I actually agree with your separation of Software Engineering and Computer Science comment, but completely disagree with the funding level statement. The notion that branches of Mathematics don't use computer resources is absolutely laughable. If anything, I don't see why the best practices for managing people who are developing the same CRUD apps over and over again needs any significant investment of any kind besides a few laptops. Computer Science can not and should not give a shit about development practices like Agile or its alternatives. CS people do own a lot of problems facing industry like machine learning and AI, and they address plenty of problems "in the core"...just nothing to do with process.
Also, this split absolutely does happen at some universities, and the funding is decided by agencies based on their priorities. One such university is the University of Waterloo, and the CS department is not wanting for funding...certainly not at the buy a whiteboard and get on with it levels you're suggesting.
I think that there is a conflation that needs sorting out. Mathematicians and Physicists may need funding at a high level, they may deserve it in a philosophical and natural justice sense as well. Computer Science argues for (and gets) high levels of funding by asserting economic rationales for funding with a justification that physics and maths struggle to match. My view is that if we are talking about the SE side of the shop then this is rational and fine, but if we are talking about theoretical CS which (tragically) effectively includes much of the database, programming language and methodology community, and much of the AI and ML community too, then this is a misallocation of capital.
In terms of CRUD apps - my jaw is on the floor... Don't you care about the harm that is inflicted on the people doing the development, their victims (everyone) and the reputation of the infrastructure that they create? What about voting machines? Compulsory XKCD link : https://www.xkcd.com/2030/
I think that the lofty disregard is fine - just don't go arguing for grant funding on the basis of real world impact.
On AI and ML - where is the work that will enable methods to be actually managed in the wild? How come the estimates of performance based on the methodologies of testing from academia are so woeful? Why has the academy been content with "it provides 94% TP in test with 99% confidence but when we ran it in production it gave us about 80% after review"!
Computer Science argues for (and gets) high levels of funding by asserting economic rationales for funding with a justification that physics and maths struggle to match.
Every single grant application makes (often bogus) economic rationale for its puported benefits to society and the economy. The trope in mathematics is that everything is relevant for either cryptography or protein folding.
theoretical CS which (tragically) effectively includes much of the database, programming language and methodology community, and much of the AI and ML community too, then this is a misallocation of capital
You may not accept it but there is a whole bunch of very theoretical mathematical work that goes on in AI and ML. There is a whole bunch of work that is more empirically grounded and less whiteboard as well. There is a whole spectrum on the whiteboard to deployed-in-the-real-world. That is why there are often Applied Physics programs, different from Physics programs, different from Engineering programs. And people in each of those have varying levels of overlaps with each other based on where they sit on the theoretical-applied spectrum.
Don't you care about the harm that is inflicted on the people doing the development, their victims (everyone) and the reputation of the infrastructure that they create? What about voting machines?
I never said anything about not caring - this is a silly red herring. I was making a statement about the computational resources needed to solve people and project management issues in software engineering as a counter to your "just give them some whiteboards" comment. I still don't see why throwing more cloud compute resources at Software Engineering departments will make your Scrum meetings more efficient. In fact I don't know if academia is well-poised to solve such problems at all.
arguing for grant funding on the basis of real world impact
The idea behind funding the sciences in academia is that we fund research that may have long-term impact on society. You don't get to throw a fit because every problem you have at work isn't being solved by someone sitting in a university.
the estimates of performance based on the methodologies of testing from academia are so woeful?
Are you claiming that every experiment that comes out of a physics lab works flawlessly out in the real world? Or every paper from a life science lab goes on to successfully become a new medical treatment? I mentioned it before but there are often several fields of study dedicated to just taking highly controlled results from labs and trying to get them to work in the real world. Not everything makes it (especially in the life sciences example). AI/ML are at least better in that they often (but they should be doing it even more) give you what you need to replicate the lab experiment on the controlled, sanitized data.
Comments
I actually agree with your separation of Software Engineering and Computer Science comment, but completely disagree with the funding level statement. The notion that branches of Mathematics don't use computer resources is absolutely laughable. If anything, I don't see why the best practices for managing people who are developing the same CRUD apps over and over again needs any significant investment of any kind besides a few laptops. Computer Science can not and should not give a shit about development practices like Agile or its alternatives. CS people do own a lot of problems facing industry like machine learning and AI, and they address plenty of problems "in the core"...just nothing to do with process.
Also, this split absolutely does happen at some universities, and the funding is decided by agencies based on their priorities. One such university is the University of Waterloo, and the CS department is not wanting for funding...certainly not at the buy a whiteboard and get on with it levels you're suggesting.
The best right out of school sysadmins I've seen were failed physicists. Apparently they run some moderately large stuff.
I had a really good one work for me - but he went off back to physics so that he could play with a real computer!
I think that there is a conflation that needs sorting out. Mathematicians and Physicists may need funding at a high level, they may deserve it in a philosophical and natural justice sense as well. Computer Science argues for (and gets) high levels of funding by asserting economic rationales for funding with a justification that physics and maths struggle to match. My view is that if we are talking about the SE side of the shop then this is rational and fine, but if we are talking about theoretical CS which (tragically) effectively includes much of the database, programming language and methodology community, and much of the AI and ML community too, then this is a misallocation of capital.
In terms of CRUD apps - my jaw is on the floor... Don't you care about the harm that is inflicted on the people doing the development, their victims (everyone) and the reputation of the infrastructure that they create? What about voting machines? Compulsory XKCD link : https://www.xkcd.com/2030/
I think that the lofty disregard is fine - just don't go arguing for grant funding on the basis of real world impact.
On AI and ML - where is the work that will enable methods to be actually managed in the wild? How come the estimates of performance based on the methodologies of testing from academia are so woeful? Why has the academy been content with "it provides 94% TP in test with 99% confidence but when we ran it in production it gave us about 80% after review"!
Every single grant application makes (often bogus) economic rationale for its puported benefits to society and the economy. The trope in mathematics is that everything is relevant for either cryptography or protein folding.
You may not accept it but there is a whole bunch of very theoretical mathematical work that goes on in AI and ML. There is a whole bunch of work that is more empirically grounded and less whiteboard as well. There is a whole spectrum on the whiteboard to deployed-in-the-real-world. That is why there are often Applied Physics programs, different from Physics programs, different from Engineering programs. And people in each of those have varying levels of overlaps with each other based on where they sit on the theoretical-applied spectrum.
I never said anything about not caring - this is a silly red herring. I was making a statement about the computational resources needed to solve people and project management issues in software engineering as a counter to your "just give them some whiteboards" comment. I still don't see why throwing more cloud compute resources at Software Engineering departments will make your Scrum meetings more efficient. In fact I don't know if academia is well-poised to solve such problems at all.
The idea behind funding the sciences in academia is that we fund research that may have long-term impact on society. You don't get to throw a fit because every problem you have at work isn't being solved by someone sitting in a university.
Are you claiming that every experiment that comes out of a physics lab works flawlessly out in the real world? Or every paper from a life science lab goes on to successfully become a new medical treatment? I mentioned it before but there are often several fields of study dedicated to just taking highly controlled results from labs and trying to get them to work in the real world. Not everything makes it (especially in the life sciences example). AI/ML are at least better in that they often (but they should be doing it even more) give you what you need to replicate the lab experiment on the controlled, sanitized data.