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Rebooting AI: Building Artificial Intelligence We Can Trust

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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 AI. Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we are led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the 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 AI. Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we are 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 winning in games like Jeopardy and go 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. These approaches are too narrow to achieve genuine intelligence. The world we live in is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Marcus and Davis show us what we need to first accomplish before we get there and argue that if we are wise along the way, we won't need to worry about a future of machine overlords. If we heed their advice, humanity can create an AI that we can trust in our homes, our cars, and our doctor's offices. Reboot provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of what we can achieve and how AI can make our lives better.


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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 AI. Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we are led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the 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 AI. Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we are 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 winning in games like Jeopardy and go 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. These approaches are too narrow to achieve genuine intelligence. The world we live in is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Marcus and Davis show us what we need to first accomplish before we get there and argue that if we are wise along the way, we won't need to worry about a future of machine overlords. If we heed their advice, humanity can create an AI that we can trust in our homes, our cars, and our doctor's offices. Reboot provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of what we can achieve and how AI can make our lives better.

30 review for Rebooting AI: Building Artificial Intelligence We Can Trust

  1. 5 out of 5

    Nick

    The central thesis of this book is that AI is not good enough. It is much closer to basic statistical inference than something that understands the world like a human. However, this is really all the authors needed. A short OpEd would be just as valuable as writing a 200-page book. They have a lot of examples, which do advance their point, but it makes the writing feel repetitive. Yes, AI today cannot understand the implied points of a sentence. However, they then end up providing a bunch of The central thesis of this book is that AI is not good enough. It is much closer to basic statistical inference than something that understands the world like a human. However, this is really all the authors needed. A short OpEd would be just as valuable as writing a 200-page book. They have a lot of examples, which do advance their point, but it makes the writing feel repetitive. Yes, AI today cannot understand the implied points of a sentence. However, they then end up providing a bunch of similar examples which don't provide additional valuable context. The proposed solutions are also unhelpful. They say that AI can't understand implied points, so the solution is to do that. Well obviously researchers would do that if they could. The author acknowledges this is hard, but doesn't seem to have any appreciation for this difficulty. Overall, it's not a great read. The first chapter provides everything you need to know, and after that there's not much point in reading.

  2. 4 out of 5

    Reza Mahmoudi

    یوشوا بنجیو یکی از برنده های جایزه تورینگ سال 2018 می گه اگر میخواهید برنده جایزه بعدی تورینگ بشید باید روی چیز دیگری غیر از یادگیری عمیق یا دیپ لرنینگ کار کنید. Gary Marcus خواندن این کتاب از نقطه خوبی برای شروع هستش

  3. 4 out of 5

    Nestor Rychtyckyj

    This well-written and very accessible book by Gary Marcus and Ernest Davis should be required reading for anybody that is overwhelmed by the current boom (and hype) in Artificial Intelligence (AI). For most people - the term AI is referring exclusively to Deep Learning and ignoring all of the other significant work that is going on in the area. When every product from golf clubs to vacuum cleaners is now advertised as being “powered by AI”, perhaps it’s time to step back and take a look at where This well-written and very accessible book by Gary Marcus and Ernest Davis should be required reading for anybody that is overwhelmed by the current boom (and hype) in Artificial Intelligence (AI). For most people - the term AI is referring exclusively to Deep Learning and ignoring all of the other significant work that is going on in the area. When every product from golf clubs to vacuum cleaners is now advertised as being “powered by AI”, perhaps it’s time to step back and take a look at where this technology actually is going to take us. This is precisely the point behind this book: Marcus and Davis actually do know what is happening behind the scenes and their scathing indictment of “AI by press release” should make us wonder how reliable these systems are and how far will a strictly data-driven approach actually take us to real “general AI”. The first part of the book shows that there has been tremendous progress by applying Deep Learning to various problems, but this progress is generally limited to narrow problem domains and this “AI” is actually pretty shallow and cannot be generalized. As we all already know - the hype over autonomous vehicles is slowly fading away with the realization that a true reliable self-driving car that can function in a real-world environment is still years away. Other headline-grabbing stories of AI replacing radiologists or human translators are similarly debunked. Yes, Deep Learning is a tremendous achievement but should not be applied to every problem and will not lead to the type of AI that will truly be game changing. In the second part of the book, Marcus and Davis do explain that data-driven approaches will never be able to solve problems that require reasoning, common sense and generalization. They then provide an excellent overview of how knowledge-driven approaches will need to be combined with Deep Learning to give us a chance to build robust and reliable AI systems that we can depend on. AI seems to bring out hyperbole and hype more than almost any other technology and makes people think that we are on the verge of Skynet. Unfortunately, this hype quickly leads to disappointment and criticism when outlandish claims are not fulfilled. Marcus and Davis have done a tremendous job in giving us an inside view of where AI really is and provide some good lessons of where AI should go to make meaningful progress in building intelligent machines.

  4. 4 out of 5

    Darnell

    The first part of this book, covering the limits of current AI research, was quite solid. The number of examples might be a bit excessive, but it helped show me that I've fallen victim to the tendency to make assumptions about rates of progress. The book was worth it for this part. Unfortunately, the book doesn't have much to offer in terms of solutions despite spending a large number of pages on it. There's no point in saying that AI would be better if we could solve extremely complex problems, The first part of this book, covering the limits of current AI research, was quite solid. The number of examples might be a bit excessive, but it helped show me that I've fallen victim to the tendency to make assumptions about rates of progress. The book was worth it for this part. Unfortunately, the book doesn't have much to offer in terms of solutions despite spending a large number of pages on it. There's no point in saying that AI would be better if we could solve extremely complex problems, especially after discussing how difficult much simpler problems have been.

  5. 5 out of 5

    Ricardo Acuña

    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 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.

  6. 5 out of 5

    CHERYL D

    Serious an thoughtful redirect for AI 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 Serious an thoughtful redirect for AI 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.

  7. 5 out of 5

    Frederick Gault

    This book is long on examples of what current "AI" based systems don't do properly. The authors contend that the current approach, neural net based deep learning based on big data, is only part of the problem space facing commercial AI based products. They would like to see more "common sense", among other things. They freely admit that right now no one seems to know how to do common sense and in fact most research is focused on the current approach. In their opinion, current research is This book is long on examples of what current "AI" based systems don't do properly. The authors contend that the current approach, neural net based deep learning based on big data, is only part of the problem space facing commercial AI based products. They would like to see more "common sense", among other things. They freely admit that right now no one seems to know how to do common sense and in fact most research is focused on the current approach. In their opinion, current research is erroneously convinced that continuing down the current path will result in robust products. Current AI can do amazing things, punctuated by bone-headed decisions that humans wouldn't make. They make a compelling argument as to why current products are incomplete and sometimes dangerous - for example: a self-driving car may lull the driver into not paying attention, when the AI gets into a situation it can't handle, the driver isn't focused. Indeed, people have been killed when these systems fail. Being right most of the time isn't really an option. In addition these systems are self learning and difficult to reverse engineer to make fixes.

  8. 4 out of 5

    Katja

    Has its ups and downs, but overall very interesting I kind of ruined it for me by reading some of the negative comments on here. Therefore, I cannot say if the first few chapters are really annoyingly pessimistic or the reviews have blown it out of proportion for me. So yeah, the first few chapters are a bit tough to get through, but it gets better. The chapters on the technology behind deep learning and the limitations of current AI techniques were just deep enough for a layman. So yeah, overall Has its ups and downs, but overall very interesting I kind of ruined it for me by reading some of the negative comments on here. Therefore, I cannot say if the first few chapters are really annoyingly pessimistic or the reviews have blown it out of proportion for me. So yeah, the first few chapters are a bit tough to get through, but it gets better. The chapters on the technology behind deep learning and the limitations of current AI techniques were just deep enough for a layman. So yeah, overall really interesting and insightful, but more often than not something was missing. 3/5

  9. 5 out of 5

    Peter O'Kelly

    Some related resources to consider: Reviews: • https://www.nyjournalofbooks.com/book... • https://www.zdnet.com/article/for-a-m... • https://www.psychologytoday.com/us/bl... • https://onezero.medium.com/a-i-isnt-a... Interview: https://www.technologyreview.com/s/61... Excerpt/adaptation: https://www.nytimes.com/2019/09/06/op...

  10. 5 out of 5

    Nguyen Tan

    The book is a bunch of examples to show that the current machine learning models are not human-level intelligence. We all know this! The book claims without any strong evidence that deep learning is not enough for AGI and the good ol' fashioned AI is the solution. In short, this book is written in an alternative universe in which animals are very good at logic and math and very bad at recognizing food and predator.

  11. 4 out of 5

    Jari Pirhonen

    Good discussion why artificial intelligence still has a long way to go in order to be generally useful. Current AI implementations are narrow and creating artificial general intelligence is difficult. The book has many examples where AI is not even close to what even a kid could do. The purpose of the book is not to criticize AI and it's possibilities, but to show the current limitations and set right expectations.

  12. 5 out of 5

    Pedro Martinez

    "Rebooting AI" from Gary Marcus and Ernest Davis is a remarkable guide to be appropriately sceptical on overstated Artificial Intelligence and Deep Learning commercial hypes and how to drive it forward for future needed right-purpose aims, in a fair and safe way. Insightful for IT professionals towards a safe, trustworthy, and reliable technology that will help humanity in the future.

  13. 5 out of 5

    Priyaranjan Padhi

    Even though the critique leveled at current 'AI' methods in this book are not new, they are necessary more so in the age of relatively unwarranted & excessive uncritical coverage of current machine learning methods such as deep learning being hailed as the ushers of the era of artificial general intelligence.

  14. 5 out of 5

    Xiaozhe晓哲

    The first 30% is good, the critics about deep learning are also sound. However, the rest parts especially the proposed solutions are just OK. Their suggestions include bringing in traditional AI, implement common sense, etc. Then they claim that after we doing these, AI can "suddenly" achieve better and be trusted results.

  15. 4 out of 5

    Leonard Papenmeier

    Might be interesting for non-experts If you are in the field of machine learning or related, this book will bring you no new insights. It might be interesting for a broader audience though.

  16. 5 out of 5

    Matthew Reinbold

    The book makes a strong case why current machine learning advances are "narrow" and media hype is overblown. However, the beat down gets repetitive and where to go from there ("deep understanding") lacks implementable substance.

  17. 4 out of 5

    HWZhu

    An astute analysis of the status quo of AI.

  18. 4 out of 5

    Subodh Shivapuja

    Informative read... nice splash of cold water.

  19. 5 out of 5

    A F

    Boring.

  20. 5 out of 5

    Justin

    Kyle rec

  21. 4 out of 5

    Yuanchu Dang

    A collection of complaints regarding deep learning without proposal for solutions beyond calling for some vague deep understanding and logic system.

  22. 4 out of 5

    Ilteris

    It's okay. Too many repetitive examples of what doesn't work but not enough clear examples of what could work.

  23. 5 out of 5

    Christopher Flesher

    Just a bunch of complaints

  24. 4 out of 5

    Jose Mendoza

    Interesting Interesting take on the advances of AI and the limitations the field currently faces. I would recommend it to anyone that has an interest in AI.

  25. 4 out of 5

    Vitor Cerqueira

  26. 5 out of 5

    Ken Williams

  27. 4 out of 5

    Timothy

  28. 4 out of 5

    Andres Umana

  29. 5 out of 5

    Brett McStotts

  30. 5 out of 5

    Luke

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