Minsky, Marvin

The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind

Average customer rating:

Excellent book on thinking machines - but misleading title
The common, but wrong approach.
AI: About Intuition
Frustrating and disappointing
thinking : critic - selector model

The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind
Marvin Minsky
Manufacturer: Simon & Schuster
ProductGroup: Book
Binding: Hardcover

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Book Description

Our minds are working all the time, but we rarely stop to think about how they work. The human mind has many different ways to think, says Marvin Minsky, the leading figure in artificial intelligence and computer science. We use these different ways of thinking in different circumstances, and some of them we don't even associate with thinking. For example, emotions, intuitions, and feelings are just other forms of thinking, according to Minsky. In his groundbreaking new work, The Emotion Machine, Minsky shows why we should expand our ideas about thinking and how thinking itself might change in the future.

The Emotion Machine explains how our minds work, how they progress from simple kinds of thought to more complex forms that enable us to reflect on ourselves -- what most people refer to as consciousness, or self-awareness. Unlike other broad theories of the mind, this book proceeds in a step-by-step fashion that draws on detailed and specific examples. It shows that thinking -- even higher-level thinking -- can be broken down into a series of specific actions. From emotional states to goals and attachments and on to consciousness and awareness of self, we can understand the process of thinking in all its intricacy. And once we understand thinking, we can build machines -- artificial intelligences -- that can assist with our thinking, machines that can follow the same thinking patterns that we follow and that can think as we do. These humanlike thinking machines would also be emotion machines -- just as we are.

This is a brilliant book that challenges many ideas about thinking and the mind. It is as insightful and provocative as it is original, the fruit of a lifetime spent thinking about thinking.

Customer Reviews:

Excellent book on thinking machines - but misleading title.......2007-06-10

I agree with the reviewer who noted how odd it was that a book titled "The Emotion Machine" does not discuss Joseph LeDoux, even if only to refute him. But I think that the problem is with the title, not the book. I found many of Minsky's insights very helpful - it is a very good book about how machines think. And if you are not a dualist, then those insights apply to people too. The book is very well organized and clearly written, and helps you think about thinking. I especially enjoyed his discussion of qualia (although he does not use the term), and why he thinks it is not quite the problem that so many philosophers want to make it.

Minsky's main take on emotions is that emotional states are not fundamentally different from other types of thinking, and that the entire dicotomy of rationality v. emotion is misleading. He prefers to view them all as different ways of thinking - of utilizing various mental resources at one's disposal, some conscious and some not. He organizes his discussion of difficult material very well, but I wish there was more grounding in the underlying neural anatomy of human emotion.

The common, but wrong approach........2007-05-25

What is so special about emotions?
Emotions is just one kind of bechavior, among many, demonstrated by reasonable systems. It is didn't matter what kind of system it is.
Machine and human, and bacteria, or dog, all reasonable systems are subjective simply because they are isolated from direct interactions with environment and capable to demonstrate the emotional behavior.
Contrary to common opinion all live creature, not human only, are emotional.
Best regards Michael Zeldich

AI: About Intuition.......2007-04-24

My brother is a computer programmer with a computer game company and he discovered something fascinating while trying to create a simulation for the movement of a crowd.

By inputing three variables: 1) be like a common member of the group but 2) stay a certain discrete distance from your neighbor while 3) moving away when everyone gets too close, he captured the seemingly naturalist choatic looking behavior of a crowd.

The point here is that the operation of a simple set of rules can create the appearance of the phenomenon of seemingly complicated and choatic behavior.

And I don't think the point is mistaken here where Minsky and his likes consider the delicate calculus of human behavior.

While his book ends by discussing the subject of self, perhaps self is perhaps the starting point for all proper discussions of consciousness and identity. This is because -- like all animate behavior -- the existence of self is uniquely keyed to the fact of animate autonomy.

In other words, the greatest of behvaioral conundrums is perhaps the simplest. In order to to decided what to eat, do or where to go, self provides that unique user perspective to allow the necessary illumination of what inbuilt needs remain unmet and which are in the most immediate need of meeting.

An effective engineer, Mother Nature has put into excellent service the process of emotion which allows the quick, effecient recording of the relevant information.

In his classic work The Astonishing Hypothesis, Francis Crick said that self was nothing more than the current state of our neurons and ganglia. Richard Dawkins has repeatedly shown that those neurons and ganglia recieve their current structure through the explanable process of natural selection. And Minsky has done well to show that as a result of that process our brains our like programs that have been worked over many times creating occassional inconsistencies.

Indeed it is perhaps these inconsistencies themselves that lay at the very heart of intuition.

Frustrating and disappointing.......2007-04-21

I recall appreciating The Society of Mind. But in this new book, his best answer to the Mystery of Experience is, "experiencing something like a color seems simple but is actually complicated". His main answer to the mega-Mystery of the Experience of Self-Awareness is, "consciousness is a suitcase term that we use to refer to many different things". It is almost like he is pretending to not experience these mysteries himself, so that he does not have to seriously engage the question of how/why our brain/minds do these things, and under what conditions other machines might. So frustrating that it makes the book hard to read -- it might have been better to skip over these matters more, if he can't deal with them more usefully.

thinking : critic - selector model.......2007-04-01

1. We don't recognize a problem as hard until we've spent some time on it without making any significant progress. For if you can diagnose the particular type of problem you face, then you can use that knowledge to switch to a more appropriate way to think.
2. Critic-selector model of thinking: Each critic object can recognize a certain species of problem type. When a critic sees enough evidence, the critic will activate a "selector", which tries to start up a set of resources that it has learned is like to act as a way to think that may help in this situation.
3. If a problem seems familiar, use reasoning by analogy. If it seems unfamiliar, change the way you're describing it. If it seems too difficult, divide it into several parts. If it still seems difficult, replace it by a simpler problem. If none of these work, ask someone for help.
4. If too many critics are aroused, then describe the problem in more detail. If too few critics are aroused, then make the description more abstract. If important resources conflict then you should try to discover a cause. If there has been a series of failures, then switch to a different set of critics.
5. Emotional reactions: cautious vs. reckless, unfriendly vs. amicable, visionary vs. practical, inattentive vs. vigilant, reclusive vs. sociable, and courageous vs. cowardly; each such emotional way to think can lead to different ways to deal with things-either by making you see things from new points of view or by increasing your courage or doggedness. If too many critics are active then your emotions would keep changing too quickly. And if those critics stopped working at all, then you'd get stuck in just one of states.
6. The best way to solve a problem is to already know a way to solve it. Searching extensively. When one has no better alternative, one could try to search through all possible chains of actions. But that method is not often practical because such searches grow exponentially.
7. Reasoning by analogy: when a problem reminds you of one that you solved in the past, you may be able to adapt that case to the present case situation.
8. Divide and conquer: if you can't solve a problem all at once, then break it down into smaller parts.
9. Reformulation: find a different representation that highlights more relevant information. Understand in a different way.
10. Planning: consider the set of subgoals and examine how they affect each other.
11. Techniques for problem solving: simplifying, elevating, and changing the subject.
12. More reflective ways to think: wishful thinking, self-reflection, impersonation.
13. Other modes of thinking: 1) logical contradiction: try to prove that your problem cannot be solved, and then look for a flaw in that argument. 2) Logical reasoning. We often try to make chains of deduction. 3) External representation. Drawing suitable diagrams 4) Imagination. What would happen if by simulating possible actions inside the mental models that one has built.
14. Creating higher level selectors and critics help to reduce the sizes of the searches we make.
15. Modes of thought: preparation, incubation, revelation, and evaluation.
16. Creative ideas must be combined with the knowledge and skills already possess-so it must not bee too different from ideas with which we're already familiar.
17. If too may critics are active then you notice flaws to correct and spend much time repairing them and never get at the important things and people perceive us as depressed. If too many critics are turned off then you ignore alarms and concerns that would help you concentrate allowing errors and flaws. The fewer the critics active, then the fewer goals pursued, making one intellectually dull.

Average customer rating:

The emotional brain
Cornucopia of Ideas
makes you think about the process of thought
epigrams of Minsky's views on the mind
Boring

Society of Mind
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Manufacturer: Simon & Schuster
ProductGroup: Book
Binding: Paperback

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Amazon.com

For some artificial intelligence researchers, Minsky's book is too far removed from hard science to be useful. For others, the high-level approach of The Society of Mind makes it a gold mine of ideas waiting to be implemented. The author, one of the undisputed fathers of the discipline of AI, sets out to provide an abstract model of how the human mind really works. His thesis is that our minds consist of a huge aggregation of tiny mini-minds or agents that have evolved to perform highly specific tasks. Most of these agents lack the attributes we think of as intelligence and are severely limited in their ability to intercommunicate. Yet rational thought, feeling, and purposeful action result from the interaction of these basic components. Minsky's theory does not suggest a specific implementation for building intelligent machines. Still, this book may prove to be one of the most influential for the future of AI.

Book Description

Marvin Minsky -- one of the fathers of computer science and cofounder of the Artificial Intelligence Laboratory at MIT -- gives a revolutionary answer to the age-old question: "How does the mind work?"

Minsky brilliantly portrays the mind as a "society" of tiny components that are themselves mindless. Mirroring his theory, Minsky boldly casts The Society of Mind as an intellectual puzzle whose pieces are assembled along the way. Each chapter -- on a self-contained page -- corresponds to a piece in the puzzle. As the pages turn, a unified theory of the mind emerges, like a mosaic. Ingenious, amusing, and easy to read, The Society of Mind is an adventure in imagination.

Customer Reviews:

The emotional brain.......2007-01-24

1. Study what seems least
2. Agents are the components of a system that give the system agency
3. Machines behave in a lifeless manner
4. People are not machines
5. The longer a conflict occurs with sub agents the weaker the agent is among competitor agents
6. Agents compete actively while interest is strong (If - Do, Condition - Response)
7. Agencies live in hierarchies and levels of administration reduce complexity and threshold pole the agents yielding a group positive or negative response.
8. High level agents control lower level agents
9. Pain reduces interest in long-term goals. Pain puts a focus on immediate problems (hunger and danger)
10. Pain repels and pleasure attracts
11. Limited exploitation creates agency: for example oscillation between work and sleep, limiting work in combination with anger, balancing hunger and pain with a focus on the problem of eating
12. Many questions are unanswerable
13. Unanswerable questions often create circular causality logic
14. Consciousness is doing and doing does not necessarily require understanding how the process works.
15. Cooperation requires complex interactions between agents. Competition is less complex and less productivity.

16. Learning Meaning has four types: Uniframe (several description combined into one), Accumulation (collections of samples, descriptions by experience, slow to make discovery by pattern match), Reformulation, and transformation.
17. Learning a new idea is possible as the individual accessing structures in the mind. Old structures provide a beginning reference and either are built upon or eventually bypassed in place of the new idea.
18. What we think is based on our spatial learning during youth about the world of space.
19. Accumulation: accumulation rarely feels satisfactory because we expect unity and disunity occurs in categorization, for example, birds fly, penguins are birds, penguins don't fly. Rules are not perfect. Rules reflect that which is typical and describe the exceptions.
20. Reformulation: reformulation is finding new descriptions that make the problem easier. When we can't solve a problem, we reformulate and seek escape by finding a new way to solve the problem.
21. Reformulation builds on what is already known
22. Reformulation connects things with goals in many different ways:
function-structure (Tables are for supporting things), end-means (If I want to reach higher, I can stand on a table), conclusion-premise (If you put something on a table, its height increases), effect-cause (I can reach higher because I start higher), and body-support (Tables hold things away from the floor).
23. The world of sensation: sensation -> reception -> recognition -> cognition
24. Each new idea must compete against a collection of skills associated with older ideas.
25. Memory: Conscience is concerned not with the present but with the past
26. Memory: Recall is not complete detail paradigm; instead, recall is done as memory fragments.
27. Memory: Whenever you answer a question without delay, it seem the answer was already in your mind.
28. Emotion opposites: fear-affection, attachment-dependancy, and hate-love.
29. Behavior can be modeled as sensors and effectors (sensor detect pattern and effectors can a systematic response)
30. Cross exclusion: An activity can suppress the activity of its competitor agent. Avalanche occurs when all agents equal compete for a resource. Agents must have a way to access resources cooperatively.
31. Each simple principle or mechanism must b controlled to operate within some limited range.

32. Memory: Create banks of memory and Associate problem agents with a separate memory banks and restrict each specialist agent to learn only while the goal is active. The agents could be interconnect and cooperative, however this is unlikely; and mostly likely they will be exploitive and competitive in acheiving their goals.
33. One gains from learning better ways to learn
34. When we violate standards we feel shame
35. In logic arguments are true or false. In real life arguments are strong or weak; we seek parallelism in our arguments as a reduncancy against failure; parallel reasoning is harder to break because there are more exists more than one way to answer a problem. We rarely need to know right or wrong and often prefer confrontation methods to fight out the best alternative.
36. The closest we can agree on meaning is in the expressions of mathematics.
37. Words: Language builds things in our minds. Words cannot be the substance of our thoughts. Instead, words control agents in our minds. It is the underlying emptiness of words that gives it potential versality. Language parts are divided into three categories: semantic, syntax, and grammer.
38. Words: Polynemes are k-line association domains for a word. A polyneme signals different agencies (color, shape, or texture agencies) too turn on process in their agences. Each agency must have a dictionary of words and memory to know how to respond to the polynemes, a bank composed of k-lines.
39. Context: Evidence is weighted threshold trigger of positive and negative values.
40. Context: Nemeic spiral: demand, inquire, vision, beliefs, social, language, shapes, touch, get, grasp, request, explain, touch, hearing, traits, physical, places, hearing, vision, move, and put.
41. Things: Whatever we may see or touch (nouns)
42. Differences: A discernible change by comparing two different things (verbs)
43. Cause: The cause of an action
44. Clause: A single phrase treated like a single word.
45. A gene expression is either off or on. Gene manufacture proteins and proteins produce specific chemicals. The cell contains many different types of proteins. Certain proteins move into the cell and serve messengers: 1. by altering other processes; change states of a specific gene 2. Certain protein combinations can turn genes on or off 3. Genes seem like small societies of agents. Certain cells emit specific chemicals and mobile cells follow the chemical scent.
46. How can genes build concepts in the mind? Genes only produce chemicals and how can a chemical create a concept. Genes determine the architecture of agencies destined to learn particular kinds of processes.

Cornucopia of Ideas.......2006-11-19

This book is different from other books on many different levels.

For instance, its organization parallels its subject. The main theme of the book is that the mind consists of a network of non-intelligent agents organized into more and more highly complex agencies. - That the function of these agencies taken together is to perform the mind (mind is thought of as a process and not a thing - the same way that in chemistry fire was eventually recognized as being a process of oxydizing and not an element). In the same way, each page of the book has a single concrete point to make or an idea to present. These ideas reference each other in a networked fashion. As a result, the meaning of the book emerges as you consider the networks of these ideas. This is a very aesthetically pleasing concept for organizing a book as it is informative.

The book introduces a lot of different ideas to its readers - such as the currencies that the mental agencies use to measure the importance of various tasks or views. It talks about the educational value of humor - in a way I never considered. It talks about organization of memories.

One thing that I thought was oversimplified was Marvin Minsky's description of how genes affect the development of the brain. He seems to treat the subject as though the environment has nothing to do with it. For example, we know that mother's consumption of alcohol can drastically affect the development of the fetal brain. What mother eats during pregnancy and even before can also affect the development of the brain. For example, consumption of Omega-3 fats seems to have a very strong effect on the brain development. The brain change is also directed by the experiences that the person has. Vision will not develop in an infant that has not been exposed to light in its first weeks of life. I think that this is a rather weak point of the book.

makes you think about the process of thought.......2006-09-12

The Society of Mind attempts to explain how the mind works. The author considers the mind to be a society of small mental machines that do simple things by themselves but combine to perform amazingly complex tasks like the walking and talking that we take for granted. As this book was written in the 1980's I am sure that it is somewhat out of date. But the questions he asks are timeless. How does memory work? How do we sense space? How do we process conflicting ideas? How do we know when to replace a memory with a more accurate version? This book will make you think about these questions and more. The Society of Mind is worth reading just for the questions it asks.

epigrams of Minsky's views on the mind.......2006-01-03

Minsky has assembled 270 of his essays into this handy compendium. Each is somewhat of an epigram; only one page long, including an occasional diagram. The topics all revolve around the mind and intelligence. You can treat this book casually, by randomly reading an occasional page. Or you can plough through its entirety.

Written over several decades, the essays give an incisive view into many aspects of intelligence. Hence, Minksy explains why some university level topics like calculus were relatively easy to program. But why "elementary" tasks like having a program do the equivalent of a 3 year old kid recognise and arrange building blocks have proved to be enormously harder.

Boring.......2005-05-31

I had great expectations for this book, with all the rave reviews and the topic looking highly relevant for my own research. But in the end, I skimmed the last third of the book in order to be able to finish it at all.

So how can this be? Sure, the book has its good moments, and a few of the aphorism-like chapters do contain worthwile insights. But most of it is either obvious or plain uninteresting.

The obviousness could come from the book's age. It's almost twenty years old now, and what groundbreaking ideas there might have been it in have now trickled down the cultural heap to anyone with a passing subject knowledge of AI, or even an intelligent reader of modern SF.

As for the uninterestingness, many chapters deal with the implementation details (not in the sense of programming language constructs, but algorithms) of Minsky's various modules/agents. The reason these are less than interesting is that even if you buy the overarching idea of the modular mind, you can't help feeling that Minsky's thinking is terrible GOFAI. Everything is framed in an old-school logic-based classical computer metaphor; you can almost see the outlines of lisp declarations thinly veiled in prose. I, and so many others in the field today, prefer to think in a lot more biologically inspired terms. Therefore, while the ideas about what types of modules there might be and how they interact are interesting, the internal details of Minsky-modules are not.

This is not to say The society of mind is a bad book. Minsky's writing is fluent and it might be of interest to novices in the field. Just don't expect it to say much about modern AI research.

Perceptrons - Expanded Edition: An Introduction to Computational Geometry

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Deja vu?
Seminal AI book

Perceptrons - Expanded Edition: An Introduction to Computational Geometry
Marvin L. Minsky , and Seymour A. Papert
Manufacturer: The MIT Press
ProductGroup: Book
Binding: Paperback

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Book Description

Perceptrons - the first systematic study of parallelism in computation - has remained a classical work on threshold automata networks for nearly two decades. It marked a historical turn in artificial intelligence, and it is required reading for anyone who wants to understand the connectionist counterrevolution that is going on today.

Artificial-intelligence research, which for a time concentrated on the programming of ton Neumann computers, is swinging back to the idea that intelligence might emerge from the activity of networks of neuronlike entities. Minsky and Papert's book was the first example of a mathematical analysis carried far enough to show the exact limitations of a class of computing machines that could seriously be considered as models of the brain. Now the new developments in mathematical tools, the recent interest of physicists in the theory of disordered matter, the new insights into and psychological models of how the brain works, and the evolution of fast computers that can simulate networks of automata have given Perceptrons new importance.

Witnessing the swing of the intellectual pendulum, Minsky and Papert have added a new chapter in which they discuss the current state of parallel computers, review developments since the appearance of the 1972 edition, and identify new research directions related to connectionism. They note a central theoretical challenge facing connectionism: the challenge to reach a deeper understanding of how "objects" or "agents" with individuality can emerge in a network. Progress in this area would link connectionism with what the authors have called "society theories of mind."

Marvin L. Minsky is Donner Professor of Science in MIT's Electrical Engineering and Computer Science Department. Seymour A. Papert is Professor of Media Technology at MIT.

Customer Reviews:

Deja vu?.......2000-11-27

In 1958, Cornell psychologist Frank Rosenblatt proposed the 'perceptron', one of the first neural networks to become widely known. A retina sensory layer projected to an association layer made up of threshold logic units which in turn connected to the third layer, the response layer. If two groups of patterns are linearly separable then the perceptron network works well in learning to classify them in separate classes. In this reference, Minsky and Papert show that assuming a diameter-limited sensory retina, a perceptron network could not always compute connectedness, ie, determining if a line figure is one connected line or two separate lines. Extrapolating the conclusions of this reference to other sorts of neural networks was a big setback to the field at the time of this reference. However, it was subsequently shown that having an additional 'hidden' layer in the neural network overcame many of the limitations. This reference figures so prominently in the field of neural networks, and is often referred to in modern works. But of even greater significance, the history of the perceptron demonstrates the complexity of analyzing neural networks. Before this reference, artificial neural networks were considered terrific, after this reference limited, and then in the 1980s terrific again. But at the time of this writing, it is realized that despite physiological plausibility, artificial neural networks do not scale well to large or complex problems that brains can easily handle, and artificial neural networks as we know them may actually be not so terrific.

Seminal AI book.......2000-04-03

This is a seminal work in the field of Artificial Intelligence. Following an initial period of enthusiasm, the field encountered a period of frustration and disrepute. Minksy and Papert's 1969 book summed up this general feeling of frustration among researchers by demonstrating the representational limitations of Perceptrons (used in neural networks). Their arguments were very influential in the field and accepted by most without further analysis.

Customer Reviews:

Still the best introduction to the research in pure AI.......2003-08-05

This book is a collection of PhD thesis from several fameous Artificial Intelligence researchers with couple of extra chapters by Minsky.

The chapters are: Introduction (by Minsky), SIR: A Computer Program for Semantic Information Retrieval (by Berthrand Raphael), Natural Language Input for a Computer Problem-Solving System (by Daniel Bobrow), Semantic Memory (by Ross Quillian), A Program for Solution of Geometry-Analogy Intelligence Test Question (by Thomas Evans), A Deductive Question-Answering System (by Fischer Black), Programs with Common Sense (by John McCarthy), Descriptive Languages and Problem Solving (by Minsky), and Matter, Mind, and Models (by Minsky).

This book might seem a bit old since it was originally published in 1968 (the copy I have is third printing from 1982) but it's still the best introduction to the approach that some of Artificial Intelligence pioneers had while working on some of the very first issues in the field.

This book gives clear guidances how one should design and write a thesis from Artificial Intelligence. The authors show where to draw the line between the theory and the practice and how to articulate research issues and present experimental results in such a fuzzy area.

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