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# Artificial Intelligence: A Guide for Thinking Humans (Pelican Books)

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## Description

'If you think you understand AI and all of the related issues, you don't. By the time you finish this exceptionally lucid and riveting book you will breathe more easily and wisely' - Michael Gazzaniga A leading computer scientist brings human sense to the AI bubble No recent scientific enterprise has been so alluring, terrifying and filled with extravagant promise and frustrating setbacks as artificial intelligence. Writing with clarity and passion, leading AI researcher Melanie Mitchell offers a captivating account of modern-day artificial intelligence. Flavoured with personal stories and a twist of humour, Artificial Intelligence illuminates the workings of machines that mimic human learning, perception, language, creativity and common sense. Weaving together advances in AI with cognitive science and philosophy, Mitchell probes the extent to which today's 'smart' machines can actually think or understand, and whether AI even requires such elusive human qualities at all. Artificial Intelligence: A Guide for Thinking Humans provides readers with an accessible and clear-eyed view of the AI landscape, what the field has actually accomplished, how much further it has to go and what it means for all of our futures.

Review: An especially insightful, accurate and readable explanation of AI limitations vis-a-vis capabilities - Thank you Prof Melanie Mitchell for the labor of love and commitment required to create your latest book, Artificial Intelligence A guide for Thinking Humans." The book is divided into four parts, with the first part serving as an introduction with appropriate historical background, and an update on current important concepts, developments and supporting terminology. Following the introduction, one core aspect of the book are the three main parts-- each with multiple chapters-- where Melanie explains the fundamentals, workings and applications of of of neural networks and image processing (Part II, Looking and Seeing), of reinforcement learning and game playing (Part III, Learning to Play), and of language processing (Part IV: Artificial Intelligence Meet Natural Language). If you are a manager or policy maker who desires a technically accurate and precise description of the foundations and key enabling mechanisms of these AI capabilities-- in order to strengthen your own understanding--- and your own "mental models" of what this technology is and how it really works--- the descriptions in this book are amongst the very best descriptions I have every come across (and I do a lot of reading in this area for both technical specialist and for broader audiences). The second core aspect of this book is the final part (Part V: The Barrier of Meaning) where Melanie beautifully develops the frameworks, concepts, illustrations and examples you need to deeply understand what it really means for humans to understand "meaning" and context, and to make intelligent inferences, predictions, abstractions and analogies based on this ability versus what very brittle and very limited ability of state-of-the-art AI systems to do so. Just these four chapters in Part V ( On Understanding; Knowledge, Abstraction, and Analogy in Artificial Intelligence; and Questions, Answers, and Speculations) justifies the effort to purchase and carefully read this book. I think Prof Melanie Mitchell has done modern society a great service by creating this book. She makes it possible for a broad range of people-- from a broad range of backgrounds--- to seriously understand the marvels of AI capabilities and accomplishments, how these capabilities and accomplishments are actually realized through computational methods, the limits of these abilities, why these limits exist, and how these machine-based computational methods that we refer to as Artificial Intelligence compare to human capabilities for understanding and intelligence. For those of you who look for this type of material to read, it is also important to know about the recently published book, "Rebooting AI" by Gary Marcus and Ernest Davis. I have read both of these books cover-to-cover, carefully. My advice-- get both of these books and read both of them. They do have overlapping concerns, and do cover some of the same types of concepts. But they go about it in very different ways. Both books are technically accurate, and have a lot of great examples. Both books will give you much deeper insight into the capabilities and limitations of state-of-the-art AI (both now, and in the foreseeable future). But they go about it in different ways, and with different styles. So I will refrain from prioritizing one book over the other, as each has its own approach, emphasis, and style. If you enjoy this type of topic, and want to learn more from people who write well, AND who have very deep understanding of these topics--- then go get both of these books, absorb them, understand them, and go on a campaign to make sure all of your friends and professional colleagues understand the key messages of both of these books.
Review: A measured book, that abhors mind numbing technicalities and arcane elaborations - René Descartes, a French philosopher, mathematician and scientist in elucidating his famous theory of dualism, expounded that there exist two kinds of foundation: mental and physical. While the mental can exist outside of the body, and the body cannot think. Popularly known as mind-body dualism or Cartesian Duality (after the theory’s proponent), the central tenet of this philosophy is that the immaterial mind and the material body, while being ontologically distinct substances, causally interact. British philosopher Gilbert Ryle‘s in describing René Descartes’ mind-body dualism, introduced the now immortal phrase, “ghost in the machine” to highlight the view of Descartes and others that mental and physical activity occur simultaneously but separately. Ray Kurzweil, the high priest of futurism and Director of Engineering at Google, takes Cartesian Duality to a higher plane with his public advocacy of concepts such as Technological Singularity and radical life extension. Kurzweil argues that with giant leaps in the domain of Artificial Intelligence, mankind will experience a radical life extension by 2045. Skeptics on the other hand bristle at this very notion, claiming such “Kurzweilian” aspirations to be mere fantasies putting to shame even the most ludicrous of pipe dreams. The advances in the field of AI have spawned a seminal debate that has a vertical cleave. On one side of the chasm are the undying optimists such as Ray Kurzweil predicting a new epoch in the history of mankind, while on the other side of the divide are placed pessimists and naysayers such as Nick Bostrom, James Barrat and even the likes of Bill Gates, Elon Musk and Stephen Hawking who advocate extreme caution and warn about existential risks. So what is the actual fact? Melanie Mitchell, a computer science professor at Portland State University takes this conundrum head on in her eminently readable book, ““Artificial Intelligence: A Guide for Thinking Humans.” A measured book, that abhors mind numbing technicalities and arcane elaborations, Ms. Mitchell’s work embodies a matter-of-fact narrative that seeks to demystify the future of both AI and its users. The book begins with a meeting organized by Blaise Agüera y Arcas, a computer scientist leading Google’s foray into machine intelligence. In the meeting, the genius AI pioneer and author of the Pulitzer Prize winning book, “Gödel, Escher, Bach: an Eternal Golden Braid” (or just “gee-ee-bee’), Douglas Hofstadter expresses downright alarm at the principle of Singularity being touted by Kurzweil. “If this actually happens, “we will be superseded. We will be relics. We will be left in the dust.” A former research assistant of Hofstadter, Ms. Mitchell is surprised to hear such an exclamation from her mentor. This spurs her on to assess the impact of AI, in an unbiased vein. Tracing the modest trajectory of the beginning of AI, Ms. Mitchell informs her reader about a small workshop in Dartmouth in 1956 where the seeds of AI were first sown. John McCarthy, universally acknowledged as the father of AI and the inventor of the term itself, persuaded Marvin Minsky, a fellow student at Princeton, Claude Shannon, the inventor of information theory and Nathaniel Rochester, a pioneering electrical engineer, to help him organize “a 2 month, 10-man study of artificial intelligence to be carried out during the summer of 1956.” What began as a muted endeavor has now morphed into a creature that is both revered and reviled, in equal measure. Ms. Mitchell lends a technical element to the book by dwelling on concepts such as symbolic and sub-symbolic AI. Ms. Mitchell, however lends a fascinating insight into the myriad ways in which various intrepid pioneers and computer experts attempted to distill the element of “learning” into a computer thereby bestowing it with immense scalability and computational skills. For example, using a technique termed, back-propagation, errors are taken away at the output units and to “propagate” the blame for that error backward so as to assign proper blame to each of the weights in the network. This allows back-propagation to determine how much to change each weight in order to reduce the error. The beauty of Ms. Mitchell’s explanations lies in its simplicity. She breaks down seemingly esoteric concepts into small chunks of ‘learnable’ elements. It is these kind of techniques that have enabled IBM’s Watson to defeat World Chess Champion Garry Kasparov, and trump over Jeopardy! Champions Ken Jennings and Brad Rutter. So with such stupendous advances, is the time where Artificial Intelligence surpasses human intelligence already upon us? Ms. Mitchell does not think so. Taking recourse to the views of Alan Turing’s “argument from consciousness,” Ms. Mitchell brings to our attention, Turing’s summary of the neurologist Geoffrey Jefferson’s quote: “Not until a machine can write a sonnet or compose a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain—that is, not only write it but know that it had written it. No mechanism could feel (and not merely artificially signal, an easy contrivance) pleasure at its successes, grief when its valves fuse, be warmed by flattery, be made miserable by its mistakes, be charmed by sex, be angry or depressed when it cannot get what it wants.” Ms. Mitchell also highlights – in a somewhat metaphysical manner – the inherent limitations of a computer to gainfully engage in the attributes of abstraction and analogy. In the words of her own mentor Hofstadter and his coauthor, the psychologist Emmanuel Sander, “Without concepts there can be no thought, and without analogies there can be no concepts.” If computers are bereft of common sense, it is not for the want of their users trying to ‘embed’ some into them. A famous case in point being Douglas Lenat’s Cyc project which ultimately turned out to be a bold, albeit futile exercise. A computer’s inherent limitation in thinking like a human being was also demonstrated by The Winograd schemas. These were schemas designed precisely to be easy for humans but tricky for computers. Hector Levesque, Ernest Davis, and Leora Morgenstern three AI researchers, “proposed using a large set of Winograd schemas as an alternative to the Turing test. The authors argued that, unlike the Turing test, a test that consists of Winograd schemas forestalls the possibility of a machine giving the correct answer without actually understanding anything about the sentence. The three researchers hypothesized (in notably cautious language) that “with a very high probability, anything that answers correctly is engaging in behaviour that we would say shows thinking in people.” Finally, Ms. Mitchell concludes by declaring that machines are as yet incapable of generalizing, understanding cause and effect, or transferring knowledge from situation to situation – skills human beings begin to develop in infancy. Thus while computers won’t dethrone man anytime soon, goading them on to bring such an endeavor to fruition might not be a wise idea, after all.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #703,321 in Books ( See Top 100 in Books ) #20 in Social Aspects of Technology #61 in Artificial Intelligence & Semantics #551 in Social Sciences (Books) |
| Customer Reviews | 4.6 out of 5 stars 1,321 Reviews |

## Images

![Artificial Intelligence: A Guide for Thinking Humans (Pelican Books) - Image 1](https://m.media-amazon.com/images/I/818o+Y27G4L.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ An especially insightful, accurate and readable explanation of AI limitations vis-a-vis capabilities
*by S***) on December 28, 2019*

Thank you Prof Melanie Mitchell for the labor of love and commitment required to create your latest book, Artificial Intelligence A guide for Thinking Humans." The book is divided into four parts, with the first part serving as an introduction with appropriate historical background, and an update on current important concepts, developments and supporting terminology. Following the introduction, one core aspect of the book are the three main parts-- each with multiple chapters-- where Melanie explains the fundamentals, workings and applications of of of neural networks and image processing (Part II, Looking and Seeing), of reinforcement learning and game playing (Part III, Learning to Play), and of language processing (Part IV: Artificial Intelligence Meet Natural Language). If you are a manager or policy maker who desires a technically accurate and precise description of the foundations and key enabling mechanisms of these AI capabilities-- in order to strengthen your own understanding--- and your own "mental models" of what this technology is and how it really works--- the descriptions in this book are amongst the very best descriptions I have every come across (and I do a lot of reading in this area for both technical specialist and for broader audiences). The second core aspect of this book is the final part (Part V: The Barrier of Meaning) where Melanie beautifully develops the frameworks, concepts, illustrations and examples you need to deeply understand what it really means for humans to understand "meaning" and context, and to make intelligent inferences, predictions, abstractions and analogies based on this ability versus what very brittle and very limited ability of state-of-the-art AI systems to do so. Just these four chapters in Part V ( On Understanding; Knowledge, Abstraction, and Analogy in Artificial Intelligence; and Questions, Answers, and Speculations) justifies the effort to purchase and carefully read this book. I think Prof Melanie Mitchell has done modern society a great service by creating this book. She makes it possible for a broad range of people-- from a broad range of backgrounds--- to seriously understand the marvels of AI capabilities and accomplishments, how these capabilities and accomplishments are actually realized through computational methods, the limits of these abilities, why these limits exist, and how these machine-based computational methods that we refer to as Artificial Intelligence compare to human capabilities for understanding and intelligence. For those of you who look for this type of material to read, it is also important to know about the recently published book, "Rebooting AI" by Gary Marcus and Ernest Davis. I have read both of these books cover-to-cover, carefully. My advice-- get both of these books and read both of them. They do have overlapping concerns, and do cover some of the same types of concepts. But they go about it in very different ways. Both books are technically accurate, and have a lot of great examples. Both books will give you much deeper insight into the capabilities and limitations of state-of-the-art AI (both now, and in the foreseeable future). But they go about it in different ways, and with different styles. So I will refrain from prioritizing one book over the other, as each has its own approach, emphasis, and style. If you enjoy this type of topic, and want to learn more from people who write well, AND who have very deep understanding of these topics--- then go get both of these books, absorb them, understand them, and go on a campaign to make sure all of your friends and professional colleagues understand the key messages of both of these books.

### ⭐⭐⭐⭐ A measured book, that abhors mind numbing technicalities and arcane elaborations
*by V***G on August 1, 2020*

René Descartes, a French philosopher, mathematician and scientist in elucidating his famous theory of dualism, expounded that there exist two kinds of foundation: mental and physical. While the mental can exist outside of the body, and the body cannot think. Popularly known as mind-body dualism or Cartesian Duality (after the theory’s proponent), the central tenet of this philosophy is that the immaterial mind and the material body, while being ontologically distinct substances, causally interact. British philosopher Gilbert Ryle‘s in describing René Descartes’ mind-body dualism, introduced the now immortal phrase, “ghost in the machine” to highlight the view of Descartes and others that mental and physical activity occur simultaneously but separately. Ray Kurzweil, the high priest of futurism and Director of Engineering at Google, takes Cartesian Duality to a higher plane with his public advocacy of concepts such as Technological Singularity and radical life extension. Kurzweil argues that with giant leaps in the domain of Artificial Intelligence, mankind will experience a radical life extension by 2045. Skeptics on the other hand bristle at this very notion, claiming such “Kurzweilian” aspirations to be mere fantasies putting to shame even the most ludicrous of pipe dreams. The advances in the field of AI have spawned a seminal debate that has a vertical cleave. On one side of the chasm are the undying optimists such as Ray Kurzweil predicting a new epoch in the history of mankind, while on the other side of the divide are placed pessimists and naysayers such as Nick Bostrom, James Barrat and even the likes of Bill Gates, Elon Musk and Stephen Hawking who advocate extreme caution and warn about existential risks. So what is the actual fact? Melanie Mitchell, a computer science professor at Portland State University takes this conundrum head on in her eminently readable book, ““Artificial Intelligence: A Guide for Thinking Humans.” A measured book, that abhors mind numbing technicalities and arcane elaborations, Ms. Mitchell’s work embodies a matter-of-fact narrative that seeks to demystify the future of both AI and its users. The book begins with a meeting organized by Blaise Agüera y Arcas, a computer scientist leading Google’s foray into machine intelligence. In the meeting, the genius AI pioneer and author of the Pulitzer Prize winning book, “Gödel, Escher, Bach: an Eternal Golden Braid” (or just “gee-ee-bee’), Douglas Hofstadter expresses downright alarm at the principle of Singularity being touted by Kurzweil. “If this actually happens, “we will be superseded. We will be relics. We will be left in the dust.” A former research assistant of Hofstadter, Ms. Mitchell is surprised to hear such an exclamation from her mentor. This spurs her on to assess the impact of AI, in an unbiased vein. Tracing the modest trajectory of the beginning of AI, Ms. Mitchell informs her reader about a small workshop in Dartmouth in 1956 where the seeds of AI were first sown. John McCarthy, universally acknowledged as the father of AI and the inventor of the term itself, persuaded Marvin Minsky, a fellow student at Princeton, Claude Shannon, the inventor of information theory and Nathaniel Rochester, a pioneering electrical engineer, to help him organize “a 2 month, 10-man study of artificial intelligence to be carried out during the summer of 1956.” What began as a muted endeavor has now morphed into a creature that is both revered and reviled, in equal measure. Ms. Mitchell lends a technical element to the book by dwelling on concepts such as symbolic and sub-symbolic AI. Ms. Mitchell, however lends a fascinating insight into the myriad ways in which various intrepid pioneers and computer experts attempted to distill the element of “learning” into a computer thereby bestowing it with immense scalability and computational skills. For example, using a technique termed, back-propagation, errors are taken away at the output units and to “propagate” the blame for that error backward so as to assign proper blame to each of the weights in the network. This allows back-propagation to determine how much to change each weight in order to reduce the error. The beauty of Ms. Mitchell’s explanations lies in its simplicity. She breaks down seemingly esoteric concepts into small chunks of ‘learnable’ elements. It is these kind of techniques that have enabled IBM’s Watson to defeat World Chess Champion Garry Kasparov, and trump over Jeopardy! Champions Ken Jennings and Brad Rutter. So with such stupendous advances, is the time where Artificial Intelligence surpasses human intelligence already upon us? Ms. Mitchell does not think so. Taking recourse to the views of Alan Turing’s “argument from consciousness,” Ms. Mitchell brings to our attention, Turing’s summary of the neurologist Geoffrey Jefferson’s quote: “Not until a machine can write a sonnet or compose a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain—that is, not only write it but know that it had written it. No mechanism could feel (and not merely artificially signal, an easy contrivance) pleasure at its successes, grief when its valves fuse, be warmed by flattery, be made miserable by its mistakes, be charmed by sex, be angry or depressed when it cannot get what it wants.” Ms. Mitchell also highlights – in a somewhat metaphysical manner – the inherent limitations of a computer to gainfully engage in the attributes of abstraction and analogy. In the words of her own mentor Hofstadter and his coauthor, the psychologist Emmanuel Sander, “Without concepts there can be no thought, and without analogies there can be no concepts.” If computers are bereft of common sense, it is not for the want of their users trying to ‘embed’ some into them. A famous case in point being Douglas Lenat’s Cyc project which ultimately turned out to be a bold, albeit futile exercise. A computer’s inherent limitation in thinking like a human being was also demonstrated by The Winograd schemas. These were schemas designed precisely to be easy for humans but tricky for computers. Hector Levesque, Ernest Davis, and Leora Morgenstern three AI researchers, “proposed using a large set of Winograd schemas as an alternative to the Turing test. The authors argued that, unlike the Turing test, a test that consists of Winograd schemas forestalls the possibility of a machine giving the correct answer without actually understanding anything about the sentence. The three researchers hypothesized (in notably cautious language) that “with a very high probability, anything that answers correctly is engaging in behaviour that we would say shows thinking in people.” Finally, Ms. Mitchell concludes by declaring that machines are as yet incapable of generalizing, understanding cause and effect, or transferring knowledge from situation to situation – skills human beings begin to develop in infancy. Thus while computers won’t dethrone man anytime soon, goading them on to bring such an endeavor to fruition might not be a wise idea, after all.

### ⭐⭐⭐⭐⭐ Very current and not at all dumbed down
*by M***S on June 3, 2020*

I cannot recommend this book enough. It offers an excellent, up-to-the-minute survey of the capabilities of artificial intelligence, the current state of the field, and sufficient background and underpinnings to show how we got to where we are today. The author, a professor and Ph.D. in computer science, writes for a general (and intelligent) audience, leaving out algorithms and programming languages from her explanations but providing well-chosen illustrations and diagrams that taught me more than all the other books and articles I've read on this topic. I was a bit skeptical during the first three chapters, when after saying she wouldn't dwell on the history of AI and its origins as a field, she seemed to be doing just that. In chapter 4, though, she dives into machine vision, what makes it hard to do, and how it works. From there on, it's a near-perfect book. The chapters build on one another and there's no redundancy. She's got new things to share right up to the very last page. And the rationale for those first three chapters becomes obvious, as the reader sees how they laid a foundation for later explanations. Best of all, the author deals with What is intelligence? and What does it mean to say a machine "learns"? and ethics and even how AI can be maliciously subverted right within the main text, while she's discussing neural nets and natural language processing and IBM's Watson and AlphaGo — not as a separate, tacked on chapter. It's the kind of book where you appreciate how the author has spent years immersed in the subject, not in a narrow academic sliver of it but broadly as well as deeply. I'd give it 6 stars if I could.

## Frequently Bought Together

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