The growing power and status of Computer Science departments

In academia, statistics is losing ground to computers:

“They [the statistical profession] lost the PR war because they never fought it.”

I assume this is a USA development. In Europe the computer departments have tended to be outgrowths of the math departments.

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.

There are a number of problems with this:

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”:

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 “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.

A minor reform is suggested:

One of the major problems is that AP Stat is usually taught by people who lack depth in the subject matter. A typical example is that a student complained to me that his AP Stat teacher could not answer his question as to why it is customary to use n-1 rather than n in the denominator of s^2 , even though he had attended a top-quality high school in the heart of Silicon Valley. But even that lapse is really minor, compared to the lack among the AP teachers of the broad overview typically possessed by Stat professors teaching university courses, in terms of what can be done with Stat, what the philosophy is, what the concepts really mean and so on. AP courses are ostensibly college level, but the students are not getting college-level instruction. The “teach to the test” syndrome that pervades AP courses in general exacerbates this problem.

The most exasperating part of all this is that AP Stat officially relies on TI-83 pocket calculators as its computational vehicle. The machines are expensive, and after all we are living in an age in which R is free! Moreover, the calculators don’t have the capabilities of dazzling graphics and analyzing of nontrivial data sets that R provides – exactly the kinds of things that motivate young people.

So, unlike the “CS usurpation problem,” whose solution is unclear, here is something that actually can be fixed reasonably simply. If I had my druthers, I would simply ban AP Stat, and actually, I am one of those people who would do away with the entire AP program. Obviously, there are too many deeply entrenched interests for this to happen, but one thing that can be done for AP Stat is to switch its computational vehicle to R.

As noted, R is free and is multi platform, with outstanding graphical capabilities. There is no end to the number of data sets teenagers would find attractive for R use, say the Million Song Data Set.