书籍介绍
A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own
In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.
AI导读
核心看点
- 揭秘机器学习五大流派，构建终极算法蓝图
- 探讨AI对商业、科学及社会的深远影响
- 解析数据主义时代的学习机器运作机制
适合谁读
- 对人工智能与机器学习感兴趣的科普读者
- 希望了解AI发展历史与未来趋势的从业者
- 关注科技哲学与社会伦理变化的思考者
读前提醒
- 中文版翻译质量争议大，建议优先阅读原版
- 内容偏向宏观综述，非专业技术教程
- 部分观点存在过度营销之嫌，需批判性阅读
读者共识
- 视角宏大直观，适合非专业人士建立认知
- 翻译水平参差不齐，严重影响阅读体验
- 干货相对较少，深入技术需参考专业教材
本导读基于书籍简介、目录、原文摘录、短评和书评生成，不等同于全文精读。
精彩摘录
- "Privacy is only one aspect of the larger issue of data sharing, and if we focus on it to the detriment of the whole, as much of the dabate to date has, we risk reaching the wrong conclusions. ... When people have to trade off privacy against other benefits, as when filling out a profile on a website"
- "In the early days of AI, the common view was that computers would replace blue-collar workers before white-collar ones, because white-collar work requires more brains. But that’s not quite how things turned out. Robots assemble cars, but they haven’t replaced construction workers. On the other hand,"
- "The first big worry, as with any technology, is that AI could fall into the wrong hands. … The second worry is that humans will voluntarily surrender control. It starts with robot rights, which seem absurd to me but not to everyone. After all, we already give rights to animals, who never asked for t"
- "很多时候，我们得考虑，如果终极算法落入坏人手中，它会做些什么。第一道防线就是确保好人第一个拿到它，或者如果它不明白谁是好人，就要保证它是开源的。第二道防线就是要意识到，无论学习算法有多好用，也只是在获得数据时好用。控制了数据的人也就控制了学习算法。"
- "The solution is to marry machine learning with game theory, something I've worked on in the past: don't just learn to defeat what your opponent does now; learn to parry what he might do against your learner. Factoring in the costs and benefits of different actions, as game theory does, can also help"
- "P and NP are the two most important classes of problems in computer science. A problem is in P if we can solve it efficiently, and it's in NP if we can efficiently check its solution. The famous P = NP question is whether every efficiently cheackable problem is also efficiently solvable."
- "The future belongs to those who understand at a very deep level how to combine their unique expertise with what algorithms do best."
- "Besides, knowledge is not just a long list of facts. Knowledge is general, and has structure."
作者简介
Pedro Domingos is a professor of computer science at the University of Washington. He is a winner of the SIGKDD Innovation Award, the highest honor in data science. A fellow of the Association for the Advancement of Artificial Intelligence, he lives near Seattle.
用户评论
有意思有意思，融合的那章迷之精彩
读不下去了，太空
弃读因为实在好无聊，像是道士卖膏药呢。。
两年前还没入这行，初看觉得很多都不了解。两年后，算入门了，觉得只是一本不错的科普。但真正要学习，还是要看教材或专业一点的书或教程。中文版翻译太烂。
计算机各个流派浅析
Recruiter 推荐的，很不适的读完了(还好和实际工作没关系！)。开头结尾有的观点只能用恶心来形容。非常不喜欢over selling(讨论人类小孩的学习能力之后介绍k-means 算法，seriously?), 虽然会让一些人激动，但能引起的反感远远大于这部分人激动的意义。而且有必要提master algorithm 么？有必要么？为什么要实现一个和人类大脑一样的算法呢？地球上已经七十多亿人了，这样的算法有什么用？算法有用就是因为它和人不一样。很难想象一个有着人类大脑一样学习能力的算法不会犯人类大脑的错误。不知这书的目标读者是什么人？感兴趣的话看公式不好吗？这个领域需要的是脚踏实地的介绍各种方法在哪些领域有用，哪些领域没用，为什么没用。不过现今这些问题即使研究者也很难回答。
20年看完，有些内容有点过时了，有些回顾了经典的发展历程，不错的科普
兼顾了文笔、可读性和逻辑，非常优秀的科普书籍。
但总有点科幻的感觉。
虽然是摆在总书记书架上的书，但确实没吸收到对自己有触动的观点
Great general intro of five tribes of machine learning algorithms, also with some deeper discussion of coordinations of various learners. Though no defined master algorithm yet, this book provided useful insights of what potential master learners may like
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