Fall Semester greetings, and an image to ponder

Happy new academic year to my students, and to whoever else might happen upon this post.  If you are a student of mine, I especially want to affirm you: the URL for this site may appear on your syllabus, but you’re otherwise not required to visit here.  If you’re here, then, it’s because you have some inclination toward going above and beyond what is asked of you.  That trait–something in you that no assessment test will ever be able to measure–will nevertheless stand you in good stead as few other traits or abilities will, and for years after your days of formal education are past.

Why we should train workers

My source for this image was via someone in my Twitter feed (which I unfortunately didn’t make note of).  Here is the article itself, from Brookings.

I am a couple of weeks late in getting this post up; the semester got up and running (it’s been good, so far), but the work of doing that and some family illnesses at home have cut into spare time for writing here.  As it happens, though, the work in class that I’m most proud of so far seems to me to run counter to the implications of the assertion in the image you see here.  We’ve done precious little thus far that overtly prepares you for work, much less prepares you as we would prepare intelligent machines for the work they do, and I’m quite proud of this fact: this past week, we’ve looked at some paintings and talked about some poems in our Comp I classes, and in Comp II we’ve talked about rhetorical appeals.  The rest of the semester, once we begin working on writing and research projects, will indeed have some value to you in your future careers and lives away from work; but, again, I won’t be training you as though you are machine-learning algorithms.  There are two pretty simple, obvious reasons for that: you already possess such an algorithm (though we still don’t quite understand how it works); and, for that matter, you’re already a far superior information processor, that even the fastest computers can only begin to approach in ability.  There’s also a third, more existential reason: You are, or should be, more than the work you will be hired to do.

It’s for these reasons that the assertion that accompanies the image is both deeply weird and more than a little lacking in awareness of what a good education should do for students.

The holy grail of machine learning is to construct an algorithm that approximates human learning.  Though I admit from the outset that I’m not even especially well-read in AI research, I can say with some confidence that we’ve only just reached the “approximates” stage.  Yet, the assertion takes as a given that that stage is good enough so that we can begin to train humans in the same way.  The problem with that is that, as a commenter on the Techtank article said on Twitter (paraphrasing here), “Okay, Bob, we’re gonna place you in a room with millions of untagged cat photos and check back with you in about 30 years to see what you’ve learned.”  The commenter’s point is that that’s what that kind of training would look like, since that’s how we train machine algorithms to learn.  You, however, learned fairly quickly, and from only a couple of examples, what a cat looks like: it is as though the way in which your brain functions is a demonstration of Plato’s theory of forms.  So, then, the Techtank essay makes a very strange suggestion.  Even more important, though, and setting aside for this post the question of whether computers can learn in the same manner that people do, it seems to forget, or regard as unimportant, a fundamental fact: computers in their essence have only one job to do.  As human beings, you have more than one job, and most of them have little to do with your employment.

This leads me to the other point I want to make: that machines don’t navel-gaze, but we do–as well we should–and school should help us become more adept at it . . . not to mention maybe, over time, becoming less self-absorbed.  (Machines, you may have noticed, are perversely self-absorbed–to the point that they aren’t even aware they are machines.)  Sure, we could train people the way we train machines, but machines don’t leave the workplace.  But you?  Who do you say you are when you go home?  When you are with family or friends?  When you worship, or see no point in worshiping?  When you yell at the news and/or march or volunteer for a cause and/or vote?  When you contemplate a sunrise or sunset or the stars or the mountains or an ocean . . . or an enormous floating mass of plastic in the ocean?  When you contemplate your mortality?  Being in school, by itself, can’t answer those questions for you, but a good education gives you a set of languages that can help you articulate your responses to them.  As I would tell my students back when I taught Introduction to Humanities, “This class won’t help you get a job, but it will make you more interesting to talk to at parties.”

To be trained like a machine or, for that matter, to value only that part of your time in college which you see as preparing you for a career, is to reduce yourself to a drone.  Whether it’s done to you or you choose to do it to yourself, it makes no difference.  Fortunately at Butler, we do what we can to provide our full-time students with a pretty-good community-college version of a liberal arts education.  We do what we can, in other words, to, yes, help make you employable but at the same time discourage you from becoming drone-like.  I look forward to doing my own small part in helping you achieve those two things.

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