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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

    Mal Warwick

    A closely-linked network of several score brilliant men and a few women are pushing the boundaries of artificial intelligence research. You’ll meet many of these high-achieving and sometimes eccentric individuals in the pages of Genius Makers. You’ll get a glimpse inside Google, Facebook, Baidu, and other major institutions where most of the cutting-edge AI research is underway. And in these pages, you’ll gain perspective on the issues and uncertainties that trouble this rarefied community. In a A closely-linked network of several score brilliant men and a few women are pushing the boundaries of artificial intelligence research. You’ll meet many of these high-achieving and sometimes eccentric individuals in the pages of Genius Makers. You’ll get a glimpse inside Google, Facebook, Baidu, and other major institutions where most of the cutting-edge AI research is underway. And in these pages, you’ll gain perspective on the issues and uncertainties that trouble this rarefied community. In a more general sense, Genius Makers will also show how the shifting currents of peer pressure influence the course of scientific research. One approach among many The principal theme in this important new book is the emergence of the promising approach to artificial intelligence that has become dominant in the field. Called deep learning, it’s grounded in artificial neural networks, which are loosely modeled on the human brain. In a neural network, scientists link together units or nodes called artificial neurons. The patterns they form allow the machine to learn from experience in a way analogous to learning in humans. Scientists “train” neural networks by exposing them to massive amounts of data. For example, to “teach” a neural network to recognize cats, they might feed it millions of images of cats. In the process, the neural network acquires an accurate enough picture of cats that it’s able to produce a credible cat image of its own. It doesn’t “understand” cats, but it will recognize an image of one. The emergence of deep learning For decades, deep learning had few adherents in the sixty-year-old field of artificial intelligence. A competing approach called “symbolic AI” held sway. “Whereas neural networks learned tasks on their own by analyzing data, symbolic AI did not.” Only a handful of maverick scientists stubbornly persisted through the dark ages beginning in the 1970s before more powerful computers allowed their work on neural networks to live up to its promise. Suddenly, the barrier in AI research was broken. The key was an important peer-reviewed article in 2012. It was “one of the most influential papers in the history of computer science,” attracting more than 60,000 citations. The high-profile events that have brought AI to the world’s attention in recent years are all based on deep learning. For example, the defeat of the world’s top chess masters and Go champions. The increasing facility of machines in understanding spoken language. The advances made in self-driving cars. And the now-widespread use of face recognition. In Genius Makers, New York Times technology reporter Cade Metz profiles the scientists who made all this possible—for good or ill. Seven key players In an appendix labeled “The Players,” Metz lists sixty-one of the characters whose names appear in the book. Seven of these—all men—play central roles in the drama, but one stands out above the others. I’ll start with him, then list the other six in alphabetical order by last name. Geoff Hinton British-Canadian cognitive psychologist and computer scientist Geoff Hinton (born 1947) Is the grand old man of the researchers profiled in Genius Makers. If anything, he is the central figure in this story, the founding father of the deep learning movement. As Metz puts it, “Hinton and his students changed the way machines saw the world.” It was he who stubbornly continued to advocate for the use of neural networks in developing artificial intelligence in the face of near-universal disapproval within the field. A 1969 book by MIT legends Marvin Minsky and Seymour Papert was the cause. The book savagely attacked AI research using that approach and turned the tide against it for decades. In Metz’s words, Hinton is “the man who didn’t sit down.” A back injury had prevented him from sitting for seven years when Metz arrived to interview him in December 2012. And Metz describes the elaborate arrangements Hinton must make when he travels. It’s quite remarkable that the man could function at all. Hinton teaches at the University of Toronto. He joined Google in 2013 but lives and continues to work with his students in Canada. Hinton received the 2018 Turing Award, together with Yoshua Bengio and Yann LeCun, for their work on deep learning. He is the great-great-grandson of logician George Boole (1815-64). Boole’s work in mathematics (Boolean algebra) much later helped ground the new field of computer science. Yoshua Benguio French-born Yoshua Benguio (born 1964) is a computer science professor at the Université de Montréal. Along with Geoff Hinton and Yann LeCun, Benguio advanced the technology of artificial neural networks and deep learning in the 1990s and 2000s when the world’s AI community had turned their backs on the technique. Demis Hassabis Demis Hassabis (born 1976) is, in Metz’s words, a “British chess prodigy, game designer, and neuroscientist who founded DeepMind, a London AI start-up that would grow into the world’s most celebrated AI lab.” DeepMind was acquired by Google in 2014. Hassabis and his team at DeepMind developed the extraordinary AI named AlphaGo. In 2016, AlphaGo beat Lee Sedol, the world’s champion at Go, which many consider the world’s most difficult game. But the AI’s programming wasn’t limited to playing games. In 2020, DeepMind made significant advances in the problem of protein folding, expanding the boundaries of AI research in medical science. Alex Krizhevsky Alex Krizhevsky, born in Ukraine and raised in Canada, was a brilliant young protégé of Geoff Hinton at the University of Toronto. He played a leading role in developing computer vision, which is now central to face recognition and numerous other applications. Krizhevsky was one of Hinton’s partners in a startup they sold to Google in 2013. Together with Geoff Hinton, he “showed that a neural network could recognize common objects with an accuracy beyond any other technology.” Krizhevsky joined Google Brain and the Google self-driving car project but left the company in 2017. His work is widely cited by computer scientists. Yann LeCun Yann LeCun was born in France in 1960 but for decades has worked in the United States, first at Bell Labs and then at New York University, where he holds an endowed chair in mathematical sciences. In addition to teaching in New York, he also oversees Facebook’s Artificial Intelligence Research Lab. Like others portrayed in these pages, LeCun long collaborated with Geoff Hinton on deep learning before signing up with Facebook. Andrew Ng Andrew Ng, born in Britain in 1976, is an adjunct professor at Stanford University with a long colorful history in machine learning and AI. He co-founded Google’s deep learning research team Google Brain; managed Baidu‘s Silicon Valley lab as the Chinese company’s chief scientist; co-founded the pioneering MOOC (massive open online course) company Coursera, through which he taught more than 2.5 million students online. And since 2018 he has run a venture capital fund that backs startups in artificial intelligence. Ilya Sutskever Canadian computer scientist Ilya Sutskever, another of Geoff Hinton’s brilliant young protégés, gravitated with him to Google Brain when they sold their startup company to the Silicon Valley giant. But he left Google to join OpenAI, an AI lab in San Francisco backed by Elon Musk to compete with Google’s London-based DeepMind. Sutskever has made important contributions to the field of deep learning, among them co-inventing AlphaGo. About the author Cade Metz covers AI research, driverless cars, robotics, virtual reality, and other emerging technologies for the New York Times. He had earlier worked for Wired magazine. Genius Makers is his first book. James Fallows’ review of the book, along with a second one on AI by another Times reporter, recently appeared in the paper’s Sunday Book Review. Fallows explains, “Much of Metz’s story runs from excitement for neural networks in the early 1960s, to an ‘A.I. winter’ in the 1970s, when that era’s computers proved too limited to do the job, to a recent revival of a neural-network approach toward ‘deep learning,’ which is essentially the result of the faster and more complex self-correction of today’s enormously capable machines.” In the notes at the conclusion of the text, Metz describes the research he conducted in writing Genius Makers. “This book is based,” he writes, “on interviews with more than four hundred people over the course of the eight years I’ve been reporting on artificial intelligence for Wired magazine and then the New York Times, as well as more than a hundred interviews conducted specifically for the book. Most people have been interviewed more than once, some several times or more.”

  4. 5 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.

  5. 5 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.

  6. 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!

  7. 5 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.

  8. 4 out of 5

    Jacob

    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.

  9. 4 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.

  10. 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.

  11. 5 out of 5

    Saket Nihal

    Whenever I used to think of the origins of AI, I used to believe that it would have been conceived at one of the secret labs of US or at either one of the big tech companies. I was totally bewildered to know that one of the most ground breaking technology of the decade had such a humble beginnings. Who would have thought that AI might have been extinct as a concept had it not been the sheer persistence of belief of one professor and his set of students? A great book for anyone interested to know Whenever I used to think of the origins of AI, I used to believe that it would have been conceived at one of the secret labs of US or at either one of the big tech companies. I was totally bewildered to know that one of the most ground breaking technology of the decade had such a humble beginnings. Who would have thought that AI might have been extinct as a concept had it not been the sheer persistence of belief of one professor and his set of students? A great book for anyone interested to know the history behind AI and the people who transformed AI from a fancy mathematical concept to a technology being used by the big tech to solve practical problems.

  12. 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...

  13. 5 out of 5

    Clark B. Herring

    I have been reading about Neural networks since the 1980s. This is a well written book that charts the progress and set back for both Neural networks and AI. This book is about the people -and they are largely men -who have created AI and less about AI itself. I found the history fascinating because I recall thinking in the 1990s what ever happened to neural networks. about 80% of the book takes place after 2010 because that was when there was enough computational power to realize the promise of I have been reading about Neural networks since the 1980s. This is a well written book that charts the progress and set back for both Neural networks and AI. This book is about the people -and they are largely men -who have created AI and less about AI itself. I found the history fascinating because I recall thinking in the 1990s what ever happened to neural networks. about 80% of the book takes place after 2010 because that was when there was enough computational power to realize the promise of neural networks.

  14. 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.

  15. 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.

  16. 5 out of 5

    Ali

    Gives a good overview of the history of AI and its development along with the competition between the major players in tech. Good to spark your interest in AI like myself (I had to Google and find out more about Alpha Go for example), but this is purely about the people behind their tech and their story - I still have no idea how does an Artificial Neural Network is even meant to work and what does even "training" one entails. Gives a good overview of the history of AI and its development along with the competition between the major players in tech. Good to spark your interest in AI like myself (I had to Google and find out more about Alpha Go for example), but this is purely about the people behind their tech and their story - I still have no idea how does an Artificial Neural Network is even meant to work and what does even "training" one entails.

  17. 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

  18. 4 out of 5

    Roland Glotzer

    A fascinating book on the history of artificial intelligence. Reads like a science fiction novel though none of it is fiction! Amazing to see how much has happened in the last 20 years and a bit scary to anticipate what may happen next : great things in healthcare but worrying developments in militay automation.

  19. 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.

  20. 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.

  21. 5 out of 5

    Prateek Jain

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

  22. 4 out of 5

    William

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

  23. 5 out of 5

    Graham Annett

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

  24. 4 out of 5

    Rishad Sadikot

  25. 4 out of 5

    Karthik Kannan

  26. 4 out of 5

    Torkel

  27. 4 out of 5

    Ye

  28. 5 out of 5

    Clyde D

  29. 4 out of 5

    Steph Hughes-Fitt

  30. 5 out of 5

    Greg Allen

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