The Most Human Human

by Brian Christian

  One metaphysical oddity: communication comes in degrees. The number of minds, the number of selves, in a body, seemingly, doesn’t. This begs odd questions. If the bandwidth of one’s corpus callosum were turned up just slightly, would that make someone somehow “closer” to one self? If the bandwidth were turned down just slightly, would that make someone somehow farther from one self? With the bandwidth right where it is now, how many selves are we, exactly?12

  This intense desire to make one of two, to be “healed” and restored to unity: this is the human condition. Not just the state of our sexuality, but the state of our minds. The eternal desire to “catch up,” to “stay connected,” in the face of flurrying activity and change. You never really gain ground and you never really lose ground. You aren’t unified but you aren’t separate.

  “They’re basically the same person,” we sometimes say of a couple. We may not be entirely kidding. There’s a Bach wedding cantata where the married couple is addressed with second-person-singular pronouns. Because in English these are the same as the second-person-plural pronouns—“you,” “your”—the effect doesn’t quite translate. However, we do sometimes see the opposite, where a coupled partner describes events that happened only to him- or herself, or only to the partner, using “we”—or, more commonly, simply talks about the couple as a unit, not as “she and I.” A recent study at UC Berkeley, led by psychology Ph.D. student Benjamin Seider, found that the tendency toward what he calls linguistic “we-ness” was greater in older couples than younger ones.

  Considering that the brain itself stays connected only by constant conversation, it’s hard to argue that our connections to others belong strictly on a lower tier. What makes the transmissions passing through the corpus callosum all that different from the transmissions passing through the air, from mouth to mouth? The intra-brain connections are stronger than the inter-brain connections, but not totally different in kind.

  If it’s communication that makes a whole of our two-hemisphere brain, there should be no reason why two people, communicating well enough, couldn’t create the four-hemisphere brain. Perhaps two become one through the same process one becomes one. It may end up being talk—the other intercourse—that heals the state of man. If we do it right.

  1. And allegedly the inspiration for Tom Cruise’s character in Magnolia (for which Cruise received an Oscar nomination and a Golden Globe).

  2. At the same time, he says, he attributes some of his success as an interviewer to a rapport that came from “an openness about myself—It was my nature to talk about whatever I was going through, in a way that wasn’t meant to disarm but it did disarm.”

  3. The IRS has indeed developed algorithms to flag “suspicious returns.”

  4. That my spell-checker balks at the word “unboundedness” rather poetically demonstrates his point.

  5. Yet it’s odd—in other domains, talking idiosyncratically, freshly, with novel metaphors, makes one more easily incriminated. It’s easier for someone to find something you said in an email by searching their in-box if you used an unusual turn of phrase or metaphor. Things spoken aloud, too, are likely more easily remembered the more unusual and distinctive they are. What’s more, in a their-word-against-yours situation, this quotation is likely to be regarded (e.g., by a jury) as more reliable the more unusual and vivid it is.

  The general principle, vis-à-vis culpability, would seem to be something along the lines of: if you can obscure your meaning by speaking non-standardly, do so; if your meaning will be clear, speak as generically as possible so as not to be memorable. Writing’s goals might be the other way around: clarity with novel ideas and novelty with familiar ones.

  6. Lest you think that this original separation is what created the two sexes, male and female, and that only straight folks have the right ideas about reassembly, remember that Aristophanes, like many Greek men of his time, was more homo- than hetero-normative. As he explains it, the “sexes were not two as they are now, but originally three in number,” corresponding to male, female, and “androgynous”; the male beings when split became gay men, the female beings became lesbians, and the androgynous beings became straight men and women. (No word on how bisexuals fit into this picture.)

  7. Most literary metaphors for romantic and sexual passion lean in one way or another toward the violent. We talk about a “stormy” romance or “tempestuous” feelings, or the orgasm as a tiny death (la petite mort, the French call it), or of “ravishing” beauty—checking my dictionary, I see that “ravishing” as an adjective means charming or gorgeous, and as a noun or verb, rape. And most slang terms for sex are violent—bang, screw—or at the very least negative. It’s hard to imagine ending up in better shape than when you started. But for Aristophanes it wasn’t violence at all, but healing—it’s no wonder his is such an endearing (and enduring) myth.

  8. Yet I think of Sean Penn’s answer, in Milk, to the question of whether men can reproduce: “No, but God knows we keep trying.”

  9. I suppose I shouldn’t say “of course”: there was actually a serious risk that the surgery would leave Warwick paralyzed. Somehow that didn’t seem to faze him.

  10. The Conestoga wagoners, for instance, taking six months to make the trip I cram into the evening before Thanksgiving, didn’t seem to have this problem.

  11. “You’re getting on my nerves,” we imagine him saying, suggestively. “Oh, you’re such a tase,” she, atingle, replies …

  12. As it turns out, “axonal diameter” (thicker neurons signal faster over long distances but take up more space) correlates with brain size for virtually all animals, except—as neurophysiologist Roberto Caminiti recently discovered—humans. Our axonal diameter is not significantly greater than chimpanzees’, despite our having larger brains. Evolution appears to have been willing to trade interhemispheric lags for a disproportionate increase in computational power.

  10. High Surprisal

  One-Sided Conversations

  Eager to book my room in Brighton, I did some quick digging around online and found an intriguing (and intriguingly named) place, just a stone’s throw from the Turing test, called “Motel Schmotel.” I called them up via Skype. Now, I don’t know if it was the spotty connection, or the woman’s low speaking volume, or the English accent, or what, but I could barely understand a word of what she was saying, and immediately found myself hanging on to the flow of the conversation for dear life:

  ____tel.

  Presumably, she’s just said something like “Hello, Motel Schmotel.” No reason not to plunge ahead with my request.

  Uh, yeah, I’d like to check the availability for a single room?

  ____ong?

  Probably “For how long?” but hard to know for sure. At any rate, the most likely thing she needs to know if I’m looking for a room is the duration, although that’s not helpful without the start date, so why don’t I nip that follow-up question (which I probably won’t hear anyway) in the bud and volunteer both:

  Um, for four nights, starting on Saturday the fifth of September?

  ____[something with downward tone]____, sorry. We only____balcony____ninety pounds.

  And here I was lost. They didn’t have something, but apparently they had something else. Not clear how to proceed. (Ninety pounds total, or extra? Per night, or for the whole stay? I couldn’t do all the math in my head at once and figure out if I could afford the room.) So I hedged my bets and said the most utterly neutral, noncommittal thing I could think of:

  Ah. Okay.

  ____rry!

  Presumably “sorry,” and said with a friendly finality that seemed to signal that she was expecting me to hang up any second: probably this balcony room was out of my league. Fair enough.

  Ok, thanks! Bye.

  ____ye, now!

  I suppose I got off the phone with a mixture of bemusement and guilt—it hadn’t actually been all that necessary to hear what she was saying. I knew what she was saying. The boilerplate, the template of the
conversation—my ability to guess at what she was asking me and what her possible responses could be—pulled me through.

  On (Not) Speaking the Language

  It occurred to me that I’d been able to pull off the same trick the last time I’d been in Europe, a two-week-long Eurail-pass whirlwind through Spain, France, Switzerland, and Italy the summer after college: though I speak only English and some Spanish, I did much of the ticket buying in France and Italy, and managed for the most part to pull it off. Granted, I did nod understandingly (and, I confess, impatiently) when the woman sold us our overnight tickets to Salzburg and kept stressing, it seemed to me unnecessarily, “est … station … est … est … station”—“This station, this one, I understand, yeah, yeah,” I replied, knowing, of course, that in Spanish “este” means this. Of Paris’s seven train stations, our overnighter would be coming to this one, right here.

  Perhaps you’re saying, “Wait, Salzburg? But he didn’t say anything about seeing Austria …” Indeed.

  What I failed to remember, of course, is that “este” in Spanish also means east—a fact that dawns on me as we stand dumbfounded on a ghostly empty train platform at midnight in Paris’s Austerlitz Station, checking our watches, realizing that not only have we blown the chance at realizing our Austrian Sound of Music Alp-running fantasies, but we’ll have to do an emergency rerouting of our entire, now Austria-less, itinerary, as we’ll be just about a million meters off course by morning. Also: it’s now half past midnight, the guidebook is describing our present location as “dicey,” and we don’t have a place to sleep. Our beds have just departed from East Station, fast on their way to Salzburg.

  Now, this rather—ahem—serious exception aside, I want to emphasize that by and large we did just fine, armed in part with my knowledge of a sibling Romance language, and in part with a guidebook that included useful phrases in every European tongue. You realize the talismanic power of language in these situations: you read some phonetically spelled gobbledygook off a sheet, and before you know it, abracadabra, beers have appeared at your table, or a hostel room has been reserved in your name, or you’ve been directed down a mazelike alley to the epicenter of late-night flamenco. “Say the magic word,” the expression goes, but all words seem, in one way or another, to conjure.

  The dark side of this is that the sub-fluent traveler risks solipsism—which can only be cracked by what linguists and information theorists call “surprisal,” which is more or less the fancy academic way of saying “surprise.” The amazing thing about surprisal, though, is that it can actually be quantified numerically. A very strange idea—and a very important one. We’ll see how exactly that quantification happens later in this chapter; for now, suffice it to say that, intuitively, a country can only become real to you, that is, step out of the shadow of your stereotypes of the place, by surprising you. Part of this requires that you pay attention—most of life’s surprises are on the small side and often go unnoticed. The other part of it requires that you put yourself into situations where surprise is possible; sometimes this requires merely the right attitude of openness on your part, but other times it’s impossible without serious effort and commitment ahead of time (e.g., learning the language). Template-based interactions—“Je voudrais un hot dog, s’il vous plaît … merci!”; “Où est le WC? … merci!”—where you more or less treat your interlocutor as a machine, are navigable for precisely the reason that they are of almost no cultural or experiential value. Even if your interlocutor’s response is surprising or interesting, you might miss it. Wielding language’s magic is intoxicating; becoming susceptible to it, even more so.

  Perhaps you’re starting to feel by now how all of this parallels the Turing test. In France I behaved, to my touristy chagrin, like a bot. Speaking was the easy part—provided I kept to the phrase book (this in itself was embarrassing, that my desires were so similar to those of every other American tourist in France that a one-size-fits-all FAQ sheet sufficed handily). But listening was almost impossible. So I tried only to have interactions that didn’t really require it.

  Interacting with humans in this way is, I believe, shameful. The Turing test, bless it, has now given us a yardstick for this shame.

  A Mathematical Theory of Communication

  It seems, at first glance, that information theory—the science of data transmission, data encryption, and data compression—would be mostly a question of engineering, having little to do with the psychological and philosophical questions that surround the Turing test and AI. But these two ships turn out to be sailing quite the same seas. The landmark paper that launched information theory is Claude Shannon’s 1948 “A Mathematical Theory of Communication,” and as it happens, this notion of scientifically evaluating “communication” binds information theory and the Turing test to each other from the get-go.

  What is it, exactly, that Shannon identified as the essence of communication? How do you measure it? How does it help us, and how does it hurt us—and what does it have to do with being human?

  These connections present themselves in all sorts of unlikely places, and one among them is your phone. Cell phones rely heavily on “prediction” algorithms to facilitate text-message typing: guessing what word you’re attempting to write, auto-correcting typos (sometimes overzealously), and the like—this is data compression in action. One of the startling results that Shannon found in “A Mathematical Theory of Communication” is that text prediction and text generation turn out to be mathematically equivalent. A phone that could consistently anticipate what you were intending to write, or at least that could do as well as a human, would be just as intelligent as the program that could write you back like a human. Meaning that the average American teenager, going by the New York Times’s 2009 statistics on cell phone texting, participates in roughly eighty Turing tests a day.

  This turns out to be incredibly useful and also incredibly dangerous. In charting the links between data compression and the Turing test’s hunt for the human spark, I’ll explore why. I want to begin with a little experiment I did recently, to see if it was possible to use a computer to quantify the literary value of James Joyce.1

  James Joyce vs. Mac OS X

  I took a passage from Ulysses at random and saved it on my computer as raw text: 1,717 bytes.

  Then I wrote the words “blah blah blah” over and over until it matched the length of the Joyce excerpt, and saved that: 1,717 bytes.

  Then I had my computer’s operating system, which happens to be Mac OS X, try to compress them. The “blah” file compressed all the way down to 478 bytes, just 28 percent of its previous size, but Ulysses only came down to 79 percent of its prior size, or 1,352 bytes—leaving it nearly three times as large as the “blah” file.

  When the compressor pushed down, something in the Joyce pushed back.

  Quantifying Information

  Imagine flipping a coin a hundred times. If it’s a fair coin, you can expect about fifty heads and fifty tails, of course, distributed randomly throughout the hundred. Now, imagine telling someone which flips came out which—it’d be a mouthful, of course. You could name all of the outcomes in a row (“heads, heads, tails, heads, tails, …”) or just the location of either just the heads (“the first, the second, the fourth, …”) or just the tails, letting the other be implicit, both of which come out to be about the same length.2

  But if it’s a biased coin, your job gets easier. If the coin comes up heads only 30 percent of the time, then you can save breath by just naming which flips came up heads. If it’s heads 80 percent of the time, you simply name which flips were tails. The more biased the coin, the easier the description becomes, all the way up to a completely biased coin, our “boundary case,” which compresses down to a single word—“heads” or “tails”—that describes the entire set of results.

  So, if the result of the flips can be expressed with less language the more biased the coin is, then we might argue that in these cases the result literally contains less infor
mation. This logic extends down, perhaps counterintuitively, perhaps eerily, into the individual events themselves—for any given flip, the more biased the coin, the less information the flip contains. There’s a sense in which flipping the seventy-thirty coin just doesn’t deliver what flipping the fifty-fifty coin does.3 This is the intuition of “information entropy”: the notion that the amount of information in something can be measured.

  “Information can be measured”—at first this sounds trivial, of course. We buy hard drives and fill them up, wonder if shelling out the extra fifty dollars for the 16 GB iPod will be worth it compared to the 8 GB one, and so on. We’re used to files having size values in bytes. But the size of a file is not the same thing as the amount of information in a file. Consider as an analogue the difference between volume and mass; consider Archimedes and the golden crown—in order to determine whether the crown’s gold was pure, Archimedes needed to figure out how to compare its mass to its volume.4 How do we arrive at the density of a file, the karat of its bytes?

  Information, Bias, and the Unexpected

  We could compress the biased coin because it was biased. Crucially, if all outcomes of a situation are equally probable—what’s called a “uniform distribution”—then entropy is at its maximum. From there it decreases, all the way to a minimum value when the outcome is fixed or certain. Thus we might say that as a file hits its compression floor, the fixities and certainties shake out; pattern and repetition shake out; predictability and expectancy shake out; the resulting file—before it’s decompressed back into its useful form—starts looking more and more random, more and more like white noise.