CS, having grown out of a research on fast-changing software and hardware systems, became accustomed to the “24-hour news cycle”–very rapid publication rates, with the venue of choice being (refereed) frequent conferences rather than slow journals. This leads to research work being less thoroughly conducted, and less thoroughly reviewed, resulting in poorer quality work. The fact that some prestigious conferences have acceptance rates in the teens or even lower doesn’t negate these realities. Because CS Depts. at research universities tend to be housed in Colleges of Engineering, there is heavy pressure to bring in lots of research funding, and produce lots of PhD students. Large amounts of time is spent on trips to schmooze funding agencies and industrial sponsors, writing grants, meeting conference deadlines and managing a small army of doctoral students–instead of time spent in careful, deep, long-term contemplation about the problems at hand. This is made even worse by the rapid change in the fashionable research topic du jour. making it difficult to go into a topic in any real depth. Offloading the actual research onto a large team of grad students can result in faculty not fully applying the talents they were hired for; I’ve seen too many cases in which the thesis adviser is not sufficiently aware of what his/her students are doing. There is rampant “reinventing the wheel.” The above-mentioned lack of “adult supervision” and lack of long-term commitment to research topics results in weak knowledge of the literature. This is especially true for knowledge of the Stat literature, which even the “adults” tend to have very little awareness of. For instance, consider a on the use of unlabeled training data in classification. (I’ll omit names.) One of the two authors is one of the most prominent names in the machine learning field, and the has been cited over 3,000 times, yet the cites nothing in the extensive Stat literature on this topic, consisting of a long stream of s from 1981 to the present. Again for historical reasons, CS research is largely empirical/experimental in nature. This causes what in my view is one of the most serious problems plaguing CS research in Stat – lack of rigor. Mind you, I am not saying that every should consist of theorems and proofs or be overly abstract; data- and/or simulation-based studies are fine. But there is no substitute for precise thinking, and in my experience, many (nominally) successful CS researchers in Stat do not have a solid understanding of the fundamentals underlying the problems they work on. For example, a recent in a top CS conference incorrectly stated that the logistic classification model cannot handle non-monotonic relations between the predictors and response variable; actually, one can add quadratic terms, and so on, to models like this. This “engineering-style” research model causes a cavalier attitude towards underlying models and assumptions. Most empirical work in CS doesn’t have any models to worry about. That’s entirely appropriate, but in my observation it creates a mentality that inappropriately carries over when CS researchers do Stat work. A few years ago, for instance, I attended a talk by a machine learning specialist who had just earned her PhD at one of the very top CS Departments. in the world. She had taken a Bayesian approach to the problem she worked on, and I asked her why she had chosen that specific prior distribution. She couldn’t answer – she had just blindly used what her thesis adviser had given her–and moreover, she was baffled as to why anyone would want to know why that prior was chosen. Again due to the history of the field, CS people tend to have grand, starry-eyed ambitions–laudable, but a double-edged sword. On the one hand, this is a huge plus, leading to highly impressive feats such as recognizing faces in a crowd. But this mentality leads to an oversimplified view of things, with everything being viewed as a paradigm shift. Neural networks epitomize this problem. Enticing phrasing such as “Neural networks work like the human brain” blinds many researchers to the fact that neural nets are not fundamentally different from other parametric and nonparametric methods for regression and classification.(Recently I was pleased to discover–“learn,” if you must–that the famous book by Hastie, Tibshirani and Friedman complains about what they call “hype” over neural networks; sadly, theirs is a rare voice on this matter.) Among CS folks, there is a failure to understand that the celebrated accomplishments of “machine learning” have been mainly the result of applying a lot of money, a lot of people time, a lot of computational power and prodigious amounts of tweaking to the given problem – not because fundamentally new technology has been invented. |
