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How Smart Machines Think

4.08  ·  Rating details ·  258 ratings  ·  33 reviews
Everything you've always wanted to know about self-driving cars, Netflix recommendations, IBM's Watson, and video game-playing computer programs.

The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM's Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But ho
Hardcover, 312 pages
Published October 30th 2018 by The MIT Press
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Average rating 4.08  · 
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Brian Clegg
Nov 06, 2018 rated it really liked it
While it will become apparent I think this book should have been titled 'How Dumb Machines Think', it was a remarkably enjoyable insight into how the well publicised AI successes - self-driving cars, image and face recognition, IBM's Jeopardy! playing Watson, along with game playing AIs in chess, Go and Atari and StarCraft, perform their dark arts.

There's no actual programming presented here, so no need for non-programmers to panic, though there is some quite detailed discussion of how the softw
Feb 17, 2020 rated it it was amazing
This is a great introduction to AI/ML for non-practitioners, especially if you're interested in getting started in the field and want a broad understanding before diving in. Gerrish does a great job of embedding clear descriptions of AI/ML algorithms and methodologies inside of entertaining stories about their development and expansion. He goes especially deep into autonomous vehicles, recommendation engines, and game-playing.

Many recent books on ML and AI discuss deep neural networks, natural l
Feb 22, 2019 rated it really liked it  ·  review of another edition
Shelves: business, audiobook
I found “How Smart Machines Think” to cover the same ground as many articles in magazines such as Wired and Fast Business, but with more in-depth examples. In fact, after I finished the book I started reading an article by Clive Thompson in Wired (12/18) who also used some of the same examples to make some of the same points. I liked “How Smart Machines Think” for its overview of the state of affairs of AI and machine learning, and its readable style. You don’t need to be a scientist or develope ...more
Evan Nordquist
Sep 03, 2019 rated it really liked it
Fascinating stuff.

I had heard about the DARPA Grand Challenge before, but I didn't know that the first year, the best autonomous vehicle made it ~12km out of the target 240km route. The exact route was a secret to the teams until hours before the race. The winning vehicle took 'brute forcing' in a literal sense by being an armour plated Humvee so that if it ran into a fence post, that would be a problem for the fence post, not the vehicle. But it got hung up after a switchback corner, and not h
Jan 11, 2019 rated it it was amazing
Shelves: audiobook, non-fic
I started this book again, knowing nothing about machine learning beyond common knowledge.
I now feel like I have a good enough grasp of machine learning to think about what we can do with it in the future, or even now. (Imagine if we trained Watson or AlphaGo to classify books for us in a new classification system, we could finally have consistency in libraries everywhere worldwide!)
Gerrish's metaphors were very spot on too, it made some of the more difficult concepts easy for a layperson to und
Feb 12, 2019 rated it really liked it  ·  review of another edition
Interesting overview of the coding behind AI, in particular neural networks. A programming background or familiarity will help but the book is still useful without that background. The book covers self-driving cars, Jeopardy playing Watson, chess master Deep Blue, Netflix recommendation engines. Special emphasis is placed on that nerdy favorite, strategy game playing AI
Jul 10, 2019 rated it really liked it
The author starts off by first explaining the background to today’s smart machines and how they have evolved from their mechanical equivalents since the growth and development of Information Technology. Automated mechanical devices (automata) can be traced back to the 1700’s. However, since the advent of electronics, automated devices have become ever more sophisticated and capable in the way they operate evolving into the so called ‘smart machine.’

The first half of the book provides some of the
Nov 10, 2020 rated it really liked it
The tension in popularizations of technical subjects is between being too technical so that a layman can't follow and being so vague that the reader doesn't actually learn anything that couldn't be gleaned from news headlines. This book succeeds in finding a middle ground. While this book still deals with abstractions several layers above the actual algorithms and never gets into any actual equations, it goes down enough layers to give a sense that you do come away knowing something meaningful a ...more
Gian Baltazar
Mar 27, 2020 rated it really liked it
Shelves: machine-learning
It´s a great walk-through all the innovation of ML along the years, it is not difficult to understand and a good book to close the gap between the ML practitioners and guys who don´t know the subject.
Some downside are that some concepts are too heavy to understand and i think there were better ways to explain it, also the books spot that you don´t need previous knowledge of ML, but by my point of view, it´s extremely necesary to know at least basic mathematics and conceps of regression and stati
Jun 23, 2019 rated it really liked it
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Thoroughly researched nitty gritty of the fascinating biologically inspired neural networks of AI. A look-see at the hardware of automaton & a more thorough examination of the particular
Nilendu Misra
Dec 29, 2018 rated it it was amazing
A wonderful and very accessible journey across the present set of applications of AI. From self-driving cars, Jeopardy, Image recognition, Speech generation, Recommendations, Playing Go etc. Even engineers working in the field will benefit from the right level of abstraction and very useful analogies to map the ideas. e.g., LSTM was described as the “SET” button in digital devices. Highly recommended read.
Bryan Proper
Oct 09, 2019 rated it liked it
This book does an excellent job of explaining the content for the lay person. It does not translate well to an audiobook. That is the reason for three stars. There are illustrations that are in the text version that would make the content more understandable. This can’t be conveyed through an audiobook.
Satheesh Payyanur
Nov 03, 2018 rated it liked it
Reasonably well written. It introduces the prominent figures/stalwarts who made advancements on machinelearning possible. But the book doesnt offer much to readers who already have the minimum understanding on AI/ML.
Apr 22, 2019 rated it really liked it
Shelves: audiobook, library, hoopla
Good overview of key concepts in machine learning and AI. Probably not the best book to listen to as it was just technical enough that spacing out for a minute means you've missed something important. ...more
Aug 25, 2019 rated it it was amazing  ·  review of another edition
Shelves: 2019-favorites
Highly readable and technically accurate account of the successes of AI, machine learning, and deep learning in recent years. The author strikes a good balance between detailed technical explanations and compelling storytelling. 5 out of 5.
Sep 23, 2018 rated it really liked it
Capable discussion of machine learning. I guess I wanted more futurism or philosophy.
Nick Dutton
Apr 17, 2019 rated it really liked it  ·  review of another edition
Solid intro into how machine learning works!
Eudy Guzman
Aug 11, 2019 rated it it was amazing
the title says it all. Highly recommend for anyone wanting to learn in depth about how far we've come and have yet to go in the machine learning world. ...more
Gino Mempin
If anyone's looking for a motivation or an overview of what to expect when getting into Machine Learning, this is the book to read. ...more
Iver Band
Oct 09, 2019 rated it it was amazing  ·  review of another edition
Very clear explanation of a wide range of AI and robotic technologies.
Khang Nguyễn
Oct 29, 2019 rated it it was amazing
Shelves: science
Wonderful book, a really good insight of how machine learning really works, the recent history of machine learning and how we came up with many types of algorithms to solve the real world problem...
Suraj Ahameed
to know how humans think to make machines to work for as
Feb 23, 2020 rated it it was amazing
A highly engaging overview of cutting edge theory and applications of machine learning.
Apr 03, 2020 rated it it was amazing
Very lucid; explains how AI works for a non-expert.
James Hennessy
Apr 06, 2020 rated it really liked it
Very good read for basics of emerging techonologies today.
Joshua Shumshere Leslie
Apr 09, 2020 rated it really liked it
Well written book about the advances in ML/AI over the past two decades. It also provides explanations and examples of terms used in the field. This is not a textbook, more a learning book.
Nasir Ali
May 01, 2020 rated it really liked it
A good reading I specially like the historical context of self driving car, Netflix recommendation algorithm... Overall am entertaining read!
Gage Abell
Jun 17, 2020 rated it liked it
A little technically dense but overall a good look into the brains behind modem machines
Jul 10, 2019 rated it it was amazing  ·  review of another edition
Shelves: 2019

Table of Contents
Foreword by Kevin Scott, CTO, Microsoft

1. The Secret of the Automaton
2. Self-Driving Cars and the DARPA Grand Challenge
3. Keeping within the Lanes: Perception in Self-Driving Cars
4. Yielding at Intersections: The Brain of a Self-Driving Car
5. Netflix and the Recommendation–Engine Challenge

6. Ensembles of Teams: The Netflix Prize Winners
7. Teaching Computers by Giving Them Treats
8. How to Beat Atari Games by Using Neural Networks
9. Artificial Neural Networks’ View of the W
tony chang
Mar 21, 2019 rated it it was amazing  ·  review of another edition
It reads like a group of related Wired or Ars Technica articles about major milestones in AI. It's a quick and easy read that Sean puts in easy to understand terms and analogies. ...more
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Sean Gerrish is a software engineer and machine learning geek. He has worked as an engineer at Teza Technologies and as an engineering manager for machine learning and data science teams at Google. He holds a PhD in machine learning from Princeton University.

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