The Master Algorithm (original) (raw)
内容简介 · · · · · ·
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 Dom...
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.
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.
原文摘录 · · · · · · ( 全部 )
- 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, the implied value of privacy that comes out is much lower than if you ask them abstract questions like “Do you care about your privacy?” But privacy debates are more often framed in terms of the latter. … Privacy is not a zero-sum game, even though it’s often treated like one. ––To share or not to share, and how and where (查看原文)
—— 引自第263页 - 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, machine-learning algorithms have replaced credit analysts and direct marketers. … The common theme is that narrowly defined tasks are easily learned from data, but tasks that require a broad combination of skills and knowledge aren’t. Most of your brain is devoted to vision and motion, which is a sign that walking around is much more complex than it seems; we just take it for granted because, having been honed to perfection by evolution, it’s mostly done subconsciously. ––A neural network stole... (查看原文)
—— 引自第263页
> 全部原文摘录
喜欢读"The Master Algorithm"的人也喜欢 · · · · · ·
The Master Algorithm的书评 · · · · · ·( 全部 42 条 )
四十而立 2017-02-09 14:16:56 中信出版社2017版
翻译太滥了--完全欠缺相关学科常识的翻译
先读了英文原版的前两章，觉得读英文还是费劲，然后买了中文版开始读。 从第三章开始读，读了没几页，就觉得蛋疼了。翻译大概是英文专业毕业，但是对自然科学毫无了解的吧。但是科普书的翻译难道是纯粹的语言翻译的问题吗？难道这类书翻译完后不需要经过任何review吗？ （1） ... (展开)
夏天吹起小北风 2016-12-21 16:38:52 中信出版社2017版
终极算法已在多大程度上影响我们的生活？
作为一位机器学习领域研习10 年以上的专业技术人员，我当初入行的时候没有想到，短短的10 年间，这项技术会如此快速地改变众多行业，并影响全球数十亿用户生活的方方面面。在今天，当你用今日头条浏览新闻资讯的时候，当你用网易云音乐查看推荐歌单的时候，当你在百度搜索信息... (展开)
雾凇 2017-01-18 23:58:03 中信出版社2017版
终极算法路在何方？
2016年李世石与阿尔法狗的人机大战牵动全世界的目光，最终李世石的落败，也让世人对今天的人工智能有了更多的期待。 人类对人工智能总有天然殷切的向往。许多人爱科幻，爱科幻中那些超现实的智能。美剧《疑犯追踪》中就曾虚构出一个令人向往的超级人工智能系统——“机器”... (展开)
衝 2018-06-20 09:35:11 中信出版社2017版
英文章节摘要 Github搬运
因为翻译不忍直视，搬运一份Github上的笔记来， 原文在此 。另注：因为原文中有些条目不是很连贯，有些摘抄没有什么意思，所以略有删节。 Prologue Chapter 1: The Machine Learning Revolution Chapter 2: The Master Algorithm Chapter 3: Hume's Problem of Induction Chapt... (展开)
Grass 2017-04-02 21:24:04 中信出版社2017版
翻译太粗糙，原文不严谨
上周参加一个会议，资料袋中放了一本《终极算法》，似乎是最近的热门畅销，出门时就顺手带上，在飞机上翻看。译者大概是文科生，看来胆子挺大：“与，或，非”翻译成“且，或，非”（页4），切！微分、积分翻译成“分化”和“整合”（页11），“NP完全问题”加了编者注（页42）... (展开)
Andrew 2017-10-08 22:04:51 中信出版社2017版
五大流派主算法的概述
人工智能的五大流派及对应的主算法概要介绍。细致原理还需要看对应专著。符号学派，决策树，感觉中规中矩，优点是生成的规则直观可理解；类推学派，最近邻等算法，基于距离或相似的定义，主要问题维度灾难；进化学派，遗传算法没啥体会；贝叶斯学派，贝叶斯定理号称统治世界的... (展开)
猛哥 2017-02-22 20:41:17 中信出版社2017版
书是好书，但翻译和编辑太差了
书是好书，对于机器学习各学派的特点和逻辑关系讲的很清楚，但翻译和编辑的质量实在是太差了。比如涉及到的一点点数学中，上下标没有表现出来，比如2的10次方就写成210，那意思就完全变了，害的我算了半天也不对。还有很多专业的机器学习术语翻译的完全不符合传统，让人看了很... (展开)
红R绿G蓝B 2022-10-13 07:45:41
通过“概念模型”认识“机器学习”
概念模型，认识事物核心框架和基础逻辑的简单模型，能帮我们迅速切入新领域学习新知识。 这本书提供了机器学习的概念模型，适合不求甚解，快速完整地触及机器学习全貌，了解其体系结构和内在联系，再考虑有目标层层深入。 作者划分出五个机器学习的基础思想： 1、符号学派 2、... (展开)
西溪近园 2017-03-24 19:14:15 中信出版社2017版
人类如何把握终极算法?
(1) 一本关于机器学习的科普著作，最适合IT学生、研究者和从业者阅读；非IT读者可能比较烧脑。 (2) 最有价值的部分是系统讲解了机器学习五大流派（符号学派、联结学派、进化学派、贝叶斯学派、类推学派）的基本思想和原理、主算法，以及发展的脉络. 这不简单。 (3) 作者以探求... (展开)
汪杨 2017-05-05 08:44:02
读 Pedro Domingos《The Master Algorithm》
虽然内容比较浅显，但重点是研究了机器学习的背景和理论基础，其实对专业人员来说也是很有启发的。 Minsky was an ardent supporter of the Cyc project, the most notorious failure in the history of AI. e goal of Cyc was to solve AI by entering into a computer all t... (展开)
> 更多书评 42篇