I was a grad student in AI at the time this book came out so I can tell you a little bit about the historical context from my personal perspective with benefit of hindsight. The field at the time was dominated by two schools of thought, called the "neats" and the "scruffies". At the risk of significant oversimplification, the neats thought that the right way to do AI was using formal logic while the scruffies took an empirical approach: noodle around with code and see what works. Both approaches led to interesting results. The neat legacy is modern-day theorem provers while the scruffy legacy is chatbots, self-driving cars, and neural nets.
SoM didn't fit neatly (no pun intended) into either camp. It wasn't empirical and it wasn't formal. It was just a collection of random loosely-associated ideas and nothing ever came of it. It was too informal to lead to interesting theoretical results, and it was too vague to be implemented and so no one could test it experimentally. And both of those things are still true today. I think it's fair to say that if the author had been anyone but Minsky no one would have paid any attention to it at all.
Another thing to be aware of with SoM is that Minsky was reading in many fields, and trying to sketch out theories informed by that.
One time, before the DL explosion, during a lull in AI, I sent a colleague a Minsky pop-sci quote from the earlier AI years, before our time, asserting that, soon, a self-teaching machine will be able to increase in power exponentially. I was making a joke about how that was more than a little over-optimistic. My colleague responded something like, "What you fail to see is that modern-day Marvin is that machine."
By the time I was bumping into AI at Brown and MIT, the students (including Minsky's protege, Push Singh, who started tackling commonsense reasoning) described SoM various ways, including:
* Minsky sketching out spaces for investigation, where each page was at least one PhD thesis someone could tackle. I see some comments here about the book seeming light and hand-wavy, but I suppose it's possible there's more thinking behind what is there than is obvious, and that it wasn't intended to be the definitive answer, but progress on a framework, and very accessible.
* Suggestion (maybe half-serious) that the different theories of human mind or AI/robotics reflect how the particular brilliant person behind the theory thinks. I recall the person said it as "I can totally believe that Marvin is a society of mind, ___ thinks by ___ ..."
I don't know anyone who held it out as a bible, but at the time it seemed probably everyone in AI would do well to be aware of the history of thinking, and the current thinking of people who founded the field and who have spent many decades at the center of the action of a lot of people's work.
Inspired me as an undergrad Industrial Design student in 1989ish that and The Media Lab: Inventing the Future at M.I.T by Stewart Brand were the two most influential technology books for me at that time.
Coincidentally enough it turns out my cousin was in the thick of it while the pre-media lab was still part of the architecture school. She would tell me stories of what she was up to in college... when I read that back I had to loop back and ask her about it.
Reading Society of Mind in undergrad is one of the things that led me to doubt AI progress and to stray away from the field [1]. It was handwavy, conceptual, and far removed from the research and progess at the time. If you held it up to Norvig's undergraduate level Artificial Intelligence: A Modern Approach, you could sense Minsky's book was as wishfully hypothetical as Kaku's pop-sci books on string theory.
[1] Recent progress has led me right back. There's no more exciting place to be right now than AI.
Even if it didn’t lead to empirical results I think most of the value of the book today is in the questions Minsky asked. How is intelligence organized in a distributed system like a neural net? ChatGPT may be able to do amazing things, but the mechanisms it uses are still very opaque. So even if the theory may not be “useful”, it is still worth pursuing IMO
It’s also pretty well written and written by someone who clearly spent a lot of mental energy on the problem
This made its way into pop culture via the X-Files, in an episode about A.I.: "Scruffy minds like me like puzzles. We enjoy walking down unpredictable avenues of thought, turning new corners but as a general rule, scruffy minds don't commit murder."
Logical vs. Analogical
or
Symbolic vs. Connectionist
or
Neat vs. Scruffy
Marvin Minsky
INTRODUCTION BY PATRICK WINSTON
Engineering and scientific education conditions us to expect everything, including intelligence, to have a simple, compact explanation. Accordingly, when people new to AI ask "What's AI all about," they seem to expect an answer that defines AI in terms of a few basic mathematical laws.
Today, some researchers who seek a simple, compact explanation hope that systems modeled on neural nets or some other connectionist idea will quickly overtake more traditional systems based on symbol manipulation. Others believe that symbol manipulation, with a history that goes back millennia, remains the only viable approach.
Minsky subscribes to neither of these extremist views. Instead, he argues that Artificial Intelligence must employ many approaches. Artificial Intelligence is not like circuit theory and electromagnetism. AI has nothing so wonderfully unifying like Kirchhoff's laws are to circuit theory or Maxwell's equations are to electromagnetism. Instead of looking for a "Right Way," Minsky believes that the time has come to build systems out of diverse components, some connectionist and some symbolic, each with its own diverse justification.
Minsky, whose seminal contributions in Artificial Intelligence are established worldwide, is one of the 1990 recipients of the prestigious Japan Prize---a prize recognizing original and outstanding achievements in science and technology.
Neat and scruffy are two contrasting approaches to artificial intelligence (AI) research. The distinction was made in the 70s and was a subject of discussion until the middle 80s. In the 1990s and 21st century AI research adopted "neat" approaches almost exclusively and these have proven to be the most successful.[1][2]
"Neats" use algorithms based on formal paradigms such as logic, mathematical optimization or neural networks. Neat researchers and analysts have expressed the hope that a single formal paradigm can be extended and improved to achieve general intelligence and superintelligence.
"Scruffies" use any number of different algorithms and methods to achieve intelligent behavior. Scruffy programs may require large amounts of hand coding or knowledge engineering. Scruffies have argued that the general intelligence can only be implemented by solving a large number of essentially unrelated problems, and that there is no magic bullet that will allow programs to develop general intelligence autonomously.
The neat approach is similar to physics, in that it uses simple mathematical models as its foundation. The scruffy approach is more like biology, where much of the work involves studying and categorizing diverse phenomena.[a]
Made-Up Minds: A Constructivist Approach to Artificial Intelligence (Artificial Intelligence Series) Paperback – January 1, 2003
Made-Up Minds addresses fundamental questions of learning and concept invention by means of an innovative computer program that is based on the cognitive-developmental theory of psychologist Jean Piaget. Drescher uses Piaget's theory as a source of inspiration for the design of an artificial cognitive system called the schema mechanism, and then uses the system to elaborate and test Piaget's theory. The approach is original enough that readers need not have extensive knowledge of artificial intelligence, and a chapter summarizing Piaget assists readers who lack a background in developmental psychology. The schema mechanism learns from its experiences, expressing discoveries in its existing representational vocabulary, and extending that vocabulary with new concepts. A novel empirical learning technique, marginal attribution, can find results of an action that are obscure because each occurs rarely in general, although reliably under certain conditions. Drescher shows that several early milestones in the Piagetian infant's invention of the concept of persistent object can be replicated by the schema mechanism.
> It was just a collection of random loosely-associated ideas and nothing ever came of it.
I remember buying this in '89 and being completely underwhelmed by it. There is nothing there imo. I stopped paying attention to the name Minsky after this introduction to the 'great man'.
I'm with you on ANKOS, but GEB is an accessible and fun (if a bit wordy) introduction to formal systems and Godel's theorem, so I wouldn't put it in the same category. GEB also was not marketed as anything revolutionary (except in its pedagogy). ANKOS and SoM were.
Thanks for this. I was going to post a comment asking how relevant SoM is to the form and structure of modern ML models. From just the title, not having read it, it seemed like SoM might have been prescient. Apparently not so much.
SoM didn't fit neatly (no pun intended) into either camp. It wasn't empirical and it wasn't formal. It was just a collection of random loosely-associated ideas and nothing ever came of it. It was too informal to lead to interesting theoretical results, and it was too vague to be implemented and so no one could test it experimentally. And both of those things are still true today. I think it's fair to say that if the author had been anyone but Minsky no one would have paid any attention to it at all.