Machine Learning, Thermostat, Wood Stove

As we encounter the far-future quasi-magical technology that is Machine Learning, I wanted to offer up a brief reflection on everyday technology, labor, and meaning that I found interesting. A reflection brought about by trying not to use my thermostat.

At it’s heart, contemporary Machine Learning systems are just fantastically complex versions of that everyday appliance, the thermostat. The thermostat (at least an old-fashioned, “dumb,” thermostat like mine) takes a single data point, the temperature in your house, and uses it to trigger a response: turning on the furnace. When the temperature gets too low, the furnace turns on. When temperature gets high enough, the furnace turns off. This is a dirt simple example of what Norbert Wiener, one of the great-grandparents of current machine learning efforts, called a “feedback loop” in his 1948 classic Cybernetics.

Modern Machine Learning systems are based on the same principle, they just use lots and lots of fancy Linear Algebra to implement a feedback loop that can learn responses to lots and lots of data points all at once. For example, it can learn that all the data points (pixels) in an image should be labelled “cat” by looking at lots of labelled images and understanding how pixels and labels are related. It does this through another feedback loop, illustrated below, a process known as “training” in Machine Learning lingo.

From the excellent “Deep Leaning with Python, Second Edition” by Francios Chollet

Once trained, the Machine Learning system is now able to trigger responses to various input data. That could mean identifying an image, drawing an image to match a label, writing text to respond to a prompt, all sorts of stuff.

Why build a machine that can respond to inputs like this? So we can delegate stuff to it, of course. Just as we delegate keeping the temperature in our homes steady to thermostats, so we’ll be able to delegate all sorts of things to Machine Leaning systems. That’s automation, the delegation of human action to machine action with in the context of various feedback loops (or at least, that’s how you might define automation, if you read Wiener and Latour back to back, like I did once upon a time).

Those arguing in favor of automation often argue that the process can free people from routine “drudgery” and give them more time for “meaningful work.” If a machine can do a form of work, this argument goes, then that form of work didn’t embody the Unique Qualities of Humanity to begin with! Better, and more meaningful for humans to do the Special Unique Human Things and leave the routine alone.

Which brings us back to my thermostat. This winter, we’ve been trying not to use our furnace much, since it’s oil powered and current prices (thanks, Putin!) make it expensive to run. That means we’ve set our thermostat low enough that it only turns the furnace on when we’re in danger of freezing the pipes.

Instead, we’ve been keeping warm by using a wood-burning stove. The stove has no thermostat, of course, the feedback loop must be manually implemented. If it’s too cold, someone has to build a fire. When it becomes too warm, a handle on the side lets us damp the fire down.

This process involves a fair share of drudgery: emptying the ash pan, placing the kindling, lighting the fire, fetching more wood from the wood pile, keeping the fire fed. It can be tiresome to stay warm this way.

And yet, I often find that building a fire feels like a profoundly meaningful act. I pile the paper and wood together, I light the match, I nurture the flame. Now my family will be warm, instead of cold.

When we think of meaning we tend to think of grand things: gods and heroes, the fate of nations, the unique insight of art. But, I suspect, meaning more often lives here: in the everyday quotidian drudgery of caring for each other.

It’s something worth thinking about, as we learn to automate yet more drudgery away.

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