Finally someone mentions this. Maybe I've been in the wrong circles, but I've been wishing I had the time to implement a society-of-mind-inspired system ever since llamacpp got started, and I never saw anyone else reference it until now.
I found and read this book from the library completely randomly like 20 years ago and I still remember a lot of the concepts. Definitely seems like a foundational approach for how to architect intelligent systems with a computer. Before I was even thinking about any of that and was just interested in the philosophy I thought his approach and fullness of his ideas was remarkable. Glad to see it becoming a more central text!
Honestly, I never really saw the point of it. It seems like introducing a whole bunch of inductive biases, which Richard Sutton's 'The Bitter Lesson' warned against.
> Rich Sutton's views are far less interesting than Minsky's IMO.
I don't think Minsky's and Sutton's views are in contradiction, they seem to be orthogonal.
Minsky: the mind is just a collection of a bunch of function specific areas/modules/whatever you want to call them
Sutton: trying to embed human knowledge into the system (i.e. manually) is the least effective way to get there. Search and learning are more effective (especially as computational capabilities increase)
Minsky talks about what the structure of a generalized intelligent system looks like. Sutton talks about the most effective way to create the system, but does not exclude the possibility that there are many different functional areas specialized to handle specific domains that combine to create the whole.
People have paraphrased Sutton as simply "scale" is the answer and I disagreed because to me learning is critical, but I just read what he actually wrote and he emphasizes learning.
I take Sutton's Bitter Lesson to basically say that compute scale tends to win over projecting what we think makes sense as a structure for thinking.
I also think that as we move away from purely von neumann architectures to more neuromorphic things, the algorithms we design and ways those systems will scale will change. Still, I think I agree that scaling compute / learning will continue to be a fruitful path.
