AI, Cognitive Architectures, and the Mind

August 27

1. Melanie Mitchell (2019). Artificial Intelligence: A Guide for Thinking Humans.
   • Mitchell did her PhD under Douglas R. Hofstadter, author of the famous book GEB (Gödel, Escher, Bach, see previous list). This book is her attempt to understand the true state of affairs in artificial intelligence, trying to not only explain what computers can do now but also what we can expect from them in the coming decades.
2. Herbert A. Simon (1996). The Sciences of the Artificial
   • Herb Simon developed a thesis that has been central to much of his work: “Certain phenomena are ‘artificial’ in a very specific sense: they are as they are only because of a system’s being molded, by goals or purposes, to the environment in which it lives. If natural phenomena have an air of “necessity” about them in their subservience to natural law, artificial phenomena have an air of “contingency” in their malleability by environment”. This famous book was the attempt to explain how a science of the artificial is possible, while also illustrating its nature.
3. Nils J. Nilsson (2010). The Quest for Artificial Intelligence: A History of Ideas and Achievements.
   • Probably the best history of AI book written (the absence of the last 10 years of progress notwithstanding). Nilsson: “I have three kinds of readers in mind. One is the intelligent lay reader interested in scientific topics who might be curious about what AI is all about. Another group, perhaps overlapping the first, consists of those in technical or professional fields who, for one reason or another, need to know about AI and would benefit from a complete picture of the field – where it has been, where it is now, and where it might be going. To both of these groups, I promise no complicated mathematics or computer jargon, lots of diagrams, and my best efforts to provide clear explanations of how AI programs and techniques work. (I also include several photographs of AI people. The selection of these is somewhat random and doesn’t necessarily indicate prominence in the field.) A third group consists of AI researchers, students, and teachers who would benefit from knowing more about the things AI has tried, what has and hasn’t worked, and good sources for historical and other information. Knowing the history of a field is important for those engaged in it. For one thing, many ideas that were explored and then abandoned might now be viable because of improved technological capabilities. For that group, I include extensive end-of-chapter notes citing source material. The general reader will miss nothing by ignoring these notes. The main text itself mentions Web sites where interesting films, demonstrations, and background can be found. (If links to these sites become broken, readers may still be able to access them using the ‘Wayback Machine’ at http://www.archive.org). The book follows a roughly chronological approach, with some backing and filling. My story may have left out some actors and events, but I hope it is reasonably representative of AI’s main ideas, controversies, successes, and limitations. I focus more on the ideas and their realizations than on the personalities involved. I believe that to appreciate AI’s history, one has to understand, at least in lay terms, something about how AI programs actually work” (pp xiii-xiv).
4. Allen Newell (1990). Unified Theories of Cognition.
   • Allen Newell is another Turing Award winner. This book is based on the William James Lectures at Harvard University. His thesis is that psychology is ready for unified theories of cognition, and this offers an important contribution to the understanding of mental architecture. Here an overview of the content of the book:
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We again take a look at two books from Marvin Minsky, one of the fathers of artificial intelligence. Truly a treat and so much grasp and process of understanding human cognitive architecture! Isaac Asimov once said: “The only people I ever met whose intellects surpasses my own were Carl Sagan and Marvin Minsky”. Here you can find a celebration of Marvin Minsky after he died PART 1, PART 2, PART 3, PART 4. Here is an interview with him on Web of Stories.

1. Marvin Minsky (1986). The Society of Mind.
2. Marvin Minsky (2006). The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind.
   • All his lectures are available on MIT OpenCourseWare: Video Lectures in 2011 (at the age of 84). A must watch (take the time for it)!

 

1. Joscha Bach (2009). Principles of Synthetic Intelligence: PSI, An Architecture of Motivated Cognition.
   • Bach is an AI expert who previously worked at the MIT Media Lab and Martin Nowak’s group Program for Evolutionary Dynamics. His work, based on his PhD, was inspired by theoretical psychologist Dietrich Dörner’s PSI theory42. According to Margaret Boden, this is: “A thought-provoking description of a long-running project on mental architecture, perhaps the most important topic in cognitive science today”.
2. John Pollock (1989). How to Build A Person: A Prolegomenon.
   • A plaidoyer to defend the conception of human beings as intelligent machines.
3. David Gelernter (1994). The Muse in the Machine: Computerizing the Poetry of Human Thought.
   • A model of human thought that puts analogy at the center of action: “The study of how thinking works is a big field – it encompasses philosophers of mind, cognitive psychologists, neurophysiologists and legions of frantically intense computer scientists bent on carrying off the greatest conjuring trick of all time, building minds out of computers. But thought science today is at sea. Despite monumental exertions, it has achieved a good grasp of no more than half the problem before it. Reasoning is one big part of human thought, and thought science has reasoning decently under control. Philosophers and psychologists understand it and computers, up to a point, can fake it. But there is one other big piece of the picture, which goes by many names: creativity, intuition, insight, metaphoric thinking, ‘holistic thinking’; all these tricks boil down at base to drawing analogies. Inventing a new analogy – hitching two thoughts together, sometimes two superficially unrelated thoughts – brings about a new metaphor and, it is generally agreed, drives creativity as well. Studies (and intuition) suggest that creativity hinges on seeing an old problem in a new way, and this so-called “restructuring” process boils down at base to the discovery of new analogies. How analogical thinking works is the great unsolved problem, the unknowable longitude, of thought science” (pp. 2-3).
4. Margaret Boden (1991). Computer Models Mind: Computational Approaches in Theoretical Psychology.
   • Boden’s goal was to show the diversity of work in the area of computational psychology. She also goes beyond that to discuss the controversial philosophical assumptions that underlie it, an aspect often swept under the carpet.
5. Margaret Boden (2016). AI: Its Nature and Future.
   • An overview of AI and how AI has evolved and continues to evolve. Content:
     • What is Artificial Intelligence?
     • General Intelligence as the Holy Grail
     • Language, Creativity, Emotion
     • Artificial Neural Networks
     • Robots and Artificial Life
     • But Is it Intelligence, Really?
     • The Singularity
6. Ray Kurzweil (2012). How to Create a Mind: The Secret of Human Thought Revealed.
   • Kurzweil: “In this book I present a thesis I call the pattern recognition theory of the mind (PRTM), which, I argue, describes the basic algorithm of the neocortex… I describe a model of how the human brain achieves this critical capability using a very clever structure designed by biological evolution” (pp. 5-8).
7. Stan Franklin (2001). Artificial Minds.
   • A book that explores the mechanisms of mind. It was an attempt to change our minds about mind.
8. Gilbert Ryle (1949). The Concept of Mind.
   • Gilbert Ryle had a strong influence on other philosophers such as Daniel C. Dennett (who did his doctoral studies under him). In the re-introduction to this classic, Dennett writes: “Where does that leave us? With a book of breathtaking ambition in one dimension and refreshing modesty in another, a book whose hints and asides have sometimes proven more influential than its major declarations, a book that may in another fifty years prove to have an even higher proportion of truth than we find in it today”.
9. Richard David Precht (2007). Who Am I? And If So, How Many? A Journey Through your Mind.
   • A well written book by the German philosopher Precht; definitely not one of the books that contribute to the unappetizing state of literature. Precht states “universities rarely foster innovation. Even today, academia privileges the regurgitation of secondary texts over intellectual creativity” (p. xiii).
10. David Perkins (2000). The Eureka Effect: The Art and Logic of Breakthrough Thinking.
    • Blurb: “Drawing on a rich knowledge of artificial intelligence and cognitive psychology, David Perkins offers a uniquely integrative theory of how breakthroughs occur”.
11. Rosalind W. Picard (2000). Affective Computing. MIT Press.
    1. Covers the quest to give computers the ability to recognize, understand, and express emotions.
12. Pamela McCorduck (2004). Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence.
    • A well-written overview. According to Herb Simon, this is the most reliable source on the first couple of decades of AI.
13. Pamela McCorduck (2019). This Could Be Important: My Life and Times with the Artificial Intelligentsia.
    • “What follows is one story (there are many) of an extraordinary half-century and more, when humans edged toward an epochal event: a new kind of intelligence emerged, designed by us, to live beside our own. But this book is about humans, not machines. As it happens, AI’s coming of age, if not its full maturity, has paralleled my own life. So this is the saga of a grand scientific quest, intertwined with my personal quest. It’s a coming of age story of a scientific field and of a naïve young woman – now slightly wiser and decidedly older”.
14. William H. Calvin (2004). A Brief History of the Mind.
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    • The book explores how the mind has expanded since the big bang 50,000 years ago. Are we now once again nearing a crossroad of mental evolution? Calvin: “There are many ways to write a book like this, depending on the author’s viewpoint. We all tend to deal with the same set of facts, but our intellectual backgrounds and interests differ. Most people writing on the subject were trained in anthropology, linguistic, psychology, or evolutionary biology. I tend to look at the problem from the standpoint of a neurobiologist, always trying to figure out how nerve cells can analyze the world, make sensible plans for movement, and manage those interneurons that convert thought into action. This is the brain mechanic’s time scale of how. I was driven to looking into the evolutionary setup for why things work the way they presently do. And, since I try to deal with brain circuitry for language and creative plans, I was looking for insights from the comparison of human brains to those of our closest cousins that lack these behaviors. I tend to be impressed by self-organization, emergent properties of neural circuitry, and fast tracks in evolution. For better or worse, this book reflects those issues more than would be found in most books on human evolution. Read widely” (p. xv). See also many other fascinating books by William Calvin listed on his Wikipedia page.
15. Daniel L. Schacter (2001). The Seven Sins of Memory: How the Mind Forgets and Remembers.
    • An exploration of memory’s imperfections and what one can do to guard against them, by the leading scholar in the area. Nobel laureate Eric Kandel: “Bravo: a tour de force. No one can better explain for the general reader the insights on memory and its distortion than Daniel Schacter, one of the most exciting and original students of the biology of memory”.
16. Norbert Wiener (1950). The Human Use of Human Being: Cybernetics and Society.
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    • Wiener – the founder of the science of cybernetics – discusses the implications of cybernetics.
17. Kim Sterelny (2003). Thought in a Hostile World: The Evolution of Human Cognition.
    • The late Fiona Cowie, an Australian philosopher who spent her career at Caltech, gave this review: “This book is a godsend for anyone wanting to understand the evolution of human cognition without buying into the wholesale modularism of recent evolutionary psychology. Densely, but elegantly, written and replete with fascinating empirical detail, this book represents a major advance in the philosophical understanding of human cognitive evolution”.
18. Thomas Fuchs (2018). Ecology of the Brain: The Phenomenology and Biology of the Embodied Mind.
    • A fight again brain centrism. For Fuchs, the brain is the organ “which mediates the relations towards the world, to other people, and to ourselves” (p. xix). Thus, the brain is an element of mediation, transformation, and resonance where its function is part of a living organism. Fuchs aims to show that this leads to an integral, personalistic concept of the human being. “All scientific endeavors to reveal the functioning of the brain are ultimately dependent on this fundament in the life-world.
19. David Eagleman (2020). Livewired: The Inside Story of the Ever-Changing Brain.
    • The most recent book by Eagleman. The core message: Our brain and machinery are not fully pre-programmed but shaped by interaction within our world. Eagleman: “The thrill of life is not about who we are but about who we are in the process of becoming”.
20. Matthew Cobb (2020). The Idea of the Brain: A History.
    • “This book tells the story of centuries of discovery, showing how brilliant minds, some of them now forgotten, first identified that the brain is the organ that produces thought and then began to show what it might be doing. It describes the extraordinary discoveries that have been made as we have attempted to understand what the brain does, and delights in the ingenious experiments that have produced these insights. But there is a significant flaw in this tale of astonishing progress, one that is rarely acknowledged in the many books that claim to explain how the brain works. Despite a solid bedrock of understanding, we have no clear comprehension about how billions, or millions, or thousands, or even tens of neurons work together to produce the brain’s activity (…) This book is not a history of neuroscience, nor a history of brain anatomy and physiology, nor a history of the study of consciousness, nor a history of psychology. It contains some of these things, but the history I tell is rather different, for two reasons. First, I want to explore the rich variety of ways in which we have thought about what brains do and how they do it, focusing on experimental evidence – this is rather different from telling the story of an academic discipline. It also means that the book does not deal solely with how we have thought about the human brain – other brains in other animals, not all of them mammals, have shed light on what is happening in our heads (…) The second reason why this is not simply a history can be seen from the contents page – the book is divided into three parts: Past, Present and Future. The conclusion of the ‘Present’ section, which deals with how our understanding of the brain has developed over the last seventy years or so under the computational metaphor, is that some researchers sense we are approaching an impasse in how we understand the brain” (pp. 2-6).
21. Thomas H. Davenport (2018). The AI Advantage: How to Put the Artificial Intelligence Revolution to Work.
    • Davenport spreads the message that AI is largely an analytical technology and a straightforward extension of what organizations do with data and analytics. His goal: to chart the path of AI and cognitive technologies in mainstream businesses.
22. Stuart Russell (2019). Human Compatible: Artificial Intelligence and the Problem of Control.
    • Russell, the co-author of the textbook Artificial Intelligence: A Modern Approach (together with Peter Norvig), has published a non-technical book that asks us to rethink AI from the ground up to avoid problems of human control. Praised by Kahneman as “the most important book I have read in quite some time” and by Judea Pearl as making him “a convert to Russell’s concern with our ability to control our upcoming creation – superintelligent machines”.
23. Carlos E. Perez (2018). Artificial Intuition: The Improbable Deep Learning Revolution.
    • Perez: “I challenge you to find a field as interesting and exciting as Deep Learning”.
24. Terrence J. Sejnowski (2018). The Deep Learning Revolution.
    • Sejnowski’s goal: To provide a personal guide to the past, present, and future of deep learning. “Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of intelligence. Nature abounds with intelligence in many forms, from humble bacterial to complex human intelligence, each adapted to its niche in nature. Artificial intelligence will also come in many forms that will take their particular places on this spectrum. As machine intelligence based on deep neural networks matures, it could provide a new conceptual framework for biological intelligence” (p. x).
25. Brian Cantwell Smith (2019). The Promise of Artificial Intelligence: Reckoning and Judgment.
    • “Neither deep learning, nor other forms of second-wave AI, nor any proposals yet advanced for third-wave, will lead to genuine intelligence. Systems currently being imagined will achieve formidable reckoning prowess, but human-level intelligence and judgment, honed over millennia, is of a different order. It requires ‘getting up out’ of internal representations and being committed to the world as world, in all its unutterable richness. Only with existential commitment, genuine stakes, and passionate resolve to hold things accountable to being in the world can a system (human or machine) genuinely refer to an object, assess ontological schemes, distinguish truth from falsity, respond appropriately to context, and shoulder responsibility” (p. xiii). Brian Cantwell Smith sees his attempt as a cartographic project to wisely choreograph the world we are developing and developing intellectual tools with which the current development is assessed.
26. Joseph E. Aoun (2017). Robot-Proof: Higher Education in the Age of Artificial Intelligence.
    • Aoun, president of Northeastern University, outlines his suggested model of higher education with the aim of developing and empowering a new generation of creators who can employ our technological wonders.
27. Max Tegmark (2017). LIFE 3.0: Being Human in the Age of Artificial Intelligence.
    • What are AI’s implications? What is the future of humanity? How can we create a benevolent future civilization while harnessing the benefits of future AI?
28. Gary Marcus and Ernest Davis (2019). Rebooting AI: Building Artificial Intelligence We Can Trust.
    • As the title suggests, this book is about understanding “why AI, so far, hasn’t been on the right track, and what we might do to work toward AI that is robust and reliable, capable of functioning in a complex and ever-changing world, such that we can genuinely trust it with our homes, our parents and children, our medical decisions, and ultimately our lives” (p. 9).
29. Sean Gerrish (2018). How Smart Machines Think.
    • What makes modern machine learning tick? “But over time, I found myself repeating this process again and again. Whenever I saw another breakthrough in artificial intelligence or machine learning hit the press, I came back to the same question: How does it work? The curious thing to me was that I’d spent countless hours studying and practicing machine learning in academia and industry, and yet I still couldn’t consistently answer that question. Perhaps I didn’t know AI and machine learning as well as I should, I thought, or perhaps college courses didn’t teach us the right material. Most college courses on these topics usually just teach the building blocks behind these breakthroughs – not how these building blocks should be put together to do interesting things” (p. xi).
30. Nikolaas Tinbergen (1989). The Study of Instinct. Clarendon Press.
    • Tinbergen’s influential book on animal behaviour has also influenced artificial intelligence (combination of if-then rules to account for behaviour). He shared the Nobel Prize in Physiology or Medicine together with Konrad Lorenz and Karl von Frisch; his brother Jan Tinbergen won the first Nobel Prize in Economics together with Ragnar Frisch.
31. Brett Frischmann and Evan Selinger (2018). Re-Engineering Humanity.
    • What happens to our lives when we embrace Big Data, predictive analytics, and smart environments?
32. Peter Dauvergne (2020). AI in the Wild: Sustainability in the Age of Artificial Intelligence.
    • What can AI achieve in preserving our natural environment? A book that explores the consequences of AI for global sustainability.
33. Arthur I. Miller (2019). The Artist in the Machine: The World of AI-Powered Creativity.
    • What is creativity? Can computers be creative? Can computers create art, literature, or music? What will a computer’s creativity be like? What will a creative computer be like? Will computers ever think like us? Do they need to? This book offers a journey exploring creativity in the age of machines.
34. Marcus du Sautoy (2019). The Creativity Code: How AI Is Learning to Write, Paint and Think
    • We will revisit Marcus du Sautoy’s book from the 2019 list: “You may ask why a mathematician is offering to take you on this journey. The simple answer is that AI, machine learning, algorithms and code are all mathematical at heart. If you want to understand how and why the algorithms that control modern life are doing what they do, you need to understand the mathematical rules that underpin them. If you don’t, you will be pushed and pulled around by the machines” (p. 6).
35. Gregory Bateson (2002). Mind and Nature: A Necessary Unit.
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    • A beautiful book that requires time and thinking to digest.
    • The Division of the Perceived Universe into Parts and Wholes Is Convenient and May Be Necessary, But No Necessity Determines How It Shall Be Done