Is conceptual purity the defining aesthetic in academic computer science?

This is Jessica. Last week the 2024 Turing award winners were announced. It went to pioneers in reinforcement learning Andrew Barto and Richard Sutton. 

Academic computer scientists get excited about Turing awards. On the positive side, people like to see hard work getting recognized and celebrate their peers or mentors. On the cringier side, award announcements give people an excuse to casually boast about their education on the awarded topic or their proximity to the winners. And suddenly everyone is very interested in hearing any pearls of wisdom the winners have to share about their specific area but also the broader research landscape that their contributions intersect. 

In this case, some bad advice once given by one of the winners trickled up to social media. It seems Sutton once gave some truly horrible advice in a talk back in 2022. There was a slide about how to go about “keeping one’s eyes on the prize of understanding intelligence” if you want to be ambitious in AI research. To do this, he suggested not being distracted by applications. Or domain knowledge. Or unnecessary problem variations. Or AI privacy, explainability, or safety. 

This advice is questionable for many reasons. Do we really want to encourage the already palpable preoccupation with “artificial general intelligence” or proving progress on benchmarks of questionable relation to real world tasks they supposedly embody? How does one not worry at all about domain knowledge, given that many real world decision pipelines where AI is deployed still depend on human oversight? And how do you reconcile an inventor of algorithms that are vulnerable to unintended behavior through “reward hacking” and the like telling people not to waste their time on topics like AI risk management or making these models interpretable to humans?  

It’s probably not what we should be telling the younger generation. But at the same time, it lines up with a certain preoccupation with conceptual purity that I see often among computer scientists, where the closer your work is to math, the more brilliant you must be. There’s a certain glamour associated with solving an old math problem (e.g., he cracked the sunflower lemma!) Not surprisingly, I see this most in CS theorists, but I don’t think it’s at all specific to theory. Conceptual purity also seems to carry weight in areas like programming languages, privacy and security, and ML. More generally, it leads to vague but recurring questions that come up in faculty hiring or grant proposal review, like “How is this advancing ‘core CS’?” 

It’s interesting how sharply this aesthetic contrasts with ideals from some of the more applied areas of computer science. In systems, it’s more about conquering messes at scale. If your code somehow tames something ugly and complex into submission, then you’ve earned a sort of valor, not unlike a firefighter putting out some raging blaze or a lawn crew bringing out the power tools to hack away a bunch of dense brush. This is the impression I get often when listening to my systems colleagues discuss faculty candidates or research in their area. In human computer interaction, there can be a preoccupation with context, with very specific things and what can’t be formalized. If you want to write a paper applying anthropological methods to understand the role of mobile interfaces in witchcraft in some small rural community, go for it. I guess on the bright side, when you combine a bunch of individually perverse aesthetics in a single discipline, things feel more balanced. 

In this case, it’s unfortunate Sutton once advocated for ignoring applications and safety and the like, because he has often spoken out against exactly the kind of arrogance that tends to accompany this kind of preoccupation with conceptual purity. But I guess the purity fetish can run deep. I know I have been guilty of it myself sometimes, e.g., when I find myself pointing to examples from the more applied areas of CS when I want to make a point about research with questionable generalizability. I’ve seen faculty use the term “pure CS” and then catch themself and ask whether it’s the right ideal. Part of this is probably what we’re conditioned to appreciate in computer science education (e.g., clever generic solutions over practical or contextually-specific ones). But perhaps we’ve selected into it as well.

I think it is also a reaction to the perceived threat of interdisciplinarity. Computer science appears to have subsumed many other fields, but this leads to a kind of threat from within that whatever it was historically will disappear. Focusing on the so-called conceptual “core” can seem to protect academic CS from being subsumed by the rapidly bleeding edges. On the flip side, the undeniable relevance of computer science to the world perhaps gives contributions to “pure CS” some kind of penultimate value that requires no rationalization as they might in other fields. One can focus on conceptual purity without the risk of being judged irrelevant to the world.