Since the 1971s the cognitive sciences have offered multidisciplinary ways of understanding the mind and cognition. The MIT Encyclopedia of the represents Sciences(MITECS)is a landmark,comprehensive reference work that represents the methodological and theoretical diversity of this changing field. For both students and researchers,MITCS will be an indispensable guide to the current state of the cognitive sciences. “The cognitive sciences emerged in recognition of the fact that scholars and scientists in many different fields shared common problems and needed to collaborate. Now at last The MIT Encyclopedia of the Cognitive Sciences has provided a forum large enough for that interaction to occur——a forum that will not only facilitate cooperation but will educate a new generation of cognitive scientists.”——George Miller,Professor of Psychology Emeritus,Princeton University “At last,a thorough,authoritative source for work in the cognitive sciences. Take the most important topics in the study of cognition,ask the worlds top authorities to summarize the state of the art,and you have it:The MIT Encyclopedia of the Cognitive Sciences. I have already used it to learn,to browse,to inform,to teach,and to update my own understanding.It doesnt matter which end you seek:the book will frequently be in use.” ——Donald A. Norman. The Nielsen Norman Group;Professor Emeritus,Department of Cognitive Science,University of California,San Diego;and author The Invisible Computer “Among the human minds proudest accomplishments is the invention of a science dedicated to understanding itself:cognitive science. In less than fifty years,deep mysteries of antiquity have been brought into the lab and captured in rigorous theories. This volume is an authoritative guide to this exhilarating new body of knowledge,written by the experts,edited with skill and good judgment.If we were to leave a time capsule for the next millennium with records of the great achievements of civilization,this volume would have to be in it.” ——Steven Pinker,Professor of Psychology,Massachusetts Institute of Technology;and author of How the Mind Works and The Language Instinct.

This is an account of the schism that developed in linguistics during the 1960s and 70s, between Noam Chomsky with his revolutionary ideas about mental structure and universal grammar, and his disciples who took his ideas in a direction he was unhappy with. The repercussions of this divisive and acrimonious dispute remain in the ways that linguists look at language and the mind.

本书是第一本从各个层面全面介绍语言技术的书，自第1版出版以来，一直好评如潮，被国外许多著名大学选为自然语言处理和计算语言学课程的主要教材。本书将深入的语言分析与健壮的统计方法结合起来，新版更是涉及了大量的现代技术，将自然语言处理、计算语言学以及语音识别等内容融合在一本书中，把各种技术相互联系起来，让读者了解怎样才能最佳地利用每种技术，怎样才能将各种技术结合起来使用。本书写作风格引人入胜，深入技术细节而又不让人感觉枯燥。
本书不仅可以作为高等学校自然语言处理和计算语言学等课程的本科生和研究生教材，对于自然语言处理相关领域的研究人员和技术人员也是不可或缺的权威参考书。

你永远不可能挽救每一只数码体
人工智能时代的小人物大命运
《软件体的生命周期》结集特德·姜最新的六篇作品：《软件体的生命周期》、《赏心悦目》、《商人和炼金术士之门》、《呼吸——宇宙的毁灭》、《前路迢迢》及《达西的新型自动机器保姆》——
安娜在蓝色伽马培育数码体，供喜爱之人购买当宠物。随着数码体市场的发展、壮大、冷淡和萧条，数码体们的命运也随之发生变迁。
戴上“审美干扰镜”，人们就无法分辨一个人相貌的美丑。这一新技术的诞生是否可以彻底根除相貌歧视，让我们听听那些当事人是怎么说的。
穿越时空之门回到过去，能够对既定的事实带来改变吗？
每天早上醒来之后，先把用完的肺换掉。来自地下的空气流源源不断，只可惜，我们耐以生存的似乎并不是空气。
当你的每一个决定都被他人精确预料，你是欣然接受，还是无助挣扎？
把孩子给保姆照料让人不放心，可如果这个保姆是个严格执行命令、百分百听指挥的机器人呢？

机器与未来是息息相关的。在战争中锻造出来的控制论一度成为了前所未有的能够预测并预见未来智能自动机的工具。与此同时，两股对立的力量共同塑造了未来的控制论愿景。一方是对于一个更加美好的世界之希望：暴力行为减少，工作变得更加人性化，游戏更加娱乐化，政治更加自由化，战争不再那么血腥。“思考的机器”会带来进步，这深深地嵌入在那些现代主义者的信仰之中。
但反对势力同样塑造了迫在眉睫的技术变革所带来的控制论假想：它充满了一种对这样一个世界的恐慌——机器人会使工人陷入失业，机器会伤害人类，核心系统会崩塌，大量的监控和隐私泄露，机械化逆行。乐观主义对抗悲观主义，解放对抗压迫，乌托邦对抗反乌托邦。
本书探讨了将控制交于机器，与机器交互或通过机器进行交互的含意。机器最终能把人类从肮脏、重复的劳动中解放出来吗？能把人类从令人抓狂的交通拥堵中解脱出来，并使得我们的工作、生活和游戏更加社会化、互联化，但同时更加安全和放心吗？ 或者，现代社会正不知不觉走入一个慢慢失去控制的危险的勇敢新世界？我们是否正在无意中建立网络化的经济，表面上这种经济直接伸进了我们的口袋和手提包中，但它随时可以戛然而止，甚至有可能在关键枢纽上崩塌？通过把前所未有的控制权委托给这些前所未有的、互联化的智能的机器，我们发达的社会需要承担多大的风险？

本书由经济学家西蒙1980年代在斯坦福大学的三个讲座整理而成。三个讲座分别探讨了：1.通过超凡模型、行为模型、直觉模型三个理性模型，以及情感对决策的影响，来理解我们是怎么做决策的；2.理性适应与生物自然选择之间的相似性，从时间维度理解理性决策；3.由个人延伸到社 会机构，有限理性对社会和政治机构决策的影响，从空间维度理解理性决策。
西蒙认为，理性它不能选择我们的最终目标，也不能调节我们追求最终目标上的单纯冲突，理性所能够做的，是帮我们更有效地实现目标。
西蒙作为20世纪全才式的学者，各领域的读者都能在里面受到启发。此书短短一百多页，就涉及经济学、管理学、政治学、生物学、心理学、计算机、文学等学科。引入此书，对经济学普及和研究有较大意义。

一部关于AlphaGo的纪录片定于4月21日在纽约翠贝卡电影节首映。该部纪录片由格雷格执导，演员表有李世石，樊麾，黄士杰，哈萨比斯和席尔瓦。影片时长90分钟。这部电影全方位展示了人机大战的过程，更尽可能多的揭示了人类思维的工作方式和人工智能未来的工作方式。

面对科技的迅猛发展，中国政府制定了《新一代人工智能发展规划》，将人工智能上升到国家战略层面，并提出：不仅人工智能产业要成为新的经济增长点，而且要在2030年达到世界领先水平，让中国成为世界主要人工智能创新中心，为跻身创新型国家前列和经济强国奠定基础。
《人工智能》一书由腾讯一流团队与工信部高端智库倾力创作。本书从人工智能这一颠覆性技术的前世今生说起，对人工智能产业全貌、最新进展、发展趋势进行了清晰的梳理，对各国的竞争态势做了深入研究。本书还对人工智能给个人、企业、社会带来的机遇与挑战进行了深入分析。对于想全面了解人工智能的读者，本书提供了重要参考，是一本必备书籍。

Tool-making or culture, language or religious belief: ever since Darwin, thinkers have struggled to identify what fundamentally differentiates human beings from other animals. In this much-anticipated book, Michael Tomasello weaves his twenty years of comparative studies of humans and great apes into a compelling argument that cooperative social interaction is the key to our cognitive uniqueness. Once our ancestors learned to put their heads together with others to pursue shared goals, humankind was on an evolutionary path all its own.

Tomasello argues that our prehuman ancestors, like today's great apes, were social beings who could solve problems by thinking. But they were almost entirely competitive, aiming only at their individual goals. As ecological changes forced them into more cooperative living arrangements, early humans had to coordinate their actions and communicate their thoughts with collaborative partners. Tomasello's "shared intentionality hypothesis" captures how these more socially complex forms of life led to more conceptually complex forms of thinking. In order to survive, humans had to learn to see the world from multiple social perspectives, to draw socially recursive inferences, and to monitor their own thinking via the normative standards of the group. Even language and culture arose from the preexisting need to work together. What differentiates us most from other great apes, Tomasello proposes, are the new forms of thinking engendered by our new forms of collaborative and communicative interaction.

A Natural History of Human Thinking is the most detailed scientific analysis to date of the connection between human sociality and cognition.

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。）

《深度学习》由全球知名的三位专家Ian Goodfellow、Yoshua Bengio 和Aaron Courville撰写，是深度学习领域奠基性的经典教材。全书的内容包括3个部分：第1部分介绍基本的数学工具和机器学习的概念，它们是深度学习的预备知识；第2部分系统深入地讲解现今已成熟的深度学习方法和技术；第3部分讨论某些具有前瞻性的方向和想法，它们被公认为是深度学习未来的研究重点。
《深度学习》适合各类读者阅读，包括相关专业的大学生或研究生，以及不具有机器学习或统计背景、但是想要快速补充深度学习知识，以便在实际产品或平台中应用的软件工程师。

本书是一本研究汉语自然语言处理方面的基础性、综合性书籍，涉及NLP的语言理论、算法和工程实践的方方面面，内容繁杂。 本书包括NLP的语言理论部分、算法部分、案例部分，涉及汉语的发展历史、传统的句法理论、认知语言学理论。需要指出的是，本书是迄今为止第一本系统介绍认知语言学和算法设计相结合的中文NLP书籍，并从认知语言学的视角重新认识和分析了NLP的句法和语义相结合的数据结构。这也是本书的创新之处。 本书适用于所有想学习NLP的技术人员，包括各大人工智能实验室、软件学院等专业机构。

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.

《心智、语言和机器:维特根斯坦哲学和人工智能科学的对话》向读者介绍人工智能科学的基本发展历史和基本技术；介绍“人工智能哲学”这门哲学分支的大致发展情况；维氏哲学将对知识表征、自然语言理解、机器人、非单调推理等人工智能的子课题做出贡献。而《心智、语言和机器:维特根斯坦哲学和人工智能科学的对话》就将负责在维氏和这些课题之间搭建桥梁。

The New York Times best-selling author of Physics of the Impossible , Physics of the Future and Hyperspace tackles the most fascinating and complex object in the known universe: the human brain .

For the first time in history, the secrets of the living brain are being revealed by a battery of high tech brain scans devised by physicists. Now what was once solely the province of science fiction has become a startling reality. Recording memories, telepathy, videotaping our dreams, mind control, avatars, and telekinesis are not only possible; they already exist.

The Future of the Mind gives us an authoritative and compelling look at the astonishing research being done in top laboratories around the world—all based on the latest advancements in neuroscience and physics. One day we might have a "smart pill" that can enhance our cognition; be able to upload our brain to a computer, neuron for neuron; send thoughts and emotions around the world on a "brain-net"; control computers and robots with our mind; push the very limits of immortality; and perhaps even send our consciousness across the universe.

Dr. Kaku takes us on a grand tour of what the future might hold, giving us not only a solid sense of how the brain functions but also how these technologies will change our daily lives. He even presents a radically new way to think about "consciousness" and applies it to provide fresh insight into mental illness, artificial intelligence and alien consciousness.

With Dr. Kaku's deep understanding of modern science and keen eye for future developments, The Future of the Mind is a scientific tour de force--an extraordinary, mind-boggling exploration of the frontiers of neuroscience.

《中文信息处理丛书：统计自然语言处理（第2版）》全面介绍了统计自然语言处理的基本概念、理论方法和最新研究进展，内容包括形式语言与自动机及其在自然语言处理中的应用、语言模型、隐马尔可夫模型、语料库技术、汉语自动分词与词性标注、句法分析、词义消歧、篇章分析、统计机器翻译、语音翻译、文本分类、信息检索与问答系统、自动文摘和信息抽取、口语信息处理与人机对话系统等，既有对基础知识和理论模型的介绍，也有对相关问题的研究背景、实现方法和技术现状的详细阐述。
《中文信息处理丛书：统计自然语言处理（第2版）》可作为高等院校计算机、信息技术等相关专业的高年级本科生或研究生的教材或参考书，也可供从事自然语言处理、数据挖掘和人工智能等研究的相关人员参考。

《实用多元统计分析(第6版)》多元统计分析是统计学中内容十分丰富、应用范围极为广泛的一个分支。在自然科学和社会科学的许多学科中，研究者都有可能需要分析处理有多个变量的数据的问题。能否从表面上看起来杂乱无章的数据中发现和提炼出规律性的结论，不仅需要对所研究的专业领域有很好的训练，而且要掌握必要的统计分析工具。对研究者来说，《实用多元统计分析》是学习掌握多元统计分析的各种模型和方法的一本有价值的参考书：首先，它做到了“浅入深出”，既可供初学者入门，又能使有较深基础的人受益；其次，它既侧重于应用，又兼顾必要的推理论证，使学习者既能学到“如何”做，又能在一定程度上了解“为什么”这样做；最后，它内涵丰富、全面，不仅基本包括各种在实际中常用的多元统计分析方法，而且对现代统计学的最新思想和进展有所介绍。

"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, co-chair of OpenAI; co-founder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

《支持向量机：理论、算法与拓展》以分类问题（模式识别、判别分析）和回归问题为背景，介绍支持向量机的基本理论、方法和应用。特别强调对所讨论的问题和处理方法的实质进行直观的解释和说明，因此具有很强的可读性。为使具有一般高等数学知识的读者能够顺利阅读，书中首先介绍了最优化的基础知识。《支持向量机：理论、算法与拓展》可作为理工类、管理学等专业的高年级本科生、研究生和教师的教材或教学参考书，也可供相关领域的科研人员和实际工作者阅读参考。