I don’t remember exactly how it happened, but I was in his office. I remember it to this day. Think it was Sue Lapin who directed me to his room, but that’s another story.
He was a classic example of a gray haired, balding, absent-minded professor, his office shelves stuffed to the ceiling with books, papers, and other flotsam and jetsam of an academia life. There we were: two different generations — he, my father’s generation, and me, a 50’s nerd baby boomer. The commonality was we were both computer nerds, interested in ideas and the nascent computer science field. At the time I was just trying to get a job to support my education: a Masters degree at University of Wisconsin, Madison, far from my home in sunny SoCal. I had driven the two thousand miles or so across the US for the first time in my life to get there.
We talked for about four hours non-stop about all kinds of things, the Chinese language is the only subject I remember: he was a fountain of knowledge, and both us could have gone on many Moore hours. I probably didn’t know the significance of it at the time, except he did point me to a job which I got to support myself in that strange land. A year and half later, I got a Masters in Computer Science, and left Madison to wander towards Enlightenment for the next 46 years, and hopefully beyond.
But what I didn’t know at the time how important Ed Moore’s idea was: Moore machines would become important to me for understanding the world. I needed to learn More about Moore and many other things — Slowly.
It took me awhile (about 40 years) to come back to his concepts and the basics of information and computer science: armed with many knowledge domains that I initially failed and succeeded at, and revisited, as I did with Ed’s fast and slow ideas.
“The purpose of computing is for insight, not numbers” — Richard Hamming
I had personally encountered previous many individuals involved with the emerging computer field as an undergraduate, like Richard Hamming and John Seely-Brown, and later in graduate school, Paul Mockapetris, and in Artificial Intelligence and robotics research work (HRL) Carver Mead, Lynn Conway, and Steve Crocker and their work would be important in seeing the underlying patterns of computing, the Internet, and reality.
But it was the work and ideas of Arvind and Kim Gostelow on PetriNets and Dataflow, while I was in graduate school, that I didn’t appreciate until recently. They concentrated on theoretical computation.
‘Invert, always invert’
(‘man muss immer umkehren’)
— Carl Gustav Jacob Jacobi
Combining the three finite cellular automata architectures: Mealy machines, Moore machines, and Petri nets in a Gestalt Science methodology is the next step along the way of this wandering enlightenment. And then there is even More Moore to be understood.
To be continued…