梅拉妮·米歇尔Melanie Mitchell

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• 梅拉妮·米歇尔Melanie Mitchell

基本信息

类别	信息
姓名：	梅拉妮·米歇尔 Melanie Mitchell
获奖：	Phi Beta Kappa科学奖（2010），赫伯特·西蒙奖（2020）
国籍：	美国
母校：	布朗大学，密歇根大学
所属机构：	密西根大学，圣达菲研究所，洛斯阿拉莫斯国家实验室，OGI科学与工程学院，波特兰州立大学
联系邮箱：	mm@pdx.edu
主要研究方向：	复杂系统，遗传算法

Melanie Mitchell is a professor of computer science at Portland State University. She has worked at the Santa Fe Institute and Los Alamos National Laboratory. Her major work has been in the areas of analogical reasoning, complex systems, genetic algorithms and cellular automata, and her publications in those fields are frequently cited.

Melanie Mitchell is a professor of computer science at Portland State University. She has worked at the Santa Fe Institute and Los Alamos National Laboratory. Her major work has been in the areas of analogical reasoning, complex systems, genetic algorithms and cellular automata, and her publications in those fields are frequently cited.[2]

梅拉妮·米歇尔 Melanie Mitchell，是波特兰州立大学 Portland State University的计算机科学教授。她曾在圣菲研究所 Santa Fe Institute和洛斯阿拉莫斯国家实验室 Los Alamos National Laboratory工作。她主要的研究领域在类比推理、复杂系统、遗传算法和元胞自动机。在这些领域中，她所出版的书籍、发表的论文经常被引用。[2]

She received her PhD in 1990 from the University of Michigan under Douglas Hofstadter and John Holland, for which she developed the Copycat cognitive architecture. She is the author of "Analogy-Making as Perception", essentially a book about Copycat. She has also critiqued Stephen Wolfram's A New Kind of Science and showed that genetic algorithms could find better solutions to the majority problem for one-dimensional cellular automata. She is the author of An Introduction to Genetic Algorithms, a widely known introductory book published by MIT Press in 1996. She is also author of Complexity: A Guided Tour (Oxford University Press, 2009), which won the 2010 Phi Beta Kappa Science Book Award, and Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux).

She received her PhD in 1990 from the University of Michigan under Douglas Hofstadter and John Holland, for which she developed the Copycat cognitive architecture. She is the author of "Analogy-Making as Perception", essentially a book about Copycat. She has also critiqued Stephen Wolfram's A New Kind of Science[3] and showed that genetic algorithms could find better solutions to the majority problem for one-dimensional cellular automata. She is the author of An Introduction to Genetic Algorithms, a widely known introductory book published by MIT Press in 1996. She is also author of Complexity: A Guided Tour (Oxford University Press, 2009), which won the 2010 Phi Beta Kappa Science Book Award, and Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux).

1990年，她在密歇根大学获得了博士学位，在侯世达和约翰·霍兰德 John Holland 的指导下，她开发了模仿的认知结构。也写下了《以类比为知觉》《Analogy-Making as Perception》一书，这本书本质上也是关于模仿。她还批评了斯蒂芬·沃尔夫拉姆 Stephen Wolfram所写的《一种新科学》《A New Kind of Science》 ，并指出遗传算法能够更好的解决一维元胞自动机的大多数问题。她撰写的《遗传算法导论》《 Genetic Algorithms》是一本广为人知的介绍性书籍，由麻省理工学院出版社于1996年出版。她也是《复杂性: 导览》《Complexity: A Guided Tour 》(牛津大学出版社，2009年)一书的作者，该书获得了2010年斐陶斐卡帕科学图书奖，并被亚马逊网站评为2009年十大最佳科学书籍之一。她的最新著作是《人工智能:思考人类指南》。

梅拉妮·米歇尔创建了圣塔菲研究所的复杂性探索者平台，提供与复杂系统领域相关的在线课程和其他教育资源。她的在线课程《复杂性入门》已经被超过25000名学生选修，并且是 Course排名前50的在线课程之一。

博士生

• Max Quinn
• Sheng Lundquist
• Erik Conser

观点

While expressing strong support for AI research, Mitchell has expressed concern about AI's vulnerability to hacking as well as its ability to inherit social biases. On artificial general intelligence, Mitchell states that "commonsense knowledge" and "humanlike abilities for abstraction and analogy making" might constitute the final step required to build superintelligent machines, but that current technology is not close to being able to solve this problem. Mitchell believes that humanlike visual intelligence would require "general knowledge, abstraction, and language", and hypothesizes that visual understanding may have to be learned as an embodied agent rather than merely viewing pictures.

While expressing strong support for AI research, Mitchell has expressed concern about AI's vulnerability to hacking as well as its ability to inherit social biases. On artificial general intelligence, Mitchell states that "commonsense knowledge" and "humanlike abilities for abstraction and analogy making" might constitute the final step required to build superintelligent machines, but that current technology is not close to being able to solve this problem.[4] Mitchell believes that humanlike visual intelligence would require "general knowledge, abstraction, and language", and hypothesizes that visual understanding may have to be learned as an embodied agent rather than merely viewing pictures.[5]

虽然梅拉妮·米歇尔强烈支持人工智能的研究，但也表达了对人工智能容易受到黑客攻击且一直受到来自社会的偏见的担忧。关于一般的人工智能，梅勒妮·米歇尔指出，”具备常识性知识”和”能够模仿人抽象和类比的能力”可能是构建超级人工智能所需的最后一步，但目前的技术还不能解决这个问题。[4]梅拉妮·米歇尔认为，像人类一样的视觉智能需要“一般知识、抽象概念和语言”，并假设视觉理解需要作为一种具体化的媒介进行学习，而不仅仅是观看图片。

代表作品

书籍

• Mitchell, Melanie (1993). Analogy-Making as Perception以类比为知觉. ISBN 0-262-13289-3. 
• Mitchell, Melanie (1998). An Introduction to Genetic Algorithms遗传算法导论. Cambridge, Massachusetts: MIT Press. ISBN 0-262-63185-7. 
• Mitchell, Melanie (2009). Complexity: A Guided Tour复杂性: 导览. Oxford, U.K.: Oxford University Press. ISBN 0-19-512441-3. 
• Mitchell, Melanie (October 15, 2019) (in English). Artificial Intelligence: A Guide for Thinking Humans 人工智能: 人类思考指南 (First ed.). Farrar, Straus and Giroux. ISBN 978-0374257835. 

文章

• Mitchell, M., Holland, J. H., and Forrest, S. (1994). "When will a genetic algorithm outperform hill climbing?". Advances in Neural Information Processing Systems. 6: 51–58.{{cite journal}}: CS1 maint: multiple names: authors list (link)
• Melanie Mitchell, Peter T. Hraber, and James P. Crutchfield (1993). "Revisiting the edge of chaos: Evolving cellular automata to perform computations" (PDF). Complex Systems. 7: 89–130.{{cite journal}}: CS1 maint: multiple names: authors list (link)
• Cowan, George; David Pines; David Elliott Meltzer (1999). Complexity : metaphors, models, and reality. Cambridge, Massachusetts: Perseus Books. pp. 731. ISBN 978-0738202327. https://archive.org/details/complexity00gcow. 
• Mitchell, M. and Newman, M. (2002). Complex systems theory and evolution. In M. Pagel (editor), Encyclopedia of Evolution , New York: Oxford University Press.

• Mitchell, M. (2001). Life and evolution in computers. History and Philosophy of the Life Sciences, 23, 361-383.

• Mitchell, M. (1998). A complex-systems perspective on the ``computation vs. dynamics debate in cognitive science. In M. A. Gernsbacher and S. J. Derry (eds.), Proceedings of the 20th Annual Conference of the Cognitive Science Society---Cogsci98, 710-715.

• Mitchell, M. (1998). Theories of structure versus theories of change. (Commentary on ``The dynamical hypothesis in cognitive science, by T. van Gelder.) Behavioral and Brain Sciences, 21, 645-646 .

• Belew, R. K., Mitchell, M., and Ackley, D. H. (1996). Computation and the natural sciences. In R. K. Belew and M. Mitchell (editors), Adaptive Individuals in Evolving Populations: Models and Algorithms. Reading, MA: Addison-Wesley.

论文

• Mitchell, M. (2020). How the Analogies We Live By Shape Our Thoughts, Transmissions, Santa Fe Institute, April, 2020.

• Mitchell, M. (2019). Can a Computer Ever Learn to Talk?, OneZero, November, 2019.

• Mitchell, M. (2019). We Shouldn’t be Scared by 'Superintelligent A.I.', New York Times, November, 2019.

• Mitchell, M. (2019). Blade Runner is set in November 2019, but what does it say about our future?, The Big Issue, November, 2019.

• Mitchell, M. (2019). AI Can Pass Standardized Tests --- But It Would Fail Preschool, Wired.

• Mitchell, M. (2019). How do you teach a car that a snowman won’t walk across the road?, Aeon.

• Mitchell, M. (2018). Artificial Intelligence Hits the Barrier of Meaning, New York Times, November, 2018.

• Forrest, S. and Mitchell,M. (2016) Adaptive computation: The multidisciplinary legacy of John H. Holland. Communications of the ACM, 59(8), 58-63.

• Mitchell, M. (2014). How Can the Study of Complexity Transform Our Understanding of the World?. In Big Questions Online.

• Mitchell, M. (2010) Biological computation. In ACM Ubiquity Symposium on "What is Computation?"

• Mitchell, M. (2008) Five questions. In C. Gershenson (editor), Complexity: 5 Questions. Automatic Press

• Mitchell, M. (2003). Review of "Conceptual Coordination: How the Mind Orders Experience in Time" by William J. Clancey. Contemporary Psychology, 48 (3).

• Mitchell, M. (2002). Review of "A New Kind of Science" by Stephen Wolfram. Science, 298, 65--68.

• Mitchell, M. (1999). Can Evolution Explain How the Mind Works? A Review of the Evolutionary Psychology Debates. Complexity , 3 (3), 17-24.

• Mitchell, M. (1998). Review of "Handbook of Genetic Algorithms" by Lawrence Davis. Artificial Intelligence, 100 (1-2), 325-330.

• Mitchell, M. (1997). Review of "Figments of Reality" by Ian Stewart and Jack Cohen. New Scientist, August 11, 1997.

• Mitchell, M. (1997). Review of "Darwin's Dangerous Idea" by Daniel Dennett. Complexity, 2 (1), 32--26.

• Mitchell, M. (1995). Review of "Out of Control: The Rise of Neo-Biological Civilization" by Kevin Kelly. Technology Review, October, 1995.

• Mitchell, M. (1993). Computer Models of Adaptive Complex Systems. New Scientist, February 13, 1993.

• Mitchell, M. (1991). Review of "The Dreams of Reason: The Computer and the Rise of the Sciences of Complexity" by Heinz Pagels. In Bulletin of the Santa Fe Institute, 6 (1).

• Mitchell, M. (1985). Artificial Intelligence and the Popular Press. Popular Computing, January, 1985.

相关链接

• Mitchell's professional homepage
• 梅拉妮·米歇尔Melanie Mitchell publications indexed by Google Scholar
• 在线课程 Introduction to Complexity , onComplexity Explorer, Santa Fe Institute. Free and accessible to all. Always available.
• 梅拉妮·米歇尔（Melanie Mitchell）关于思考人类的AI指南
• 梅拉妮·米歇尔（Melanie Mitchell）：人工智能的概念，类比，常识和未来

编者推荐

集智学园课程链接

圣塔菲课程: Introdution to Complexity