Perhaps some inspiration: you’ve convinced at least this grad student to harp on all of these principles when I yearly teach an introduction to probability for engineers, applied physicists, and applied mathematicians. I’m sure many before you have noted these deficiencies… I have routine conversations with my adviser about the sad state of machine learning literature in engineering. The fad is to just slap together a few existing algorithms, eek out a 3% improvement in efficiency or something, and publish 6 papers out of that. The pressure to publish for tenure is maddening. The job market for PhDs is disappointing… half of the deficiencies you mention exist because people get tired of working very hard to be underpaid as post-docs and sweep things under the rug to find some kind of geodesic path to life-success/money/not-spending-12-hours-per-day-in-the-lab. These aren’t excuses, mind you, but the realities of being a “scientist” leave open a lot of room for this. And that one guy (right now, me) sitting in the corner of lab with my colleagues and constantly wanting to talk about bayescraft, is like the annoying guy who always takes the stairs instead of the elevator and sits with perfect posture. Colleagues just want to ignore me, churn out their rudimentary permutations of existing methods, and go home at night to do hobby X.
I worked in a government research lab for a few years before grad school, and this is all even worse in that sort of environment. One can become rather cynical about our race rather quickly when the supposed experts ask you to model a radar gain pattern with functions that aren’t even integrable, for example, and don’t know what you mean by integrable when the area under their curves blows up to infinity and ruins their numerical simulations. And these people graduated with honor from a PhD program at Berkeley or Stanford or Harvard or MIT and have been doing science for 20+ years.
But like you’ve said elsewhere: this is our Earth. It won’t be any better until we change it. One semester of probability theory at a time.
Perhaps some inspiration: you’ve convinced at least this grad student to harp on all of these principles when I yearly teach an introduction to probability for engineers, applied physicists, and applied mathematicians. I’m sure many before you have noted these deficiencies… I have routine conversations with my adviser about the sad state of machine learning literature in engineering. The fad is to just slap together a few existing algorithms, eek out a 3% improvement in efficiency or something, and publish 6 papers out of that. The pressure to publish for tenure is maddening. The job market for PhDs is disappointing… half of the deficiencies you mention exist because people get tired of working very hard to be underpaid as post-docs and sweep things under the rug to find some kind of geodesic path to life-success/money/not-spending-12-hours-per-day-in-the-lab. These aren’t excuses, mind you, but the realities of being a “scientist” leave open a lot of room for this. And that one guy (right now, me) sitting in the corner of lab with my colleagues and constantly wanting to talk about bayescraft, is like the annoying guy who always takes the stairs instead of the elevator and sits with perfect posture. Colleagues just want to ignore me, churn out their rudimentary permutations of existing methods, and go home at night to do hobby X.
I worked in a government research lab for a few years before grad school, and this is all even worse in that sort of environment. One can become rather cynical about our race rather quickly when the supposed experts ask you to model a radar gain pattern with functions that aren’t even integrable, for example, and don’t know what you mean by integrable when the area under their curves blows up to infinity and ruins their numerical simulations. And these people graduated with honor from a PhD program at Berkeley or Stanford or Harvard or MIT and have been doing science for 20+ years.
But like you’ve said elsewhere: this is our Earth. It won’t be any better until we change it. One semester of probability theory at a time.