I am not sure why the author thinks science is stagnating — it is hard to measure progress, and even if we were stagnating, it is possible that many fields have matured and now have to tackle...
I am not sure why the author thinks science is stagnating — it is hard to measure progress, and even if we were stagnating, it is possible that many fields have matured and now have to tackle extremely difficult problems. (I guess in complexity theory, we would want to show that P is not NP, although I think very few people actually work directly on this problem.) There are many people working on more empirical questions in all of the natural sciences, for example. There are fewer theorists in these departments than experimentalists, and I have the feeling that the proportion has not favored theorists more in recent years.
It is true that academia tends to work on more "fundamental" or "theoretical" questions, but this also seems acceptable — industry has the financial power and incentives to work on problems that are closer to having impact, like "cheaper rockets, cures for cancer, [and] software that is efficient." And in the modern day, we do see many companies working on these problems. There is not enough money or people in academia to successfully compete with industry in these areas, so they work on problems that have longer timescales. When they do see translational application, those who work in academia often partner with their industry counterparts to make it happen (this is common in software and in pharmaceutical applications).
I do agree that empirical work is often undervalued by people who work on more theoretical topics, and they do not necessarily have the same respect for their creativity and intellectual capability as they would for other theorists. However, as a person who works on computer science theory, most of my colleagues recognize that working on theory, versus, say, compiler design or machine learning, just require different skillsets. I think the point about college courses not suiting their audiences needs is an important point, but is mostly a separate discussion.
I am not sure why the author thinks science is stagnating — it is hard to measure progress, and even if we were stagnating, it is possible that many fields have matured and now have to tackle extremely difficult problems. (I guess in complexity theory, we would want to show that P is not NP, although I think very few people actually work directly on this problem.) There are many people working on more empirical questions in all of the natural sciences, for example. There are fewer theorists in these departments than experimentalists, and I have the feeling that the proportion has not favored theorists more in recent years.
It is true that academia tends to work on more "fundamental" or "theoretical" questions, but this also seems acceptable — industry has the financial power and incentives to work on problems that are closer to having impact, like "cheaper rockets, cures for cancer, [and] software that is efficient." And in the modern day, we do see many companies working on these problems. There is not enough money or people in academia to successfully compete with industry in these areas, so they work on problems that have longer timescales. When they do see translational application, those who work in academia often partner with their industry counterparts to make it happen (this is common in software and in pharmaceutical applications).
I do agree that empirical work is often undervalued by people who work on more theoretical topics, and they do not necessarily have the same respect for their creativity and intellectual capability as they would for other theorists. However, as a person who works on computer science theory, most of my colleagues recognize that working on theory, versus, say, compiler design or machine learning, just require different skillsets. I think the point about college courses not suiting their audiences needs is an important point, but is mostly a separate discussion.