Tag: Technology

Marshall McLuhan: The Here And Now


“In a culture like ours, long accustomed to splitting and dividing all things as a means of control, it is sometimes a bit of a shock to be reminded that, in operational and practical fact, the medium is the message.”


In this passage from Understanding Media, Marshall McLuhan, reminds us of the difficulty that frictionless connection brings with it and how technological media advances have worked not to preserve but rather to ‘abolish history.’

Perfection of the means of communication has meant instantaneity. Such an instantaneous network of communication is the body-mind unity of each of us. When a city or a society achieves a diversity and equilibrium of awareness analogous to the body-mind network, it has what we tend to regard as a high culture.

But the instantaneity of communication makes free speech and thought difficult if not impossible, and for many reasons. Radio extends the range of the casual speaking voice, but it forbids that many should speak. And when what is said has such range of control, it is forbidden to speak any but the most acceptable words and notions. Power and control are in all cases paid for by loss of freedom and flexibility.

Today the entire globe has a unity in point of mutual interawareness, which exceeds in rapidity the former flow of information in a small city—say Elizabethan London with its eighty or ninety thousand inhabitants. What happens to existing societies when they are brought into such intimate contact by press, picture stories, newsreels, and jet propulsion? What happens when the Neolithic Eskimo is compelled to share the time and space arrangements of technological man? What happens in our minds as we become familiar with the diversity of human cultures which have come into existence under innumerable circumstances, historical and geographical? Is what happens comparable to that social revolution which we call the American melting pot?

When the telegraph made possible a daily cross section of the globe transferred to the page of newsprint, we already had our mental melting pot for cosmic man—the world citizen.The mere format of the page of newsprint was more revolutionary in its intellectual and emotional consequences than anything that could be said about any part of the globe.

When we juxtapose news items from Tokyo, London, New York, Chile, Africa, and New Zealand, we are not just manipulating space. The events so brought together belong to cultures widely separated in time. The modern world abridges all historical times as readily as it reduces space. Everywhere and every age have become here and now. History has been abolished by our new media.

What A Rembrandt Can Teach you about Software and Programmers


A thoughtful passage by David Gelernter in Mirror Worlds: or the Day Software Puts the Universe in a Shoebox…How It Will Happen and What It Will Mean on how looking at a Rembrandt can teach us to better understand not only software but the craft behind it.

Suppose you visit an art museum and walk up to a painting. I say “Ah ha! I see you’re admiring some powdered pigments, mixed with oil and smeared onto what appears to be a canvas panel.” You say “No, you moron. I’m admiring a Rembrandt.” Good. You’re three-quarters of the way towards a deep understanding of software.

How did this happen?

Well clearly we may, if we choose, regard a painting as a coming-together of two separate elements. The paints and canvas—the physical stuff; and the form-giving mind-plan. I’ll call these two elements the body and the disembodied painting respectively.

Both are necessary to the finished product. But they are unequally decisive to its character. If Rembrandt had (while trying to shake out a tablecloth) accidentally chucked his favorite paint set into a canal on the very morning he was destined to make our painting; if he’d accordingly been forced to go down to the basement and hunt up another set—the finished product would be the same. But if he’d altered his mind-plan—the disembodied painting—before setting to work, our finished painting would obviously have been different.

In fact, the disembodied painting is a painting in and of itself— albeit a painting of a special kind, namely an unbodied one. Rembrandt is perfectly entitled to tell his wife “I have a painting in mind” before setting to work. But plainly the mere body is no painting, not in and of itself. If the paints on Rembrandt’s table went around telling people “Hey look at us, we’re a painting,” no-one would believe them.

This distinction is the key to software and its special character. A running program is a machine of a certain kind, an information machine. The program text—the words and symbols that the programmer composes, that “tell the computer what to do” – is a disembodied information machine. Your computer provides a body.

Unlike Rembrandt’s mind plan, a disembodied information machine must be written down precisely and in full. It’s a bit like the engineering drawings for a new toaster in this regard; the machine designer leaves nothing to chance. Unlike Rembrandt’s mind plan or the toaster drawings, on the other hand, a disembodied information machine can be “embodied” automatically. No skill, judgement or human intervention is required. Merely hand your text to a computer (it’s probably stored inside the computer already); the computer itself performs the “embodying.”

So: A running program—an information machine or infomachine for short—is the embodiment of a disembodied machine. In saying this, we have said a lot. A fairly simple point first, then a subtle and deeply important one—

Some people believe that, when they see a program running, the machine they are watching is a “computer.” True, but not true enough. The computer, that impressive-looking box with the designer logo, is merely the paint, not the painting. When you say I’m watching this computer do its stuff, you are saying in effect I’m admiring not this Rembrandt but some paint smeared on canvas. Some people imagine the computer as a gifted actor (say) who is handed a program and declaims it feelingly. No: bad image. The computer itself is of the utmost triviality to the workings of the infomachine you are watching. It may decide how fast or slowly the thing runs, and may effect its behaviour just a little around the fringes, but essentially, it is of no logical significance whatever. It is a mere body, and bodies are a dime a dozen.

The second point is harder.

People often find it difficult to keep in mind that, when they see a program text, what they’re looking at is a machine. The fact that, for the time being, the machine they’re looking at has no body confuses them With good reason: This is a subtle, maybe a confusing point. They leap to the conclusion that what programmers do amounts to arranging symbols on paper (or in a computer file) in a certain way. They look at a program and see merely a highly specialized kind of document …

This mistake is fatal to any real understanding of what software is.

Understanding software doesn’t mean understanding how program texts are arranged, it means understanding what the working infomachine itself is like—what actually happens when you embody the thing and turn it on-what kind of structure you are creating when you organize those squiggles-the shape of the finished product, the way information hums through it, the way it grows, shrinks and changes as it runs, the look and feel of the actual computational landscape. This is where software creativity is exercised. This is where the field evolves, metamorphoses and explodes. Talented software designers work with some image of the actual running program uppermost in mind. Failing to see through the program text to the machine it represents is like trying to understand musical notation without grasping that those little sticks and ellipsoids represent sounds.

This kind of information is hard to convey. You can’t directly see a running program. You can sense its workings indirectly, but you can’t open the hood and look right at the mechanism. An ironic reversal of the Rembrandt experience: Here the mind-plan is tangible, but the embodied thing itself is not.

Finally concluding

[I]f you get carried away, and start asserting that “music is the mechanical manipulation of symbols on staff paper,” “programming is mathematics,” you have committed intellectual suicide. You’ve mistaken the means for the end. You’ve cut yourself off absolutely from all real inspiration, creativity and growth. And you have failed, profoundly, to understand the character of your field.

A dangerous mistake. Where software is concerned, an all-too-natural one.

Real vs. Simulated Memories

Blue Brain

Software memory is increasingly doing more and more for us. Yet it lacks one important element of human memory: emotion.

This thought-provoking excerpt comes from Mirror Worlds: or the Day Software Puts the Universe in a Shoebox…How It Will Happen and What It Will Mean, a book recommended by Marc Andreessen.

When an expert remembers a patient, he doesn’t remember a mere list of words. He remembers an experience, a whole galaxy of related perceptions. No doubt he remembers certain words—perhaps a name, a diagnosis, maybe some others. But he also remembers what the patient looked like, sounded like; how the encounter made him feel (confident, confused?) … Clearly these unrecorded perceptions have tremendous information content. People can revisit their experiences, examine their stored perceptions in retrospect. In reducing a “memory” to mere words, and a quick-march parade step of attribute, value, attribute, value at that, we are giving up a great deal. We are reducing a vast mountaintop panorama to a grainy little black-and-white photograph.

There is, too, a huge distance between simulated remembering—pulling cases out of the database—and the real thing. To a human being, an experience means a set of coherent sensations, which are wrapped up and sent back to the storeroom for later recollection. Remembering is the reverse: A set of coherent sensations is trundled out of storage and replayed—those archived sensations are re-experienced. The experience is less vivid on tape (so to speak) than it was in person, and portions of the original may be smudged or completely missing, but nonetheless—the Rememberer gets, in essence, another dose of the original experience. For human beings, in other words, remembering isn’t merely retrieving, it is re-experiencing.

And this fact is important because it obviously impinges (probably in a large way) on how people do their remembering. Why do you “choose” to recall something? Well for one thing, certain memories make you feel good. The original experience included a “feeling good” sensation, and so the tape has “feel good” recorded on it, and when you recall the memory—you feel good. And likewise, one reason you choose (or unconsciously decide) not to recall certain memories is that they have “feel bad” recorded on them, and so remembering them makes you feel bad. (If you don’t believe me check with Freud, who based the better part of a profoundly significant career on this observation, more or less.) It’s obvious that the emotions recorded in a memory have at least something to do with steering your solitary rambles through Memory Woods.

But obviously, the software version of remembering has no emotional compass. To some extent, that’s good: Software won’t suppress, repress or forget some illuminating case because (say) it made a complete fool of itself when the case was first presented. Objectivity is powerful.

On the other hand, we are brushing up here against a limitation that has a distinctly fundamental look. We want our Mirror Worlds to “remember” intelligently—to draw just the right precedent or two from a huge database. But human beings draw on reason and emotion when they perform all acts of remembering. An emotion can be a concise, nuanced shorthand for a whole tangle of facts and perceptions that you never bothered to sort out. How did you feel on your first day at work or school, your child’s second birthday, last year’s first snowfall? Later you might remember that scene; you might be reminded merely by the fact that you now feel the same as you did then. Why do you feel the same? If you think carefully, perhaps you can trace down the objective similarities between the two experiences. But their emotional resemblance was your original clue. And it’s quite plausible that “expertise” works this way also, at least occasionally: I’m reminded of a past case, not because of any objective similarity, but rather because I now feel the same as I did then.

The Glass Cage: Automation and US

The Glass Cage

The impact of technology is all around us. Maybe we’re at another Gutenberg moment and maybe we’re not.

Marshall McLuhan said it best.

When any new form comes into the foreground of things, we naturally look at it through the old stereos. We can’t help that. This is normal, and we’re still trying to see how will our previous forms of political and educational patterns persist under television. We’re just trying to fit the old things into the new form, instead of asking what is the new form going to do to all the assumptions we had before.

He also wrote that “a new medium is never an addition to an old one, nor does it leave the old one in peace.”

In The Glass Cage: Automation and US, Nick Carr, one of my favorite writers, enters the debate about the impact automation has on us, “examining the personal as well as the economic consequences of our growing dependence on computers.”

We know that the nature of jobs is going to change in the future thanks to technology. Tyler Cowen argues “If you and your skills are a complement to the computer, your wage and labor market prospects are likely to be cheery. If your skills do not complement the computer, you may want to address that mismatch.”

Carr’s book shows another side to the argument – the broader human consequences to living in a world where computers and software do the things we used to do.

Computer automation makes our lives easier, our chores less burdensome. We’re often able to accomplish more in less time—or to do things we simply couldn’t do before. But automation also has deeper, hidden effects. As aviators have learned, not all of them are beneficial. Automation can take a toll on our work, our talents, and our lives. It can narrow our perspectives and limit our choices. It can open us to surveillance and manipulation. As computers become our constant companions, our familiar, obliging helpmates, it seems wise to take a closer look at exactly how they’re changing what we do and who we are.

On the autonomous automobile, for example, Carr agues that while they have a ways to go before they start chauffeuring us around, there are broader questions that need to be answered first.

Although Google has said it expects commercial versions of its car to be on sale by the end of the decade, that’s probably wishful thinking. The vehicle’s sensor systems remain prohibitively expensive, with the roof-mounted laser apparatus alone going for eighty thousand dollars. Many technical challenges remain to be met, such as navigating snowy or leaf-covered roads, dealing with unexpected detours, and interpreting the hand signals of traffic cops and road workers. Even the most powerful computers still have a hard time distinguishing a bit of harmless road debris (a flattened cardboard box, say) from a dangerous obstacle (a nail-studded chunk of plywood). Most daunting of all are the many legal, cultural, and ethical hurdles a driverless car faces-Where, for instance, will culpability and liability reside should a computer-driven automobile cause an accident that kills or injures someone? With the car’s owner? With the manufacturer that installed the self-driving system? With the programmers who wrote the software? Until such thorny questions get sorted out, fully automated cars are unlikely to grace dealer showrooms.

Tacit and Explicit Knowledge

Self-driving cars are just one example of a technology that forces us “to change our thinking about what computers and robots can and can’t do.”

Up until that fateful October day, it was taken for granted that many important skills lay beyond the reach of automation. Computers could do a lot of things, but they couldn’t do everything. In an influential 2004 book, The New Division of Labor: How Computers Are Creating the Next Job Market, economists Frank Levy and Richard Murnane argued, convincingly, that there were practical limits to the ability of software programmers to replicate human talents, particularly those involving sensory perception, pattern recognition, and conceptual knowledge. They pointed specifically to the example of driving a car on the open road, a talent that requires the instantaneous interpretation of a welter of visual signals and an ability to adapt seamlessly to shifting and often unanticipated situations. We hardly know how we pull off such a feat ourselves, so the idea that programmers could reduce all of driving’s intricacies, intangibilities, and contingencies to a set of instructions, to lines of software code, seemed ludicrous. “Executing a left turn across oncoming traffic,” Levy and Murnane wrote, “involves so many factors that it is hard to imagine the set of rules that can replicate a drivers behavior.” It seemed a sure bet, to them and to pretty much everyone else, that steering wheels would remain firmly in the grip of human hands.

In assessing computers’ capabilities, economists and psychologists have long drawn on a basic distinction between two kinds of knowledge: tacit and explicit. Tacit knowledge, which is also sometimes called procedural knowledge, refers to all the stuff we do without actively thinking about it: riding a bike, snagging a fly ball, reading a book, driving a car. These aren’t innate skills—we have to learn them, and some people are better at them than others—but they can’t be expressed as a simple recipe, a sequence of precisely defined steps. When you make a turn through a busy intersection in your car, neurological studies have shown, many areas of your brain are hard at work, processing sensory stimuli, making estimates of time and distance, and coordinating your arms and legs. But if someone asked you to document everything involved in making that turn, you wouldn’t be able to, at least not without resorting to generalizations and abstractions.The ability resides deep in your nervous system outside the ambit of your conscious mind. The mental processing goes on without your awareness.

Much of our ability to size up situations and make quick judgments about them stems from the fuzzy realm of tacit knowledge. Most of our creative and artistic skills reside there too. Explicit knowledge, which is also known as declarative knowledge, is the stuff you can actually write down: how to change a flat tire, how to fold an origami crane, how to solve a quadratic equation. These are processes that can be broken down into well-defined steps. One person can explain them to another person through written or oral instructions: do this, then this, then this.

Because a software program is essentially a set of precise, written instructions—do this, then this, then this—we’ve assumed that while computers can replicate skills that depend on explicit knowledge, they’re not so good when it comes to skills that flow from tacit knowledge. How do you translate the ineffable into lines of code, into the rigid, step-by-step instructions of an algorithm? The boundary between the explicit and the tacit has always been a rough one—a lot of our talents straddle the line—but it seemed to offer a good way to define the limits of automation and, in turn, to mark out the exclusive precincts of the human. The sophisticated jobs Levy and Murnane identified as lying beyond the reach of computers—in addition to driving, they pointed to teaching and medical diagnosis—were a mix of the mental and the manual, but they all drew on tacit knowledge.

Google’s car resets the boundary between human and computer, and it does so more dramatically, more decisively, than have earlier breakthroughs in programming. It tells us that our idea of the limits of automation has always been something of a fiction. Were not as special as we think we are. While the distinction between tacit and explicit knowledge remains a useful one in the realm of human psychology, it has lost much of its relevance to discussions of automation.


That doesn’t mean that computers now have tacit knowledge, or that they’ve started to think the way we think, or that they’ll soon be able to do everything people can do. They don’t, they haven’t, and they won’t. Artificial intelligence is not human intelligence. People are mindful; computers are mindless. But when it comes to performing demanding tasks, whether with the brain or the body, computers are able to replicate our ends without replicating our means. When a driverless car makes a left turn in traffic, it’s not tapping into a well of intuition and skill; it’s following a program. But while the strategies are different, the outcomes, for practical purposes, are the same. The superhuman speed with which computers can follow instructions, calculate probabilities, and receive and send data means that they can use explicit knowledge to perform many of the complicated tasks that we do with tacit knowledge. In some cases, the unique strengths of computers allow them to perform what we consider to be tacit skills better than we can perform them ourselves. In a world of computer-controlled cars, you wouldn’t need traffic lights or stop signs. Through the continuous, high-speed exchange of data, vehicles would seamlessly coordinate their passage through even the busiest of intersections—just as computers today regulate the flow of inconceivable numbers of data packets along the highways and byways of the internet. What’s ineffable in our own minds becomes altogether effable in the circuits of a microchip.

Many of the cognitive talents we’ve considered uniquely human, it turns out, are anything but. Once computers get quick enough, they can begin to replicate our ability to spot patterns, make judgments, and learn from experience.

It’s not only vocations that are increasingly being computerized, avocations are too.

Thanks to the proliferation of smartphones, tablets, and other small, affordable, and even wearable computers, we now depend on software to carry out many of our daily chores and pastimes. We launch apps to aid us in shopping, cooking, exercising, even finding a mate and raising a child. We follow turn-by-turn GPS instructions to get from one place to the next. We use social networks to maintain friendships and express our feelings. We seek advice from recommendation engines on what to watch, read, and listen to. We look to Google, or to Apple’s Siri, to answer our questions and solve our problems. The computer is becoming our all-purpose tool for navigating, manipulating, and understanding the world, in both its physical and its social manifestations. Just think what happens these days when people misplace their smartphones or lose their connections to the net. Without their digital assistants, they feel helpless.

As Katherine Hayles, a literature professor at Duke University, observed in her 2012 book How We Think, “When my computer goes down or my Internet connection fails, I feel lost, disoriented, unable to work—in fact, I feel as if my hands have been amputated.”

While our dependency on computers is “disconcerting at times,” we welcome it.

We’re eager to celebrate and show off our whizzy new gadgets and apps—and not only because they’re so useful and so stylish. There’s something magical about computer automation. To watch an iPhone identify an obscure song playing over the sound system in a bar is to experience something that would have been inconceivable to any previous generation.


The trouble with automation is “that it often gives us what we don’t need at the cost of what we do.”

To understand why that’s so, and why we’re eager to accept the bargain, we need to take a look at how certain cognitive biases—flaws in the way we think—can distort our perceptions. When it comes to assessing the value of labor and leisure, the mind’s eye can’t see straight.

Mihaly Csikszentmihalyi, a psychology professor and author of the popular 1990 book Flow, has described a phenomenon that he calls “the paradox of work.” He first observed it in a study conducted in the 1980s with his University of Chicago colleague Judith LeFevre. They recruited a hundred workers, blue-collar and white-collar, skilled and unskilled, from five businesses around Chicago. They gave each an electronic pager (this was when cell phones were still luxury goods) that they had programmed to beep at seven random moments a day over the course of a week. At each beep, the subjects would fill out a short questionnaire. They’d describe the activity they were engaged in at that moment, the challenges they were facing, the skills they were deploying, and the psychological state they were in, as indicated by their sense of motivation, satisfaction, engagement, creativity, and so forth. The intent of this “experience sampling,” as Csikszentmihalyi termed the technique, was to see how people spend their time, on the job and off, and how their activities influence their “quality of experience.”

The results were surprising. People were happier, felt more fulfilled by what they were doing, while they were at work than during their leisure hours. In their free time, they tended to feel bored and anxious. And yet they didn’t like to be at work. When they were on the job, they expressed a strong desire to be off the job, and when they were off the job, the last thing they wanted was to go back to work. “We have,” reported Csikszentmihalyi and LeFevre, “the paradoxical situation of people having many more positive feelings at work than in leisure, yet saying that they wish to be doing something else when they are at work, not when they are in leisure.” We’re terrible, the experiment revealed, at anticipating which activities will satisfy us and which will leave us discontented. Even when we’re in the midst of doing something, we don’t seem able to judge its psychic consequences accurately.

Those are symptoms of a more general affliction, on which psychologists have bestowed the poetic name miswanting. We’re inclined to desire things we don’t like and to like things we don’t desire. “When the things we want to happen do not improve our happiness, and when the things we want not to happen do,” the cognitive psychologists Daniel Gilbert and Timothy Wilson have observed, “it seems fair to say we have wanted badly.” And as slews of gloomy studies show, we’re forever wanting badly. There’s also a social angle to our tendency to misjudge work and leisure. As Csikszentmihalyi and LeFevre discovered in their experiments, and as most of us know from our own experience, people allow themselves to be guided by social conventions—in this case, the deep-seated idea that being “at leisure” is more desirable, and carries more status, than being “at work”—rather than by their true feelings. “Needless to say,” the researchers concluded, “such a blindness to the real state of affairs is likely to have unfortunate consequences for both individual wellbeing and the health of society.” As people act on their skewed perceptions, they will “try to do more of those activities that provide the least positive experiences and avoid the activities that are the source of their most positive and intense feelings.” That’s hardly a recipe for the good life.

It’s not that the work we do for pay is intrinsically superior to the activities we engage in for diversion or entertainment. Far from it. Plenty of jobs are dull and even demeaning, and plenty of hobbies and pastimes are stimulating and fulfilling. But a job imposes a structure on our time that we lose when we’re left to our own devices. At work, were pushed to engage in the kinds of activities that human beings find most satisfying. We’re happiest when we’re absorbed in a difficult task, a task that has clear goals and that challenges us not only to exercise our talents but to stretch them. We become so immersed in the flow of our work, to use Csikszentmihalyi s term, that we tune out distractions and transcend the anxieties and worries that plague our everyday lives. Our usually wayward attention becomes fixed on what we’re doing. “Every action, movement, and thought follows inevitably from the previous one,” explains Csikszentmihalyi. “Your whole being is involved, and you’re using your skills to the utmost.” Such states of deep absorption can be produced by all manner of effort, from laying tile to singing in a choir to racing a dirt bike. You don’t have to be earning a wage to enjoy the transports of flow.

More often than not, though, our discipline flags and our mind wanders when we’re not on the job. We may yearn for the workday to be over so we can start spending our pay and having some fun, but most of us fritter away our leisure hours. We shun hard work and only rarely engage in challenging hobbies. Instead, we watch TV or go to the mall or log on to Facebook. We get lazy. And then we get bored and fretful. Disengaged from any outward focus, our attention turns inward, and we end up locked in what Emerson called the jail of self-consciousness. Jobs, even crummy ones, are “actually easier to enjoy than free time,” says Csikszentmihalyi, because they have the “built-in” goals and challenges that “encourage one to become involved in one’s work, to concentrate and lose oneself in it.” But that’s not what our deceiving minds want us to believe. Given the opportunity, we’ll eagerly relieve ourselves of the rigors of labor. We’ll sentence ourselves to idleness.

Automation offers us innumerable promises. Our lives, we think, will be greater if more things are automated. Yet as Carr explores in The Glass Cage, automation extracts a cost. Removing “complexity from jobs, diminishing the challenge they present and hence the level of engagement they promote.” This doesn’t mean that Carr is anti-automation. He’s not. He just wants us to see another side.

“All too often,” Carr warns, “automation frees us from that which makes us feel free.”

Paul Graham: On Arguing With Idiots, Where Ideas Come From, and What Makes Good Programmers


Paul Graham is a programmer, writer, and investor. His 2004 anthology Hackers and Painters explores the world and the people who inhabit it. He calls the book an “intellectual wild west,” and I agree.


When looking for where to find great ideas, we tend to end up in the combination space. Combining ideas from different disciplines creates new ideas. It’s repurposing something.

Graham, however, expands on this view and offers another source of new ideas as repurposing overlooked ideas.

Great work tends to grow out of ideas that others have overlooked, and no idea is so overlooked as one that’s unthinkable.

Arguing with Idiots

What happens when you argue with an idiot? You become one.

Argue with idiots, and you become an idiot.
The most important thing is to be able to think what you want, not to say what you want. And if you feel you have to say everything you think, it may inhibit you from thinking improper thoughts.

What Makes Good Programmers?

Few people would have better insight into the hacker culture than Graham. He believes, and I agree, that an undercurrent of disobedience can lead to healthy outcomes. Organizations spend a great deal of money hiring these people only to annoy them with pointless bureaucracy.

Those in authority tend to be annoyed by hackers’ general attitude of disobedience. But that disobedience is a byproduct of the qualities that make them good programmers. They may laugh at the CEO when he talks in generic corporate newspeech, but they also laugh at someone who tells them a certain problem can’t be solved. Suppress one, and you suppress the other.

Still Curious?

All of the essays in Hackers & Painters: Big Ideas from the Computer Age are worth reading and thinking about.

Claude Shannon: The Man Who Turned Paper Into Pixels

"The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point. Frequently the messages have meaning."— Claude Shannon (1948)
“The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point. Frequently the messages have meaning.”— Claude Shannon (1948)

Claude Shannon is the most important man you’ve probably never heard of. If Alan Turing is to be considered the father of modern computing, then the American mathematician Claude Shannon is the architect of the Information Age.

The video, created by the British filmmaker Adam Westbrook, echoes the thoughts of Nassim Taleb that boosting the signal does not mean you remove the noise, in fact, just the opposite: you amplify it.

Any time you try to send a message from one place to another something always gets in the way. The original signal is always distorted. Where ever there is signal there is also noise.

So what do you do? Well, the best anyone could do back then was to boost the signal. But then all you do is boost the noise.

Thing is we were thinking about information all wrong. We were obsessed with what a message meant.

A Renoir and a receipt? They’re different, right? Was there a way to think of them in the same way? Like so many breakthroughs the answer came from an unexpected place. A brilliant mathematician with a flair for blackjack.


The transistor was invented in 1948, at Bell Telephone Laboratories. This remarkable achievement, however, “was only the second most significant development of that year,” writes James Gleick in his fascinating book: The Information: A History, a Theory, a Flood. The most important development of 1948 and what still underscores modern technology is the bit.

An invention even more profound and more fundamental came in a monograph spread across seventy-nine pages of The Bell System Technical Journal in July and October. No one bothered with a press release. It carried a title both simple and grand “A Mathematical Theory of Communication” and the message was hard to summarize. But it was a fulcrum around which the world began to turn. Like the transistor, this development also involved a neologism: the word bit, chosen in this case not by committee but by the lone author, a thirty-two-year -old named Claude Shannon. The bit now joined the inch, the pound, the quart, and the minute as a determinate quantity— a fundamental unit of measure.

But measuring what? “A unit for measuring information,” Shannon wrote, as though there were such a thing, measurable and quantifiable, as information.


Shannon’s theory made a bridge between information and uncertainty; between information and entropy; and between information and chaos. It led to compact discs and fax machines, computers and cyberspace, Moore’s law and all the world’s Silicon Alleys. Information processing was born, along with information storage and information retrieval. People began to name a successor to the Iron Age and the Steam Age.

Gleick also recounts the relationship between Turing and Shannon:

In 1943 the English mathematician and code breaker Alan Turing visited Bell Labs on a cryptographic mission and met Shannon sometimes over lunch, where they traded speculation on the future of artificial thinking machines. (“ Shannon wants to feed not just data to a Brain, but cultural things!” Turing exclaimed. “He wants to play music to it!”)

Commenting on vitality of information, Gleick writes:

(Information) pervades the sciences from top to bottom, transforming every branch of knowledge. Information theory began as a bridge from mathematics to electrical engineering and from there to computing. … Now even biology has become an information science, a subject of messages, instructions, and code. Genes encapsulate information and enable procedures for reading it in and writing it out. Life spreads by networking. The body itself is an information processor. Memory resides not just in brains but in every cell. No wonder genetics bloomed along with information theory. DNA is the quintessential information molecule, the most advanced message processor at the cellular level— an alphabet and a code, 6 billion bits to form a human being. “What lies at the heart of every living thing is not a fire, not warm breath, not a ‘spark of life,’” declares the evolutionary theorist Richard Dawkins. “It is information, words, instructions.… If you want to understand life, don’t think about vibrant, throbbing gels and oozes, think about information technology.” The cells of an organism are nodes in a richly interwoven communications network, transmitting and receiving, coding and decoding. Evolution itself embodies an ongoing exchange of information between organism and environment.

The bit is the very core of the information age.

The bit is a fundamental particle of a different sort: not just tiny but abstract— a binary digit, a flip-flop, a yes-or-no. It is insubstantial, yet as scientists finally come to understand information, they wonder whether it may be primary: more fundamental than matter itself. They suggest that the bit is the irreducible kernel and that information forms the very core of existence.

In the words of John Archibald Wheeler, the last surviving collaborator of both Einstein and Bohr, information gives rise to “every it— every particle, every field of force, even the spacetime continuum itself.”

This is another way of fathoming the paradox of the observer: that the outcome of an experiment is affected, or even determined, when it is observed. Not only is the observer observing, she is asking questions and making statements that must ultimately be expressed in discrete bits. “What we call reality,” Wheeler wrote coyly, “arises in the last analysis from the posing of yes-no questions.” He added: “All things physical are information-theoretic in origin, and this is a participatory universe.” The whole universe is thus seen as a computer —a cosmic information-processing machine.

The greatest gift of Prometheus to humanity was not fire after all: “Numbers, too, chiefest of sciences, I invented for them, and the combining of letters, creative mother of the Muses’ arts, with which to hold all things in memory .”

Information technologies are both relative in the time they were created and absolute in terms of the significance. Gleick writes:

The alphabet was a founding technology of information. The telephone, the fax machine, the calculator, and, ultimately, the computer are only the latest innovations devised for saving, manipulating, and communicating knowledge. Our culture has absorbed a working vocabulary for these useful inventions. We speak of compressing data, aware that this is quite different from compressing a gas. We know about streaming information, parsing it, sorting it, matching it, and filtering it. Our furniture includes iPods and plasma displays, our skills include texting and Googling, we are endowed, we are expert, so we see information in the foreground. But it has always been there. It pervaded our ancestors’ world, too, taking forms from solid to ethereal, granite gravestones and the whispers of courtiers. The punched card, the cash register, the nineteenth-century Difference Engine, the wires of telegraphy all played their parts in weaving the spiderweb of information to which we cling. Each new information technology, in its own time, set off blooms in storage and transmission. From the printing press came new species of information organizers: dictionaries, cyclopaedias, almanacs— compendiums of words, classifiers of facts, trees of knowledge. Hardly any information technology goes obsolete. Each new one throws its predecessors into relief. Thus Thomas Hobbes, in the seventeenth century, resisted his era’s new-media hype: “The invention of printing, though ingenious, compared with the invention of letters is no great matter.” Up to a point, he was right. Every new medium transforms the nature of human thought. In the long run, history is the story of information becoming aware of itself.

The Information: A History, a Theory, a Flood is a fascinating read.

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