个不可避免的必然产物，正是致使其有时会偶然得到一种全新的解决问题的方法的缘由。这一点彷佛能够从一个更广为众人所知的事实方面来获得确认，既最可靠的人每每不能得到解决问题的新方法。

我争论的观点是：那种可以高真模仿人类行为的机器是能够被造出来的。它们有时会犯错误，有时会得出十分新颖有趣的结果，总的来讲，它们的结果输出能够与人类的输出同样,被予以等量齐观的关注。这个结果的内容在于对真实结果的更高的几率指望，而对于这一点，我认为它偏偏不可能被给予一个精确的描述。例如，你不能由于一台具体的机器早晚能够得出全部可能的结果就简单地说机器早晚会得出任何正确的结果。咱们也知道如何去建造这种机器，它们会以基本相同的几率输出正确的或错误的结果，若是是这样，那么它们输出的结果是没有任何意义的。并且若是这真的能够被证实的话，那么机器对客观情境的真实反应会证实个人论点。

让咱们更深刻细致地进入到“证据”自己。制造一台在任何程度的测试下都能获得良好结果的机器是彻底可能的，条件是机器被作得足够精细。然而，这仍然不可能被视为是一个足够的证据。这样的机器将会由于重复犯一样的错误，且不可能自我纠错或者只能依赖于外界干涉（来纠错）而大现原形。

若是一台机器能以某种方式“根据经验学习”，那将会给人以深入的印象。由于若是这是真的，那么看起来咱们没有任何理由不从一个相对而言较为简单的机器来开始这一切，经过让其经历一系列所谓的“经验”，逐步将其转化为一套精巧得多的机器，并且还可以被用来处理更为普遍的各种偶发事件。这个过程有可能以经过对其所需听从的经验进行适当的选择而被加速，这能够被称为“教育”。但这里咱们必须得谨慎从事。由于自动地使机器的结构以名为迭代、实为套用之前刻意设定的特定模式来安排相应的经验是十分容易作到的。显而易见，这就是一种彻头彻尾的做弊，由于这几乎等同于安排一我的躲在机器里面。这里再重复一下，这里所采用的关于认定何种教育内容是合理的标准是不可以随便套用所谓的数学体系的。可是我认为以下的陈述在实践中被证实是足够有用的：假定这个机器懂英文，因为它既没有手脚，也不用吃饭，更没有烟瘾，它会把时间主要用于下国际象棋和围棋，甚至桥牌。这个机器拥有一个键盘，任何想对它说的话均可以经过键盘被键入，同时它也能够输出任何它想说的话。针对这个机器的教育，我认为其应该被交给一些可以胜任且同时也对该项目感兴趣的教师。固然，该教师会被禁止得到关于这台机器内部功能的任何相关细节。建造这台机器的技术人员，能够被安排来保证这个机器的正常运行。若是他怀疑机器的运行不正常，他能够将机器设定回任何一种其先前的状态，同时让教师再从那一点开始，从新授课，但他本人并不参与整个的授课过程。因为这个步骤只是用来检验这个技术人员的善意与否，几乎不用说，在之后真正的实验阶段里，它并不会被采纳。实际上，在我看来，这个教育过程将会成为在合理的较短期内生产出一台具有合理智能的机器的相当重要的部分。与人类自身所进行的类比也恰好说明这一点。

如今，我能够给出一些关于这样一台机器在将来将会以何种方式进行运行的特征描述。首先，这台机器会有一个内存，这一点并不须要作十分复杂详尽的解释。它应该是一个针对全部的陈述的简单的列表，包括为它而做的陈述以及由它而做的陈述，还有在游戏中它采起的全部的行动，以及它曾出过的全部的牌。这个列表将会按照时间前后来进行排序。在这些简单的存储以外，还会有一些“基于经验的索引”。为了解释这一理念，我将引入一个这种索引可能会用到的表格。它是一个针对被用到的词汇按照首字母进行排序的，同时还能给出其被使用的次数的索引表格。由此，它们能够在内存中被查询。另外，一个这样类型的索引能够包含人员的类型，或者对弈中的围棋盘面的几个部分。在对机器进行教育的较晚阶段，内存能够被扩展，用以记录在每个时刻机器配置的重要零件，换句话说，机器将开始记住它曾经的想法。这将会产生出更多的新型的索引方式。在一些已使用过的索引中所观察到的特征指标意味着能够引入新型的索引方式。这些索引模式将会如下面这些方式被使用：不管什么时候，在要作出一个关于下一步将要作什么的选则时，当前状态的特征值会在已有的索引系统中被查询，在这种状况下，之前其在相似状况下所作过的选择，连同其相应结果，不管好坏与否，都会被发现。同时，相应的，新的选择也被制定出来。这就提出了一些新的问题：既若是一些指标是有利的，而另一些指标是不利的，该如何是好？关于这一点，不一样的机器会有不一样的答案，同时，答案也会随其受教育程度的不一样而有所变化。在最初阶段，可能一些十分粗糙的规则就足够了，例如，去统计谁得到来自支持者的选票最多等等。在整个教育过程至关靠后的阶段，机器自身可能会察觉到在这种情形下整个的关于步骤的问题，经过采用一些索引方式，能够产生出一些高度复杂的，并且如同其所但愿的那种使人很是满意的规则形式。这看起来是彻底可能的，尽管这些规则形式显得相对比较粗糙，但其自身就是至关使人满意的。因此，总的来讲，尽管所选择的规则显得十分粗糙，但那个进程仍然能够被执行。这一点能够被一些看起来很复杂的工程问题有时竟然是被那种基于经验的最粗糙的方法所解决而获得证实，而那些经验一般只是被用来处理那些问题最表层的部分。举例来讲，函数是否随着它的一个变量的变化而增减。关于“决定行为”的方式所对应的的图景，其所提出的另一个问题是“有利结果”的理念。若是没有这样一些和心理学家所倡导的“快乐原则”相对应的理念，继续向前迈进将会是很是的困难。因此，向机器引入相似的理念是很是天然的事情。我建议教师应该熟练使用两种关键的方法诀窍，其分别表明快乐与疼痛两种理念。在教育的较晚阶段，机器会识别出一些特定的其余的真切的情景感觉，好比渴望，这因为它们曾经在过去被密切地常常性地与快乐联系在一块儿，固然也有其余的一些相似的理念，好比不被须要等等。甚至来讲，来自教师的特定的生气表情也会被机器识别为坏事将近，并且这些表情不会被机器忽略，所以，教师会发现他也不必再使用教鞭来督促机器的学习了。

在现阶段，沿着现有方向继续提出建议不会有太多效果，由于它除了对教育孩子所使用的真实的手段进行分析以外，很可能再没有别的有价值的东西了。尽管如此，还有一个特点，我想把它引入到机器里，这就是“随机要素”，每台机器将被提供一个磁带，里面有一个随机的数字序列，例如，相等数量的0和1，这个数字序列会被用于机器所作的选择，这会致使机器的行为不管以任何一种形式，都不会彻底取决于其所服从的经验，并且，在人们和它互动实验时，这一点还会有别的有价值的用途。在某种程度上，经过制定欺骗性的选择，咱们能够控制机器的发展方向。好比，人们能够坚持其所作的一个特定的选择，比方说，10个特定的数位（十位数？），这意味着在每1024台或者更多的机器当中，大约会有一台机器的智慧程度会发展到与被欺骗的那台机器同样的程度。因为“发展程度”这个理念所具有的主观本质属性，对这一点，我不能完美地给出一个很精确的描述，更不用说还有这样一个可能的事实：也许那台被欺骗的机器在其不被欺骗的状况下，其所作选择，已是足够幸运了。

如今让咱们假定，为了论据的充实，这些机器在将来的出现将会是一个切实的可能，而后看一下建造他们的后果。尽管自伽利略时代起咱们已经在宗教容忍方面取得如此巨大的进步，可是这样作固然会招致来自部分人的强烈地反对。在反对的人们当中，确定会有来自知识分子群体的强烈的抵触，由于他们惧怕这样的机器确定会致使他们失业。虽然关于这一点，知识分子们的认知多是错误的，但实际上，这的确是可能的。固然，咱们还有至关的工做须要去作，来尽力了解机器所要试图表达的意思，好比说，若是在将来，人们竟然须要努力方能让本身的智能达到机器的水准，这样的话，彷佛一旦机器的思惟方法获得许可，被容许正式开始独立使用，其超越咱们那微弱的智力是指日可待的事情，因此，这一切看起来是可能的，并不是都是危言耸听。并且，机器自己不会有死亡的问题，它们也会经过相互交流学习来增进它们的智慧。因此在将来的某个阶段，咱们不得不指望机器采起相应手段进行自我控制，好比采用前面提到过的Samuel Butler在其著做《Erewhon》所说起的方法。

“图灵论著专研与精译工做群”－刘德欣校译2018/4/13

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02图灵1951年报告Intelligent Machinery, A Heretical Theory原英文 (2185字)

智能机器，被视为异端的理论Intelligent machinery, aheretical theory

A.M.图灵，1951年BBC的The'51 Society电台节目

This posthumous essay beginsanoccasionalfeature in which willappear documents, usually translations, otherwise not readilyavailable.

Intelligent Machinery, A HereticalTheory•

A. M.TURING

‘You cannotmakea machinetothink foryou.’Thisisacommonplacethat is usually acceptedwithout question. Itwill bethe purposeof thispaper to question it.

Mostmachinery developedfor commercialpurposes  isintended tocarry outsomeveryspecificjob,andtocarryitoutwithcertainty andconsiderablespeed. Very oftenit does thesame series ofoperations overandover again without any variety.Thisfact about the actualmachinery available isapowerful argumenttomany infavourofthesloganquoted above. To amathematical logician thisargument isnotavailable,forithas been shownthat therearemachinestheoretically possiblewhichwilldosomethingvery closetothinking. Theywill,forinstance, testthevalidity ofaformalproof in thesystem of Principia Mathematica,or eventell ofa formulaof thatsystemwhether itisprovableordisprovable.Inthecase thattheformulais neitherprovable nor disprovablesuchamachine certainlydoes  not behavein averysatisfactory manner, for itcontinues towork indefinitelywithout producing any result atall, butthis cannot be regarded as very different fromthereaction ofthemathematicians, whohave forinstance worked for hundredsof yearsonthequestion as to whether Fermant’s last theorem is true or not.For the case of machines ofthiskindamoresubtlekindofagreement isnecessary. By Gödel’sfamous theorem, or somesimilarargument, one canshowthat however the machine isconstructedtherearebound to be cases where the machinefails to give an answer, but a mathematician would beableto. On theotherhand, the machine has certainadvantagesover the mathematician.Whatever it does  can be reliedupon, assuming no mechanical ‘breakdown’，whereas themathematician makes  a certainproportion of mistakes.I believe thatthis dangerof themathematician makingmistakesisanunavoidable corollaryofhispowerofsometimes hitting uponanentirelynewmethod. This seemstobeconfirmed by the well known factthat themost reliablepeople will not usually hitupon reallynew methods.

My contention is that machines can be constructed which will simulate the behaviour of thehuman mind very closely. They will make mistakes at times, and at times they may make new and very interesting statements, and on the whole the output of them willbe worth attention to the same sort of extent as the output of a human mind.The content of this statement lies in the greater frequencyexpected for the true statements,and it cannot, I think, be given an exact statement. It would not, forinstance, be sufficient to say simply that the machine will make any truestatement sooner or later, for an example of such a machine would be one whichmakes all possible statementssooner or later. We know how to construct these, and as they would (probably) produce true and falsestatements about equally frequently, their verdicts would be quite worthless.It would be the actual reaction of the machine to circumstances that wouldprove my contention, if indeed it can be proved at all.

Let us go rather more carefully into the nature of this ‘proof'. It is clearly possible to produce amachine which would give a very good account of itself for any range of tests, if the machine were made sufficiently elaborate. However,this again would hardly be considered an adequate proof. Such a machine wouldgive itself away by making the same sort of mistake over and over again, andbeing quite unable to correct itself, or to be corrected by argument fromoutside. If the machine were able in some way to ‘ learn by experience ’ itwould be much more impressive. If this were the case there seems to be no real reason why one should not start from a comparatively simplemachine, and, by subjecting it to a suitable range of ‘experience’ transform itinto one which was much more elaborate, and was able to deal with a far greaterrange of contingencies. This process  could propably be hastened by a suitable selection of the experiences to which it was subjected. This might becalled ‘education ’. But here we have to be careful. It would be quite easy toarrange the experiences in such a way that theyautomatically caused the structure of the machine to build up into a previously intendedform, and this would obviously be a gross form of cheating, almost on a parwith having a man inside the machine. Here again the criterion as to what wouldbe considered reasonable in the way of ‘education’ cannot be put intomathematical terms, but I suggest that the following would be adequate inpractice. Let us suppose that it is intended that the machine shall understandEnglish, and that owing to its having no hands or feet, and not needing to eat,not desiring to smoke, it will occupy its time mostly in playing games such as Chess and GO,and possibly Bridge. The machine isprovided with a typewriter keyboard on which any remarks to it are typed, andit also types out any remarks that it wishes to make. I suggest that theeducation of the machine should be entrusted to some highly competent schoolmasterwho is interested in the project but who is forbidden any detailed knowledge ofthe inner workings of the machine. The mechanic who has constructed themachine, however, is permitted to keep the machine in running order, and if hesuspects that the machine has been operating incorrectly may put it back to oneof its previous positions and ask the schoolmaster to repeat his lessons fromthat point on, but he may not take any part in the teaching. Since this procedurewould only serve to test the bona fides of the mechanic, I need hardly saythat it would not be adopted in the experimental stages. As I see it, thiseducation process would in practice be an essential to the production of areasonably intelligent machine within a reasonably short space of time. Thehuman analogy alone suggests this.

I maynow give some indication of the way in which such a machine might be expectedto function. The machine would incorporate a memory. This does not need verymuch explanation. It would simply be a list of all the statements that had beenmade to it or by it, and all the moves it had made and the cards it had playedin its games. These would be listed in chronological order. Besides thisstraightforward memory there would be a number of ‘indexes of experience’. Toexplain this idea I will suggest the form which one such index might possiblytake. It might be an alphabetical index of the words that had been used givingthe 'times’ at which they had been used, so that they could be looked up in thememory. Another such index might contain patterns of men or parts of a GO boardthat had occurred. At comparatively late stages of education the memory mightbe extended to include important parts of the configuration of the machine ateach moment, or in other words it would begin to remember what its thoughts hadbeen. This would give rise to fruitful new forms of indexing. New forms ofindex might be introduced on account of special features observed in the indexesalready used. The indexes would be used in this sort of way. Whenever a choicehas to be made as to what to do next features of the present situation arelooked up in the indexes available, and the previous choice in the similarsituations, and the outcome, good or bad, is discovered. The new choice is madeaccordingly. This raises a number of problems. If some of the indications arefavourable and some are unfavourable what is one to do? The answer to this willprobably differ from machine to machine and will also vary with its degree ofeducation. At first probably some quite crude rule will suffice, e.g., to dowhichever has the greatest number of votes in its favour. At a very late stageof education the whole question of procedure in such cases will probably havebeen investigated by the machine itself, by means of some kind of index, andthis may result in some highly sophisticated, and, one hopes, highlysatisfactory, form of rule. It seems probable however that the comparativelycrude forms of rule will themselves be reasonably satisfactory, so that progresscan on the whole be made in spite of the crudeness of the choice rules. Thisseems to be verified by the fact that Engineering problems are sometimes solvedby the crudes t rule of thumb procedure which only deals with the mostsuperficial aspects of the problem, e.g., whether a function increases or decreaseswith one of its variables. Another problem raised by this picture of the waybehaviour is determined is the idea of 'favourable outcome’. Without some such idea,corresponding to the ‘pleasure principle' of the psychologists, itis very difficult to see how to proceed. Certainly it would be most natural tointroduce some such thing into the machine. I suggest that there should be twokeys which can be manipulated by the schoolmaster, and which represent theideas of pleasure and pain. At later stages in education the machine wouldrecognise certain other conditions as desirable owing to their having beenconstantly associated in the past with pleasure, and likewise certain others asundesirable. Certain expressions of anger on the part of the schoolmastermight, for instance, be recognised as so ominous that they could never beoverlooked, so that the schoolmaster would find that it became unnecessary to ‘apply the cane’any more.

To makefurther suggestions along these lines would perhaps be unfruitful at thisstage, as they are likely to consi.st of nothing more than an analysis ofactual methods of education applied to human children. There is, however, onefeature that I would like suggest should be incorporated in the machines, andthat is a ‘randomelement'. Each machine should be supplied with a tape bearing arandom series of figures, e.g., 0 and 1 in equal quantities, and this series offigures should be used in the choices made by the machine. This would result inthe behaviour of the machine not being by any means completely determined bythe experiences to which it was subjected, and would have some valuable uses whenone was experimenting with it. By faking the choices made one would be able tocontrol the development of the machine to some extent. One might, for instance,insist on the choice made being a particular one at, say, 10 particular places,and this would mean that about one machine in 1024 or more would develop to ashigh a degree as the one which had been faked. This cannot very well be givenan accurate statement because of the subjective nature of the idea of ‘degreeof development’ to say nothing of the fact that the machine that had been fakedmight have been also fortunate in its unfaked choices.

Let us nowassume, for the sake of argument, that these machines are a genuine possibility,and look at the consequences of constructing them. To do so would of course meetwith great opposition, unless we have advanced greatly in religious tolerationfrom thedays of Galileo. There would be great opposition from theintellectuals who were afraid of being put out of a job. It is probable thoughthat the intellectuals would be mistaken about this. There would be plenty todo in trying, say, to keep one's intelligence up to the standard set by themachines, for it seems probable that once the machine thinking method hadstarted, it would not take long to outstrip our feeble powers. There would beno question of the machines dying, and they would be able to converse with eachother to sharpen their wits. At some stage therefore we should have to expectthe machines to take control, in the way that is mentioned in Samuel Butler’s Erewhon.

 ABSTRACT.In this posthumous essay, Turing contends that it may be possible to constructa machine in which there would be an element of randomness and an analogue ofthe pleasure principle of psychology, that could be taught, and that couldeventually be more intelligent than humans.

英文PDF网址http://viola.informatik.uni-bremen.de/typo/fileadmin/media/lernen/Turing-_Intelligent_Machinery.pdf[2]

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参考文献(145字)

1. 刘德欣n科学网柳渝博客．图灵的文章“Intelligent machinery, a heretical theory”译文．[EB/OL]http://blog.sciencenet.cn/home.php?mod=space&uid=2322490&do=blog&id=1109814，2018-4-19．

2. Alan Turing,Intelligentmachinery, a heretical theory，1951，http://viola.informatik.uni-bremen.de/typo/fileadmin/media/lernen/Turing-_Intelligent_Machinery.pdf.Downloaded from http://philmat.oxfordjournals.org/ at SUB Bremen on June 20,2012．

3. B. Jack. Copeland,The Essential Turing, 2004．

x.秦陇纪．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；纯文本数据神经网络训练；大数据简化之技术体系[EB/OL]．数据简化DataSimp（微信公众号）http://www.datasimp.org，2017-06-06．

 

图灵1951年报告IntelligentMachinery, A Heretical Theory中英文（7075字)

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1.   图灵1951年报告IntelligentMachinery, A Heretical Theory中英文（7075字)..... 1

01图灵1951年报告Intelligent Machinery, A Heretical Theory汉译文 (4461字)..... 1

02图灵1951年报告Intelligent Machinery, A Heretical Theory原英文 (2185字)..... 3

2.   参考文献(145字)..... 6

3.  Appx(626字).数据简化DataSimp社区简介..... 6

 

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