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

a very good book. AI from many different angles. the things it can do and those it can't, yet and maybe never. the controversies and the successes. a little dated but thought provoking and much basic information. clear and concise.i only hope she will update it.

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.

Yes, the book is “old” if you consider the latest AI craziness between 2022 and now. But it gives you a clear understanding of the history of the field, as well a of how AI works. A very good book that I will use as a reference for a college course that I am teaching in the soring.

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.

Dies ist ein sehr verständliches und ausgewogenes Buch über künstliche Intelligenz. Melanie Mitchell erklärt in einfachen Worten, was KI ist, wie sie sich historisch entwickelt hat und wo ihre tatsächlichen Grenzen liegen. Besonders gut hat mir gefallen, dass sie weder übertreibt noch Angst schürt, sondern einen ruhigen und realistischen Ton beibehält, der hilft, das Thema besser zu verstehen. Der einzige Nachteil ist, dass das Buch bereits 2019 erschienen ist und deshalb die neuesten Entwicklungen bis 2025, wie große Sprachmodelle oder die breite Nutzung von KI im Alltag, nicht abdeckt. Die grundlegenden Ideen bleiben jedoch vollkommen aktuell und geben das theoretische Fundament, um die heutigen Fortschritte nachvollziehen zu können. Insgesamt halte ich es für eine hervorragende Einführung für alle, die künstliche Intelligenz ohne Übertreibungen verstehen möchten, und ich kann es uneingeschränkt empfehlen.

I genuinely believe Mitchell's book is a must-read for anyone interested in understanding AI beyond the buzzwords. It's not just about how AI works but how it fits into our society and lives. The book deepened my understanding and appreciation of AI's complexities and future trajectory.

I read this as an introduction to AI and focused mainly on the first half of the book. Although the book is partially outdated with the evolution of AI since it was written, especially regarding NLP (Natural Language Processing), it explains a lot of the core concepts which are still totally relevant. Although AI can do extraordinary things, so far it has still not been able to emulate the "common sense" of humans and Mitchell explains really well why this is fundamentally difficult for an AI. I give it five stars because it outlines the history of AI and the fundamental concepts and limitations of AI so clearly.

This book gave me the information that I wanted and more again. I remember in 1980 doing a "computer" course to find out what a floppy disk was. I read this book to find out more about this topic that I kept reading about, AI, and is it really going to make all humans redundant in the near future? The narrative was pitched at the correct level for me, I wanted to understand how AI worked without needing to be a mathematician or programming geek. I can understand the concept of how it works and what its strengths and weaknesses, and future challenges are. My understanding is that Artificial "Intelligence" is perhaps misleading. It isn't really intelligence as would be described in humans but actually sophisticated rules, systems and computing power. The author explained it as more like "idiot savant" which doesn't understand what it is doing, cannot explain it, and sometimes makes decisions based on totally false premise (but still provides the correct answer in "most" cases. She gives some good examples of that. BUT, does this mean AI is not useful. Quite the opposite, it is a very powerful tool for certain uses in well defined situations. There are lots of examples of that in today's world. In summary, a valuable book if you want to lear what all of the hype is. And it is dished up with a little bit of wry humour.

A autora elabora uma concisa e preciosa evolução histórica da Inteligência Artificial, desde seu início nos anos 1950. Aborda diversos tópicos, sobre imagens e linguagem natural, as técnicas usadas, de forma didática tornando de fácil compreensão de como funcionam as IAs. No final do livro ela coloca suas opiniões, que ao longo do livro foram bem humoradas, com muitos exemplos, faz citação de todas as referências. Um guia rápido e completo para quem deseja saber mais sobre IA e as diversas técnicas como o deep reinforcement learning. Mitchell foi orientada no seu doutorado, simplesmente, do grande cientista Douglas Hofstadter, comentando diversos diálogos entre eles. Excelente livro para quem deseja entender e conhecer a história da IA.