Genius Makers: The Mavericks Who Brought A.I. to Google, Facebook, and the World
- Downloads：6895
- Type：Epub+TxT+PDF+Mobi
- Create Date：2021-04-13 09:31:39
- Update Date：2025-09-06
- Status：finish
- Author：Cade Metz
- ISBN：B08CD1M43L
- Environment：PC/Android/iPhone/iPad/Kindle
Reviews
Simone Scardapane
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Good overview of some of the main characters behind the "deep learning revolution"， all the way up to the 2019 Turing prize。 Some chapters feel slightly out of pace and you can feel the editorial team hurrying up in cobbling everything together， but the author is very balanced and also good at explaining some of the technical side。 Recommended reading for anyone working in the field or interested in the historical aspects。 Good overview of some of the main characters behind the "deep learning revolution"， all the way up to the 2019 Turing prize。 Some chapters feel slightly out of pace and you can feel the editorial team hurrying up in cobbling everything together， but the author is very balanced and also good at explaining some of the technical side。 Recommended reading for anyone working in the field or interested in the historical aspects。 。。。more
Ridhi Garg
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Many books proclaim that true artificial intelligence is on the horizon， and this expert overview makes a convincing case that genuine AI is…on the horizon。New York Times technology correspondent Metz tells his engrossing story through the lives of a dozen geniuses， scores of brilliant men （mostly）， and an ongoing， cutthroat industrial and academic arms race。 He begins with a history of neural networks， an idea developed in the 1950s when it became clear that sheer calculating speed would never Many books proclaim that true artificial intelligence is on the horizon， and this expert overview makes a convincing case that genuine AI is…on the horizon。New York Times technology correspondent Metz tells his engrossing story through the lives of a dozen geniuses， scores of brilliant men （mostly）， and an ongoing， cutthroat industrial and academic arms race。 He begins with a history of neural networks， an idea developed in the 1950s when it became clear that sheer calculating speed would never produce a smart computer。 As the author astutely points out， calling it “artificial intelligence” may be a mistake。 Today’s neural nets capable of “deep learning” don’t think， but they’re superb at pattern recognition。 A must-read， fully-up-to-date report on the holy grail of computing。 。。。more
JJ
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Absolutely an enjoyable and informative readingThis book is for anyone who is living in a world in which is AI is here to stay and go beyond anything the mankind has faced in its entire history。
Jacob Mainwaring
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I found this to be really interesting！ It did not go into much technical detail on how deep learning works but was more focused on its history and its role within the artificial intelligence community。 I liked hearing about some of the field’s big names， like Geoffrey Hinton， Yann Lecun， Ian Goodfellow， and Demis Hassabis。 More interesting， though， was the discussion of how researchers crossed over from academia into industry。 AI research labs at companies like Facebook and Google have redefined I found this to be really interesting！ It did not go into much technical detail on how deep learning works but was more focused on its history and its role within the artificial intelligence community。 I liked hearing about some of the field’s big names， like Geoffrey Hinton， Yann Lecun， Ian Goodfellow， and Demis Hassabis。 More interesting， though， was the discussion of how researchers crossed over from academia into industry。 AI research labs at companies like Facebook and Google have redefined the way those two partner together。 Lastly， I thought the contrast between how the US and China approach AI research was interesting， if not a bit concerning。 I don’t know enough about the topic to weigh in on how much farther deep learning will take us but its progress thus far cannot be ignored and I’m glad to have learned more about its evolution。 。。。more
Prateek Jain
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It is an amazing book for the AI enthusiasts， a must read
William
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An entertaining and accessible history on one of the most important technological breakthroughs of our generation。
Peter O'Kelly
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Some reviews to consider:t• https://www。nytimes。com/2021/03/19/bo。。。t• https://www。kirkusreviews。com/book-re。。。t• https://www。latimes。com/entertainment。。。t• https://www。washingtonpost。com/outloo。。。t• https://www。ft。com/content/52163178-0。。。 Some reviews to consider:t• https://www。nytimes。com/2021/03/19/bo。。。t• https://www。kirkusreviews。com/book-re。。。t• https://www。latimes。com/entertainment。。。t• https://www。washingtonpost。com/outloo。。。t• https://www。ft。com/content/52163178-0。。。 。。。more
Tathagat Varma
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The fast-evolving world of #artificialintelligencetechnology， especially led by #machinelearning， #deeplearning and a whole slew of newer innovations that have come about in last few years have had a long and interesting past。 In fact the whole story of how some of the fathers of AI worked hard to kill off the newly created #neuralnetworks back in 50s and 60s is an interesting story by itself。 This new book traces the history of AI right from its inception in mid-50s right to this date， and is a The fast-evolving world of #artificialintelligencetechnology， especially led by #machinelearning， #deeplearning and a whole slew of newer innovations that have come about in last few years have had a long and interesting past。 In fact the whole story of how some of the fathers of AI worked hard to kill off the newly created #neuralnetworks back in 50s and 60s is an interesting story by itself。 This new book traces the history of AI right from its inception in mid-50s right to this date， and is a great resource for anyone looking to understand how the world of research， academic， and business has been so tightly integrated， that has led to the third resurgence of the field of AI， following two #AIwinter before in 70s and 80s。 Surely， we have much better fundamentals this time， and coupled with the matching hardware power， hopefully the field of AI is poised for a much higher take-off than ever before。 。。。more
Moritz Mueller-Freitag
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How does it feel to see your life’s work go up in smoke？ In the early 2000s， the computational linguist Chris Brockett had a sudden panic attack when he realized that a new crop of machine learning methods would make his research obsolete。 The anxiety set in when it dawned on him that he had wasted nearly seven years of his life writing down linguistic rules for natural language processing。 His colleagues thought he was having a heart attack and rushed him to the hospital。 “My fifty-two-year-old How does it feel to see your life’s work go up in smoke？ In the early 2000s， the computational linguist Chris Brockett had a sudden panic attack when he realized that a new crop of machine learning methods would make his research obsolete。 The anxiety set in when it dawned on him that he had wasted nearly seven years of his life writing down linguistic rules for natural language processing。 His colleagues thought he was having a heart attack and rushed him to the hospital。 “My fifty-two-year-old body had one of those moments when I saw a future where I wasn’t involved，” he later reflected。Many AI researchers experienced a similar shock in 2012 when Geoff Hinton and two of his grad students showed that deep neural networks could beat state-of-the-art AI systems in image recognition。 Hinton belonged to a small group of academic contrarians – the “neural network underground” – who bet their careers on a concept that was long dismissed as a technological dead end。 “Neural networks are for people who don’t understand stats，” they were told。 But Hinton’s gang had the last laugh – much to the dismay of their detractors who had invested themselves in “shallow learning” methods。Progress， of course， didn’t stop with image recognition。 Since 2012， neural networks have achieved similar breakthroughs across previously intractable problems， ranging from machine translation and voice synthesis to solving the conundrum of protein folding。 These advances have changed the technology industry in profound ways and set off a global arms race for top AI talent。 It has also led to a fundamental shift in how software is being developed: instead of programming software by writing explicit instructions， we now increasingly train software by showing labeled examples。 The new mantra is to throw just enough training data at a problem until it’s solved。 I’ve witnessed this shift myself over the years when I co-founded a company with one of Hinton’s former doctoral students。Cade Metz’s new book， Genius Makers， chronicles the AI miracles of the past decade from the vantage point of its creators。 It’s a very readable and informative history of modern AI aimed at a general audience。 The great strength of the book is that it avoids the common pitfall of tipping into hyperbole。 Instead， it reminds us that technology always reflects the values， biases， and incentive systems of its makers。 Although the narrative holds few groundbreaking revelations for people who are active in the field， it’s still fun to read about a subject when you’ve met many of the key protagonists in the flesh。 And let’s be honest: Hinton’s oft-quoted wry sense of humor is worth the price of admission alone。 。。。more
Kaustubh Sule
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This book should be treated as history of AI/ ML starting with Frank Reozenblatt perceptron。 It gives you a great point of view about struggles and success of AI pioneers in implementing their thought process and belief。 A great read for those trying to make sense of applications of ML/AI in their own field as well as where it is heading
Mike
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AI is such a juggernaut today that it's hard to remember how little respect and attention it got in the 1980s and 1990s among computer scientists generally。 I began my career in earnest then， and no one I knew in academia or industry was working in the field。 After some signal failures to deliver in the 1970s， the entire field fell into disrepute。Metz does an exceptional job of chronicling the research that changed all that， and especially the key people who stubbornly stayed focused on the work AI is such a juggernaut today that it's hard to remember how little respect and attention it got in the 1980s and 1990s among computer scientists generally。 I began my career in earnest then， and no one I knew in academia or industry was working in the field。 After some signal failures to deliver in the 1970s， the entire field fell into disrepute。Metz does an exceptional job of chronicling the research that changed all that， and especially the key people who stubbornly stayed focused on the work。 He correctly highlights the key technical contributors as well -- advent of huge amounts of data， enormous distributed storage and compute capacity， the happy accident of GPUs designed for rendering video games working amazingly well on the math required by machine learning。 It's all written in a really accessible way。 He explains what convolutional neural networks are in a way that an ordinary person can understand。The book discusses the tension between folks who believe in artificial general intelligence and those who think that accomplishment is in the distant future。 The people debating that point， and doing the research， talked to Metz， and he uses their words directly to explain the different points of view。This is an excellent history， taking the field right up to the present day。 No doubt there will be plenty of fodder for a sequel， in ten or twenty years！ 。。。more
Abhilash
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It's hard to write a review of a non-fiction book。 It's always a mismatch of expectations and reality。 It's a good history book about AI from both academic and corporation pov。 It covers almost everything。 But it doth not offer insights or make predictions。 The author is a journalist and hence he never planned to or make claims about the path AI is to take。 If you are excited about AGI， this book brings you back on the ground。 Microsoft's response to AI vs that of Google and Facebook comes out r It's hard to write a review of a non-fiction book。 It's always a mismatch of expectations and reality。 It's a good history book about AI from both academic and corporation pov。 It covers almost everything。 But it doth not offer insights or make predictions。 The author is a journalist and hence he never planned to or make claims about the path AI is to take。 If you are excited about AGI， this book brings you back on the ground。 Microsoft's response to AI vs that of Google and Facebook comes out really well in this one。 Also， covers China's plan to dominate AI by 2030 and it's scary。 。。。more
Patrick Pilz
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