- File Size: 18293 KB
- Print Length: 290 pages
- Publisher: Pantheon (September 10, 2019)
- Publication Date: September 10, 2019
- Sold by: Random House LLC
- Language: English
- ASIN: B07MYLGQLB
- Text-to-Speech: Enabled
- Word Wise: Enabled
- Lending: Not Enabled
- Amazon Best Sellers Rank: #94,297 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Rebooting AI: Building Artificial Intelligence We Can Trust Kindle Edition
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—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.”
“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
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.
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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.
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.
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.
Top international reviews
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.
Insgesamt eine spannende Lektüre, die ich jedem AI-Interessierten ohne Einschränkung empfehlen kann.
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.
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