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作者简介
For Einstein, Gertrude Stein, Wittgenstein, and Frankenstein 献给爱因斯坦、格特鲁德·斯坦、维特根斯坦和弗兰肯斯坦
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赞誉
真正的互联网思想家，又一部科技思想荟萃的杰作！布罗克曼的个人魅力总是能聚集互联网、人工智能领域的最强大脑，展现深刻的技术思想和前沿洞察。段永朝苇草智酷创始合伙人建筑学家威廉·J.米切尔曾有一个比喻:人不过是猿猴的1.0版。现在，经由各种比特的武装，人类终于将自己升级到猿猴2.0版。他们将如何处理自己的原子之身呢？这是今日顶尖思想者不得不回答的“大问题”
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总序
1981年，我成立了一个名为“现实俱乐部”（Reality Club）的组织，试图把那些探讨后工业时代话题的人们聚集在一起。1997年，“现实俱乐部”上线，更名为Edge。在Edge中呈现出来的观点都是经过推敲的，它们代表着诸多领域的前沿，比如进化生物学、遗传学、计算机科学、神经学、心理学、宇宙学和物理学等。从这些参与者的观点中，涌现出一种新的自然哲学：一
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引言人工智能的机遇与风险
人工智能是今天的神话，也是其他一切故事背后的故事。它既是新的开始，也是末世毁灭，两种结局分别对应了好的人工智能和恶的人工智能。本书集结了诸多来自人工智能领域内外的重要思想家的对话，探讨了人工智能的定义及含义。该对话是基于一个叫作“可能的心智”的项目，正式开始于2016年9月在康涅狄格州华盛顿的五月花格瑞斯酒店召开的一次会议，本书的一些撰稿人也参与了这次会议。
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01 虽然是谬误，却比以往更靠谱 WRONG, BUT MORE RELEVANT THAN EVER
It is exactly in the extension of the cybernetic idea to human beings that Wiener's conceptions missed their target. 维纳的错误就在于他把控制论的理念用到了人类身上。塞思·劳埃德 Seth Lloyd 塞思·劳埃德是麻省理工学院理论物理学家，
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02 不透明学习机器的局限性 THE LIMITATIONS OF OPAQUE LEARNING MACHINES
Deep learning has its own dynamics, it does its own repair and its own optimization, and it gives you the right results most of the time. But when it doesn't, you don't have a clue
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03 给机器输入使命 THE PURPOSE PUT INTO THE MACHINE
We may face the prospect of superintelligent machines—their actions by definition unpredictable by us and their imperfectly specified objectives conflicting with our own—whose moti
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04 人工智能第三定律 THE THIRD LAW
Any system simple enough to be understandable will not be complicated enough to behave intelligently, while any system complicated enough to behave intelligently will be too compli
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05 我们将如何应对？ WHAT CAN WE DO?
We don't need artificial conscious agents. We need intelligent tools. 我们不需要有意识的人工主体。我们需要的是智能工具。丹尼尔·丹尼特 Daniel C. Dennett 丹尼尔·丹尼特是塔夫茨大学奥斯丁·弗莱彻哲学讲席教授和认知研究中心主任。他著有10多本著作，包括《直觉泵和其他思考工
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06 我们的机器使我们陷入非人类混乱 THE INHUMAN MESS OUR MACHINES HAVE GOTTEN US INTO
We are in a much more complex situation today than Wiener foresaw, and I am worried that it is much more pernicious than even his worst imagined fears. 我们今天的处境比维纳预想的要复杂得多，我担心这比他预想的
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07 智能的统一 THE UNITY OF INTELLIGENCE
The advantages of artificial over natural intelligence appear permanent, while the advantages of natural over artificial intelligence, though substantial at present, appear transie
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08 让我们心怀渴望，超越自我 LET’S ASPIRE TO MORE THAN MAKING OURSELVES OBSOLETE
We should analyze what could go wrong with AI to ensure that it goes right. 我们应该分析人工智能可能会出什么问题，以确保它能正常运行。迈克斯·泰格马克 Max Tegmark 迈克斯·泰格马克是麻省理工学院物理学家、人工智能研究者、未来生命研究所所长、基础问题研究所的科学主任。著有
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09 反对派报告 DISSIDENT MESSAGES
Continued progress in AI can precipitate a change of cosmic proportions—a runaway process that will likely kill everyone. 人工智能的持续发展会造成宇宙规模的变化，这个失控的过程可能会杀死每一个人。扬·塔里安 Jaan Tallinn 扬
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11 超越奖惩 BEYOND REWARD AND PUNISHMENT
Misconceptions about human thinking and human origins are causing corresponding misconceptions about AGI and how it might be created. 对人类思维和人类起源的误解，相应地导致了对通用人工智能以及如何创建通用人工智能的误解。戴维
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12 对人类的人工利用 THE ARTIFICIAL USE OF HUMAN BEINGS
Automated intelligent systems that will make good inferences about what people want must have good generative models for human behavior. 能够对人类需求做出很好推断的自动智能系统，必须具有良好的人类行为生成模型。汤姆·格里
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13 把人类放进人工智能的方程式中 PUTTING THE HUMAN INTO THE AI EQUATION
In the real world, an AI must interact with people and reason about them. "People" will have to formally enter the AI problem definition somewhere. 在现实世界中工作，机器人必须与人们实际互动，并理智地对待他们。“
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14 梯度下降 GRADIENT DESCENT
Just because AI systems sometimes end up in local minima, don't conclude that this makes them any less like life. Humans—indeed, probably all life-forms—are often stuck in local mi
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15 “信息”之于维纳、香农及我们 “INFORMATION" FOR WIENER, FOR SHANNON, AND FOR US
Many of the central arguments in The Human Use of Human Beings seem closer to the 19th century than the 21st. Wiener seems not to have fully embraced Shannon's notion of informatio
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16 伸缩性 SCALING
Although machine making and machine thinking might appear to be unrelated trends, they lie in each other's futures. 虽然机器制造和机器思维可能看起来是毫不相关的两个趋势，但它们存在于彼此的未来。尼尔·格申斐尔德 Neil Gershenfel
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17 第一批机器智能 THE FIRST MACHINE INTELLIGENCES
Hybrid superintelligences such as nation-states and corporations have their own emergent goals and their actions are not always aligned to the interests of the people who created t
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18 计算机会成为我们的霸主吗？ WILL COMPUTERS BECOME OUR OVERLORDS?
Our fears about AI reflect the belief that our intelligence is what makes us special. 人们对人工智能的恐惧反映出这样的信念：正是我们的智能才使我们与众不同。文卡·拉马克里希南 Venki Ramakrishnan 文卡·拉马克里希南是剑桥大学分子生物学医学研究委员会实验室
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19 人类策略 THE HUMAN STRATEGY
How can we make a good human-artificial ecosystem, something that's not a machine society but a cyberculture in which we can all live as humans—a culture with a human feel to it? 我
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20 使看不见的为人所见：当艺术遇见人工智能 MAKING THE INVISIBLE VISIBLE: ART MEETS AI
Many contemporary artists are articulating various doubts about the promises of AI and reminding us not to associate the term“artificial intelligence" solely with positive outcomes
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21 人工智能与4岁儿童的对比 AIS VERSUS FOUR-YEAR-OLDS
Looking at what children do may give programmers useful hints about directions for computer learning. 看看孩子们的行为，这可能会给程序员提供一些有关计算机学习方向的有用提示。艾莉森·高普尼克 Alison Gopnik 艾莉森·高普尼克是加州大学伯克利分校
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22 算法学家的客观梦想 ALGORISTS DREAM OF OBJECTIVITY
By now, the legal, ethical, formal, and economic dimensions of algorithms are all quasi-infinite. 到目前为止，算法的法律、伦理、形式和经济尺度都是准无限的。彼得·加里森 Peter Galison 彼得·加里森是一位科学史学家，哈佛大学约瑟夫·佩莱格里诺校级教
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23 机器的权利 THE RIGHTS OF MACHINES
Probably we should be less concerned about us-versus-them and more concerned about the rights of all sentients in the face of an emerging unprecedented diversity of minds. 或许，面对一种前
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24 控制论生物的艺术应用 THE ARTISTIC USE OF CYBERNETIC BEINGS
The work of cybernetically inclined artists concerns the emergent behaviors of life that elude AI in its current condition. 倾向于控制论的艺术家的作品关注的是在现状下人工智能无法触及的生命的涌现行为。卡罗琳·琼斯 Caroline A
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25 人工智能与文明的未来 ARTIFICIAL INTELLIGENCE AND THE FUTURE OF CIVILIZATION
The most dramatic discontinuity will surely be when we achieve effective human immortality. Whether this will be achieved biologically or digitally isn't clear, but inevitably it w
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02 不透明学习机器的局限性 THE LIMITATIONS OF OPAQUE LEARNING MACHINES

Deep learning has its own dynamics, it does its own repair and its own optimization, and it gives you the right results most of the time. But when it doesn't, you don't have a clue about what went wrong and what should be fixed.

深度学习有自己的动力学机制，它能自我修复，找出最优化组合，绝大多数时候都会给出正确的结果。可一旦结果错了，你不会知道哪里出了问题，也不知道该如何修复。

朱迪亚·珀尔

Judea Pearl

朱迪亚·珀尔是计算机科学教授，加州大学洛杉矶分校认知系统实验室主任。他与达娜·麦肯齐（Dana Mackenzie）合著了《为什么：关于因果关系的新科学》（The Book of Why: The New Science of Cause and Effect）。