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Mind Over Machine: The Power of Human Intuition and Expertise in the Era of the Computer

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Defining the limits of computer technology, the authors make a compelling case that binary logic will always be inferior to human intuitive ability. A stunning reaffirmation of human intelligence.


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Defining the limits of computer technology, the authors make a compelling case that binary logic will always be inferior to human intuitive ability. A stunning reaffirmation of human intelligence.

30 review for Mind Over Machine: The Power of Human Intuition and Expertise in the Era of the Computer

  1. 5 out of 5

    Chris

    It was very interesting to read this book so long after its publication in 1987. It was enlightening to see that much of the 'hype' surrounding artificial intelligence was already in place at the time, and many of the predictions we see today regarding the great strides that AI will make in the next two decades were made as far back as the 1960s, by serious AI researchers. Given that this book is more than 30 years old, many of the factual aspects are out of date - there is a focus on contemporan It was very interesting to read this book so long after its publication in 1987. It was enlightening to see that much of the 'hype' surrounding artificial intelligence was already in place at the time, and many of the predictions we see today regarding the great strides that AI will make in the next two decades were made as far back as the 1960s, by serious AI researchers. Given that this book is more than 30 years old, many of the factual aspects are out of date - there is a focus on contemporaneous technologies, many of which are now obsolete. These parts can be read almost as a primary historical source - what was the state of AI research at the time, where have we come in the subsequent years, and what issues, apparent 30 years ago, continue to plague the field? An interesting aspect to note is that the 'neural net/machine learning/deep learning' approach, so ubiquitous today that is seems almost terminologically interchangeable with AI - at least to an industry outsider such as myself - is one of many approaches describes in the book. Clearly, at the time it had not emerged as the champion AI technique it seems to be today. The parts of the book which focus on the state of 1980s AI research are then clearly at risk of redundancy, and many of the assertions made have not aged particularly well. The author appears to have an interest in chess, and many paragraphs cover the application of AI to beating human chess players. At the time, no AI program had convincingly demonstrated superiority to the world's best chess players, and the author suggests that one never might. A decade after the book's publication, IBM's Deep Blue beat the reigning world champion Garry Kasparov in a six-game match - not without controversy, it must be noted - and subsequent research efforts have developed AI programs capable of beating the world's best players at many games - most recently, Go and six-man Hold 'Em poker. The author is also pessimistic on the viability of computer vision applications, most conspicuously on the topic of facial recognition. We have seen great strides in facial recognition technology in recent years - for better of worse - and as such this is another prediction which has not aged well. Despite this, I do believe that the book contains a great deal of timeless wisdom and insight. For one, it illustrates how overlapping are the areas of neuroscience, philosophy, and artificial intelligence research. For one such as myself, whose exposure to AI research is limited to that covered in the media and the odd machine learning-focused practical workshop, it can seem that AI is a purely technical issue - provide the model with better data, train it long and well enough, and eventually we can train computers to perform any number of tasks. But this book explains very well the more fundamental issues at play. I feel that the author made a convincing case in these parts. I can recommend this book but suggest that it be supplemented with more up-to-date accounts of the state of AI research.

  2. 5 out of 5

    Chetan Vashisht

    3.5/5 Written in 1987, the things talked in the book are still very relevant today. His model for skill acquisition and expertise is extremely well written. They also deal with some very interesting topics surrounding technology like it's use in education and management. But these topics were fairly dry in the book. Some of the sections in the book were extremely boring to read (and very out of date), but it's still fun to understand the history of the subject and read about it's evolution. Takeaw 3.5/5 Written in 1987, the things talked in the book are still very relevant today. His model for skill acquisition and expertise is extremely well written. They also deal with some very interesting topics surrounding technology like it's use in education and management. But these topics were fairly dry in the book. Some of the sections in the book were extremely boring to read (and very out of date), but it's still fun to understand the history of the subject and read about it's evolution. Takeaways from the book are the five stage model of learning to expert. The novice, expert beginner, Competent, Proficient and Expert. In each step of the journey, the decision making shifts from analytical towards intuitive and the commitment shown increases as well. The novice uses rule based reasoning to get to answers and the expert makes almost does no rule based analysis. But this means that when an expert says that they feel a certain way and they can't explain why, it's because they really don't know why. They've seen so many cases over the years and often the causality and the rules for choosing a particular path might not be a straight forward to explain. But this does put an expert into the dealing with the problem of cognitive tunnelling. Computers and technology should be used where human expertise and intuition is at a minimum (ideally zero) and bots by far out perform humans here. If you have an operation which can be performed using a set of rules, then bots will excel here and it works for the human as well. Lastly, intuition is that ability of yours to explain what you understand without knowing "why" or "how" you arrived at it. A good example trying to transfer the knowledge of riding a bicycle to another soul. Although you know how, you can never express it in words.

  3. 4 out of 5

    Leonardo Longo

    I've read this book primarily for understanding of Dreyfus model of skill acquisition, of how learners acquire skills through formal instruction and practicing, but I was truly impressed on the authors point of view on the role of Artificial intelligence in our society, it's potentialities and barriers. More than approaching AI from a merely technical point of view, they use the theories from Plato, Socrates, Aristotles, Descartes and many other philosophers in order to analyze the society's way I've read this book primarily for understanding of Dreyfus model of skill acquisition, of how learners acquire skills through formal instruction and practicing, but I was truly impressed on the authors point of view on the role of Artificial intelligence in our society, it's potentialities and barriers. More than approaching AI from a merely technical point of view, they use the theories from Plato, Socrates, Aristotles, Descartes and many other philosophers in order to analyze the society's way of thinking and reacting, which is something more than valuable nowadays, with people discussing just the bits and bytes.

  4. 5 out of 5

    Chant

    The text is dated, which of course makes sense! It was published in 1986, so of course the advancements in artificial intelligence has grown. However, as the book points out, the problems of "common-sense" or general "know-how", are still very much relevant for the 21st century. I however do have a word of caution, if you've read Hubert Dreyfus's other book on AI "What Computers Can't Do" it'll be more or less the same fair in this book (which he co-wrote with his brother Stuart). The text is dated, which of course makes sense! It was published in 1986, so of course the advancements in artificial intelligence has grown. However, as the book points out, the problems of "common-sense" or general "know-how", are still very much relevant for the 21st century. I however do have a word of caution, if you've read Hubert Dreyfus's other book on AI "What Computers Can't Do" it'll be more or less the same fair in this book (which he co-wrote with his brother Stuart).

  5. 4 out of 5

    Simon Roberts

    A seminal book for anyone interested in the field of AI. Dated but still highly relevant to current debates about the potential and limitations of artificial intelligence.

  6. 5 out of 5

    Lou

    Read this while working on the concept of knowing back in the day.

  7. 4 out of 5

    Eduardo Rodríguez

    It reads today as it did 40 years ago. AI hype and hubris has not been corrected: then again, there's a lot of money in maintaining its mistakes alive. Human knowledge acquisition is not one problem among many: it is THE problem at the center of all knowledge. Some of the smartest people in History have grappled with it without reaching any definitive conclusions, but here comes a bunch of MIT nerds and Silicon Valley billionaires thinking they can solve it with a handful of mathematical party t It reads today as it did 40 years ago. AI hype and hubris has not been corrected: then again, there's a lot of money in maintaining its mistakes alive. Human knowledge acquisition is not one problem among many: it is THE problem at the center of all knowledge. Some of the smartest people in History have grappled with it without reaching any definitive conclusions, but here comes a bunch of MIT nerds and Silicon Valley billionaires thinking they can solve it with a handful of mathematical party tricks and billions of dollars. And no matter how many times they get it wrong, they don't concede or take up reading Aristotle. The AI community complained that Dreyfus was derisive and that constructive criticism instead of sarcasm may have had more of an impact. I doubt it. What drives Dreyfus' sarcasm is his outrage at seeing these philosophical amateurs delving into a well known field of study with complete disregard for anything that came before and making obvious and predictable mistakes as a result. Apparently, Edward Feigenbaum said of Dreyfus: "What does he offer us? Phenomenology! That ball of fluff. That cotton candy!" Whatever tone Dreyfus may have used, these people would have refused to engage because they wanted to see themselves as engineers (as serious and precise people of science) and not as philosophers, whom they viewed with disdain as an inferior species. And that has not changed either.

  8. 5 out of 5

    Darian

  9. 4 out of 5

    Ethan Wood

  10. 4 out of 5

    Todd

  11. 5 out of 5

    Reid

  12. 5 out of 5

    Paul Ganzon

  13. 4 out of 5

    A S

  14. 5 out of 5

    Steven Strasnick

  15. 5 out of 5

    Bog

  16. 4 out of 5

    Ioannis Polymenis

  17. 4 out of 5

    Jade Corcoran

  18. 4 out of 5

    LPenting

  19. 5 out of 5

    Leo Loos

  20. 4 out of 5

    David

  21. 4 out of 5

    Thomas Moe

  22. 4 out of 5

    Idan Regev

  23. 5 out of 5

    Mackenzie Drysdale

  24. 4 out of 5

    Kostas

  25. 4 out of 5

    Les Johnson

  26. 4 out of 5

    Josh

  27. 5 out of 5

    Robert Fitkin

  28. 5 out of 5

    Ken

  29. 4 out of 5

    Rebecca Baker

  30. 5 out of 5

    Marc van den Berg

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