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梅勒妮·米歇尔 Melanie Mitchell (查看源代码)

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| 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
 
| 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
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| 联系邮箱：  ||  mm@pdx.edu
 
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| 主要研究方向： || [[复杂系统]]，[[遗传算法]]
 
| 主要研究方向： || [[复杂系统]]，[[遗传算法]]
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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).
 
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).
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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。此外，她的新书《人工智能: 人类思考指南》也获得了不错的反响。
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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》。
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梅勒妮·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。
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==博士生==
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*[https://www.linkedin.com/in/max-quinn-589804172/ Max Quinn]
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*[https://www.linkedin.com/pub/sheng-lundquist/84/439/409  Sheng Lundquist]
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*[https://www.linkedin.com/in/econser/  Erik Conser]
 
==观点==
 
==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.<ref>{{cite news |title=Fears about robot overlords are (perhaps) premature |url=https://www.csmonitor.com/Books/Book-Reviews/2019/1025/Fears-about-robot-overlords-are-perhaps-premature |accessdate=10 May 2020 |work=Christian Science Monitor |date=25 October 2019}}</ref> 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.<ref>{{cite news |title=What Is Computer Vision? |url=https://www.pcmag.com/news/what-is-computer-vision |accessdate=10 May 2020 |work=PCMAG |date=9 February 2020 |language=en}}</ref>
 
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.<ref>{{cite news |title=Fears about robot overlords are (perhaps) premature |url=https://www.csmonitor.com/Books/Book-Reviews/2019/1025/Fears-about-robot-overlords-are-perhaps-premature |accessdate=10 May 2020 |work=Christian Science Monitor |date=25 October 2019}}</ref> 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.<ref>{{cite news |title=What Is Computer Vision? |url=https://www.pcmag.com/news/what-is-computer-vision |accessdate=10 May 2020 |work=PCMAG |date=9 February 2020 |language=en}}</ref>
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*{{Cite journal | author=Melanie Mitchell, Peter T. Hraber, and [[James P. Crutchfield]]| title= Revisiting the edge of chaos: Evolving cellular automata to perform computations | journal=[[Complex Systems (journal)|Complex Systems]] | year=1993 | volume=7 | pages=89–130 | url=http://web.cecs.pdx.edu/~mm/rev-edge.pdf  }}
 
*{{Cite journal | author=Melanie Mitchell, Peter T. Hraber, and [[James P. Crutchfield]]| title= Revisiting the edge of chaos: Evolving cellular automata to perform computations | journal=[[Complex Systems (journal)|Complex Systems]] | year=1993 | volume=7 | pages=89–130 | url=http://web.cecs.pdx.edu/~mm/rev-edge.pdf  }}
 
*{{cite book|last=Cowan |first=George | authorlink = George Cowan |author2=[[David Pines]] |author3=David Elliott Meltzer|title=Complexity : metaphors, models, and reality|url=https://archive.org/details/complexity00gcow |url-access=limited |year=1999|publisher=[[Perseus Books]]|location=Cambridge, Massachusetts|isbn=978-0738202327|pages=[https://archive.org/details/complexity00gcow/page/n749 731]}}
 
*{{cite book|last=Cowan |first=George | authorlink = George Cowan |author2=[[David Pines]] |author3=David Elliott Meltzer|title=Complexity : metaphors, models, and reality|url=https://archive.org/details/complexity00gcow |url-access=limited |year=1999|publisher=[[Perseus Books]]|location=Cambridge, Massachusetts|isbn=978-0738202327|pages=[https://archive.org/details/complexity00gcow/page/n749 731]}}
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*Mitchell, M. and Newman, M. (2002). Complex systems theory and evolution. In M. Pagel (editor), Encyclopedia of Evolution , New York: Oxford University Press.
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*Mitchell, M. (2001). Life and evolution in computers. History and Philosophy of the Life Sciences, 23, 361-383.
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*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.
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*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 .
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*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.
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==论文==
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*Mitchell, M. (2020). How the Analogies We Live By Shape Our Thoughts, Transmissions, Santa Fe Institute, April, 2020.
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*Mitchell, M. (2019). Can a Computer Ever Learn to Talk?, OneZero, November, 2019.
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*Mitchell, M. (2019). We Shouldn’t be Scared by 'Superintelligent A.I.', New York Times, November, 2019.
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*Mitchell, M. (2019). Blade Runner is set in November 2019, but what does it say about our future?, The Big Issue, November, 2019.
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*Mitchell, M. (2019). AI Can Pass Standardized Tests --- But It Would Fail Preschool, Wired.
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*Mitchell, M. (2019). How do you teach a car that a snowman won’t walk across the road?, Aeon.
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*Mitchell, M. (2018). Artificial Intelligence Hits the Barrier of Meaning, New York Times, November, 2018.
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*Forrest, S. and Mitchell,M. (2016) Adaptive computation: The multidisciplinary legacy of John H. Holland. Communications of the ACM, 59(8), 58-63.
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*Mitchell, M. (2014). How Can the Study of Complexity Transform Our Understanding of the World?. In Big Questions Online.
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*Mitchell, M. (2010) Biological computation. In ACM Ubiquity Symposium on "What is Computation?"
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*Mitchell, M. (2008) Five questions. In C. Gershenson (editor), Complexity: 5 Questions. Automatic Press
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*Mitchell, M. (2003). Review of "Conceptual Coordination: How the Mind Orders Experience in Time" by William J. Clancey. Contemporary Psychology, 48 (3).
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*Mitchell, M. (2002). Review of "A New Kind of Science" by Stephen Wolfram. Science, 298, 65--68.
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*Mitchell, M. (1999). Can Evolution Explain How the Mind Works? A Review of the Evolutionary Psychology Debates. Complexity , 3 (3), 17-24.
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*Mitchell, M. (1998). Review of "Handbook of Genetic Algorithms" by Lawrence Davis. Artificial Intelligence, 100 (1-2), 325-330.
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*Mitchell, M. (1997). Review of "Figments of Reality" by Ian Stewart and Jack Cohen. New Scientist, August 11, 1997.
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*Mitchell, M. (1997). Review of "Darwin's Dangerous Idea" by Daniel Dennett. Complexity, 2 (1), 32--26.
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*Mitchell, M. (1995). Review of "Out of Control: The Rise of Neo-Biological Civilization" by Kevin Kelly. Technology Review, October, 1995.
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*Mitchell, M. (1993). Computer Models of Adaptive Complex Systems. New Scientist, February 13, 1993.
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*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).
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*Mitchell, M. (1985). Artificial Intelligence and the Popular Press. Popular Computing, January, 1985.
    
==参考文献==
 
==参考文献==
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* [http://www.cs.pdx.edu/~mm Mitchell's professional homepage]
 
* [http://www.cs.pdx.edu/~mm Mitchell's professional homepage]
 
* {{google scholar id|k4gbv2AAAAAJ}}
 
* {{google scholar id|k4gbv2AAAAAJ}}
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* 在线课程 [https://www.complexityexplorer.org/courses/104-introduction-to-complexity Introduction to Complexity] , on[https://www.complexityexplorer.org/ Complexity Explorer], Santa Fe Institute. Free and accessible to all. Always available.
    
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