网范文：“ Statistics:Losing Ground to CS” 美国统计协会(ASA)过去几年经历一段时间的焦虑,他们担心统计领域走向未来的重要性会逐渐降低,该领域在很大程度上是被其他学科,特别是计算机科学(CS)所替代。努力使该领域对学生的吸引力在很大程度上是不成功的。该篇主要研讨了这两方面。 The American Statistical Association (ASA) leadership, and many in Statistics academia. have been undergoing a period of angst the last few years, They worry that the field of Statistics is headed for a future of reduced national influence and importance, with the feeling that: The field is to a large extent being usurped by other disciplines, notably Computer Science (CS). Efforts to make the field attractive to students have largely been unsuccessful. I had been aware of these issues for quite a while, and thus was pleasantly surprised last year to see then-ASA president Marie Davidson write a plaintive editorial titled, “Aren’t We Data Science?” Good, the ASA is taking action, I thought. But even then I was startled to learn during JSM 2017 (a conference tellingly titled “Statistics: Global Impact, Past, Present and Future”) that the ASA leadership is so concerned about these problems that it has now retained a PR firm. This is probably a wise move–most large institutions engage in extensive PR in one way or another–but it is a sad statement about how complacent the profession has become. Indeed, it can be argued that the action is long overdue; as a friend of mine put it, “They [the statistical profession] lost the PR war because they never fought it.” In this post, I’ll tell you the rest of the story, as I see it, viewing events as a statistician, computer scientist and R activist. CS vs. Statistics Let’s consider the CS issue first. Recently a number of new terms have arisen, such as data science, Big Data, and analytics, and the popularity of the term machine learning has grown rapidly. To many of us, though, this is just “old wine in new bottles,” with the “wine” being Statistics. But the new “bottles” are disciplines outside of Statistics–especially CS. I have a foot in both the Statistics and CS camps. I’ve spent most of my career in the Computer Science Department at the University of California, Davis, but I began my career in Statistics at that institution. My mathematics doctoral thesis at UCLA was in probability theory, and my first years on the faculty at Davis focused on statistical methodology. I was one of the seven charter members of the Department of Statistics. Though my departmental affiliation later changed to CS, I never left Statistics as a field, and most of my research in Computer Science has been statistical in nature. With such “dual loyalties,” I’ll refer to people in both professions via third-person pronouns, not first, and I will be critical of both groups. However, in keeping with the theme of the ASA’s recent actions, my will be Stat-centric: What is poor Statistics to do? Well then, how did CS come to annex the Stat field? The primary cause, I believe, came from the CS subfield of Artificial Intelligence (AI). Though there always had been some probabilistic analysis in AI, in recent years the interest has been almost exclusively in predictive analysis–a core area of Statistics. That switch in AI was due largely to the emergence of Big Data. No one really knows what the term means, but people “know it when they see it,” and they see it quite often these days. Typical data sets range from large to huge to astronomical (sometimes literally the latter, as cosmology is one of the application fields), necessitating that one pay key attention to the computational aspects. Hence the term data science, combining quantitative methods with speedy computation, and hence another reason for CS to become involved. Involvement is one thing, but usurpation is another. Though not a deliberate action by any means, CS is eclipsing Stat in many of Stat’s central areas. This is dramatically demonstrated by statements that are made like, “With machine learning methods, you don’t need statistics”–a punch in the gut for statisticians who realize that machine learning really IS statistics. ML goes into great detail in certain aspects, e.g. text mining, but in essence it consists of parametric and nonparametric curve estimation methods from Statistics, such as logistic regression, LASSO, nearest-neighbor classification, random forests, the EM algorithm and so on. Though the Stat leaders seem to regard all this as something of an existential threat to the well-being of their profession, I view it as much worse than that. The problem is not that CS people are doing Statistics, but rather that they are doing it poorly: Generally the quality of CS work in Stat is weak. It is not a problem of quality of the researchers themselves; indeed, many of them are very highly talented. Instead, there are a number of systemic reasons for this, structural problems with the CS research “business model”: |
