2011 朱迪亚·珀尔

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Judea Pearl
  Photo-Essay

BIRTH:
September 4, 1936, Tel Aviv.

EDUCATION:
B.S., Electrical Engineering (Technion, 1960); M.S., Electronics (Newark College of Engineering, 1961); M.S., Physics (Rutgers University, 1965); Ph.D., Electrical Engineering (Polytechnic Institute of Brooklyn, 1965).

EXPERIENCE:
Research Engineer, New York University Medical School (1960–1961); Instructor, Newark College of Engineering (1961); Member of Technical Staff, RCA Research Laboratories, Princeton, New Jersey (1961–1965); Director, Advanced Memory Devices, Electronic Memories, Inc., Hawthorne, California (1966–1969); Assistant Professor of Engineering Systems, UCLA (1969–1970); Associate Professor of Computer Science, UCLA (1970–1976); Director, Cognitive Systems Laboratory, UCLA (from 1978); Professor of Computer Science, UCLA (from 1976—Emeritus  since 1994); Professor of Statistics, UCLA (from 1996–Emeritus since 1994); President, Daniel Pearl Foundation (from 2002); International Advisory Board, NGO Monitor (from 2011).



HONORS AND AWARDS:
RCA Laboratories Achievement Award (1963); NATO Senior Fellowship in Science (1974); Pattern Recognition Society Award for an Outstanding Contribution (1978); Fellow, IEEE (1988); Fellow, American Association of Artificial Intelligence (1990); Named “The Most Published Scientist in the Artificial Intelligence Journal,” (1991); Member, National Academy of Engineering (1995); UCLA Faculty Research Lecturer of the Year (1996); IJCAI Research Excellence Award (1999); AAAI Classic Paper Award (2000); Lakatos Award, London School of Economics and Political Science (2001); Corresponding Member, Spanish Academy of Engineering (2002); Pekeris Memorial Lecture (2003); ACM Allen Newell Award (2003); Purpose Prize (2006); Honorary Doctorate, University of Toronto (2007); Honorary Doctorate, Chapman University (2008); Benjamin Franklin Medal in Computers and Cognitive Science (2008); Festschrift and Symposium in honor of Judea Pearl (2010); Rumelhart Prize Symposium in honor of Judea Pearl (2011); David E. Rumelhart Prize (2011); IEEE Intelligent Systems’ AI Hall of Fame (2011); ACM Turing Award (2011); Harvey Prize (2012); elected to National Academy of Sciences (2014).


PRESS RELEASE
MEDIA COVERAGE
JUDEA PEARL DL Author Profile link
United States – 2011
CITATION
For fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning.

SHORT ANNOTATED
BIBLIOGRAPHY
ACM TURING AWARD
LECTURE VIDEO
RESEARCH
SUBJECTS
ADDITIONAL
MATERIALS
VIDEO INTERVIEW
Judea Pearl created the representational and computational foundation for the processing of information under uncertainty.

He is credited with the invention of Bayesian networks, a mathematical formalism for defining complex probability models, as well as the principal algorithms used for inference in these models. This work not only revolutionized the field of artificial intelligence but also became an important tool for many other branches of engineering and the natural sciences. He later created a mathematical framework for causal inference that has had significant impact in the social sciences.

Judea Pearl was born on September 4, 1936, in Tel Aviv, which was at that time administered under the British Mandate for Palestine. He grew up in Bnei Brak, a Biblical town his grandfather went to reestablish in 1924.

Pearl discusses the influence of his inspirational high school teachers in Tel Aviv.       
In 1956, after serving in the Israeli army and joining a Kibbutz, Judea decided to study engineering. He attended the Technion, where he met his wife, Ruth, and received a B.S. degree in Electrical Engineering in 1960. Recalling the Technion faculty members in a 2012 interview in the Technion Magazine, he emphasized the thrill of discovery:

Professor Franz Olendorf always spoke as if he was personally present in Cavendish laboratory, where the electron was discovered, Professor Abraham Ginzburg made us feel the winds blowing in our face as we travelled along those line integrals in the complex plane. And Professor Amiram Ron gave us the feeling that there is still something we can add to Maxwell’s theory of electromagnetic waves.

Judea then went to the United States for graduate study, receiving an M.S. in Electronics from Newark College of Engineering in 1961, an M.S. in Physics from Rutgers University in 1965, and a Ph.D. in electrical engineering from the Polytechnic Institute of Brooklyn in the same year. The title of his Ph.D. thesis was “Vortex Theory of Superconductive Memories;” the term “Pearl vortex” has become popular among physicists to describe the type of superconducting current he studied. He worked at RCA Research Laboratories in Princeton, New Jersey on superconductive parametric amplifiers and storage devices, and at Electronic Memories, Inc. in Hawthorne, California on advanced memory systems. Despite the apparent focus on physical devices, Pearl reports being motivated even then by potential applications to intelligent systems.

When industrial research on magnetic and superconducting memories was curtailed by the advent of large-scale semiconductor memories, Pearl decided to move into academia to pursue his long-term interest in logic and reasoning. In 1969, he joined the faculty of the University of California, Los Angeles, initially in Engineering Systems, and in 1970 he received tenure in the newly formed Computer Science Department. In 1976 he was promoted to full professor. In 1978 he founded the Cognitive Systems Laboratory – a title that emphasized his desire to understand human cognition. The laboratory’s research facility was Pearl’s office, on the door of which hung a permanent sign reading, “Don’t knock. Experiments in Progress.”

Pearl explains that free will is an illusion that might need to be programmed into robots.       
Pearl’s reputation in computer science was established initially not in probabilistic reasoning –a highly controversial topic at that time – but in combinatorial search. A series of journal papers beginning in 1980 culminated in the publication of the book, Heuristics: Intelligent Search Strategies for Computer Problem Solving, [6] in 1984. This work included many new results on traditional search algorithms such as A*, and on game-playing algorithms, raising AI research to a new level of rigor and depth. It also set out new ideas on how admissible heuristics might be derived automatically from relaxed problem definitions, an approach that has led to dramatic advances in planning systems. Despite the book’s formal style, it drew its inspiration from, as Pearl said, “the ever-amazing observation of how much people can accomplish with that simplistic, unreliable information source known as intuition.” Ira Pohl wrote in 2011 that “The impact of Pearl’s monograph was transformative … [The book] was a tour de force summarizing the work of three decades.”

Soon after arriving at UCLA, Pearl began teaching courses on probability and decision theory, which was a rarity in computer science departments at that time. Probabilistic methods had been tried in the 1960s and found wanting; a system for estimating the probability of a disease given n possible symptoms was thought to require a set of probability parameters whose size is exponential in n. The 1970s, on the other hand, saw the rise of knowledge-based systems, based primarily on logical rules or on rules augmented with “certainty factors.”

Pearl believed that sound probabilistic analysis of a problem would give intuitively correct results, even in those cases where rule-based systems behaved incorrectly. One such case had to do with the ability to reason both causally (from cause to effect) and diagnostically (from effect to cause). “If you used diagnostic rules, you could not do prediction, and if you used predictive rules you could not reason diagnostically, and if you used both, you ran into positive-feedback instabilities, something we never encountered in probability theory.” Another case concerned the “explaining-away” phenomenon, whereby the degree of belief in any cause of a given effect is increased when the effect is observed, but then decreases when some other cause is found to be responsible for the observed effect. Rule-based systems could not exhibit the explaining-away phenomenon, whereas it happens automatically in probabilistic analysis.

In addition to these basic qualitative questions, Pearl was motivated by David Rumelhart’s 1976 paper on reading comprehension. As he wrote later in his 1988 book,

In this paper, Rumelhart presented compelling evidence that text comprehension must be a distributed process that combines both top-down and bottom-up inferences. Strangely, this dual mode of inference, so characteristic of Bayesian analysis, did not match the capabilities of either the “certainty factors” calculus or the inference networks of PROSPECTOR[1]−the two major contenders for uncertainty management in the 1970s. I thus began to explore the possibility of achieving distributed computation in a “pure” Bayesian framework.

Pearl realized that the concept of conditional independence would be the key to constructing complex probability models with polynomially many parameters and to organizing distributed probability computations. The paper “Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach”[8] introduced probability models defined by directed acyclic graphs and derived an exact, distributed, asynchronous, linear-time inference algorithm for trees – an algorithm we now call belief propagation, the basis for turbocodes. There followed a period of remarkable creative output for Pearl, with more than 50 papers covering exact inference for general graphs, approximate inference algorithms using Markov chain Monte Carlo, conditional independence properties, learning algorithms, and more, leading up to the publication of Probabilistic Reasoning in Intelligent Systems[15] in 1988. This monumental work combined Pearl’s philosophy, his theories of human cognition, and all his technical material into a persuasive whole that sparked a revolution in the field of artificial intelligence. Within just a few years, leading researchers from both the logical and the neural-network camps within AI had adopted a probabilistic – often called simply the modern – approach to AI.

Pearl’s Bayesian networks provided a syntax and a calculus for multivariate probability models, in much the same way that George Boole provided a syntax and a calculus for logical models. Theoretical and algorithmic questions associated with Bayesian networks form a significant part of the modern research agenda for machine learning and statistics, Their use has also permeated other areas, such as natural language processing, computer vision, robotics, computational biology, and cognitive science. As of 2012, some 50,000 publications have appeared with Bayesian networks as a primary focus.

Even while developing the theory and technology of Bayesian probability networks, Pearl suspected that a different approach was needed to address the issue of causality, which had been one of his concerns for many years. In his 2000 book on Causality [20], he described his early interest as follows:

I got my first hint of the dark world of causality during my junior year of high school. My science teacher, Dr. Feuchtwanger, introduced us to the study of logic by discussing the 19th century finding that more people died from smallpox inoculations than from smallpox itself. Some people used this information to argue that inoculation was harmful when, in fact, the data proved the opposite, that inoculation was saving lives by eradicating smallpox.

And here is where logic comes in,” concluded Dr. Feuchtwanger, “To protect us from cause-effect fallacies of this sort.” We were all enchanted by the marvels of logic, even though Dr. Feuchtwanger never actually showed us how logic protects us from such fallacies.

It doesn’t, I realized years later as an artificial intelligence researcher. Neither logic, nor any branch of mathematics had developed adequate tools for managing problems, such as the smallpox inoculations, involving cause-effect relationships.

A Bayesian network such as Smoking --> Cancer fails to capture causal information; indeed, it is mathematically equivalent to the network Cancer --> Smoking. The key characteristic of a causal network is the way in which it captures the potential effect of exogenous intervention. In a causal network X --> Y, intervening to set the value of Y should leave one’s prior belief in X unchanged and simply breaks the link from X to Y; thus, Smoking --> Cancer as a causal network captures our beliefs about how the world works (inducing cancer in a subject does not change one’s belief in whether the subject is a smoker), whereas Cancer --> Smoking does not (inducing a subject to smoke does change one’s belief that the subject will develop cancer). This simple analysis, which Pearl calls the do-calculus, leads to a complete mathematical framework for formulating causal models and for analyzing data to determine causal relationships. This work has overturned the long-held belief in statistics that causality can be determined only from controlled random trials – which are impossible in areas such as the biological and social sciences. Referring to this work, Phil Dawid (Professor of Statistics at Cambridge) remarks that Pearl is “the most original and influential thinker in statistics today.” Chris Winship (Professor of Sociology at Harvard) writes that, “Social science will be forever in his debt.”

Pearl reflects on the importance of rebellion to science.       
In 2010 a Symposium was held at UCLA in Pearl’s honor, and a Festschrift was published containing papers in all the areas covered by his research. The volume also contains reminiscences from former students and other researchers in the field. Ed Purcell, Pearl’s first PhD student, wrote, “In class I was immediately impressed and enchanted by Judea’s knowledge, intelligence, brilliance, warmth and humor. His teaching style was engaging, interactive, informative and fun.” Hector Geffner, a PhD student in the late 1980s, wrote, “He was humble, fun, unassuming, respectful, intelligent, enthusiastic, full of life, very easy to get along with, and driven by a pure and uncorrupted passion for understanding.” Nils Nilsson, former professor and Chair of the Computer Science Department at Stanford and an AI pioneer, described Pearl as “a towering figure in our field.”

Pearl’s outside interests include music (several early conferences were entertained by his impromptu piano renditions and very realistic trumpet imitations), philosophy, and early books – particularly the great works of science throughout history, of which he possesses several first editions. Judea and Ruth Pearl had three children, Tamara, Michelle, and Daniel. Since Daniel’s kidnap and murder in Pakistan in 2002, Professor Pearl has devoted a significant fraction of his time and energy to the Daniel Pearl Foundation, which he and his wife founded to promote Daniel’s values of “uncompromised objectivity and integrity; insightful and unconventional perspective; tolerance and respect for people of all cultures; unshaken belief in the effectiveness of education and communication; and the love of music, humor, and friendship.”

Pearl will donate a major portion of the Turing Prize money to support the projects of the Daniel Pearl Foundation and another portion to promote the introduction of causal inference in statistics education.

Author: Stuart J. Russell


[1] An expert system that finds ore deposits from geological information; created in the 1970s by Richard Duda, Peter Hart, and others at Stanford Research Institute (SRI).



茱蒂亚珍珠
  照片-论文

诞生。
1936年9月4日，特拉维夫。

学历：电气工程学士（Technion，1960年）；电子学硕士（Newark工程学院，1961年）。
电气工程学士（Technion，1960）；电子学硕士（Newark工程学院，1961）；物理学硕士（Rutgers大学，1965）；电气工程博士（Polytechnic Institute of Brooklyn，1965）。

工作经验。
纽约大学医学院研究工程师（1960-1961年）；纽瓦克工程学院讲师（1961年）；新泽西州普林斯顿RCA研究实验室技术员（1961-1965年）；电子记忆公司高级记忆装置主任。加利福尼亚州霍桑（1966-1969）；加州大学洛杉矶分校工程系统助理教授（1969-1970）；加州大学洛杉矶分校计算机科学副教授（1970-1976）；加州大学洛杉矶分校认知系统实验室主任（1978年起）；加州大学洛杉矶分校计算机科学教授（1976年起-1994年起退休）；加州大学洛杉矶分校统计学教授（1996年起-1994年起退休）；Daniel Pearl基金会主席（2002年起）；非政府组织监测组织国际顾问委员会（2011年起）。



荣誉和奖项。
RCA实验室成就奖（1963年）；北约科学高级研究员（1974年）；模式识别协会杰出贡献奖（1978年）；IEEE会员（1988年）；美国人工智能协会会员（1990年）；被评为 "在人工智能杂志上发表最多的科学家"（1991年）；国家工程院院士（1995年）；加州大学洛杉矶分校年度教师研究讲师（1996年）；IJCAI研究优秀奖（1999年）；AAAI经典论文奖（2000年）。伦敦政治经济学院拉卡托斯奖（2001年）；西班牙工程院通讯院士（2002年）；Pekeris纪念讲座（2003年）；ACM Allen Newell奖（2003年）；目的奖（2006年）；多伦多大学荣誉博士（2007年）。查普曼大学名誉博士（2008年）；本杰明-富兰克林计算机和认知科学奖（2008年）；纪念朱迪亚-珀尔的节选和研讨会（2010年）；纪念朱迪亚-珀尔的鲁梅尔哈特奖研讨会（2011年）；大卫E. 鲁梅尔哈特奖（2011年）；IEEE智能系统的人工智能名人堂（2011年）；ACM图灵奖（2011年）；哈维奖（2012年）；当选为美国国家科学院院士（2014年）。


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JUDEA PEARL DL作者简介链接
美国 - 2011年
嘉奖
通过开发概率和因果推理的微积分，对人工智能做出了根本性贡献。

短篇注释
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亚马逊图灵奖
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朱迪亚-珀尔为处理不确定情况下的信息创建了表征和计算基础。

他发明了贝叶斯网络，这是一种用于定义复杂概率模型的数学形式主义，以及用于这些模型推理的主要算法。这项工作不仅彻底改变了人工智能领域，而且还成为许多其他工程和自然科学分支的重要工具。他后来创建了一个因果推理的数学框架，对社会科学产生了重大影响。

朱迪亚-珀尔于1936年9月4日出生在特拉维夫，当时特拉维夫是在英国对巴勒斯坦的委任统治下管理的。他在Bnei Brak长大，这是一个他的祖父在1924年去重新建立的圣经小镇。

珍珠讨论了他在特拉维夫的高中老师的鼓舞人心的影响。       
1956年，在以色列军队服役并加入一个基布兹后，朱迪亚决定学习工程学。他在以色列理工学院学习，在那里他遇到了他的妻子露丝，并在1960年获得了电子工程学士学位。在2012年《以色列理工学院杂志》的采访中，他回忆起以色列理工学院的教师，强调了发现的快感。

弗朗茨-奥伦道夫教授总是说得好像他本人就在卡文迪许实验室一样，电子就是在那里被发现的。亚伯拉罕-金兹伯格教授让我们在复平面内沿着那些线积分旅行时感到风在我们脸上吹。而阿米拉姆-罗恩教授让我们感觉到，麦克斯韦的电磁波理论中还有我们可以补充的东西。

随后，朱迪亚去美国读研究生，1961年在纽瓦克工程学院获得电子学硕士学位，1965年在罗格斯大学获得物理学硕士学位，同年在布鲁克林理工学院获得电气工程博士学位。他的博士论文题目是 "超导记忆的涡流理论"；"珍珠涡流 "一词已在物理学家中流行，用来描述他研究的超导电流类型。他曾在新泽西州普林斯顿的RCA研究实验室研究超导参数放大器和存储设备，并在加利福尼亚州霍桑的电子记忆公司研究先进的记忆系统。尽管表面上专注于物理设备，但珀尔报告说，即使在那时，他也被智能系统的潜在应用所激励。

当磁性和超导存储器的工业研究因大规模半导体存储器的出现而受到限制时，珀尔决定转入学术界，追求他对逻辑和推理的长期兴趣。1969年，他加入了加州大学洛杉矶分校的教师队伍，最初是在工程系统系，1970年他在新成立的计算机科学系获得了终身教职。1976年，他被提升为全职教授。1978年，他成立了认知系统实验室--这个名称强调了他对了解人类认知的渴望。实验室的研究设施是珀尔的办公室，门上挂着一个永久的标志，上面写着 "不要敲门。实验正在进行中"。

珍珠解释说，自由意志是一种幻觉，可能需要对机器人进行编程。       
珀尔在计算机科学领域的声誉最初不是在概率推理方面建立的--这在当时是一个极具争议性的话题--而是在组合搜索方面。从1980年开始的一系列期刊论文最终导致了《启发式方法》一书的出版。计算机问题解决的智能搜索策略，[6]于1984年出版。这项工作包括许多关于传统搜索算法（如A*）和游戏算法的新结果，将人工智能研究提高到一个新的严格和深入的水平。它还提出了如何从宽松的问题定义中自动推导出可接受的启发式算法的新观点，这种方法导致了规划系统的巨大进步。尽管这本书的风格很正式，但它的灵感来自于，正如珀尔所说，"人们可以通过被称为直觉的简单、不可靠的信息来源完成多少事情，这种观察永远是令人惊奇的。" Ira Pohl在2011年写道："Pearl的专著的影响是变革性的......[这本书]是对三十年来工作的总结。

来到加州大学洛杉矶分校后不久，珀尔开始教授概率和决策理论课程，这在当时的计算机科学系中是很罕见的。概率方法在20世纪60年代曾被尝试过，但发现不尽人意；一个估计一种疾病的概率的系统，给定了n个可能的症状，被认为需要一组概率参数，其大小是n的指数级。

珀尔认为，对一个问题进行合理的概率分析会给出直观正确的结果，即使是在那些基于规则的系统表现不正确的情况下。其中一种情况与因果关系（从因到果）和诊断关系（从果到因）的推理能力有关。"如果你使用诊断性规则，你就不能进行预测，如果你使用预测性规则，你就不能进行诊断性推理，如果你同时使用这两种规则，你就会遇到正反馈不稳定性，这是我们在概率论中从未遇到的。" 另一个案例涉及 "解释-远离 "现象，即当观察到某一特定效果时，对该效果的任何原因的相信程度会增加，但当发现其他原因对观察到的效果负有责任时，则会减少。基于规则的系统不能表现出解释-远离现象，而它在概率分析中自动发生。

除了这些基本的定性问题外，Pearl还受到David Rumelhart 1976年关于阅读理解的论文的激励。正如他后来在1988年的书中所写的那样。

在这篇论文中，鲁梅尔哈特提出了令人信服的证据，表明文本理解必须是一个分布式的过程，结合了自上而下和自下而上的推理。奇怪的是，这种具有贝叶斯分析特点的双重推理模式与 "确定性因素 "微积分或PROSPECTOR[1]的推理网络的能力都不匹配--这是在1970年代不确定性管理的两个主要竞争者。因此，我开始探索在一个 "纯 "贝叶斯框架中实现分布式计算的可能性。

珍珠意识到，条件独立性的概念将是构建具有多项参数的复杂概率模型和组织分布式概率计算的关键。论文《推理引擎上的贝叶斯牧师。分布式分层方法"[8] 介绍了由有向无环图定义的概率模型，并推导出一种精确的、分布式的、异步的、线性时间的树推理算法--这种算法我们现在称之为信念传播，是涡轮码的基础。随后，Pearl有一段时期的创作成果非常突出，有50多篇论文涉及一般图的精确推理、使用马尔科夫链蒙特卡洛的近似推理算法、条件独立性属性、学习算法等等，最终在1988年出版了《智能系统的概率推理》[15]。这部不朽的作品将Pearl的哲学、他的人类认知理论和他所有的技术材料结合在一起，成为一个有说服力的整体，引发了人工智能领域的革命。在短短几年内，人工智能领域的逻辑阵营和神经网络阵营的主要研究人员都采用了概率论--通常被简单称为现代--的方法来研究人工智能。

佩尔的贝叶斯网络为多变量概率模型提供了一种语法和微积分，就像乔治-布尔为逻辑模型提供了一种语法和微积分一样。与贝叶斯网络相关的理论和算法问题构成了现代机器学习和统计学研究议程的重要部分，其使用也渗透到其他领域，如自然语言处理、计算机视觉、机器人学、计算生物学和认知科学。截至2012年，以贝叶斯网络为主要内容的出版物已经出现了约50,000篇。

即使在发展贝叶斯概率网络的理论和技术时，珀尔怀疑需要一种不同的方法来解决因果关系的问题，这是他多年来关注的问题之一。在他2000年出版的《因果关系》一书中[20]，他对自己早期的兴趣描述如下。

我是在高三时第一次接触到因果关系的黑暗世界的。我的科学老师Feuchtwanger博士通过讨论19世纪的发现，即死于天花接种的人比死于天花本身的人更多，将我们引入了逻辑学的研究。一些人利用这一信息来争辩说接种是有害的，而事实上，数据证明情况恰恰相反，接种通过根除天花来拯救生命。

Feuchtwanger博士总结说："这就是逻辑的作用，""保护我们免受这种因果谬误的影响"。我们都被逻辑的奇妙之处迷住了，尽管费特万格博士从未向我们展示过逻辑是如何保护我们免受这种谬误的影响。

多年后，作为一名人工智能研究者，我意识到它并没有。无论是逻辑学，还是任何数学分支，都没有开发出足够的工具来管理问题，例如天花接种，涉及因果关系。

一个贝叶斯网络，如吸烟-->癌症，未能捕捉到因果信息；事实上，它在数学上等同于网络癌症-->吸烟。因果网络的关键特征是它捕捉外源性干预的潜在效果的方式。在一个X->Y的因果网络中，为设定Y的值而进行的干预应该使人对X的先前信念保持不变，而只是打破了从X到Y的联系；因此，吸烟->癌症作为一个因果网络捕捉到了我们对世界如何运作的信念（诱发主体的癌症不会改变人对主体是否吸烟的信念），而癌症->吸烟则不会（诱使主体吸烟会改变人对主体会患癌症的信念）。这个简单的分析，珀尔称之为 "做-微积分"，导致了一个完整的数学框架，用于制定因果模型和分析数据以确定因果关系。这项工作推翻了统计学中长期以来的信念，即因果关系只能从受控随机试验中确定--这在生物和社会科学等领域是不可能的。谈到这项工作，菲尔-达维德（剑桥大学统计学教授）说，珀尔是 "当今统计学界最具原创性和影响力的思想家"。克里斯-温希普（哈佛大学社会学教授）写道："社会科学将永远欠他的债"。

珍珠反思了反叛对科学的重要性。       
2010年，加州大学洛杉矶分校为纪念珀尔举行了一次研讨会，并出版了一本Festschrift，其中包含他的研究所涉及的所有领域的论文。该卷还包含了以前的学生和该领域的其他研究人员的回忆文章。珀尔的第一个博士生埃德-珀塞尔（Ed Purcell）写道："在课堂上，我立即被朱迪亚的知识、智慧、才华、热情和幽默所打动和陶醉。他的教学风格引人入胜，互动性强，内容丰富，趣味性强"。20世纪80年代末的博士生赫克托-格夫纳（Hector Geffner）写道："他谦虚、有趣、不苟言笑、尊重他人、聪明、热情、充满生机，非常容易相处，并被一种纯粹的、不受腐蚀的理解热情所驱动"。斯坦福大学计算机科学系前教授和主席、人工智能先驱尼尔斯-尼尔森（Nils Nilsson）将珀尔描述为 "我们这个领域的一个杰出人物"。

珀尔的外部兴趣包括音乐（早期的几次会议都是由他的即兴钢琴演奏和非常逼真的小号模仿来娱乐的）、哲学和早期书籍--特别是历史上伟大的科学作品，他拥有几个初版的书籍。朱迪亚和露丝-珀尔有三个孩子，塔玛拉、米歇尔和丹尼尔。自从丹尼尔2002年在巴基斯坦被绑架和谋杀以来，珀尔教授将他的大部分时间和精力投入到丹尼尔-珀尔基金会，他和他的妻子成立该基金会是为了促进丹尼尔的价值观，即 "不妥协的客观性和完整性；有洞察力和非传统的观点；对所有文化的人的宽容和尊重；对教育和沟通的有效性的坚定信念；以及对音乐、幽默和友谊的热爱。"

珀尔将把图灵奖奖金的主要部分捐赠给支持丹尼尔-珀尔基金会的项目，另一部分用于促进在统计学教育中引入因果推理。

作者。Stuart J. Russell


[1] 一种从地质信息中寻找矿藏的专家系统；由斯坦福研究所（SRI）的理查德-杜达、彼得-哈特等人于1970年代创建。