《情感计算》主要内容：目前这个世界与我当时写这《情感计算》时大不一样。当时情感计算几乎是令人困惑的，只有极 少数的计算机界科学家或工程师愿意投入此项工作。计算机本身具有类似于情感机制的这种 观点不是新的，它在Cap&Brother剧本R．U．R．创造出世界“机器人”这一词时就出现了，但具有有效情感机制的计算机实际上并不存在。有关人工智能的会议要么是忽视情感，要么是把情感边缘化。情感智能的观念在心理学和认知科学中变得越发重要，但没有人把它应用到人机交互中。神经科学和心理学上早已发现关于情感在决策、感知、创造性等方面的作用，而计算科学在很大程度上并不知晓。许多人不知道情感有助于理性和智能行为，普遍认为计算机的情感是一种空洞无聊的东西，就像蛋糕表面上的一层糖霜，可以用来使之更为悦目，但没有真正实质上的意义。 我感谢当时与我讨论情感计算的几个同事。在出版《情感计算》的前一年，我记得，麻省理工学院(MIT)人工智能实验室的一批研究人员邀请我发言，他们对以下问题十分感兴趣，即赋予计算机以类似情感机制这件事的重要程度如何，以及为什么这件事能对人工智能有用?情感即使带来好处，是否会造成更多的麻烦?答案不太明显，需要加以解释；而他们很欢迎这方面的证据。媒体实验室及其他单位的一些人员愿意听我的论证、提出问题、提出他们的想法和批评意见，甚至协作研究，得出了新的悟解。有些人特别是一些尚未取得终身任职的学术界的同行告诉我说：我的想法是荒唐的，我已经享有严肃研究者的声誉，致力于机器具有情感的研究可能会毁掉我的名誉。我记得我曾深刻内省，以决定是否继续从事这项研究。坦率地说，如果我工作在传统的学术部门，而不是在这样一个实验室，那里的领导层，特别是JerryWiesner和NicholasNegronte，经常公开地称颂大胆的想法并强烈鼓励冒险，我是不会像这样全心全意投入这项研究的。 已经过去五年多了。今天，很难想像当时我竟会感到那样害怕。计算机中情感的研究，已经为很多学术界和工业界顶级的研究实验室接受，并引起了国际上的重大关注。 自从《情感计算》出版后，已经有20多个专题讨论会、会议以及特定集会，其主题均围绕情感和计算机运算，而且通常把情感计算列为一个学科领域。我不能、也不会对世界上这种改变自我居功。事实是，真理不依赖任何个人的努力而找到自己前进的道路。在追求它的过程中，我们在黑暗中苦苦探求、苦苦摸索，而不变的真理按自己的条件终于显露出来。试图理解情感就是试图认识事物的真实面目。最终，我们可以清楚地看到事物的原貌，理解人工智能和一切人类进程是如何进行的。

From the inventor of the PalmPilot comes a new and compelling theory of intelligence, brain function, and the future of intelligent machines Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself. Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines. The brain is not a computer, but a memory system that stores experiences in a way that reflects the true structure of the world, remembering sequences of events and their nested relationships and making predictions based on those memories. It is this memory-prediction system that forms the basis of intelligence, perception, creativity, and even consciousness. In an engaging style that will captivate audiences from the merely curious to the professional scientist, Hawkins shows how a clear understanding of how the brain works will make it possible for us to build intelligent machines, in silicon, that will exceed our human ability in surprising ways. Written with acclaimed science writer Sandra Blakeslee, "On Intelligence" promises to completely transfigure the possibilities of the technology age. It is a landmark book in its scope and clarity.

1.大脑如何产生新想法？思维如何产生，又是如何运作的？意识缘何形成？什么是情感、感觉、想法？如果将人类大脑看成一台机器，那么这是否有益于我们设计出能够像人一样能理解、会思考的高级人工智能——情感机器？ 2.情感是人类特有的一种思维方式，如果机器具备了情感，是不是就可以取代人类？ 在《情感机器》中，人工智能之父马文•明斯基有力地论证了：情感、直觉和情绪并不是与众不同的东西，而只是一种人类特有的思维方式。也同时揭示了为什么人类思维有时需要理性推理,而有时又会转向情感的奥秘。通过对人类思维方式建模,他为我们剖析了人类思维的本质，为大众提供了一幅创建能理解、会思考、具备人类意识、常识性思考能力，乃至自我观念的情感机器的路线图。

转载自amazon.com: 　　 　　Marvin Minsky -- one of the fathers of computer science and cofounder of the Artificial Intelligence Laboratory at MIT -- gives a revolutionary answer to the age-old question: _How does the mind work?_ 　　 　　（马文。明斯基————电脑科学的鼻祖，麻省理工学院的人工智能实验室的创始人之一————在本书里对相传以久的问题，“思维是怎么一回事儿？”，做出了革命性的回答。） 　　 　　Minsky brilliantly portrays the mind as a _society_ of tiny components that are themselves mindless. Mirroring his theory, Minsky boldly casts The Society of Mind as an intellectual puzzle whose pieces are assembled along the way. Each chapter -- on a self-contained page -- corresponds to a piece in the puzzle. As the pages turn, a unified theory of the mind emerges, like a mosaic. Ingenious, amusing, and easy to read, The Society of Mind is an adventure in imagination. 　　 　　（明斯基的精彩理论把思维描画成由本身不具备思维的微小部件组成的“社会”。本书章节段落之间结构跟他的理论相呼应，每一页纸独立成为一章，讨论整个问题里的单个环节。翻过这一篇篇书页，关于思维的统一理论渐渐成型，《意识社会》一书妙趣横生，是在想象空间里的一场历险。）

Knowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top down, putting the focus on what an agent needs to know in order to behave intelligently, how this knowledge can be represented symbolically, and how automated reasoning procedures can make this knowledge available as needed. This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way. Each of the various styles of representation is presented in a simple and intuitive form, and the basics of reasoning with that representation are explained in detail. This approach gives readers a solid foundation for understanding the more advanced work found in the research literature. The presentation is clear enough to be accessible to a broad audience, including researchers and practitioners in database management, information retrieval, and object-oriented systems as well as artificial intelligence. This book provides the foundation in knowledge representation and reasoning that every AI practitioner needs. *Authors are well-recognized experts in the field who have applied the techniques to real-world problems * Presents the core ideas of KR&R in a simple straight forward approach, independent of the quirks of research systems *Offers the first true synthesis of the field in over a decade

当机器智能超越了人类智能时会发生什么？人工智能会拯救人类还是毁灭人类？ 作者提到，我们不是这个星球上速度最快的生物，但我们发明了汽车、火车和飞机。我们虽然不是最强壮的，但我们发明了推土机。我们的牙齿不是最锋利的，但我们可以发明比任何动物的牙齿更坚硬的刀具。我们之所以能控制地球，是因为我们的大脑比即使最聪明的动物的大脑都要复杂得多。如果机器比人类聪明，那么我们将不再是这个星球的主宰。当这一切发生的时候，机器的运转将超越人类。 人类大脑拥有一些其他动物大脑没有的功能。正是这些独特的功能使我们的种族得以拥有主导地位。如果机器大脑在一般智能方面超越了人类，那么这种新兴的超级智能可能会极其强大，并且有可能无法控制。正如现在大猩猩的命运更多的掌握在人类手中而不是自己手中一样，人类未来的命运也会取决于机器超级智能的行为。 但是，我们有一项优势：我们有机会率先采取行动。是否有可能建造一个种子人工智能，创造特定的初始条件，使得智能爆发的结果能够允许人类的生存？我们如何实现这种可控的引爆？ 作者相信，超级智能对我们人类将是一个巨大的威胁。在这本书中，作者谈到了超级智能的优势所带来的风险，也谈到了人类如何解决这种风险。作者认为，他的这本书提到的问题将是我们人类所面临的最大风险。 这本书目标宏大，且有独创性，开辟了人工智能领域的新道路。本书会带你开启一段引人入胜的旅程，把你带到对人类状况和智慧生命未来思索的最前沿。尼克•波斯特洛姆的新书为理解人类和智慧生命的未来奠定了基础，不愧是对我们时代根本任务的一次重新定义。

From the inventor of the PalmPilot comes a new and compelling theory of intelligence, brain function, and the future of intelligent machines Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself. Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines. The brain is not a computer, but a memory system that stores experiences in a way that reflects the true structure of the world, remembering sequences of events and their nested relationships and making predictions based on those memories. It is this memory-prediction system that forms the basis of intelligence, perception, creativity, and even consciousness. In an engaging style that will captivate audiences from the merely curious to the professional scientist, Hawkins shows how a clear understanding of how the brain works will make it possible for us to build intelligent machines, in silicon, that will exceed our human ability in surprising ways. Written with acclaimed science writer Sandra Blakeslee, On Intelligence promises to completely transfigure the possibilities of the technology age. It is a landmark book in its scope and clarity.

《计算机与人脑》是自动机（以电子计算机为代表）理论研究中的重要材料之一。原书是冯·诺意曼在1955-1956年准备讲演用的未完成稿。著者从数学的角度，主要是从逻辑和统计数学的角度，探讨计算机的运算和人脑思维的过程，进行了一些比较研究。书中的许多技术推论带有预测性，尚待今后实验研究及进一步探讨才能判断其是否正确。

The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For computer professionals, linguists, and cognitive scientists interested in artificial intelligence.

This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.

This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. It also motivates the use of various forms of error functions, and reviews the principal algorithms for error function minimization. As well as providing a detailed discussion of learning and generalization in neural networks, the book also covers the important topics of data processing, feature extraction, and prior knowledge. The book concludes with an extensive treatment of Bayesian techniques and their applications to neural networks.

《人工智能:一种现代方法》(第2版中文版)以详尽和丰富的资料，从理性智能体的角度，全面阐述了人工智能领域的核心内容，并深入介绍了各个主要的研究方向，是一本难得的综合性教材。全书分为八大部分：第一部分“人工智能” ，第二部分“问题求解” ，第三部分“ 知识与推理” ，第四部分“规划” ，第五部分“不确定知识与推理” ，第六部分“学习” ，第七部分“通讯、感知与行动” ，第八部分“ 结论” 。

《人工智能复杂问题求解的结构和策略(原书第6版)》是一本经典的人工智能教材，全面阐述了人工智能的基础理论，有效结合了求解智能问题的数据结构以及实现的算法，把人工智能的应用程序应用于实际环境中，并从社会和哲学、心理学以及神经生理学角度对人工智能进行了独特的讨论。新版中增加了对“基于随机方法的机器学习”的介绍，并提出了一些新的主题，如涌现计算、本体论、随机分割算法等。 《人工智能复杂问题求解的结构和策略(原书第6版)》适合作为高等院校计算机专业人工智能教材，也可供人工智能领域的研究者及相关工程技术人员参考。

《模式分类》(原书第2版)的第1版《模式分类与场景分析》出版于1973年，是模式识别和场景分析领域奠基性的经曲名著。在第2版中，除了保留了第1版的关于统计模式识别和结构模式识别的主要内容以外，读者将会发现新增了许多近25年来的新理论和新方法，其中包括神经网络、机器学习、数据挖掘、进化计算、不变量理论、隐马尔可夫模型、统计学习理论和支持向量机等。作者还为未来25年的模式识别的发展指明了方向。书中包含许多实例，各种不同方法的对比，丰富的图表，以及大量的课后习题和计算机练习。

Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

本书被全世界89个国家的900多所大学用作教材。 本书以详尽和丰富的资料，从理性智能体的角度，全面阐述了人工智能领域的核心内容，并深入介绍了各个主要的研究方向。全书分为8大部分：第一部分“人工智能”，第二部分“问题求解”，第三部分“知识与推理”，第四部分“规划”，第五部分“不确定知识与推理”，第六部分“学习”，第七部分“通信、感知与行动”，第八部分“结论”。本书既详细介绍了人工智能的基本概念、思想和算法，还描述了其各个研究方向最前沿的进展，同时收集整理了详实的历史文献与事件。另外，本书的配套网址 本书适合于不同层次和领域的研究人员及学生，是高等院校本科生和研究生人工智能课的首选教材，也是相关领域的科研与工程技术人员的重要参考书。

《机器学习》展示了机器学习中核心的算法和理论，并阐明了算法的运行过程。《机器学习》综合了许多的研究成果，例如统计学、人工智能、哲学、信息论、生物学、认知科学、计算复杂性和控制论等，并以此来理解问题的背景、算法和其中的隐含假定。《机器学习》可作为计算机专业 本科生、研究生教材，也可作为相关领域研究人员、教师的参考书。