Wednesday, November 17, 2021

CS Slow to Change?

Back in March of 2019 I wrote

I was also going to post about Yann LeCun's Facebook rant about stodgy CS departments but then Yann goes ahead and wins a Turing award with Geoffrey Hinton and Yoshua Bengio for their work on machine learning. I knew Yann from when we worked together at NEC Research in the early 2000's and let's just congratulate him and the others and let them bask in glory for truly transforming how we think of computing today. I'll get back to his post soon enough.

So not that soon. Yann's post was from 2015 where he went after "stodgy" CS departments naming Yale, Harvard, Princeton and Chicago.

CS is a quickly evolving field.  Because of excess conservatism, these departments have repeatedly missed important trends in CS and related field, such as Data Science. They seem to view CS as meaning strictly theory, crypto, systems and programming  languages, what some have called "core CS", paying lip service to graphics, vision, machine learning, AI, HCI, robotics, etc. But these areas are the ones that have been expanding the fastest in the last decades, particularly machine learning and computer vision in the last decade....It is quite common, and somewhat natural, that newer areas (eg ML) be looked down upon by members of older, more established areas (eg Theory and Systems). After all, scientists are professional skeptics. But in a fast evolving disciplines like CS and now Data Science, an excessive aversion to risk and change is a recipe for failure.

We've seen some changes since. Yale's Statistics Department is now Statistics and Data Science. The University of Chicago has a new Data Science undergrad major and institute.

I wonder if that's the future. CS doesn't really change that much, at least not quickly. Data science, and perhaps cybersecurity, evolve as separate fields which only have limited intersection with traditional CS. The CS degree itself just focuses on those interested in how the machines work and the theory behind them. We're busy trying to figure this out at Illinois Tech as are most other schools. And what about augmented/virtual reality and the metaverse, quantum computing, fintech, social networks, human and social factors and so on? How do you choose which bets to make? 

Most of all, universities, traditionally slowly moving machines, need to far more agile even in fields outside computing since the digital transformation is affecting everything. How do you plan degrees when the computing landscape when students graduate is different from when they start? 


  1. It sounds like the PhD students are beating a path to machine learning. Just look at the Taulbee report and the number of papers submitted to ICML or NeurIPS.

  2. Cybersecurity requires knowing about "core CS." Theory: lots of things one wants to do algorithmically for defense are {P,NP,APX}-complete/hard. Folks need to know that stuff as well as SAT/SMT. Crypto: goes without saying. Systems: these are what is being secured/threatened. PLs: compilers (RE/VR; DSLs), formal methods are important.