I recently read Martin Ford’s Rise of the Robots with the UMD CS faculty book club. The book considers the impact of the growth of information technology (IT) on the human labor market, and how the trend towards greater automation could eventually eliminate a substantial number of jobs. The result could be a radical, and disruptive, reshaping of the global economy.
I would recommend the book. I found it well-written and thought provoking. Ford capably argues from past economic and technology trends and also digs into particular problems, products, and research in order to extrapolate future impact. Of the ten faculty who discussed the book, nine of us (including me) were convinced that future automation will be increasingly disruptive to human labor markets.
While reading the book, I found myself wondering about my own role, and that of my field, in addressing this situation we’ve contributed to. Many computer scientists have high-minded ideals and wish to help society through IT innovation. What can we do to ensure that those ideals are realized, rather than perverted into the dystopian future that Ford is warning us about? Continue reading
In the computing stack, PL sits between algorithms and systems. Without algorithms to implement or computer systems to run them on, there would be no need for programming languages. However, the research communities that study algorithms, PL, and systems don’t really have much of an overlap. This is perhaps unavoidable: computer science is now a mature field, and researchers in mature fields tend to pursue specialized and technical research questions.
At the same time, it seems important that the approaches — assumptions and methods — of different subfields of computing be compatible to some extent. At the end of the day, computer science aims to produce solutions for the whole computing stack. An “impedence mismatch” between its subfields compromises our ability to come up with such end-to-end solutions.
This suggests that the comparative study of assumptions, techniques and cultures of different CS fields (a sort of “comp lit” for computer science) is potentially valuable.
Personally, I have always been intrigued by the relationship between the fields of programming languages and algorithms. In this post, I discuss similarities and differences between these two areas, and argue that their synthesis could be interesting for both research and teaching.