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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]
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，但她也表达了对人工智能易受黑客攻击以及其继承社会偏见的担忧。对于通用人工智能，梅拉妮·米歇尔指出，“常识知识”和“人类抽象和类比的能力”可能是建造超级智能机器所需的最后一步，但目前的技术还不足以解决这个问题。<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>
“So, in AI, people talk about this notion of distribution, which is kind of a statistics idea that your data has a certain distribution. The dogs in your training data have a certain range of features that your system learns and if you show it a new thing that is within that range of features then it can recognize it, but if it's outside of that distribution, it won't be able to transfer its knowledge to that. And that's something that we humans are able to do. One of the things that kind of surprised me: there's a huge focus in AI on this thing called 'transfer learning,' which is exactly what we're talking about. That is: learn one thing, learn to play chess, be able to transfer your knowledge to variations of chess or to checkers.”
“So, in AI, people talk about this notion of distribution, which is kind of a statistics idea that your data has a certain distribution. The dogs in your training data have a certain range of features that your system learns and if you show it a new thing that is within that range of features then it can recognize it, but if it's outside of that distribution, it won't be able to transfer its knowledge to that. And that's something that we humans are able to do. One of the things that kind of surprised me: there's a huge focus in AI on this thing called 'transfer learning,' which is exactly what we're talking about. That is: learn one thing, learn to play chess, be able to transfer your knowledge to variations of chess or to checkers.”
梅拉妮·米歇尔觉得人类和人工智能的区别在于人类可以进行“迁移学习Transfer Learning”，比如辨认出动画片中的狗，但是如果机器的训练集中没有动画片中的狗，它就不能很好地对这个图做出分类。人工智能无法把知识迁移到自己从未见过的范围内。
“'Intelligence' is one of those words that means different things in different contexts. （It means different things to different people. Here we are sitting in Washington, DC, and I think a lot of the country, a lot of people in the country think, 'Oh, Congress--there there's no intelligence there.' But, when I go around giving talks about AI and I say, 'Well, computers aren't very intelligent yet,' people tell me, 'Well, human beings aren't very intelligent, either.' But they're using the term just very differently. Intelligence isn't just one thing. ）It's not a yes or no thing either. And I think one of the problems is we don't have a good sense of what intelligence is. We don't understand our own intelligence very well. Our state of understanding the brain is still quite limited. Our understanding of human psychology is still rather limited. And I think intelligence is the one of those terms that's a placeholder for things we don't understand yet. It's kind of a phenomenon that we kind of have a general idea of what it is but we don't know specifically, and it's just waiting for more scientific advances to replace it with something more useful.”
“'Intelligence' is one of those words that means different things in different contexts. （It means different things to different people. Here we are sitting in Washington, DC, and I think a lot of the country, a lot of people in the country think, 'Oh, Congress--there there's no intelligence there.' But, when I go around giving talks about AI and I say, 'Well, computers aren't very intelligent yet,' people tell me, 'Well, human beings aren't very intelligent, either.' But they're using the term just very differently. Intelligence isn't just one thing. ）It's not a yes or no thing either. And I think one of the problems is we don't have a good sense of what intelligence is. We don't understand our own intelligence very well. Our state of understanding the brain is still quite limited. Our understanding of human psychology is still rather limited. And I think intelligence is the one of those terms that's a placeholder for things we don't understand yet. It's kind of a phenomenon that we kind of have a general idea of what it is but we don't know specifically, and it's just waiting for more scientific advances to replace it with something more useful.”
“智能”这个词在不同的语境中有不同的含义。梅拉妮·米歇尔觉得人类还不太了解自己的智慧，对什么是智能，以及大脑的工作方式的认知都非常有限，但人类对于“智能”有一个大致的概念。
==代表作品==
==代表作品==