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梅拉妮·米歇尔Melanie Mitchell (查看源代码)

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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'''[[圣塔菲研究所 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。<ref>[https://scholar.google.com/scholar?q=author:%22Melanie+Mitchell%22&lr=&start=0&sa=N Google Scholar search for Melanie Mitchell]</ref>

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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).

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 Artificial Intelligence: A Guide for Thinking Humans》。

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==观点==

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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。<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>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。<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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