A few years ago, I found myself investigating the thorny problem of Shakespearean authorship. I wanted to know if the anonymous Renaissance play Arden of Faversham (1590) was written partly or entirely by William Shakespeare. Perhaps, I thought, an AI could look over a field of plays divided into just two categories—Shakespeare on one side of the fence and everyone else on the other—and place Arden of Faversham decisively on the correct side.
The AI considered what words Shakespeare and only Shakespeare tended to use, as well as those words that Shakespeare and only Shakespeare avoided. We put Shakespeare’s plays on one side of a fence and every other Renaissance play on the other. We then unleashed an AI, tasking it with figuring out what sorts of features are common to Shakespeare’s plays and, even more importantly, what features are only common to Shakespeare’s plays. So when Arden was thrown at the AI, it would choose to place Arden on the Shakespearean or non-Shakespearean side of the fence based on which “Shakespearean” words it had.
The result, it turns out, is inconclusive. The field happens to be far less neat than I have portrayed. AIs don’t see the fence I mentioned that divides categories. What they do, instead, is build that fence. Here is where the problem arises. If, after drawing the fence, the plays separate cleanly on either side, then we have a neat cleavage between the two categories of Shakespearean and non-Shakespearean plays. But if that separation is not so neat, then it becomes far more difficult to be certain of our classification.
As you would perhaps expect, Renaissance plays don’t cluster so nicely into Shakespearean and non-Shakespearean plays. Shakespeare’s style and verbiage are so varied and dynamic that he intrudes into other authors’ spaces—as other authors frequently do to one another. And word frequencies alone are likely not enough to prove authorship definitively. We need to take other features into consideration, like word sequence and grammar, in the hopes of finding a field on which a fence can be neatly drawn. We have yet to find it. The same goes for the lines between abusive and nonabusive language that Perspective AI—a project from Google that launched in 2017 with the aim of filtering out abusive language from internet conversations and comments—had such trouble identifying, or even a chatbot’s inability to determine appropriate versus inappropriate responses.
The failure of AI in classifying Arden of Faversham can be attributed to several different causes. Perhaps there simply aren’t enough plays to correctly train an AI. Or perhaps there is something about the nature of the data of Renaissance plays that causes AI to have a harder time with particular types of classification problems. I would argue that it’s the nature of the data itself. The particular kind of data that foils AI more than anything is human language. Unfortunately, human language is also a primary form of data on the meganet. As language confounds deep-learning applications, AI—and meganets—will learn to avoid it in favor of numbers and images, a move that stands to imperil how humans use language with each other.
Meganets are what I’m calling the persistent, evolving, and opaque data networks that control (or at least heavily influence) how we see the world. They’re bigger than any one platform or algorithm; rather, meganets are a way to describe how all of these systems become tangled up in each other. They accumulate data about all our daily activities, vital statistics, and our very inner selves. They construct social groupings that could not have even existed 20 years ago. And, as the new minds of the world, they constantly modify themselves in response to user behavior, resulting in collectively authored algorithms none of us intend—not even the corporations and governments operating them. AI is the part of the meganet that looks most like a brain. But by themselves, deep-learning networks are brains without vision processing, speech centers, or an ability to grow or act.