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How Smart Machines Think

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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 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 how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today's machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world—and to play Atari video games better than humans. He explains Watson's famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution—at least for now. Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people.


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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 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 how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today's machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world—and to play Atari video games better than humans. He explains Watson's famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution—at least for now. Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people.

30 review for How Smart Machines Think

  1. 4 out of 5

    Brian Clegg

    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 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 software architectures are structured and how the different components - for example neural networks - do their job, but it isn't anything too scary if you take it slowly. One thing that comes across very strongly, despite the AI types' insistence that their programs are of general use, is how very specifically tailored programs like the AlphaGo software that beat champions at the game Go, and the Watson computer that won at the US TV quiz show Jeopardy! were - incredibly finely designed to meet use and that use only. The reason I make the remark about dumb machines is that what doesn't come across sufficiently in Sean Gerrish's book is that, because these programs are not in any sense intelligent, when they get things wrong, they often get things dramatically wrong. So some of Watson's answers on Jeopardy! did not make any sense at all. Similarly, image recognition software can be fooled by apparently abstract patterns that happen to have the right components to appear to be a distinguishable object. And when you bear in mind we're suggesting putting this kind of software in charge of cars that 'getting it dramatically wrong' bit is more than a little unnerving. There was, though, a great section on the development of self-driving cars, from the original feeble attempts, where all the competitors in a race failed before completing 10 percent of the course, through to more recent and more successful versions that can handle basic traffic scenarios - though it would have been nice if Gerrish had gone beyond the old prize challenges to describe what the latest Google and Uber vehicles do. (It may be that their approaches are too proprietary.) However, what was missing was any serious assessment of the big problems still faced. There have been two excellent books recently on the huge holes in AI that practitioners rarely admit to - The AI Delusion and Common Sense, The Turing Test and the Quest for Real AI - Gerrish would have produced an even better book if he could have addressed the concerns that these books raise. Even so, in How Smart Machines Think we have a hugely informative and very readable book for anyone with an interest in finding out just what the much-trumpeted AI systems really do, and what lies beneath the hype.

  2. 4 out of 5

    Robert

    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 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 language processing, and reinforcement learning but, at least in the ones I've read, search algorithms get short shrift. As powerful as some of the newer algorithms might be, less glamorous techniques like search will be key in the development of many AIs.

  3. 5 out of 5

    Jay

    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 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 developer to enjoy this.

  4. 5 out of 5

    Evan Nordquist

    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 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 having a contingency plan for this occurrence, spun it's tires in the desert sand until they caught fire. The future looked bleak for self driving cars in 2004. Then the second year of the contest (2005), along comes Sebastian Thrun who uses machine learning to teach his car "Stanley" (from Stanford University), how to react to road obstacles. It was 'supervised machine learning', where the car would be trained by having it observe hours of human drivers reacting to different road situations. Each of the 23 vehicles in this second challenge were started at 5 minutes intervals so that they wouldn't run into one another. Since the sensors at the time could only see a few 10's of meters ahead, and it makes no sense to drive faster than the vehicles could see, most vehicles would drive no faster than 25mph.) Stanley, on the other hand, also had cameras and AI to detect if it was looking at an open straightaway or not. If yes, then go 40mph until it is no longer on a straightaway. Stanley had to be paused 3 times during the race because it kept catching up to the cars ahead of it. Eventually the referees just had to pause everyone else to let Stanley go through. Instead of no cars finishing, this time 5 cars finished the 200+km race. That was an early chapter on machine learning, there are fascinating chapters later on more and more sophisticated AI like unsupervised machine learning and deep learning. Thrilling stuff.

  5. 4 out of 5

    Khiana

    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 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 understand. I really appreciated the way that he delved into the stories of the people behind these inventions. Even if sometimes it felt a bit like name dropping the big wigs in the field, the stories of how some of these inventions (like self driving cars) even came about is fascinating. Even though I was very much alive during many of these events, as a kid they somehow passed me by. I definitely recommend this book even if you only have a passing interest in the field as it's both technical and relatable.

  6. 4 out of 5

    Anthony

    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

  7. 5 out of 5

    BCS

    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 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 key ideas surrounding how smart machines perceive and interact with the world around them using a number of case studies. The first case study in this section is based around self-driving cars and the 2004 DARPA robot car race through the Mojave Desert. The operation and technology behind one competitor (the self-driving Humvee) explains how its construction keeps it on the road, and how neural networks allowed it to observe and navigate through its external environment. The second case study is contained in Chapter 5. It deals with the Netflix recommendation engine challenge. The grand prize on offer was $1M to the team coming up with a movie recommendation engine for their physical DVD rental service in order to better satisfy customer’s requirements. The teams and their approach to the Netflix challenge and prize winners are discussed in the chapter which follows. The next short section of the book is taken up with explaining how reinforcement learning can be employed to enhance machine thinking. This leads into three chapters on the technology and use of neural networks followed by a close look at how computers are set up to play games such as Alpha Go and Deep Blue. Finally, the author outlines the difficulties in constructing intelligent agents to play video games that up until now have not been solved, using StarCraft as an example. I found the book interesting and entertaining. The technical explanations of the theory behind the technology are supplemented by easy to follow diagrams and illustrations. The book will be of interest to anyone wishing to take their understanding of thinking machines to a higher level. It also contains annotated references for anyone with a need to continue with further study. I award the book 7 out 10 for its coverage of the topic and excellent references. Review by Jim McGhie MBA, CEng, MBCS, CITP

  8. 5 out of 5

    Cade

    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 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 about how the applications discussed do the things they do to make headlines. Make headlines is not a figure of speech. Every single one of these topics is one I recognize from major news headlines, and I think they are well chosen to cover the span of applications of AI that are at the forefront of our collective consciousness. The main way this book is able to go into more depth without becoming opaquely technical is through the extensive use of diagrams. These diagrams are clean and easy to follow but informative, reinforcing the truism that a picture is worth a thousand words. Consider reading the text and not (only) listening to the audiobook. My main complaint is that after reading this book I'd like to understand even more about these fascinating subjects although it isn't really fair to ask that of a book intended for a non-technical audience.

  9. 4 out of 5

    Gian Baltazar

    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 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 statistical learning to get the whole idea of this book. But overall, this book is a great summary written with good words so any of us can give his point of view of what is gonna be the next tech-innovation of tomorrow

  10. 4 out of 5

    Sabrina

    01001110 01101111 01110111 00100000 01100100 01101111 01110111 01101110 00100000 01110100 01101111 00100000 01101111 01110101 01110010 00100000 01100011 01101111 01101101 01110000 01110101 01110100 01100101 01110010 00100000 01101111 01110110 01100101 01110010 01101100 01101111 01110010 01100100 01110011 00101110 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 01001110 01101111 01110111 00100000 01100100 01101111 01110111 01101110 00100000 01110100 01101111 00100000 01101111 01110101 01110010 00100000 01100011 01101111 01101101 01110000 01110101 01110100 01100101 01110010 00100000 01101111 01110110 01100101 01110010 01101100 01101111 01110010 01100100 01110011 00101110 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 particulars of how neural networks function in gaming & applications. Stanley Watson Deep Blue Google Brain Deep Mind OpenAI Alphabet NVIDIA

  11. 5 out of 5

    Nilendu Misra

    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.

  12. 4 out of 5

    Bryan Proper

    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.

  13. 5 out of 5

    Satheesh Payyanur

    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.

  14. 5 out of 5

    Sam

    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. 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.

  15. 4 out of 5

    Oge

    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.

  16. 4 out of 5

    Francine

    Capable discussion of machine learning. I guess I wanted more futurism or philosophy.

  17. 5 out of 5

    Nick Dutton

    Solid intro into how machine learning works!

  18. 4 out of 5

    Eudy Guzman

    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. 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.

  19. 4 out of 5

    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. 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.

  20. 5 out of 5

    Iver Band

    Very clear explanation of a wide range of AI and robotic technologies.

  21. 5 out of 5

    Khang Nguyễn

    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...

  22. 4 out of 5

    Suraj Ahameed

    to know how humans think to make machines to work for as

  23. 5 out of 5

    Matt

    A highly engaging overview of cutting edge theory and applications of machine learning.

  24. 4 out of 5

    E.G.

    Very lucid; explains how AI works for a non-expert.

  25. 5 out of 5

    James Hennessy

    Very good read for basics of emerging techonologies today.

  26. 5 out of 5

    Joshua Shumshere Leslie

    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.

  27. 5 out of 5

    Nasir Ali

    A good reading I specially like the historical context of self driving car, Netflix recommendation algorithm... Overall am entertaining read!

  28. 5 out of 5

    Gage Abell

    A little technically dense but overall a good look into the brains behind modem machines

  29. 4 out of 5

    azamaali

    Table of Contents Foreword by Kevin Scott, CTO, Microsoft Preface 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 Table of Contents Foreword by Kevin Scott, CTO, Microsoft Preface 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 World 10. Looking Under the Hood of Deep Neural Networks 11. Neural Networks that Can Hear, Speak, and Remember 12. Understanding Natural Language (and Jeopardy! Questions) 13. Mining the Best Jeopardy! Answer 14. Brute-Force Search Your Way to a Good Strategy 15. Expert-Level Play for the Game of Go 16. Real-Time AI and StarCraft 17. Five Decades (or More) from Now

  30. 4 out of 5

    tony chang

    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. 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.

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