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Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World Hardcover – March 16, 2021
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—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 among 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?
- Print length384 pages
- LanguageEnglish
- PublisherDutton
- Publication dateMarch 16, 2021
- Dimensions6.22 x 1.26 x 9.27 inches
- ISBN-101524742678
- ISBN-13978-1524742676
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Editorial Reviews
Review
—The Los Angeles Times
"[An] engaging new book... [Metz’s] straightforward writing perfectly translates industry jargon for technologically un-savvy readers (like me) who might be unfamiliar with what it means for a machine to engage in 'deep learning' or master tasks through its own experiences."
—Christian Science Monitor
"Carving a narrative out of a complex and ever-changing cast of characters... The book is filled with enlightening anecdotes that add texture and drama to the story. Genius Makers opens with Geoffrey Hinton, the Brit turned Canadian who is widely recognized as having played the most critical role in developing deep learning, the branch of AI that is changing the world today."
—Washington Post
"A ringside seat at what may turn out to be the pivotal episode in human history... Metz has a breezy style that is easy and fun to read... undeniably charming."
—Forbes
"Colorful and readable... draws on extensive access and meticulous research."
—Financial Times
"Valuably suggests a framework for the right questions to ask now about AI and its use. Genius Makers is about the people who have built the AI world."
—James Fallows, The New York Times Book Review
"The first book to chronicle the rise of savant-like artificial intelligence (AI), and the last we’ll ever need… A ripping good read."
—William Softky, Fair Observer
"An informative, enjoyable work… With vivid detail, Metz has crafted an accessible narrative that will keep readers turning the pages."
—Library Journal (starred review)
"A must-read, fully-up-to-date report on the holy grail of computing."
—Kirkus Reviews (starred review)
"With well-crafted storytelling and extensive research, Metz captures the thrill and promise of technological innovation."
—Booklist
"Written by an expert who has exclusive access to each of these companies—and others who are working in this field—this is a rich, character-driven narrative that captures an extraordinary moment in the history of technology."
—Irish Tech News
"In Genius Makers, Cade Metz delivers the definitive take on how AI technology came to be and what its arrival will mean for us humans. The book relies on tireless reporting and delightful writing to bring to life one of the most surprising and important stories of our time. If you want to read one book to understand AI, this is the one."
—Ashlee Vance, New York Times bestselling author of Elon Musk
"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, #1 New York Times bestselling author of Leonardo da Vinci, Steve Jobs, and The Innovators
"Cade Metz has produced an enthralling narrative of the advance of artificial intelligence. He describes the key personalities, the seminal meetings and the crucial breakthroughs with his customary eye for detail, building them into a dramatic history of this era-defining technology."
—Kai-Fu Lee, author of AI Superpowers
"This is the inside story of how AI entered Google, Facebook, and the rest of high tech. It is also the story of how Silicon Valley and its megabucks infiltrated AI and changed its course. Chock full of behind-the-scenes anecdotes and wry humor—we learn the true tale of the technology that is transforming humanity."
—Oren Etzioni, chief executive, Allen Institute for Artificial Intelligence
"One day soon, when computers are safely driving our roads and speaking to us in complete sentences, we'll look back at Cade Metz's elegant, sweeping Genius Makers as their birth story—the Genesis for an age of sentient machines."
—Brad Stone, author of The Everything Store and The Upstarts
"Genius Makers is an enthralling, definitive modern history of artificial intelligence. Cade Metz's detailed narrative reveals the crucial decisions made by executives, developers and investors—and foreshadows the disproportionately large effect they will have on our futures."
—Amy Webb, author of The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity
About the Author
Excerpt. © Reprinted by permission. All rights reserved.
1. Genesis
"Frankenstein Monster Designed by Navy That Thinks."
On July 7, 1958, several men gathered around a machine inside the offices of the United States Weather Bureau in Washington, D.C., about fifteen blocks west of the White House. As wide as a kitchen refrigerator, twice as deep, and nearly as tall, the machine was just one piece of a mainframe computer that fanned across the room like a multipiece furniture set. It was encased in silvery plastic, reflecting the light from above, and the front panel held row after row of small round lightbulbs, red square buttons, and thick plastic switches, some white and some gray. Normally, this $2 million machine ran calculations for the Weather Bureau, the forerunner of the National Weather Service, but on this day, it was on loan to the U.S. Navy and a twenty-nine-year-old Cornell University professor named Frank Rosenblatt.
As a newspaper reporter looked on, Rosenblatt and his Navy cohorts fed two white cards into the machine, one marked with a small square on the left, the other marked on the right. Initially, the machine couldn't tell them apart, but after it read another fifty cards, that changed. Almost every time, it correctly identified where the card was marked-left or right. As Rosenblatt explained it, the machine had learned this skill on its own, thanks to a mathematical system modeled on the human brain. He called it a Perceptron. In the future, he said, this system would learn to recognize printed letters, handwritten words, spoken commands, and even people's faces, before calling out their names. It would translate one language into another. And in theory, he added, it could clone itself on an assembly line, explore distant planets, and cross the line from computation into sentience.
"The Navy revealed the embryo of an electronic computer today that it expects will be able to walk, talk, see, write, reproduce itself, and be conscious of its existence," read the article that appeared the next morning in the New York Times. A second article, in the Sunday edition, said that Navy officials hesitated to call this a machine because it was "so much like a human being without life." Rosenblatt grew to resent the way the popular press covered the event, particularly a headline in Oklahoma ("Frankenstein Monster Designed by Navy That Thinks"). In later years, among colleagues and in his published writings, he described the project in more measured terms. He insisted it was not an attempt at artificial intelligence, and he acknowledged its limitations. Still, the idea slipped from his grasp.
The Perceptron was one of the first neural networks, an early incarnation of the technology Geoff Hinton would auction to the highest bidder more than fifty years later. But before it reached that $44 million moment, let alone the extravagant future predicted across the pages of the New York Times in the summer of 1958, it descended into academic obscurity. By the early 1970s, after those lavish predictions met the limitations of Rosenblatt's technology, the idea was all but dead.
Frank Rosenblatt was born on July 11, 1928, in New Rochelle, New York, just north of the Bronx. He attended Bronx Science, the elite public high school that eventually produced eight Nobel laureates, six Pulitzer Prize winners, eight National Medal of Science winners, and three recipients of the Turing Award, the worldÕs top computer science prize. A small, thin man with fleshy jowls and short, dark, wavy hair who wore standard-issue black-rimmed glasses, Rosenblatt was trained in psychology, but his interests were much wider. In 1953, the New York Times published a small story describing an early computer he used to crunch data for his PhD thesis. Called EPAC-short for electronic profile-analyzing computer-it analyzed the psychological profiles of his patients. As the years passed, he came to believe that machines could provide an even greater understanding of the mind. After finishing his PhD, he joined the Cornell Aeronautical Laboratory in Buffalo, about a hundred and fifty miles from the universityÕs main campus in Ithaca, New York. Donated to Cornell by a company that designed aircraft during the Second World War, this flight research center morphed into a more eclectic lab in the postwar years, operating with little oversight from the administration back in Ithaca. It was here that Rosenblatt designed the Perceptron, backed by funding from the Office of Naval Research.
Rosenblatt saw the project as a window into the inner workings of the brain. If he could re-create the brain as a machine, he believed, he could plumb the mysteries of what he called "natural intelligence." Drawing on ideas initially proposed by two researchers at the University of Chicago a decade earlier, the Perceptron analyzed objects and looked for patterns that could identify these objects (for example, whether a card had a mark on the left-hand or right-hand side). It did this using a series of mathematical calculations that operated (in a very broad sense) like the network of neurons in the brain. As the Perceptron examined and tried to identify each object, it got some right and others wrong. But it could learn from its mistakes, methodically adjusting each of those mathematical calculations until the mistakes were few and far between. Much like a neuron in the brain, each calculation was nearly meaningless on its own-just an input for a larger algorithm. But the larger algorithm-a kind of mathematical recipe-could actually do something useful. Or at least that was the hope. At the Weather Bureau in the summer of 1958, Rosenblatt showed off the beginnings of this idea-a simulation of the Perceptron that ran on the bureau's IBM 704, the leading commercial computer of the day. Then, back at the lab in Buffalo, working alongside a team of engineers, he began building an entirely new machine around the same idea. He called it the Mark I. Unlike other machines of the day, it was designed to see the world around it. "For the first time, a non-biological system will achieve an organization of its external environment in a meaningful way," he told a reporter later that year, during another trip to meet with his backers in Washington.
His primary collaborator at the Office of Naval Research did not view the Perceptron in the same extravagant terms. But Rosenblatt was unmoved. "My colleague disapproves of all the loose talk one hears nowadays about mechanical brains," he told the reporter, over a cup of coffee. "But that is exactly what it is." A small, silver pitcher of cream sat on the table in front of him, and he picked it up. Though this was the first time he'd laid eyes on the pitcher, Rosenblatt said, he could still recognize it as a pitcher. The Perceptron, he explained, could do much the same. It could draw the conclusions needed to distinguish, say, a dog from a cat. He admitted that the technology was a long way from having practical uses: It lacked depth perception and "the refinements of judgment." But he was confident of its potential. One day, he said, the Perceptron would travel into space and send its observations back to Earth. When the reporter asked if there was anything the Perceptron was not capable of, Rosenblatt threw up his hands. "Love. Hope. Despair. Human nature, in short," he said. "If we don't understand the human sex drive, how should we expect a machine to?"
That December, the New Yorker hailed Rosenblatt's creation as the first serious rival to the brain. Previously, the magazine had marveled that the IBM 704 could play a game of chess. Now it described the Perceptron as an even more remarkable machine, a computer that could achieve "what amounts to human thought." Though scientists claimed that only biological systems could see, feel, and think, the magazine said, the Perceptron behaved "as if it saw, felt, and thought." Rosenblatt had not yet built the machine, but this was dismissed as a minor obstacle. "It is only a question of time (and money) before it comes into existence," the magazine said.
Rosenblatt completed the Mark I in 1960. It spanned six racks of electrical equipment, each the size of a kitchen fridge, and it plugged into what seemed to be a camera. This was a camera, though engineers had removed the film loader, swapping in a small square device covered with four hundred black dots. These were photocells that responded to changes in light. Rosenblatt and his engineers would print block letters on squares of cardboard-A, B, C, D, etc.-and when they placed these squares on an easel in front of the camera, the photocells could read the black lines of the letters against the white space of the cardboard. As they did, the Mark I learned to recognize the letters, much like the IBM mainframe inside the Weather Bureau learned to recognize the marked cards. This required a little help from the humans in the room: As it worked to identify the letters, a technician would tell the machine whether it was right or wrong. But eventually, the Mark I would learn from its own hits and misses, pinpointing the patterns that identified the slanted line of an A or the double curve of a B. When demonstrating the machine, Rosenblatt had a way of proving this was learned behavior. He would reach into the racks of electrical equipment and pull out a few wires, breaking the connections between the motors that acted as faux-neurons. When he reconnected the wires, the machine once again struggled to recognize the letters, but then, after examining more cards and relearning the same skill, it worked as it did before.
This electrical contraption worked well enough to attract interest beyond the Navy. Over the next several years, the Stanford Research Institute, or SRI, a lab in Northern California, began exploring the same ideas, and Rosenblatt's own lab won contracts with both the U.S. Postal Service and the Air Force. The Postal Service needed a way of reading addresses on envelopes, and the Air Force hoped to identify targets in aerial photos. But all that was still in the future. Rosenblatt's system was only marginally effective when reading printed letters, a relatively simple task. As the system analyzed cards printed with the letter A, each photocell examined a particular spot on the card-say, an area near the lower right-hand corner. If the spot was black more often than white, the Mark I assigned it a high "weight," meaning it would play a more important role in the mathematical calculation that ultimately determined what was an A and what wasn't. When reading a new card, the machine could recognize an A if most of the highly weighted spots were colored black. That was about it. The technology wasn't nearly nimble enough to read the irregularities of handwritten digits.
Despite the system's obvious deficiencies, Rosenblatt remained optimistic about its future. Others, too, believed the technology would improve in the years to come, learning more complex tasks in more complex ways. But it faced a significant obstacle: Marvin Minsky.
Frank Rosenblatt and Marvin Minsky had been contemporaries at Bronx Science. In 1945, MinskyÕs parents moved him to Phillips Andover, the model American prep school, and after the war, he enrolled at Harvard. But he complained that neither could match his experience at Science, where the coursework had been more challenging and the students more ambitious-Ópeople you could discuss your most elaborate ideas with and no one would be condescending,Ó he said. After Rosenblatt died, Minsky pointed to his old schoolmate as the kind of creative thinker who walked the halls of Science, and like Rosenblatt, Minsky was a pioneer in the field of artificial intelligence. But he viewed the field through a different lens.
As an undergraduate at Harvard, using over three thousand vacuum tubes and a few parts from an old B-52 bomber, Minsky built what may have been the first neural network, a machine he called SNARC. Then, as a graduate student in the early '50s, he continued to explore the mathematical concepts that eventually gave rise to the Perceptron. But he came to see artificial intelligence as a larger endeavor. He was among the small group of scientists who crystalized AI as its own field of study during a gathering at Dartmouth College in the summer of 1956. A Dartmouth professor named John McCarthy had urged the wider academic community to explore an area of research he called "automata studies," but that didn't mean much to anyone else. So he recast it as artificial intelligence, and that summer, he organized a conference alongside several like-minded academics and other researchers. The agenda at the Dartmouth Summer Research Conference on Artificial Intelligence included "neuron nets," but also "automatic computers," "abstractions," and "self-improvement." Those who attended the conference would lead the movement into the 1960s, most notably McCarthy, who eventually took his research to Stanford University on the West Coast; Herbert Simon and Alan Newell, who built a lab at Carnegie Mellon in Pittsburgh; and Minsky, who settled at the Massachusetts Institute of Technology in New England. They aimed to re-create human intelligence using whatever technology could get them there, and they were sure it wouldn't take very long, some arguing that a machine would beat the world chess champion and discover its own mathematical theorem within a decade. Bald from a young age, with wide ears and an impish grin, Minsky became an AI evangelist, but his evangelism didn't extend to neural networks. A neural network was just one way of building artificial intelligence, and Minsky, like many of his colleagues, began exploring other avenues. By the mid-'60s, as other techniques grabbed his attention, he questioned whether neural networks could handle anything beyond the simple tasks Rosenblatt demonstrated at his lab in upstate New York.
Minsky was part of a larger backlash against Rosenblatt's ideas. As Rosenblatt himself wrote in his 1962 book Principles of Neurodynamics, the Perceptron was a controversial concept among academics, and he placed much of the blame on the press. The reporters who wrote about his work in the late 1950s, Rosenblatt said, "fell to the task with all of the exuberance and sense of discretion of a pack of happy bloodhounds." He lamented, in particular, headlines like the one in Oklahoma, saying they were a long way from inspiring confidence in his work as a serious scientific pursuit. Four years after the event in Washington, pulling back on his own early claims, he insisted the Perceptron was not an attempt at artificial intelligence-at least not as researchers like Minsky saw AI. "The perceptron program is not primarily concerned with the invention of devices for 'artificial intelligence,' but rather with investigating the physical structures and neurodynamic principles which underlie 'natural intelligence,'" he wrote. "Its utility is in enabling us to determine the physical conditions for the emergence of various psychological properties." In other words, he wanted to understand how the human brain worked, rather than send a new brain out into the world. Because the brain was a mystery, he couldn't re-create it. But he believed he could use machines to explore this mystery, and maybe even solve it.
Product details
- Publisher : Dutton (March 16, 2021)
- Language : English
- Hardcover : 384 pages
- ISBN-10 : 1524742678
- ISBN-13 : 978-1524742676
- Item Weight : 1.25 pounds
- Dimensions : 6.22 x 1.26 x 9.27 inches
- Best Sellers Rank: #192,697 in Books (See Top 100 in Books)
- #103 in Robotics & Automation (Books)
- #177 in Computers & Technology Industry
- #446 in Artificial Intelligence & Semantics
- Customer Reviews:
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Customers find the book enlightening about AI progress and issues. They appreciate the author's ability to make difficult concepts understandable and seamlessly blend technical brilliance with human stories. The storytelling is described as brilliant, compelling, and the definitive AI narrative. Readers also mention that it's a good read.
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Customers find the book engaging and enlightening about deep learning and its progress. They praise the author's ability to make difficult concepts understandable, seamlessly blending technical brilliance with human stories. Readers describe it as an important account of recent developments and major players that led to today's AI. It is described as a must-read for tech enthusiasts and anyone curious about the future of humanity.
"...Makers is a fascinating journey through the history and evolution of artificial intelligence, offering a glimpse into the minds of those shaping our..." Read more
"...is a masterclass in storytelling, seamlessly blending the technical brilliance of artificial intelligence with the human stories of the visionaries..." Read more
"...This work is an important account of the recent developments and major players that led to today's discussions and efforts around artificial general..." Read more
"...If you are not directly involved, the book is still a great read to understand how the current AI technology came to be as it becomes omnipresent..." Read more
Customers find the storytelling brilliant and compelling. They describe it as the definitive AI narrative, a story of ambition, persistence, and the relentless pursuit of building the future one layer at a time. The author does a great job describing the circumstances that led to major tech companies.
"...It’s a story of ambition, persistence, and the relentless pursuit of building the future, one layer at a time." Read more
"This book is a masterclass in storytelling, seamlessly blending the technical brilliance of artificial intelligence with the human stories of the..." Read more
"...Metz does a wonderful job of storytelling around Geoff Hinton, following his career and ideas from the early 1980s to present, while integrating..." Read more
"...The author does a great job of describing the circumstances that led to the major tech companies (Google, Facebook, Microsoft, Nvidia etc) adopting..." Read more
Top reviews from the United States
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- Reviewed in the United States on November 24, 2024Cade Metz’s Genius Makers is a fascinating journey through the history and evolution of artificial intelligence, offering a glimpse into the minds of those shaping our future. From early concepts like Perceptron to breakthroughs like backpropagation and capsule networks, the book makes complex ideas accessible, showing how AI systems learn to recognize patterns and solve problems beyond human capability.
The human stories are just as compelling as the science. Geoffrey Hinton’s decision to prioritize “the right home for his research” over profit reflects the idealism that drives many AI pioneers. Metz also captures the contrasts in their approaches, like Hinton’s flood of ideas versus Yann LeCun’s sharp focus, illustrating the diversity of thought shaping the field.
The book doesn’t shy away from the challenges and risks of AI, highlighting ethical concerns like the need for “moral responsibilities to be baked into their design by default.” It also critiques the narrow reach of current AI systems, which often benefit only a small segment of society.
As the Los Angeles Times notes, “You could even call it essential for any policymakers, politicians, police, lawyers, judges, and decision-makers who will be contending with the social forces unleashed by artificial intelligence. Which, soon, will mean all of them.”
Whether you’re a tech enthusiast, a policymaker, or simply curious about AI, Genius Makers is an inspiring and thought-provoking read. It’s a story of ambition, persistence, and the relentless pursuit of building the future, one layer at a time.
- Reviewed in the United States on June 29, 2021This who's-who history of the VIP's of deep learning (which is the hottest form of machine learning and the source of most of the "AI" hype in recent years) heavily emphasizes the importance of that technology -- and places the few most famous folks on a somewhat-deserved if grandiose pedestal -- but lacks substance when it comes to the world-changing, money-making potential of how this technology is specifically to be used. Instead, it punctuates and demonstrates its importance mostly by way of the dazzling sums those VIPs got for selling their start-ups and by way of deep learning's (extremely impressive) technical performance on games (e.g., chess, Go, video games) and a few select applications (e.g., image recognition, machine translation). It doesn't go far substantiating concretely which of those apps can really earn a company money or otherwise provably deliver an impact. So it's great to see the recent history of these scientists uniquely laid out in one place, but then it feels a bit vacuous, since the bios and history aren't met proportionately with the actually world-changing, *concrete*, deployed accomplishments (and potential accomplishments) of deep learning. Understanding those concrete deployment scenarios, IMO, is a question that the author should have more emphatically included during each of the interviews he conducted with these VIPs when preparing the book. The gravitas given to these VIPs is overblown, replete with minutia about who met with whom at what restaurant, which serves to convey the "royal" importance (and stature) of these people more than that of their ideas or the tech's realized value. There’s a much more compelling and informed story to be told about the real (and potential) deployed wins of this tech, such as improving search results (well, that's pretty much only incremental, after a point) and the immense savings in cooling of Google's huge server banks (wow! but only briefly mentioned; buried), and much more.
I also need to lodge a major complaint about the degree to which this book gives credence to artificial general intelligence. Now, the author is ostensibly just reporting both sides - the proponents and the skeptics - but the book certainly is heavier on the former, not to mention that giving even just half the platform to the fictional, ill-defined, ultimately meaningless concept of AGI is about as neutral as doing the same for flat-earth proponents. (Flat-earther's don't need a platform -- they already have one! Sorry, couldn't resist.)
I would also say a popular, lay-friendly book like this has a responsibility to go much further and come down much more strongly on the ethical / social justice concerns.
- Eric Siegel, Ph.D., author, "Predictive Analytics"
- Reviewed in the United States on November 16, 2024This book is a masterclass in storytelling, seamlessly blending the technical brilliance of artificial intelligence with the human stories of the visionaries who shaped it. Cade Metz makes complex concepts accessible while keeping the narrative exciting. A must-read for tech enthusiasts and anyone curious about the future of humanity shaped by AI!
- Reviewed in the United States on February 21, 2023Cade Metz has written a mesmerizing account of how deep learning, and its long-time proponents, have come to dominate AI and shift the balance of power in the tech world. The book briefly traces the roots of neural network and connectionist approaches that were discredited by most early AI pioneers like Minsky and also held with contempt by top researchers and tech company executives as recently as 10 years ago. Metz does a wonderful job of storytelling around Geoff Hinton, following his career and ideas from the early 1980s to present, while integrating most of the important AI researchers along the way. Metz has also revealed extraordinary details of how the tech giants raced to acquire talent and develop deep learning capabilities. This work is an important account of the recent developments and major players that led to today's discussions and efforts around artificial general intelligence. Genius Makers is a more compelling read than Walter Isaacson's The Innovators- and that's saying something!
Top reviews from other countries
- Amazon CustomerReviewed in Canada on April 26, 2024
5.0 out of 5 stars Historical perspective on AI
Enjoy reading it (I am about 40%), it provides an historical academic/big lab view of AI
- Rafael AzevedoReviewed in Brazil on June 3, 2023
5.0 out of 5 stars Very good
Very interesting book. It deserves an update, as AI has seem to be in 2023 in a tip point.
-
Cliente AmazonReviewed in Spain on May 2, 2024
5.0 out of 5 stars Interesante
Interessant
- Vaibhav PantReviewed in India on December 30, 2023
5.0 out of 5 stars Liked the book a lot
Liked the book a lot. Nice history of how the stalwarts have brought about the latest AI revolution. Worth a read.
- SGReviewed in Germany on May 21, 2022
5.0 out of 5 stars Geoff Hinton and the Deep Learning Conspiracy Examined
I really enjoyed this book. It's the best book I could have wished to read in the month in which I launch an AI startup. There so much in Metz's book about the value of ideas, science and research. That value is absolutely idealistic but also the numbers speak for themselves if the sales prices of startups like DeepMind and OpenAI are discussed or figures which researchers were given for part-time work at a major tech giant are given. If you're a researcher or scientist whose struggling to convince people why science and research are worth investment or even pursuing this is a book that even translates the original blue-sky research on brain models into actual dollar amounts. If you're trying to justify why a startup should develop future tech instead of just building a product, there's a lot to find here. I've also been critical of Deep Learning and Geoff Hinton in the past - now I see them in a different light. However, while the book truly is a great read and I kept quoting ideas, facts, concepts, and lines from the book over the past weeks, there is an element of repetition with respect to the victim narrative of Hinton and LeCun always portraying themselves as overcoming unbelievable odds as Neural Networks were not always "flavour of the months". I also felt details were missing. Pitts and McCulloch were only mentioned as "two researchers from Chicago a decade earlier" (approx.) and Google's VP of Machine Learning Peter Norvig in my recollection had quite a significant role in the rise of machine learning at Google but isn't mentioned at all.
Overall, a cool read.