Historians are a lot better explainers than algorithmaticians. (Is there such a word? If not, I just made it up.) Let me argue my case.
Harvard’s Jill Lepore, one of the most fascinating and truly humorous historical writers, has superb explanatory insight on algorithmic prediction. That’s not a surprise. She approaches history from perspectives that have never crossed this historian’s mind--and does it with a lot of verve. She’s one of the few with a steady hand when the rest of us are lurching around with churning stomachs about artificial intelligence and moronic politicians.
With two talkative algorithmic geniuses in my own family, a son-in-law from Harvard and a recently minted Carnegie-Mellon data-scientist grandson, with whom I spend hours listening and making sense of their conversations, Lepore is an absolute delight at simplifying algorithmic insight.
She regularly seems to have the ability to condense an entire discipline, the whole shebang into a single readable paragraph. Here’s the first paragraph from her New Yorker article, entitled simply “Unforeseen.” Prophecy is a mug’s game. But then, lately, most of us are mugs. 2018 was a banner year for the art of prediction, which is not to say the science, because there really is no science of prediction. Predictive algorithms start out as historians: they study historical data to detect patterns. Then they become prophets: they devise mathematical formulas that explain the pattern, test the formulas against historical data withheld for the purpose, and use the formulas to make predictions about the future. That’s why Amazon, Google, Facebook, and everyone else are collecting your data to feed to their algorithms: they want to turn your past into your future.
So, what’s going on here? Let’s do a close read, identifying what’s deeper in her paragraph. By that I mean if you look at her underlying verbal and rhetorical processes, are there names for it that’ll help you understand it better? And perhaps learn how to do similarly? After all, no matter one’s technical expertise, wordsmiths with technical expertise always do better in business than the rest of the gang. They always capture attention. It’s one of the ways I built my marvelous business—and how plenty of others have done the same. One of my most successful mentees started out as an English major—and has become highly successful, developing a lucrative career in a free-lance business. The other also began as an English major, ending up as EVP in a multi-billion-dollar, international food product business. Pay close attention: wordsmithing is what distinguishes ordinary technicians from the common pack of technicians. So what’s in Lepore’s wordsmithing?
The first and most obvious strategy is the use of historical “connection.” Lepore understands that history is nothing more nor nothing less than a human, all-too-human enterprise. History is not the raw stuff of the past—it is the connection we are able to make with the past. And her connection is all about comparing history patterning to algorithms (another form of pattern).
The second strategy is "analogy." Analogy has a long, successful use in most any kind of argument. Kepler used it in the seventeenth century to explain the planets’ movement around the sun. He argued that the sun rotates on its axis and the planets rotate around it like boatmen caught in a fast, whirling current. Deep analogical thinking is the practice of recognizing conceptual similarities in different scenarios that may seem to have little in common on the surface. So, we have historical patterns and algorithmic patterns of data, both gathered over time periods. Furthermore, analogies can be used not only to describe, but even more significantly—to resolve problems.
Her third strategy is "narrative"—mini-narrative. Her little story not only explains the context, but tells the reader how they, too, can manage their little world of data and make it both obvious and enlightening.
Story form is powerful of and by itself. Although it’s not obvious to most and initially unbelievable, the story form is actually more powerful for influencing people than the content. Form is the pattern the message follows and content is the information the message contains. If you want to send congratulations to a friend at work for a promotion, you can send pretty much the same content through the different forms of a letter, a poem, an email, a text message or a conversation. Notice that the patterns of each of these forms is different. They follow different rules of both production and consumption. Furthermore, notice that each form is probably familiar to your friend although he’s never before received this promotion. We all know how letters “work,” while each new letter conveys new information. That, in fact, is an important characteristic of form. It follows familiar patterns that are widely understood within a culture. Thus, the narrative form is powerful of itself—and Lepore uses it widely and regularly. So do highly successful businesspeople.
So, to go back to Jill Lepore’s explanation of algorithmic work: connection, analogy and narrative. There’s are several reasons why smart historians are so great at explanation and analogy. It’s also why if you want to be a great data scientist, a few humanities courses in history or philosophy or even painting could be of great value.
**If you really are a book-nut like me, pick up Jill Lepore’s book: These truths: A history of the United States. But if you are a bit lazy, just google her New Yorker articles for bedtime reading.
There is a reason historians can be so great at such explanations. It’s also why if you want to be a data scientist, a few humanities’ courses in history could be of great value.
How’s that for taking down the algorithm’s pants and revealing it all?