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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 foref 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 foref 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. 4 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 simi 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

    Mehrsa

    I've read a lot of books on AI and the future of tech and economics in general and this is by far the most mature and sober. It's not a downer like some of the books that are all "everything that is capitalism is bad" but it's also not a breathless "AI and tech will save us and change everything." AI is really good at a few things--like playing Go, Jeopardy, finding facts, sorting, etc etc. But it's really bad at all the things that humans basically learn by the time they turn 5--like common sen I've read a lot of books on AI and the future of tech and economics in general and this is by far the most mature and sober. It's not a downer like some of the books that are all "everything that is capitalism is bad" but it's also not a breathless "AI and tech will save us and change everything." AI is really good at a few things--like playing Go, Jeopardy, finding facts, sorting, etc etc. But it's really bad at all the things that humans basically learn by the time they turn 5--like common sense, reading other people, changing course, just basically walking and stuff too. But, of course. Humans are a product of millions of years of evolution and if you want to think of our brain as an algorithm (as some scientists have), then we are just a super sophisticated one and we barely understand how our own algorithm works. But the book is careful to not be a wet towel. We should definitely push ahead on developing AI, but let's see the snake oil for what it is. In short, we will not have the Jetsons at any point soon and doctors are likely to keep their jobs, but hopefully our Roomba's will stop bumping into things and just quitting and running out of batteries at some point (mine does this every night).

  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. 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... 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...

  5. 5 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.

  6. 5 out of 5

    Kyle

    This is a nice, fairly short, introduction to the current limits to deep learning and AI. The authors point out how to watch for hype, explain where we actually are currently, and give suggestions on how we should approach making general AIs rather than the narrow AIs we currently have. As somewhat of a skeptic when it comes to AI as it is now (I wouldn't trust a self-driving car right now), it is nice to see a comprehensive accounting for the problems AI now has while still acknowledging the ama This is a nice, fairly short, introduction to the current limits to deep learning and AI. The authors point out how to watch for hype, explain where we actually are currently, and give suggestions on how we should approach making general AIs rather than the narrow AIs we currently have. As somewhat of a skeptic when it comes to AI as it is now (I wouldn't trust a self-driving car right now), it is nice to see a comprehensive accounting for the problems AI now has while still acknowledging the amazing advancements made in the area. The problem does seem to be that common sense is not easy to program or learn (for machines) with our current methods. I also like that the authors focus us on practical AI problems rather than the theoretical ones of superintelligences that are very likely far in the future. While I found their discussions of a different approach interesting on how to get towards giving AI common sense, the suggestions still seem rather abstract to me. It's not clear to me how exactly one should go about doing it with computer programming after reading the book. It seems like coming up with a good way of properly conceptualizing and representing common sense is the problem, so I can't really fault them for that. If you'd like to have a very readable introduction to AI and what to look out for, then I'd strongly recommend the book. It is skeptical without being too negative, also giving praise where it is due.

  7. 5 out of 5

    Yunke Xiang

    This book tries to argue that we need some paradigm change for the current AI development. Instead of building machines that’s primarily fueled by big data and can handle specific tasks, we should have bolder vision and action and design machines that actually understands the world (have common sense, capable of reasoning). The book has offered a lot of examples on where current AI long on promise but short on delivery. I enjoyed reading it because these are all most up to date examples from the This book tries to argue that we need some paradigm change for the current AI development. Instead of building machines that’s primarily fueled by big data and can handle specific tasks, we should have bolder vision and action and design machines that actually understands the world (have common sense, capable of reasoning). The book has offered a lot of examples on where current AI long on promise but short on delivery. I enjoyed reading it because these are all most up to date examples from the big development (e.g. criticism for IBM Watson’s from the oncologists who actually used it, the most recent Tesla car accident). I do feel that the book itself has started to make some empty and vague promise itself when the authors start to lay out their ideas of the “better” path for AI which needs to have hybrid structure that incorporate innate knowledge and abilities that represent knowledge compositionally. They also suggest we need to inject common knowledge and sense into AI and enable them to judge when run into extreme situations. As much as I hope AI will do that. I do feel these are more like “nice wishes”. It is good to write them out like this as suggestions for real AI researchers but not so nice to complain while other people are doing the real work. That said, I still enjoyed reading this book and a lot of the descriptions on algorithms are accurate and informative. Nice read!

  8. 5 out of 5

    Kiwi Begs2Differ ✎

    Disappointed by this book. Besides stating the obvious, it barely scratches the surface of the AI topic. If you have a good understanding of the subject and have read a few recent technical articles you are not likely to learn much new. The authors highlight the limits of current AI research and development (predominantly based on deep learning) but they hardly add anything of value in terms of the direction that AI development should go instead. What this book proposes is a long term goal/vision Disappointed by this book. Besides stating the obvious, it barely scratches the surface of the AI topic. If you have a good understanding of the subject and have read a few recent technical articles you are not likely to learn much new. The authors highlight the limits of current AI research and development (predominantly based on deep learning) but they hardly add anything of value in terms of the direction that AI development should go instead. What this book proposes is a long term goal/vision of an AI with “common sense”, leaning towards implementing a replica of a human logic. It’s known that machines are best at tasks that humans perform less well (e.g. quick analysis of large quantities of data and identification of patterns) and vice versa, things that come naturally to humans are difficult to implement in machines (e.g. when it comes to ambiguity and applying context). That doesn't mean that AI, even with its limits, is not valuable and research to improve current systems is hopeless. It’s easy to criticise, but far more difficult to propose possible solutions to address the current AI weaknesses and limits, and in terms of providing any concrete alternatives this book doesn’t deliver. Far too shallow, repetitive and negative for me.

  9. 5 out of 5

    Filip Ilievski

    Brilliant storytelling and a balanced view of today's AI. As an AI researcher, this book was very suitable for me, though I expect it to be easy to follow by laymen too. I especially enjoyed the many examples throughout the book. The writing could be more compact, but I can leave with that

  10. 5 out of 5

    Tom Satterthwaite

    Provides great insight into the state of AI, how far it has come and how far it still has to go to attain the current levels of hype.

  11. 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.

  12. 5 out of 5

    A F

    Boring.

  13. 5 out of 5

    Christopher Flesher

    Just a bunch of complaints

  14. 5 out of 5

    Becky B

    A realistic look at the current abilities and limitations of modern AI and the author's suggestions about what needs to happen in order to get AI to the place where it could take care of household chores or put Grandpa to bed without calamity. This was a good dose of reality to combat all the bells and whistles that AI makers throw at you in press releases. AI is nowhere near understanding language, it is lightyears away from any common sense, and the fact that search results or voice recognition A realistic look at the current abilities and limitations of modern AI and the author's suggestions about what needs to happen in order to get AI to the place where it could take care of household chores or put Grandpa to bed without calamity. This was a good dose of reality to combat all the bells and whistles that AI makers throw at you in press releases. AI is nowhere near understanding language, it is lightyears away from any common sense, and the fact that search results or voice recognition software works are thanks to lots of tricks and short cuts. Watson may have won Jeopardy, but it was quickly fired when it tried to work for a real life field of study. Why? Because Watson can't problem solve or reason. It is basically a glorified search engine. Current approaches to programing AI focus on deep learning (which is basically throwing tons of examples at them and hoping they recognize what's similar). As the authors point out, deep learning is extremely different from deep understanding. The AI can just recognize a few pixels in a certain arrangement, they don't truly understand any concepts. The other, older approach is to program in everything. But programming in everything you and I automatically understand takes a long time. (They cite one company that was working on an AI program in this way and after 3 decades they still were nowhere near a finished product.) The ability to recognize an object, identify it as a pencil and understand that you can use it for writing and then write with it takes more thoughts and actions than we normally realize until you try to break it down for a very stupid, very literal computer. I think the most hilarious part of the book were the authors' recommendations about how to thwart a modern robot uprising. It just shows how far AI has to go to get anywhere near truly automated driving (they have a lot to say on the so-called automated cars out there), let alone robots that can plot a human overthrow. If you're curious. Step one of avoiding the current robot uprising is to close your door and lock it. Most current robots have serious problems with doorknobs and keys just compound that problem. Step three is to put on a shirt with a picture of something else. You can fool a current robot into thinking you are a toaster by wearing a t-shirt with a toaster on it. So while Hollywood may be intent on making believe that the robot apocalypse is imminent and your iPhone secretly hates you, the truth is our robots are stupider than most of us even realize, and you can laugh at Hollywood by climbing on your table to avoid your rogue Roomba (or just about every robot currently out there). A very interesting read and a good dose of reality to combat hype. Recommended to tech-lovers, and those worried about the robot uprising (you'll sleep easy after reading this). Notes on content: 3 strong swear words in quotes.

  15. 5 out of 5

    Derek Bridge

    This book starts off as a good appraisal of the state-of-play in AI, especially the limitations of deep learning (which is the current in-vogue version of subsymbolic AI). The book is very clear about what deep learning is lacking, in particular representations of relational knowledge (especially common-sense knowledge ) and (relatedly) compositionality. The book acknowledges too that "classical AI" (symbolic AI) is brittle. So, the diagnosis is good. But there are two problems. First is that the This book starts off as a good appraisal of the state-of-play in AI, especially the limitations of deep learning (which is the current in-vogue version of subsymbolic AI). The book is very clear about what deep learning is lacking, in particular representations of relational knowledge (especially common-sense knowledge ) and (relatedly) compositionality. The book acknowledges too that "classical AI" (symbolic AI) is brittle. So, the diagnosis is good. But there are two problems. First is that the version of deep learning that Marcus and Davis criticize is something of a strawman. They fail to acknowledge that there is a recognition of these problems among the better thinkers in the deep learning community and there is on-going work - even if none of it has the maturity yet to seem convincing. I appreciate that some of this on-going work is mathematical and abstruse, not suitable for description in a popular science polemic. But nothing that Marcus in particular says or writes seems to show any cognizance or even interest in this new work. His critique remains insouciantly high-level. The second problem is the cure that the authors propose. Long on aspiration, short on credible detail, they propose a hybrid of deep learning and classical AI. Here again we see no attempt to discuss the state-of-the-art: the plethora of already-extant marriages of learning, probabilities and logic. And, taken at face value, a hybrid would seem to take us in the wrong direction. Hybrids are all-too-often proposed as ways of achieving complementarity: the strengths of one component of the hybrid are supposed to compensate for the weaknesses of the other component. But sometimes, that's not what we get. Instead, we get a technology that inherits the weaknesses of both components. I fear the latter will be the result of any marriage of deep learning with classical AI. In my view, the solution relies instead on reaffirming the realization that symbolic AI and subsymbolic AI are not partners lying on the same plane. They co-exist at different levels of a hierarchy. They are different levels of descriptions (different models) of the same phenomenon. What we need is to find ways in which the subsymbolic (including out-growths from deep learning) can implement something like the symbolic virtual machine that the cognitive scientists describe. What we need includes: finding ways to capture more complex relations in the deeper layers of our networks (layers that lie beyond the convolutions); moving beyond simplistic output layers; and exploiting the complex relational representations to provide the inductive bias needed for one-class learning from few examples.

  16. 5 out of 5

    Robert

    Humans have been trying to make computers that think at least since the 1950s and during that time I think it's fair to say that there have been two main camps: (1) A group that feels it is important to develop systems with a solid philosophical, mathematical and scientific foundation, in such a way that we understand what they are doing, why they are doing it, and whether what they are doing truly constitutes "intelligence." (2) A group that feels it is more important to first develop systems tha Humans have been trying to make computers that think at least since the 1950s and during that time I think it's fair to say that there have been two main camps: (1) A group that feels it is important to develop systems with a solid philosophical, mathematical and scientific foundation, in such a way that we understand what they are doing, why they are doing it, and whether what they are doing truly constitutes "intelligence." (2) A group that feels it is more important to first develop systems that perform well on specific tasks (that require intelligence when performed by humans) and then hope we can make them even more "intelligent" and perhaps understand why they work. Since about 2012 the terrific performance of Deep Learning systems on tasks like (i) Language Translation (ii) Recognizing objects (iii) Speech recognition (iv) Playing board games (v) Self Driving Cars has led to an explosion in popularity for the second approach. This book is a forceful argument for the first approach. A major focus of this book is pointing out numerous weaknesses in the deep learning approach and arguing why a deep learning centric research program will NEVER lead to "intelligent" behavior. I found his arguments basically convincing, as he reinforced what I already thought, but he spelled it out in much more detail and with examples that I hadn't thought of. One central point I took way is the importance of EXPLICIT REPRESENTATION. I have come to believe that to have flexible and truly "intelligent" behavior a program needs to have a DETAILED CONCEPTUAL UNDERSTANDING of the world it is operating in. Just having statistical correlations (as arguably you get with deep learning) is not enough. So in conclusion, after reading this I am even less enthusiastic about deep learning than I was before and I now see it as a bit of a dead end from the point of view of understanding intelligence. It's a nice engineering tool but I doubt it will ever do much beyond it's current tasks (i.e. very narrow tasks that do no require common sense or any broader understanding). I would love to be wrong about this, as it would mean AI would advance faster, but I seriously doubt that I am.

  17. 4 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 erroneo 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.

  18. 5 out of 5

    J.

    I received a finished copy from the publisher via a friend, with no expectation to leave a personal review. This is a fun, accessible, and balanced approach to AI--which helpfully gives context for the nearly messianic claims newspapers so often provide about how AI is developing--from authors who, unexpectedly, are in favor of its further development. I didn't realize until I read the first chapter that that would be the case; I had just assumed that a title like this, despite the happy cover, w I received a finished copy from the publisher via a friend, with no expectation to leave a personal review. This is a fun, accessible, and balanced approach to AI--which helpfully gives context for the nearly messianic claims newspapers so often provide about how AI is developing--from authors who, unexpectedly, are in favor of its further development. I didn't realize until I read the first chapter that that would be the case; I had just assumed that a title like this, despite the happy cover, would come from skeptics like myself. I did not finish this--I only read the first chapter and part of the second--but that's not because I necessarily wanted to stop reading. It's because I started reading this for my thesis, then COVID happened, and I had to move across the country with only two bags. I didn't have room to take this, but assuming I get it mailed to me or have the time, it's a book I'd be happy to possibly return to in the future. Judging from reviews, it looks like I already got the main idea from the book, and the rest of it is just more examples--but, hey, I like examples, so why not.

  19. 5 out of 5

    Flaviu Vescan

    The book reflects a belief I've been having for some time now, mainly that we are currently barking up the wrong tree when it comes to AI. We are making amazing tools, thats certain, but most people see the latest improvements in solving a very specific problem and extrapolate from that to believe we are close to having a general purpose AI, or True AI. It compares classical AI with modern ML driven AI and talks about the strengths and weaknesses of both. Modern AI is amazing, but the flawed cla The book reflects a belief I've been having for some time now, mainly that we are currently barking up the wrong tree when it comes to AI. We are making amazing tools, thats certain, but most people see the latest improvements in solving a very specific problem and extrapolate from that to believe we are close to having a general purpose AI, or True AI. It compares classical AI with modern ML driven AI and talks about the strengths and weaknesses of both. Modern AI is amazing, but the flawed classical AI with all of it's impracticality is the only way we can ever achieve AGI, not by improving neural networks or making ML more efficient. My belief is that when it comes to developing true AI we need to go back to the drawing board and classical AI is better at helping achieve it than the modern approaches that are being overhyped by Media with hyperboles, clickbait and in some cases fraud. If you share this belief than you'll enjoy this book.

  20. 5 out of 5

    Charlie

    Half read, half listened to. This was a very interesting book that echoed so many of my views on current trends to use more machine learning in more and more applications. They reached a similar conclusion that ML will not be enough on it’s own, that something more is needed. The start and middle of the book were great - fast paced, clear examples, sharp arguments and points. The final chapters were a lot more subdued and seemed to drag a bit. I understand that there are no solutions to these har Half read, half listened to. This was a very interesting book that echoed so many of my views on current trends to use more machine learning in more and more applications. They reached a similar conclusion that ML will not be enough on it’s own, that something more is needed. The start and middle of the book were great - fast paced, clear examples, sharp arguments and points. The final chapters were a lot more subdued and seemed to drag a bit. I understand that there are no solutions to these har problems yet, but the difference between the chapters where the authors were talking about an area of expertise vs. the chapters where they were speculating is stark. Would have preferred the book without the fuzzy bits, but I’m not sure how else they could have ended it.

  21. 5 out of 5

    Adam Sherman

    I thought this was a very tempered look at AI from an intelligence perspective. Pointing out specific issues and problems that AI doesn't seem to be able to solve easily with current approaches. The necessity of instituting logic, moral values drawn from logic, and hard rules with probabilistic Bayesian and neural networks. Gary Marcus disagrees with many of the potential problems AI will cause in the upcoming future but lays out the more pragmatic and realistic ones as opposed to existential is I thought this was a very tempered look at AI from an intelligence perspective. Pointing out specific issues and problems that AI doesn't seem to be able to solve easily with current approaches. The necessity of instituting logic, moral values drawn from logic, and hard rules with probabilistic Bayesian and neural networks. Gary Marcus disagrees with many of the potential problems AI will cause in the upcoming future but lays out the more pragmatic and realistic ones as opposed to existential issues which he only touches on briefly and tends to be dismissive of the claims being made by others.

  22. 4 out of 5

    Nick

    Pretty interesting read. Takes a skeptical approach about the current state of AI. The authors believe we are still a long way off from any sort of machine take over. The most repeated idea seemed to be the gap between human intelligence and machines is the development of common sense. Their arguments were persuasive, but they kept beating the drum so I found myself skimming from time to time. I liked the Chapter about trust the most. Discussed Asimov's rules, why Bostroms paper clip example can Pretty interesting read. Takes a skeptical approach about the current state of AI. The authors believe we are still a long way off from any sort of machine take over. The most repeated idea seemed to be the gap between human intelligence and machines is the development of common sense. Their arguments were persuasive, but they kept beating the drum so I found myself skimming from time to time. I liked the Chapter about trust the most. Discussed Asimov's rules, why Bostroms paper clip example can be bypassed, and some possible legal and ethical considerations between machines (bus full of kids vs self driving car)

  23. 5 out of 5

    Prashant Singh

    A good book explains what current AI is capable of which is already quite impressive. It also points out how media and companies making AI are hyping minor incremental advances. This unnecessary hyping has also made people to have either unnecessary expectations ranging from a star trek world in a decade to terminator like ending where ai will take over humans. The book in my opinion is written well so as a non technical person who wishes to know current progress of ai in little detail. I am imp A good book explains what current AI is capable of which is already quite impressive. It also points out how media and companies making AI are hyping minor incremental advances. This unnecessary hyping has also made people to have either unnecessary expectations ranging from a star trek world in a decade to terminator like ending where ai will take over humans. The book in my opinion is written well so as a non technical person who wishes to know current progress of ai in little detail. I am impressed by how advances in different sub fields are important before making a expert level ai. The book also entails suggested reading for topics like deep learning and common sense.

  24. 5 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

  25. 5 out of 5

    Adriel N.

    Pretty accessible book that looks at the current, limited state of Artificial Intelligence and presents suggestions on how to improve the field in the future. This was my first foray into AI related literature and I found it engaging and thought provoking. The book fell apart a bit at the end by providing sweeping generalizations of where the field needs to go moving forward but, all in all, I enjoyed it and I would recommend to anyone who has some interest or general curiosity about Artificial Pretty accessible book that looks at the current, limited state of Artificial Intelligence and presents suggestions on how to improve the field in the future. This was my first foray into AI related literature and I found it engaging and thought provoking. The book fell apart a bit at the end by providing sweeping generalizations of where the field needs to go moving forward but, all in all, I enjoyed it and I would recommend to anyone who has some interest or general curiosity about Artificial Intelligence.

  26. 5 out of 5

    Tuan Nguyen-Duy-Anh

    it’s a good book for casual read while the author has provided many examples of the shortcomings of the AI in the current form, not much “deep” discussion is provided in terms of technical other than “what” we need to do. I think for those who are causally following news about AI would probably think of those “what”. the most important thing, “how”, unfortunately is largely untouched in this book. I do not expect the author to revolutionize the AI fields, but it would be more useful if the author it’s a good book for casual read while the author has provided many examples of the shortcomings of the AI in the current form, not much “deep” discussion is provided in terms of technical other than “what” we need to do. I think for those who are causally following news about AI would probably think of those “what”. the most important thing, “how”, unfortunately is largely untouched in this book. I do not expect the author to revolutionize the AI fields, but it would be more useful if the author could delve deeper into the possible directions that the author mentioned.

  27. 5 out of 5

    Rhea D'Costa

    I picked this book to get my foot in the door of AI. Truly a light introduction into the topic with loads of relevant examples about technology developed by big name companies. A surprisingly realistic perspective on the pace of AI development. Insightful exploration of a major theme that we need to move beyond big data and deep learning alone and towards AI carefully engineered with values, common sense and a deep understanding of the world. The author's footnotes and reading recommendations al I picked this book to get my foot in the door of AI. Truly a light introduction into the topic with loads of relevant examples about technology developed by big name companies. A surprisingly realistic perspective on the pace of AI development. Insightful exploration of a major theme that we need to move beyond big data and deep learning alone and towards AI carefully engineered with values, common sense and a deep understanding of the world. The author's footnotes and reading recommendations allow for a guided and deeper exploration of many sub-topics.

  28. 4 out of 5

    Jeremy

    Although some criticisms to AI from this book seem to be outdated or superficial, AI researchers and engineers should still always keep these questions in mind. Like, what’s real “intelligence”? How do we keep justice and fairness in machine learning? Etc. Many other Deep Learning enthusiasts would probably give a low rating of this book but again I appreciate the efforts of the authors. The industry needs someone like them.

  29. 5 out of 5

    Bill Vosse

    Provides a very good overview if what AI, especially that based on machine learning is, but more importantly, is not, or not yet able to do. Makes the point that narrow AI is already quite good at lot of very specific things, it still makes too many mistakes and can easily be spoofed. General AI, while wide assumed to be available soon, will still take decades to achieve, if we will ever see it the way some people imagine.

  30. 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.

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