I’ve been experimenting with a daily email with Colin Nagy called Why Is This Interesting? This is from today’s edition. If you’re interested in checking it out, drop me a line (I’ll post something here when we launch in publicly).
Because this is something we’ve been worried about forever (literally). In Phaedrus, Plato worried about roughly the same thing as it related to writing: “If men learn this, it will implant forgetfulness in their souls; they will cease to exercise memory because they rely on that which is written, calling things to remembrance no longer from within themselves, but by means of external marks. What you have discovered is a recipe not for memory, but for reminder.”
The reality is that all technology affects culture in expected and unexpected ways. “We shape our tools and thereafter our tools shape us” is one of my favorite aphorisms (misattributed to McLuhan). The irony, of course, is that the complaints in this article are perfectly expected. We come to rely on automation because it’s mostly better. In fact, the strangest part of the whole piece is the way the evidence of backup camera safety is presented. “Between 2008 and 2011,” the author writes, “the percentage of new cars sold with backup cameras doubled, but the backup fatality rate declined by less than a third while backup injuries dropped only 8 percent.” I think the implication is that those numbers aren’t all that impressive, but a 20 or 30 percent drop in backup fatalities seems pretty excellent to me.
The Times piece is effectively an explorations of McLuhan’s four effects. The backup camera enhances our senses by giving us eyes in the back of our heads, obsolescing the car’s mirrors, and retrieving a time when cars were smaller, but, as the article points out, when pushed to its extreme it reverses our own role as driver, giving control entirely over the tech. While the points are valid, we should be less surprised that this keeps happening and try to keep things in perspective.
Another very strong piece from Rebecca Solnit on Lithub about Trump. Here’s a snippet: “The Trump family aspires to mafia status, a thuggocracy, but they are manipulable and bumbling where Putin and company are disciplined and Machiavellian. They hire fools and egomaniacs and compromised figures—Scaramucci, Giuliani, Bannon, Flynn, Nunberg, the wifebeating Rob Porter—and then fire them, with a soap opera’s worth of drama; the competent ones quit, as have many lawyers hired to help Trump navigate his scandals. The Trumps don’t hide things well or keep their mouths shut or manage the plunder they grab successfully, and they keep committing crimes in public.”
“In another respect, the drive to identify reasons for committing extreme violence runs opposite to the very logic of terrorism. I am using the term ‘terrorism’ in its broadest possible meaning, to denote acts of violence intended primarily to terrify. This works only when the violence is unpredictable—when it’s senseless. This is as true of state terror and political terrorism as it is of a school shooting—especially one perpetrated by the shy kid who never seemed to say a word about girls. It is so frightening precisely because most of these shy, unpopular kids who are ignored and spurned by others will never hurt a fly. Nor will most other people, including most of those who claim to want to blow up the world, whether because they are not getting enough sex or because they want to live in a caliphate.”
This episode of the podcast 80,000 Hours with computational neuroscientist Anders Sandberg is really fun (if you’re into talking about stuff like the Fermi Paradox). I particularly liked Sandberg’s “Aestivation Hypothesis”. Aestivation is the opposite of hibernation (sleeping during the summer instead of the winter) and the gist of the hypothesis is that maybe the reason we haven’t heard from the aliens is because they’re waiting for the stars to die out so it gets cold enough that they can efficiently run massively complex calculations that would otherwise take tons of power to cool:
So if you imagine the real advanced civilization that has seen a lot of galaxy expanded long distances, once you’ve seen a hundred elliptical galaxies and a hundred spiral galaxies, how many surprises are we going to be there? Now most of the interesting stuff your civilization is doing is going to be culture, science, philosophy, and all the other internal stuff. The external universe is nice scenery, but you’ve seen much of it. So this leads to this possibility that maybe advanced civilization is actually an estimate. They slow down, they freeze themselves, and wait until a much later era because we get so much more. It turns out that you can calculate how much more they can get. So the background radiation of the universe is declining exponentially.
As promised, here’s an interesting snippet from the book I’m reading, How to Thinkon how we don’t actually “think for ourselves”:
“Ah, a wonderful account of what happens when a person stops believing what she’s told and learns to think for herself.” But here’s the really interesting and important thing: that’s not at all what happened. Megan Phelps-Roper didn’t start “thinking for herself”—she started thinking with different people. To think independently of other human beings is impossible, and if it were possible it would be undesirable. Thinking is necessarily, thoroughly, and wonderfully social. Everything you think is a response to what someone else has thought and said. And when people commend someone for “thinking for herself” they usually mean “ceasing to sound like people I dislike and starting to sound more like people I approve of.”
Bipartisan posturing of this kind would be absurd in a healthy democracy, even at the best of times—after all, one of the reasons we elect people is so that they can debate and disagree. If you’re not fighting with anyone, you’re not fighting for anything. But given the stated agenda of the current administration, not to mention countless other Republican-led administrations across the country, bipartisanship is perilous and counterproductive almost by definition.
That’s it for this week. Thanks for reading and have a great weekend and memorial day.
West calls his struggle the right to be a “free thinker,” and he is, indeed, championing a kind of freedom—a white freedom, freedom without consequence, freedom without criticism, freedom to be proud and ignorant; freedom to profit off a people in one moment and abandon them in the next; a Stand Your Ground freedom, freedom without responsibility, without hard memory; a Monticello without slavery, a Confederate freedom, the freedom of John C. Calhoun, not the freedom of Harriet Tubman, which calls you to risk your own; not the freedom of Nat Turner, which calls you to give even more, but a conqueror’s freedom, freedom of the strong built on antipathy or indifference to the weak, the freedom of rape buttons, pussy grabbers, and fuck you anyway, bitch; freedom of oil and invisible wars, the freedom of suburbs drawn with red lines, the white freedom of Calabasas.
This hits close to home: Your coffee addiction, by decade. “‘No sugar,’ you declare. ‘I take it black.’ Shoot a side-eyed glance at that kid over there with his blended-ice drink—amateur hour. Sorry they don’t serve Shirley Temples, geez.”
On the podcast front, I’ve been enjoying Real Famous, which features interviews with ad people (many of whom are my friends). Paul Feldwick, author of the awesome book Anatomy of a Humbug, is an excellent listen.
Multitasking, in short, is not only not thinking, it impairs your ability to think. Thinking means concentrating on one thing long enough to develop an idea about it. Not learning other people’s ideas, or memorizing a body of information, however much those may sometimes be useful. Developing your own ideas. In short, thinking for yourself. You simply cannot do that in bursts of 20 seconds at a time, constantly interrupted by Facebook messages or Twitter tweets, or fiddling with your iPod, or watching something on YouTube.
You could say the trouble for Rodger started when, around puberty, he began to know—and, in writing, recite—the first and last names of every boy he considered a sexual competitor, while at the same time referring to girls almost always collectively. Girls. Pretty girls. Pretty blond girls. Only three girls (or perhaps, by this time, women) are listed by name in My Twisted World, vis-a-vis dozens of boys (I’m not including family members). By the end of his writing and life, he’s failed to distinguish between any groups of humans at all, to the point where he considers his 6-year-old brother yet another budding Romeo who, because “he will grow up enjoying the life [Rodger has] craved for,” must die. “Girls will love him,” Rodger says. “He will become one of my enemies.” Rodger begs our most individuating question—“why don’t you love me?”—by proving himself repeatedly unable to individuate another. In erotic coupling, the ego finds relief in its equal. But had Elliot Rodger ever found his equal and opposite in another human being, he would, by all indications, have been repulsed. Reading him, I kept remembering Rooney Mara’s kiss-off in The Social Network: “You are going to go through life thinking that girls don’t like you because you’re a nerd.1 [Or short. Or half-Asian. Or bad at football, or not a real ladies’ man, or somehow else disappointing to the ur-dads of America.] And I want you to know, from the bottom of my heart, that isn’t true. It’ll be because you’re an asshole.”
Chunking was originally conceptualized in the groundbreaking work of Herbert Simon in his analysis of chess—chunks were envisioned as the varying neural counterparts of different chess patterns. Gradually, neuroscientists came to realize that experts such as chess grand masters are experts because they have stored thousands of chunks of knowledge about their area of expertise in their long-term memory. Chess masters, for example, can recall tens of thousands of different chess patterns. Whatever the discipline, experts can call up to consciousness one or several of these well-knit-together, chunked neural subroutines to analyze and react to a new learning situation. This level of true understanding, and ability to use that understanding in new situations, comes only with the kind of rigor and familiarity that repetition, memorization, and practice can foster.
The computer takes a reading from a Geiger counter that measures radiation in the surrounding air, specifically the radioactive isotope Americium-241. The reading is expressed as a long number of code; that number gives the generator its true randomness. The random number is called the seed, and the seed is plugged into the algorithm, a pseudorandom number generator called the Mersenne Twister. At the end, the computer spits out the winning lottery numbers.
If you haven’t heard the Google Duplex calls, go have a listen. Some interesting comments from Twitter:
Jessi Hempel: “Reading about Google’s Duplex: Design is a series of choices, and creating voice tech designed to let humans trick other humans is a choice humans are making, not an inevitable consequence of technology’s evolution.”
Stewart Brand: “This sounds right. The synthetic voice of synthetic intelligence should sound synthetic. Successful spoofing of any kind destroys trust. When trust is gone, what remains becomes vicious fast.”
The New York Times’s Weinstein report was a believability project years in the making: it systematized abuse, turned it into a pattern your eye could follow. There were interviews, emails, audio recordings, legal documents; facts were double- and triple-checked. But its paradoxical consequence was to set the bar far too high for every subsequent story whose breaking it had made possible. What’s a little masturbation between friends when the king of Hollywood kingmakers had employed former agents of the Israel Defense Forces to silence his accusers? In one final act of gaslighting, Weinstein made all other abuse look not so bad and all other evidence look not so good.
Every year I take a trip out to Montana to teach at a weekend seminar series that’s part of the University of Montana’s Entertainment Management. I’m 11 years in and I work really hard to create original content for each year. This time around I talked about mental models, theories of communications and information, and a bit about machine learning. I wanted to try to take a bit of the content I shared there and repurpose it. As always, you can subscribe by email here.
Shannon and McLuhan were two of the most important thinkers of the 20th century. Without Shannon we’d have no computers and without McLuhan we wouldn’t examine the effects of media, communications, and technology on society with the urgency we do. With that said, they’re very different in their science and approach. Shannon was fundamentally a mathematician while McLuhan was a scholar of literature. In their work Shannon examined huge questions around how communications works technically, while McLuhan examined how it works tactically. When asked, McLuhan drew the distinction as questions of “transportation” versus “transformation”:
My kind of study of communication is really a study of transformation, whereas Information Theory and all the existing theories of communication I know of are theories of transportation… Information Theory … has nothing to do with the effects these forms have on you… So mine is a transformation theory: how people are changed by the instruments they employ.
I want to take some time to go through both, as they are fascinating in their own ways.
Of course, information existed before Shannon, just as objects had inertia before Newton. But before Shannon, there was precious little sense of information as an idea, a measurable quantity, an object fitted out for hard science. Before Shannon, information was a telegram, a photograph, a paragraph, a song. After Shannon, information was entirely abstracted into bits.
The intellectual leaps Shannon made in his paper “A Mathematical Theory of Communications” were miraculous. What starts off as a question about how to reduce noise in the transmission of information turned into a complete theory of information that paved the way for the computing we all rely on. At the base of the whole thing is a recognition that information is probabilistic, which he explains in a kind of beautiful way. Here’s my best attempt to take you through his logic (which some extra explanation from me).
Let’s start by thinking about English for a second. If we wanted to create a list of random letters we could put the numbers 1-27 in a hat (alphabet + space) and pick out numbers one by one and then write down their letter equivalent. When Shannon did this he got:
But letters aren’t random at all. If you open a book up and counted all the letters you wouldn’t find 26 letters each occurring 3.8% of the time. On the contrary, letters occur probabilistically “e” occurs more often than “a,” and “a” occurs more often than “g,” which in turn occurs more often than “x.” Put it all together and it looks something like this:
So now imagine we put all our letters (and a space) in a hat. But instead of 1 letter each, we have 100 total tiles in the hat and they alight with the chart above: 13 tiles for “e”, 4 tiles for “d”, 1 tile for “v”. Here’s what Shannon got when he did this:
OCRO HLI RGWR NMIELWIS EU LL NBNESEBYA TH EEI ALHENHTTPA OOBTTVA NAH BRL
He called this “first-order approximation” and while it still doesn’t make much sense, it’s a lot less random than the first example.
What’s wrong with that last example is that letters don’t operate independently. Let’s play a game for a second. I’m going to say a letter and you guess the next one. If I say “T” the odds are most of you are going to say “H”. That makes lots of sense since “the” is the most popular word in the English language. So instead of just picking letters at random based on probability what Shannon did next is pick one letter and then match it with it’s probabilistic pair. These are called bigrams and just like we had letter frequencies, we can chart these out.
This time Shannon took a slightly different approach. Rather than loading up a bunch of bigrams in a hat and picking them out at random he turned to a random page in a book and choose a random letter. He then turned to another random page in the same book and found the first occurance of recorded the letter immediately after it. What came out starts to look a lot more like English:
ON IE ANTSOUTINYS ARE T INCTORE ST BE S DEAMY ACHIN D ILONASIVE TUCOOWE AT TEASONARE FUSO TIZIN ANDY TOBE SEACE CTISBE
For his “first-order approximation” he picks random words from the book. It looks a lot like a sentence because words don’t occur randomly. There’s a good chance an “and” will come after a word because “and” is likely the third most popular word in the book. Here’s what came out:
REPRESENTING AND SPEEDILY IS AN GOOD APT OR COME CAN DIFFERENT NATURAL HERE HE THE A IN CAME THE TO OF TO EXPERT GRAY COME TO FURNISHES THE LINE MESSAGE HAD BE THESE.
Second-order approximation works just like bigrams, but instead of letters it uses pairs of words.
THE HEAD AND IN FRONTAL ATTACK ON AN ENGLISH WRITER THAT THE CHARACTER OF THIS POINT IS THEREFORE ANOTHER METHOD FOR THE LETTERS THAT THE TIME OF WHO EVER TOLD THE PROBLEM FOR AN UNEXPECTED.
As Shannon put it, “The resemblance to ordinary English text increases quite noticeably at each of the above steps.”
While all that’s cool, much of it was pretty well known at the time. Shannon had worked on cryptography during World War II and used many of these ideas to encrypt/decrypt messages. Where the leap came was how he used this to think about the quantity of information any message contains. He basically realized that the first example, with 27 random symbols (A-Z plus a space), carried with it much more information than his second- or third-order approximation, where subsequent letters were chosen based on their probabilities. That’s because there are fewer “choices” to be made as we introduce bigrams and trigrams, and “choices”, or lack-thereof, are the essence of information.
Until then, communication wasn’t a unified science … There was one medium for voice transmission, another medium for radio, still others for data. Claude showed that all communication was fundamentally the same-and furthermore, that you could take any source and represent it by digital data.
But Shannon didn’t stop there, he goes on to show that all language has redundancy and it can be used to fight noise. The whole thing is pretty mind-blowing and, like I said, underpins all modern computing. (There’s a whole other theory about the relationship between information theory and creativity that I’ll save for another day.)
In retrospect, it’s easy easy to blame old games like Doom and Duke Nukem for stimulating the fantasy of male adolescent power. But that choice was made less deliberately at the time. Real-time 3-D worlds are harder to create than it seems, especially on the relatively low-powered computers that first ran games like Doom in the early 1990s. It helped to empty them out as much as possible, with surfaces detailed by simple textures and objects kept to a minimum. In other words, the first 3-D games were designed to be empty so that they would run.
An empty space is most easily interpreted as one in which something went terribly wrong. Add a few monsters that a powerful player-dude can vanquish, and the first-person shooter is born. The lone, soldier-hero against the Nazis, or the hell spawn, or the aliens.
A perfect case of the medium being the message.
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