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This colorful page-turner puts artificial intelligence into a human perspective. Through the lives of Geoff Hinton and other major players, Metz explains this transformative technology and makes the quest thrilling. --Walter Isaacson, author of The Code Breaker Recipient of starred reviews in both Kirkus and Library Journal THE UNTOLD TECH STORY OF OUR TIME What does it m This colorful page-turner puts artificial intelligence into a human perspective. Through the lives of Geoff Hinton and other major players, Metz explains this transformative technology and makes the quest thrilling. --Walter Isaacson, author of The Code Breaker Recipient of starred reviews in both Kirkus and Library Journal THE UNTOLD TECH STORY OF OUR TIME What does it mean to be smart? To be human? What do we really want from life and the intelligence we have, or might create? With deep and exclusive reporting, across hundreds of interviews, New York Times Silicon Valley journalist Cade Metz brings you into the rooms where these questions are being answered. Where an extraordinarily powerful new artificial intelligence has been built into our biggest companies, our social discourse, and our daily lives, with few of us even noticing. Long dismissed as a technology of the distant future, artificial intelligence was a project consigned to the fringes of the scientific community. Then two researchers changed everything. One was a sixty-four-year-old computer science professor who didn't drive and didn't fly because he could no longer sit down--but still made his way across North America for the moment that would define a new age of technology. The other was a thirty-six-year-old neuroscientist and chess prodigy who laid claim to being the greatest game player of all time before vowing to build a machine that could do anything the human brain could do. They took two very different paths to that lofty goal, and they disagreed on how quickly it would arrive. But both were soon drawn into the heart of the tech industry. Their ideas drove a new kind of arms race, spanning Google, Microsoft, Facebook, and OpenAI, a new lab founded by Silicon Valley kingpin Elon Musk. But some believed that China would beat them all to the finish line. Genius Makers dramatically presents the fierce conflict between national interests, shareholder value, the pursuit of scientific knowledge, and the very human concerns about privacy, security, bias, and prejudice. Like a great Victorian novel, this world of eccentric, brilliant, often unimaginably yet suddenly wealthy characters draws you into the most profound moral questions we can ask. And like a great mystery, it presents the story and facts that lead to a core, vital question: How far will we let it go?


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This colorful page-turner puts artificial intelligence into a human perspective. Through the lives of Geoff Hinton and other major players, Metz explains this transformative technology and makes the quest thrilling. --Walter Isaacson, author of The Code Breaker Recipient of starred reviews in both Kirkus and Library Journal THE UNTOLD TECH STORY OF OUR TIME What does it m This colorful page-turner puts artificial intelligence into a human perspective. Through the lives of Geoff Hinton and other major players, Metz explains this transformative technology and makes the quest thrilling. --Walter Isaacson, author of The Code Breaker Recipient of starred reviews in both Kirkus and Library Journal THE UNTOLD TECH STORY OF OUR TIME What does it mean to be smart? To be human? What do we really want from life and the intelligence we have, or might create? With deep and exclusive reporting, across hundreds of interviews, New York Times Silicon Valley journalist Cade Metz brings you into the rooms where these questions are being answered. Where an extraordinarily powerful new artificial intelligence has been built into our biggest companies, our social discourse, and our daily lives, with few of us even noticing. Long dismissed as a technology of the distant future, artificial intelligence was a project consigned to the fringes of the scientific community. Then two researchers changed everything. One was a sixty-four-year-old computer science professor who didn't drive and didn't fly because he could no longer sit down--but still made his way across North America for the moment that would define a new age of technology. The other was a thirty-six-year-old neuroscientist and chess prodigy who laid claim to being the greatest game player of all time before vowing to build a machine that could do anything the human brain could do. They took two very different paths to that lofty goal, and they disagreed on how quickly it would arrive. But both were soon drawn into the heart of the tech industry. Their ideas drove a new kind of arms race, spanning Google, Microsoft, Facebook, and OpenAI, a new lab founded by Silicon Valley kingpin Elon Musk. But some believed that China would beat them all to the finish line. Genius Makers dramatically presents the fierce conflict between national interests, shareholder value, the pursuit of scientific knowledge, and the very human concerns about privacy, security, bias, and prejudice. Like a great Victorian novel, this world of eccentric, brilliant, often unimaginably yet suddenly wealthy characters draws you into the most profound moral questions we can ask. And like a great mystery, it presents the story and facts that lead to a core, vital question: How far will we let it go?

30 review for Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World

  1. 5 out of 5

    Will Byrnes

    [In 2016] Ed Boyton, a Princeton University professor who specialized in nascent technologies for sending information between machines and the human brain…told [a] private audience that scientists were approaching the point where they could create a complete map of the brain and then simulate it with a machine. The question was whether the machine, in addition to acting like a human, would actually feel what it was like to be human. This, they said, was the same question explored in Westworld [In 2016] Ed Boyton, a Princeton University professor who specialized in nascent technologies for sending information between machines and the human brain…told [a] private audience that scientists were approaching the point where they could create a complete map of the brain and then simulate it with a machine. The question was whether the machine, in addition to acting like a human, would actually feel what it was like to be human. This, they said, was the same question explored in Westworld. AI, Artificial Intelligence, is a source of active concern in our culture. Tales abound in film, television, and written fiction about the potential for machines to exceed human capacities for learning, and ultimately gain self-awareness, which will lead to them enslaving humanity, or worse. There are hopes for AI as well. Language recognition is one area where there has been growth. However much we may roll our eyes at Siri or Alexa’s inability to, first, hear, the words we say properly, then interpret them accurately, it is worth bearing in mind that Siri was released a scant ten years ago, in 2011, Alexa following in 2014. We may not be there yet, but self-driving vehicles are another AI product that will change our lives. It can be unclear where AI begins and the use of advanced algorithms end in the handling of our on-line searching, and in how those with the means use AI to market endless products to us. Cade Metz – image from Wired So what is AI? Where did it come from? What stage of development is it currently at and where might it take us? Cade Metz, late of Wired Magazine and currently a tech reporter with the New York Times, was interested in tracking the history of AI. There are two sides to the story of any scientific advance, the human and the technological. No chicken and egg problem to be resolved here, the people came first. In telling the tales of those, Metz focuses on the brightest lights in the history of AI development, tracking their progress from the 1950s to the present, leading us through the steps, and some mis-steps, that have brought us to where we are today, from a seminal conference in the late 1950s to Frank Rosenblatt’s Perceptron in 1958, from the Boltzmann Machine to the development of the first neural network, SNARC, cadged together from remnant parts of old B-24s by Marvin Minsky, from the AI winter of governmental disinvestment that began in 1971 to its resumption in the 1980s, from training machines to beat the most skilled humans at chess, and then Go, to training them to recognize faces, from gestating in universities to being hooked up to steroidal sources of computing power at the world’s largest corporations, from early attempts to mimic the operations of the human brain to shifting to the more achievable task of pattern recognition, from ignoring social elements to beginning to see how bias can flow through people into technology, from shunning military uses to allowing, if not entirely embracing them. This is one of 40 artificial neurons used in Marvin Minsky’s SPARC machine - image from The Scientist Metz certainly has had a ringside seat for this, drawing from hundreds of interviews he conducted with the players in his reportorial day jobs, eight years at Wired and another two at the NY Times. He did another hundred or so interviews just for the book. Some personalities shine through. We meet Geoffrey Hinton in the prologue, as he auctions his services (and the services of his two assistants) off to the highest corporate bidder, the ultimate figure a bit startling. Hinton is the central figure in this AI history, a Zelig-like-character who seems to pop up every time there is an advance in the technology. He is an interesting, complicated fellow, not just a leader in his field, but a creator of it and a mentor to many of the brightest minds who followed. It must have helped his recruiting that he had an actual sense of humor. He faced more than his share of challenges, suffering a back condition that made it virtually impossible for him to sit. Makes those cross country and trans-oceanic trips by train and plane just a wee bit of a problem. He suffered in other ways as well, losing two wives to cancer, providing a vast incentive for him to look at AI and neural networking as tools to help develop early diagnostic measures for diverse medical maladies. Marvin Minsky in a lab at M.I.T. in 1968.Credit...M.I.T. - image and caption from NY Times Where there are big ideas there are big egos, and sometimes an absence of decency. At a 1966 conference, when a researcher presented a report that did not sit well with Marvin Minsky, he interrupted the proceedings from the floor at considerable personal volume. “How can an intelligent young man like you,” he asked, “waste your time with something like this?” This was not out of character for the guy, who enjoyed provoking controversy, and, clearly, pissing people off. He single-handedly short-circuited a promising direction in AI research with his strident opposition. Skynet’s Employee of the month One of the developmental areas on which Metz focuses is deep learning, namely, feeding vast amounts of data to neural networks that are programmed to analyze the incomings for commonalities, in order to then be able to recognize unfamiliar material. For instance, examine hundreds of thousands of images of ducks and the system is pretty likely to be able to recognize a duck when it sees one. Frankly, it does not seem all that deep, but it is broad. Feeding a neural net vast quantities of data in order to train it to recognize particular things is the basis for a lot of facial recognition software in use today. Of course, the data being fed into the system reflects the biases of those doing the feeding. Say, for instance, that you are looking to identify faces, and most of the images that have been fed in are of white people, particularly white men. In 2015, when Google’s foto recognition app misidentified a black person as a gorilla, Google’s response was not to re-work its system ASAP, but to remove the word “gorilla” from its AI system. So, GIGO rules, fed by low representation by women and non-white techies. Metz addresses the existence of such inherent bias in the field, flowing from tech people in the data they use to feed neural net learning, but it is not a major focus of the book. He addresses it more directly in interviews. Frank Rosenblatt and his Perceptron - image from Cornell University On the other hand, by feeding systems vast amounts of information, it may be possible, for example, to recognize early indicators of public health or environmental problems that narrower examination of data would never unearth, and might even be able to give individuals a heads up that something might merit looking into. He gives a lot of coverage to the bouncings back and forth of this, that, and the other head honcho researcher from institution to institution, looking at why such changes were made. A few of these are of interest, like why Hinton crossed the Atlantic to work, or why he moved from the states to Canada, and then stayed where he was based once he settled, regardless of employer. But a lot of the personnel movement was there to illustrate how strongly individual corporations were committed to AI development. This sometimes leads to odd, but revealing, images, like researchers having been recruited by a major company, and finding when they get there, that the equipment they were expected to use was trivial compared to the project they were working on. When researchers realized that running neural networks would require vast numbers of Graphics Processing Units, GPUs (comparable to the Central Processing Units (CPUs) that are at the heart of every computer, but dedicated to a narrower range of activities) some companies dove right in while others balked. This is the trench warfare that I found most interesting, the specific command decisions that led to or impeded progress. Rehoboam – the quantum supercomputer at the core of WestWorld - Image from The Sun There are a lot of names in The Genius Makers. I would imagine that Metz and his editors pared quite a few out, but it can be a bit daunting at times, trying to figure out which ones merit retaining, unless you already know that there is a manageable number of these folks. It can slow down reading. It would have been useful for Dutton to have provided a graphic of some sort, a timeline indicating this idea began here, that idea began then, and so on. It is indeed possible that such a welcome add-on is present in the final hardcover book. I was working from an e-ARE. Sometimes the jargon was just a bit too much. Overall, the book is definitely accessible for the general, non-technical, reader, if you are willing to skip over a phrase and a name here and there, or enjoy, as I do, looking up EVERYTHING. The stories Metz tells of these pioneers, and their struggles are worth the price of admission, but you will also learn a bit about artificial intelligence (whatever that is) and the academic and corporate environments in which AI existed in the past, and is pursued today. You will not get a quick insight into what AI really is or how it works, but you will learn how what we call AI today began and evolved, and get a taste of how neural networking consumes vast volumes of data in a quest to amass enough knowledge to make AI at least somewhat…um…knowledgeable. Intelligence is a whole other thing, one of the dreams that has eluded developers and concerned the public. It is one of the ways in which AI has always been bedeviled by the curse of unrealistic expectations. (left to right) Yann LeCun, Geoffrey Hinton, Yoshua Bengio - Image from Eyerys Metz is a veteran reporter, so knows how to tell stories. It shows in his glee at telling us about this or that event. He includes a touch of humor here and there, a lightly sprinkled spice. Nothing that will make you shoot your coffee out your nose, but enough to make you smile. Here is an example. …a colleague introduced [Geoff Hinton] at an academic conference as someone who had failed at physics, dropped out of psychology, and then joined a field with no standards at all: artificial intelligence. It was a story Hinton enjoyed repeating, with a caveat. “I didn’t fail at physics and drop out of psychology,” he would say. “I failed at psychology and dropped out of physics—which is far more reputable.” The Genius Makers is a very readable bit of science history, aimed at a broad public, not the techie crowd, who would surely be demanding a lot more detail in the theoretical and implementation ends of decision-making and the construction of hardware and software. It will give you a clue as to what is going on in the AI world, and maybe open your mind a bit to what possibilities and perils we can all look forward to. There are many elements involved in AI. But the one (promoted by Elon Musk) we tend to be most concerned about is that it will develop, frighteningly portrayed in many sci-fi films and TV series, as a dark, all-powerful entity driven to subjugate weak humans. This is called AGI, for Artificial General Intelligence and is something that we do not know how to achieve. Bottom line for that is pass the popcorn and enjoy the show. Skynet may take over in one fictional future, but it ain’t gonna happen in our real one any time soon. Review posted – April 16, 2021 Publication date – March 16, 2021 I received an e-book ARE from Dutton in return for…I’m gonna need a lot more data before I can answer that accurately. ==========In the summer of 2019 GR reduced the allowable review size by 25%, from 20,000 to 15,000 characters. In order to accommodate the text beyond that I have moved it to the comments section directly below.

  2. 5 out of 5

    Moritz Mueller-Freitag

    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.

  3. 4 out of 5

    Patrick Pilz

    I think Cade Metz writes an important book here. As a top journalist, he covers in this latest book the the story of the people who made Artificial Intelligence what it is today. This is rather somber reporting, in which Cade Metz just lays out the facts along with condensed memoirs of all the main actors who brought us to where we are today. His writing is stellar and the journey interesting. Most importantly, Cade choses to keep the technical details in the background, which makes this book ve I think Cade Metz writes an important book here. As a top journalist, he covers in this latest book the the story of the people who made Artificial Intelligence what it is today. This is rather somber reporting, in which Cade Metz just lays out the facts along with condensed memoirs of all the main actors who brought us to where we are today. His writing is stellar and the journey interesting. Most importantly, Cade choses to keep the technical details in the background, which makes this book very accessible for anyone with any background. It does an ok job on balancing the rewards and benefits while also outlining some dangers and limitations. You can certainly tell that he is more in the camp of proponents of AI, but he is not ignorant of the risks either. All in all a book that deserves top spots on the non-fiction bestseller lists, just like "Tools and Weapons" by Brad Smith and Kai-Fu Lee's "AI Superpowers", probably the great read of the year on this subject.

  4. 4 out of 5

    Abhilash

    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.

  5. 5 out of 5

    Mike

    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!

  6. 4 out of 5

    Tathagat Varma

    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.

  7. 4 out of 5

    Jacob Mainwaring

    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.

  8. 5 out of 5

    Ridhi Garg

    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.

  9. 5 out of 5

    Ty

    While this is the author's first book, he has been a writer for Wired magazine and the New York Times for many years, so I was familiar with his work and was looking forward to the book. While the book often reads like a series of in-depth magazine articles, the result is very good. Metz takes the many complex technical topics around Artificial Intelligence and explains them well, without even a single equation. Perhaps he focuses too much on some of the big personalities in the field, but it is While this is the author's first book, he has been a writer for Wired magazine and the New York Times for many years, so I was familiar with his work and was looking forward to the book. While the book often reads like a series of in-depth magazine articles, the result is very good. Metz takes the many complex technical topics around Artificial Intelligence and explains them well, without even a single equation. Perhaps he focuses too much on some of the big personalities in the field, but it is kind of refreshing to see the story of the uber-nerds being told. I highly recommend this book for anyone who wants to understand more about what is going on at the leading edge of technology today.

  10. 4 out of 5

    Peter O'Kelly

    Some reviews to consider: • https://www.nytimes.com/2021/03/19/bo... • https://www.kirkusreviews.com/book-re... • https://www.latimes.com/entertainment... • https://www.washingtonpost.com/outloo... • https://www.ft.com/content/52163178-0... Some reviews to consider: • https://www.nytimes.com/2021/03/19/bo... • https://www.kirkusreviews.com/book-re... • https://www.latimes.com/entertainment... • https://www.washingtonpost.com/outloo... • https://www.ft.com/content/52163178-0...

  11. 5 out of 5

    Stephanie Zhang

    Even though nothing new for me from a technology perspective since I'm quite familiar already with most ML/DL models, the book is still very thought-provoking. Makes me reflect on my career decisions. It just dawned on me how big of a difference the industry and field one's career and the timing can have. If you are in the AI world today, very likely you are hot on the market and can easily make a big impact. Whereas being in the AI field 50 years ago, you'd find it hard to even get a job. Even though nothing new for me from a technology perspective since I'm quite familiar already with most ML/DL models, the book is still very thought-provoking. Makes me reflect on my career decisions. It just dawned on me how big of a difference the industry and field one's career and the timing can have. If you are in the AI world today, very likely you are hot on the market and can easily make a big impact. Whereas being in the AI field 50 years ago, you'd find it hard to even get a job.

  12. 4 out of 5

    Simone Scardapane

    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.

  13. 4 out of 5

    Kaustubh Sule

    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

  14. 4 out of 5

    Mark Bergen

    Neural nets! Backpropagation! Generative adversarial networks! All this math and code that increasingly runs our lives and is exceedingly difficult to understand -- people have tried explaining it to me many times. Nothing really stuck until I read Cade Metz's lucid, absurdly thorough and enjoyable book. Neural nets! Backpropagation! Generative adversarial networks! All this math and code that increasingly runs our lives and is exceedingly difficult to understand -- people have tried explaining it to me many times. Nothing really stuck until I read Cade Metz's lucid, absurdly thorough and enjoyable book.

  15. 4 out of 5

    JJ

    Absolutely an enjoyable and informative reading This 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.

  16. 5 out of 5

    Prateek Jain

    It is an amazing book for the AI enthusiasts, a must read

  17. 5 out of 5

    William

    An entertaining and accessible history on one of the most important technological breakthroughs of our generation.

  18. 4 out of 5

    Graham Annett

    i enjoyed the personal backgrounds on most of the researchers i already know of

  19. 4 out of 5

    Rishad Sadikot

  20. 5 out of 5

    Torkel

  21. 5 out of 5

    Steph Hughes-Fitt

  22. 4 out of 5

    Greg Allen

  23. 5 out of 5

    David Ward

  24. 5 out of 5

    Matteo

  25. 4 out of 5

    ross mastroianni

  26. 4 out of 5

    Brian MacAskill

  27. 5 out of 5

    Pearse Keane

  28. 5 out of 5

    Shivakanth

  29. 5 out of 5

    Dave

  30. 4 out of 5

    Saul Klein

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