Famous Computing Quotes

Friedrich Nietzsche, who famously gave us the ‘God is dead’ phrase was interested in the sources of morality. He warned that the emergence of something (whether an organ, a legal institution, or a religious ritual) is never to be confused with its acquired purpose: ‘Anything in existence, having somehow come about, is continually interpreted anew, requisitioned anew, transformed and redirected to a new purpose.’ This is a liberating thought, which teaches us to never hold the history of something against its possible applications. Even if computers started out as calculators, that doesn’t prevent us from playing games on them. (47) (quoting Nietzsche, the Genealogy of Morals)

We consume indie music and films, and generate our own online content. We “think different” (even if we got the idea from Apple Computer’s famous ad campaign).

It's impossible that James Joyce could have mentioned "talk-tapes" in his writing, Asher thought. Someday I'm going to get my article published; I'm going to prove that Finnegan's Wake is an information pool based on computer memory systems that didn't exist until a century after James Joyce's era; that Joyce was plugged into a cosmic consciousness from which he derived the inspiration for his entire corpus of work. I'll be famous forever.

Well, bingo, his name popped up in the database on this crime ring’s computer as one of their own. Sloane, Wilma, KazuKen, Celi-hag, BunnyMuff, were all part of the illegal and criminal cyber-bullying ring that used blackmail to extort celebrities and famous authors, musicians, schools like Aunt Sookie Acting Academy for money or they will post lies, false rumors, photo shopped fake photos, and accusations of fake awards, fake credentials on the internet. They did that to Summer and tried to do that with Aunt Sookie, apparently. But as seemingly innocent as they seem, using young girls’ photos as their supposed fake identities, they really were part of a larger crime ring.”, Loving Summer by Kailin Gow

We like to believe that we live in a grand age of creative individualism. We look back at the midcentury era in which the Berkeley researchers conducted their creativity studies, and feel superior. Unlike the starched-shirted conformists of the 1950s, we hang posters of Einstein on our walls, his tongue stuck out iconoclastically. We consume indie music and films, and generate our own online content. We “think different” (even if we got the idea from Apple Computer’s famous ad campaign). But the way we organize many of our most important institutions—our schools and our workplaces—tells a very different story.

The computer is also not famous for having mercy.

There are too many famous Steve Jobs anecdotes to count, but several of them revolve around one theme: his unwillingness to leave well enough alone. His products had to be perfect; they had to do what they promised, and then some. And even though deadlines loomed and people would have to work around the clock, he would regularly demand more from his teams than they thought they could provide. The result? The most successful company in the history of the world and products that inspire devotion that is truly unusual for a personal computer or cell phone.

Apple Computers is a famous example: it was founded by (mostly Republi­can) computer engineers who broke from IBM in Silicon Valley in the 198os, forming little democratic circles of twenty to forty people with their laptops in each other's garages.

It's impossible that James Joyce could have mentioned "talk-tapes" in his writing, Asher thought. Someday I'm going to get my article published; I'm going to prove that Finnegan's Wake is an information pool based on computer memory systems that didn't exist until a century after James Joyce's era; that Joyce was plugged into a cosmic consciousness from which he derived the inspiration for his entire corpus of work. I'll be famous forever.

The famous computer scientist Melvin Conway coined an adage that is often referred to as Conway's Law. It states that any organization that designs a system will produce a design whose structure mirrors the organization's structure. Another way to say this is to beware of shipping your org chart.

I try not to hate anybody. "Hate is a four-letter word," like the bumper sticker says. But I hate book reviewers. Book reviewers are the most despicable, loathsome order of swine that ever rooted about the earth. They are sniveling, revolting creatures who feed their own appetites for bile by gnawing apart other people's work. They are human garbage. They all deserve to be struck down by awful diseases described in the most obscure dermatology journals. Book reviewers live in tiny studios that stink of mothballs and rotting paper. Their breath reeks of stale coffee. From time to time they put on too-tight shirts and pants with buckles and shuffle out of their lairs to shove heaping mayonnaise-laden sandwiches into their faces, which are worn in to permanent snarls. Then they go back to their computers and with fat stubby fingers they hammer out "reviews." Periodically they are halted as they burst into porcine squeals, gleefully rejoicing in their cruelty. Even when being "kindly," book reviewers reveal their true nature as condescending jerks. "We look forward to hearing more from the author," a book reviewer might say. The prissy tones sound like a second-grade piano teacher, offering you a piece of years-old strawberry hard candy and telling you to practice more. But a bad book review is just disgusting. Ask yourself: of all the jobs available to literate people, what monster chooses the job of "telling people how bad different books are"? What twisted fetishist chooses such a life?

Timothy Leary declared that personal computers had become the new LSD and years later revised his famous mantra to proclaim, “Turn on, boot up, jack in.

As Marshall McLuhan famously quipped, “Anyone who tries to make a distinction between education and entertainment doesn’t know the first thing about either.

It was a computer repair man who taught Taylor Swift to play the 3 chords in the guitar when she was just 12. After that she wrote her famous song, Lucky You.

There are two moments in the course of education where a lot of kids fall off the math train. The first comes in the elementary grades, when fractions are introduced. Until that moment, a number is a natural number, one of the figures 0, 1, 2, 3 . . . It is the answer to a question of the form “how many.”* To go from this notion, so primitive that many animals are said to understand it, to the radically broader idea that a number can mean “what portion of,” is a drastic philosophical shift. (“God made the natural numbers,” the nineteenth-century algebraist Leopold Kronecker famously said, “and all the rest is the work of man.”) The second dangerous twist in the track is algebra. Why is it so hard? Because, until algebra shows up, you’re doing numerical computations in a straightforwardly algorithmic way. You dump some numbers into the addition box, or the multiplication box, or even, in traditionally minded schools, the long-division box, you turn the crank, and you report what comes out the other side. Algebra is different. It’s computation backward. When you’re asked to solve

Neurologically speaking, though, there are reasons we develop a confused sense of priorities when we’re in front of our computer screens. For one thing, email comes at unpredictable intervals, which, as B. F. Skinner famously showed with rats seeking pellets, is the most seductive and habit-forming reward pattern to the mammalian brain. (Think about it: would slot machines be half as thrilling if you knew when, and how often, you were going to get three cherries?) Jessie would later say as much to me when I asked her why she was “obsessed”—her word—with her email: “It’s like fishing. You just never know what you’re going to get.” More to the point, our nervous systems can become dysregulated when we sit in front of a screen. This, at least, is the theory of Linda Stone, formerly a researcher and senior executive at Microsoft Corporation. She notes that we often hold our breath or breathe shallowly when we’re working at our computers. She calls this phenomenon “email apnea” or “screen apnea.” “The result,” writes Stone in an email, “is a stress response. We become more agitated and impulsive than we’d ordinarily be.

The personalized environment is very good at answering the questions we have but not at suggesting questions or problems that are out of our sight altogether. It brings to mind the famous Pablo Picasso quotation: “Computers are useless. They can only give you answers.

What was once an anonymous medium where anyone could be anyone—where, in the words of the famous New Yorker cartoon, nobody knows you’re a dog—is now a tool for soliciting and analyzing our personal data. According to one Wall Street Journal study, the top fifty Internet sites, from CNN to Yahoo to MSN, install an average of 64 data-laden cookies and personal tracking beacons each. Search for a word like “depression” on Dictionary.com, and the site installs up to 223 tracking cookies and beacons on your computer so that other Web sites can target you with antidepressants. Share an article about cooking on ABC News, and you may be chased around the Web by ads for Teflon-coated pots. Open—even for an instant—a page listing signs that your spouse may be cheating and prepare to be haunted with DNA paternity-test ads. The new Internet doesn’t just know you’re a dog; it knows your breed and wants to sell you a bowl of premium kibble.

And yet they obey and will continue to obey the orders that come to them. As in the famous Charge of the Light Brigade, these soldiers give up their lives, trusting that their commanders are using them well. While we sit safely here in these simulator rooms, playing an elaborate computer game, they are obeying, dying so that all of humankind can live.

1981, for example, Adam Osborne released the first truly portable personal computer. It was not great—it had a five-inch screen and not much memory—but it worked well enough. As Osborne famously declared, “Adequacy is sufficient. All else is superfluous.” Jobs found that approach to be morally appalling, and he spent days making fun of Osborne. “This guy just doesn’t get it,” Jobs repeatedly railed as he wandered the Apple corridors. “He’s not

Like gamblers, baseball fans and television networks, fishermen are enamored of statistics. The adoration of statistics is a trait so deeply embedded in their nature that even those rarefied anglers the disciples of Jesus couldn't resist backing their yarns with arithmetic: when the resurrected Christ appears on the morning shore of the Sea of Galilee and directs his forlorn and skunked disciples to the famous catch of John 21, we learn that the net contained not "a boatload" of fish, nor "about a hundred and a half," nor "over a gross," but precisely "a hundred and fifty three." This is, it seems to me, one of the most remarkable statistics ever computed. Consider the circumstances: this is after the Crucifixion and the Resurrection; Jesus is standing on the beach newly risen from the dead, and it is only the third time the disciples have seen him since the nightmare of Calvary. And yet we learn that in the net there were "great fishes" numbering precisely "a hundred and fifty three." How was this digit discovered? Mustn't it have happened thus: upon hauling the net to shore, the disciples squatted down by that immense, writhing fish pile and started tossing them into a second pile, painstakingly counting "one, two, three, four, five, six, seven... " all the way up to a hundred and fifty three, while the newly risen Lord of Creation, the Sustainer of all their beings, He who died for them and for Whom they would gladly die, stood waiting, ignored, till the heap of fish was quantified. Such is the fisherman's compulsion toward rudimentary mathematics! ....Concerning those disciples huddled over the pile of fish, another possibility occurs to me: perhaps they paid the fish no heed. Perhaps they stood in a circle adoring their Lord while He, the All-Curious Son of His All-Knowing Dad, counted them all Himself!

LEACH: You write by hand and, famously, do not own a computer. Is there some kind of physical pleasure to be taken in writing by hand? BERRY: Yes, but I don’t know how I’d prove it. I have a growing instinct to avoid mechanical distractions and screens because I want to be in the presence of this place. I like to write by the ambient daylight because I don’t want to miss it. As I grow older, I grieve over every moment I’m gone from this place, because it is inexhaustibly interesting to me.

Most people think the Lego corporation assembled a crack team of world-class experts to engineer Mini-Florida on a computer, but I’m not buying it.” “You aren’t?” asked Coleman. “It’s way too good.” Serge pointed at a two-story building in Key West. “Examine the meticulous green shutters on Hemingway’s house. No, my money is on a lone-wolf manic type like the famous Latvian Edward Leedskalnin, who single-handedly built the Coral Castle back in the twenties. He operated in secret, moving multi-ton hewn boulders south of Miami, and nobody knows how he did it. Probably happened here as well: The Lego people conducting an exhaustive nationwide search among the obsessive-compulsive community. But they had to be selective and stay away from the ones whose entire houses are filled to the ceiling with garbage bags of their own hair. Then they most likely found some cult guru living in a remote Lego ashram south of Pueblo with nineteen wives, offered him unlimited plastic blocks and said, ‘Knock yourself out.

a famous 1925 lecture given by Professor Francis Peabody to the Harvard medical student body:             The good physician knows his patients through and through, and his knowledge is bought dearly. Time, sympathy, and understanding must be lavishly dispensed, but the reward is to be found in that personal bond which forms the greatest satisfaction of the practice of medicine. One of the essential qualities of the clinician is interest in humanity, for the secret of the care of the patient is in caring for the patient.

Our fate is reflected in our most famous invention: the computer. Those local area networks that sprang up like cities in the eighties and nineties got connected at the turn of the century by the internet. Just like European colonization connected the globe. Globalization is to the human race what the internet is to computers—a method for sharing resources and ideas. Ideas can now move around the world in nanoseconds. We have a platform for enabling the strongest minds to transform their thoughts into reality—and deploy that reality for the good of the masses.

There has been an enduring misunderstanding that needs to be cleared up. Turing’s core message was never “If a machine can imitate a man, the machine must be intelligent.” Rather, it was “Inability to imitate does not rule out intelligence.” In his classic essay on the Turing test, Turing encouraged his readers to take a broader perspective on intelligence and conceive of it more universally and indeed more ethically. He was concerned with the possibility of unusual forms of intelligence, our inability to recognize those intelligences, and the limitations of the concept of indistinguishability as a standard for defining what is intelligence and what is not. In section two of the paper, Turing asks directly whether imitation should be the standard of intelligence. He considers whether a man can imitate a machine rather than vice versa. Of course the answer is no, especially in matters of arithmetic, yet obviously a man thinks and can think computationally (in terms of chess problems, for example). We are warned that imitation cannot be the fundamental standard or marker of intelligence. Reflecting on Turing’s life can change one’s perspective on what the Turing test really means. Turing was gay. He was persecuted for this difference in a manner that included chemical castration and led to his suicide. In the mainstream British society of that time, he proved unable to consistently “pass” for straight. Interestingly, the second paragraph of Turing’s famous paper starts with the question of whether a male or female can pass for a member of the other gender in a typed conversation. The notion of “passing” was of direct personal concern to Turing and in more personal settings Turing probably did not view “passing” as synonymous with actually being a particular way.

There are two moments in the course of education where a lot of kids fall off the math train. The first comes in the elementary grades, when fractions are introduced. Until that moment, a number is a natural number, one of the figures 0, 1, 2, 3 . . . It is the answer to a question of the form “how many.”* To go from this notion, so primitive that many animals are said to understand it, to the radically broader idea that a number can mean “what portion of,” is a drastic philosophical shift. (“God made the natural numbers,” the nineteenth-century algebraist Leopold Kronecker famously said, “and all the rest is the work of man.”) The second dangerous twist in the track is algebra. Why is it so hard? Because, until algebra shows up, you’re doing numerical computations in a straightforwardly algorithmic way. You dump some numbers into the addition box, or the multiplication box, or even, in traditionally minded schools, the long-division box, you turn the crank, and you report what comes out the other side. Algebra is different. It’s computation backward.

As Reagan’s first budget director, Stockman, a former two-term congressman from Michigan, was the point man for the supply-side economics the new administration was pushing— the theory that taxes should be lowered to stimulate economic activity, which would in turn produce more tax revenue to compensate for the lower rates. With his wonky whiz-kid persona, computer-like mental powers, and combative style, he browbeat Democratic congressmen and senators who challenged his views. But he soon incurred the wrath of political conservatives when he confessed to Atlantic reporter William Greider that supply-side economics was really window dressing for reducing the rates on high incomes. Among other acts of apostasy, he called doctrinaire supply-siders “naive.” The 1981 article created a sensation and prompted Reagan to ask him over lunch, “You have hurt me. Why?” Stockman famously described the meeting as a “trip to the woodshed.” Though the president himself forgave him, Stockman’s loose lips undercut his power at the White House, and in 1985 he left government to become an investment banker at Salomon Brothers.

We were working on the idea about dogs’ Internet searches, and first we debated whether the sketch should feature real dogs or Henrietta and Viv in dog costumes (because cast members were always, unfailingly, trying to get more air time, we quickly went with the latter). Then we discussed where it should take place (the computer cluster in a public library, but, even though all this mattered for was the establishing shot, we got stalled on whether that library should be New York’s famous Main Branch building on Fifth Avenue, with the lion statues in front, a generic suburban library in Kansas City, or a generic suburban library in Jacksonville, Florida, which was where Viv was from). Then we really got stalled on the breeds of dogs. Out of loyalty to my stepfather and Sugar, I wanted at least one to be a beagle. Viv said that it would work best if one was really big and one was really little, and Henrietta said she was fine with any big dog except a German Shepherd because she’d been bitten by her neighbor’s German Shepherd in third grade. After forty minutes we’d decided on a St. Bernard and a Chihuahua—I eventually conceded that Chihuahuas were funnier than beagles. We decided to go with the Florida location for the establishing shot because the lions in front of the New York Main Branch could preempt or diminish the appearance of the St. Bernard. Then we’d arrived at the fun part, which was the search terms. With her mouth full of beef kebab, Viv said, “Am I adopted?” With my mouth full of spanakopita, I said, “Am I a good girl?” With her mouth full of falafel, Henrietta said, “Am I five or thirty-five?” “Why is thunder scary?” I said. “Discreet crotch-sniffing techniques,” Henrietta said. “Cheap mani-pedis in my area,” Viv said. “Oh, and cheapest self-driving car.” “Best hamburgers near me,” I said. “What is halitosis,” Henrietta said. “Halitosis what to do,” I said. “Where do humans pee,” Viv said. “Taco Bell Chihuahua male or female,” I said. “Target bull terrier married,” Viv said. “Lassie plastic surgery,” Henrietta said. “Funny cat videos,” I said. “Corgis embarrassing themselves YouTube,” Viv said. “YouTube little dog scares away big dog,” I said. “Doghub two poodles and one corgi,” Henrietta said. “Waxing my tail,” I said. “Is my tail a normal size,” Viv said.

If this is true—if solitude is an important key to creativity—then we might all want to develop a taste for it. We’d want to teach our kids to work independently. We’d want to give employees plenty of privacy and autonomy. Yet increasingly we do just the opposite. We like to believe that we live in a grand age of creative individualism. We look back at the midcentury era in which the Berkeley researchers conducted their creativity studies, and feel superior. Unlike the starched-shirted conformists of the 1950s, we hang posters of Einstein on our walls, his tongue stuck out iconoclastically. We consume indie music and films, and generate our own online content. We “think different” (even if we got the idea from Apple Computer’s famous ad campaign). But the way we organize many of our most important institutions—our schools and our workplaces—tells a very different story. It’s the story of a contemporary phenomenon that I call the New Groupthink—a phenomenon that has the potential to stifle productivity at work and to deprive schoolchildren of the skills they’ll need to achieve excellence in an increasingly competitive world. The New Groupthink elevates teamwork above all else. It insists that creativity and intellectual achievement come from a gregarious place. It has many powerful advocates. “Innovation—the heart of the knowledge economy—is fundamentally social,” writes the prominent journalist Malcolm Gladwell. “None of us is as smart as all of us,” declares the organizational consultant Warren Bennis,

At the top of Anonybitch’s feed, there is a video of a boy and a girl making out in a hot tub. Anonybitch is particularly famous for her hot tub videos. She tags them #rubadub. This one’s a little grainy, like it was zoomed in from far away. I click play. The girl is sitting in the boy’s lap, her body draped over his, legs hooked around his waist, arms around his neck. She’s wearing a red nightgown, and it billows in the water like a full sail. The back of her head obscures the boy. Her hair is long, and the ends dip into the hot tub like calligraphy brushes in ink. The boy runs his hands down her spine like she is a cello and he is playing her. I’m so entranced I don’t notice at first that Kitty is watching with me. Both of our heads are tilted, trying to suss out what it is we’re looking at. “You shouldn’t be looking at this,” I say. “Are they doing it?” she asks. “It’s hard to say because of her nightgown.” But maybe? Then the girl touches the boy’s cheek, and there is something about the movement, the way she touches him like she is reading braille. Something familiar. The back of my neck goes icy cold, and I am hit with a gust of awareness, of humiliating recognition. That girl is me. Me and Peter, in the hot tub on the ski trip. Oh my God. I scream. Margot comes racing in, wearing one of those Korean beauty masks on her face with slits for eyes, nose, and mouth. “What? What?” I try to cover the computer screen with my hand, but she pushes it out of the way, and then she lets out a scream too. Her mask falls off. “Oh my God! Is that you?” Oh my God oh my God oh my God. “Don’t let Kitty see!” I shout. Kitty’s wide-eyed. “Lara Jean, I thought you were a goody-goody.” “I am!” I scream.

Moore’s Law, the rule of thumb in the technology industry, tells us that processor chips—the small circuit boards that form the backbone of every computing device—double in speed every eighteen months. That means a computer in 2025 will be sixty-four times faster than it is in 2013. Another predictive law, this one of photonics (regarding the transmission of information), tells us that the amount of data coming out of fiber-optic cables, the fastest form of connectivity, doubles roughly every nine months. Even if these laws have natural limits, the promise of exponential growth unleashes possibilities in graphics and virtual reality that will make the online experience as real as real life, or perhaps even better. Imagine having the holodeck from the world of Star Trek, which was a fully immersive virtual-reality environment for those aboard a ship, but this one is able to both project a beach landscape and re-create a famous Elvis Presley performance in front of your eyes. Indeed, the next moments in our technological evolution promise to turn a host of popular science-fiction concepts into science facts: driverless cars, thought-controlled robotic motion, artificial intelligence (AI) and fully integrated augmented reality, which promises a visual overlay of digital information onto our physical environment. Such developments will join with and enhance elements of our natural world. This is our future, and these remarkable things are already beginning to take shape. That is what makes working in the technology industry so exciting today. It’s not just because we have a chance to invent and build amazing new devices or because of the scale of technological and intellectual challenges we will try to conquer; it’s because of what these developments will mean for the world.

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In fact, the same basic ingredients can easily be found in numerous start-up clusters in the United States and around the world: Austin, Boston, New York, Seattle, Shanghai, Bangalore, Istanbul, Stockholm, Tel Aviv, and Dubai. To discover the secret to Silicon Valley’s success, you need to look beyond the standard origin story. When people think of Silicon Valley, the first things that spring to mind—after the HBO television show, of course—are the names of famous start-ups and their equally glamorized founders: Apple, Google, Facebook; Jobs/ Wozniak, Page/ Brin, Zuckerberg. The success narrative of these hallowed names has become so universally familiar that people from countries around the world can tell it just as well as Sand Hill Road venture capitalists. It goes something like this: A brilliant entrepreneur discovers an incredible opportunity. After dropping out of college, he or she gathers a small team who are happy to work for equity, sets up shop in a humble garage, plays foosball, raises money from sage venture capitalists, and proceeds to change the world—after which, of course, the founders and early employees live happily ever after, using the wealth they’ve amassed to fund both a new generation of entrepreneurs and a set of eponymous buildings for Stanford University’s Computer Science Department. It’s an exciting and inspiring story. We get the appeal. There’s only one problem. It’s incomplete and deceptive in several important ways. First, while “Silicon Valley” and “start-ups” are used almost synonymously these days, only a tiny fraction of the world’s start-ups actually originate in Silicon Valley, and this fraction has been getting smaller as start-up knowledge spreads around the globe. Thanks to the Internet, entrepreneurs everywhere have access to the same information. Moreover, as other markets have matured, smart founders from around the globe are electing to build companies in start-up hubs in their home countries rather than immigrating to Silicon Valley.

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Every day, the markets were driven less directly by human beings and more directly by machines. The machines were overseen by people, of course, but few of them knew how the machines worked. He knew that RBC’s machines—not the computers themselves, but the instructions to run them—were third-rate, but he had assumed it was because the company’s new electronic trading unit was bumbling and inept. As he interviewed people from the major banks on Wall Street, he came to realize that they had more in common with RBC than he had supposed. “I’d always been a trader,” he said. “And as a trader you’re kind of inside a bubble. You’re just watching your screens all day. Now I stepped back and for the first time started to watch other traders.” He had a good friend who traded stocks at a big-time hedge fund in Stamford, Connecticut, called SAC Capital. SAC Capital was famous (and soon to be infamous) for being one step ahead of the U.S. stock market. If anyone was going to know something about the market that Brad didn’t know, he figured, it would be them. One spring morning he took the train up to Stamford and spent the day watching his friend trade. Right away he saw that, even though his friend was using technology given to him by Goldman Sachs and Morgan Stanley and the other big firms, he was experiencing exactly the same problem as RBC: The market on his screens was no longer the market. His friend would hit a button to buy or sell a stock and the market would move away from him. “When I see this guy trading and he was getting screwed—I now see that it isn’t just me. My frustration is the market’s frustration. And I was like, Whoa, this is serious.” Brad’s problem wasn’t just Brad’s problem. What people saw when they looked at the U.S. stock market—the numbers on the screens of the professional traders, the ticker tape running across the bottom of the CNBC screen—was an illusion. “That’s when I realized the markets are rigged. And I knew it had to do with the technology. That the answer lay beneath the surface of the technology. I had absolutely no idea where. But that’s when the lightbulb went off that the only way I’m going to find out what’s going on is if I go beneath the surface.

Beyoncé and Rihanna were pop stars. Pop stars were musical performers whose celebrity had exploded to the point where they could be identified by single words. You could say BEYONCÉ or RIHANNA to almost anyone anywhere in the industrialized world and it would conjure a vague neurological image of either Beyoncé or Rihanna. Their songs were about the same six subjects of all songs by all pop stars: love, celebrity, fucking, heartbreak, money and buying ugly shit. It was the Twenty-First Century. It was the Internet. Fame was everything. Traditional money had been debased by mass production. Traditional money had ceased to be about an exchange of humiliation for food and shelter. Traditional money had become the equivalent of a fantasy world in which different hunks of vampiric plastic made emphatic arguments about why they should cross the threshold of your home. There was nothing left to buy. Fame was everything because traditional money had failed. Fame was everything because fame was the world’s last valid currency. Beyoncé and Rihanna were part of a popular entertainment industry which deluged people with images of grotesque success. The unspoken ideology of popular entertainment was that its customers could end up as famous as the performers. They only needed to try hard enough and believe in their dreams. Like all pop stars, Beyoncé and Rihanna existed off the illusion that their fame was a shared experience with their fans. Their fans weren’t consumers. Their fans were fellow travelers on a journey through life. In 2013, this connection between the famous and their fans was fostered on Twitter. Beyoncé and Rihanna were tweeting. Their millions of fans were tweeting back. They too could achieve their dreams. Of course, neither Beyoncé nor Rihanna used Twitter. They had assistants and handlers who packaged their tweets for maximum profit and exposure. Fame could purchase the illusion of being an Internet user without the purchaser ever touching a mobile phone or a computer. That was a difference between the rich and the poor. The poor were doomed to the Internet, which was a wonderful resource for watching shitty television, experiencing angst about other people’s salaries, and casting doubt on key tenets of Mormonism and Scientology. If Beyoncé or Rihanna were asked about how to be like them and gave an honest answer, it would have sounded like this: “You can’t. You won’t. You are nothing like me. I am a powerful mixture of untamed ambition, early childhood trauma and genetic mystery. I am a portal in the vacuum of space. The formula for my creation is impossible to replicate. The One True God made me and will never make the like again. You are nothing like me.

A famous British writer is revealed to be the author of an obscure mystery novel. An immigrant is granted asylum when authorities verify he wrote anonymous articles critical of his home country. And a man is convicted of murder when he’s connected to messages painted at the crime scene. The common element in these seemingly disparate cases is “forensic linguistics”—an investigative technique that helps experts determine authorship by identifying quirks in a writer’s style. Advances in computer technology can now parse text with ever-finer accuracy. Consider the recent outing of Harry Potter author J.K. Rowling as the writer of The Cuckoo’s Calling , a crime novel she published under the pen name Robert Galbraith. England’s Sunday Times , responding to an anonymous tip that Rowling was the book’s real author, hired Duquesne University’s Patrick Juola to analyze the text of Cuckoo , using software that he had spent over a decade refining. One of Juola’s tests examined sequences of adjacent words, while another zoomed in on sequences of characters; a third test tallied the most common words, while a fourth examined the author’s preference for long or short words. Juola wound up with a linguistic fingerprint—hard data on the author’s stylistic quirks. He then ran the same tests on four other books: The Casual Vacancy , Rowling’s first post-Harry Potter novel, plus three stylistically similar crime novels by other female writers. Juola concluded that Rowling was the most likely author of The Cuckoo’s Calling , since she was the only one whose writing style showed up as the closest or second-closest match in each of the tests. After consulting an Oxford linguist and receiving a concurring opinion, the newspaper confronted Rowling, who confessed. Juola completed his analysis in about half an hour. By contrast, in the early 1960s, it had taken a team of two statisticians—using what was then a state-of-the-art, high-speed computer at MIT—three years to complete a project to reveal who wrote 12 unsigned Federalist Papers. Robert Leonard, who heads the forensic linguistics program at Hofstra University, has also made a career out of determining authorship. Certified to serve as an expert witness in 13 states, he has presented evidence in cases such as that of Christopher Coleman, who was arrested in 2009 for murdering his family in Waterloo, Illinois. Leonard testified that Coleman’s writing style matched threats spray-painted at his family’s home (photo, left). Coleman was convicted and is serving a life sentence. Since forensic linguists deal in probabilities, not certainties, it is all the more essential to further refine this field of study, experts say. “There have been cases where it was my impression that the evidence on which people were freed or convicted was iffy in one way or another,” says Edward Finegan, president of the International Association of Forensic Linguists. Vanderbilt law professor Edward Cheng, an expert on the reliability of forensic evidence, says that linguistic analysis is best used when only a handful of people could have written a given text. As forensic linguistics continues to make headlines, criminals may realize the importance of choosing their words carefully. And some worry that software also can be used to obscure distinctive written styles. “Anything that you can identify to analyze,” says Juola, “I can identify and try to hide.

What is your name?” she said crossing her legs. “I am Raj Singhania, owner of Singhania group of Industries and I am on my way to sign a 1000 crore deal.” “Oh my God, Oh my God!” she said laughing and looked at Bobby from top to bottom. “What’s with this OMG thing and girls, stop saying that. I am not going to propose you anytime soon. But it’s OK. I can understand how girls feel when they meet famous dudes like me,” Bobby said smiling. “What kind of an idiot are you?” she said laughing. “Indeed, a very rare one. The one that you find after searching for millions of years,” Bobby said. “Do you always talk like this?” she said laughing. “Only to strangers on bus or whenever I get bored,” Bobby said. “OK, tell me your real name,” she said. “My name is Mogaliputta Tissa and I am here to save the world.” “Oh no not again!” she said squeezing her head with both her hands. “I know you are dying inside to kiss me,” Bobby said flashing a smile. “Why would I kiss you?” she said with a pretended sternness. “Because, you are impressed with my intelligence level and the hotness quotient, I can see that in your eyes.” “You think you are hot! Oh no! You look like that cartoon guy in 7 up commercial,” she said laughing. “Thank you. He was the coolest guy I saw on TV,” Bobby said. “OK fine, let’s calm down. Tell me your real name,” she said calmly. “I don’t remember my name,” Bobby said calmly. “What kind of idiot forgets his name?” she said staring into Bobby’s eyes. “I am suffering from multiple personality disorder and I forgot my present personality’s name. Can you help me out?” Bobby said with an innocent look on his face. “I will kill you with my hair clip. Leave me alone,” she said and closed her eyes. “You look like a Pomeranian puppy,” Bobby said looking at her hair. “Don’t talk to me,” she said. “You look very beautiful,” Bobby said. “Nice try but I am not going to open my eyes,” she said. “Your ear rings are very nice. But I think that girl in the last seat has better rings,” Bobby said. “She is not wearing any ear rings. I know because I saw her when I was getting inside. It takes just 5 seconds for a girl to know what other girls around her are wearing,” she said with her eyes still closed. “Hey, look. They are selling porn CDs at a roadside shop,” Bobby said. “I have loads of porn in my personal computer. I don’t need them,” she said. “OMG, that girl looks hotter than you,” Bobby said. “I will not open my eyes no matter what. Even if an earthquake hits the road, I will not open my eyes,” she said crossing her arms over her chest. Bobby turned back and waved his hand to the kid who was poking his mom’s ear. The kid came running and halted at Bobby’s seat. “This aunty wants to give you a chocolate if you tell her your name,” Bobby whispered to the kid and the kid perked up smiling. “Hello Aunty! Wake up, my name is Bintu. Give me my chocolate, Aunty, please!” the kid said yanking at the girl’s hand. All of a sudden, she opened her eyes and glared at the kid. “Don’t call me aunty. What would everyone think? I am a teenage girl. Go away. I don’t have anything to give you,” she said and the kid went back to his seat. “This is what happens when you mess with an intelligent person like me,” Bobby said laughing. “Shut up,” she said. “OK dude.” “I am not a dude. Stop it.” “OK sexy. Oops! OK Saxena,” “I will scream.” “OK. Where do you study?” “Why should I tell you?” “Are you suffering from split personality disorder like me?” Bobby said staring into her eyes. “Shut up. Don’t talk to me,” she said with a pout. “What the hell! I have enlightened your mind with my thoughts, told you my name and now you are acting like you don’t know me. Girls are mad.

I do not believe that ‘employment outside the house’ is as valuable or important or satisfying as employment at home, for either men or women,” wrote Wendell Berry in his famous 1987 essay “Why I Am Not Going to Buy a Computer.

Where people were once dazzled to be online, now their expectations had soared, and they did not bother to hide their contempt for those who sought to curtail their freedom on the Web. Nobody was more despised than a computer science professor in his fifties named Fang Binxing. Fang had played a central role in designing the architecture of censorship, and the state media wrote admiringly of him as the “father of the Great Firewall.” But when Fang opened his own social media account, a user exhorted others, “Quick, throw bricks at Fang Binxing!” Another chimed in, “Enemies of the people will eventually face trial.” Censors removed the insults as fast as possible, but they couldn’t keep up, and the lacerating comments poured in. People called Fang a “eunuch” and a “running dog.” Someone Photoshopped his head onto a voodoo doll with a pin in its forehead. In digital terms, Fang had stepped into the hands of a frenzied mob. Less than three hours after Web users spotted him, the Father of the Great Firewall shut down his account and recoiled from the digital world that he had helped create. A few months later, in May 2011, Fang was lecturing at Wuhan University when a student threw an egg at him, followed by a shoe, hitting the professor in the chest. Teachers tried to detain the shoe thrower, a science student from a nearby college, but other students shielded him and led him to safety. He was instantly famous online. People offered him cash and vacations in Hong Kong and Singapore. A female blogger offered to sleep with him.

Clutching a printed e-mail, Vivian moved towards the most famous desk in the world, her voice shaking with emotion. “Mr. President, tell me this Politico e-news article that just hit our computer screens is wrong. You’re not seriously considering dumping the Vice President and replacing him with Hilde? Tell me it ain’t so! Not Hilde Ramona Calhoun.” “Now, Vivian, calm down….you know I won’t do anything that major without talking first to you….and….of course, talking….to…The Wife.” “So, you are thinking about it? I knew it.” “I have to, Vivian, you know that, we all know the Veep can’t open his mouth without sticking in both feet.

I used Harvard’s computer system only once as an undergraduate, to run regressions for my senior thesis on the economics of spousal abuse. The data was stored on large, heavy magnetic tapes that I had to lug in big boxes across campus, cursing the entire way and arriving in a sweaty mess at the sole computer center, which was populated exclusively with male students. I then had to stay up all night spinning the tapes to input the data. When I tried to execute my final calculations, I took down the entire system. That’s right. Years before Mark famously crashed that same Harvard system, I beat him to it.

Your mind operates on the famous computing principle of GIGO-garbage in, garbage out. If you do ill, speak ill and think ill, the residue is going to leave you sick. If you do well, speak well and think well, the outcome is going to be well.—OM SWAMI, A MILLION THOUGHTS.

Charles Proteus Steinmetz famously asked, “Where does this heat go”? This heat “freezes” the possible into reality and creates what we collectively call “the past.” For humans, the Past is singular tense and the Future gets pluralized.

Cogito ergo sum is Descartes famous sentence. It means “I think; therefore, I am.” This philosophy led the Western World to equate their being, their identity, with their mind rather than their whole organism.

Software can have serious bugs and still be wildly successful. Lotus 1-2-3 famously mistook 1900 for a leap year, but it was so popular that versions of Excel to this day have to be programmed to honor that mistake to ensure backward compatibility. And because Excel’s popularity ultimately dwarfed that of Lotus 1-2-3, the bug is now part of the ECMA Office Open XML specification.

Python is a mainstream programming language that is commonly used to solve cognitive and mathematical problems. Many Python modules and useful Python libraries, such as IPython, Pandas, SciPy, and others, are most commonly used for these tasks. Usage of Business Applications Python is used by many engineers to assemble and maintain their commercial programs or apps. Python is used by many designers to maintain their web-based company sites. An application that runs on the console Python can be used to create help-based software. IPython, for example, can be used to create a variety of support-based applications. Audio or Video-based Application Programming Python is an excellent programming language for a variety of video and audio projects. Python is used by many professionals to create a variety of media applications. You can do this with the help of cplay, another Python compiler. 3D based Computer-Aided Drafting Applications Python is used by many designers to create 3D-based Computer-Aided Drafting systems. Fandango is a very useful Python-based application that allows you to see all of the capabilities of CAD to expand these types of applications. Applications for Business Python is used by many Python experts to create a variety of apps that can be used in a business. Tryton and Picalo are the most famous applications in this regard.

What is so important about Engelbart’s legacy is that he saw the computer as primarily a tool to augment—not replace—human capability. In our current era, by contrast, much of the financing flowing out of Silicon Valley is aimed at building machines that can replace humans. In a famous encounter in 1953 at MIT, Marvin Minsky, the father of research on artificial intelligence, declared: “We’re going to make machines intelligent. We are going to make them conscious!” To which Doug Engelbart replied: “You’re going to do all that for the machines? What are you going to do for the people?

Jobs noticed that when the heart gave him an intuition, it was for him a command that he had to follow, regardless of the opinions of others. The only thing that mattered was finding a way to give shape to the intuition. For Jobs, the vegan diet, Zen meditation, a life immersed in nature, abstention from alcohol and coffee were necessary to nourish his inner voice, the voice of his heart and strengthen his ability to intuit the future. At the same time, this caused great difficulties. He was sensitive, intuitive, irrational and nervous. He was aware of the limitations that his irrationality caused in handling a large company, such as Apple Computer, and chose a rationalist manager to run the company: John Sculley, a famous manager he admired but with whom he entered continually in conflict, to the point that in 1985 the board of directors decided to fire Jobs from Apple, the company he had founded. Apple Computer continued to make money for a while with the products designed by Jobs, but after a few years the decline began and in the mid-1990s it came to the brink of bankruptcy. On December 21, 1996, the board of directors asked Jobs to return as the president’s personal advisor. Jobs accepted. He asked for a salary of one dollar a year in exchange for the guarantee that his insights, even if crazy, were accepted unconditionally. In a few months he revolutionized the products and on September 16, 1997 he became interim CEO. Apple Computer resurrected in less than a year. How did he manage? He believed that we should not let the noise of others’ opinions dull our inner voice. And, more importantly, he repeated that we must always have the courage to believe in our heart and in our intuitions, because they already know the future and know where we need to go. For Jobs, everything else was secondary.

Blockchains are possible because of consensus protocols – sets of rules that determine what kinds of blocks can become part of the chain and thus the “truth.” These consensus protocols are designed to resist malicious tampering up to a certain security bound. The blockchains we focus on currently use the proof of work (PoW) consensus protocol, which relies on a computationally and energy intensive lottery to determine which block to add. The participants agree that the longest chain of blocks is the truth. If attackers want to make a longer chain that contains malicious transactions, they must outpace all the computational work of the entire rest of the network. In theory, they would need most of the network power (“hash rate”) to accomplish this – hence, the famous 51 percent attack being the boundary of PoW security. Luckily, it is extraordinarily difficult for any actor, even an entire country, to amass this much network power on the most widely used blockchains, such as Bitcoin or Ethereum.

Endeavour could be contacted only through PLANETCOM, which was an autonomous corporation famous for the strictness and efficiency of its accounting. It took a long time to establish a line of credit with PLANETCOM. Somewhere, someone was working on this, but, at the moment, PLANETCOM’s hardhearted computers did not recognize the existence of the Rama Committee.

The Instagram versus Hipstamatic story is perhaps the canonical example of a strategy made famous by Chris Dixon’s 2015 essay “Come for the tool, stay for the network.” Chris writes: A popular strategy for bootstrapping networks is what I like to call “come for the tool, stay for the network.” The idea is to initially attract users with a single-player tool and then, over time, get them to participate in a network. The tool helps get to initial critical mass. The network creates the long term value for users, and defensibility for the company.40 There are many other examples across many sectors beyond photo apps: The Google Suite provides stand-alone tools for people to create documents, spreadsheets, and presentations, but also network features around collaborative editing, and comments. Games like Minecraft or even classics like Street Fighter can be played in single-player mode where you play against the computer, or multiplayer mode where you play with friends. Yelp started out effectively as a directory tool for people to look up local businesses, showing addresses and phone numbers, but the network eventually built out the database of photos and reviews. LinkedIn started as a tool to put your resume online, but encouraged you to build up your professional network over time. “Come for the tool, stay for the network” circumvents the Cold Start Problem and makes it easier to launch into an entire network—with PR, paid marketing, influencers, sales, or any number of tried-and-true channels. It minimizes the size requirement of an atomic network and in turn makes it easy to take on an entire network. Whether it’s photo-sharing apps or restaurant directories, in the framework of the Cold Start Theory, this strategy can be visualized. In effect, a tool can be used to “prop up” the value of the network effects curve when the network is small.

Enterprise deals or “how to lose your freedom in 5 minutes” Being able to use our product for sales prospecting, I decided to go after some big names at the enterprise level. After one week I had booked meetings with companies like Uber, Facebook, etc. This is where the fun begins…or not… I spent 3 months doing between 4 to 9 meetings for each enterprise company I had booked meetings with. Every meeting leads to the next one as you go up the chain of command. And then comes the pilot phase. Awesome you might think! Well, not really… Working with enterprise-level clients requires a lot of custom work and paperwork. And when I say “a lot” I mean a sh*t ton of work. You need an entire department to handle the legal aspect, and hire another 10 people to entirely change your tech department to meet their requirements. During 4 months I went from being super excited to work with the most famous companies in the world to “this deal will transform our company entirely and we’ll have to start doing custom everything”. Losing my freedom and flexibility quickly became a no-go. The issue here is, with all these meetings I thought that they would adapt to our standards. That they understood from the start that we were a startup and that we couldn’t comply with all their needs. But it doesn’t work like this. It’s actually the other way around even though the people you meet working at these companies tell you otherwise. The bottleneck often comes from the legal department. It doesn’t matter if everyone is excited to use your product, if you don’t comply with their legal requirements or try to negotiate it will never work out. To give you an example, we had enterprise companies asking us to specifically have all our employee’s computers locked down in the office after they end their day. Knowing that we’re a remote company, it’s impossible to comply with that... If you want to target enterprise accounts, do it. But make sure to know that you need a lot of time and effort to make things work. It won’t be quick. I was attracted to the BIG names thinking that it would be an amazing way to grow faster, but instead, I should have been 100% focused on our target market (startups, SMBs).

The insatiable need for more processing power -- ideally, located as close as possible to the user but, at the very least, in nearby indus­trial server farms -- invariably leads to a third option: decentralized computing. With so many powerful and often inactive devices in the homes and hands of consumers, near other homes and hands, it feels inevitable that we'd develop systems to share in their mostly idle pro­cessing power. "Culturally, at least, the idea of collectively shared but privately owned infrastructure is already well understood. Anyone who installs solar panels at their home can sell excess power to their local grid (and, indirectly, to their neighbor). Elon Musk touts a future in which your Tesla earns you rent as a self-driving car when you're not using it yourself -- better than just being parked in your garage for 99% of its life. "As early as the 1990s programs emerged for distributed computing using everyday consumer hardware. One of the most famous exam­ples is the University of California, Berkeley's SETl@HOME, wherein consumers would volunteer use of their home computers to power the search for alien life. Sweeney has highlighted that one of the items on his 'to-do list' for the first-person shooter Unreal Tournament 1, which shipped in 1998, was 'to enable game servers to talk to each other so we can just have an unbounded number of players in a single game session.' Nearly 20 years later, however, Sweeney admitted that goal 'seems to still be on our wish list.' "Although the technology to split GPUs and share non-data cen­ter CPUs is nascent, some believe that blockchains provide both the technological mechanism for decentralized computing as well as its economic model. The idea is that owners of underutilized CPUs and GPUs would be 'paid' in some cryptocurrency for the use of their processing capabilities. There might even be a live auction for access to these resources, either those with 'jobs' bidding for access or those with capacity bidding on jobs. "Could such a marketplace provide some of the massive amounts of processing capacity that will be required by the Metaverse? Imagine, as you navigate immersive spaces, your account continuously bidding out the necessary computing tasks to mobile devices held but unused by people near you, perhaps people walking down the street next to you, to render or animate the experiences you encounter. Later, when you’re not using your own devices, you would be earning tokens as they return the favor. Proponents of this crypto-exchange concept see it as an inevitable feature of all future microchips. Every computer, no matter how small, would be designed to be auctioning off any spare cycles at all times. Billions of dynamically arrayed processors will power the deep compute cycles of event the largest industrial customers and provide the ultimate and infinite computing mesh that enables the Metaverse.

The first thing companies did with computer technology back in the 1980s was to multiply the number of choices for their customers. More colors, more styles, more features, more models, more messages, more channels, more services, more brand extensions, more SKUs. The siren call of “consumer choice” proved impossible for companies to resist. If a little choice was good, they reasoned, more choice was better. Customers loved it. For about 15 minutes. Today their lives are so cluttered by choice that they can barely breathe. Americans now see that a little choice increases their freedom, but too much takes it away. Do you really want to spend three hours learning how to use the features on your new Samsung TV? Or sort through 17 varieties each time you buy Crest toothpaste at the supermarket? Or deal with the 3,000 pages of items shown in Restoration Hardware’s 15-pound set of catalogs? Not if you have a life. Of course, none of us wants to give up this lavish banquet of choice. We just want it off the floor and out of the way. “It’s not information over-load,” media consultant Clay Shirky famously said. “It’s filter failure.” Our brains can’t handle the deluge. We’re desperate for a way to organize, access, and make use of so many options. Amazon founder Jeff Bezos called it “cognitive overhead.

Aren’t fears of disappearing jobs something that people claim periodically, like with both the agricultural and industrial revolution, and it’s always wrong?” It’s true that agriculture went from 40 percent of the workforce in 1900 to 2 percent in 2017 and we nonetheless managed to both grow more food and create many wondrous new jobs during that time. It’s also true that service-sector jobs multiplied in many unforeseen ways and absorbed most of the workforce after the Industrial Revolution. People sounded the alarm of automation destroying jobs in the 19th century—the Luddites destroying textile mills in England being the most famous—as well as in the 1920s and the 1960s, and they’ve always been wildly off the mark. Betting against new jobs has been completely ill-founded at every point in the past. So why is this time different? Essentially, the technology in question is more diverse and being implemented more broadly over a larger number of economic sectors at a faster pace than during any previous time. The advent of big farms, tractors, factories, assembly lines, and personal computers, while each a very big deal for the labor market, were orders of magnitude less revolutionary than advancements like artificial intelligence, machine learning, self-driving vehicles, advanced robotics, smartphones, drones, 3D printing, virtual and augmented reality, the Internet of things, genomics, digital currencies, and nanotechnology. These changes affect a multitude of industries that each employ millions of people. The speed, breadth, impact, and nature of the changes are considerably more dramatic than anything that has come before.

front of a bookshelf. John didn’t have very many books, maybe a couple dozen on this bookshelf. She took her hand out of her mouth and tapped on the spine of one of the books. “What’s in this book? Monsters and Myths.” “Just like it sounds. It’s a compendium of monsters and mythology.” “Like vampires and werewolves?” John nodded. “As well as the more famous gods and demons throughout human history.” “You mind if I look through it?” said Emma. “Be my guest. I haven’t looked at that thing in years.” Emma pulled the book down and sat on the couch and began leafing through it. I looked back at the computer and pointed to it. “Can you check again?” John rolled his eyes. He tapped a couple keys and then said, “Still nothing new.” “Should we go surfing then?” I said. “But,

Speaking at the Chaos Communication Congress, an annual computer hacker conference held in Berlin, Germany, Tobias Engel, founder of Sternraute, and Karsten Nohl, chief scientist for Security Research Labs, explained that they could not only locate cell-phone callers anywhere in the world, they could also listen in on their phone conversations. And if they couldn’t listen in real time, they could record the encrypted calls and texts for later decryption.

This equivalence wasn’t discovered until the 1930s, most notably by Claude Elwood Shannon (born 1916), whose famous 1938 M.I.T. master’s thesis was entitled “A Symbolic Analysis of Relay and Switching Circuits.

In his essay “Self-Reliance,” Ralph Waldo Emerson famously wrote, “A foolish consistency is the hobgoblin of little minds”; in the same passage, he worried that individuals were getting stuck in “a reverence for our past act or word because the eyes of others have no other data for computing our orbit than our past acts, and we are loath to disappoint them.” Data. Computing. That was written in 1841,

How Ma Bell Helped Us Build the Blue Box In 1955, the Bell System Technical Journal published an article entitled “In Band Signal Frequency Signaling” which described the process used for routing telephone calls over trunk lines with the signaling system at the time. It included all the information you’d need to build an interoffice telephone system, but it didn’t include the MF (multifrequency) tones you needed for accessing the system and dialing. But nine years later, in 1964, Bell revealed the other half of the equation, publishing the frequencies used for the digits needed for the actual routing codes. Now, anybody who wanted to get around Ma Bell was set. The formula was there for the taking. All you needed were these two bits of information found in these two articles. If you could build the equipment to emit the frequencies needed, you could make your own free calls, skipping Ma Bell’s billing and monitoring system completely. Famous “phone phreaks” of the early 1970s include Joe Engressia (a.k.a. Joybubbles), who was able to whistle (with his mouth) the high E tone needed to take over the line. John Draper (a.k.a. Captain Crunch) did the same with the free whistle that came inside boxes of Cap’n Crunch. A whole subculture was born. Eventually Steve Jobs (a.k.a. Oaf Tobar) and I (a.k.a. Berkeley Blue) joined the group, making and selling our own versions of the Blue Boxes. We actually made some good money at this.

He was a sudden multimillionaire; she was a world-famous celebrity, but sweetly down-to-earth and not all that wealthy. She didn’t know what to make of him then, and still found him puzzling when she talked about him almost thirty years later. At one dinner early in their relationship, Jobs started talking about Ralph Lauren and his Polo Shop, which she admitted she had never visited. “There’s a beautiful red dress there that would be perfect for you,” he said, and then drove her to the store in the Stanford Mall. Baez recalled, “I said to myself, far out, terrific, I’m with one of the world’s richest men and he wants me to have this beautiful dress.” When they got to the store, Jobs bought a handful of shirts for himself and showed her the red dress. “You ought to buy it,” he said. She was a little surprised, and told him she couldn’t really afford it. He said nothing, and they left. “Wouldn’t you think if someone had talked like that the whole evening, that they were going to get it for you?” she asked me, seeming genuinely puzzled about the incident. “The mystery of the red dress is in your hands. I felt a bit strange about it.” He would give her computers, but not a dress, and when he brought

Only years later would scientists again need to harness the power of multiple processors at once, when massively parallel processing would become an integral part of supercomputing. Years later, too, the genealogy of Shoch’s worm would come full circle. Soon after he published a paper about the worm citing The Shockwave Rider, he received a letter from John Brunner himself. It seemed that most science fiction writers harbored an unspoken ambition to write a book that actually predicted the future. Their model was Arthur C. Clarke, the prolific author of 2001: A Space Odyssey, who had become world-famous for forecasting the invention of the geosynchronous communications satellite in an earlier short story. “Apparently they’re all jealous of Arthur Clarke,” Shoch reflected. “Brunner wrote that his editor had sent him my paper. He said he was ‘really delighted to learn, that like Arthur C. Clarke, I predicted an event of the future.’” Shoch briefly considered replying that he had only borrowed the tapeworm’s name but that the concept was his own and that, unfortunately, Brunner did not really invent the worm. But he let it pass.

Was there anything about Ada Lovelace?” I asked. Nightingale gave me a funny look. “Byron’s daughter?” he asked. “I’m not sure I understand the connection.” “She worked with Babbage on the difference engine,” I said. “In what capacity?” “She was a famously gifted mathematician,” I said. Who I mostly knew about from reading Steampunk, but I wasn’t going to mention that. “Generally considered to have written the first true computer program.” “Ah,” said Nightingale. “So now we know who to blame.

Your mind operates on the famous computing principle of GIGO - garbage in, garbage out. If you do ill, speak ill and think ill, the residue is going to leave you sick. If you do well, speak well and think well, the outcome is going to be well.

The early Wittgenstein and the logical positivists that he inspired are often thought to have their roots in the philosophical investigations of René Descartes.9 Descartes’s famous dictum “I think, therefore I am” has often been cited as emblematic of Western rationalism. This view interprets Descartes to mean “I think, that is, I can manipulate logic and symbols, therefore I am worthwhile.” But in my view, Descartes was not intending to extol the virtues of rational thought. He was troubled by what has become known as the mind-body problem, the paradox of how mind can arise from nonmind, how thoughts and feelings can arise from the ordinary matter of the brain. Pushing rational skepticism to its limits, his statement really means “I think, that is, there is an undeniable mental phenomenon, some awareness, occurring, therefore all we know for sure is that something—let’s call it I—exists.” Viewed in this way, there is less of a gap than is commonly thought between Descartes and Buddhist notions of consciousness as the primary reality. Before 2030, we will have machines proclaiming. Descartes’s dictum. And it won’t seem like a programmed response. The machines will be earnest and convincing. Should we believe them when they claim to be conscious entities with their own volition?

I suppose that this viewpoint-that physical systems are to be regarded as merely computational entities-stems partly from the powerful and increasing role that computational simulations play in modern twentieth-century science, and also partly from a belief that physical objects are themselves merely 'patterns of information', in some sense, that are subject to computational mathematical laws. Most of the material of our bodies and brains, after all, is being continuously replaced, and it is just its pattern that persists. Moreover, matter itself seems to have merely a transient existence since it can be converted from one form into another. Even the mass of a material body, which provides a precise physical measure of the quantity of matter that the body contains, can in appropriate circumstances be converted into pure energy (according to Einstein's famous E=mc^2)-so even material substance seems to be able to convert itself into something with a theoretical mathematical actuality. Furthermore, quantum theory seemst o tell us that material particles are merely 'waves' of information. (We shall examine these issues more thoroughly in Part II.) Thus, matter itself is nebulous and transient; and it is not at all unreasonable to suppose that the persistence of 'self' might have more to do with the preservation of patterns than of actual material particles.

You are created to be a creator. God does not give anyone a finished product. God did not create the telephone, car, computers, Facebook, amazon, ebay, God did not make a chair, He created a tree for you to produce the chair. He gave you the raw materials to look at and ask yourself what can I do with this? Are you a producer or a consumer? .....ponder

donated skeletal collection; one more skull was just a final drop in the bucket. Megan and Todd Malone, a CT technician in the Radiology Department at UT Medical Center, ran skull 05-01 through the scanner, faceup, in a box that was packed with foam peanuts to hold it steady. Megan FedExed the scans to Quantico, where Diana and Phil Williams ran them through the experimental software. It was with high hopes, shortly after the scan, that I studied the computer screen showing the features ReFace had overlaid, with mathematical precision, atop the CT scan of Maybe-Leoma’s skull. Surely this image, I thought—the fruit of several years of collaboration by computer scientists, forensic artists, and anthropologists—would clearly settle the question of 05-01’s identity: Was she Leoma or was she Not-Leoma? Instead, the image merely amplified the question. The flesh-toned image on the screen—eyes closed, the features impassive—could have been a department-store mannequin, or a sphinx. There was nothing in the image, no matter how I rotated it in three dimensions, that said, “I am Leoma.” Nor was there anything that said, “I am not Leoma.” To borrow Winston Churchill’s famous description of Russia, the masklike face on the screen was “a riddle wrapped in a mystery inside an enigma.” Between the scan, the software, and the tissue-depth data that the software merged with the

The minute I dropped out I could stop taking the required classes that didn’t interest me, and begin dropping in on the ones that looked interesting. It wasn’t all romantic. I didn’t have a dorm room, so I slept on the floor in friends’ rooms, I returned coke bottles for the 5¢ deposits to buy food with, and I would walk the seven miles across town every Sunday night to get one good meal a week at the Hare Krishna temple. I loved it. And much of what I stumbled into by following my curiosity and intuition turned out to be priceless later on. Let me give you one example: Reed College at that time offered perhaps the best calligraphy instruction in the country. Throughout the campus every poster, every label on every drawer, was beautifully hand calligraphed. Because I had dropped out and didn’t have to take the normal classes, I decided to take a calligraphy class to learn how to do this. I learned about serif and san serif typefaces, about varying the amount of space between different letter combinations, about what makes great typography great. It was beautiful, historical, artistically subtle in a way that science can’t capture, and I found it fascinating. None of this had even a hope of any practical application in my life. But ten years later, when we were designing the first Macintosh computer, it all came back to me. And we designed it all into the Mac. It was the first computer with beautiful typography. If I had never dropped in on that single course in college, the Mac would have never had multiple typefaces or proportionally spaced fonts. And since Windows just copied the Mac, it’s likely that no personal computer would have them. If I had never dropped out, I would have never dropped in on this calligraphy class, and personal computers might not have the wonderful typography that they do. Of course it was impossible to connect the dots looking forward when I was in college. But it was very, very clear looking backwards ten years later. Again, you can’t connect the dots looking forward; you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future. You have to trust in something—your gut, destiny, life, karma, whatever. This approach has never let me down, and it has made all the difference in my life. The narrator of this story is Steve Jobs, the legendary CEO of Apple. The story was part of his famous Stanford commencement speech in 2005.[23] It’s a perfect illustration of how passion and purpose drive success, not the crossing of an imaginary finish line in the future. Forget the finish line. It doesn’t exist. Instead, look for passion and purpose directly in front of you. The dots will connect later, I promise—and so does Steve.

- “The consequence?” - “Globalization.” - “Implication?” - “Our fate is reflected in our most famous invention: the computer. Those local area networks that sprang up like cities in the eighties and nineties got connected at the turn of the century by the internet. Just like European colonization connected the globe. Globalization is to the human race what the internet is to computers—a method for sharing resources and ideas. Ideas can now move around the world in nanoseconds. We have a platform for enabling the strongest minds to transform their thoughts into reality—and deploy that reality for the good of the masses. If you think about it, vision—fictive simulation—remains the most powerful human ability. Look at the Forbes list of the richest people. The individuals listed are very different, but they all share one trait: vision. The ability to imagine a future that doesn’t exist—to imagine what the world would be like if something changed, if a product or service existed. And these people’s fortunes were made because their visions were accurate—they correctly predicted that something that didn’t already exist both could be created and would be valuable to a specific group of people.

Right across the species, everything must be stored away and put under seal - including the famous genome - doubtless for the use of a later race, who will exploit it as fossil material. We shall ourselves, by the combined pressure of the mass of computer data and the continental drift, be transformed into a metamorphic deposit (the Unconscious already seems like a psychical residue of the Carboniferous). Right now, one has the impression the human race is merely turning in on itself and its origins, desperately gathering together its distress flares and dematerializing to transform itself into a message. But a message to whom? Everyone is looking for a safe area, some form of permanent plot that can eclipse existence as a primary abode and protect us from death. The unfortunate thing is there aren't even any plots held in perpetuity in the cemeteries any more.

The University of Michigan opened its new Computer Center in 1971, in a brand-new building on Beal Avenue in Ann Arbor, with beige-brick exterior walls and a dark-glass front. The university’s enormous mainframe computers stood in the middle of a vast white room, looking, as one faculty member remembers, “like one of the last scenes in the movie 2001: A Space Odyssey.” Off to the side were dozens of keypunch machines—what passed in those days for computer terminals. In 1971, this was state of the art. The University of Michigan had one of the most advanced computer science programs in the world, and over the course of the Computer Center’s life, thousands of students passed through that white room, the most famous of whom was a gawky teenager named Bill Joy.

The truth of the New Democrats’ purpose was presented by the journalist Joe Klein in his famous 1996 roman à clef about Clinton’s run for the presidency, Primary Colors. Although the novel contains more than a nod to Clinton’s extramarital affairs, Klein seems broadly sympathetic to the man from Arkansas as well as to the DLC project more generally. Toward the equality-oriented politics of the Democratic past he is forthrightly contemptuous. Old people who recall fondly the battles of the Thirties, for example, are objects of a form of ridicule that Klein thinks he doesn’t even need to explain; it is self-evident that people who care about workers are fools. And when an old-school “prairie populist” challenges the Clinton character for the nomination, Klein describes him as possessing “a voice made for crystal radio sets” and “offering Franklin Roosevelt’s jobs program (forestry, road-building) to out-of-work computer jockeys.” Get it? His views are obsolete! “It was like running against a museum.” That was the essential New Democrat idea: The world had changed, but certain Democratic voters expected their politicians to help them cling to a status that globalization had long since revoked. However,

Nevertheless, the Icelandic language remains a source of pride and identity. Famously, rather than adopt foreign terms for new technologies and concepts, various committees establish new Icelandic words to enter the lexicon: tölva (a mixture of “number” and “prophetess”) for computer, friðþjófur (“thief of peace”) for a pager, and skriðdreki (“crawling dragon”) for an armored tank.

The computer scientist Gerald Weinberg is famous for saying, “No matter what the problem is, it’s a people problem.

One of the first things Tessa asked me was whether I had seen the whole Metz speech, not just the famous quote, which she repeated word for word:4 We are living in a computer-programmed reality, and the only clue we have to it is when some variable is changed, and some alteration in our reality occurs.

Age: 11 Height: 5’5 Favourite animal: Wolf   Chris loves to learn. When he’s not reading books explaining how planes work or discovering what lives at the bottom of the ocean, he’s watching the Discovery Channel on TV to learn about all the world’s animal and plant life. How things work is one of Chris’ main interests, and for this reason he has a special appreciation for electrical and mechanical things, everything from computers to trains. He considers himself a train expert and one day dreams of riding on famous trains, such as the Orient Express and the Trans-Siberian Railway.   Chris dreams of one day being a great engineer, like Isambard Kingdom Brunel. He knows this will involve going to university, so he studies hard at school. Beatrix is his study partner, and when they aren’t solving mysteries in the Cluefinders Club they can be found in the garden poring over text books. Like Ben, he loves to read comic books, and his favourite super-hero is Iron Man, who is a genius engineer and businessman. Chris says, “One day I’ll invent a new form of transport that will revolutionise world travel!”    

One thing that we conclude from all this is that the 'learning robot' procedure for doing mathematics is not the procedure that actually underlies human understanding of mathematics. In any case, such bottom-up-dominated procedure would appear to be hopelessly bad for any practical proposal for the construction of a mathematics-performing robot, even one having no pretensions whatever for simulating the actual understandings possessed by a human mathematician. As stated earlier, bottom-up learning procedures by themselves are not effective for the unassailable establishing of mathematical truths. If one is to envisage some computational system for producing unassailable mathematical results, it would be far more efficient to have the system constructed according to top-down principles (at least as regards the 'unassailable' aspects of its assertions; for exploratory purposes, bottom-up procedures might well be appropriate). The soundness and effectiveness of these top-down procedures would have to be part of the initial human input, where human understanding an insight provide the necesssary additional ingredients that pure computation is unable to achieve. In fact, computers are not infrequently employed in mathematical arguments, nowadays, in this kind of way. The most famous example was the computer-assisted proof, by Kenneth Appel and Wolfgang Haken, of the four-colour theorem, as referred to above. The role of the computer, in this case, was to carry out a clearly specified computation that ran through a very large but finite number of alternative possibilities, the elimination of which had been shown (by the human mathematicians) to lead to a general proof of the needed result. There are other examples of such computer-assisted proofs and nowadays complicated algebra, in addition to numerical computation, is frequently carried out by computer. Again it is human understanding that has supplied the rules and it is a strictly top-down action that governs the computer's activity.

She was not alone. “There’s a definite panic on the hip scene in Cambridge,” wrote student radical Raymond Mungo that year, “people going to uncommonly arduous lengths (debt, sacrifice, the prospect of cold toes and brown rice forever) to get away while there’s still time.” And it wasn’t just Cambridge. All over the nation at the dawn of the 1970s, young people were suddenly feeling an urge to get away, to leave the city behind for a new way of life in the country. Some, like Mungo, filled an elderly New England farmhouse with a tangle of comrades. Others sought out mountain-side hermitages in New Mexico or remote single-family Edens in Tennessee. Hilltop Maoists traversed their fields with horse-drawn plows. Graduate students who had never before held a hammer overhauled tobacco barns and flipped through the Whole Earth Catalog by the light of kerosene lamps. Vietnam vets hand-mixed adobe bricks. Born-and-bred Brooklynites felled cedar in Oregon. Former debutants milked goats in Humboldt County and weeded strawberry beds with their babies strapped to their backs. Famous musicians forked organic compost into upstate gardens. College professors committed themselves to winter commutes that required swapping high heels for cross-country skis. Computer programmers turned the last page of Scott and Helen Nearing’s Living the Good Life and packed their families into the car the next day. Most had no farming or carpentry experience, but no matter. To go back to the land, it seemed, all that was necessary was an ardent belief that life in Middle America was corrupt and hollow, that consumer goods were burdensome and unnecessary, that protest was better lived than shouted, and that the best response to a broken culture was to simply reinvent it from scratch.

Minsky was an ardent supporter of the Cyc project, the most notorious failure in the history of AI. The goal of Cyc was to solve AI by entering into a computer all the necessary knowledge. When the project began in the 1980s, its leader, Doug Lenat, confidently predicted success within a decade. Thirty years later, Cyc continues to grow without end in sight, and commonsense reasoning still eludes it. Ironically, Lenat has belatedly embraced populating Cyc by mining the web, not because Cyc can read, but because there’s no other way. Even if by some miracle we managed to finish coding up all the necessary pieces, our troubles would be just beginning. Over the years, a number of research groups have attempted to build complete intelligent agents by putting together algorithms for vision, speech recognition, language understanding, reasoning, planning, navigation, manipulation, and so on. Without a unifying framework, these attempts soon hit an insurmountable wall of complexity: too many moving parts, too many interactions, too many bugs for poor human software engineers to cope with. Knowledge engineers believe AI is just an engineering problem, but we have not yet reached the point where engineering can take us the rest of the way. In 1962, when Kennedy gave his famous moon-shot speech, going to the moon was an engineering problem. In 1662, it wasn’t, and that’s closer to where AI is today. In industry, there’s no sign that knowledge engineering will ever be able to compete with machine learning outside of a few niche areas. Why pay experts to slowly and painfully encode knowledge into a form computers can understand, when you can extract it from data at a fraction of the cost? What about all the things the experts don’t know but you can discover from data? And when data is not available, the cost of knowledge engineering seldom exceeds the benefit. Imagine if farmers had to engineer each cornstalk in turn, instead of sowing the seeds and letting them grow: we would all starve.

The first eye-opener came in the 1970s, when DARPA, the Pentagon’s research arm, organized the first large-scale speech recognition project. To everyone’s surprise, a simple sequential learner of the type Chomsky derided handily beat a sophisticated knowledge-based system. Learners like it are now used in just about every speech recognizer, including Siri. Fred Jelinek, head of the speech group at IBM, famously quipped that “every time I fire a linguist, the recognizer’s performance goes up.” Stuck in the knowledge-engineering mire, computational linguistics had a near-death experience in the late 1980s. Since then, learning-based methods have swept the field, to the point where it’s hard to find a paper devoid of learning in a computational linguistics conference. Statistical parsers analyze language with accuracy close to that of humans, where hand-coded ones lagged far behind. Machine translation, spelling correction, part-of-speech tagging, word sense disambiguation, question answering, dialogue, summarization: the best systems in these areas all use learning. Watson, the Jeopardy! computer champion, would not have been possible without it.

Computer pioneer Alan Turing famously proved that if a computer can perform a certain bare minimum set of operations, then, given enough time and memory, it can be programmed to do anything that any other computer can do. Machines exceeding this critical threshold are called universal computers (aka Turing-universal computers); all of today’s smartphones and laptops are universal in this sense.

dominate. Bill Gates did not start out with the business model that made him rich and famous. Initially, after dropping out of Harvard, he and Paul Allen sold their own line of BASIC software for the Altair 8800.7 It was only when the company was about five years old, in 1981, that Gates found out that IBM was seeking an operating system for its proposed personal computer line. He bought an operating system from another company in Seattle, called it MS-DOS, licensed it to IBM, and built Microsoft into a juggernaut. You may not find the right business model on the first cut, so you may have to adapt—before you run out of cash.

glory, at the Science Museum of London. Charles Babbage was a well-known scientist and inventor of the time. He had spent years working on his Difference Engine, a revolutionary mechanical calculator. Babbage was also known for his extravagant parties, which he called “gatherings of the mind” and hosted for the upper class, the well-known, and the very intelligent.4 Many of the most famous people from Victorian England would be there—from Charles Darwin to Florence Nightingale to Charles Dickens. It was at one of these parties in 1833 that Ada glimpsed Babbage’s half-built Difference Engine. The teenager’s mathematical mind buzzed with possibilities, and Babbage recognized her genius immediately. They became fast friends. The US Department of Defense uses a computer language named Ada in her honor. Babbage sent Ada home with thirty of his lab books filled with notes on his next invention: the Analytic Engine. It would be much faster and more accurate than the Difference Engine, and Ada was thrilled to learn of this more advanced calculating machine. She understood that it could solve even harder, more complex problems and could even make decisions by itself. It was a true “thinking machine.”5 It had memory, a processor, and hardware and software just like computers today—but it was made from cogs and levers, and powered by steam. For months, Ada worked furiously creating algorithms (math instructions) for Babbage’s not-yet-built machine. She wrote countless lines of computations that would instruct the machine in how to solve complex math problems. These algorithms were the world’s first computer program. In 1840, Babbage gave a lecture in Italy about the Analytic Engine, which was written up in French. Ada translated the lecture, adding a set of her own notes to explain how the machine worked and including her own computations for it. These notes took Ada nine months to write and were three times longer than the article itself! Ada had some awesome nicknames. She called herself “the Bride of Science” because of her desire to devote her life to science; Babbage called her “the Enchantress of Numbers” because of her seemingly magical math

Your mind operates on the famous computing principle of GIGO - garbage in, garbage out. If you do ill, speak ill and think ill, the residue is going to leave you sick. If you do well, speak well and think well, the outcome is going to be well. ​— ​Om Swami, A Million Thoughts.

Moreover, Netflix produces exactly what it knows its customers want based on their past viewing habits, eliminating the waste of all those pilots, and only loses customers when they make a proactive decision to cancel their subscription. The more a person uses Netflix, the better Netflix gets at providing exactly what that person wants. And increasingly, what people want is the original content that is exclusive to Netflix. The legendary screenwriter William Goldman famously wrote of Hollywood, “Nobody knows anything.” To which Reed Hastings replies, “Netflix does.” And all this came about because Hastings had the insight and persistence to wait nearly a decade for Moore’s Law to turn his long-term vision from an impossible pipe dream into one of the most successful media companies in history. Moore’s Law has worked its magic many other times, enabling new technologies ranging from computer animation (Pixar) to online file storage (Dropbox) to smartphones (Apple). Each of those technologies followed the same path from pipe dream to world-conquering reality, all driven by Gordon Moore’s 1965 insight.

John von Neumann, one of the founding fathers of computer science, famously said that “with four parameters I can fit an elephant, and with five I can make him wiggle his trunk.

Turing’s vision was shared by his fellow computer scientists in America, who codified their curiosity in 1956 with a now famous Dartmouth College research proposal in which the term “artificial intelligence” was coined.