Rebooting AI: Building Artificial Intelligence We Can Trust Kindle Edition

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.

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.

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.

O autor coloca diversos pontos com os quais concordo. A inteligência artificial é vista hoje como a panacéia, mas a verdade é que ela ainda está muito longe de ser capaz de fazer as maravilhas que muitos autores advogam. O caminho será longo, árduo e teremos que ter uma real disrrupção de como vemos e trabalhamos IA para chegar no fufuro sonhado onde robôs e inteligência artificial possam nos ajudar a melhorar nossas vidas.

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.

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.

中々面白かったです。ただAIの新しい方向性が書かれてる、というわけではなく、deep lerningの過大評価をおさえこもうとしたりする、あくまで現状のAI研究に対する、”再起動”が主題の本です。

the book pages are unviewable in kindle app for windows