Skip to main content

Automated Academics

This WSJ piece on the US income gains over the past decade (summary: unless you have a PhD or MD, you didn't have any income gains) got me thinking:

I'm actually pretty cautious about that number, I would be more interested in the range of the distribution, I think the percent change is being pulled up by all of those physics PhDs who went into finance.

Then again, considering in the that over the past few weeks I've been learning how to automate the collection of data that used to be done by people with masters degrees, maybe PhDs are going to be the ones who automate all of the former undergraduate and masters level work out of existence, keeping the productivity gains for ourselves (conditional on the tax structure). (see also Farhod Manjoo's recent series on this issue in Slate.)

One thing I gleaned from a talk given by the Governor of the California Board of Education last night was that academics largely doesn't even need PhDs (at least at all levels except the very top). You can just have a few PhDs design standardised courses and then have them administered by less trained people. This has already been happening in K-12 education, but it gets even better in higher education, as many for-profits already know.

Since adults will complete their work electronically without as much oversight as children need you can cut out much of the administration. Monitoring instructor performance--assessing how well students are completing the standardised work--can be fully automated and done in real-time.

Conclusion: like in much of the rest of the economy, you have a few highly trained people who organise the system and everyone else just implements it with minimal need for training. The former captures most of the productivity gains.

Implication: do well in school. . . no, not just ok, but very good. Also, do well in something that allows you to design larger processes, rather than just implementing an established routine.

Comments

Popular posts from this blog

Slide: one function for lag/lead variables in data frames, including time-series cross-sectional data

I often want to quickly create a lag or lead variable in an R data frame. Sometimes I also want to create the lag or lead variable for different groups in a data frame, for example, if I want to lag GDP for each country in a data frame.I've found the various R methods for doing this hard to remember and usually need to look at old blogposts. Any time we find ourselves using the same series of codes over and over, it's probably time to put them into a function. So, I added a new command–slide–to the DataCombine R package (v0.1.5).Building on the shift function TszKin Julian posted on his blog, slide allows you to slide a variable up by any time unit to create a lead or down to create a lag. It returns the lag/lead variable to a new column in your data frame. It works with both data that has one observed unit and with time-series cross-sectional data.Note: your data needs to be in ascending time order with equally spaced time increments. For example 1995, 1996, 1997. ExamplesNot…

A Link Between topicmodels LDA and LDAvis

Carson Sievert and Kenny Shirley have put together the really nice LDAvis R package. It provides a Shiny-based interactive interface for exploring the output from Latent Dirichlet Allocation topic models. If you've never used it, I highly recommend checking out their XKCD example (this paper also has some nice background).LDAvis doesn't fit topic models, it just visualises the output. As such it is agnostic about what package you use to fit your LDA topic model. They have a useful example of how to use output from the lda package.I wanted to use LDAvis with output from the topicmodels package. It works really nicely with texts preprocessed using the tm package. The trick is extracting the information LDAvis requires from the model and placing it into a specifically structured JSON formatted object.To make the conversion from topicmodels output to LDAvis JSON input easier, I created a linking function called topicmodels_json_ldavis. The full function is below. To use it follow …

Showing results from Cox Proportional Hazard Models in R with simPH

Update 2 February 2014: A new version of simPH (Version 1.0) will soon be available for download from CRAN. It allows you to plot using points, ribbons, and (new) lines. See the updated package description paper for examples. Note that the ribbons argument will no longer work as in the examples below. Please use type = 'ribbons' (or 'points' or 'lines'). Effectively showing estimates and uncertainty from Cox Proportional Hazard (PH) models, especially for interactive and non-linear effects, can be challenging with currently available software. So, researchers often just simply display a results table. These are pretty useless for Cox PH models. It is difficult to decipher a simple linear variable’s estimated effect and basically impossible to understand time interactions, interactions between variables, and nonlinear effects without the reader further calculating quantities of interest for a variety of fitted values.So, I’ve been putting together the simPH R p…