Yitang “Tom” Zhang spent the seven years following the completion of his Ph.D. in mathematics floating between Kentucky and Queens, working for a chain of Subway restaurants, and doing odd accounting work. Now he is on a lecture tour that includes stops at Harvard, Columbia, Caltech, and Princeton, is fielding multiple professorship offers, and spends two hours a day dealing with the press. That’s because, in April, Zhang proved a theorem that had eluded mathematicians for a century or more. When we called Zhang to see what he thought of being thrust into the spotlight, we found a shy, modest man, genuinely disinterested in all the fuss.
In the language of hydraulic engineering, the process eroding the foundation is known as “solutioning.” If that problem is not addressed, what happens next is “piping”: water begins to travel between the voids, moving horizontally beneath the dam. To illustrate, American engineers have devised a triangular chart. The process begins, at the apex, with solutioning, advances through cavity formation and piping, and ends with core collapse and, finally, dam breach—like a Florida sinkhole opening up, unannounced, beneath a shopping center. Engineers jokingly refer to the chart as the “triangle of death.” Schnittker told me, “Once piping begins, there is no going back. In twelve hours, the dam is gone.”
And yet the rise of machine learning makes it more difficult for us to carve out a special place for us. If you believe, with Searle, that there is something special about human “insight,” you can draw a clear line that separates the human from the automated. If you agree with Searle’s antagonists, you can’t. It is understandable why so many people cling fast to the former view. At a 2015 M.I.T. conference about the roots of artificial intelligence, Noam Chomsky was asked what he thought of machine learning. He pooh-poohed the whole enterprise as mere statistical prediction, a glorified weather forecast. Even if neural translation attained perfect functionality, it would reveal nothing profound about the underlying nature of language. It could never tell you if a pronoun took the dative or the accusative case. This kind of prediction makes for a good tool to accomplish our ends, but it doesn’t succeed by the standards of furthering our understanding of why things happen the way they do. A machine can already detect tumors in medical scans better than human radiologists, but the machine can’t tell you what’s causing the cancer.
Most of us are slaves to our chronological age, behaving, as the saying goes, age-appropriately. For example, young people often take steps to recover from a minor injury, whereas someone in their 80s may accept the pain that comes with the injury and be less proactive in addressing the problem. “Many people, because of societal expectations, all too often say, ‘Well, what do you expect, as you get older you fall apart,’ ” says Langer. “So, they don’t do the things to make themselves better, and it becomes a self-fulfilling prophecy.”
It’s this perception of one’s age, or subjective age, that interests Antonio Terracciano, a psychologist and gerontologist at Florida State University College of Medicine. Horvath’s work shows that biological age is correlated with diseases. Can one say the same thing about subjective age?
Given that thoughts are a jumble of fragments and pieces, it occurred to me that a recorded transcript of those jumbled pieces actually might not be very illuminating. It might not even be intelligible. Meanwhile the (admittedly much more arduous) process of writing down my thoughts had been surprisingly enlightening. In one swoop, my brain was capable of detecting the patchy notions swirling in my mind, filling in their gaps to make them whole—that is, adding the stripes—and then evaluating them for their credibility and value, or lack thereof.
In other words, my own brain was a brain decoder. It required a lot more effort than merely using a digital recorder as I’d imagined, but it was also a whole lot more sophisticated—say, a trillion times more—than anything scientists have conceived of inventing.
In science, the question of when to believe is a deep and ancient problem. There is no universal answer, and evaluating the merits of any potential discovery always includes considering the prior beliefs of the people involved. There is no way around this.
• • •
This was the genius of the fake signal injection: Whatever the prior belief of an individual scientist might be, it gave him or her reason to doubt it. A scientist who believed that the current generation of instruments was simply not up to the task would have to allow for the possibility that it was. A scientist tempted to elevate a signal because of the benefits of a real detection would have to temper his or her enthusiasm to avoid making a false claim. The fake injection bugaboo forced us to keep an open mind, apply skepticism and reason, and examine the evidence at face value.
At first glance, this brightly decorated room is no different from that of any other elementary school. Shelves are filled with storybooks; on the chalkboard, a vertical line of words reads ”prudence,” ”pretzel,” ”prairie,” ”purple.” But the nervous agitation of the boys’ hands, punctuated by occasional odd flapping gestures, betrays the fact that something is off kilter. There is also a curious poster on one of the walls with a circle of human faces annotated with words like ”sad,” ”proud” and ”lonely.” When I ask Cacciabaudo about it, she explains that her students do not know how to read the basic expressions of the human face. Instead, they must learn them by rote.