I have in front of me Alexander Lerner's 1967 _Foundations of Cybernetics_ (Начала кибернетики in the original Russian), which more or less instantiates Forsythe's synnoetics program. It covers dynamical systems, signals, regulation, optimal control, automata and Turing machines, information theory, computation, adaptation, games, learning and pattern recognition, large-scale systems, operations research, neurophysiology of the brain, and reciprocal relations between humans and machines. Throughout the book, there are potted biographies of key thinkers, including Wiener, Lyapunov, Turing, von Neumann. I cannot think of a similar text in the Western literature at that time. (Incidentally, Lerner was Vapnik's PhD advisor at the Moscow Institute of Control Sciences.)
I wonder how the autograding you mentioned of the BOLGOL programs was done. Wonderful piece. Thanks for sharing this. Silos are normative for humans, is my suspicion.
Since the assignments were such simple linear algebraic computations, the autograding was simply testing them on random instances. The student code had their ID number, so the grades could be printed out on a line printer. Forsythe credits the idea of autograding to AJ Perlis, the first Turing Award Laureate.
I don't know, I feel like the interdisciplinary dream has panned out to a large extent.
E.g. you can have people in CS departments happily working on topics owned by other fields. Examples I can think of off the top of my head
- computational chemistry and physics (property of chemistry and physics)
- comp bio/genomics/etc. (property of biology)
- quantum information (partially property of physics)
- micro theory (property of economics)
- techniques for causal inference (property of economics, statistics, political science)
- NLP (property of linguistics)
Maybe it's not the norm everywhere, but there are definitely departments where you could have 6 PhD students working in these 6 areas all scrounging for free food from the same buffet. Hard to imagine that in any other department.
In fact, I've worked on most of these in the course of my academic career (all degrees in EE). Working on some genomics and systems bio with some colleagues and a postdoc right now.
In 1962, the only piece missing was the lack of connected computers. When I did my first degree, the campus had a mainframe computer and the only distribution was the siting of line printer terminals. A field station could communicate with this mainframe via a 300 baud acoustic coupler. Even as late as 1985, the university I was at still had a Burroughs minicomputer, and the first few IBM PCs. [I owned an Apple II at the time.] LANs were big in the early 1990s, just as the Internet was about to be democratized. It is almost hard to imagine life without cheap computers, from desktops to smartphones. Software is no longer expensive, much of it free. For programmers, there is a wealth of free languages from Lisp to Go, with old standbys like Fortran and Cobol available. It is so different from the early 1990s when one had to buy C/C++ compilers in boxes with a lot of floppy disks. The other change is that one need no longer haunt computer bookshelves for learning a programming language. While I maintain at least one book per language I use, it is far easier to see what is available online. There is really no barrier to learning a computer language anymore. The problem is choice. Where once BASIC was the starter language, then Pascal or C, now there are so many, each with their pros and cons, depending on your interests.
Apart from working through a curriculum and doing the coursework, it is hard to see the value of basic CS courses compared to what may be available online, other than the sheepskin at the end of the course and an entry into paid employment.
I have in front of me Alexander Lerner's 1967 _Foundations of Cybernetics_ (Начала кибернетики in the original Russian), which more or less instantiates Forsythe's synnoetics program. It covers dynamical systems, signals, regulation, optimal control, automata and Turing machines, information theory, computation, adaptation, games, learning and pattern recognition, large-scale systems, operations research, neurophysiology of the brain, and reciprocal relations between humans and machines. Throughout the book, there are potted biographies of key thinkers, including Wiener, Lyapunov, Turing, von Neumann. I cannot think of a similar text in the Western literature at that time. (Incidentally, Lerner was Vapnik's PhD advisor at the Moscow Institute of Control Sciences.)
I wonder how the autograding you mentioned of the BOLGOL programs was done. Wonderful piece. Thanks for sharing this. Silos are normative for humans, is my suspicion.
Since the assignments were such simple linear algebraic computations, the autograding was simply testing them on random instances. The student code had their ID number, so the grades could be printed out on a line printer. Forsythe credits the idea of autograding to AJ Perlis, the first Turing Award Laureate.
I don't know, I feel like the interdisciplinary dream has panned out to a large extent.
E.g. you can have people in CS departments happily working on topics owned by other fields. Examples I can think of off the top of my head
- computational chemistry and physics (property of chemistry and physics)
- comp bio/genomics/etc. (property of biology)
- quantum information (partially property of physics)
- micro theory (property of economics)
- techniques for causal inference (property of economics, statistics, political science)
- NLP (property of linguistics)
Maybe it's not the norm everywhere, but there are definitely departments where you could have 6 PhD students working in these 6 areas all scrounging for free food from the same buffet. Hard to imagine that in any other department.
EE, easy
In fact, I've worked on most of these in the course of my academic career (all degrees in EE). Working on some genomics and systems bio with some colleagues and a postdoc right now.
this is a fair point, although I guess a lot of EE departments became EECS around the same time CS departments were getting started.
In 1962, the only piece missing was the lack of connected computers. When I did my first degree, the campus had a mainframe computer and the only distribution was the siting of line printer terminals. A field station could communicate with this mainframe via a 300 baud acoustic coupler. Even as late as 1985, the university I was at still had a Burroughs minicomputer, and the first few IBM PCs. [I owned an Apple II at the time.] LANs were big in the early 1990s, just as the Internet was about to be democratized. It is almost hard to imagine life without cheap computers, from desktops to smartphones. Software is no longer expensive, much of it free. For programmers, there is a wealth of free languages from Lisp to Go, with old standbys like Fortran and Cobol available. It is so different from the early 1990s when one had to buy C/C++ compilers in boxes with a lot of floppy disks. The other change is that one need no longer haunt computer bookshelves for learning a programming language. While I maintain at least one book per language I use, it is far easier to see what is available online. There is really no barrier to learning a computer language anymore. The problem is choice. Where once BASIC was the starter language, then Pascal or C, now there are so many, each with their pros and cons, depending on your interests.
Apart from working through a curriculum and doing the coursework, it is hard to see the value of basic CS courses compared to what may be available online, other than the sheepskin at the end of the course and an entry into paid employment.