Rebooting outlet sale lowest AI: Building Artificial Intelligence We Can Trust outlet online sale

Rebooting outlet sale lowest AI: Building Artificial Intelligence We Can Trust outlet online sale

Rebooting outlet sale lowest AI: Building Artificial Intelligence We Can Trust outlet online sale

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Two leaders in the field offer a compelling analysis of the current state of the art and reveal the steps we must take to achieve a truly robust artificial intelligence.

Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we have been led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer beating a human in Jeopardy! does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements in the field thus far have occurred in closed systems with fixed sets of rules, and these approaches are too narrow to achieve genuine intelligence.

The real world, in contrast, is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Taking inspiration from the human mind, Marcus and Davis explain what we need to advance AI to the next level, and suggest that if we are wise along the way, we won''t need to worry about a future of machine overlords. If we focus on endowing machines with common sense and deep understanding, rather than simply focusing on statistical analysis and gatherine ever larger collections of data, we will be able to create an AI we can trust—in our homes, our cars, and our doctors'' offices.  Rebooting AI provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of how a new generation of AI can make our lives better.

Review

“Artificial intelligence is among the most consequential issues facing humanity, yet much of today’s commentary has been less than intelligent: awe-struck, credulous, apocalyptic, uncomprehending. Gary Marcus and Ernest Davis, experts in human and machine intelligence, lucidly explain what today’s AI can and cannot do, and point the way to systems that are less A and more I.”
—Steven Pinker, Johnstone Professor of Psychology, Harvard University, and the author of How the Mind Works and The Stuff of Thought
 
“Finally, a book that tells us what AI is, what AI is not, and what AI could become if only we are ambitious and creative enough. No matter how smart and useful our intelligent machines are today, they don’t know what really matters.  Rebooting AI dares to imagine machine minds that goes far beyond the closed systems of games and movie recommendations to become real partners in every aspect of our lives.”  
—Garry Kasparov, Former World Chess Champion and author of Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins
 
“Finally, a book that says aloud what so many AI experts are really thinking. Every CEO should read it, and everyone else at the company, too. Then they’ll be able to separate the AI wheat from the chaff, and know where we are, how far we have to go, and how to get there.”
—Pedro Domingos, Professor of computer science at the University of Washington and author of The Master Algorithm
 
“A welcome antidote to the hype that has engulfed AI over the past decade and a realistic look at how far AI and robotics still have to go.”
—Rodney Brooks, former director of the MIT Computer Science and Artificial Intelligence Laboratory
 
“AI is achieving superhuman performance in many narrow applications, but the reality is that we are still very far from artificial general intelligence that truly understands the world. Marcus and Davis explain the pitfalls of current approaches with humor and insight, and provide a compelling path toward the kind of robust AI that can earn our trust.”
—Erik Brynjolfsson, Professor at MIT and co-author of The Second Machine Age and Machine | Platform | Crowd

 
Rebooting AI is a blast to read. It''s erudite, it''s witty, and it neatly unpacks why today''s AI has such trouble doing truly smart tasks—and what it''ll take to reach that goal.”
—Clive Thompson, Wired magazine columnist and author of Coders: The Making of a New Tribe and the Remaking of the World
 
“Will machines overtake humans in the foreseeable future, or is it just hype? Marcus and Davis lay out their answer with elegant prose and a sure quill, drawing the distinction between today’s deep-learning based narrow, brittle artificial “intelligence” and the ever-elusive artificial general intelligence. Common sense and trust, which are intrinsically human, emerge as grand challenges for the field. If you plan to read one book to keep up with AI—this is an outstanding choice!”
—Oren Etzioni, CEO of Allen institute for AI & Professor of computer science at University of Washington. 
 
“Artificial intelligence is here to stay. What are its achievements, its prospects, its pitfalls and misdirected initiatives—and how might these be remedied and overcome? This lucid and deeply informed account, from a critical but sympathetic perspective, is a valuable guide to developments that will surely have a major impact on the social order and intellectual culture.”
—Noam Chomsky

“When I was a child I saw  2001: A Space Odyssey and then read everything I could about AI. All the smart people said it was twenty years away.  Twenty years later I was an adult and the smart people said that AI was twenty years away. Twenty years after that we passed 2001 and the smart people said it was about twenty years away.  Yup, it’s getting better and better, but it still ain’t HAL. It can tag photos pretty good but on understanding stories my son passed all the AI before he went to his stupid preschool. Now is the time to listen to  smarter people: in  Rebooting AI, Gary Marcus and Ernest Davis do a great job separating truth from bullshit to understand why we might not have real A.I. in twenty years and what we can do to get way closer.”
—Penn Jillette, Emmy-winning magician and actor and New York Times best-belling author

“A must-read for anyone who cares about the future of artificial intelligence, filled with masterful storytelling and clear and easy-to-digest examples. Simultaneously puncturing hype and plotting a new course towards toward truly successful AI,  Rebooting AI offers the first rational look at what AI can and can’t do, and what it will take to build AI that we can genuinely trust. And it does it in a way that engages the reader and ultimately celebrates both what AI has accomplished and the strengths and power of the human mind.”
—Annie Duke, best-selling author of Thinking in Bets: Making Smarter Decisions When You Don''t Have All the Facts


About the Author

GARY MARCUS is a scientist, best-selling author, and entrepreneur. He is the founder and CEO of Robust.AI and was founder and CEO of Geometric Intelligence, a machine-learning company acquired by Uber in 2016. He is the author of five books, including Kluge, The Birth of the Mind, and the New York Times best seller Guitar Zero.

ERNEST DAVIS is a professor of computer science at the Courant Institute of Mathematical Science, New York University. One of the world''s leading scientists on commonsense reasoning for artificial intelligence, he is the author of four books, including Representations of Commonsense Knowledge and Verses for the Information Age.

Learn more about the authors and their work at rebooting.ai.

Excerpt. © Reprinted by permission. All rights reserved.

from Chapter 1:
 
MIND THE GAP
 
Since its earliest days, artificial intelligence has been long on prom­ise, short on delivery. In the 1950s and 1960s, pioneers like Marvin Minsky, John McCarthy, and Herb Simon genuinely believed that AI could be solved before the end of the twentieth century. “Within a generation,” Marvin Minsky famously wrote, in 1967, “the prob­lem of artificial intelligence will be substantially solved.” Fifty years later, those promises still haven’t been fulfilled, but they have never stopped coming. In 2002, the futurist Ray Kurzweil made a public bet that AI would “surpass native human intelligence” by 2029. In November 2018 Ilya Sutskever, co-founder of OpenAI, a major AI research institute, suggested that “near term AGI [artificial general intelligence] should be taken seriously as a possibility.” Although it is still theoretically possible that Kurzweil and Sutskever might turn out to be right, the odds against this happening are very long. Getting to that level—general-purpose artificial intelligence with the flexibility of human intelligence—isn’t some small step from where we are now; instead it will require an immense amount of foundational progress—not just more of the same sort of thing that’s been accomplished in the last few years, but, as we will show, something entirely different.
 
Even if not everyone is as bullish as Kurzweil and Sutskever, ambi­tious promises still remain common, for everything from medicine to driverless cars. More often than not, what is promised doesn’t mate­rialize. In 2012, for example, we heard a lot about how we would be seeing “autonomous cars [in] the near future.” In 2016, IBM claimed that Watson, the AI system that won at Jeopardy!, would “revo­lutionize healthcare,” stating that Watson Health’s “cognitive sys­tems [could] understand, reason, learn, and interact” and that “with [recent advances in] cognitive computing . . . we can achieve more than we ever thought possible.” IBM aimed to address problems ranging from pharmacology to radiology to cancer diagnosis and treatment, using Watson to read the medical literature and make rec­ommendations that human doctors would miss. At the same time, Geoffrey Hinton, one of AI’s most prominent researchers, said that “it is quite obvious we should stop training radiologists.”
 
In 2015 Facebook launched its ambitious and widely covered project known simply as M, a chatbot that was supposed to be able to cater to your every need, from making dinner reservations to planning your next vacation.
 
As yet, none of this has come to pass. Autonomous vehicles may someday be safe and ubiquitous, and chatbots that can cater to every need may someday become commonplace; so too might superintel­ligent robotic doctors. But for now, all this remains fantasy, not fact.
 
The driverless cars that do exist are still primarily restricted to highway situations with human drivers required as a safety backup, because the software is too unreliable. In 2017, John Krafcik, CEO at Waymo, a Google spinoff that has been working on driverless cars for nearly a decade, boasted that Waymo would shortly have driverless cars with no safety drivers. It didn’t happen. A year later, as Wired put it, the bravado was gone, but the safety drivers weren’t. Nobody really thinks that driverless cars are ready to drive fully on their own in cities or in bad weather, and early optimism has been replaced by widespread recognition that we are at least a decade away from that point—and quite possibly more.
 
IBM Watson’s transition to health care similarly has lost steam. In 2017, MD Anderson Cancer Center shelved its oncology collabo­ration with IBM. More recently it was reported that some of Wat­son’s recommendations were “unsafe and incorrect.” A 2016 project to use Watson for the diagnosis of rare diseases at the Marburg, Ger­many, Center for Rare and Undiagnosed Diseases was shelved less than two years later, because “the performance was unacceptable.” In one case, for instance, when told that a patient was suffering from chest pain, the system missed diagnoses that would have been obvious even to a first year medical student, such as heart attack, angina, and torn aorta. Not long after Watson’s troubles started to become clear, Facebook’s M was quietly canceled, just three years after it was announced.
 
Despite this history of missed milestones, the rhetoric about AI remains almost messianic. Eric Schmidt, the former CEO of Google, has proclaimed that AI would solve climate change, poverty, war, and cancer. XPRIZE founder Peter Diamandis made similar claims in his book Abundance, arguing that strong AI (when it comes) is “definitely going to rocket us up the Abundance pyramid.” In early 2018, Google CEO Sundar Pichai claimed that “AI is one of the most important things humanity is working on . . . more pro­found than . . . electricity or fire.” (Less than a year later, Google was forced to admit in a note to investors that products and ser­vices “that incorporate or utilize artificial intelligence and machine learning, can raise new or exacerbate existing ethical, technological, legal, and other challenges.”)
 
Others agonize about the potential dangers of AI, often in ways that show a similar disconnect from current reality. One recent non­fiction bestseller by the Oxford philosopher Nick Bostrom grappled with the prospect of superintelligence taking over the world, as if that were a serious threat in the foreseeable future. In the pages of The Atlantic, Henry Kissinger speculated that the risk of AI might be so profound that “human history might go the way of the Incas, faced with a Spanish culture incomprehensible and even awe-inspiring to them.” Elon Musk has warned that working on AI is “summoning the demon” and a danger “worse than nukes,” and the late Stephen Hawking warned that AI could be “the worst event in the history of our civilization.”
 
But what AI, exactly, are they talking about? Back in the real world, current-day robots struggle to turn doorknobs, and Teslas driven in “Autopilot” mode keep rear-ending parked emergency vehi­cles (at least four times in 2018 alone). It’s as if people in the four­teenth century were worrying about traffic accidents, when good hygiene might have been a whole lot more helpful.
 
[ . . . ]

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4.5 out of 54.5 out of 5
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Top reviews from the United States

Kenneth Forbus
5.0 out of 5 starsVerified Purchase
Best single non-technical book to read about where AI is and where it should be going
Reviewed in the United States on September 22, 2019
While there has been significant and important progress in artificial intelligence, there has also been a tidal wave of hype. Pundits hyperventilate about super-intelligent systems and less discerning (and in a few cases, less scrupulous) researchers overstate progress in... See more
While there has been significant and important progress in artificial intelligence, there has also been a tidal wave of hype. Pundits hyperventilate about super-intelligent systems and less discerning (and in a few cases, less scrupulous) researchers overstate progress in ways that result in misleading media stories. Marcus & Davis'' new book, Rebooting AI, is a perfect corrective to such hype. They do an excellent job of describing where the field actually is, what the strengths and weaknesses are of some current technologies, where they think the field is going wrong, and what we should be doing instead. Most importantly, it is written for the general reader, lavishly illustrated with many examples and a good dose of humor where appropriate. If you want one book to read to catch up on where AI is and should be going, this is the book for you.

Here is a chapter by chapter breakdown. Chapter 1 does an excellent job of laying out the basic argument, that today''s AI systems are narrow and only by moving beyond the big data/statistical learning focus of much of today''s work will we achieve flexible AI systems. The discussion of overattribution, illusory progress, and the robustness gap are especially useful for understanding the difference between what often gets reported versus where the state of the art is. Demonstrations and laboratory experiments are (hopefully) on the path to robust technologies, but the distance is often not clear to outsiders. Chapter 2 explains why the problems with today''s AI technologies matters, focusing mostly on bias found in machine learning systems.

Chapter 3 dissects deep learning, which is the revolution in AI that everyone knows about, due both to real progress but also media attention. (There are two others, as noted below.) They provide a non-technical overview of neural networks and deep learning, and point out both their strengths and weaknesses in a balanced way. Many who only read popular press accounts of deep learning will find the examples and arguments about brittleness surprising, but the phenomena are quite replicable. My only fault with Chapter 3 is that the picture it paints of modern AI is a bit oversimplified, even for this level of discussion. There are two other revolutions in AI. The first is knowledge graphs, where structured, relational representations straight out of the classic AI playbook have been applied to many tasks (mostly via semantic web technologies), and at industrial scale. Google and Microsoft both use billion-fact knowledge graphs in their search engines and other products, for example, and the technology is spreading quickly (even Spotify has its own knowledge graph). The second is high-performance reasoning systems, where satisfiability solvers are part of the constraint solvers used every day by logistics companies and other industrial concerns for planning and scheduling. (Marcus and Davis do bring up one line of this revolution, model checking, on page 187). I can see why, rhetorically, focusing only on deep learning makes sense for them, it simplifies the main argument considerably. On the other hand, these other two revolutions lend credence to their call for revisiting ideas from classical AI. A common claim by neural network modelers has always been that symbolic representations and reasoning over them cannot scale, but the same rising tide of massive data and computation that lifted deep learning has also lifted work in knowledge representation and reasoning, although these are not receiving the same attention that deep learning is. So to my mind, these other revolutions make the approach argued for in Chapter 7 even stronger.

Chapters 4 and 5 dissect the state of the art in machine reading and robotics, two areas where there is an astonishing amount of hype. Their examples do an excellent job of pointing out what can and cannot be done today, and just how far we are from systems that can read as humans do, or operate in the physical world the way we do.

Chapters 6 and 7 chart their alternate course. Chapter 6 provides a capsule summary of the kinds of insights that AI could be taking from other areas of cognitive science. It is a sad comment on the current state of AI education that many of the eleven hard-won insights listed here will be news to many of today''s graduate students and even some AI practitioners. Chapter 7 sketches some ideas about common sense. They carefully walk readers through some basic ideas about knowledge representation, to get across some of the pitfalls as well as the power, and argue that time, space, and causality are the three key areas to focus on. As with Chapter 3, so much more could be said -- and Davis has written an excellent book about this, albeit for a technical audience -- but the key thing is, you will come out of this chapter with a good sense of the overall approach.

Chapter 8 is about trust, and its relationship with good engineering practices. They do a fine job at outlining basics of software development that are relevant to understanding how people build safe and reliable software. Their handling of ethical questions is very sensible.

To summarize: This is an excellent non-technical book which debunks hype about AI while pointing out both real progress and the daunting open questions that remain on the road to understanding how to build intelligent systems with human-like flexibility and breadth. If you are interested in AI, or its possible impacts, you should read it.
28 people found this helpful
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T. Nield
5.0 out of 5 starsVerified Purchase
Finally a good appraisal that looks past the hype and sensationalism
Reviewed in the United States on September 16, 2019
I''ve been waiting for this book for awhile. Gary Marcus has always been on the forefront calling out the sensationalism and hype of modern AI trends. Many of us have forgotten or were not born when this all happened before in the 1960''s and 1980''s. This AI rhetoric and... See more
I''ve been waiting for this book for awhile. Gary Marcus has always been on the forefront calling out the sensationalism and hype of modern AI trends. Many of us have forgotten or were not born when this all happened before in the 1960''s and 1980''s. This AI rhetoric and sensationalism has happened before, and was always followed by an AI winter.

What Gary and Ernest do well is to not to leave readers without a possible solution. Sure, they take a critical appraisal of deep learning and how limited it is in practical use cases. But they also offer a path and possible research areas where "AI" may be better realized. However, it is going to be long and hard, and general AI seems unlikely to happen in our lifetimes.
8 people found this helpful
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Steven Miller (SMU, Singapore)
5.0 out of 5 starsVerified Purchase
Today''s AI systems are so high performing. Yet have no real sense of understanding or intelligence
Reviewed in the United States on November 23, 2019
What I especially liked about this book is the way it used many examples to demonstrate and explain a very important concept: That while today''s AI systems-- based mostly on Deep Learning (multi-level neural networks)-- are so very capable within the confines of specific... See more
What I especially liked about this book is the way it used many examples to demonstrate and explain a very important concept: That while today''s AI systems-- based mostly on Deep Learning (multi-level neural networks)-- are so very capable within the confines of specific and narrow task performance, and rapidly getting even more capable and high performing-again within the confines of specific and narrow task performance, they do not have any real understanding of what they are doing, and they are not really "intelligent." This contradiction is hard for the broader public (non-specialist in AI or Cognitive Science) to grasp and make sense of. The authors Markus and Davis help the rest of the world (the non-specialists) to understand this.

Their Chapter 6, "Insights from the Human Mind" is beautifully crafted. They use insights from properties of human intelligence to explain the general principles and properties of what they mean by "deep understanding" and "flexible intelligence."

Their core point is that we need to move from the our current situation of AI-enabled machines based on Deep Learning (multi-level neural networks) to a future situation of AI-enabled machines which have the capacity for actual understanding, and eventually Deep Understanding. Throughout the book, they give many informative examples of this gap, and also provide strategies for moving AI in this direction.

People who have absolutely no technical background in AI, and who have no in-depth understanding of AI applications will find this book very easy to read and understand, and highly informative. Remarkably, at the same time, people (like myself) who know a lot about AI methods and technology, and real-world AI applications, will also find this book easy to read and understand, and highly informative.
One person found this helpful
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G. Bestick
4.0 out of 5 starsVerified Purchase
Clear-Eyed Look at Current State of Play
Reviewed in the United States on November 4, 2020
Gary Marcus and Ernest Davis believe that artificial intelligence is going down a narrow path that will lead to more dead ends than breakthroughs. Despite its big leaps in speech and object recognition, they think that deep learning, the current darling of the field, is... See more
Gary Marcus and Ernest Davis believe that artificial intelligence is going down a narrow path that will lead to more dead ends than breakthroughs. Despite its big leaps in speech and object recognition, they think that deep learning, the current darling of the field, is “greedy, opaque and brittle.” Greedy because it needs huge amounts of data to come up with answers, opaque because it’s hard to analyze how it comes up with those answers, and brittle because it only works well on narrow, well-defined problem sets.

They argue for opening up the field by integrating principles of human cognition into AI development. To achieve any level of breakthrough from where we are today, we need machines that have a working model of the world they operate in, the ability to generalize, and a database of real world experiences to draw on in order to adapt to and integrate new information. They’d also toss in a healthy dose of common sense. Until a domestic robot or driverless car can sort out the world the way a human brain can, they won’t be trustworthy enough (accurate, reliable, safe, ethical) to cede control to them.

Marcus and Peters believe we’re far away from this type of robust AI, and getting there won’t be easy or cheap.
Although they worry about the potential dystopian outcomes AI might generate in automation, finance, content delivery, politics and surveillance, they are cautiously optimistic over the long term. Whether the long term is ten years or ten thousand, they don’t say.

Rebooting AI is accessible, well-researched and understandable by the non-technical. Recommended as an antidote to both the hype and the doomsaying among the popular press.
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Chris
5.0 out of 5 starsVerified Purchase
Sheds light on the current state of AI
Reviewed in the United States on October 18, 2019
I currently work as an engineer in the autonomous vehicle space, and I find that this book sheds light on the current state of AI. Basically, I found this book because at work I ran in to a couple of the current fundamental problems of deep learning. One, deep... See more
I currently work as an engineer in the autonomous vehicle space, and I find that this book sheds light on the current state of AI.

Basically, I found this book because at work I ran in to a couple of the current fundamental problems of deep learning. One, deep learning struggles to adapt with a low number of human-labeled examples. Two, deep learning also struggles to adapt when the distribution of the input shifts or the task changes.

I was poring through recent deep learning research papers attempting to solve these problems, and I found that I was mostly unsatisfied by their results and the directions that they were going.

I think, at the time of this writing, AI is often conflated with deep learning, and after reading this book I have a greater appreciation about the broader scope of AI, both in its problem and solution space. We have a long way to go in creating truly intelligent systems.
One person found this helpful
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CHERYL
5.0 out of 5 starsVerified Purchase
Serious an thoughtful redirect for AI
Reviewed in the United States on October 20, 2019
I have been waiting for the industry to finally get critical with itself. Thanks for this thoughtful treatment of a subject that has had misleading and frothy treatment from the media, unrealistic and misguided expectation-setting from software vendors, and too little... See more
I have been waiting for the industry to finally get critical with itself. Thanks for this thoughtful treatment of a subject that has had misleading and frothy treatment from the media, unrealistic and misguided expectation-setting from software vendors, and too little serious self examination from practitioners. The authors have set out a pretty good set of “functional requirements” for a generalized AI here. They’ve done a good job of articulating 1. Why the current data-driven deep learning cannot progress beyond simple and very narrow tasks, 2. A common sense understanding of the world is missing from these approaches 3. a conceptual faculty able to learn without 10,000 high quality labeled examples beforehand is needed; and 4. What potential corrective actions might be taken.

Importantly, they have suggested a reunification of the two divergent AI traditions (the original, in the Minsky tradition, and the current data driven, tabulated rasa deep learning approach) is ultimately what is needed to progress and fix the current course. Additionally they implied (or I read into it) that a more trans-disciplinary approach is needed, given the stated need for understanding the functionality of the brain, not just at a biological/ chemical level, but at a fundamental metaphysical / philosophical level.

Probably the biggest contribution is the authors’ “recipe for achieving common sense, and ultimately general intelligence,” too long to quote here but I couldn’t agree more.

The book paints a clear, yet challenging road forward, but as they argue, “tough love” for this young (teenager?) AI is what is needed if there is any needed hope of it achieving its potential.
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Fred Simkin
5.0 out of 5 starsVerified Purchase
To quote Elmo "IMPORTANT, IMPORTANT, IMPORTANT
Reviewed in the United States on December 5, 2019
It seems everyday there is a constant stream across the media sphere all focused on one acronym "A.I." The public is treated to pundits, politicians and marketing hucksters to either warn or promise us that, the robots are coming to steal our jobs and burn our brains or... See more
It seems everyday there is a constant stream across the media sphere all focused on one acronym "A.I." The public is treated to pundits, politicians and marketing hucksters to either warn or promise us that, the robots are coming to steal our jobs and burn our brains or usher in a new eden of "smart" everything, cities, cars, soda water and anything we can imagine, neither of which is true as the authors demonstrate.

It is true that the tools/techniques/approaches that fall within the field of AI (and AI is a field not a product, you cannot build "an AI") have the potential to have a profound effect on our society. And as a society we need to be able to make good choices about the adoption (or rejection) of the applications built using these tools/techniques/approaches. To do that we need good information. Gary Marcus and Ernest Davis have ridden to our rescue with a accessible critique of the current state of the space and what can and should be done going forward. What "AI" is and what it is not. What the the tools/techniques/approaches can do, and what the can not.
It also an antidote to the endless marketing hype that one critic has correctly labeled as "AIwashing". The process of appending "smart" or "intelligent" to any product without actually describing what that means.

This is an important book and worth your time.
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A M
4.0 out of 5 starsVerified Purchase
An interesting criticism of deep learning as the tool that will bring AGI
Reviewed in the United States on October 20, 2019
Almost all the computer science researchers and programmers unanimously adopt machine learning in general and deep learning in particular as the algorithm that will bring AI and AGI (artificial general intelligence). This book begs to differ. It bravely criticizes the... See more
Almost all the computer science researchers and programmers unanimously adopt machine learning in general and deep learning in particular as the algorithm that will bring AI and AGI (artificial general intelligence). This book begs to differ. It bravely criticizes the prospects of deep learning to deliver AGI, by presenting cases where this approach fails and suggests some half-baked ideas in regards to other approaches such as using Predicate Calculus (in Logic) in order to implement common-sense knowledge. Some of the text is repetitive.
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Top reviews from other countries

Henner Hinze
3.0 out of 5 starsVerified Purchase
Not much substance in here Prof Marcus
Reviewed in Germany on October 17, 2019
A good portion of the book looses itself in flowery examples of the uncertainties of everyday trivialities that humans master withy ease but machines are far from solving. Towards the end the authors muse at length how much easier live would be once we only would have...See more
A good portion of the book looses itself in flowery examples of the uncertainties of everyday trivialities that humans master withy ease but machines are far from solving. Towards the end the authors muse at length how much easier live would be once we only would have machines intellectually as capable as humans. But when it comes to a clear plan on how to get there it all remains very abstract and vague-because that’s the difficult part. This is not to say Marcus and Davis don’t have a point. They have and a good one indeed: the current cure-all in AI, Deep Learning, might be practically useful but is also clearly a dead end towards artificial “deep understanding” and trustworthy systems. This could have been laid out in a journal article though, it might not have needed a whole book. If you’re new to the topic of AI and just have a general interest, this book could be for you. If you are an enthusiast already and want to widen your horizon it probably won’t do the job.
A good portion of the book looses itself in flowery examples of the uncertainties of everyday trivialities that humans master withy ease but machines are far from solving. Towards the end the authors muse at length how much easier live would be once we only would have machines intellectually as capable as humans. But when it comes to a clear plan on how to get there it all remains very abstract and vague-because that’s the difficult part.
This is not to say Marcus and Davis don’t have a point. They have and a good one indeed: the current cure-all in AI, Deep Learning, might be practically useful but is also clearly a dead end towards artificial “deep understanding” and trustworthy systems. This could have been laid out in a journal article though, it might not have needed a whole book.
If you’re new to the topic of AI and just have a general interest, this book could be for you. If you are an enthusiast already and want to widen your horizon it probably won’t do the job.
5 people found this helpful
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C. Jaensch
5.0 out of 5 starsVerified Purchase
Spannende sowie visionäre Lektüre zum Thema KI
Reviewed in Germany on October 9, 2019
Ich bin noch nicht ganz durch, doch schon die ersten Kapitel begeistern. Die Autoren begutachten kritisch den aktuellen Stand (State of the Art) der KI-Methoden und verweisen unverhohlen auf Defizite, die sonst in den Medien stiefmütterlich oder garnicht behandelt werden....See more
Ich bin noch nicht ganz durch, doch schon die ersten Kapitel begeistern. Die Autoren begutachten kritisch den aktuellen Stand (State of the Art) der KI-Methoden und verweisen unverhohlen auf Defizite, die sonst in den Medien stiefmütterlich oder garnicht behandelt werden. Im Vergleich zu anderen kritischen Büchern zum Thema AI sind die Autoren aber keine AI-Gegner, nein Ihr Anliegen ist: die aktuellen Defizite direkt beim Namen zu nennen, aber auch Wege und Möglichkeiten zu skizzieren, wie diese überwunden werden könnten. Insgesamt eine spannende Lektüre, die ich jedem AI-Interessierten ohne Einschränkung empfehlen kann.
Ich bin noch nicht ganz durch, doch schon die ersten Kapitel begeistern. Die Autoren begutachten kritisch den aktuellen Stand (State of the Art) der KI-Methoden und verweisen unverhohlen auf Defizite, die sonst in den Medien stiefmütterlich oder garnicht behandelt werden. Im Vergleich zu anderen kritischen Büchern zum Thema AI sind die Autoren aber keine AI-Gegner, nein Ihr Anliegen ist: die aktuellen Defizite direkt beim Namen zu nennen, aber auch Wege und Möglichkeiten zu skizzieren, wie diese überwunden werden könnten.
Insgesamt eine spannende Lektüre, die ich jedem AI-Interessierten ohne Einschränkung empfehlen kann.
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Ricardo
4.0 out of 5 starsVerified Purchase
A critical review of the current development of AI, a contrasting view of AI''s current hype.
Reviewed in Mexico on January 7, 2020
Throughout the history, there are generally cycles that oscillate between the extremes of two dialectically opposed positions resulting in a new stage in the historical development of contraries. REBOOTING AI analyzes the current hype of the AI, and especially the "Deep...See more
Throughout the history, there are generally cycles that oscillate between the extremes of two dialectically opposed positions resulting in a new stage in the historical development of contraries. REBOOTING AI analyzes the current hype of the AI, and especially the "Deep Learning". The AI has reached such a point that it covers a good part of startup investments, technological developments, new products, and even politics. REBOOTING AI on this sense analyzes this current AI hype emphasizing that AI is essentially a set of statistical algorithms, which are still far from a real and strong intelligence. The rhetoric existing in publications, announcements of new products, developments or research has messianic dyes according to G. Marcus. The problem is that the industry exaggerate the announcements, capabilities, functionalities and possibilities of AI. The truth is that the current AI has a very short and reduced scope. The tasks AI can do are very specific, within a delimited domain. The present AI is a kind of digital idiot savant, very capable in pattern detection but with zero understanding. AI cannot deal with a real world that is open, and that is not limited in specific contexts. The book argues extensively and with many examples that Deep Learning is not the panacea to AI in the long term. Deep Learning has many limitations and it is not foreseeable that in the future it cannot be a solution to achieve strong AI. AI can only work with a large amount of data to learn and statistical algorithms to identify patterns. This restraint is becoming increasingly evident. G. Marcus proposes that you need to use cognitive architectures, using the concepts and research of classical AI, cognitive psychology and neurosciences. G. Marcus details throughout the book, the difficulties of AI in linguistics and natural understanding of language. The examples are profuse, and sometimes repetitive. With just one example, it would be enough to capture the idea. Although the book is for the general people reading, I consider that some sections are a bit hard and repetitive, explaining the cognitive processes and semantic analysis of texts that are required for AI. G. Marcus''s summary and proposal to the current limitations of AI is that AI requires to use complex computational cognitive models and not just neural networks with pattern detection. Although G. Markus refers to several books and publications related to the subject, it seems to me that it would have been good to talk about research and advances in Computational Psychology (for example: The Cambridge Handbook of Computational Psychology). G. Markus says that we need a new generation of AI researchers who know well and appreciate classical AI, machine learning and computer science more broadly, and take advantage of AI''s historical knowledge base. AI must evolve and reboot going from just recognizing patterns without understanding, to an understanding of what it perceives, to have common sense and to deal with causality. AI is, in general, on the wrong path, with limited intelligence for just narrow tasks, learned with big data and without deep understanding. G. Markus''s proposal is to achieve an AI that has a) common sense, b) cognitive models, and c) reasoning. However given the AI current limitation is worth to consider that AI is increasingly playing an important role that impact our daily lives, in the social, political, industrial, health and commercial realms. Undoubtedly AI is deeply transforming how we purchase, decide, socialize and care our health. I think . REBOOTING AI is a good book that provides a critical review of the current development of AI. It provides a contrasting view of AI''s current hype.
Throughout the history, there are generally cycles that oscillate between the extremes of two dialectically opposed positions resulting in a new stage in the historical development of contraries. REBOOTING AI analyzes the current hype of the AI, and especially the "Deep Learning". The AI has reached such a point that it covers a good part of startup investments, technological developments, new products, and even politics. REBOOTING AI on this sense analyzes this current AI hype emphasizing that AI is essentially a set of statistical algorithms, which are still far from a real and strong intelligence.

The rhetoric existing in publications, announcements of new products, developments or research has messianic dyes according to G. Marcus. The problem is that the industry exaggerate the announcements, capabilities, functionalities and possibilities of AI. The truth is that the current AI has a very short and reduced scope. The tasks AI can do are very specific, within a delimited domain. The present AI is a kind of digital idiot savant, very capable in pattern detection but with zero understanding. AI cannot deal with a real world that is open, and that is not limited in specific contexts.

The book argues extensively and with many examples that Deep Learning is not the panacea to AI in the long term. Deep Learning has many limitations and it is not foreseeable that in the future it cannot be a solution to achieve strong AI. AI can only work with a large amount of data to learn and statistical algorithms to identify patterns. This restraint is becoming increasingly evident. G. Marcus proposes that you need to use cognitive architectures, using the concepts and research of classical AI, cognitive psychology and neurosciences.

G. Marcus details throughout the book, the difficulties of AI in linguistics and natural understanding of language. The examples are profuse, and sometimes repetitive. With just one example, it would be enough to capture the idea. Although the book is for the general people reading, I consider that some sections are a bit hard and repetitive, explaining the cognitive processes and semantic analysis of texts that are required for AI.

G. Marcus''s summary and proposal to the current limitations of AI is that AI requires to use complex computational cognitive models and not just neural networks with pattern detection. Although G. Markus refers to several books and publications related to the subject, it seems to me that it would have been good to talk about research and advances in Computational Psychology (for example: The Cambridge Handbook of Computational Psychology). G. Markus says that we need a new generation of AI researchers who know well and appreciate classical AI, machine learning and computer science more broadly, and take advantage of AI''s historical knowledge base.

AI must evolve and reboot going from just recognizing patterns without understanding, to an understanding of what it perceives, to have common sense and to deal with causality. AI is, in general, on the wrong path, with limited intelligence for just narrow tasks, learned with big data and without deep understanding. G. Markus''s proposal is to achieve an AI that has a) common sense, b) cognitive models, and c) reasoning.

However given the AI current limitation is worth to consider that AI is increasingly playing an important role that impact our daily lives, in the social, political, industrial, health and commercial realms. Undoubtedly AI is deeply transforming how we purchase, decide, socialize and care our health.

I think . REBOOTING AI is a good book that provides a critical review of the current development of AI. It provides a contrasting view of AI''s current hype.
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Sandy
5.0 out of 5 starsVerified Purchase
A critical review today''s AI moving on a parochial path with little understanding of the world.
Reviewed in India on February 16, 2020
Summary : Contemporary AI is progressing on a parochial path of supervised learning, requiring an humungous amount of labelled (curated) data. This approach is not only making AI processes centralised, at the hands of a few large companies like the googles'', Amazons and...See more
Summary : Contemporary AI is progressing on a parochial path of supervised learning, requiring an humungous amount of labelled (curated) data. This approach is not only making AI processes centralised, at the hands of a few large companies like the googles'', Amazons and facebooks, but it is also making progress along a narrow path which is devoid of real world "intelligence". AI had promised (perhaps hyped) flying cars, but it seems to be failing even at basis task of driving cars safely out of driveway. Artificial Intelligence is premised on the basis of machine learning tool called "neural network" which uses a method called back propagation while adjusting weight and bias in the network. This method requires huge set of curated training data to "train" the network. It falters pretty quickly at a "black swan" moment like for e.g. a self driving car suddenly encountering a white pickup trailer truck and thus completely ignoring it. Today''s AI system are quite "dumb" in its understanding of the world and work well in a very narrow set of environments like a chess or go game which are essentially limited by the number of cells in the environment. The computer which mastered GO games had to play over 30 million events to master the game and when the scope of game was even slightly altered it went back to square one. Gary Marcus thus argues that AI system need to robust and resilient to manage everyday task. They argue AI needs to pick up a different direction which is not based on "huge data processing" but rather learning with unsupervised and unstructured data set . The book is also in a way a celebration of human (thus all living beings) brain which quite unique in its ability to operate under a variety of circumstance with very little training
Summary : Contemporary AI is progressing on a parochial path of supervised learning, requiring an humungous amount of labelled (curated) data. This approach is not only making AI processes centralised, at the hands of a few large companies like the googles'', Amazons and facebooks, but it is also making progress along a narrow path which is devoid of real world "intelligence". AI had promised (perhaps hyped) flying cars, but it seems to be failing even at basis task of driving cars safely out of driveway. Artificial Intelligence is premised on the basis of machine learning tool called "neural network" which uses a method called back propagation while adjusting weight and bias in the network. This method requires huge set of curated training data to "train" the network. It falters pretty quickly at a "black swan" moment like for e.g. a self driving car suddenly encountering a white pickup trailer truck and thus completely ignoring it.

Today''s AI system are quite "dumb" in its understanding of the world and work well in a very narrow set of environments like a chess or go game which are essentially limited by the number of cells in the environment. The computer which mastered GO games had to play over 30 million events to master the game and when the scope of game was even slightly altered it went back to square one.

Gary Marcus thus argues that AI system need to robust and resilient to manage everyday task. They argue AI needs to pick up a different direction which is not based on "huge data processing" but rather learning with unsupervised and unstructured data set .

The book is also in a way a celebration of human (thus all living beings) brain which quite unique in its ability to operate under a variety of circumstance with very little training
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Josh
5.0 out of 5 starsVerified Purchase
Excellent book on limitation of deep learning
Reviewed in Canada on August 24, 2020
A lot of people look at the progress today and assume we are getting closer to the holy grail of intelligent robots when in fact we are taking a shortcut. I have often said statistics is a shortcut to derive at an answer when there are too many variables and that''s what...See more
A lot of people look at the progress today and assume we are getting closer to the holy grail of intelligent robots when in fact we are taking a shortcut. I have often said statistics is a shortcut to derive at an answer when there are too many variables and that''s what deep learning is today, a massive shortcut. Instead of programming robots from the ground up with intelligence, we use deep learning to find statistical correlations meaning robots often guess right but we don''t understand when or why they will be wrong. The authors don''t aay deep learning is bad just that today people see it as the silver bullet when it''s just a powerful tool in narrow AI that can fool people into thinking it has intelligence. For real AI, deep learning won''t be enough or that important. My personal opinion is narrow AI can still solve real problems like automated driving and many other cool things but I agree with the authors that it''s not taking us closer to real artificial intelligence and deep learning is sort of a bad name. It should really be called something less grand like advanced correlation testing as to not pretend it''s something it''s not.
A lot of people look at the progress today and assume we are getting closer to the holy grail of intelligent robots when in fact we are taking a shortcut.

I have often said statistics is a shortcut to derive at an answer when there are too many variables and that''s what deep learning is today, a massive shortcut.

Instead of programming robots from the ground up with intelligence, we use deep learning to find statistical correlations meaning robots often guess right but we don''t understand when or why they will be wrong.

The authors don''t aay deep learning is bad just that today people see it as the silver bullet when it''s just a powerful tool in narrow AI that can fool people into thinking it has intelligence. For real AI, deep learning won''t be enough or that important.

My personal opinion is narrow AI can still solve real problems like automated driving and many other cool things but I agree with the authors that it''s not taking us closer to real artificial intelligence and deep learning is sort of a bad name. It should really be called something less grand like advanced correlation testing as to not pretend it''s something it''s not.
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